Precision Prediction of Neoadjuvant Chemotherapy Efficacy in Breast Cancer: Integrating Multimodal Imaging and Clinical Features

Precision Prediction of Neoadjuvant Chemotherapy Efficacy in Breast Cancer: Integrating Multimodal Imaging and Clinical Features | 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 Precision Prediction of Neoadjuvant Chemotherapy Efficacy in Breast Cancer: Integrating Multimodal Imaging and Clinical Features Xianglong Chen, Luo Yong, Zhiming Xie, Yun Wen, Fangsheng Mou, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5396093/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objectives To assess the predictive value of combining DCE-MRI, DKI, IVIM parameters, and clinical characteristics for neoadjuvant chemotherapy (NAC) efficacy in invasive ductal carcinoma. Methods We conducted a retrospective study of 77 patients with invasive ductal carcinoma, analyzing MRI data collected before NAC. Parameters extracted included DCE-MRI (Ktrans, Kep, Ve, wash-in, wash-out, TTP, iAUC), DKI (MK, MD), and IVIM (D, D*, f). Differences between NAC responders and non-responders were assessed using t-tests or Mann-Whitney U tests. ROC curves and Spearman correlation analyses evaluated predictive accuracy. Results NAC responders had higher DCE-Kep, DKI-MD, IVIM-D, and IVIM-f values. Non-responders had higher DCE-Ve, DKI-MK, IVIM-D (kurtosis, skewness, entropy), and IVIM-f (entropy). The mean DKI-MK had the highest AUC (0.724), and IVIM-D interquartile range showed the highest sensitivity (94.12%). Combined parameters had the highest AUC (0.969), sensitivity (94.12%), and specificity (90.70%). HER2 status and lesion margins were independent predictors of poor response. Conclusions Combining DCE-MRI, DKI, and IVIM parameters effectively predicts NAC efficacy, providing valuable preoperative assessment insights. Nuclear Medicine & Medical Imaging Invasive ductal carcinoma Neoadjuvant chemotherapy Diffusion kurtosis imaging Intravoxel incoherent motion imaging Dynamic contrast-enhanced Histogram parameters. Figures Figure 1 Figure 2 Figure 3 Figure 4 Advances in knowledge This study innovatively integrates DCE-MRI, DKI, and IVIM imaging techniques and utilizes histogram-based analysis to enhance the prediction accuracy of neoadjuvant chemotherapy efficacy. It not only broadens the application range of imaging parameters but also supports the development of personalized treatment plans based on comprehensive data. Introduction Breast cancer remains the most common malignant tumor in women, with increasing incidence and mortality rates that severely threaten women's quality of life and health [1]. Invasive ductal carcinoma (IDC) is the most prevalent pathological subtype of breast cancer, characterized by strong invasiveness, with tendencies to infiltrate the chest wall and skin or metastasize distantly. Effective treatment is urgently needed, and neoadjuvant chemotherapy (NAC) is the preferred treatment option for locally advanced breast cancer (LABC). Widely applied in clinical settings, effective preoperative NAC can downstage tumors, reduce tumor size, eliminate or reduce micrometastases, increase surgical options, and improve both disease-free survival and overall survival rates for patients [2].Traditional imaging modalities for breast cancer include mammography, ultrasound, CT, and MRI. Among these, magnetic resonance imaging (MRI) is currently the most sensitive and advanced imaging technique for breast cancer diagnosis, enhancing detection rates, characterizing lesions, and evaluating the extent of surrounding soft tissue involvement [3]. In addition to dynamic contrast-enhanced (DCE) MRI, functional MRI techniques such as magnetic resonance spectroscopy (MRS), diffusion-weighted imaging (DWI), diffusion kurtosis imaging (DKI), and intravoxel incoherent motion (IVIM) imaging improve the accuracy of differentiating between benign and malignant tumors, aiding clinical treatment planning and therapeutic efficacy assessment. DCE-MRI accurately reflects the density of tumor neovasculature, blood flow, and vascular wall permeability; DKI indicates the non-Gaussian diffusion of water molecules in tissue, with key quantitative parameters including mean diffusivity (MD) and mean kurtosis (MK), which provide insight into the microstructure of the tissue. IVIM imaging, an extension of diffusion-weighted imaging (DWI), utilizes a bi-exponential model to obtain diffusion coefficient (D), pseudo-diffusion coefficient (D*), and perfusion fraction (f) parameters, allowing for non-invasive quantification of tissue water molecule diffusion and microcirculation perfusion [4][5][6].Compared to traditional imaging analysis, histogram analysis offers a broader range of quantitative indicators (10th percentile, 90th percentile, energy, entropy, interquartile range, kurtosis, maximum, minimum, mean, median, range, skewness, uniformity, variance), providing a comprehensive, non-invasive assessment of the complexity and heterogeneity of the microstructure in tumor tissues. This improves the differentiation between benign and malignant lesions and enhances the assessment of clinical therapeutic efficacy [7][8]. Materials and Methods 1.1 Study Population This retrospective study collected data on 152 female breast cancer patients who underwent MRI examinations at our hospital from January 2021 to October 2023. After screening, 77 patients met the inclusion criteria. Inclusion criteria were: (1) histopathologically confirmed breast cancer (invasive ductal carcinoma); (2) completion of 3.0T MRI routine, DCE, DKI, and IVIM sequence scans before NAC; (3) completion of 6–8 cycles of NAC; (4) surgical treatment with subsequent pathological evaluation of NAC efficacy using the Miller-Payne (MP) grading system. Exclusion criteria were: (1) poor image quality; (2) incomplete basic patient information or clinical data. Patients who met the criteria were assessed for NAC efficacy based on changes in tumor cell density as graded by the Miller-Payne system. Grade I: no reduction in tumor cell density; Grade II: tumor cell density reduction 90% reduction in tumor cell density with remaining clustered or scattered tumor cells; Grade V: no residual tumor cells, though ductal carcinoma in situ may persist [9][10]. Grades IV and V were classified as the significant response group (43 patients), while Grades I, II, and III were classified as the non-significant response group (34 patients). This study protocol has been registered with the National Medical Research Registration System and was reviewed and approved by our medical institution and relevant management departments. 1.2 MRI Examination Protocol MRI examinations were conducted using a Siemens 3.0T MRI scanner with an 18-channel dedicated breast phased-array coil. Scanning parameters were as follows: Axial T1WI: TR 6.04 ms, TE 2.46 ms, slice thickness 1.6 mm, FOV 360 mm × 360 mm, acquisition matrix 448 × 384, one excitation; Axial T2WI: TR 6500 ms, TE 84 ms, slice thickness 4 mm, acquisition matrix 448 × 448, one excitation; DKI/IVIM: TR 6100 ms, TE 54 ms, FOV 340 mm × 340 mm, b-values of 0, 30, 50, 100, 150, 200, 400, 800, 1200, 1600, and 2000 s/mm²; DCE-MRI: TR 7.14 ms, TE 3.69 ms, flip angle 8°, slice thickness 4 mm, matrix 448 × 448, FOV 340 mm × 340 mm, scan time 9.6 seconds per phase, for a total of 35 phases. Gadopentetate dimeglumine contrast agent (20 mL) was injected intravenously via a high-pressure injector at 2.0 mL/s. 1.3 Image Processing and Analysis MR breast images for DCE, DKI, and IVIM sequences meeting inclusion criteria were exported from the PACS system in DICOM format and processed using Body Station software. Pseudocolor maps of MD, MK, D, f, and D* values were generated. Regions of interest (ROIs) encompassing the entire lesion were manually outlined layer by layer, referencing contrast-enhanced MRI images to avoid non-tumor tissues (e.g., normal tissue, necrotic areas, large blood vessels) as much as possible. DKI parameters including mean kurtosis (MK) and mean diffusivity (MD), as well as IVIM parameters diffusion coefficient (D), pseudo-diffusion coefficient (D*), and perfusion fraction (f), were obtained as whole-field histogram parameters.For DCE-MRI images, MR Tissue4D software was used to select the core region of the lesion and manually outline the ROI to acquire quantitative parameters: transfer constant (Ktrans), rate constant (Kep), and volume ratio of the extravascular extracellular space (Ve), as well as semi-quantitative parameters including the rate of concentration increase per minute (wash-in), rate of concentration decrease per minute (wash-out), time to peak (TTP), and initial area under the curve (iAUC) within the first 60 seconds post-contrast administration. Image processing and analysis were independently performed under blinded conditions by two radiologists. 