Quantifying tumor morphological complexity based on pretreatment MRI fractal analysis for predicting pathologic complete response and survival in breast cancer: a retrospective, multicenter study

Quantifying tumor morphological complexity based on pretreatment MRI fractal analysis for predicting pathologic complete response and survival in breast cancer: a retrospective, multicenter study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Quantifying tumor morphological complexity based on pretreatment MRI fractal analysis for predicting pathologic complete response and survival in breast cancer: a retrospective, multicenter study Yao Huang, Ying Cao, Huifang Chen, Xiaosong Lan, Sun Tang, Zhitao Zhang, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4294410/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 20 May, 2025 Read the published version in Breast Cancer Research → Version 1 posted 7 You are reading this latest preprint version Abstract Background: The tumor morphological complexity is closely associated with treatment response and prognosis in patients with breast cancer. However, conveniently quantifiable tumor morphological complexity methods are currently lacking. Methods: Women with breast cancer who underwent NAC and pretreatment MRI were retrospectively enrolled at four centers from May 2010 to April 2023. MRI-based fractal analysis was used to calculate fractal dimensions (FDs), quantifying tumor morphological complexity. Features associated with pCR were identified using multivariable logistic regression analysis, upon which a nomogram model was developed, and assessed by the area under the receiver operating characteristic curve (AUC). Cox proportional hazards analysis was used to identify independent prognostic factors for disease-free survival (DFS) and overall survival (OS) and develop nomogram models. Results: A total of 1109 patients (median age, 49 years [IQR, 43-54 years]) were enrolled; 435, 351, and 323 patients were recruited in the training, external validation cohorts 1 and 2, respectively. HR status (odds ratio [OR], 0.234 [0.135, 0.406]; P < 0.001), HER2 status (OR, 3.320 [1.923, 5.729]; P < 0.001), and Global FD (OR, 0.352 [0.261, 0.480]; P < 0.001) were independent predictors of pCR. The nomogram model for predicting pCR achieved AUCs of 0.80 (95% CI: 0.75, 0.86) and 0.74 (95% CI: 0.68, 0.79) in the external validation cohorts. The nomogram model, which integrated global FD and clinicopathological variables can stratify prognosis into low-risk and high-risk groups ( log-rank test , DFS: P = 0.04; OS: P < 0.001). Conclusions: Global FD can quantify tumor morphological complexity and the model that combines global FD and clinicopathological variables showed good performance in predicting pCR to NAC and survival in patients with breast cancer. Breast cancer MRI Pathological complete response Neoadjuvant chemotherapy Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Neoadjuvant chemotherapy (NAC) is the preferred treatment for locally advanced breast cancer, effectively reducing tumor size and raising the possibility of breast-conserving surgery [1, 2]. Patients who achieved pathological complete response (pCR) after NAC showed improved disease-free survival (DFS) and overall survival (OS) [3]. However, non-responders not only face increased economic burden but also potential drug side effects [4, 5]. Therefore, a reliable approach to predict pCR and prognosis is urgently required in patients with breast cancer for personalized treatment. Tumor morphological complexity has been a manifestation of heterogeneity in breast cancer [6, 7]. Patients with higher tumor sphericity and smaller volumes are more likely to achieve pCR [8-10]. A greater size and more irregular morphology of the tumor are linked to increased risks of metastasis and recurrence [11, 12]. Consequently, there is a close correlation between tumor morphological features and treatment response and prognosis [13-15]. Despite the variety of features used to describe tumor morphology, many techniques essentially assess the same tumor characteristics, such as tumor size, indicated by both diameter and volume. Utilizing these characteristics for predicting pCR and prognosis yields moderate performance. Therefore, it is necessary to develop a quantitative parameter to quantify the morphological complexity of tumors, which could prove valuable in predicting pCR and survival outcomes. The fractal analysis quantifies tumor morphological complexity by measuring self-similarity across various spatial scales, utilizing fractal dimension (FD) as a quantitative parameter [16]. A higher FD value indicates greater tumor morphological complexity, implying poorer treatment outcomes and prognosis [17, 18]. Within breast cancer diagnosis, FD identifies malignancy by analyzing tumor heterogeneity in ultrasound, mammography, and MRI images, with higher FD values more indicative of malignant lesions [19-22]. However, the efficacy of FD in predicting pCR to NAC and the survival outcomes for breast cancer is still unclear and requires further research. This study aimed to quantify tumor morphological complexity using multi-dimension FD based on pretreatment MRI and explore the predictive value of a model combining this quantitative measure with clinicopathologic variables to predict pCR to NAC and survival prognosis in patients with breast cancer. Materials And Methods Study Cohort This multicenter retrospective study received approval from the institutional review boards of each participating center, and the requirement for patient written informed consent was waived. The study was conducted following the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) guidelines [23]. A checklist is provided in Supplementary Table S1. From January 2017 to April 2023, this study recruited female patients from three centers. Centers A and B served as the training cohort, while center C and center D (the publicly available I-SPY2 dataset from The Cancer Imaging Archive, conducted between May 2010 and November 2016) served as external validation cohorts [9]. The inclusion criteria were: ( a ) biopsy-confirmed invasive breast cancer without distant metastasis, ( b ) MRI conducted before NAC, and ( c ) post-NAC pathological confirmation of pCR. The exclusion criteria involved: ( a ) inadequate MRI quality, ( b ) lack of histopathologic data, ( c ) previous history of breast cancer, ( d ) external institution surgery or unassessed pCR, ( e ) incomplete clinical data (excluding uncollected characteristics), and ( f ) lack of follow-up records for survival prognosis analysis in center A (Fig. 1A). NAC Regimens and Histopathology Analysis All patients received an anthracyclines and/or taxanes-based NAC regimen. For human epidermal growth factor receptor2 (HER2) positive patients, treatment included trastuzumab alone or in combination with pertuzumab. In the study, pre-neoadjuvant chemotherapy (NAC) core needle biopsies were conducted, and immunohistochemistry determined estrogen receptor (ER), progesterone receptor (PR) status, HER2 status, and Ki-67 index status for patients from centers A, B, and C. Tumors with ≥1% nuclear staining were defined as ER (+) / PR (+), while those with < 1% as ER (-)/PR (-) [24, 25]. HR positivity is defined as ER (+) and/or PR (+). HER2 status was categorized as negative for 0 or 1+ immunohistochemistry scores, and positive for 3+ scores. For 2+ scores, fluorescence in situ hybridization (FISH) determined HER2 status; amplification signified HER2 (+), and lack thereof indicated HER2 (-) [26, 27]. Additionally, a Ki-67 index threshold of 20% was set, where ≥ 20% denoted high expression and < 20% indicated low expression [28]. The pCR was defined based on surgical pathology as the absence of residual invasive cancer, possibly with residual ductal carcinoma in situ, and no lymph node invasion in the axillary lymph nodes (ypT0/is ypN0). MRI Procedure and Image Processing All MRI examinations were performed with 1.5T or 3.0T scanners. Detailed protocols are provided in Supplementary Table S2. To minimize variability in imaging protocols, B-spline interpolation was used to resample images to 1×1×1 mm 3 , and z-score normalization was used for image intensity standardization. The peak enhancement phase of dynamic contrast-enhanced MRI was used. Preliminary semi-automatic tumor region segmentation was conducted using the Dr. Wise platform ( https://keyan.deepwise.com ). Subsequently, two radiologists (*.*.*., and *.*.*., with 6 and 10 years of experience, respectively, in breast MRI) manually corrected the segmentation. If there were multiple lesions, only the largest one was considered [29]. Image Analysis FDs were calculated by MATLAB (version, R2020a) using the box-counting method [30]. Initially, post-segmentation images underwent binarization. Subsequently, boxes of incremental sizes covered tumor areas, recording the minimum needed for full coverage, with sizes increasing up to 45% of image dimensions [17, 31]. Finally, logarithmic transformation was applied to box sizes and corresponding quantities for linear regression via the least-squares method, as illustrated: where N L is the minimum count of boxes, each with side length L , needed to cover the ROI areas, and FD is the opposite value of k . For each patient, 4 two-dimensional FDs (max FD, median FD, min FD, mean FD) and 1 three-dimensional FD (global FD) were calculated (Fig. 1B). Meanwhile, 14 morphological features describing tumor size and geometric shape were extracted using PyRadiomics (version 3.1) [32], details of which are described in Supplementary materials. Follow-up Data Collection For surveillance of recurrence and distant metastasis, patients underwent post-surgery follow-ups every six months with chest radiography and/or chest CT scans, along with annual bone scans and abdominal CT scans or ultrasounds. Follow-up was conducted in center A by two radiologists (*.*., and *.*., each with 6 and 10 years of experience), who recorded DFS and OS from the surgery date (the time origin) to the first recurrence or death, respectively. Patients without recurrence or death were censored at their last follow-up date. Statistical Analyses Statistical analysis was conducted using R (version 4.3.1) and Python (version 3.9.5). To assess the reproducibility of fractal analysis, images from 30 patients (15 patients with pCR and 15 patients with non-pCR) randomly selected were segmented twice by a radiologist at one-month intervals and once by another radiologist. Bland-Altman statistics was used to evaluate intra-observer consistency, and a mixed-effects model with random effects on intercept and slope was used for variance-component analysis to assess the reproducibility of fractal analysis [33]. The intraclass correlation coefficient (ICC) was used to assess interobserver consistency, with an ICC > 0.75 indicating good consistency. The X 2 test or Fisher exact test was used to compare differences in categorical variables between pCR and non-pCR groups. The Kolmogorov-Smirnov test was used to assess the normal distribution of continuous variables, and the Levene test was applied to assess homogeneity. Group differences were compared using the t -test or the Mann-Whitney U test. Univariable and multivariable logistic regression analyses were performed to assess the association between features and pCR, and molecular subtypes were excluded from the multivariable logistic regression model because of their collinearity with both HR status and HER2 status. Independent predictors of pCR were used to create a nomogram model. Model performance was assessed by area under the receiver operating characteristic curve (AUC), and accuracy, sensitivity specificity, positive predictive value, and negative predictive value were also calculated. AUCs of different models were compared using the Delong test. The Hosmer-Lemeshow test assessed the calibration of models, and decision curves were used to evaluate the benefit of models. The correlation coefficients were calculated using Spearman correlation analysis. The survival analysis used Cox proportional hazards analysis to identify factors associated with survival outcomes (DFS and OS) and develop nomogram models. Patients were divided into high and low-score groups based on the median score of the nomogram model. Differences in DFS and OS between these groups were compared using Kaplan-Meier curves and the Log-rank test. Two-tailed P < .05 was deemed statistically significant. The codes used for analyses are available from https://github.com/YaoHuang1123/FD . Results Baseline Characteristics In this study, 1318 patients were recruited from four centers. Patients were excluded for inadequate MRI quality ( n = 20), lack of histopathologic data ( n = 34), previous history of breast cancer ( n = 13), external institution surgery or unassessed pCR ( n = 81), and incomplete clinical data ( n = 61), resulting in 1109 patients being included in the study (Fig. 1A). For predicting pCR, the training cohort comprised 435 patients (center A [ n = 413], center B [ n = 22]; median age, 51 years [IQR, 46–55 years]). Two external validation cohorts consisted of 351 patients from center C (median age, 48 years [IQR, 43–52 years]) and 323 patients from center D (median age, 48 years [IQR, 40–56 years]). In all cohorts, significant differences were observed in HR status, HER2 status, max FD, and global FD between pCR and non-pCR groups (all P < 0.05) (Table 1). Reproducibility of Fractal Analysis The FDs for both 3D and 2D fractal analyses showed good consistency, the Bland-Altman repeatability coefficients ranging from 0.11 to 0.19 (Supplementary Table S3 and Fig. S2). Variance-components analysis indicated that the variance between patients (variance:0.0172-0.0195) exceeded the variance between readings (variance: 0.0001-0.0009) for both 3D and 2D fractal analyses. The coefficient of variation (COV) for 2D FDs (COV: 3.21-6.78) between readings was found to be higher than that for 3D (COV: 2.65), whereas the coefficient of variation for 2D FDs (COV: 377.94-467.28) between patients was lower compared to 3D (486.94). Furthermore, five FDs showed good inter-observer consistency (ICC: 0.87-0.93) (Supplementary Table S4). Correlation Analysis Spearman correlation analysis indicated that global FD strongly positively correlated with max, median, and mean FD (correlation coefficient [ r ]: 0.69 to 0.81, P < 0.05), and negatively with HER2 status ( r : -0.12 to -0.01, P ≤ 0.04). Global FD showed negative correlations with sphericity and surface area to volume ratio ( r : -0.65 to -0.18, P ≤ 0.04), and positively with diameter and volume ( r : 0.54 to 0.68, P ≤ 0.04) (Fig. 2, Supplementary Table S5 and S6). Variables Associated with pCR Univariable logistic regression analysis showed that HR status, HER2 status, Ki-67 status, Clinical T stage, and global FD were associated with pCR. After adjustment of the multivariable model for variables with P < 0.05 in the univariable analysis, HR status (odds ratio [OR], 0.234 [95% CI: 0.135, 0.406]; P < 0.001), HER2 status (OR, 3.320 [95% CI: 1.923, 5.729]; P < 0.001), and global FD (OR, 0.352 [95% CI: 0.261, 0.480]; P < 0.001) were independent predictors for pCR (Table 2). These independent predictors were then used to develop the nomogram model-1. Following the same process, clinicopathological variables (HR and HER2 status) and morphological features (sphericity, major axis length, and maximum 2D diameter [row]) were identified to develop the nomogram model-2 (Supplementary Table S7). Performance of Models for Prediction of pCR For predicting pCR to NAC, the AUCs ranging from 0.52 to 0.73 were observed for five FD univariable models across two external validation cohorts (Supplementary Fig. S2). Global FD achieved AUCs of 0.73 (95% CI: 0.67, 0.79) and 0.68 (95% CI: 0.61, 0.74), significantly outperforming morphological models with AUCs of 0.61 (95% CI: 0.54, 0.64) and 0.55 (95% CI: 0.49, 0.62) in the two external validation cohorts (Delong test, all P < 0.001), respectively (Fig. 3A-C, Table 3, Supplementary Table S8). The nomogram model-1 achieved AUCs of 0.80 (95% CI: 0.75, 0.86) and 0.74 (95% CI: 0.68, 0.79) (Fig. 3A-C, Fig. 4), significantly outperforming nomogram model-2 with AUCs of 0.74 (95% CI: 0.68, 0.80) and 0.69 (95% CI: 0.62, 0.74) in two external validation cohorts, respectively (Delong test, P < 0.001) (Supplementary Table S8). The calibration between predicted and observed probabilities was good for nomogram model-1 ( Hosmer-Lemeshow test, P : 0.35-0.78) (Fig. 3D-F). Decision curve analysis showed that nomogram model-1 offered greater clinical benefit across most threshold ranges and demonstrated net benefits in two external validation cohorts at thresholds of 0.07 to 0.68 and 0.13 to 0.66 (Fig. 3G-I). Model Performance for Prediction of pCR in Patient Subgroups Four subgroup analyses were conducted based on molecular subtypes, age, menopausal status, and Ki-67 status. In two external validation cohorts, the global FD for prediction of pCR to NAC achieved AUCs ranging from 0.65-0.83 for patients with four molecular subtypes (HR+/HER2-, HR+/HER2+, HR-/HER2-, and HR-/HER+) (Supplementary Fig. S3). The nomogram model-1 achieved AUCs ranging from 0.72-0.83 for patients with age ≤ 45 years or age > 45 years, and 0.74-0.82 for premenopausal or postmenopausal patients in two external validation cohorts. For patients with high and low Ki-67 expression, the nomogram model-1 achieved AUCs of 0.77 (95% CI: 0.69-0.85) and 0.80 in external validation cohort 1 (Supplementary Fig. S4). Survival Analysis For survival analysis, 171 patients from center A (median age, 50 years [IQR, 45-55 years]) were enrolled. During the follow-up (DFS: median, 29 months [IQR, 15-44 months]; OS: median, 37 months [IQR, 18.05-48.85 months]), 52 patients had recurrence and 14 patients died (Table 4). Cox proportional hazards analysis identified menopausal status (hazard ratio [HR], 1.88 [95% CI: 1.08, 3.28]; P = 0.03), NAC treatment response (HR, 3.75 [95% CI: 1.14, 12.33]; P = 0.03) and global FD (HR, 2.03 [95% CI: 1.08, 3.81]; P = 0.03) were independent prognostic factors for DFS. While cT4 stage (HR, 5.92 [95% CI: 1.50, 23.34]; P = 0.01) and global FD (HR, 4.85 [95% CI: 1.05, 22.46]; P = 0.04) were independent prognostic factors for OS (Fig. 5). For DFS, the cutoff for dividing high and low-risk groups was 11.71, while for OS, the cutoff value was 42.01. Kaplan-Meier analysis for DFS and OS revealed significant differences between the low and high-risk groups (log-rank test, DFS: P = 0.04; OS: P < 0.001), with the low-risk group exhibiting better DFS and OS (Fig. 6). Discussion Accurately predicting pCR to NAC and prognosis in breast cancer patients is crucial for clinical decision-making. This study used fractal analysis to quantify the tumor morphological complexity. The nomogram model combining global FD and clinicopathologic variables (HR status and HER2 status) showed good performance in predicting pCR to NAC. Additionally, the nomogram model that integrated global FD and clinicopathological variables could be used for prognostic stratification in patients with breast cancer. Previously, tumor morphology descriptions relied mainly on subjective assessments by radiologists and imaging shape features, showing diversity but lacking a quantitative indicator reflecting both tumor size and regularity. FD quantifies tumor morphological complexity without dependence on imaging techniques, which is suitable for broad clinical applications. Previous studies often focused on 2D measurements [17-19]. Variance-components analysis revealed that, compared to global FD, 2D FDs showed a higher coefficient of variation between readings and a lower coefficient of variation between patients. This indicates that global FD may provide a more robust measure and may be beneficial in reflecting patient differences. Spearman correlation analysis revealed that global FD negatively correlates with sphericity, and the surface area to volume ratio, but positively with tumor size; higher morphological complexity (i.e., lower sphericity and larger tumor size) is reflected in increased global FD. Previous studies have focused on predicting pCR to NAC using clinical TNM staging, HR status, HER2 status, and Ki-67 expression, yet predictions based solely on clinicopathologic variables have shown limitations [34, 35]. Models developed by Li et al [9], based on tumor morphological features, achieved AUCs between 0.69 and 0.81 without further validation. Based on MRI, the radiomics model developed by Liu et al [36] achieved AUCs ranging from 0.71 to 0.80. Zhuang et al enhanced predictive model performance by combining radiomics and clinicopathologic variables, the combined model achieved an AUC of 0.826 [37]. In this study, the nomogram model-1 (combining global FD, HR status, and HER2 status) achieved AUCs of 0.80 and 0.74, on par with previous studies. Global FD and nomogram model-1 also showed good performance in predicting pCR in subgroup analyses. Notably, global FD offers clinicians an easily understandable quantitative feature, providing interpretability through its depiction of tumor morphology. Our findings suggest the potential of global FD as an imaging biomarker in assisting clinicians to identify pCR before NAC, which was more convenient to calculate than radiomics features. This study explored the application of global FD in predicting DFS and OS. Our findings indicated that patients with lower nomogram model (combining clinicopathological variables and global FD) scores exhibited better DFS and OS. Cox proportional hazards models indicated global FD was an independent prognostic factor for both DFS and OS. Previous studies have substantiated the importance of MRI tumor morphological features like tumor size in predicting breast cancer prognosis [14, 38, 39]. Our results offer a new perspective on prognostic prediction of breast cancer using non-invasive MRI technology to quantify tumor morphological complexity. This study has several limitations. First, as a retrospective analysis incorporating data from four centers, the global FD's clinical applicability and effectiveness need further validation through prospective analysis. Second, while this study used semi-automatic segmentation to ensure accuracy in FD calculation, fully automatic techniques could further augment stability and reduce subjectivity. Additionally, the prognostic analysis was based on a limited single-center sample and needs exploration in larger, multi-center cohorts to ascertain the value of global FD in prognosis prediction. Moreover, a comprehensive consideration of tumor morphology and spatial distribution could more fully quantify intratumoral heterogeneity. Finally, given that tumor morphology changes with treatment, reliance on pretreatment images may have limitations. Exploring the value of longitudinal changes in global FD is necessary to predict pCR to NAC and prognosis. In conclusion, the global fractal dimension developed from pretreatment MRI offers a non-invasive and practical approach to quantify the tumor morphological complexity and can predict pCR and prognosis in breast cancer. The generalizability and reproducibility of the prediction model based on the global fractal dimension should be validated with larger prospective data sets. Abbreviations AUC area under the receiver operating characteristic curve DFS disease-free survival FD fractal dimension HER2 human epidermal growth factor receptor2 HR hormone receptor NAC neoadjuvant chemotherapy OR odds ratio OS overall survival pCR pathologic complete response Declarations Ethics approval and consent to participate This study was approved by Chongqing University Cancer Hospital institutional review board, Southwest Hospital institutional review board, and Chongqing Hospital of Traditional Chinese Medicine. Consent for publication Not applicable. Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding This study has received funding by the Graduate Research and Innovation Foundation of Chongqing (CYB23070), the Fundamental Research Funds for the Central Universities (2023CDJYGRH-YB04), the Chongqing Medical Research Project of Combination of Science and Medicine (No. 2024MSXM171), the Chongqing University Cancer Hospital Scientific Research Capacity Improvement Project (2023nlts004), and the Natural Science Foundation of Chongqing municipality (CSTB2023NSCQ-MSX0787). Authors' contributions Guarantors of integrity of entire study, Y.H., X.W., J.Z.; study concepts/study design or data acquisition or data analysis/interpretation, all authors; manuscript drafting or manuscript revision for important intellectual content, all authors; approval of final version of submitted manuscript, all authors; agrees to ensure any questions related to the work are appropriately resolved, all authors; literature research, Y.H., X.W., Y.C., J.Z.; clinical studies, Y.H., Y.C., H.C., X.L., S.T., J.Z.; experimental studies, Y.H., X.W., Y.C., Z.Z., T.Y.; statistical analysis, Y.H., X.W., J.Z.; and manuscript editing, Y.H., X.W., Y.C., J.Z. Acknowledgements The authors thank all patients who participated in the study. References Korde LA, Somerfield MR, Carey LA, Crews JR, Denduluri N, Hwang ES, Khan SA, Loibl S, Morris EA, Perez A et al : Neoadjuvant Chemotherapy, Endocrine Therapy, and Targeted Therapy for Breast Cancer: ASCO Guideline . J Clin Oncol 2021, 39 (13):1485-1505. Gradishar WJ, Anderson BO, Abraham J, Aft R, Agnese D, Allison KH, Blair SL, Burstein HJ, Dang C, Elias AD et al : Breast Cancer, Version 3.2020, NCCN Clinical Practice Guidelines in Oncology . J Natl Compr Canc Netw 2020, 18 (4):452-478. Cortazar P, Zhang L, Untch M, Mehta K, Costantino JP, Wolmark N, Bonnefoi H, Cameron D, Gianni L, Valagussa P et al : Pathological complete response and long-term clinical benefit in breast cancer: the CTNeoBC pooled analysis . Lancet 2014, 384 (9938):164-172. Romeo V, Accardo G, Perillo T, Basso L, Garbino N, Nicolai E, Maurea S, Salvatore M: Assessment and Prediction of Response to Neoadjuvant Chemotherapy in Breast Cancer: A Comparison of Imaging Modalities and Future Perspectives . Cancers (Basel) 2021, 13 (14). Wolf DM, Yau C, Wulfkuhle J, Brown-Swigart L, Gallagher RI, Lee PRE, Zhu Z, Magbanua MJ, Sayaman R, O'Grady N et al : Redefining breast cancer subtypes to guide treatment prioritization and maximize response: Predictive biomarkers across 10 cancer therapies . Cancer Cell 2022, 40 (6):609-623.e606. Caudle AS, Gonzalez-Angulo AM, Hunt KK, Liu P, Pusztai L, Symmans WF, Kuerer HM, Mittendorf EA, Hortobagyi GN, Meric-Bernstam F: Predictors of tumor progression during neoadjuvant chemotherapy in breast cancer . J Clin Oncol 2010, 28 (11):1821-1828. Braman N, Prasanna P, Whitney J, Singh S, Beig N, Etesami M, Bates DDB, Gallagher K, Bloch BN, Vulchi M et al : Association of Peritumoral Radiomics With Tumor Biology and Pathologic Response to Preoperative Targeted Therapy for HER2 (ERBB2)-Positive Breast Cancer . JAMA Netw Open 2019, 2 (4):e192561. Hylton NM, Blume JD, Bernreuter WK, Pisano ED, Rosen MA, Morris EA, Weatherall PT, Lehman CD, Newstead GM, Polin S et al : Locally advanced breast cancer: MR imaging for prediction of response to neoadjuvant chemotherapy--results from ACRIN 6657/I-SPY TRIAL . Radiology 2012, 263 (3):663-672. Li W, Newitt DC, Gibbs J, Wilmes LJ, Jones EF, Arasu VA, Strand F, Onishi N, Nguyen AA, Kornak J et al : Predicting breast cancer response to neoadjuvant treatment using multi-feature MRI: results from the I-SPY 2 TRIAL . NPJ Breast Cancer 2020, 6 (1):63. Li W, Newitt DC, Yun B, Jones EF, Arasu V, Wilmes LJ, Gibbs J, Nguyen AA, Onishi N, Kornak J et al : Tumor Sphericity Predicts Response in Neoadjuvant Chemotherapy for Invasive Breast Cancer . Tomography 2020, 6 (2):216-222. Gong X, Wu J, Wu J, Liu J, Gu H, Shen H: Correlation of SASH1 expression and ultrasonographic features in breast cancer . Onco Targets Ther 2017, 10 :271-276. Sopik V, Narod SA: The relationship between tumour size, nodal status and distant metastases: on the origins of breast cancer . Breast Cancer Res Treat 2018, 170 (3):647-656. Uemoto Y, Katsuta E, Kondo N, Wanifuchi-Endo Y, Fujita T, Asano T, Hisada T, Terada M, Kato A, Okuda K et al : Low HECTD1 mRNA expression is associated with poor prognosis and may be correlated with increased mitochondrial respiratory function in breast cancer . Am J Cancer Res 2022, 12 (4):1593-1605. Guirguis MS, Adrada BE, Candelaria RP, Sun J, Whitman GJ, Yang WT, Boge M, Mohamed RM, Elshafeey NA, Lane DL et al : Prediction of response to neoadjuvant systemic therapy in triple negative breast cancer using baseline tumor MRI characteristics and imaging patterns of response . Cancer Res 2022, 82 (4):3. Zhang W, Wang Z, Yang S, Wang Y, Xiang S, Guo Z, Hou B, Dong X, Yuan Z, Xu B et al : Preoperative Rim Enhancement on Magnetic Resonance Imaging Indicates Larger Tumor Size and Poor Prognosis in Chinese Basal-Like Breast Cancer Patients . Cancer Biother Radiopharm 2022, 37 (8):729-736. Fernández E, Jelinek HF: Use of fractal theory in neuroscience: methods, advantages, and potential problems . Methods 2001, 24 (4):309-321. Tochigi T, Kamran SC, Parakh A, Noda Y, Ganeshan B, Blaszkowsky LS, Ryan DP, Allen JN, Berger DL, Wo JY et al : Response prediction of neoadjuvant chemoradiation therapy in locally advanced rectal cancer using CT-based fractal dimension analysis . Eur Radiol 2022, 32 (4):2426-2436. Wang J, Li Y, Yang F, Bravo L, Wan K, Xu Y, Cheng W, Sun J, Zhu Y, Zhu T et al : Fractal Analysis: Prognostic Value of Left Ventricular Trabecular Complexity Cardiovascular MRI in Participants with Hypertrophic Cardiomyopathy . Radiology 2021, 298 (1):71-79. Sherminie LPG, Jayatilake ML: Fractal Dimension Analysis of Pixel Dynamic Contrast Enhanced-Magnetic Resonance Imaging Pharmacokinetic Parameters for Discrimination of Benign and Malignant Breast Lesions . JCO Clin Cancer Inform 2023, 7 :e2200101. Alvarado-Cruz LB, Delgadillo-Herrera M, Toxqui-Quitl C, Padilla-Vivanco A, Castro-Ortega R, Arreola-Esquivel M: Fractal analysis for classification of breast lesions . In: Conference on Current Developments in Lens Design and Optical Engineering XX: Aug 12 2019; San Diego, CA : Spie-Int Soc Optical Engineering; 2019. Garra BS, Krasner BH, Horii SC, Ascher S, Mun SK, Zeman RK: Improving the distinction between benign and malignant breast lesions: the value of sonographic texture analysis . Ultrason Imaging 1993, 15 (4):267-285. Byng JW, Boyd NF, Fishell E, Jong RA, Yaffe MJ: Automated analysis of mammographic densities . Phys Med Biol 1996, 41 (5):909-923. Collins GS, Reitsma JB, Altman DG, Moons KG: Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement . Bmj 2015, 350 :g7594. Kwan ML, Bernard PS, Kroenke CH, Factor RE, Habel LA, Weltzien EK, Castillo A, Gunderson EP, Maxfield KS, Stijleman IJ et al : Breastfeeding, PAM50 tumor subtype, and breast cancer prognosis and survival . J Natl Cancer Inst 2015, 107 (7). Newman LA, Stark A, Chitale D, Pepe M, Longton G, Worsham MJ, Nathanson SD, Miller P, Bensenhaver JM, Proctor E et al : Association Between Benign Breast Disease in African American and White American Women and Subsequent Triple-Negative Breast Cancer . JAMA Oncol 2017, 3 (8):1102-1106. Tutt A, Tovey H, Cheang MCU, Kernaghan S, Kilburn L, Gazinska P, Owen J, Abraham J, Barrett S, Barrett-Lee P et al : Carboplatin in BRCA1/2-mutated and triple-negative breast cancer BRCAness subgroups: the TNT Trial . Nat Med 2018, 24 (5):628-637. Buisseret L, Pommey S, Allard B, Garaud S, Bergeron M, Cousineau I, Ameye L, Bareche Y, Paesmans M, Crown JPA et al : Clinical significance of CD73 in triple-negative breast cancer: multiplex analysis of a phase III clinical trial . Ann Oncol 2018, 29 (4):1056-1062. Gómez-Aleza C, Nguyen B, Yoldi G, Ciscar M, Barranco A, Hernández-Jiménez E, Maetens M, Salgado R, Zafeiroglou M, Pellegrini P et al : Inhibition of RANK signaling in breast cancer induces an anti-tumor immune response orchestrated by CD8+ T cells . Nat Commun 2020, 11 (1):6335. Leithner D, Horvat JV, Marino MA, Bernard-Davila B, Jochelson MS, Ochoa-Albiztegui RE, Martinez DF, Morris EA, Thakur S, Pinker K: Radiomic signatures with contrast-enhanced magnetic resonance imaging for the assessment of breast cancer receptor status and molecular subtypes: initial results . Breast Cancer Res 2019, 21 (1):106. Kempkes SN, Slot MR, Freeney SE, Zevenhuizen SJM, Vanmaekelbergh D, Swart I, Smith CM: Design and characterization of electrons in a fractal geometry . Nat Phys 2019, 15 (2):127-131. Liu S, Fan X, Zhang C, Wang Z, Li S, Wang Y, Qiu X, Jiang T: MR imaging based fractal analysis for differentiating primary CNS lymphoma and glioblastoma . Eur Radiol 2019, 29 (3):1348-1354. van Griethuysen JJM, Fedorov A, Parmar C, Hosny A, Aucoin N, Narayan V, Beets-Tan RGH, Fillion-Robin JC, Pieper S, Aerts H: Computational Radiomics System to Decode the Radiographic Phenotype . Cancer Res 2017, 77 (21):e104-e107. Sanghera B, Banerjee D, Khan A, Simcock I, Stirling JJ, Glynne-Jones R, Goh V: Reproducibility of 2D and 3D fractal analysis techniques for the assessment of spatial heterogeneity of regional blood flow in rectal cancer . Radiology 2012, 263 (3):865-873. Arici S, Sengiz Erhan S, Geredeli C, Cekin R, Sakin A, Cihan S: The Clinical Importance of Androgen Receptor Status in Response to Neoadjuvant Chemotherapy in Turkish Patients with Local and Locally Advanced Breast Cancer . Oncol Res Treat 2020, 43 (9):435-440. Ouldamer L, Bendifallah S, Pilloy J, Arbion F, Body G, Brisson C, Lavoué V, Lévêque J, Daraï E: Risk scoring system for predicting breast conservation after neoadjuvant chemotherapy . Breast J 2019, 25 (4):696-701. Liu Z, Li Z, Qu J, Zhang R, Zhou X, Li L, Sun K, Tang Z, Jiang H, Li H et al : Radiomics of Multiparametric MRI for Pretreatment Prediction of Pathologic Complete Response to Neoadjuvant Chemotherapy in Breast Cancer: A Multicenter Study . Clin Cancer Res 2019, 25 (12):3538-3547. Zhuang X, Chen C, Liu Z, Zhang L, Zhou X, Cheng M, Ji F, Zhu T, Lei C, Zhang J et al : Multiparametric MRI-based radiomics analysis for the prediction of breast tumor regression patterns after neoadjuvant chemotherapy . Transl Oncol 2020, 13 (11):100831. Kim JY, Kim JJ, Hwangbo L, Suh HB, Kim S, Choo KS, Nam KJ, Kang T: Kinetic Heterogeneity of Breast Cancer Determined Using Computer-aided Diagnosis of Preoperative MRI Scans: Relationship to Distant Metastasis-Free Survival . Radiology 2020, 295 (3):517-526. Liang T, Hu B, Du H, Zhang Y: Predictive value of T2-weighted magnetic resonance imaging for the prognosis of patients with mass-type breast cancer with peritumoral edema . Oncol Lett 2020, 20 (6):314. Tables Table 1: The Characteristics of Patients in Three Cohorts. Characteristics Training cohort ( n = 435) P Value External validation cohort 1 ( n = 351) P Value External validation cohort 2 ( n = 323) P Value pCR ( n = 114) Non-pCR ( n = 321) pCR ( n = 83) Non-pCR ( n = 268) pCR ( n = 97) Non-pCR ( n = 226) Age (years)* 51 (45-55) 50 (46-55) .58 ‡ 49 ± 8 47 ± 9 .11 † 49 (41-56) 47 (40-56) .72 ‡ Age group ≤ 45 40 (35) 104 (32) 21 (25) 112 (42) 40 (41) 45 (41) > 45 74 (65) 217 (68) 62 (75) 156 (58) 57 (59) 134 (59) Menopausal status .94 ⁑ .34 ⁑ .27 ⁑ Premenopausal 70 (61) 194 (60) 50 (60) 179 (67) 51 (53) 140 (62) Postmenopausal 44 (39) 127 (40) 33 (40) 89 (33) 39 (40) 75 (33) Perimenopausal 0 (0) 0 (0) 0 (0) 0 (0) 7 (7) 11 (5) HR status < .001 ⁑ < .001 ⁑ < .001 ⁑ Negative 76 (67) 91 (28) 49 (59) 78 (29) 59 (61) 78 (35) Positive 38 (33) 230 (72) 34 (41) 190 (71) 38 (39) 148 (65) HER2 status < .001 ⁑ < .001 ⁑ .02 ⁑ Negative 45 (39) 224 (70) 45 (54) 206 (77) 66 (68) 183 (81) Positive 69 (61) 97 (30) 38 (46) 62 (23) 31 (32) 43 (19) Ki-67 status .01 ⁑ < .001 ⁑ Low (< 20%) 9 (8) 63 (20) 8 (10) 83 (31) 0 (0) 0 (0) High (≥ 20%) 105 (92) 258 (80) 75 (90) 185 (69) 0 (0) 0 (0) Not available 0 (0) 0 (0) 0 (0) 0 (0) 97 (100) 226 (100) Clinical T stage .11 ⁑ cT1 11 (9) 25 (8) 0 (0) 0 (0) 0 (0) 0 (0) cT2 61 (54) 136 (42) 0 (0) 0 (0) 0 (0) 0 (0) cT3 17 (15) 57 (18) 0 (0) 0 (0) 0 (0) 0 (0) cT4 25 (22) 103 (32) 0 (0) 0 (0) 0 (0) 0 (0) Not available 0 (0) 0 (0) 83 (100) 268 (100) 97 (100) 226 (100) Clinical N stage 0.37 ⁑ cN0 11 (10) 39 (12) 0 (0) 0 (0) 0 (0) 0 (0) cN1 45 (39) 103 (32) 0 (0) 0 (0) 0 (0) 0 (0) cN2 43 (38) 120 (38) 0 (0) 0 (0) 0 (0) 0 (0) cN3 15 (13) 59 (18) 0 (0) 0 (0) 0 (0) 0 (0) Not available 0 (0) 0 (0) 83 (100) 268 (100) 97 (100) 226 (100) Molecular subtypes < .001 ⁑ < .001 ⁑ < .001 ⁑ HR+ / HER2- 11 (10) 165 (52) 19 (23) 153 (57) 22 (23) 115 (51) HR+ / HER2+ 27 (23) 65 (20) 15 (18) 37 (14) 16 (17) 33 (15) HR- / HER2- 34 (30) 59 (18) 26 (31) 53 (20) 44 (45) 68 (30) HR- / HER2+ 42 (37) 32 (10) 23 (28) 25 (9) 15 (15) 10 (4) FD Max FD 1.37 ± 0.21 1.46 ± 0.15 < .001 ‡ 1.34 ± 0.18 1.40 ± 0.17 .01 ‡ 1.51 ± 0.15 1.55 ± 0.14 .01 ‡ Median FD 1.26 ± 0.20 1.34 ± 0.15 < .001 ‡ 1.20 ± 0.19 1.26 ± 0.18 .01 ‡ 1.42 ± 0.14 1.46 ± 0.14 .06 ‡ Min FD 0.97 ± 0.18 1.02 ± 0.18 .01 † 1.01 ± 0.20 0.99 ± 0.21 .796 † 1.26 ± 0.17 1.28 ± 0.18 .12 ‡ Mean FD 1.23 ± 0.19 1.31 ± 0.14 < .001 ‡ 1.19 ± 0.18 1.24 ± 0.17 .046 ‡ 1.41 ± 0.14 1.44 ± 0.14 .06 ‡ Global FD 1.75 ± 0.31 2.00 ± 0.21 < .001 ‡ 1.77 ± 0.24 1.95 ± 0.22 < .001 ‡ 1.89 ± 0.20 2.02 ± 0.20 < .001 † Note: Unless otherwise indicated, numbers represent the count of patients, with percentages in parentheses. P values indicate the comparison of characteristics between the pCR and non-pCR groups across different cohorts. FD = fractal dimension, HER2 = human epidermal growth factor receptor2, HR = hormone receptor, pCR = pathologic complete response. * Data are medians, with IQRs in parentheses. ‡ Mann–Whitney U test. † t -test. ⁑ c 2 test or fisher exact test. Table 2: Univariable and Multivariable Logistic Regression Analysis of Characteristics Associated with pCR in the Training Cohort. Characteristics Univariable P Value Multivariable P Value Odds Ratio (95% CI) Odds Ratio (95% CI) Age 0.896 (0.725, 1.1082) 0.312 Menopausal status Premenopausal Reference Postmenopausal 0.960 (0.619, 1.489) 0.856 HR status Negative Reference Positive 0.197 (0.125, 0.313) < 0.001 0.234 (0.135, 0.406) < 0.001 HER2 status Negative Reference Positive 3.541 (2.270, 5.524) <0 .001 3.320 (1.923, 5.729) < 0.001 Ki-67 status Low Reference High 2.849 (1.367, 5.937) 0.005 2.023 (0.785, 5.214) 0.145 Clinical T stage cT1 Reference cT2 1.222 (0.734, 2.036) 0.441 0.903 (0.331, 2.468) 0.843 cT3 0.812 (0.450, 1.463) 0.488 0.718 (0.220, 2.335) 0.581 cT4 0.595 (0.360, 0.982) 0.042 0.497 (0.161, 1.527) 0.222 Clinical N stage cN0 Reference cN1 1.380 (0.887, 2.149) 0.154 cN2 1.014 (0.653, 1.577) 0.949 cN3 0.673 (0.365, 1.241) 0.204 Molecular subtypes HR+ / HER2- Reference HR+ / HER2+ 1.222 (0.734, 2.036) 0.441 HR- / HER2- 1.887 (1.155, 3.083) 0.011 HR- / HER2+ 5.268 (3.109, 8.927) < 0.001 FD Global FD 0.340 (0.256, 0.451) < 0.001 0.352 (0.261, 0.480) < 0.001 Note: Data in parentheses are 95% CI. Molecular subtypes were excluded from the multivariable logistic regression model because of their collinearity with both HR status and HER2 status. CI = confidence interval, FD = fractal dimension, HER2 = human epidermal growth factor receptor2, HR = hormone receptor, pCR = pathologic complete response. Table 3: Performances of Different Models for Predicting pCR to NAC in Three Cohorts Cohorts Models AUC (95% CI) ACC (%) SEN (%) SPE (%) PPV (%) NPV (%) TC Global FD 0.75 (0.70, 0.80) 70.1 73.7 68.8 45.7 88.0 Morphological model 0.61 (0.55, 0.67) 58.2 63.2 56.4 34.0 81.2 Nomogram model-1 0.83 (0.78, 0.87) 80.5 71.1 83.8 60.9 89.1 Nomogram model-2 0.78 (0.73, 0.82) 66.9 82.5 61.4 43.1 90.8 EVC-1 Global FD 0.73 (0.67, 0.79) 67.2 79.5 63.4 40.2 90.9 Morphological model 0.61 (0.54, 0.67) 63.0 60.2 63.8 34.0 83.8 Nomogram model-1 0.80 (0.75, 0.86) 78.3 67.5 81.7 53.3 89.0 Nomogram model-2 0.74 (0.68, 0.80) 70.7 71.1 70.5 42.8 88.7 EVC-2 Global FD 0.68 (0.61, 0.74) 65.6 53.6 70.8 44.1 78.0 Morphological model 0.55 (0.49, 0.62) 52.9 60.8 49.6 34.1 74.7 Nomogram model-1 0.74 (0.68, 0.79) 72.4 38.1 87.2 56.1 76.7 Nomogram model-2 0.69 (0.62, 0.74) 66.9 64.9 67.7 46.3 81.8 Note: AUC = area under the receiver operating characteristic curve, ACC = accuracy, CI = confidence interval, SEN = sensitivity, SPE = specificity, PPV = positive predictive value, NPV = negative predictive value, TC = training cohort, EVC-1 = external validation cohort 1, EVC-2 = external validation cohort 2. Nomogram model-1: combining hormone receptor, human epidermal growth factor receptor-2 and global FD. Nomogram model-2: combining hormone receptor, human epidermal growth factor receptor-2, sphericity, major axis length and maximum 2D diameter (row). Table 4: The Characteristics of Patients for Survival Analysis Characteristics Total ( n = 171 ） Age (years)* 50 (45-55) Age group ≤ 45 48 (28) > 45 123 (72) Menopausal status Premenopausal 102 (60) Postmenopausal 69 (40) HR status Negative 74 (43) Positive 97 (57) HER2 status Negative 108 (63) Positive 63 (37) Ki-67 status Low (< 20%) 22 (13) High (≥ 20%) 149 (87) Clinical T stage cT1 17 (10) cT2 90 (53) cT3 36 (21) cT4 28 (16) Clinical N stage cN0 20 (12) cN1 55 (32) cN2 67 (39) cN3 29 (17) Treatment response Non-pCR 133 (78) pCR 38 (22) Global_FD 1.94 ± 0.25 DFS Follow-up time (DFS)* 20.1 (5.85-36.0) Recurrence 52 (30) No recurrence 119 (70) OS Follow-up time (OS)* 36.9 (18.05-48.85) Death 14 (8) Survival 157 (92) Note: Unless otherwise indicated, numbers represent the count of patients, with percentages in parentheses. DFS = Disease-free survival, FD = fractal dimension, HER2 = human epidermal growth factor receptor2, HR = hormone receptor, OS = overall survival. * Data are medians, with IQRs in parentheses. Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterials.docx Cite Share Download PDF Status: Published Journal Publication published 20 May, 2025 Read the published version in Breast Cancer Research → Version 1 posted Editorial decision: Revision requested 25 Aug, 2024 Reviews received at journal 14 Jun, 2024 Reviewers agreed at journal 27 May, 2024 Reviewers invited by journal 27 May, 2024 Editor assigned by journal 23 Apr, 2024 Submission checks completed at journal 23 Apr, 2024 First submitted to journal 19 Apr, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4294410","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":294736307,"identity":"3f416937-28fe-41c4-9647-5254cd5540e4","order_by":0,"name":"Yao Huang","email":"","orcid":"","institution":"Chongqing University","correspondingAuthor":false,"prefix":"","firstName":"Yao","middleName":"","lastName":"Huang","suffix":""},{"id":294736308,"identity":"751617c0-f26b-4016-a1e2-6652deb26457","order_by":1,"name":"Ying Cao","email":"","orcid":"","institution":"Chongqing University","correspondingAuthor":false,"prefix":"","firstName":"Ying","middleName":"","lastName":"Cao","suffix":""},{"id":294736309,"identity":"7e88a8e2-77fe-4ed6-b0a0-dfbb2ca65a13","order_by":2,"name":"Huifang Chen","email":"","orcid":"","institution":"Chongqing University Cancer Hospital","correspondingAuthor":false,"prefix":"","firstName":"Huifang","middleName":"","lastName":"Chen","suffix":""},{"id":294736310,"identity":"e5ae745a-6f32-41e7-ade5-5837ce47a0dc","order_by":3,"name":"Xiaosong Lan","email":"","orcid":"","institution":"Chongqing University Cancer Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xiaosong","middleName":"","lastName":"Lan","suffix":""},{"id":294736311,"identity":"9d6d8094-4e02-4668-9b2f-f7739db8576a","order_by":4,"name":"Sun Tang","email":"","orcid":"","institution":"Chongqing University Cancer Hospital","correspondingAuthor":false,"prefix":"","firstName":"Sun","middleName":"","lastName":"Tang","suffix":""},{"id":294736312,"identity":"fa0a5ab2-2893-493c-b4b4-ab1092698096","order_by":5,"name":"Zhitao Zhang","email":"","orcid":"","institution":"Siemens Healthineers Digital Technology (Shanghai) Co., Ltd","correspondingAuthor":false,"prefix":"","firstName":"Zhitao","middleName":"","lastName":"Zhang","suffix":""},{"id":294736313,"identity":"81ac745c-ede6-405b-a65c-a9c938eab93f","order_by":6,"name":"Ting Yin","email":"","orcid":"","institution":"MR Collaborations, Siemens Healthineers Ltd","correspondingAuthor":false,"prefix":"","firstName":"Ting","middleName":"","lastName":"Yin","suffix":""},{"id":294736314,"identity":"6fd533a9-51c2-4bed-819e-847d27846bf1","order_by":7,"name":"Xiaoxia Wang","email":"","orcid":"","institution":"Chongqing University Cancer Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xiaoxia","middleName":"","lastName":"Wang","suffix":""},{"id":294736316,"identity":"aa890aed-0d29-44c7-a63b-27ea6d656916","order_by":8,"name":"Jiuquan Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABD0lEQVRIiWNgGAWjYDACCRDBA2YaHGAosOEHMxMKiNZikCbZANZiQEgLBACVGRyGaGHAo0V+dvOzh19krBnM2w9vPPDB4LwEv0R24ocHBgzy/GIHsGphnHPM3FiGJ51B5kxawcEZBrclJHvObpYAOsxw5uwErFqYJRLMpCV4DgMdmGNwmMfgdp3B8d4NIC0JBrexa2GTSP8G0cL/BqTlnITBYd7NP/Bp4ZHIMZP8ANIiAbblgATQlm14bQGqLJNmAPpFQuIZyC/JIL9ss0gwkMDpF/kZ6dskf/ZYAx2WvPnDhwo7YIjlbr75o8JGnl8auxZwEPD2MNc3oFuPUzkIMP74wYxXwSgYBaNgFIxwAABUVleLnNp0vAAAAABJRU5ErkJggg==","orcid":"","institution":"Chongqing University Cancer Hospital","correspondingAuthor":true,"prefix":"","firstName":"Jiuquan","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2024-04-19 16:57:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4294410/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4294410/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s13058-025-02034-5","type":"published","date":"2025-05-20T15:57:27+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":55538958,"identity":"44a6d350-762c-4173-9266-4cca25d72724","added_by":"auto","created_at":"2024-04-29 16:53:46","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1225221,"visible":true,"origin":"","legend":"\u003cp\u003ePatient inclusion and exclusion process for four center (A) and FD calculation flowchart (B). Data from center D are publicly available from the I-SPY2 dataset of The Cancer Imaging Archive. NAC = neoadjuvant chemotherapy，FD = fractal dimension, pCR = pathologic complete response.