1.4 Statistical Analysis Statistical analysis was conducted using SPSS version 25.0. Interclass correlation coefficient (ICC) tests were applied to assess the consistency of histogram parameters from DCE, DKI, and IVIM across two groups. The ICC values for DCE parameters ranged from 0.821 to 0.997; for DKI, MD values were between 0.873 and 0.988, and MK values ranged from 0.917 to 0.985; for IVIM, D values ranged from 0.830 to 0.977, f values from 0.803 to 0.911, and D* values from 0.902 to 0.990, all ICC values exceeding 0.7, indicating good consistency between the data from the two groups. The Kolmogorov-Smirnov test was used to evaluate the normality of measurement data; normally distributed data were described by mean ± standard deviation (SD) and compared using independent sample t-tests. Non-normally distributed data were reported as median and interquartile range, M (P25, P75), and compared using the Mann-Whitney U test. Categorical data were analyzed using the Chi-square test (χ²) or Fisher's exact test, with counts (percentages) for statistical description. Spearman rank correlation analysis was performed to evaluate the association between various parameters and the NAC non-significant response. Receiver Operating Characteristic (ROC) curves were created using Medcalc version 22.0, and the area under the curve (AUC) was used to assess the diagnostic efficacy of each parameter in differentiating NAC efficacy. The DeLong test was applied to assess the statistical significance of differences between AUC values, with P < 0.05 considered statistically significant. Variables identified by univariate logistic regression (categorical and continuous variables) with P < 0.01 were included in a multivariate logistic regression analysis to identify independent influencing factors. Results 2.1 Comparison of Clinical Data and MRI Image Characteristics According to the Miller-Payne grading system, the 77 patients were divided into a significant response group (43 patients) and a non-significant response group (34 patients). No significant differences were observed between the significant and non-significant response groups in terms of age, menstrual status, lesion quadrant, tumor maximum diameter, enhancement pattern, peritumoral edema, tumor necrosis, chest wall involvement, skin thickening, nipple retraction, p53 status, immunohistochemical subtypes, or axillary lymph node metastasis (P > 0.05). In the significant response group, 35 patients (81%) had regular lesion margins, while 8 (19%) had spiculated margins, compared to 15 (44%) and 19 (56%) in the non-significant response group, respectively. For intralesional enhancement, heterogeneous enhancement and ring/segmental enhancement were observed in 32 (74%) and 11 (26%) patients in the significant response group, versus 17 (50%) and 17 (50%) in the non-significant response group.In terms of immunohistochemical expression, low ER expression (≤10%) and medium/high ER expression (>10%) were found in 32 patients (74%) and 11 patients (26%) in the significant response group, respectively, compared with 14 (41%) and 20 (59%) in the non-significant response group. Low PR expression (≤10%) and medium/high PR expression (>10%) were observed in 37 (86%) and 6 (14%) patients in the significant response group, respectively, compared with 18 (53%) and 16 (47%) in the non-significant response group. For Ki-67 expression, low expression (≤14%) and medium/high expression (>14%) were found in 1 patient (2%) and 42 patients (98%) in the significant response group, compared to 27 (79%) and 7 (21%) in the non-significant response group. HER2-negative and HER2-positive statuses were observed in 14 patients (33%) and 29 patients (67%) in the significant response group, respectively, compared to 23 (68%) and 11 (32%) in the non-significant response group. All of these comparisons were statistically significant (P < 0.05). Detailed clinical data and MRI imaging characteristics of breast invasive ductal carcinoma patients before NAC are presented in Table 1and Figure 3. 2.2 Analysis of DCE, DKI, and IVIM Parameters In breast cancer patients, the NAC significant response group showed higher values of DCE-Kep, DKI-MD (90th Percentile, Mean, Median, Max, Range), IVIM-D (90th Percentile, Interquartile Range, Mean, Median, Variance, Uniformity), and IVIM-f (Interquartile Range) compared to the non-significant response group. Conversely, DCE-Ve, DKI-MK (Mean, Median), IVIM-D (Kurtosis, Skewness, Entropy), and IVIM-f (Entropy) were lower in the significant response group than in the non-significant response group, with all parameters showing statistically significant differences (P < 0.05). In univariate logistic regression analyses of categorical and continuous variables, significant differences (P < 0.05) with NAC efficacy were found for lesion margin, internal enhancement, ER expression, PR expression, HER2 status, Ki67 expression, DKI-D (90th Percentile, Max, Mean, Median, Range), DKI-K (Mean, Median), IVIM-D (90th Percentile, Entropy, Interquartile Range, Mean, Median, Kurtosis, Skewness, Uniformity, Variance), and IVIM-f (Entropy, Interquartile Range). Variables with P < 0.01 were included in a multivariate binary logistic regression analysis, identifying HER2 status and lesion margin as independent factors influencing the non-significant NAC response in breast cancer (P 0, P < 0.05) with NAC efficacy in breast cancer were observed for DCE-Kep, DKI-MD (90th Percentile, Mean, Median, Max, Range), IVIM-D (90th Percentile, Interquartile Range, Mean, Median, Variance, Uniformity), and IVIM-f (Interquartile Range), indicating that higher values of these parameters are associated with greater NAC efficacy. Negative correlations (rs < 0, P < 0.05) were noted for DCE-Ve, DKI-MK (Mean, Median), IVIM-D (Kurtosis, Skewness, Entropy), and IVIM-f (Entropy), suggesting that higher values of these parameters are associated with a reduced NAC efficacy. Among DCE parameters, Ve showed the highest correlation with NAC efficacy (rs = 0.267); for DKI-MD, Max and Range had the strongest correlation (rs = 0.357); within DKI-MK, Mean demonstrated the highest correlation (rs = 0.386); for IVIM-D, Mean correlated most significantly with NAC efficacy (rs = 0.367); and for IVIM-f, Interquartile Range had the strongest correlation (rs = 0.296). Details are provided in Table 5. 2.4 Diagnostic Efficacy Analysis of Individual and Combined Whole-Histogram Parameters Among individual parameters, the largest AUC was observed for DKI-MK Mean (AUC = 0.724). IVIM-D Interquartile Range demonstrated the highest sensitivity (94.12%), while DKI-MK Mean and IVIM-D Median exhibited the highest specificity (90.70%). In terms of combined parameters, the integration of DCE with DKI and IVIM histogram parameters achieved the highest AUC (0.969), sensitivity (94.12%), and specificity (90.70%). No significant differences were observed between AUCs of individual parameters (P > 0.05). However, significant differences were found within the combined parameters, where the combined DCE and IVIM, DCE and DKI+IVIM, and DCE+DKI+IVIM showed statistically significant differences (P < 0.05). Additionally, significant differences were observed in combined DKI parameters with DKI+IVIM, DCE+DKI+IVIM, and in combined IVIM parameters with DKI+IVIM, DCE+DKI+IVIM (P < 0.05). Further details can be found in Table 5 and Figure 4. Discussion 3.1 Predictive Value of Pre-NAC MRI Imaging Characteristics, Clinical Features, and Immunohistochemical Factors for NAC Efficacy in Invasive Ductal Carcinoma of the Breast In this study, MRI characteristics such as lesion margin, internal enhancement patterns, as well as ER, PR, Ki67 expression, and HER2 status, demonstrated significant associations with NAC efficacy. The number of patients with significant NAC response was notably lower in those with spiculated lesion margins compared to those with smooth margins. This may be due to the greater tumor cell activity, rapid proliferation rate, and high cellular density of tumors with spiculated margins, which invade surrounding tissues and elicit a fibrotic response that contributes to the spiculated appearance leads to a diminished NAC efficacy.Patients with heterogeneous internal enhancement were significantly more likely to exhibit a substantial NAC response compared to those with ring or segmented enhancement. This could be attributed to inadequate angiogenesis, resulting in necrotic or fibrotic regions within the tumor that often have limited blood and oxygen supply. Consequently, these poorly vascularized areas may inhibit effective drug penetration, thereby limiting therapeutic efficacy against the tumor . According to thedelines from the American Society of Clinical Oncology (ASCO) and the College of American Pathologists (CAP), ER and PR immunohistochemical (IHC) staining between 1% and 10% is categorized as low expression. In this study, patients with low ER and PR expression were significantly more likely to achieve a notable NAC response compared to those with medium or high expression. Breast cancers with low ER and PR expression may rely less on estrogen and progesterone for growth, exhibiting higher resistance to hormone therapy. Thus, these patients may respond more favorably to NAC, which operates through mechanisms independent of hormone signaling . HER2 expression, classifiee from 0 to 3+, is considered positive with an IHC score of 3+ or a score of 2+ combined with a positive in situ hybridization (ISH) result. In this study, a significantly higher number of HER2-positive patients exhibited a marked NAC response compared to HER2-negative patients. HER2, a cell membrane receptor, plays a critical role in regulating cell growth and division. Breast cancer cells overexpressing HER2 generally have higher proliferative activity, potentially making them more susceptible to chemotherapeutic agents . Previous studies have defined low IHC staining range of 1% to 14% . Our results show that patients with low Ki67 expression hantly fewer notable NAC responses compared to those with medium or high Ki67 expression. Elevated Ki67 expression is indicative of higher tumor cell proliferation. Since NAC predominantly targets rapidly proliferating cancer cells, tumors with medium or high Ki67 expression are more likely to be susceptible to chemotherapeutic agents . 3.2 Value of Pre-Treatment Quantitative Parameters (Ktrans, Kep, Ve) and Semi-Quantitative Parameters (Wash-in, Wash-out, TTP, and iAUC) in Predicting NAC Efficacy in Invasive Ductal Carcinoma of the Breast DCE-MRI quantitative and semi-quantitative parameters can accurately reflect tumor microvessel density, blood flow, and vascular permeability. In this study, pre-NAC quantitative parameters Kep and Ve showed statistically significant associations with NAC efficacy. The average Kep value was higher in the effective NAC group, while the average Ve value was lower in this group compared to the non-effective group. Kep reflects the rate of exchange between the extracellular extravascular space (EES) and blood vessels, while Ve represents the EES volume fraction, both indicating vascular permeability. Increased values of Kep and Ve suggest greater drug penetration into the tumor, potentially enhancing NAC efficacy. However, in this study, the Ve value in the effective NAC group was lower than in the non-effective group. Previous studies have explored the predictive value of changes in Kep following early NAC treatment (e.g., after the second cycle) on overall NAC efficacy. The results showed that Kep values in the pCR group significantly decreased after two weeks of NAC treatment, whereas Ve values remained unchanged. However, predicting NAC efficacy before treatment initiation is more clinically desirable. Given the limitations of single-parameter predictions, pre-treatment multi-parametric MRI analyses are essential for accurately predicting NAC efficacy. 3.3 Value of Histogram Parameters MD and MK from the DKI Model in Predicting NAC Efficacy in Invasive Ductal Carcinoma of the Breast In the DKI model, MD reflects the overall diffusion level and resistance of water molecules, while MK reflects the complexity of tissue microstructure. In this study, MD parameters (90th percentile, mean, median, max, and range) were higher in the effective NAC group than in the non-effective group. The 90th percentile MD value may represent areas of lower cellular density, such as necrotic, liquefied, or cystic regions. Higher mean and median MD values indicate lower cellular density, increased interstitial spaces, and greater water diffusion, with reduced resistance and higher water molecule content in the tissue. High maximum and range values likely indicate larger areas of free water diffusion and lower cell density. In the effective NAC group, the MK parameters (mean and median) were lower than in the non-effective group. Lower mean and median MK values suggest less structural complexity and heterogeneity in the tumor microstructure. These findings demonstrate that higher MD (90th percentile, mean, median, max, range) and lower MK (mean, median) values are associated with significant NAC efficacy, indicating lower tumor cell density, heterogeneity, and structural complexity before NAC. These findings align with previous baseline studies by Zheng et al. 3.3 Predictive Value of Histogram Parameters D, f, and D from the IVIM Model in Pre-Treatment Assessment of NAC Efficacy for Invasive Ductal Carcinoma of the Breast In the IVIM model, the D and f values indicate the true diffusion coefficient and perfusion information, respectivelystudy, the effective NAC group showed significantly higher values for IVIM-D (90th percentile, interquartile range, mean, median, variance, and uniformity) and IVIM-f (interquartile range) than the non-effective group. Higher mean, median, and variance values for IVIM-D may suggest lower tumor cell density and larger extracellular space, resulting in less resistance to water molecule diffusion. These areas of lower diffusion resistance are consistent with the findings for DKI-MD values in this study. Higher interquartile range values for IVIM-D and IVIM-f might indicate a broader distribution range of water molecule diffusion or perfusion values within the tissue, allowing for more effective drug penetration during NAC treatment. In contrast, the effective NAC group had lower values for IVIM-D (kurtosis, skewness, and entropy) and IVIM-f (entropy) than the non-effective group, potentially indicating greater tissue heterogeneity, uneven water molecule diffusion, and higher distribution irregularity, which may contribute to less effective NAC outcomes. These findings are consistent with those reported by Kim et al. . Althougstudies such as this cannot capture the dynamic changes of variables during NAC, combining multiparametric MRI with clinical characteristics allows for a more comprehensive assessment of the relationship between tumor characteristics and NAC efficacy, providing a valuable reference for pre-treatment clinical decision-making. 3.4 Limitations of This Study First, this is a single-center study with a relatively small sample size. Second, the DCE-MRI quantitative parameters did not undergo histogram analysis. Third, this study focused on the short-term efficacy of NAC without long-term follow-up to evaluate patient prognosis. Declarations Ethical Statement The present study does not involve any human or animal subjects, and thus, formal ethics approval and consent to participate were not required. Consent to Publish All authors have provided consent for the publication of this manuscript. Competing interests The authors declare no competing interests or conflicts of interest relevant to this work. Funding This study was supported by Chongqing Regional Key Disciplines (zdxk202116) Availability of data and materials This study was approved and supervised by the ethics committee of Three Gorges Hospital Affiliated to Chongqing University.(MR-50-23-026780) Contributors Xianglong Chen, and Luo Yong contributed equally to this work as first authors, Xianglong Chen, and Luo Yong developed the concept and discussed experiments and collaboratively drafting the initial version of the manuscript. Zhiming Xie and Yun Wen collected patient samples and data. Fangsheng Mou and Wenbing Zhen served as co-corresponding authors, providing overall guidance and supervision for the organization and development of the manuscript. All authors participated in the critical review, revision, and approval of the final version of the manuscript. Associated This section collects any data citations, data availability statements, or supplementary materials included in this article.Data Data Availability Statement The data that support the findings of this study are available from the corresponding author upon reasonable request. References Freddie,Bray,Jacques,et al.Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries.[J].CA: a cancer journal for clinicians, 2018.DOI:10.3322/caac.21492. Maccoll C E ,Guillaume Paré, Salehi A ,et al.Postneoadjuvant Pure and Predominantly Pure Intralymphatic Breast Carcinoma: Case Series and Literature Review[J].The American journal of surgical pathology, 2020.DOI:10.1097/PAS.0000000000001610. HUSSEIN H, ABBAS E, KESHAVARZI S, et al. Supplemental Breast Cancer Screening in Women with Dense Breasts and Negative Mammography: A Systematic Review and Meta-Analysis[J/OL].Radiology,2023,306(3). DOI:10.1148/radiol.221785. Comparison of the pre-treatment functional MRI metrics' efficacy in predicting Locoregionally advanced nasopharyngeal carcinoma response to induction chemotherapy[J].Cancer Imaging, 2021, 21(1):1-12.DOI:10.1186/s40644-021-00428-0. FUSCO R, SANSONE M, GRANATA V, et al. Diffusion and perfusion MR parameters to assess preoperative short-course radiotherapy response in locally advanced rectal cancer: a comparative explorative study among Standardized Index of Shape by DCE-MRI, intravoxel incoherent motion- and diffusion kurtosis imaging-derived parameters[J/OL]. Abdominal Radiology,2019,44(11):3683-3700. DOI:10.1007/s00261-018-1801-z. GRANATA V, FUSCO R, SANSONE M, et al. Magnetic resonance imaging in the assessment of pancreatic cancer with quantitative parameter extraction by means of dynamic contrast-enhanced magnetic resonance imaging, diffusion kurtosis imaging and intravoxel incoherent motion diffusion-weighted imaging[J/OL]. Therapeutic Advances in Gastroenterology, 2019: 175628481988505. DOI:10.1177/1756284819885052. Li, Hao, Zhao, Sheng, Fan, Hai Y, et al. The Effect of Histogram Analysis of DCE-MRI Parameters on Differentiating Renal Tumors. Clinical laboratory, 2023 Nov 1;69(11).DOI:10.7754/Clin.Lab.2023.221126. Li Q , Xiao Q , Yang M ,et al.Histogram analysis of quantitative parameters from synthetic MRI: correlations with prognostic factors and molecular subtypes in invasive ductal breast cancer[J].European Journal of Radiology, 2021(3):109697.DOI:10.1016/j.ejrad.2021.109697. Zhao D , Fu X , Rohr J ,et al.Poor histologic tumor response after adjuvant therapy in basal-like HER2-positive breast carcinoma[J].Pathology - Research and Practice, 2021, 228:153677-.DOI:10.1016/j.prp.2021.153677. Huang Y , Le J , Miao A ,et al.Prediction of treatment responses to neoadjuvant chemotherapy in breast cancer using contrast-enhanced ultrasound.