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4294410/v1/89fbb9af979e9b3464e4285b.png"},{"id":55538955,"identity":"994c5b3c-4399-44c4-a460-32cfa143bcfa","added_by":"auto","created_at":"2024-04-29 16:53:46","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1210690,"visible":true,"origin":"","legend":"\u003cp\u003eThe Spearman correlation coefficient network diagrams (A-C) between FDs and clinicopathologic variables, and the Spearman correlation coefficient heat maps (D-F) between FDs and morphological featuresin the training cohort, external validation cohort 1, and external validation cohort 2, respectively. FD = fractal dimension, HER2 = human epidermal growth factor receptor 2, HR = hormone receptor.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4294410/v1/aa0e559a0d786c737e3fb3fb.png"},{"id":55538953,"identity":"4af6d689-e17b-411b-a2aa-259973985aae","added_by":"auto","created_at":"2024-04-29 16:53:46","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":404121,"visible":true,"origin":"","legend":"\u003cp\u003eThe receiver operating characteristic curves (A-C), calibration curve (D-F) and decision curves (G-I) of different models in the training cohort, external validation cohort 1, and external validation cohort 2, respectively. The morphological model incorporates sphericity, major axis length and maximum 2D diameter (row). The nomogram model-1 integrates hormone receptor, human epidermal growth factor receptor-2, and global FD. The nomogram model-2 integrates hormone receptor, human epidermal growth factor receptor-2, sphericity, major axis length and maximum 2D diameter (row). AUC = area under the receiver operating characteristic curve, CI = confidence interval, FD = fractal dimension.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4294410/v1/3a3f2404235db527d603f228.png"},{"id":55538956,"identity":"b1bbbb9b-8145-47d5-9dcb-5e984a2e782f","added_by":"auto","created_at":"2024-04-29 16:53:46","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":331299,"visible":true,"origin":"","legend":"\u003cp\u003eA nomogram model incorporating hormone receptor, human epidermal growth factor receptor-2 and global FD for predicting pCR to NAC. FD = fractal dimension, HER2 = human epidermal growth factor receptor2, HR = hormone receptor, pCR = pathologic complete response.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4294410/v1/fc3a65797646f36ee7b74d89.png"},{"id":55538954,"identity":"cbfca50b-b98b-48e3-824c-1c5601d1bf88","added_by":"auto","created_at":"2024-04-29 16:53:46","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":574662,"visible":true,"origin":"","legend":"\u003cp\u003eForest plots of univariate and multivariate Coxproportional hazards regression analysis of DFS (A-B) and OS (C-D). Variables with a p-value of less than 0.05 in the univariate Cox proportional hazards regression analysis were included in the multivariate Cox proportional hazards regression analysis. CI = confidence interval, DFS = disease-free survival, FD = fractal dimension, HER2 = human epidermal growth factor receptor2, HR = hormone receptor, OS = overall survival.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-4294410/v1/b60798c83faa6a2298bbff14.png"},{"id":55538957,"identity":"d9d3e30c-d09d-4708-a7b9-776351167176","added_by":"auto","created_at":"2024-04-29 16:53:46","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":309753,"visible":true,"origin":"","legend":"\u003cp\u003ePrognostic nomogram models and Kaplan-Meier survival curves for DFS (A-B) and OS (C-D). DFS = disease-free survival, FD = fractal dimension, OS = overall survival.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-4294410/v1/a34bae21f84fb842fd485ec1.png"},{"id":83460619,"identity":"3017525a-d25d-4cc4-b4cf-1f416bff0a74","added_by":"auto","created_at":"2025-05-26 16:12:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7636616,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4294410/v1/7940cec0-ff8c-4c4e-8b69-69aa26fa71a1.pdf"},{"id":55538959,"identity":"595c6b31-8a78-4600-873f-94164016c36a","added_by":"auto","created_at":"2024-04-29 16:53:46","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":569015,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-4294410/v1/960fe43978f6a19016ffcd50.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Quantifying tumor morphological complexity based on pretreatment MRI fractal analysis for predicting pathologic complete response and survival in breast cancer: a retrospective, multicenter study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eNeoadjuvant chemotherapy (NAC) is the preferred treatment for locally advanced breast cancer, effectively reducing tumor size and raising the possibility of breast-conserving surgery\u0026nbsp;[1, 2].\u0026nbsp;Patients who achieved pathological complete response (pCR) after NAC showed improved disease-free survival (DFS) and overall survival (OS)\u0026nbsp;[3]. However, non-responders not only face increased economic burden but also potential drug side effects\u0026nbsp;[4, 5]. Therefore, a reliable approach to predict pCR and prognosis is urgently required in patients with breast cancer for personalized treatment.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTumor morphological complexity has been a manifestation of heterogeneity in breast cancer\u0026nbsp;[6, 7]. Patients with higher tumor sphericity and smaller volumes are more likely to achieve pCR\u0026nbsp;[8-10]. A greater size and more irregular morphology of the tumor are linked to increased risks of metastasis and recurrence\u0026nbsp;[11, 12]. Consequently, there is a close correlation between tumor morphological features and treatment response and prognosis\u0026nbsp;[13-15]. Despite the variety of features used to describe tumor morphology, many techniques essentially assess the same tumor characteristics, such as tumor size, indicated by both diameter and volume. Utilizing these characteristics for predicting pCR and prognosis yields moderate performance. Therefore, it is necessary to develop a quantitative parameter to quantify the morphological complexity of tumors, which could prove valuable in predicting pCR and survival outcomes.\u003c/p\u003e\n\u003cp\u003eThe fractal analysis quantifies tumor morphological complexity by measuring self-similarity across various spatial scales, utilizing fractal dimension (FD) as a quantitative parameter\u0026nbsp;[16]. A higher FD value indicates greater tumor morphological complexity, implying poorer treatment outcomes and prognosis\u0026nbsp;[17, 18]. Within breast cancer diagnosis, FD identifies malignancy by analyzing tumor heterogeneity in ultrasound, mammography, and MRI images, with higher FD values more indicative of malignant lesions\u0026nbsp;[19-22]. However, the efficacy of FD in predicting pCR to NAC and the survival outcomes for breast cancer is still unclear and requires further research.\u003c/p\u003e\n\u003cp\u003eThis study aimed to quantify tumor morphological complexity using multi-dimension FD based on pretreatment MRI and explore the predictive value of a model combining this quantitative measure with clinicopathologic variables to predict pCR to NAC and survival prognosis in patients with breast cancer.\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy Cohort\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis multicenter retrospective study received approval from the institutional review boards of each participating center, and the requirement for patient written informed consent was waived. The study was conducted following the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) guidelines\u0026nbsp;[23]. A checklist is provided in Supplementary Table S1.\u003c/p\u003e\n\u003cp\u003eFrom January 2017 to April 2023, this study recruited female patients from three centers. Centers A and B served as the training cohort, while center C and center D (the publicly available I-SPY2 dataset from The Cancer Imaging Archive, conducted between May 2010 and November 2016) served as external validation cohorts\u0026nbsp;[9]. The inclusion criteria were: (\u003cem\u003ea\u003c/em\u003e) biopsy-confirmed invasive breast cancer without distant metastasis, (\u003cem\u003eb\u003c/em\u003e) MRI conducted before NAC, and (\u003cem\u003ec\u003c/em\u003e) post-NAC pathological confirmation of pCR. The exclusion criteria involved: (\u003cem\u003ea\u003c/em\u003e) inadequate MRI quality, (\u003cem\u003eb\u003c/em\u003e) lack of histopathologic data, (\u003cem\u003ec\u003c/em\u003e) previous history of breast cancer, (\u003cem\u003ed\u003c/em\u003e) external institution surgery or unassessed pCR, (\u003cem\u003ee\u003c/em\u003e) incomplete clinical data (excluding uncollected characteristics), and (\u003cem\u003ef\u003c/em\u003e) lack of follow-up records for survival prognosis analysis in center A (Fig. 1A).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNAC Regimens and Histopathology Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll patients received an anthracyclines and/or taxanes-based NAC regimen. For human epidermal growth factor receptor2 (HER2) positive patients, treatment included trastuzumab alone or in combination with pertuzumab.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn the study, pre-neoadjuvant chemotherapy (NAC) core needle biopsies were conducted, and immunohistochemistry determined estrogen receptor (ER), progesterone receptor (PR) status, HER2 status, and Ki-67 index status for patients from centers A, B, and C. Tumors with \u0026ge;1% nuclear staining were defined as ER (+) / PR (+), while those with \u0026lt; 1% as ER (-)/PR (-) [24, 25]. HR positivity is defined as ER (+) and/or PR (+). HER2 status was categorized as negative for 0 or 1+ immunohistochemistry scores, and positive for 3+ scores. For 2+ scores, fluorescence in situ hybridization (FISH) determined HER2 status; amplification signified HER2 (+), and lack thereof indicated HER2 (-) [26, 27]. Additionally, a Ki-67 index threshold of 20% was set, where \u0026ge; 20% denoted high expression and \u0026lt; 20% indicated low expression [28].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe pCR was defined based on surgical pathology as the absence of residual invasive cancer, possibly with residual ductal carcinoma in situ, and no lymph node invasion in the axillary lymph nodes (ypT0/is ypN0).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMRI Procedure and Image Processing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll MRI examinations were performed with 1.5T or 3.0T scanners. Detailed protocols are provided in Supplementary Table S2.\u003c/p\u003e\n\u003cp\u003eTo minimize variability in imaging protocols, B-spline interpolation was used to resample images to 1\u0026times;1\u0026times;1 mm\u003csup\u003e3\u003c/sup\u003e, and z-score normalization was used for image intensity standardization. The peak enhancement phase of dynamic contrast-enhanced MRI was used. Preliminary semi-automatic tumor region segmentation was conducted using the Dr. Wise platform (\u003cem\u003ehttps://keyan.deepwise.com\u003c/em\u003e). Subsequently, two radiologists (*.*.*., and *.*.*., with 6 and 10 years of experience, respectively, in breast MRI) manually corrected the segmentation. If there were multiple lesions, only the largest one was considered [29].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImage\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFDs were calculated by MATLAB (version, R2020a) using the box-counting method [30]. Initially, post-segmentation images underwent binarization. Subsequently, boxes of incremental sizes covered tumor areas, recording the minimum needed for full coverage, with sizes increasing up to 45% of image dimensions [17, 31]. Finally, logarithmic transformation was applied to box sizes and corresponding quantities for linear regression via the least-squares method, as illustrated:\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAIUAAAAfCAYAAADJJw4ZAAADz0lEQVR4Ae2VDbHqQAyFawENWMADEtCABRzgAAcoQAEGMIADPPTNx8y5N6Tbn2237zI7yUyn3b8k5+Rk27RhwYBjoHHjGAYDbYgiRNBhIETRoSQmQhShgQ4DIYoOJTERoggNdBgIUXQoiYlBURyPx7ZpmvZ+vxdlarvdvv2ez+cfv3wTa7/f/8yt+aF4p9Npdpjr9drBMdtZ27bkAgc8z+dziavkWWGmrkM2KAoOriGK2+32Lv5ut/vIjViPx+Njbs0B8SnsEkPEVtxLfL1erzffl8tlshsaNqeR2DuG+U9EQVKA8R3hRTKZmZkbubGWduQaosjJKVcUUzBniwIVbzabd0Epou1sOkZrFJwn1UVcXwA/HA6tugJwY9fazNonjxGfXDF9zxGlFwW+mAM7/u3vCa60Jn54y7hBKVqO5YiCmwj/5MG7TyC/GfVkQtIExngDVEKgiAKhNUghOASr4N61yOfGgCQM8Yxda94PZyy5qW9/RmMKgCgx8JATeeeaFwVjiRu/8CVcrEkkzEmUigke5aS5sXeOKMAMTuXDd6pps0RBwgJFsvoHAh7nKjBrfixwnBFwBEQhNSexae+ab3CQI0RZTKmY5Kuc/boVBfkLj/bhW2dZU4Ox7sf46msk+dM5zvY9qUJzzubC2Ob+4d8OUt828ZQTrUOubhGKnNqLf/bZpKVc3zWpXErOEVekqnP6/E8VBQXHpzXbHMTULSK+4ErG2dzGyLkpiG/9M06J8BOBsjNvFZ0pyPFdZYEgBIrLHHstYLnkvO0WSGMvZ2Ui185pzb45S6yhx+7XN3lxhpuKIqlQWs95W/Gnbgrhwye4xA+/XStGnbWxx/DL55R9wiz/uqV5e8sShf6DUhtkojaMuTFySQwy6BKZyIA8a17Vdm3pt4qDH2EiN5/DlDhWFOwHn3gAGyIQXsSfKgLn6FhbXPKy475cwDJlHznQCOAkB/j1Da4Yg6IAHI7s1Y4jqZ1kJBACaV6dyzpJyNTZHoQXCr6YW8ts90qokGRznRKbwgmreOCNL/FmhQZ32q91fBBXZ+y6PduXz1RR4ItY8EqMPkEQZ1AUfYmk5glqQYhsks41umYo6Vx/37BffFjh+dvhG/Ikh2KioPttIXVFq4NyANM1OofPOcLKifc/9ooP/T4QBzcxv5Rvs2KisNcm15P/JUwFzm1jr1C+azEEbn+xiMLeHN+Csx7Gv4XRCvIIUVRQxNIQQhSlGa3AX4iigiKWhhCiKM1oBf5CFBUUsTSEEEVpRivwF6KooIilIYQoSjNagb8QRQVFLA3hH/mCDEidXV1zAAAAAElFTkSuQmCC\" height=\"31\" width=\"133\"\u003e\u003c/p\u003e\n\u003cp\u003ewhere \u003cem\u003eN\u003csub\u003eL\u003c/sub\u003e\u003c/em\u003e is the minimum count of boxes, each with side length \u003cem\u003eL\u003c/em\u003e, needed to cover the ROI areas, and FD is the opposite value of \u003cem\u003ek\u003c/em\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor each patient, 4 two-dimensional FDs (max FD, median FD, min FD, mean FD) and 1 three-dimensional FD (global FD) were calculated (Fig. 1B). Meanwhile, 14 morphological features describing tumor size and geometric shape were extracted using PyRadiomics (version 3.1)\u0026nbsp;[32], details of which are described in Supplementary materials.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFollow-up Data Collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor surveillance of recurrence and distant metastasis, patients underwent post-surgery follow-ups every six months with chest radiography and/or chest CT scans, along with annual bone scans and abdominal CT scans or ultrasounds. Follow-up was conducted in center A by two radiologists (*.*., and *.*., each with 6 and 10 years of experience), who recorded DFS and OS from the surgery date (the time origin) to the first recurrence or death, respectively. Patients without recurrence or death were censored at their last follow-up date.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analyses\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStatistical analysis was conducted using R (version 4.3.1) and Python (version 3.9.5). To assess the reproducibility of fractal analysis, images from 30 patients (15 patients with pCR and 15 patients with non-pCR) randomly selected were segmented twice by a radiologist at one-month intervals and once by another radiologist. Bland-Altman statistics was used to evaluate intra-observer consistency, and a mixed-effects model with random effects on intercept and slope was used for variance-component analysis to assess the reproducibility of fractal analysis\u0026nbsp;[33]. The intraclass correlation coefficient (ICC) was used to assess interobserver consistency, with an ICC \u0026gt; 0.75 indicating good consistency.\u003c/p\u003e\n\u003cp\u003eThe \u003cem\u003eX\u003c/em\u003e\u003cem\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e test or \u003cem\u003eFisher\u003c/em\u003e exact test was used to compare differences in categorical variables between pCR and non-pCR groups. The \u003cem\u003eKolmogorov-Smirnov\u003c/em\u003e test was used to assess the normal distribution of continuous variables, and the \u003cem\u003eLevene\u003c/em\u003e test was applied to assess homogeneity. Group differences were compared using the \u003cem\u003et\u003c/em\u003e-test or the \u003cem\u003eMann-Whitney U\u003c/em\u003e test. Univariable and multivariable logistic regression analyses were performed to assess the association between features and pCR, and molecular subtypes were excluded from the multivariable logistic regression model because of their collinearity with both HR status and HER2 status. Independent predictors of pCR were used to create a nomogram model. Model performance was assessed by area under the receiver operating characteristic curve (AUC), and accuracy, sensitivity specificity, positive predictive value, and negative predictive value were also calculated. AUCs of different models were compared using the Delong test. The \u003cem\u003eHosmer-Lemeshow\u003c/em\u003e test assessed the calibration of models, and decision curves were used to evaluate the benefit of models. The correlation coefficients were calculated using Spearman correlation analysis.\u003c/p\u003e\n\u003cp\u003eThe survival analysis used Cox proportional hazards analysis to identify factors associated with survival outcomes (DFS and OS) and develop nomogram models. Patients were divided into high and low-score groups based on the median score of the nomogram model. Differences in DFS and OS between these groups were compared using Kaplan-Meier curves and the \u003cem\u003eLog-rank\u003c/em\u003e test. Two-tailed \u003cem\u003eP\u003c/em\u003e \u0026lt; .05 was deemed statistically significant. The codes used for analyses are available from \u003cem\u003ehttps://github.com/YaoHuang1123/FD\u003c/em\u003e.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eBaseline Characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, 1318 patients were recruited from four centers. Patients were excluded for inadequate MRI quality (\u003cem\u003en\u003c/em\u003e = 20), lack of histopathologic data (\u003cem\u003en\u003c/em\u003e = 34), previous history of breast cancer (\u003cem\u003en\u003c/em\u003e = 13), external institution surgery or unassessed pCR (\u003cem\u003en\u003c/em\u003e = 81), and incomplete clinical data (\u003cem\u003en\u003c/em\u003e = 61), resulting in 1109 patients being included in the study (Fig. 1A). For predicting pCR, the training cohort comprised 435 patients (center A [\u003cem\u003en\u003c/em\u003e = 413], center B [\u003cem\u003en\u003c/em\u003e = 22]; median age, 51 years [IQR, 46\u0026ndash;55 years]). Two external validation cohorts consisted of 351 patients from center C (median age, 48 years [IQR, 43\u0026ndash;52 years]) and 323 patients from center D (median age, 48 years [IQR, 40\u0026ndash;56 years]).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn all cohorts, significant differences were observed in HR status, HER2 status, max FD, and global FD between pCR and non-pCR groups (all \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05) (Table 1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eReproducibility of Fractal Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe FDs for both 3D and 2D fractal analyses showed good consistency, the Bland-Altman repeatability coefficients ranging from 0.11 to 0.19 (Supplementary Table S3 and Fig. S2). Variance-components analysis indicated that the variance between patients (variance:0.0172-0.0195) exceeded the variance between readings (variance: 0.0001-0.0009) for both 3D and 2D fractal analyses. The coefficient of variation (COV) for 2D FDs (COV: 3.21-6.78) between readings was found to be higher than that for 3D (COV: 2.65), whereas the coefficient of variation for 2D FDs (COV: 377.94-467.28) between patients was lower compared to 3D (486.94). Furthermore, five FDs showed good inter-observer consistency (ICC: 0.87-0.93) (Supplementary Table S4).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorrelation Analysis\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSpearman correlation analysis indicated that global FD strongly positively correlated with max, median, and mean FD (correlation coefficient [\u003cem\u003er\u003c/em\u003e]: 0.69 to 0.81, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05), and negatively with HER2 status (\u003cem\u003er\u003c/em\u003e: -0.12 to -0.01, \u003cem\u003eP\u003c/em\u003e \u0026le; 0.04). Global FD showed negative correlations with sphericity and surface area to volume ratio (\u003cem\u003er\u003c/em\u003e: -0.65 to -0.18, \u003cem\u003eP\u003c/em\u003e \u0026le; 0.04), and positively with diameter and volume (\u003cem\u003er\u003c/em\u003e: 0.54 to 0.68, \u003cem\u003eP\u003c/em\u003e \u0026le; 0.04) (Fig. 2, Supplementary Table S5 and S6).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVariables Associated with pCR\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUnivariable logistic regression analysis showed that HR status, HER2 status, Ki-67 status, Clinical T stage, and global FD were associated with pCR. After adjustment of the multivariable model for variables with \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05 in the univariable analysis, HR status (odds ratio [OR], 0.234 [95% CI: 0.135, 0.406]; \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001), HER2 status (OR, 3.320 [95% CI: 1.923, 5.729]; \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001), and global FD (OR, 0.352 [95% CI: 0.261, 0.480]; \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001) were independent predictors for pCR (Table 2). These independent predictors were then used to develop the nomogram model-1. Following the same process, clinicopathological variables (HR and HER2 status) and morphological features (sphericity, major axis length, and maximum 2D diameter\u0026nbsp;[row]) were identified to develop the nomogram model-2 (Supplementary Table S7).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePerformance of Models for Prediction of pCR\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor predicting pCR to NAC, the AUCs ranging from 0.52 to 0.73 were observed for five FD univariable models across two external validation cohorts (Supplementary Fig. S2). Global FD achieved AUCs of 0.73 (95% CI: 0.67, 0.79) and 0.68 (95% CI: 0.61, 0.74), significantly outperforming morphological models with AUCs of 0.61 (95% CI: 0.54, 0.64) and 0.55 (95% CI: 0.49, 0.62) in the two external validation cohorts (Delong test, all \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001), respectively (Fig. 3A-C, Table 3, Supplementary Table S8).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe nomogram model-1 achieved AUCs of 0.80 (95% CI: 0.75, 0.86) and 0.74 (95% CI: 0.68, 0.79) (Fig. 3A-C, Fig. 4), significantly outperforming nomogram model-2 with AUCs of 0.74 (95% CI: 0.68, 0.80) and 0.69 (95% CI: 0.62, 0.74) in two external validation cohorts, respectively (Delong test, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001) (Supplementary Table S8).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe calibration between predicted and observed probabilities was good for nomogram model-1 (\u003cem\u003eHosmer-Lemeshow\u003c/em\u003e test, \u003cem\u003eP\u003c/em\u003e: 0.35-0.78) (Fig. 3D-F). Decision curve analysis showed that nomogram model-1 offered greater clinical benefit across most threshold ranges and demonstrated net benefits in two external validation cohorts at thresholds of 0.07 to 0.68 and 0.13 to 0.66 (Fig. 3G-I).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eModel Performance for Prediction of pCR in Patient Subgroups\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFour subgroup analyses were conducted based on molecular subtypes, age, menopausal status, and Ki-67 status. In two external validation cohorts, the global FD for prediction of pCR to NAC achieved AUCs ranging from 0.65-0.83 for patients with four molecular subtypes (HR+/HER2-, HR+/HER2+, HR-/HER2-, and HR-/HER+) (Supplementary Fig. S3).\u003c/p\u003e\n\u003cp\u003eThe nomogram model-1 achieved AUCs ranging from 0.72-0.83 for patients with age \u0026le; 45 years or age \u0026gt; 45 years, and 0.74-0.82 for premenopausal or postmenopausal patients in two external validation cohorts. For patients with high and low Ki-67 expression, the nomogram model-1 achieved AUCs of 0.77 (95% CI: 0.69-0.85) and 0.80 in external validation cohort 1 (Supplementary Fig. S4).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSurvival Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor survival analysis, 171 patients from center A (median age, 50 years [IQR, 45-55 years]) were enrolled. During the follow-up (DFS: median, 29 months [IQR, 15-44 months]; OS: median, 37 months [IQR, 18.05-48.85 months]), 52 patients had recurrence and 14 patients died (Table 4).\u003c/p\u003e\n\u003cp\u003eCox proportional hazards analysis identified menopausal status (hazard ratio [HR], 1.88 [95% CI: 1.08, 3.28]; \u003cem\u003eP\u0026nbsp;\u003c/em\u003e= 0.03), NAC treatment response (HR, 3.75 [95% CI: 1.14, 12.33]; \u003cem\u003eP\u0026nbsp;\u003c/em\u003e= 0.03) and global FD (HR, 2.03 [95% CI: 1.08, 3.81]; \u003cem\u003eP\u0026nbsp;\u003c/em\u003e= 0.03) were independent prognostic factors for DFS. While cT4 stage (HR, 5.92 [95% CI: 1.50, 23.34]; \u003cem\u003eP\u0026nbsp;\u003c/em\u003e= 0.01) and global FD (HR, 4.85 [95% CI: 1.05, 22.46]; \u003cem\u003eP\u0026nbsp;\u003c/em\u003e= 0.04) were independent prognostic factors for OS (Fig. 5).\u003c/p\u003e\n\u003cp\u003eFor DFS, the cutoff for dividing high and low-risk groups was 11.71, while for OS, the cutoff value was 42.01. Kaplan-Meier analysis for DFS and OS revealed significant differences between the low and high-risk groups (log-rank test, DFS: \u003cem\u003eP\u003c/em\u003e = 0.04; OS: \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001), with the low-risk group exhibiting better DFS and OS (Fig. 6).\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eAccurately predicting pCR to NAC and prognosis in breast cancer patients is crucial for clinical decision-making. This study used fractal analysis to quantify the tumor morphological complexity. The nomogram model combining global FD and clinicopathologic variables (HR status and HER2 status) showed good performance in predicting pCR to NAC. Additionally, the nomogram model that integrated global FD and clinicopathological variables could be used for prognostic stratification in patients with breast cancer.\u003c/p\u003e\n\u003cp\u003ePreviously, tumor morphology descriptions relied mainly on subjective assessments by radiologists and imaging shape features, showing diversity but lacking a quantitative indicator reflecting both tumor size and regularity. FD quantifies tumor morphological complexity without dependence on imaging techniques, which is suitable for broad clinical applications. Previous studies often focused on 2D measurements\u0026nbsp;[17-19]. Variance-components analysis revealed that, compared to global FD, 2D FDs showed a higher coefficient of variation between readings and a lower coefficient of variation between patients. This indicates that global FD may provide a more robust measure and may be beneficial in reflecting patient differences. Spearman correlation analysis revealed that global FD negatively correlates with sphericity, and the surface area to volume ratio, but positively with tumor size; higher morphological complexity (i.e., lower sphericity and larger tumor size) is reflected in increased global FD.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePrevious studies have focused on predicting pCR to NAC using clinical TNM staging, HR status, HER2 status, and Ki-67 expression, yet predictions based solely on clinicopathologic variables have shown limitations\u0026nbsp;[34, 35]. Models developed by Li et al\u0026nbsp;[9], based on tumor morphological features, achieved AUCs between 0.69 and 0.81 without further validation. Based on MRI, the radiomics model developed by Liu et al\u0026nbsp;[36]\u0026nbsp;achieved AUCs ranging from 0.71 to 0.80. Zhuang et al enhanced predictive model performance by combining radiomics and clinicopathologic variables, the combined model achieved an AUC of 0.826\u0026nbsp;[37]. In this study, the nomogram model-1 (combining global FD, HR status, and HER2 status) achieved AUCs of 0.80 and 0.74, on par with previous studies. Global FD and nomogram model-1 also showed good performance in predicting pCR in subgroup analyses. Notably, global FD offers clinicians an easily understandable quantitative feature, providing interpretability through its depiction of tumor morphology. Our findings suggest the potential of global FD as an imaging biomarker in assisting clinicians to identify pCR before NAC, which was more convenient to calculate than radiomics features.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study explored the application of global FD in predicting DFS and OS. Our findings indicated that patients with lower nomogram model (combining clinicopathological variables and global FD) scores exhibited better DFS and OS. Cox proportional hazards models indicated global FD was an independent prognostic factor for both DFS and OS. Previous studies have substantiated the importance of MRI tumor morphological features like tumor size in predicting breast cancer prognosis\u0026nbsp;[14, 38, 39]. Our results offer a new perspective on prognostic prediction of breast cancer using non-invasive MRI technology to quantify tumor morphological complexity.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study has several limitations. First, as a retrospective analysis incorporating data from four centers, the global FD\u0026apos;s clinical applicability and effectiveness need further validation through prospective analysis. Second, while this study used semi-automatic segmentation to ensure accuracy in FD calculation, fully automatic techniques could further augment stability and reduce subjectivity. Additionally, the prognostic analysis was based on a limited single-center sample and needs exploration in larger, multi-center cohorts to ascertain the value of global FD in prognosis prediction. Moreover, a comprehensive consideration of tumor morphology and spatial distribution could more fully quantify intratumoral heterogeneity. Finally, given that tumor morphology changes with treatment, reliance on pretreatment images may have limitations. Exploring the value of longitudinal changes in global FD is necessary to predict pCR to NAC and prognosis.\u003c/p\u003e\n\u003cp\u003eIn conclusion, the global fractal dimension developed from pretreatment MRI offers a non-invasive and practical approach to quantify the tumor morphological complexity and can predict pCR and prognosis in breast cancer. The generalizability and reproducibility of the prediction model based on the global fractal dimension should be validated with larger prospective data sets.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAUC area under the receiver operating characteristic curve\u003c/p\u003e\n\u003cp\u003eDFS disease-free survival\u003c/p\u003e\n\u003cp\u003eFD fractal dimension\u003c/p\u003e\n\u003cp\u003eHER2 human epidermal growth factor receptor2\u003c/p\u003e\n\u003cp\u003eHR hormone receptor\u003c/p\u003e\n\u003cp\u003eNAC neoadjuvant chemotherapy\u003c/p\u003e\n\u003cp\u003eOR odds ratio\u003c/p\u003e\n\u003cp\u003eOS overall survival\u003c/p\u003e\n\u003cp\u003epCR pathologic complete response\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by Chongqing University Cancer Hospital institutional review board, Southwest Hospital institutional review board, and Chongqing Hospital of Traditional Chinese Medicine.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study has received funding by the Graduate Research and Innovation Foundation of Chongqing (CYB23070), the Fundamental Research Funds for the Central Universities (2023CDJYGRH-YB04), the Chongqing Medical Research Project of Combination of Science and Medicine (No. 2024MSXM171), the Chongqing University Cancer Hospital Scientific Research Capacity Improvement Project (2023nlts004), and the Natural Science Foundation of Chongqing municipality (CSTB2023NSCQ-MSX0787).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGuarantors of integrity of entire study, Y.H., X.W., J.Z.; study concepts/study design or data acquisition or data analysis/interpretation, all authors; manuscript drafting or manuscript revision for important intellectual content, all authors; approval of final version of submitted manuscript, all authors; agrees to ensure any questions related to the work are appropriately resolved, all authors; literature research, Y.H., X.W., Y.C., J.Z.; clinical studies, Y.H., Y.C., H.C., X.L., S.T., J.Z.; experimental studies, Y.H., X.W., Y.C., Z.Z., T.Y.; statistical analysis, Y.H., X.W., J.Z.; and manuscript editing, Y.H., X.W., Y.C., J.Z.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank all patients who participated in the study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eKorde LA, Somerfield MR, Carey LA, Crews JR, Denduluri N, Hwang ES, Khan SA, Loibl S, Morris EA, Perez A\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eNeoadjuvant Chemotherapy, Endocrine Therapy, and Targeted Therapy for Breast Cancer: ASCO Guideline\u003c/strong\u003e. \u003cem\u003eJ Clin Oncol \u003c/em\u003e2021, \u003cstrong\u003e39\u003c/strong\u003e(13):1485-1505.\u003c/li\u003e\n\u003cli\u003eGradishar WJ, Anderson BO, Abraham J, Aft R, Agnese D, Allison KH, Blair SL, Burstein HJ, Dang C, Elias AD\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eBreast Cancer, Version 3.2020, NCCN Clinical Practice Guidelines in Oncology\u003c/strong\u003e. \u003cem\u003eJ Natl Compr Canc Netw \u003c/em\u003e2020, \u003cstrong\u003e18\u003c/strong\u003e(4):452-478.\u003c/li\u003e\n\u003cli\u003eCortazar P, Zhang L, Untch M, Mehta K, Costantino JP, Wolmark N, Bonnefoi H, Cameron D, Gianni L, Valagussa P\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003ePathological complete response and long-term clinical benefit in breast cancer: the CTNeoBC pooled analysis\u003c/strong\u003e. \u003cem\u003eLancet \u003c/em\u003e2014, \u003cstrong\u003e384\u003c/strong\u003e(9938):164-172.\u003c/li\u003e\n\u003cli\u003eRomeo V, Accardo G, Perillo T, Basso L, Garbino N, Nicolai E, Maurea S, Salvatore M: \u003cstrong\u003eAssessment and Prediction of Response to Neoadjuvant Chemotherapy in Breast Cancer: A Comparison of Imaging Modalities and Future Perspectives\u003c/strong\u003e. \u003cem\u003eCancers (Basel) \u003c/em\u003e2021, \u003cstrong\u003e13\u003c/strong\u003e(14).\u003c/li\u003e\n\u003cli\u003eWolf DM, Yau C, Wulfkuhle J, Brown-Swigart L, Gallagher RI, Lee PRE, Zhu Z, Magbanua MJ, Sayaman R, O\u0026apos;Grady N\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eRedefining breast cancer subtypes to guide treatment prioritization and maximize response: Predictive biomarkers across 10 cancer therapies\u003c/strong\u003e. \u003cem\u003eCancer Cell \u003c/em\u003e2022, \u003cstrong\u003e40\u003c/strong\u003e(6):609-623.e606.\u003c/li\u003e\n\u003cli\u003eCaudle AS, Gonzalez-Angulo AM, Hunt KK, Liu P, Pusztai L, Symmans WF, Kuerer HM, Mittendorf EA, Hortobagyi GN, Meric-Bernstam F: \u003cstrong\u003ePredictors of tumor progression during neoadjuvant chemotherapy in breast cancer\u003c/strong\u003e. \u003cem\u003eJ Clin Oncol \u003c/em\u003e2010, \u003cstrong\u003e28\u003c/strong\u003e(11):1821-1828.\u003c/li\u003e\n\u003cli\u003eBraman N, Prasanna P, Whitney J, Singh S, Beig N, Etesami M, Bates DDB, Gallagher K, Bloch BN, Vulchi M\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eAssociation of Peritumoral Radiomics With Tumor Biology and Pathologic Response to Preoperative Targeted Therapy for HER2 (ERBB2)-Positive Breast Cancer\u003c/strong\u003e. \u003cem\u003eJAMA Netw Open \u003c/em\u003e2019, \u003cstrong\u003e2\u003c/strong\u003e(4):e192561.\u003c/li\u003e\n\u003cli\u003eHylton NM, Blume JD, Bernreuter WK, Pisano ED, Rosen MA, Morris EA, Weatherall PT, Lehman CD, Newstead GM, Polin S\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eLocally advanced breast cancer: MR imaging for prediction of response to neoadjuvant chemotherapy--results from ACRIN 6657/I-SPY TRIAL\u003c/strong\u003e. \u003cem\u003eRadiology \u003c/em\u003e2012, \u003cstrong\u003e263\u003c/strong\u003e(3):663-672.\u003c/li\u003e\n\u003cli\u003eLi W, Newitt DC, Gibbs J, Wilmes LJ, Jones EF, Arasu VA, Strand F, Onishi N, Nguyen AA, Kornak J\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003ePredicting breast cancer response to neoadjuvant treatment using multi-feature MRI: results from the I-SPY 2 TRIAL\u003c/strong\u003e. \u003cem\u003eNPJ Breast Cancer \u003c/em\u003e2020, \u003cstrong\u003e6\u003c/strong\u003e(1):63.\u003c/li\u003e\n\u003cli\u003eLi W, Newitt DC, Yun B, Jones EF, Arasu V, Wilmes LJ, Gibbs J, Nguyen AA, Onishi N, Kornak J\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eTumor Sphericity Predicts Response in Neoadjuvant Chemotherapy for Invasive Breast Cancer\u003c/strong\u003e. \u003cem\u003eTomography \u003c/em\u003e2020, \u003cstrong\u003e6\u003c/strong\u003e(2):216-222.\u003c/li\u003e\n\u003cli\u003eGong X, Wu J, Wu J, Liu J, Gu H, Shen H: \u003cstrong\u003eCorrelation of SASH1 expression and ultrasonographic features in breast cancer\u003c/strong\u003e. \u003cem\u003eOnco Targets Ther \u003c/em\u003e2017, \u003cstrong\u003e10\u003c/strong\u003e:271-276.\u003c/li\u003e\n\u003cli\u003eSopik V, Narod SA: \u003cstrong\u003eThe relationship between tumour size, nodal status and distant metastases: on the origins of breast cancer\u003c/strong\u003e. \u003cem\u003eBreast Cancer Res Treat \u003c/em\u003e2018, \u003cstrong\u003e170\u003c/strong\u003e(3):647-656.\u003c/li\u003e\n\u003cli\u003eUemoto Y, Katsuta E, Kondo N, Wanifuchi-Endo Y, Fujita T, Asano T, Hisada T, Terada M, Kato A, Okuda K\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eLow HECTD1 mRNA expression is associated with poor prognosis and may be correlated with increased mitochondrial respiratory function in breast cancer\u003c/strong\u003e. \u003cem\u003eAm J Cancer Res \u003c/em\u003e2022, \u003cstrong\u003e12\u003c/strong\u003e(4):1593-1605.\u003c/li\u003e\n\u003cli\u003eGuirguis MS, Adrada BE, Candelaria RP, Sun J, Whitman GJ, Yang WT, Boge M, Mohamed RM, Elshafeey NA, Lane DL\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003ePrediction of response to neoadjuvant systemic therapy in triple negative breast cancer using baseline tumor MRI characteristics and imaging patterns of response\u003c/strong\u003e. \u003cem\u003eCancer Res \u003c/em\u003e2022, \u003cstrong\u003e82\u003c/strong\u003e(4):3.\u003c/li\u003e\n\u003cli\u003eZhang W, Wang Z, Yang S, Wang Y, Xiang S, Guo Z, Hou B, Dong X, Yuan Z, Xu B\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003ePreoperative Rim Enhancement on Magnetic Resonance Imaging Indicates Larger Tumor Size and Poor Prognosis in Chinese Basal-Like Breast Cancer Patients\u003c/strong\u003e. \u003cem\u003eCancer Biother Radiopharm \u003c/em\u003e2022, \u003cstrong\u003e37\u003c/strong\u003e(8):729-736.\u003c/li\u003e\n\u003cli\u003eFern\u0026aacute;ndez E, Jelinek HF: \u003cstrong\u003eUse of fractal theory in neuroscience: methods, advantages, and potential problems\u003c/strong\u003e. \u003cem\u003eMethods \u003c/em\u003e2001, \u003cstrong\u003e24\u003c/strong\u003e(4):309-321.\u003c/li\u003e\n\u003cli\u003eTochigi T, Kamran SC, Parakh A, Noda Y, Ganeshan B, Blaszkowsky LS, Ryan DP, Allen JN, Berger DL, Wo JY\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eResponse prediction of neoadjuvant chemoradiation therapy in locally advanced rectal cancer using CT-based fractal dimension analysis\u003c/strong\u003e. \u003cem\u003eEur Radiol \u003c/em\u003e2022, \u003cstrong\u003e32\u003c/strong\u003e(4):2426-2436.\u003c/li\u003e\n\u003cli\u003eWang J, Li Y, Yang F, Bravo L, Wan K, Xu Y, Cheng W, Sun J, Zhu Y, Zhu T\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eFractal Analysis: Prognostic Value of Left Ventricular Trabecular Complexity Cardiovascular MRI in Participants with Hypertrophic Cardiomyopathy\u003c/strong\u003e. \u003cem\u003eRadiology \u003c/em\u003e2021, \u003cstrong\u003e298\u003c/strong\u003e(1):71-79.\u003c/li\u003e\n\u003cli\u003eSherminie LPG, Jayatilake ML: \u003cstrong\u003eFractal Dimension Analysis of Pixel Dynamic Contrast Enhanced-Magnetic Resonance Imaging Pharmacokinetic Parameters for Discrimination of Benign and Malignant Breast Lesions\u003c/strong\u003e. \u003cem\u003eJCO Clin Cancer Inform \u003c/em\u003e2023, \u003cstrong\u003e7\u003c/strong\u003e:e2200101.\u003c/li\u003e\n\u003cli\u003eAlvarado-Cruz LB, Delgadillo-Herrera M, Toxqui-Quitl C, Padilla-Vivanco A, Castro-Ortega R, Arreola-Esquivel M: \u003cstrong\u003eFractal analysis for classification of breast lesions\u003c/strong\u003e. In: \u003cem\u003eConference on Current Developments in Lens Design and Optical Engineering XX: Aug 12 2019; San Diego, CA\u003c/em\u003e: Spie-Int Soc Optical Engineering; 2019.\u003c/li\u003e\n\u003cli\u003eGarra BS, Krasner BH, Horii SC, Ascher S, Mun SK, Zeman RK: \u003cstrong\u003eImproving the distinction between benign and malignant breast lesions: the value of sonographic texture analysis\u003c/strong\u003e. \u003cem\u003eUltrason Imaging \u003c/em\u003e1993, \u003cstrong\u003e15\u003c/strong\u003e(4):267-285.\u003c/li\u003e\n\u003cli\u003eByng JW, Boyd NF, Fishell E, Jong RA, Yaffe MJ: \u003cstrong\u003eAutomated analysis of mammographic densities\u003c/strong\u003e. \u003cem\u003ePhys Med Biol \u003c/em\u003e1996, \u003cstrong\u003e41\u003c/strong\u003e(5):909-923.\u003c/li\u003e\n\u003cli\u003eCollins GS, Reitsma JB, Altman DG, Moons KG: \u003cstrong\u003eTransparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement\u003c/strong\u003e. \u003cem\u003eBmj \u003c/em\u003e2015, \u003cstrong\u003e350\u003c/strong\u003e:g7594.\u003c/li\u003e\n\u003cli\u003eKwan ML, Bernard PS, Kroenke CH, Factor RE, Habel LA, Weltzien EK, Castillo A, Gunderson EP, Maxfield KS, Stijleman IJ\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eBreastfeeding, PAM50 tumor subtype, and breast cancer prognosis and survival\u003c/strong\u003e. \u003cem\u003eJ Natl Cancer Inst \u003c/em\u003e2015, \u003cstrong\u003e107\u003c/strong\u003e(7).