[J].AME Publishing Company, 2021(4).DOI:10.21037/GS-20-836. Galati F , Rizzo V , Moffa G ,et al.Radiologic-pathologic correlation in breast cancer: do MRI biomarkers correlate with pathologic features and molecular subtypes?[J].European Radiology Experimental, 2022, 6(1):1-13.DOI:10.1186/s41747-022-00289-7. Wang S , Zhang Y , Yang X ,et al.Shrink pattern of breast cancer after neoadjuvant chemotherapy and its correlation with clinical pathological factors[J].World Journal of Surgical Oncology, 2013, 11(1):166-166.DOI:10.1186/1477-7819-11-166. RAMTOHUL T, TESCHER C, VAFLARD P, et al. Prospective Evaluation of Ultrafast Breast MRI for Predicting Pathologic Response after Neoadjuvant Therapies[J/OL]. Radiology,2022,305(3):565-574. DOI:10.1148/radiol.220389. Dou H , Li F , Wang Y ,et al.Estrogen receptor-negative/progesterone receptor-positive breast cancer has distinct characteristics and pathologic complete response rate after neoadjuvant chemotherapy[J].Diagnostic Pathology, 2024, 19(1).DOI:10.1186/s13000-023-01433-6. Leon-Ferre R A , Hieken T J , Boughey J C .The Landmark Series: Neoadjuvant Chemotherapy for Triple-Negative and HER2-Positive Breast Cancer[J].Annals of Surgical Oncology, 2021, 28(4):2111-2119.DOI:10.1245/s10434-020-09480-9. TERUYA N, INOUE H, HORII R, et al. Intratumoral heterogeneity, treatment response, and survival outcome of ER‐positive HER2‐positive breast cancer[J/OL].Cancer Medicine,2023,12(9): 10526-10535. DOI:10.1002/cam4.5788. Peng J H , Zhang X , Song J L ,et al.Neoadjuvant chemotherapy reduces the expression rates of ER, PR, HER2, Ki67, and P53 of invasive ductal carcinoma[J].Medicine, 2019, 98(2).DOI:10.1097/MD.0000000000013554. Zhang H, Wang Z, Liu W, et al. Breast-Conserving Surgery in Triple-Negative Breast Cancer: A Retrospective Cohort Study[J/OL]. Evidence-Based Complementary and Alternative Medicine,2023,2023:1-8. DOI:10.1155/2023/5431563. Chen W, Li F X, Lu D L, et al. Differences between the efficacy of HER2(2+)/FISH-positive and HER2(3+) in breast cancer during dual-target neoadjuvant therapy[J/OL]. The Breast,2023,71:69-73. DOI:10.1016/j.breast.2023.07.008. Liang X , Chen X , Yang Z ,et al.Early prediction of pathological complete response to neoadjuvant chemotherapy combining DCE-MRI and apparent diffusion coefficient values in breast Cancer[J].BMC cancer, 2022, 22(1):1250.DOI:10.1186/s12885-022-10315-x. Guo W, Zhang Y, Luo D, et al. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for pretreatment prediction of neoadjuvant chemotherapy response in locally advanced hypopharyngeal cancer[J/OL]. The British Journal of Radiology, 2020, 93(1115): 20200751. DOI:10.1259/bjr.20200751. Zhang D, Geng X, Suo S, et al. The predictive value of DKI in breast cancer: Does tumour subtype affect pathological response evaluations?[J/OL]. Magnetic Resonance Imaging,2021,85:28-34. DOI:10.1016/j.mri.2021.10.013. Liu W , Wei C , Bai J ,et al.Histogram analysis of diffusion kurtosis imaging in the differentiation of malignant from benign breast lesions[J].European Journal of Radiology, 2019, 117:156-163.DOI:10.1016/j.ejrad.2019.06.008. Histogram analysis in predicting the grade and histological subtype of meningiomas based on diffusion kurtosis imaging:[J].Acta Radiologica, 2020, 61(9):1228-1239.DOI:10.1177/0284185119898656. Zheng D , Lai G , Chen Y ,et al.Integrating dynamic contrast-enhanced magnetic resonance imaging and diffusion kurtosis imaging for neoadjuvant chemotherapy assessment of nasopharyngeal carcinoma.[J].Journal of Magnetic Resonance Imaging, 2018.DOI:10.1002/jmri.26164. Ai Z , Han Q , Huang Z ,et al.The value of multiparametric histogram features based on intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) for the differential diagnosis of liver lesions.[J].Annals of Translational Medicine,2020(18).DOI:10.21037/ATM-20-5109. Kim Y , Kim S H , Lee H W ,et al.Intravoxel incoherent motion diffusion-weighted MRI for predicting response to neoadjuvant chemotherapy in breast cancer[J].Magnetic Resonance Imaging, 2018, 48:27-33.DOI:10.1016/j.mri.2017.12.018. Tables Tables 1 to 5 are available in the Supplementary Files section Additional Declarations The authors declare no competing interests. Supplementary Files Tables.docx Cite Share Download PDF Status: Posted Version 1 posted 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-5396093","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":374426934,"identity":"035e445f-b763-4a5f-b39b-f089b8ffd6d5","order_by":0,"name":"Xianglong Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA40lEQVRIie3PsYrCQBCA4V0Cm2a4tCuCzzCHECyCvopLYG0CZ3nlhICVDxDwJXyExME8QwrBQtA2lVgIntZ3JLnOYv9iqvkYRgiX6x3Tr7GMIPBTKhqMpj0J2tFgzWmZL23cl/AY60XG0OwkdYlgk1UnQM9QYYgjLDzh837beuRQLT5zVCalkjjBw4cAa+s2gjoJdYNgMpm+yNkTGsIO8nXVc9Rm5UniCbKkbpKo5xUcg3oS0Yfo2oaDHOcjDZLKNdpYdf0S5PF5CN8PmB0vp+Z2j6aBz1Ur+Z3637rL5XK5/uoHXzNMgEUFiOcAAAAASUVORK5CYII=","orcid":"","institution":"School of Medical Imaging,North Sichuan Medical Univesiyt,Nanchong,Sichuan Province,China","correspondingAuthor":true,"prefix":"","firstName":"Xianglong","middleName":"","lastName":"Chen","suffix":""},{"id":374426935,"identity":"426291e2-bcd4-4914-9a6f-12a20d2cad66","order_by":1,"name":"Luo Yong","email":"","orcid":"","institution":"Department of Radiology, Three Gorges Hospital Affiliated to Chongqing University","correspondingAuthor":false,"prefix":"","firstName":"Luo","middleName":"","lastName":"Yong","suffix":""},{"id":374426936,"identity":"c108dcda-e52f-4102-b73e-7cfd629d00fe","order_by":2,"name":"Zhiming Xie","email":"","orcid":"","institution":"School of Medicine of Chongqing University","correspondingAuthor":false,"prefix":"","firstName":"Zhiming","middleName":"","lastName":"Xie","suffix":""},{"id":374426937,"identity":"275f717e-512a-4e89-9fe5-b1d1661812ca","order_by":3,"name":"Yun Wen","email":"","orcid":"","institution":"Department of Radiology, Three Gorges Hospital Affiliated to Chongqing University","correspondingAuthor":false,"prefix":"","firstName":"Yun","middleName":"","lastName":"Wen","suffix":""},{"id":374426938,"identity":"a71222e4-5285-4cdd-bce4-98a0da4375c5","order_by":4,"name":"Fangsheng Mou","email":"","orcid":"","institution":"Department of Radiology, Three Gorges Hospital Affiliated to Chongqing University","correspondingAuthor":false,"prefix":"","firstName":"Fangsheng","middleName":"","lastName":"Mou","suffix":""},{"id":374426939,"identity":"0bedab83-1b53-44d8-b764-1ea9f8e46af7","order_by":5,"name":"Wenbing Zhen","email":"","orcid":"","institution":"Department of Radiology, Three Gorges Hospital Affiliated to Chongqing University","correspondingAuthor":false,"prefix":"","firstName":"Wenbing","middleName":"","lastName":"Zhen","suffix":""}],"badges":[],"createdAt":"2024-11-05 14:12:10","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-5396093/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5396093/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":68384153,"identity":"7678b14b-3281-40af-987d-2a40b7a437fc","added_by":"auto","created_at":"2024-11-06 17:11:37","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":566450,"visible":true,"origin":"","legend":"\u003cp\u003eA~F represents patients in the non-significant neoadjuvant chemotherapy (NAC) group, with tumor lesions in the upper outer quadrant of the breast. Pathological results indicate invasive ductal carcinoma with heterogeneous enhancement. Regions of interest (ROI) were delineated globally on the DKI raw images (A), then copied onto the color maps of MD (B), MK (C), D* value (D), D value (E), and f value (F), with global histogram parameters calculated.G~L represents patients in the significant neoadjuvant chemotherapy (NAC) group, with tumor lesions in the right upper outer quadrant of the breast. Pathological results indicate invasive ductal carcinoma with heterogeneous enhancement. Regions of interest (ROI) were delineated globally on the DKI raw images (G), then copied onto the color maps of MD (H), MK (I), D* value (J), D value (K), and f value (L), with global histogram parameters calculated.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5396093/v1/14ba91616c7e035ce39c982b.png"},{"id":68384643,"identity":"8e00e77f-0d95-4e26-bf23-1c8d781f8720","added_by":"auto","created_at":"2024-11-06 17:19:37","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":61287,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of whole-lesion histogram parameters between significant efficacy group and non-significant efficacy group (P\u0026lt;0.05).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5396093/v1/e33a239831cacedf5d807731.png"},{"id":68384154,"identity":"3f1a9f39-a5d2-4b8f-8e65-40f6ead4eeab","added_by":"auto","created_at":"2024-11-06 17:11:37","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":63079,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of categorical variables between the significant efficacy group and the non-significant efficacy group (P\u0026lt;0.05).