\u003c/li\u003e\n\u003cli\u003eNewman LA, Stark A, Chitale D, Pepe M, Longton G, Worsham MJ, Nathanson SD, Miller P, Bensenhaver JM, Proctor E\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eAssociation Between Benign Breast Disease in African American and White American Women and Subsequent Triple-Negative Breast Cancer\u003c/strong\u003e. \u003cem\u003eJAMA Oncol \u003c/em\u003e2017, \u003cstrong\u003e3\u003c/strong\u003e(8):1102-1106.\u003c/li\u003e\n\u003cli\u003eTutt A, Tovey H, Cheang MCU, Kernaghan S, Kilburn L, Gazinska P, Owen J, Abraham J, Barrett S, Barrett-Lee P\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eCarboplatin in BRCA1/2-mutated and triple-negative breast cancer BRCAness subgroups: the TNT Trial\u003c/strong\u003e. \u003cem\u003eNat Med \u003c/em\u003e2018, \u003cstrong\u003e24\u003c/strong\u003e(5):628-637.\u003c/li\u003e\n\u003cli\u003eBuisseret L, Pommey S, Allard B, Garaud S, Bergeron M, Cousineau I, Ameye L, Bareche Y, Paesmans M, Crown JPA\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eClinical significance of CD73 in triple-negative breast cancer: multiplex analysis of a phase III clinical trial\u003c/strong\u003e. \u003cem\u003eAnn Oncol \u003c/em\u003e2018, \u003cstrong\u003e29\u003c/strong\u003e(4):1056-1062.\u003c/li\u003e\n\u003cli\u003eG\u0026oacute;mez-Aleza C, Nguyen B, Yoldi G, Ciscar M, Barranco A, Hern\u0026aacute;ndez-Jim\u0026eacute;nez E, Maetens M, Salgado R, Zafeiroglou M, Pellegrini P\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eInhibition of RANK signaling in breast cancer induces an anti-tumor immune response orchestrated by CD8+ T cells\u003c/strong\u003e. \u003cem\u003eNat Commun \u003c/em\u003e2020, \u003cstrong\u003e11\u003c/strong\u003e(1):6335.\u003c/li\u003e\n\u003cli\u003eLeithner D, Horvat JV, Marino MA, Bernard-Davila B, Jochelson MS, Ochoa-Albiztegui RE, Martinez DF, Morris EA, Thakur S, Pinker K: \u003cstrong\u003eRadiomic signatures with contrast-enhanced magnetic resonance imaging for the assessment of breast cancer receptor status and molecular subtypes: initial results\u003c/strong\u003e. \u003cem\u003eBreast Cancer Res \u003c/em\u003e2019, \u003cstrong\u003e21\u003c/strong\u003e(1):106.\u003c/li\u003e\n\u003cli\u003eKempkes SN, Slot MR, Freeney SE, Zevenhuizen SJM, Vanmaekelbergh D, Swart I, Smith CM: \u003cstrong\u003eDesign and characterization of electrons in a fractal geometry\u003c/strong\u003e. \u003cem\u003eNat Phys \u003c/em\u003e2019, \u003cstrong\u003e15\u003c/strong\u003e(2):127-131.\u003c/li\u003e\n\u003cli\u003eLiu S, Fan X, Zhang C, Wang Z, Li S, Wang Y, Qiu X, Jiang T: \u003cstrong\u003eMR imaging based fractal analysis for differentiating primary CNS lymphoma and glioblastoma\u003c/strong\u003e. \u003cem\u003eEur Radiol \u003c/em\u003e2019, \u003cstrong\u003e29\u003c/strong\u003e(3):1348-1354.\u003c/li\u003e\n\u003cli\u003evan Griethuysen JJM, Fedorov A, Parmar C, Hosny A, Aucoin N, Narayan V, Beets-Tan RGH, Fillion-Robin JC, Pieper S, Aerts H: \u003cstrong\u003eComputational Radiomics System to Decode the Radiographic Phenotype\u003c/strong\u003e. \u003cem\u003eCancer Res \u003c/em\u003e2017, \u003cstrong\u003e77\u003c/strong\u003e(21):e104-e107.\u003c/li\u003e\n\u003cli\u003eSanghera B, Banerjee D, Khan A, Simcock I, Stirling JJ, Glynne-Jones R, Goh V: \u003cstrong\u003eReproducibility of 2D and 3D fractal analysis techniques for the assessment of spatial heterogeneity of regional blood flow in rectal cancer\u003c/strong\u003e. \u003cem\u003eRadiology \u003c/em\u003e2012, \u003cstrong\u003e263\u003c/strong\u003e(3):865-873.\u003c/li\u003e\n\u003cli\u003eArici S, Sengiz Erhan S, Geredeli C, Cekin R, Sakin A, Cihan S: \u003cstrong\u003eThe Clinical Importance of Androgen Receptor Status in Response to Neoadjuvant Chemotherapy in Turkish Patients with Local and Locally Advanced Breast Cancer\u003c/strong\u003e. \u003cem\u003eOncol Res Treat \u003c/em\u003e2020, \u003cstrong\u003e43\u003c/strong\u003e(9):435-440.\u003c/li\u003e\n\u003cli\u003eOuldamer L, Bendifallah S, Pilloy J, Arbion F, Body G, Brisson C, Lavou\u0026eacute; V, L\u0026eacute;v\u0026ecirc;que J, Dara\u0026iuml; E: \u003cstrong\u003eRisk scoring system for predicting breast conservation after neoadjuvant chemotherapy\u003c/strong\u003e. \u003cem\u003eBreast J \u003c/em\u003e2019, \u003cstrong\u003e25\u003c/strong\u003e(4):696-701.\u003c/li\u003e\n\u003cli\u003eLiu Z, Li Z, Qu J, Zhang R, Zhou X, Li L, Sun K, Tang Z, Jiang H, Li H\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eRadiomics of Multiparametric MRI for Pretreatment Prediction of Pathologic Complete Response to Neoadjuvant Chemotherapy in Breast Cancer: A Multicenter Study\u003c/strong\u003e. \u003cem\u003eClin Cancer Res \u003c/em\u003e2019, \u003cstrong\u003e25\u003c/strong\u003e(12):3538-3547.\u003c/li\u003e\n\u003cli\u003eZhuang X, Chen C, Liu Z, Zhang L, Zhou X, Cheng M, Ji F, Zhu T, Lei C, Zhang J\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eMultiparametric MRI-based radiomics analysis for the prediction of breast tumor regression patterns after neoadjuvant chemotherapy\u003c/strong\u003e. \u003cem\u003eTransl Oncol \u003c/em\u003e2020, \u003cstrong\u003e13\u003c/strong\u003e(11):100831.\u003c/li\u003e\n\u003cli\u003eKim JY, Kim JJ, Hwangbo L, Suh HB, Kim S, Choo KS, Nam KJ, Kang T: \u003cstrong\u003eKinetic Heterogeneity of Breast Cancer Determined Using Computer-aided Diagnosis of Preoperative MRI Scans: Relationship to Distant Metastasis-Free Survival\u003c/strong\u003e. \u003cem\u003eRadiology \u003c/em\u003e2020, \u003cstrong\u003e295\u003c/strong\u003e(3):517-526.\u003c/li\u003e\n\u003cli\u003eLiang T, Hu B, Du H, Zhang Y: \u003cstrong\u003ePredictive value of T2-weighted magnetic resonance imaging for the prognosis of patients with mass-type breast cancer with peritumoral edema\u003c/strong\u003e. \u003cem\u003eOncol Lett \u003c/em\u003e2020, \u003cstrong\u003e20\u003c/strong\u003e(6):314.\u003cstrong\u003e\u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1: The Characteristics of Patients in Three Cohorts.\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"954\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.867924528301888%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.230607966457022%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eTraining cohort\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(\u003cem\u003en\u003c/em\u003e = 435)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.4423480083857445%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.81132075471698%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eExternal validation cohort 1 (\u003cem\u003en\u003c/em\u003e = 351)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.918238993710692%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.81132075471698%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eExternal validation cohort 2 (\u003cem\u003en\u003c/em\u003e = 323)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.918238993710692%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.535901926444833%\"\u003e\n \u003cp\u003e\u003cstrong\u003epCR\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(\u003cem\u003en\u003c/em\u003e = 114)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.26444833625219%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-pCR\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(\u003cem\u003en\u003c/em\u003e = 321)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.8861646234676%\"\u003e\n \u003cp\u003e\u003cstrong\u003epCR\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(\u003cem\u003en\u003c/em\u003e = 83)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.21366024518389%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-pCR\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(\u003cem\u003en\u003c/em\u003e = 268)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.21366024518389%\"\u003e\n \u003cp\u003e\u003cstrong\u003epCR\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(\u003cem\u003en\u003c/em\u003e = 97)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.8861646234676%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-pCR\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(\u003cem\u003en\u003c/em\u003e = 226)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.867924528301888%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (years)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.70020964360587%\"\u003e\n \u003cp\u003e51 (45-55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.530398322851154%\"\u003e\n \u003cp\u003e50 (46-55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.4423480083857445%\"\u003e\n \u003cp\u003e.58\u003csup\u003e\u0026Dagger;\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.90985324947589%\"\u003e\n \u003cp\u003e49\u0026nbsp;\u0026plusmn;\u0026nbsp;8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90146750524109%\"\u003e\n \u003cp\u003e47\u0026nbsp;\u0026plusmn;\u0026nbsp;9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.918238993710692%\"\u003e\n \u003cp\u003e.11\u003cstrong\u003e\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90146750524109%\"\u003e\n \u003cp\u003e49 (41-56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.90985324947589%\"\u003e\n \u003cp\u003e47 (40-56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.918238993710692%\"\u003e\n \u003cp\u003e.72\u003csup\u003e\u0026Dagger;\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.867924528301888%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.70020964360587%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.530398322851154%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.4423480083857445%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.90985324947589%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90146750524109%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.918238993710692%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90146750524109%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.90985324947589%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.918238993710692%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.867924528301888%\"\u003e\n \u003cp\u003e\u0026le;\u0026nbsp;45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.70020964360587%\"\u003e\n \u003cp\u003e40 (35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.530398322851154%\"\u003e\n \u003cp\u003e104 (32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.4423480083857445%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.90985324947589%\"\u003e\n \u003cp\u003e21 (25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90146750524109%\"\u003e\n \u003cp\u003e112 (42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.918238993710692%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90146750524109%\"\u003e\n \u003cp\u003e40 (41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.90985324947589%\"\u003e\n \u003cp\u003e45 (41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.918238993710692%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.867924528301888%\"\u003e\n \u003cp\u003e\u0026gt; 45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.70020964360587%\"\u003e\n \u003cp\u003e74 (65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.530398322851154%\"\u003e\n \u003cp\u003e217 (68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.4423480083857445%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.90985324947589%\"\u003e\n \u003cp\u003e62 (75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90146750524109%\"\u003e\n \u003cp\u003e156 (58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.918238993710692%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90146750524109%\"\u003e\n \u003cp\u003e57 (59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.90985324947589%\"\u003e\n \u003cp\u003e134 (59)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.918238993710692%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.867924528301888%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMenopausal status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.70020964360587%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.530398322851154%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.4423480083857445%\"\u003e\n \u003cp\u003e.94\u003csup\u003e⁑\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.90985324947589%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90146750524109%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.918238993710692%\"\u003e\n \u003cp\u003e.34\u003csup\u003e⁑\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90146750524109%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.90985324947589%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.918238993710692%\"\u003e\n \u003cp\u003e.27\u003csup\u003e⁑\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.867924528301888%\"\u003e\n \u003cp\u003ePremenopausal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.70020964360587%\"\u003e\n \u003cp\u003e70 (61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.530398322851154%\"\u003e\n \u003cp\u003e194 (60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.4423480083857445%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.90985324947589%\"\u003e\n \u003cp\u003e50 (60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90146750524109%\"\u003e\n \u003cp\u003e179 (67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.918238993710692%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90146750524109%\"\u003e\n \u003cp\u003e51 (53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.90985324947589%\"\u003e\n \u003cp\u003e140 (62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.918238993710692%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.867924528301888%\"\u003e\n \u003cp\u003ePostmenopausal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.70020964360587%\"\u003e\n \u003cp\u003e44 (39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.530398322851154%\"\u003e\n \u003cp\u003e127 (40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.4423480083857445%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.90985324947589%\"\u003e\n \u003cp\u003e33 (40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90146750524109%\"\u003e\n \u003cp\u003e89 (33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.918238993710692%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90146750524109%\"\u003e\n \u003cp\u003e39 (40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.90985324947589%\"\u003e\n \u003cp\u003e75 (33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.918238993710692%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.867924528301888%\"\u003e\n \u003cp\u003ePerimenopausal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.70020964360587%\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.530398322851154%\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.4423480083857445%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.90985324947589%\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90146750524109%\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.918238993710692%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90146750524109%\"\u003e\n \u003cp\u003e7 (7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.90985324947589%\"\u003e\n \u003cp\u003e11 (5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.918238993710692%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.867924528301888%\"\u003e\n \u003cp\u003e\u003cstrong\u003eHR status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.70020964360587%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.530398322851154%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.4423480083857445%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; .001\u003c/strong\u003e\u003csup\u003e⁑\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.90985324947589%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90146750524109%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.918238993710692%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; .001\u003c/strong\u003e\u003csup\u003e⁑\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90146750524109%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.90985324947589%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.918238993710692%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; .001\u003c/strong\u003e\u003csup\u003e⁑\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.867924528301888%\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.70020964360587%\"\u003e\n \u003cp\u003e76 (67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.530398322851154%\"\u003e\n \u003cp\u003e91 (28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.4423480083857445%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.90985324947589%\"\u003e\n \u003cp\u003e49 (59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90146750524109%\"\u003e\n \u003cp\u003e78 (29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.918238993710692%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90146750524109%\"\u003e\n \u003cp\u003e59 (61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.90985324947589%\"\u003e\n \u003cp\u003e78 (35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.918238993710692%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.867924528301888%\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.70020964360587%\"\u003e\n \u003cp\u003e38 (33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.530398322851154%\"\u003e\n \u003cp\u003e230 (72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.4423480083857445%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.90985324947589%\"\u003e\n \u003cp\u003e34 (41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90146750524109%\"\u003e\n \u003cp\u003e190 (71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.918238993710692%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90146750524109%\"\u003e\n \u003cp\u003e38 (39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.90985324947589%\"\u003e\n \u003cp\u003e148 (65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.918238993710692%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.867924528301888%\"\u003e\n \u003cp\u003e\u003cstrong\u003eHER2 status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.70020964360587%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.530398322851154%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.4423480083857445%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; .001\u003c/strong\u003e\u003csup\u003e⁑\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.90985324947589%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90146750524109%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.918238993710692%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; .001\u003c/strong\u003e\u003csup\u003e⁑\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90146750524109%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.90985324947589%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.918238993710692%\"\u003e\n \u003cp\u003e\u003cstrong\u003e.02\u003c/strong\u003e\u003csup\u003e⁑\u003c/sup\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.867924528301888%\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.70020964360587%\"\u003e\n \u003cp\u003e45 (39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.530398322851154%\"\u003e\n \u003cp\u003e224 (70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.4423480083857445%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.90985324947589%\"\u003e\n \u003cp\u003e45 (54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90146750524109%\"\u003e\n \u003cp\u003e206 (77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.918238993710692%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90146750524109%\"\u003e\n \u003cp\u003e66 (68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.90985324947589%\"\u003e\n \u003cp\u003e183 (81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.918238993710692%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.867924528301888%\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.70020964360587%\"\u003e\n \u003cp\u003e69 (61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.530398322851154%\"\u003e\n \u003cp\u003e97 (30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.4423480083857445%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.90985324947589%\"\u003e\n \u003cp\u003e38 (46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90146750524109%\"\u003e\n \u003cp\u003e62 (23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.918238993710692%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90146750524109%\"\u003e\n \u003cp\u003e31 (32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.90985324947589%\"\u003e\n \u003cp\u003e43 (19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.918238993710692%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.867924528301888%\"\u003e\n \u003cp\u003e\u003cstrong\u003eKi-67 status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.70020964360587%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.530398322851154%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.4423480083857445%\"\u003e\n \u003cp\u003e\u003cstrong\u003e.01\u003c/strong\u003e\u003csup\u003e⁑\u003c/sup\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.90985324947589%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90146750524109%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.918238993710692%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; .001\u003c/strong\u003e\u003csup\u003e⁑\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90146750524109%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.90985324947589%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.918238993710692%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.867924528301888%\"\u003e\n \u003cp\u003eLow (\u0026lt; 20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.70020964360587%\"\u003e\n \u003cp\u003e9 (8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.530398322851154%\"\u003e\n \u003cp\u003e63 (20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.4423480083857445%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.90985324947589%\"\u003e\n \u003cp\u003e8 (10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90146750524109%\"\u003e\n \u003cp\u003e83 (31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.918238993710692%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90146750524109%\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.90985324947589%\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.918238993710692%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.867924528301888%\"\u003e\n \u003cp\u003eHigh (\u0026ge;\u0026nbsp;20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.70020964360587%\"\u003e\n \u003cp\u003e105 (92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.530398322851154%\"\u003e\n \u003cp\u003e258 (80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.4423480083857445%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.90985324947589%\"\u003e\n \u003cp\u003e75 (90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90146750524109%\"\u003e\n \u003cp\u003e185 (69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.918238993710692%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90146750524109%\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.90985324947589%\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.918238993710692%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.867924528301888%\"\u003e\n \u003cp\u003eNot available\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.70020964360587%\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.530398322851154%\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.4423480083857445%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.90985324947589%\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90146750524109%\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.918238993710692%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90146750524109%\"\u003e\n \u003cp\u003e97 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.90985324947589%\"\u003e\n \u003cp\u003e226 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.918238993710692%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.867924528301888%\"\u003e\n \u003cp\u003e\u003cstrong\u003eClinical T stage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.70020964360587%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.530398322851154%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.4423480083857445%\"\u003e\n \u003cp\u003e.11\u003csup\u003e⁑\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.90985324947589%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90146750524109%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.918238993710692%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90146750524109%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.90985324947589%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.918238993710692%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.867924528301888%\"\u003e\n \u003cp\u003ecT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.70020964360587%\"\u003e\n \u003cp\u003e11 (9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.530398322851154%\"\u003e\n \u003cp\u003e25 (8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.4423480083857445%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.90985324947589%\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90146750524109%\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.918238993710692%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90146750524109%\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.90985324947589%\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.918238993710692%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.867924528301888%\"\u003e\n \u003cp\u003ecT2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.70020964360587%\"\u003e\n \u003cp\u003e61 (54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.530398322851154%\"\u003e\n \u003cp\u003e136 (42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.4423480083857445%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.90985324947589%\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90146750524109%\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.918238993710692%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90146750524109%\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.90985324947589%\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.918238993710692%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.867924528301888%\"\u003e\n \u003cp\u003ecT3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.70020964360587%\"\u003e\n \u003cp\u003e17 (15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.530398322851154%\"\u003e\n \u003cp\u003e57 (18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.4423480083857445%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.90985324947589%\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90146750524109%\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.918238993710692%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90146750524109%\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.90985324947589%\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.918238993710692%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.867924528301888%\"\u003e\n \u003cp\u003ecT4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.70020964360587%\"\u003e\n \u003cp\u003e25 (22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.530398322851154%\"\u003e\n \u003cp\u003e103 (32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.4423480083857445%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.90985324947589%\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90146750524109%\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.918238993710692%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90146750524109%\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.90985324947589%\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.918238993710692%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.867924528301888%\"\u003e\n \u003cp\u003eNot available\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.70020964360587%\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.530398322851154%\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.4423480083857445%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.90985324947589%\"\u003e\n \u003cp\u003e83 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90146750524109%\"\u003e\n \u003cp\u003e268 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.918238993710692%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90146750524109%\"\u003e\n \u003cp\u003e97 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.90985324947589%\"\u003e\n \u003cp\u003e226 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.918238993710692%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.867924528301888%\"\u003e\n \u003cp\u003e\u003cstrong\u003eClinical N stage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.70020964360587%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.530398322851154%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.4423480083857445%\"\u003e\n \u003cp\u003e0.37\u003csup\u003e⁑\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.90985324947589%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90146750524109%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.918238993710692%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90146750524109%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.90985324947589%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.918238993710692%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.867924528301888%\"\u003e\n \u003cp\u003ecN0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.70020964360587%\"\u003e\n \u003cp\u003e11 (10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.530398322851154%\"\u003e\n \u003cp\u003e39 (12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.4423480083857445%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.90985324947589%\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90146750524109%\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.918238993710692%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90146750524109%\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.90985324947589%\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.918238993710692%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.867924528301888%\"\u003e\n \u003cp\u003ecN1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.70020964360587%\"\u003e\n \u003cp\u003e45 (39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.530398322851154%\"\u003e\n \u003cp\u003e103 (32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.4423480083857445%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.90985324947589%\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90146750524109%\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.918238993710692%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90146750524109%\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.90985324947589%\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.918238993710692%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.867924528301888%\"\u003e\n \u003cp\u003ecN2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.70020964360587%\"\u003e\n \u003cp\u003e43 (38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.530398322851154%\"\u003e\n \u003cp\u003e120 (38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.4423480083857445%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.90985324947589%\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90146750524109%\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.918238993710692%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90146750524109%\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.90985324947589%\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.918238993710692%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.867924528301888%\"\u003e\n \u003cp\u003ecN3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.70020964360587%\"\u003e\n \u003cp\u003e15 (13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.530398322851154%\"\u003e\n \u003cp\u003e59 (18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.4423480083857445%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.90985324947589%\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90146750524109%\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.918238993710692%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90146750524109%\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.90985324947589%\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.918238993710692%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.867924528301888%\"\u003e\n \u003cp\u003eNot available\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.70020964360587%\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.530398322851154%\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.4423480083857445%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.90985324947589%\"\u003e\n \u003cp\u003e83 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90146750524109%\"\u003e\n \u003cp\u003e268 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.918238993710692%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90146750524109%\"\u003e\n \u003cp\u003e97 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.90985324947589%\"\u003e\n \u003cp\u003e226 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.918238993710692%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.867924528301888%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMolecular subtypes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.70020964360587%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.530398322851154%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.4423480083857445%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; .001\u003c/strong\u003e\u003csup\u003e⁑\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.90985324947589%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90146750524109%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.918238993710692%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; .001\u003c/strong\u003e\u003csup\u003e⁑\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90146750524109%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.90985324947589%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.918238993710692%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; .001\u003c/strong\u003e\u003csup\u003e⁑\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.867924528301888%\"\u003e\n \u003cp\u003eHR+ / HER2-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.70020964360587%\"\u003e\n \u003cp\u003e11 (10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.530398322851154%\"\u003e\n \u003cp\u003e165 (52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.4423480083857445%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.90985324947589%\"\u003e\n \u003cp\u003e19 (23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90146750524109%\"\u003e\n \u003cp\u003e153 (57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.918238993710692%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90146750524109%\"\u003e\n \u003cp\u003e22 (23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.90985324947589%\"\u003e\n \u003cp\u003e115 (51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.918238993710692%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.867924528301888%\"\u003e\n \u003cp\u003eHR+ / HER2+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.70020964360587%\"\u003e\n \u003cp\u003e27 (23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.530398322851154%\"\u003e\n \u003cp\u003e65 (20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.4423480083857445%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.90985324947589%\"\u003e\n \u003cp\u003e15 (18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90146750524109%\"\u003e\n \u003cp\u003e37 (14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.918238993710692%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90146750524109%\"\u003e\n \u003cp\u003e16 (17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.90985324947589%\"\u003e\n \u003cp\u003e33 (15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.918238993710692%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.867924528301888%\"\u003e\n \u003cp\u003eHR- / HER2-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.70020964360587%\"\u003e\n \u003cp\u003e34 (30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.530398322851154%\"\u003e\n \u003cp\u003e59 (18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.4423480083857445%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.90985324947589%\"\u003e\n \u003cp\u003e26 (31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90146750524109%\"\u003e\n \u003cp\u003e53 (20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.918238993710692%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90146750524109%\"\u003e\n \u003cp\u003e44 (45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.90985324947589%\"\u003e\n \u003cp\u003e68 (30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.918238993710692%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.867924528301888%\"\u003e\n \u003cp\u003eHR- / HER2+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.70020964360587%\"\u003e\n \u003cp\u003e42 (37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.530398322851154%\"\u003e\n \u003cp\u003e32 (10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.4423480083857445%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.90985324947589%\"\u003e\n \u003cp\u003e23 (28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90146750524109%\"\u003e\n \u003cp\u003e25 (9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.918238993710692%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90146750524109%\"\u003e\n \u003cp\u003e15 (15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.90985324947589%\"\u003e\n \u003cp\u003e10 (4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.918238993710692%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.867924528301888%\"\u003e\n \u003cp\u003e\u003cstrong\u003eFD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.70020964360587%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.530398322851154%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.4423480083857445%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.90985324947589%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90146750524109%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.918238993710692%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90146750524109%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.90985324947589%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.918238993710692%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.867924528301888%\"\u003e\n \u003cp\u003eMax FD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.70020964360587%\"\u003e\n \u003cp\u003e1.37 \u0026plusmn; 0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.530398322851154%\"\u003e\n \u003cp\u003e1.46 \u0026plusmn; 0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.4423480083857445%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; .001\u003c/strong\u003e\u003csup\u003e\u0026Dagger;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.90985324947589%\"\u003e\n \u003cp\u003e1.34 \u0026plusmn; 0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90146750524109%\"\u003e\n \u003cp\u003e1.40 \u0026plusmn; 0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.918238993710692%\"\u003e\n \u003cp\u003e\u003cstrong\u003e.01\u003c/strong\u003e\u003csup\u003e\u0026Dagger;\u003c/sup\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90146750524109%\"\u003e\n \u003cp\u003e1.51 \u0026plusmn; 0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.90985324947589%\"\u003e\n \u003cp\u003e1.55 \u0026plusmn; 0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.918238993710692%\"\u003e\n \u003cp\u003e\u003cstrong\u003e.01\u003c/strong\u003e\u003csup\u003e\u0026Dagger;\u003c/sup\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.867924528301888%\"\u003e\n \u003cp\u003eMedian FD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.70020964360587%\"\u003e\n \u003cp\u003e1.26 \u0026plusmn; 0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.530398322851154%\"\u003e\n \u003cp\u003e1.34 \u0026plusmn; 0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.4423480083857445%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; .001\u003c/strong\u003e\u003csup\u003e\u0026Dagger;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.90985324947589%\"\u003e\n \u003cp\u003e1.20 \u0026plusmn; 0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90146750524109%\"\u003e\n \u003cp\u003e1.26 \u0026plusmn; 0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.918238993710692%\"\u003e\n \u003cp\u003e\u003cstrong\u003e.01\u003c/strong\u003e\u003csup\u003e\u0026Dagger;\u003c/sup\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90146750524109%\"\u003e\n \u003cp\u003e1.42 \u0026plusmn; 0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.90985324947589%\"\u003e\n \u003cp\u003e1.46 \u0026plusmn; 0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.918238993710692%\"\u003e\n \u003cp\u003e.06\u003csup\u003e\u0026Dagger;\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.867924528301888%\"\u003e\n \u003cp\u003eMin FD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.70020964360587%\"\u003e\n \u003cp\u003e0.97 \u0026plusmn; 0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.530398322851154%\"\u003e\n \u003cp\u003e1.02 \u0026plusmn; 0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.4423480083857445%\"\u003e\n \u003cp\u003e\u003cstrong\u003e.01\u003c/strong\u003e\u003cstrong\u003e\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.90985324947589%\"\u003e\n \u003cp\u003e1.01 \u0026plusmn; 0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90146750524109%\"\u003e\n \u003cp\u003e0.99 \u0026plusmn; 0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.918238993710692%\"\u003e\n \u003cp\u003e.796\u003cstrong\u003e\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90146750524109%\"\u003e\n \u003cp\u003e1.26 \u0026plusmn; 0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.90985324947589%\"\u003e\n \u003cp\u003e1.28 \u0026plusmn; 0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.918238993710692%\"\u003e\n \u003cp\u003e.12\u003csup\u003e\u0026Dagger;\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.867924528301888%\"\u003e\n \u003cp\u003eMean FD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.70020964360587%\"\u003e\n \u003cp\u003e1.23 \u0026plusmn; 0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.530398322851154%\"\u003e\n \u003cp\u003e1.31 \u0026plusmn; 0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.4423480083857445%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; .001\u003c/strong\u003e\u003csup\u003e\u0026Dagger;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.90985324947589%\"\u003e\n \u003cp\u003e1.19 \u0026plusmn; 0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90146750524109%\"\u003e\n \u003cp\u003e1.24 \u0026plusmn; 0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.918238993710692%\"\u003e\n \u003cp\u003e.046\u003csup\u003e\u0026Dagger;\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90146750524109%\"\u003e\n \u003cp\u003e1.41 \u0026plusmn; 0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.90985324947589%\"\u003e\n \u003cp\u003e1.44 \u0026plusmn; 0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.918238993710692%\"\u003e\n \u003cp\u003e.06\u003csup\u003e\u0026Dagger;\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.867924528301888%\"\u003e\n \u003cp\u003eGlobal FD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.70020964360587%\"\u003e\n \u003cp\u003e1.75 \u0026plusmn; 0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.530398322851154%\"\u003e\n \u003cp\u003e2.00 \u0026plusmn; 0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.4423480083857445%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; .001\u003c/strong\u003e\u003csup\u003e\u0026Dagger;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.90985324947589%\"\u003e\n \u003cp\u003e1.77 \u0026plusmn; 0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90146750524109%\"\u003e\n \u003cp\u003e1.95 \u0026plusmn; 0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.918238993710692%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; .001\u003c/strong\u003e\u003csup\u003e\u0026Dagger;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90146750524109%\"\u003e\n \u003cp\u003e1.89 \u0026plusmn; 0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.90985324947589%\"\u003e\n \u003cp\u003e2.02 \u0026plusmn; 0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.918238993710692%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; .001\u003c/strong\u003e\u003cstrong\u003e\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"10\"\u003e\n \u003cp\u003eNote: Unless otherwise indicated, numbers represent the count of patients, with percentages in parentheses. \u003cem\u003eP\u003c/em\u003e values indicate the comparison of characteristics between the pCR and non-pCR groups across different cohorts. FD = fractal dimension, HER2 = human epidermal growth factor receptor2, HR = hormone receptor, pCR = pathologic complete response.