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5396093/v1/1c5abdebc1d0a60189eae0c9.png"},{"id":68384151,"identity":"8207d93c-53e0-4d5f-bfc0-87ba6b0907bd","added_by":"auto","created_at":"2024-11-06 17:11:37","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":26304,"visible":true,"origin":"","legend":"\u003cp\u003eThe ROC curve for independent histogram parameters with higher diagnostic efficacy (A) and the ROC curve for combined histogram parameters (B).\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5396093/v1/1e1338ae1d7ecd79030ee8fa.png"},{"id":68385664,"identity":"5da54a0c-0e07-4bc6-88d6-d18aa171400e","added_by":"auto","created_at":"2024-11-06 17:27:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1341009,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5396093/v1/3732da27-a7f9-40c5-8902-03744d7fd282.pdf"},{"id":68384150,"identity":"58c04f2f-4618-4b4c-a76c-72eae4f65078","added_by":"auto","created_at":"2024-11-06 17:11:37","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":52312,"visible":true,"origin":"","legend":"","description":"","filename":"Tables.docx","url":"https://assets-eu.researchsquare.com/files/rs-5396093/v1/4363f340444a0c1a37a6ada8.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003ePrecision Prediction of Neoadjuvant Chemotherapy Efficacy in Breast Cancer: Integrating Multimodal Imaging and Clinical Features\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Advances in knowledge ","content":"\u003cp\u003eThis study innovatively integrates DCE-MRI, DKI, and IVIM imaging techniques and utilizes histogram-based analysis to enhance the prediction accuracy of neoadjuvant chemotherapy efficacy. It not only broadens the application range of imaging parameters but also supports the development of personalized treatment plans based on comprehensive data.\u003c/p\u003e\n"},{"header":"Introduction","content":"\u003cp\u003eBreast cancer remains the most common malignant tumor in women, with increasing incidence and mortality rates that severely threaten women's quality of life and health [1]. Invasive ductal carcinoma (IDC) is the most prevalent pathological subtype of breast cancer, characterized by strong invasiveness, with tendencies to infiltrate the chest wall and skin or metastasize distantly. Effective treatment is urgently needed, and neoadjuvant chemotherapy (NAC) is the preferred treatment option for locally advanced breast cancer (LABC). Widely applied in clinical settings, effective preoperative NAC can downstage tumors, reduce tumor size, eliminate or reduce micrometastases, increase surgical options, and improve both disease-free survival and overall survival rates for patients [2].Traditional imaging modalities for breast cancer include mammography, ultrasound, CT, and MRI. Among these, magnetic resonance imaging (MRI) is currently the most sensitive and advanced imaging technique for breast cancer diagnosis, enhancing detection rates, characterizing lesions, and evaluating the extent of surrounding soft tissue involvement [3]. In addition to dynamic contrast-enhanced (DCE) MRI, functional MRI techniques such as magnetic resonance spectroscopy (MRS), diffusion-weighted imaging (DWI), diffusion kurtosis imaging (DKI), and intravoxel incoherent motion (IVIM) imaging improve the accuracy of differentiating between benign and malignant tumors, aiding clinical treatment planning and therapeutic efficacy assessment.\u003c/p\u003e\n\u003cp\u003eDCE-MRI accurately reflects the density of tumor neovasculature, blood flow, and vascular wall permeability; DKI indicates the non-Gaussian diffusion of water molecules in tissue, with key quantitative parameters including mean diffusivity (MD) and mean kurtosis (MK), which provide insight into the microstructure of the tissue. IVIM imaging, an extension of diffusion-weighted imaging (DWI), utilizes a bi-exponential model to obtain diffusion coefficient (D), pseudo-diffusion coefficient (D*), and perfusion fraction (f) parameters, allowing for non-invasive quantification of tissue water molecule diffusion and microcirculation perfusion [4][5][6].Compared to traditional imaging analysis, histogram analysis offers a broader range of quantitative indicators (10th percentile, 90th percentile, energy, entropy, interquartile range, kurtosis, maximum, minimum, mean, median, range, skewness, uniformity, variance), providing a comprehensive, non-invasive assessment of the complexity and heterogeneity of the microstructure in tumor tissues. This improves the differentiation between benign and malignant lesions and enhances the assessment of clinical therapeutic efficacy [7][8].\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003e1.1 Study Population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis retrospective study collected data on 152 female breast cancer patients who underwent MRI examinations at our hospital from January 2021 to October 2023. After screening, 77 patients met the inclusion criteria. Inclusion criteria were: (1) histopathologically confirmed breast cancer (invasive ductal carcinoma); (2) completion of 3.0T MRI routine, DCE, DKI, and IVIM sequence scans before NAC; (3) completion of 6–8 cycles of NAC; (4) surgical treatment with subsequent pathological evaluation of NAC efficacy using the Miller-Payne (MP) grading system. Exclusion criteria were: (1) poor image quality; (2) incomplete basic patient information or clinical data.\u003c/p\u003e\n\u003cp\u003ePatients who met the criteria were assessed for NAC efficacy based on changes in tumor cell density as graded by the Miller-Payne system. Grade I: no reduction in tumor cell density; Grade II: tumor cell density reduction \u0026lt;30%; Grade III: reduction between 30% and 90%; Grade IV: \u0026gt;90% reduction in tumor cell density with remaining clustered or scattered tumor cells; Grade V: no residual tumor cells, though ductal carcinoma in situ may persist [9][10]. Grades IV and V were classified as the significant response group (43 patients), while Grades I, II, and III were classified as the non-significant response group (34 patients). This study protocol has been registered with the National Medical Research Registration System and was reviewed and approved by our medical institution and relevant management departments.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.2 MRI Examination Protocol\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMRI examinations were conducted using a Siemens 3.0T MRI scanner with an 18-channel dedicated breast phased-array coil. Scanning parameters were as follows: Axial T1WI: TR 6.04 ms, TE 2.46 ms, slice thickness 1.6 mm, FOV 360 mm × 360 mm, acquisition matrix 448 × 384, one excitation; Axial T2WI: TR 6500 ms, TE 84 ms, slice thickness 4 mm, acquisition matrix 448 × 448, one excitation; DKI/IVIM: TR 6100 ms, TE 54 ms, FOV 340 mm × 340 mm, b-values of 0, 30, 50, 100, 150, 200, 400, 800, 1200, 1600, and 2000 s/mm²; DCE-MRI: TR 7.14 ms, TE 3.69 ms, flip angle 8°, slice thickness 4 mm, matrix 448 × 448, FOV 340 mm × 340 mm, scan time 9.6 seconds per phase, for a total of 35 phases. Gadopentetate dimeglumine contrast agent (20 mL) was injected intravenously via a high-pressure injector at 2.0 mL/s.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.3 Image Processing and Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMR breast images for DCE, DKI, and IVIM sequences meeting inclusion criteria were exported from the PACS system in DICOM format and processed using Body Station software. Pseudocolor maps of MD, MK, D, f, and D* values were generated. Regions of interest (ROIs) encompassing the entire lesion were manually outlined layer by layer, referencing contrast-enhanced MRI images to avoid non-tumor tissues (e.g., normal tissue, necrotic areas, large blood vessels) as much as possible. DKI parameters including mean kurtosis (MK) and mean diffusivity (MD), as well as IVIM parameters diffusion coefficient (D), pseudo-diffusion coefficient (D*), and perfusion fraction (f), were obtained as whole-field histogram parameters.For DCE-MRI images, MR Tissue4D software was used to select the core region of the lesion and manually outline the ROI to acquire quantitative parameters: transfer constant (Ktrans), rate constant (Kep), and volume ratio of the extravascular extracellular space (Ve), as well as semi-quantitative parameters including the rate of concentration increase per minute (wash-in), rate of concentration decrease per minute (wash-out), time to peak (TTP), and initial area under the curve (iAUC) within the first 60 seconds post-contrast administration. Image processing and analysis were independently performed under blinded conditions by two radiologists.