\u003c/p\u003e\n \u003cp\u003e* Data are medians, with IQRs in parentheses.\u003c/p\u003e\n \u003cp\u003e\u0026Dagger; Mann\u0026ndash;Whitney U test.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026dagger;\u003c/strong\u003e \u003cem\u003et\u003c/em\u003e-test.\u003c/p\u003e\n \u003cp\u003e⁑\u0026nbsp;c\u003csup\u003e2\u003c/sup\u003e test or fisher exact test.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2: Univariable and Multivariable Logistic Regression Analysis of Characteristics Associated with pCR in the Training Cohort.\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"558\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.788530465949822%\" rowspan=\"2\" style=\"width: 18.8552%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.655913978494624%\" style=\"width: 31.5366%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnivariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.21505376344086%\" rowspan=\"2\" style=\"width: 9.3442%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.72043010752688%\" style=\"width: 20.0233%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMultivariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.21505376344086%\" rowspan=\"2\" style=\"width: 8.8436%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.24390243902439%\" style=\"width: 31.5366%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOdds Ratio (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"53.96341463414634%\" style=\"width: 20.0233%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOdds Ratio (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.788530465949822%\" style=\"width: 18.8552%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.655913978494624%\" style=\"width: 31.5366%;\"\u003e\n \u003cp\u003e0.896 (0.725, 1.1082)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.21505376344086%\" style=\"width: 9.3442%;\"\u003e\n \u003cp\u003e0.312\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.72043010752688%\" style=\"width: 20.0233%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.21505376344086%\" style=\"width: 8.8436%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.788530465949822%\" style=\"width: 18.8552%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMenopausal status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.655913978494624%\" style=\"width: 31.5366%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.21505376344086%\" style=\"width: 9.3442%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"31.72043010752688%\" style=\"width: 20.0233%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.21505376344086%\" style=\"width: 8.8436%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.788530465949822%\" style=\"width: 18.8552%;\"\u003e\n \u003cp\u003ePremenopausal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.655913978494624%\" style=\"width: 31.5366%;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.21505376344086%\" style=\"width: 9.3442%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"31.72043010752688%\" style=\"width: 20.0233%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.21505376344086%\" style=\"width: 8.8436%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.788530465949822%\" style=\"width: 18.8552%;\"\u003e\n \u003cp\u003ePostmenopausal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.655913978494624%\" style=\"width: 31.5366%;\"\u003e\n \u003cp\u003e0.960 (0.619, 1.489)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.21505376344086%\" style=\"width: 9.3442%;\"\u003e\n \u003cp\u003e0.856\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.72043010752688%\" style=\"width: 20.0233%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.21505376344086%\" style=\"width: 8.8436%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.788530465949822%\" style=\"width: 18.8552%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHR status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.655913978494624%\" style=\"width: 31.5366%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.21505376344086%\" style=\"width: 9.3442%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"31.72043010752688%\" style=\"width: 20.0233%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.21505376344086%\" style=\"width: 8.8436%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.788530465949822%\" style=\"width: 18.8552%;\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.655913978494624%\" style=\"width: 31.5366%;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.21505376344086%\" style=\"width: 9.3442%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"31.72043010752688%\" style=\"width: 20.0233%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.21505376344086%\" style=\"width: 8.8436%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.788530465949822%\" style=\"width: 18.8552%;\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.655913978494624%\" style=\"width: 31.5366%;\"\u003e\n \u003cp\u003e0.197 (0.125, 0.313)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.21505376344086%\" style=\"width: 9.3442%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.72043010752688%\" style=\"width: 20.0233%;\"\u003e\n \u003cp\u003e0.234 (0.135, 0.406)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.21505376344086%\" style=\"width: 8.8436%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.788530465949822%\" style=\"width: 18.8552%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHER2 status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.655913978494624%\" style=\"width: 31.5366%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.21505376344086%\" style=\"width: 9.3442%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"31.72043010752688%\" style=\"width: 20.0233%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.21505376344086%\" style=\"width: 8.8436%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.788530465949822%\" style=\"width: 18.8552%;\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.655913978494624%\" style=\"width: 31.5366%;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.21505376344086%\" style=\"width: 9.3442%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"31.72043010752688%\" style=\"width: 20.0233%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.21505376344086%\" style=\"width: 8.8436%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.788530465949822%\" style=\"width: 18.8552%;\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.655913978494624%\" style=\"width: 31.5366%;\"\u003e\n \u003cp\u003e3.541 (2.270, 5.524)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.21505376344086%\" style=\"width: 9.3442%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0 .001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.72043010752688%\" style=\"width: 20.0233%;\"\u003e\n \u003cp\u003e3.320 (1.923, 5.729)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.21505376344086%\" style=\"width: 8.8436%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.788530465949822%\" style=\"width: 18.8552%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eKi-67 status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.655913978494624%\" style=\"width: 31.5366%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.21505376344086%\" style=\"width: 9.3442%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"31.72043010752688%\" style=\"width: 20.0233%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.21505376344086%\" style=\"width: 8.8436%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.788530465949822%\" style=\"width: 18.8552%;\"\u003e\n \u003cp\u003eLow\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.655913978494624%\" style=\"width: 31.5366%;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.21505376344086%\" style=\"width: 9.3442%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"31.72043010752688%\" style=\"width: 20.0233%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.21505376344086%\" style=\"width: 8.8436%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.788530465949822%\" style=\"width: 18.8552%;\"\u003e\n \u003cp\u003eHigh\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.655913978494624%\" style=\"width: 31.5366%;\"\u003e\n \u003cp\u003e2.849 (1.367, 5.937)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.21505376344086%\" style=\"width: 9.3442%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.005\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.72043010752688%\" style=\"width: 20.0233%;\"\u003e\n \u003cp\u003e2.023 (0.785, 5.214)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.21505376344086%\" style=\"width: 8.8436%;\"\u003e\n \u003cp\u003e0.145\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.788530465949822%\" style=\"width: 18.8552%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eClinical T stage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.655913978494624%\" style=\"width: 31.5366%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.21505376344086%\" style=\"width: 9.3442%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"31.72043010752688%\" style=\"width: 20.0233%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.21505376344086%\" style=\"width: 8.8436%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.788530465949822%\" style=\"width: 18.8552%;\"\u003e\n \u003cp\u003ecT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.655913978494624%\" style=\"width: 31.5366%;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.21505376344086%\" style=\"width: 9.3442%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"31.72043010752688%\" style=\"width: 20.0233%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.21505376344086%\" style=\"width: 8.8436%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.788530465949822%\" style=\"width: 18.8552%;\"\u003e\n \u003cp\u003ecT2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.655913978494624%\" style=\"width: 31.5366%;\"\u003e\n \u003cp\u003e1.222 (0.734, 2.036)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.21505376344086%\" style=\"width: 9.3442%;\"\u003e\n \u003cp\u003e0.441\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.72043010752688%\" style=\"width: 20.0233%;\"\u003e\n \u003cp\u003e0.903 (0.331, 2.468)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.21505376344086%\" style=\"width: 8.8436%;\"\u003e\n \u003cp\u003e0.843\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.788530465949822%\" style=\"width: 18.8552%;\"\u003e\n \u003cp\u003ecT3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.655913978494624%\" style=\"width: 31.5366%;\"\u003e\n \u003cp\u003e0.812 (0.450, 1.463)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.21505376344086%\" style=\"width: 9.3442%;\"\u003e\n \u003cp\u003e0.488\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.72043010752688%\" style=\"width: 20.0233%;\"\u003e\n \u003cp\u003e0.718 (0.220, 2.335)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.21505376344086%\" style=\"width: 8.8436%;\"\u003e\n \u003cp\u003e0.581\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.788530465949822%\" style=\"width: 18.8552%;\"\u003e\n \u003cp\u003ecT4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.655913978494624%\" style=\"width: 31.5366%;\"\u003e\n \u003cp\u003e0.595 (0.360, 0.982)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.21505376344086%\" style=\"width: 9.3442%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.042\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.72043010752688%\" style=\"width: 20.0233%;\"\u003e\n \u003cp\u003e0.497 (0.161, 1.527)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.21505376344086%\" style=\"width: 8.8436%;\"\u003e\n \u003cp\u003e0.222\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.788530465949822%\" style=\"width: 18.8552%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eClinical N stage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.655913978494624%\" style=\"width: 31.5366%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.21505376344086%\" style=\"width: 9.3442%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"31.72043010752688%\" style=\"width: 20.0233%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.21505376344086%\" style=\"width: 8.8436%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.788530465949822%\" style=\"width: 18.8552%;\"\u003e\n \u003cp\u003ecN0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.655913978494624%\" style=\"width: 31.5366%;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.21505376344086%\" style=\"width: 9.3442%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"31.72043010752688%\" style=\"width: 20.0233%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.21505376344086%\" style=\"width: 8.8436%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.788530465949822%\" style=\"width: 18.8552%;\"\u003e\n \u003cp\u003ecN1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.655913978494624%\" style=\"width: 31.5366%;\"\u003e\n \u003cp\u003e1.380 (0.887, 2.149)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.21505376344086%\" style=\"width: 9.3442%;\"\u003e\n \u003cp\u003e0.154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.72043010752688%\" style=\"width: 20.0233%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.21505376344086%\" style=\"width: 8.8436%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.788530465949822%\" style=\"width: 18.8552%;\"\u003e\n \u003cp\u003ecN2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.655913978494624%\" style=\"width: 31.5366%;\"\u003e\n \u003cp\u003e1.014 (0.653, 1.577)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.21505376344086%\" style=\"width: 9.3442%;\"\u003e\n \u003cp\u003e0.949\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.72043010752688%\" style=\"width: 20.0233%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.21505376344086%\" style=\"width: 8.8436%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.788530465949822%\" style=\"width: 18.8552%;\"\u003e\n \u003cp\u003ecN3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.655913978494624%\" style=\"width: 31.5366%;\"\u003e\n \u003cp\u003e0.673 (0.365, 1.241)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.21505376344086%\" style=\"width: 9.3442%;\"\u003e\n \u003cp\u003e0.204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.72043010752688%\" style=\"width: 20.0233%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.21505376344086%\" style=\"width: 8.8436%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.788530465949822%\" style=\"width: 18.8552%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMolecular subtypes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.655913978494624%\" style=\"width: 31.5366%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.21505376344086%\" style=\"width: 9.3442%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.72043010752688%\" style=\"width: 20.0233%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.21505376344086%\" style=\"width: 8.8436%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.788530465949822%\" style=\"width: 18.8552%;\"\u003e\n \u003cp\u003eHR+ / HER2-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.655913978494624%\" style=\"width: 31.5366%;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.21505376344086%\" style=\"width: 9.3442%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.72043010752688%\" style=\"width: 20.0233%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.21505376344086%\" style=\"width: 8.8436%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.788530465949822%\" style=\"width: 18.8552%;\"\u003e\n \u003cp\u003eHR+ / HER2+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.655913978494624%\" style=\"width: 31.5366%;\"\u003e\n \u003cp\u003e1.222 (0.734, 2.036)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.21505376344086%\" style=\"width: 9.3442%;\"\u003e\n \u003cp\u003e0.441\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.72043010752688%\" style=\"width: 20.0233%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.21505376344086%\" style=\"width: 8.8436%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.788530465949822%\" style=\"width: 18.8552%;\"\u003e\n \u003cp\u003eHR- / HER2-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.655913978494624%\" style=\"width: 31.5366%;\"\u003e\n \u003cp\u003e1.887 (1.155, 3.083)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.21505376344086%\" style=\"width: 9.3442%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.011\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.72043010752688%\" style=\"width: 20.0233%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.21505376344086%\" style=\"width: 8.8436%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.788530465949822%\" style=\"width: 18.8552%;\"\u003e\n \u003cp\u003eHR- / HER2+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.655913978494624%\" style=\"width: 31.5366%;\"\u003e\n \u003cp\u003e5.268 (3.109, 8.927)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.21505376344086%\" style=\"width: 9.3442%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.72043010752688%\" style=\"width: 20.0233%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.21505376344086%\" style=\"width: 8.8436%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.788530465949822%\" style=\"width: 18.8552%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.655913978494624%\" style=\"width: 31.5366%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.21505376344086%\" style=\"width: 9.3442%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"31.72043010752688%\" style=\"width: 20.0233%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.21505376344086%\" style=\"width: 8.8436%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.788530465949822%\" style=\"width: 18.8552%;\"\u003e\n \u003cp\u003eGlobal FD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.655913978494624%\" style=\"width: 31.5366%;\"\u003e\n \u003cp\u003e0.340 (0.256, 0.451)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.21505376344086%\" style=\"width: 9.3442%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.72043010752688%\" style=\"width: 20.0233%;\"\u003e\n \u003cp\u003e0.352 (0.261, 0.480)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.21505376344086%\" style=\"width: 8.8436%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"5\" style=\"width: 92.9413%;\"\u003e\n \u003cp\u003eNote: Data in parentheses are 95% CI. Molecular subtypes were excluded from the multivariable logistic regression model because of their collinearity with both HR status and HER2 status. CI = confidence interval, FD = fractal dimension, HER2 = human epidermal growth factor receptor2, HR = hormone receptor, pCR = pathologic complete response.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cbr\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3: Performances of Different Models for Predicting pCR\u003c/strong\u003e \u003cstrong\u003eto NAC in Three Cohorts\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"614\" style=\"margin-right: calc(33%); width: 67%;\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.120521172638437%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCohorts\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.661237785016286%\"\u003e\n \u003cp\u003e\u003cstrong\u003eModels\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.472312703583063%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAUC (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.749185667752442%\"\u003e\n \u003cp\u003e\u003cstrong\u003eACC (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.749185667752442%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSEN (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.749185667752442%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSPE (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.749185667752442%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePPV (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.749185667752442%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eNPV (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.120521172638437%\" rowspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eTC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.661237785016286%\"\u003e\n \u003cp\u003eGlobal FD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.472312703583063%\"\u003e\n \u003cp\u003e0.75 (0.70, 0.