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.4 Statistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStatistical analysis was conducted using SPSS version 25.0. Interclass correlation coefficient (ICC) tests were applied to assess the consistency of histogram parameters from DCE, DKI, and IVIM across two groups. The ICC values for DCE parameters ranged from 0.821 to 0.997; for DKI, MD values were between 0.873 and 0.988, and MK values ranged from 0.917 to 0.985; for IVIM, D values ranged from 0.830 to 0.977, f values from 0.803 to 0.911, and D* values from 0.902 to 0.990, all ICC values exceeding 0.7, indicating good consistency between the data from the two groups. The Kolmogorov-Smirnov test was used to evaluate the normality of measurement data; normally distributed data were described by mean ± standard deviation (SD) and compared using independent sample t-tests. Non-normally distributed data were reported as median and interquartile range, M (P25, P75), and compared using the Mann-Whitney U test. Categorical data were analyzed using the Chi-square test (χ²) or Fisher's exact test, with counts (percentages) for statistical description. Spearman rank correlation analysis was performed to evaluate the association between various parameters and the NAC non-significant response. Receiver Operating Characteristic (ROC) curves were created using Medcalc version 22.0, and the area under the curve (AUC) was used to assess the diagnostic efficacy of each parameter in differentiating NAC efficacy. The DeLong test was applied to assess the statistical significance of differences between AUC values, with P \u0026lt; 0.05 considered statistically significant. Variables identified by univariate logistic regression (categorical and continuous variables) with P \u0026lt; 0.01 were included in a multivariate logistic regression analysis to identify independent influencing factors.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003e2.1 Comparison of Clinical Data and MRI Image Characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAccording to the Miller-Payne grading system, the 77 patients were divided into a significant response group (43 patients) and a non-significant response group (34 patients). No significant differences were observed between the significant and non-significant response groups in terms of age, menstrual status, lesion quadrant, tumor maximum diameter, enhancement pattern, peritumoral edema, tumor necrosis, chest wall involvement, skin thickening, nipple retraction, p53 status, immunohistochemical subtypes, or axillary lymph node metastasis (P \u0026gt; 0.05). In the significant response group, 35 patients (81%) had regular lesion margins, while 8 (19%) had spiculated margins, compared to 15 (44%) and 19 (56%) in the non-significant response group, respectively. For intralesional enhancement, heterogeneous enhancement and ring/segmental enhancement were observed in 32 (74%) and 11 (26%) patients in the significant response group, versus 17 (50%) and 17 (50%) in the non-significant response group.In terms of immunohistochemical expression, low ER expression (≤10%) and medium/high ER expression (\u0026gt;10%) were found in 32 patients (74%) and 11 patients (26%) in the significant response group, respectively, compared with 14 (41%) and 20 (59%) in the non-significant response group. Low PR expression (≤10%) and medium/high PR expression (\u0026gt;10%) were observed in 37 (86%) and 6 (14%) patients in the significant response group, respectively, compared with 18 (53%) and 16 (47%) in the non-significant response group. For Ki-67 expression, low expression (≤14%) and medium/high expression (\u0026gt;14%) were found in 1 patient (2%) and 42 patients (98%) in the significant response group, compared to 27 (79%) and 7 (21%) in the non-significant response group. HER2-negative and HER2-positive statuses were observed in 14 patients (33%) and 29 patients (67%) in the significant response group, respectively, compared to 23 (68%) and 11 (32%) in the non-significant response group. All of these comparisons were statistically significant (P \u0026lt; 0.05). Detailed clinical data and MRI imaging characteristics of breast invasive ductal carcinoma patients before NAC are presented in Table 1and\u0026nbsp;Figure\u0026nbsp;3.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Analysis of DCE, DKI, and IVIM Parameters\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn breast cancer patients, the NAC significant response group showed higher values of DCE-Kep, DKI-MD (90th Percentile, Mean, Median, Max, Range), IVIM-D (90th Percentile, Interquartile Range, Mean, Median, Variance, Uniformity), and IVIM-f (Interquartile Range) compared to the non-significant response group. Conversely, DCE-Ve, DKI-MK (Mean, Median), IVIM-D (Kurtosis, Skewness, Entropy), and IVIM-f (Entropy) were lower in the significant response group than in the non-significant response group, with all parameters showing statistically significant differences (P \u0026lt; 0.05). In univariate logistic regression analyses of categorical and continuous variables, significant differences (P \u0026lt; 0.05) with NAC efficacy were found for lesion margin, internal enhancement, ER expression, PR expression, HER2 status, Ki67 expression, DKI-D (90th Percentile, Max, Mean, Median, Range), DKI-K (Mean, Median), IVIM-D (90th Percentile, Entropy, Interquartile Range, Mean, Median, Kurtosis, Skewness, Uniformity, Variance), and IVIM-f (Entropy, Interquartile Range). Variables with P \u0026lt; 0.01 were included in a multivariate binary logistic regression analysis, identifying HER2 status and lesion margin as independent factors influencing the non-significant NAC response in breast cancer (P \u0026lt; 0.05). Detailed results are shown in Tables 2-4 and Figure 1-2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Correlation Analysis of DCE, DKI, and IVIM Parameters with NAC Efficacy\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePositive correlations (rs \u0026gt; 0, P \u0026lt; 0.05) with NAC efficacy in breast cancer were observed for DCE-Kep, DKI-MD (90th Percentile, Mean, Median, Max, Range), IVIM-D (90th Percentile, Interquartile Range, Mean, Median, Variance, Uniformity), and IVIM-f (Interquartile Range), indicating that higher values of these parameters are associated with greater NAC efficacy. Negative correlations (rs \u0026lt; 0, P \u0026lt; 0.05) were noted for DCE-Ve, DKI-MK (Mean, Median), IVIM-D (Kurtosis, Skewness, Entropy), and IVIM-f (Entropy), suggesting that higher values of these parameters are associated with a reduced NAC efficacy. Among DCE parameters, Ve showed the highest correlation with NAC efficacy (rs = 0.267); for DKI-MD, Max and Range had the strongest correlation (rs = 0.357); within DKI-MK, Mean demonstrated the highest correlation (rs = 0.386); for IVIM-D, Mean correlated most significantly with NAC efficacy (rs = 0.367); and for IVIM-f, Interquartile Range had the strongest correlation (rs = 0.296). Details are provided in Table 5.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4 Diagnostic Efficacy Analysis of Individual and Combined Whole-Histogram Parameters\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAmong individual parameters, the largest AUC was observed for DKI-MK Mean (AUC = 0.724). IVIM-D Interquartile Range demonstrated the highest sensitivity (94.12%), while DKI-MK Mean and IVIM-D Median exhibited the highest specificity (90.70%). In terms of combined parameters, the integration of DCE with DKI and IVIM histogram parameters achieved the highest AUC (0.969), sensitivity (94.12%), and specificity (90.70%). No significant differences were observed between AUCs of individual parameters (P \u0026gt; 0.05). However, significant differences were found within the combined parameters, where the combined DCE and IVIM, DCE and DKI+IVIM, and DCE+DKI+IVIM showed statistically significant differences (P \u0026lt; 0.05). Additionally, significant differences were observed in combined DKI parameters with DKI+IVIM, DCE+DKI+IVIM, and in combined IVIM parameters with DKI+IVIM, DCE+DKI+IVIM (P \u0026lt; 0.05). Further details can be found in Table 5 and Figure 4.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e\u003cstrong\u003e3.1 Predictive Value of Pre-NAC MRI Imaging Characteristics, Clinical Features, and Immunohistochemical Factors for NAC Efficacy in Invasive Ductal Carcinoma of the Breast\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, MRI characteristics such as lesion margin, internal enhancement patterns, as well as ER, PR, Ki67 expression, and HER2 status, demonstrated significant associations with NAC efficacy. The number of patients with significant NAC response was notably lower in those with spiculated lesion margins compared to those with smooth margins. This may be due to the greater tumor cell activity, rapid proliferation rate, and high cellular density of tumors with spiculated margins, which invade surrounding tissues and elicit a fibrotic response that contributes to the spiculated appearance leads to a diminished NAC efficacy.Patients with heterogeneous internal enhancement were significantly more likely to exhibit a substantial NAC response compared to those with ring or segmented enhancement. This could be attributed to inadequate angiogenesis, resulting in necrotic or fibrotic regions within the tumor that often have limited blood and oxygen supply. Consequently, these poorly vascularized areas may inhibit effective drug penetration, thereby limiting therapeutic efficacy against the tumor .\u003c/p\u003e\n\u003cp\u003eAccording to thedelines from the American Society of Clinical Oncology (ASCO) and the College of American Pathologists (CAP), ER and PR immunohistochemical (IHC) staining between 1% and 10% is categorized as low expression. In this study, patients with low ER and PR expression were significantly more likely to achieve a notable NAC response compared to those with medium or high expression. Breast cancers with low ER and PR expression may rely less on estrogen and progesterone for growth, exhibiting higher resistance to hormone therapy. Thus, these patients may respond more favorably to NAC, which operates through mechanisms independent of hormone signaling .\u003c/p\u003e\n\u003cp\u003eHER2 expression, classifiee from 0 to 3+, is considered positive with an IHC score of 3+ or a score of 2+ combined with a positive in situ hybridization (ISH) result. In this study, a significantly higher number of HER2-positive patients exhibited a marked NAC response compared to HER2-negative patients. HER2, a cell membrane receptor, plays a critical role in regulating cell growth and division. Breast cancer cells overexpressing HER2 generally have higher proliferative activity, potentially making them more susceptible to chemotherapeutic agents .\u003c/p\u003e\n\u003cp\u003ePrevious studies have defined low IHC staining range of 1% to 14% . Our results show that patients with low Ki67 expression hantly fewer notable NAC responses compared to those with medium or high Ki67 expression. Elevated Ki67 expression is indicative of higher tumor cell proliferation. Since NAC predominantly targets rapidly proliferating cancer cells, tumors with medium or high Ki67 expression are more likely to be susceptible to chemotherapeutic agents .\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Value of Pre-Treatment Quantitative Parameters (Ktrans, Kep, Ve) and Semi-Quantitative Parameters (Wash-in, Wash-out, TTP, and iAUC) in Predicting NAC Efficacy in Invasive Ductal Carcinoma of the Breast\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDCE-MRI quantitative and semi-quantitative parameters can accurately reflect tumor microvessel density, blood flow, and vascular permeability. In this study, pre-NAC quantitative parameters Kep and Ve showed statistically significant associations with NAC efficacy. The average Kep value was higher in the effective NAC group, while the average Ve value was lower in this group compared to the non-effective group. Kep reflects the rate of exchange between the extracellular extravascular space (EES) and blood vessels, while Ve represents the EES volume fraction, both indicating vascular permeability. Increased values of Kep and Ve suggest greater drug penetration into the tumor, potentially enhancing NAC efficacy. However, in this study, the Ve value in the effective NAC group was lower than in the non-effective group.\u003c/p\u003e\n\u003cp\u003ePrevious studies have explored the predictive value of changes in Kep following early NAC treatment (e.g., after the second cycle) on overall NAC efficacy. The results showed that Kep values in the pCR group significantly decreased after two weeks of NAC treatment, whereas Ve values remained unchanged. However, predicting NAC efficacy before treatment initiation is more clinically desirable. Given the limitations of single-parameter predictions, pre-treatment multi-parametric MRI analyses are essential for accurately predicting NAC efficacy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Value of Histogram Parameters MD and MK from the DKI Model in Predicting NAC Efficacy in Invasive Ductal Carcinoma of the Breast\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the DKI model, MD reflects the overall diffusion level and resistance of water molecules, while MK reflects the complexity of tissue microstructure. In this study, MD parameters (90th percentile, mean, median, max, and range) were higher in the effective NAC group than in the non-effective group. The 90th percentile MD value may represent areas of lower cellular density, such as necrotic, liquefied, or cystic regions. Higher mean and median MD values indicate lower cellular density, increased interstitial spaces, and greater water diffusion, with reduced resistance and higher water molecule content in the tissue. High maximum and range values likely indicate larger areas of free water diffusion and lower cell density.\u003c/p\u003e\n\u003cp\u003eIn the effective NAC group, the MK parameters (mean and median) were lower than in the non-effective group. Lower mean and median MK values suggest less structural complexity and heterogeneity in the tumor microstructure. These findings demonstrate that higher MD (90th percentile, mean, median, max, range) and lower MK (mean, median) values are associated with significant NAC efficacy, indicating lower tumor cell density, heterogeneity, and structural complexity before NAC. These findings align with previous baseline studies by Zheng et al.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Predictive Value of Histogram Parameters D, f, and D\u0026nbsp;from the IVIM Model in Pre-Treatment Assessment of NAC Efficacy for Invasive Ductal Carcinoma of the Breast\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the IVIM model, the D and f values indicate the true diffusion coefficient and perfusion information, respectivelystudy, the effective NAC group showed significantly higher values for IVIM-D (90th percentile, interquartile range, mean, median, variance, and uniformity) and IVIM-f (interquartile range) than the non-effective group. Higher mean, median, and variance values for IVIM-D may suggest lower tumor cell density and larger extracellular space, resulting in less resistance to water molecule diffusion. These areas of lower diffusion resistance are consistent with the findings for DKI-MD values in this study. Higher interquartile range values for IVIM-D and IVIM-f might indicate a broader distribution range of water molecule diffusion or perfusion values within the tissue, allowing for more effective drug penetration during NAC treatment.\u003c/p\u003e\n\u003cp\u003eIn contrast, the effective NAC group had lower values for IVIM-D (kurtosis, skewness, and entropy) and IVIM-f (entropy) than the non-effective group, potentially indicating greater tissue heterogeneity, uneven water molecule diffusion, and higher distribution irregularity, which may contribute to less effective NAC outcomes. These findings are consistent with those reported by Kim et al. . Althougstudies such as this cannot capture the dynamic changes of variables during NAC, combining multiparametric MRI with clinical characteristics allows for a more comprehensive assessment of the relationship between tumor characteristics and NAC efficacy, providing a valuable reference for pre-treatment clinical decision-making.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 Limitations of This Study\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFirst, this is a single-center study with a relatively small sample size. Second, the DCE-MRI quantitative parameters did not undergo histogram analysis. Third, this study focused on the short-term efficacy of NAC without long-term follow-up to evaluate patient prognosis.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe present study does not involve any human or animal subjects, and thus, formal ethics approval and consent to participate were not required.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; All authors have provided consent for the publication of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests or conflicts of interest relevant to this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp;This study was supported by Chongqing Regional Key Disciplines (zdxk202116)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved and supervised by the ethics committee of Three Gorges Hospital Affiliated to Chongqing University.(MR-50-23-026780)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXianglong Chen, and Luo Yong contributed equally to this work as first authors, Xianglong Chen, and Luo Yong developed the concept and discussed experiments and collaboratively drafting the initial version of the manuscript. Zhiming Xie and Yun Wen collected patient samples and data. Fangsheng Mou and Wenbing Zhen served as co-corresponding authors, providing overall guidance and supervision for the organization and development of the manuscript. All authors participated in the critical review, revision, and approval of the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssociated\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis section collects any data citations, data availability statements, or supplementary materials included in this article.Data\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eFreddie,Bray,Jacques,et al.Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries.[J].CA: a cancer journal for clinicians, 2018.DOI:10.3322/caac.21492.\u003c/li\u003e\n \u003cli\u003eMaccoll C E ,Guillaume Par\u0026eacute;, Salehi A ,et al.Postneoadjuvant Pure and Predominantly Pure Intralymphatic Breast Carcinoma: Case Series and Literature Review[J].The American journal of surgical pathology, 2020.DOI:10.1097/PAS.0000000000001610.\u003c/li\u003e\n \u003cli\u003eHUSSEIN H, ABBAS E, KESHAVARZI S, et al. Supplemental Breast Cancer Screening in Women with Dense Breasts and Negative Mammography: A Systematic Review and Meta-Analysis[J/OL].Radiology,2023,306(3). DOI:10.1148/radiol.221785.\u003c/li\u003e\n \u003cli\u003eComparison of the pre-treatment functional MRI metrics\u0026apos; efficacy in predicting Locoregionally advanced nasopharyngeal carcinoma response to induction chemotherapy[J].Cancer Imaging, 2021, 21(1):1-12.DOI:10.1186/s40644-021-00428-0.