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.749185667752442%\"\u003e\n \u003cp\u003e70.1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.749185667752442%\"\u003e\n \u003cp\u003e73.7\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.749185667752442%\"\u003e\n \u003cp\u003e68.8\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.749185667752442%\"\u003e\n \u003cp\u003e45.7\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.749185667752442%\" colspan=\"2\"\u003e\n \u003cp\u003e88.0\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.835125448028673%\"\u003e\n \u003cp\u003eMorphological model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.025089605734767%\"\u003e\n \u003cp\u003e0.61 (0.55, 0.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\n \u003cp\u003e58.2\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\n \u003cp\u003e63.2\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\n \u003cp\u003e56.4\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\n \u003cp\u003e34.0\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\" colspan=\"2\"\u003e\n \u003cp\u003e81.2\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.835125448028673%\"\u003e\n \u003cp\u003eNomogram model-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.025089605734767%\"\u003e\n \u003cp\u003e0.83 (0.78, 0.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\n \u003cp\u003e80.5\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\n \u003cp\u003e71.1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\n \u003cp\u003e83.8\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\n \u003cp\u003e60.9\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\" colspan=\"2\"\u003e\n \u003cp\u003e89.1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.835125448028673%\"\u003e\n \u003cp\u003eNomogram model-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.025089605734767%\"\u003e\n \u003cp\u003e0.78 (0.73, 0.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\n \u003cp\u003e66.9\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\n \u003cp\u003e82.5\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\n \u003cp\u003e61.4\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\n \u003cp\u003e43.1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\" colspan=\"2\"\u003e\n \u003cp\u003e90.8\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.120521172638437%\" rowspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eEVC-1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.661237785016286%\"\u003e\n \u003cp\u003eGlobal FD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.472312703583063%\"\u003e\n \u003cp\u003e0.73 (0.67, 0.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.749185667752442%\"\u003e\n \u003cp\u003e67.2\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.749185667752442%\"\u003e\n \u003cp\u003e79.5\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.749185667752442%\"\u003e\n \u003cp\u003e63.4\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.749185667752442%\"\u003e\n \u003cp\u003e40.2\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.749185667752442%\" colspan=\"2\"\u003e\n \u003cp\u003e90.9\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.835125448028673%\"\u003e\n \u003cp\u003eMorphological model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.025089605734767%\"\u003e\n \u003cp\u003e0.61 (0.54, 0.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\n \u003cp\u003e63.0\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\n \u003cp\u003e60.2\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\n \u003cp\u003e63.8\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\n \u003cp\u003e34.0\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\" colspan=\"2\"\u003e\n \u003cp\u003e83.8\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.835125448028673%\"\u003e\n \u003cp\u003eNomogram model-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.025089605734767%\"\u003e\n \u003cp\u003e0.80 (0.75, 0.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\n \u003cp\u003e78.3\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\n \u003cp\u003e67.5\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\n \u003cp\u003e81.7\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\n \u003cp\u003e53.3\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\" colspan=\"2\"\u003e\n \u003cp\u003e89.0\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.835125448028673%\"\u003e\n \u003cp\u003eNomogram model-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.025089605734767%\"\u003e\n \u003cp\u003e0.74 (0.68, 0.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\n \u003cp\u003e70.7\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\n \u003cp\u003e71.1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\n \u003cp\u003e70.5\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\n \u003cp\u003e42.8\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\" colspan=\"2\"\u003e\n \u003cp\u003e88.7\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.120521172638437%\" rowspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eEVC-2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.661237785016286%\"\u003e\n \u003cp\u003eGlobal FD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.472312703583063%\"\u003e\n \u003cp\u003e0.68 (0.61, 0.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.749185667752442%\"\u003e\n \u003cp\u003e65.6\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.749185667752442%\"\u003e\n \u003cp\u003e53.6\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.749185667752442%\"\u003e\n \u003cp\u003e70.8\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.749185667752442%\"\u003e\n \u003cp\u003e44.1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.749185667752442%\" colspan=\"2\"\u003e\n \u003cp\u003e78.0\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.835125448028673%\"\u003e\n \u003cp\u003eMorphological model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.025089605734767%\"\u003e\n \u003cp\u003e0.55 (0.49, 0.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\n \u003cp\u003e52.9\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\n \u003cp\u003e60.8\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\n \u003cp\u003e49.6\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\n \u003cp\u003e34.1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\" colspan=\"2\"\u003e\n \u003cp\u003e74.7\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.835125448028673%\"\u003e\n \u003cp\u003eNomogram model-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.025089605734767%\"\u003e\n \u003cp\u003e0.74 (0.68, 0.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\n \u003cp\u003e72.4\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\n \u003cp\u003e38.1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\n \u003cp\u003e87.2\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\n \u003cp\u003e56.1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\" colspan=\"2\"\u003e\n \u003cp\u003e76.7\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.835125448028673%\"\u003e\n \u003cp\u003eNomogram model-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.025089605734767%\"\u003e\n \u003cp\u003e0.69 (0.62, 0.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\n \u003cp\u003e66.9\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\n \u003cp\u003e64.9\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\n \u003cp\u003e67.7\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\n \u003cp\u003e46.3\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.827956989247312%\" colspan=\"2\"\u003e\n \u003cp\u003e81.8\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"98.37133550488599%\" colspan=\"8\" style=\"width: 98.2056%;\"\u003e\n \u003cp\u003eNote: AUC = area under the receiver operating characteristic curve, ACC = accuracy, CI = confidence interval, SEN = sensitivity, SPE = specificity, PPV = positive predictive value, NPV = negative predictive value, TC = training cohort, EVC-1 = external validation cohort 1, EVC-2 = external validation cohort 2.\u003c/p\u003e\n \u003cp\u003eNomogram model-1: combining hormone receptor, human epidermal growth factor receptor-2 and global FD.\u003c/p\u003e\n \u003cp\u003eNomogram model-2: combining hormone receptor, human epidermal growth factor receptor-2, sphericity, major axis length and maximum 2D diameter (row).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4: The Characteristics of Patients for Survival Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"397\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"61.9647355163728%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.0352644836272%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal (\u003cem\u003en\u003c/em\u003e = 171\u003c/strong\u003e\u003cstrong\u003e）\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"61.9647355163728%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (years)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.0352644836272%\"\u003e\n \u003cp\u003e50 (45-55)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"61.9647355163728%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.0352644836272%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"61.9647355163728%\"\u003e\n \u003cp\u003e\u0026le;\u0026nbsp;45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.0352644836272%\"\u003e\n \u003cp\u003e48 (28)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"61.9647355163728%\"\u003e\n \u003cp\u003e\u0026gt; 45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.0352644836272%\"\u003e\n \u003cp\u003e123 (72)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"61.9647355163728%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMenopausal status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.0352644836272%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"61.9647355163728%\"\u003e\n \u003cp\u003ePremenopausal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.0352644836272%\"\u003e\n \u003cp\u003e102 (60)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"61.9647355163728%\"\u003e\n \u003cp\u003ePostmenopausal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.0352644836272%\"\u003e\n \u003cp\u003e69 (40)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"61.9647355163728%\"\u003e\n \u003cp\u003e\u003cstrong\u003eHR status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.0352644836272%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"61.9647355163728%\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.0352644836272%\"\u003e\n \u003cp\u003e74 (43)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"61.9647355163728%\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.0352644836272%\"\u003e\n \u003cp\u003e97 (57)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"61.9647355163728%\"\u003e\n \u003cp\u003e\u003cstrong\u003eHER2 status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.0352644836272%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"61.9647355163728%\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.0352644836272%\"\u003e\n \u003cp\u003e108 (63)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"61.9647355163728%\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.0352644836272%\"\u003e\n \u003cp\u003e63 (37)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"61.9647355163728%\"\u003e\n \u003cp\u003e\u003cstrong\u003eKi-67 status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.0352644836272%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"61.9647355163728%\" valign=\"top\"\u003e\n \u003cp\u003eLow (\u0026lt; 20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.0352644836272%\"\u003e\n \u003cp\u003e22 (13)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"61.9647355163728%\" valign=\"top\"\u003e\n \u003cp\u003eHigh (\u0026ge;\u0026nbsp;20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.0352644836272%\"\u003e\n \u003cp\u003e149 (87)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"61.9647355163728%\"\u003e\n \u003cp\u003e\u003cstrong\u003eClinical T stage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.0352644836272%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"61.9647355163728%\"\u003e\n \u003cp\u003ecT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.0352644836272%\"\u003e\n \u003cp\u003e17 (10)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"61.9647355163728%\"\u003e\n \u003cp\u003ecT2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.0352644836272%\"\u003e\n \u003cp\u003e90 (53)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"61.9647355163728%\"\u003e\n \u003cp\u003ecT3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.0352644836272%\"\u003e\n \u003cp\u003e36 (21)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"61.9647355163728%\"\u003e\n \u003cp\u003ecT4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.0352644836272%\"\u003e\n \u003cp\u003e28 (16)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"61.9647355163728%\"\u003e\n \u003cp\u003e\u003cstrong\u003eClinical N stage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.0352644836272%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"61.9647355163728%\"\u003e\n \u003cp\u003ecN0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.0352644836272%\"\u003e\n \u003cp\u003e20 (12)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"61.9647355163728%\"\u003e\n \u003cp\u003ecN1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.0352644836272%\"\u003e\n \u003cp\u003e55 (32)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"61.9647355163728%\"\u003e\n \u003cp\u003ecN2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.0352644836272%\"\u003e\n \u003cp\u003e67 (39)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"61.9647355163728%\"\u003e\n \u003cp\u003ecN3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.0352644836272%\"\u003e\n \u003cp\u003e29 (17)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"61.9647355163728%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTreatment response\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.0352644836272%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"61.9647355163728%\"\u003e\n \u003cp\u003eNon-pCR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.0352644836272%\"\u003e\n \u003cp\u003e133 (78)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"61.9647355163728%\"\u003e\n \u003cp\u003epCR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.0352644836272%\"\u003e\n \u003cp\u003e38 (22)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"61.9647355163728%\"\u003e\n \u003cp\u003e\u003cstrong\u003eGlobal_FD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.0352644836272%\"\u003e\n \u003cp\u003e1.94 \u0026plusmn; 0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"61.9647355163728%\"\u003e\n \u003cp\u003e\u003cstrong\u003eDFS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.0352644836272%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"61.9647355163728%\"\u003e\n \u003cp\u003eFollow-up time (DFS)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.0352644836272%\"\u003e\n \u003cp\u003e20.1 (5.85-36.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"61.9647355163728%\"\u003e\n \u003cp\u003eRecurrence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.0352644836272%\"\u003e\n \u003cp\u003e52 (30)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"61.9647355163728%\"\u003e\n \u003cp\u003eNo recurrence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.0352644836272%\"\u003e\n \u003cp\u003e119 (70)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"61.9647355163728%\"\u003e\n \u003cp\u003e\u003cstrong\u003eOS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.0352644836272%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"61.9647355163728%\"\u003e\n \u003cp\u003eFollow-up time (OS)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.0352644836272%\"\u003e\n \u003cp\u003e36.9 (18.05-48.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"61.9647355163728%\"\u003e\n \u003cp\u003eDeath\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.0352644836272%\"\u003e\n \u003cp\u003e14 (8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"61.9647355163728%\"\u003e\n \u003cp\u003eSurvival\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.0352644836272%\"\u003e\n \u003cp\u003e157 (92)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"2\"\u003e\n \u003cp\u003eNote: Unless otherwise indicated, numbers represent the count of patients, with percentages in parentheses. DFS = Disease-free survival, FD = fractal dimension, HER2 = human epidermal growth factor receptor2, HR = hormone receptor, OS = overall survival.\u003c/p\u003e\n \u003cp\u003e* Data are medians, with IQRs in parentheses.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"breast-cancer-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"brcr","sideBox":"Learn more about [Breast Cancer Research](http://breast-cancer-research.biomedcentral.com)","snPcode":"13058","submissionUrl":"https://submission.nature.com/new-submission/13058/3","title":"Breast Cancer Research","twitterHandle":"@BCRJournal","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Breast cancer, MRI, Pathological complete response, Neoadjuvant chemotherapy","lastPublishedDoi":"10.21203/rs.3.rs-4294410/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4294410/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e The tumor morphological complexity is closely associated with treatment response and prognosis in patients with breast cancer. However, conveniently quantifiable tumor morphological complexity methods are currently lacking.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eWomen with breast cancer who underwent NAC and pretreatment MRI were retrospectively enrolled at four centers from May 2010 to April 2023. MRI-based fractal analysis was used to calculate fractal dimensions (FDs), quantifying tumor morphological complexity. Features associated with pCR were identified using multivariable logistic regression analysis, upon which a nomogram model was developed, and assessed by the area under the receiver operating characteristic curve (AUC). Cox proportional hazards analysis was used to identify independent prognostic factors for disease-free survival (DFS) and overall survival (OS) and develop nomogram models.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eA total of 1109 patients (median age, 49 years [IQR, 43-54 years]) were enrolled; 435, 351, and 323 patients were recruited in the training, external validation cohorts 1 and 2, respectively. HR status (odds ratio [OR], 0.234 [0.135, 0.406]; \u003cem\u003eP\u003c/em\u003e\u0026lt; 0.001), HER2 status (OR, 3.320 [1.923, 5.729]; \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001), and Global FD (OR, 0.352 [0.261, 0.480]; \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001) were independent predictors of pCR. The nomogram model for predicting pCR achieved AUCs of 0.80 (95% CI: 0.75, 0.86) and 0.74 (95% CI: 0.68, 0.79) in the external validation cohorts. The nomogram model, which integrated global FD and clinicopathological variables can stratify prognosis into low-risk and high-risk groups (\u003cem\u003elog-rank test\u003c/em\u003e, DFS: \u003cem\u003eP\u003c/em\u003e = 0.04; OS: \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eGlobal FD can quantify tumor morphological complexity and the model that combines global FD and clinicopathological variables showed good performance in predicting pCR to NAC and survival in patients with breast cancer.\u003c/p\u003e","manuscriptTitle":"Quantifying tumor morphological complexity based on pretreatment MRI fractal analysis for predicting pathologic complete response and survival in breast cancer: a retrospective, multicenter study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-29 16:53:41","doi":"10.21203/rs.3.rs-4294410/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-08-25T18:51:58+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-14T04:10:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"47411743901748008828115405882121356638","date":"2024-05-27T17:35:36+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-05-27T17:32:08+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-04-23T11:56:29+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-04-23T07:16:58+00:00","index":"","fulltext":""},{"type":"submitted","content":"Breast Cancer Research","date":"2024-04-19T16:55:36+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"breast-cancer-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"brcr","sideBox":"Learn more about [Breast Cancer Research](http://breast-cancer-research.biomedcentral.com)","snPcode":"13058","submissionUrl":"https://submission.nature.com/new-submission/13058/3","title":"Breast Cancer Research","twitterHandle":"@BCRJournal","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"47126d16-28d3-44a5-8634-de940d0ae64c","owner":[],"postedDate":"April 29th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-05-26T16:08:31+00:00","versionOfRecord":{"articleIdentity":"rs-4294410","link":"https://doi.org/10.1186/s13058-025-02034-5","journal":{"identity":"breast-cancer-research","isVorOnly":false,"title":"Breast Cancer Research"},"publishedOn":"2025-05-20 15:57:27","publishedOnDateReadable":"May 20th, 2025"},"versionCreatedAt":"2024-04-29 16:53:41","video":"","vorDoi":"10.1186/s13058-025-02034-5","vorDoiUrl":"https://doi.org/10.1186/s13058-025-02034-5","workflowStages":[]},"version":"v1","identity":"rs-4294410","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4294410","identity":"rs-4294410","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}