\u003c/li\u003e\n \u003cli\u003eFUSCO R, SANSONE M, GRANATA V, et al. Diffusion and perfusion MR parameters to assess preoperative short-course radiotherapy response in locally advanced rectal cancer: a comparative explorative study among Standardized Index of Shape by DCE-MRI, intravoxel incoherent motion- and diffusion kurtosis imaging-derived parameters[J/OL]. Abdominal Radiology,2019,44(11):3683-3700. DOI:10.1007/s00261-018-1801-z.\u003c/li\u003e\n \u003cli\u003eGRANATA V, FUSCO R, SANSONE M, et al. Magnetic resonance imaging in the assessment of pancreatic cancer with quantitative parameter extraction by means of dynamic contrast-enhanced magnetic resonance imaging, diffusion kurtosis imaging and intravoxel incoherent motion diffusion-weighted imaging[J/OL]. Therapeutic Advances in Gastroenterology, 2019: 175628481988505. DOI:10.1177/1756284819885052.\u003c/li\u003e\n \u003cli\u003eLi, Hao, Zhao, Sheng, Fan, Hai Y, et al. The Effect of Histogram Analysis of DCE-MRI Parameters on Differentiating Renal Tumors. Clinical laboratory, 2023 Nov 1;69(11).DOI:10.7754/Clin.Lab.2023.221126.\u003c/li\u003e\n \u003cli\u003eLi Q , Xiao Q , Yang M ,et al.Histogram analysis of quantitative parameters from synthetic MRI: correlations with prognostic factors and molecular subtypes in invasive ductal breast cancer[J].European Journal of Radiology, 2021(3):109697.DOI:10.1016/j.ejrad.2021.109697.\u003c/li\u003e\n \u003cli\u003eZhao D , Fu X , Rohr J ,et al.Poor histologic tumor response after adjuvant therapy in basal-like HER2-positive breast carcinoma[J].Pathology - Research and Practice, 2021, 228:153677-.DOI:10.1016/j.prp.2021.153677.\u003c/li\u003e\n \u003cli\u003eHuang Y , Le J , Miao A ,et al.Prediction of treatment responses to neoadjuvant chemotherapy in breast cancer using contrast-enhanced ultrasound.[J].AME Publishing Company, 2021(4).DOI:10.21037/GS-20-836.\u003c/li\u003e\n \u003cli\u003eGalati F , Rizzo V , Moffa G ,et al.Radiologic-pathologic correlation in breast cancer: do MRI biomarkers correlate with pathologic features and molecular subtypes?[J].European Radiology Experimental, 2022, 6(1):1-13.DOI:10.1186/s41747-022-00289-7.\u003c/li\u003e\n \u003cli\u003eWang S , Zhang Y , Yang X ,et al.Shrink pattern of breast cancer after neoadjuvant chemotherapy and its correlation with clinical pathological factors[J].World Journal of Surgical Oncology, 2013, 11(1):166-166.DOI:10.1186/1477-7819-11-166.\u003c/li\u003e\n \u003cli\u003eRAMTOHUL T, TESCHER C, VAFLARD P, et al. Prospective Evaluation of Ultrafast Breast MRI for Predicting Pathologic Response after Neoadjuvant Therapies[J/OL]. Radiology,2022,305(3):565-574. DOI:10.1148/radiol.220389.\u003c/li\u003e\n \u003cli\u003eDou H , Li F , Wang Y ,et al.Estrogen receptor-negative/progesterone receptor-positive breast cancer has distinct characteristics and pathologic complete response rate after neoadjuvant chemotherapy[J].Diagnostic Pathology, 2024, 19(1).DOI:10.1186/s13000-023-01433-6.\u003c/li\u003e\n \u003cli\u003eLeon-Ferre R A , Hieken T J , Boughey J C .The Landmark Series: Neoadjuvant Chemotherapy for Triple-Negative and HER2-Positive Breast Cancer[J].Annals of Surgical Oncology, 2021, 28(4):2111-2119.DOI:10.1245/s10434-020-09480-9.\u003c/li\u003e\n \u003cli\u003eTERUYA N, INOUE H, HORII R, et al. Intratumoral heterogeneity, treatment response, and survival outcome of ER‐positive HER2‐positive breast cancer[J/OL].Cancer Medicine,2023,12(9): 10526-10535. DOI:10.1002/cam4.5788.\u003c/li\u003e\n \u003cli\u003ePeng J H , Zhang X , Song J L ,et al.Neoadjuvant chemotherapy reduces the expression rates of ER, PR, HER2, Ki67, and P53 of invasive ductal carcinoma[J].Medicine, 2019, 98(2).DOI:10.1097/MD.0000000000013554.\u003c/li\u003e\n \u003cli\u003eZhang H, Wang Z, Liu W, et al. Breast-Conserving Surgery in Triple-Negative Breast Cancer: A Retrospective Cohort Study[J/OL]. Evidence-Based Complementary and Alternative Medicine,2023,2023:1-8. DOI:10.1155/2023/5431563.\u003c/li\u003e\n \u003cli\u003eChen W, Li F X, Lu D L, et al. Differences between the efficacy of HER2(2+)/FISH-positive and HER2(3+) in breast cancer during dual-target neoadjuvant therapy[J/OL]. The Breast,2023,71:69-73. DOI:10.1016/j.breast.2023.07.008.\u003c/li\u003e\n \u003cli\u003eLiang X , Chen X , Yang Z ,et al.Early prediction of pathological complete response to neoadjuvant chemotherapy combining DCE-MRI and apparent diffusion coefficient values in breast Cancer[J].BMC cancer, 2022, 22(1):1250.DOI:10.1186/s12885-022-10315-x.\u003c/li\u003e\n \u003cli\u003eGuo W, Zhang Y, Luo D, et al. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for pretreatment prediction of neoadjuvant chemotherapy response in locally advanced hypopharyngeal cancer[J/OL]. The British Journal of Radiology, 2020, 93(1115): 20200751. DOI:10.1259/bjr.20200751.\u003c/li\u003e\n \u003cli\u003eZhang D, Geng X, Suo S, et al. The predictive value of DKI in breast cancer: Does tumour subtype affect pathological response evaluations?[J/OL]. Magnetic Resonance Imaging,2021,85:28-34. DOI:10.1016/j.mri.2021.10.013.\u003c/li\u003e\n \u003cli\u003eLiu W , Wei C , Bai J ,et al.Histogram analysis of diffusion kurtosis imaging in the differentiation of malignant from benign breast lesions[J].European Journal of Radiology, 2019, 117:156-163.DOI:10.1016/j.ejrad.2019.06.008.\u003c/li\u003e\n \u003cli\u003eHistogram analysis in predicting the grade and histological subtype of meningiomas based on diffusion kurtosis imaging:[J].Acta Radiologica, 2020, 61(9):1228-1239.DOI:10.1177/0284185119898656.\u003c/li\u003e\n \u003cli\u003eZheng D , Lai G , Chen Y ,et al.Integrating dynamic contrast-enhanced magnetic resonance imaging and diffusion kurtosis imaging for neoadjuvant chemotherapy assessment of nasopharyngeal carcinoma.[J].Journal of Magnetic Resonance Imaging, 2018.DOI:10.1002/jmri.26164.\u003c/li\u003e\n \u003cli\u003eAi Z , Han Q , Huang Z ,et al.The value of multiparametric histogram features based on intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) for the differential diagnosis of liver lesions.[J].Annals of Translational Medicine,2020(18).DOI:10.21037/ATM-20-5109.\u003c/li\u003e\n \u003cli\u003eKim Y , Kim S H , Lee H W ,et al.Intravoxel incoherent motion diffusion-weighted MRI for predicting response to neoadjuvant chemotherapy in breast cancer[J].Magnetic Resonance Imaging, 2018, 48:27-33.DOI:10.1016/j.mri.2017.12.018.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 to 5 are available in the Supplementary Files section\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[{"identity":"3e1360a0-3391-4b59-9274-76bf65c814cf","identifier":"10.13039/501100002369","name":"Chongqing University","awardNumber":"zdxk202116","order_by":0}],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"School of Medical Imaging,North Sichuan Medical Univesiyt,Nanchong,Sichuan Province,China","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Invasive ductal carcinoma, Neoadjuvant chemotherapy, Diffusion kurtosis imaging, Intravoxel incoherent motion imaging, Dynamic contrast-enhanced, Histogram parameters.","lastPublishedDoi":"10.21203/rs.3.rs-5396093/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5396093/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjectives \u003c/strong\u003eTo assess the predictive value of combining DCE-MRI, DKI, IVIM parameters, and clinical characteristics for neoadjuvant chemotherapy (NAC) efficacy in invasive ductal carcinoma.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods \u003c/strong\u003eWe conducted a retrospective study of 77 patients with invasive ductal carcinoma, analyzing MRI data collected before NAC. Parameters extracted included DCE-MRI (Ktrans, Kep, Ve, wash-in, wash-out, TTP, iAUC), DKI (MK, MD), and IVIM (D, D*, f). Differences between NAC responders and non-responders were assessed using t-tests or Mann-Whitney U tests. ROC curves and Spearman correlation analyses evaluated predictive accuracy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults \u003c/strong\u003eNAC responders had higher DCE-Kep, DKI-MD, IVIM-D, and IVIM-f values. Non-responders had higher DCE-Ve, DKI-MK, IVIM-D (kurtosis, skewness, entropy), and IVIM-f (entropy). The mean DKI-MK had the highest AUC (0.724), and IVIM-D interquartile range showed the highest sensitivity (94.12%). Combined parameters had the highest AUC (0.969), sensitivity (94.12%), and specificity (90.70%). HER2 status and lesion margins were independent predictors of poor response.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions \u003c/strong\u003eCombining DCE-MRI, DKI, and IVIM parameters effectively predicts NAC efficacy, providing valuable preoperative assessment insights.\u003c/p\u003e","manuscriptTitle":"Precision Prediction of Neoadjuvant Chemotherapy Efficacy in Breast Cancer: Integrating Multimodal Imaging and Clinical Features","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-06 17:11:32","doi":"10.21203/rs.3.rs-5396093/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"051834a0-2f2c-41f7-95d4-aadf9d97c63f","owner":[],"postedDate":"November 6th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":39853943,"name":"Nuclear Medicine \u0026 Medical Imaging"}],"tags":[],"updatedAt":"2024-11-06T17:11:32+00:00","versionOfRecord":[],"versionCreatedAt":"2024-11-06 17:11:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5396093","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5396093","identity":"rs-5396093","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}