Early Identification of Breast Cancer Patients Achieving Radiological Complete Response to NAC: A Clinicopathological and MRI-Based Approach

Early Identification of Breast Cancer Patients Achieving Radiological Complete Response to NAC: A Clinicopathological and MRI-Based Approach | 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 Early Identification of Breast Cancer Patients Achieving Radiological Complete Response to NAC: A Clinicopathological and MRI-Based Approach Bowen Yue, Tianxiang Zhu, Zhuozhi Dai, Yi Chen, Hao Zhang, Xiaohong Chen, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8796639/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Background Radiologic complete response (rCR) after neoadjuvant chemotherapy (NAC) is increasingly recognized as a prognostic indicator in breast cancer, yet predictive models integrating baseline clinicopathological and multiparametric MRI features remain underdeveloped. We aimed to develop and validate a combined model for pretreatment prediction of rCR. Methods This retrospective study analyzed 286 consecutive breast cancer patients who received NAC between 2016 and 2023. rCR was strictly defined as complete absence of enhancement on post-treatment DCE-MRI per RECIST 1.1 criteria. Pretreatment clinicopathological variables (including ER, PR, HER2 status, Ki-67 index, and serum CA15-3 level) and multiparametric MRI characteristics (tumor size, morphology, enhancement kinetics, and ADC values) were evaluated. Variable selection was performed using multivariable logistic regression with variance inflation factor restriction (VIF < 5) to identify independent predictors. Model performance was assessed via receiver operating characteristic (ROC) analysis. Results Significant differences in ER/PR/HER2 status, Ki-67, CA15-3, tumor diameter, and morphology were observed between rCR (13.3%, 38/286) and non-rCR groups (all P < 0.05). Multivariate analysis identified high Ki-67 (OR = 9.009), low CA15-3 (OR = 0.098), tumor diameter < 3.15 cm (OR = 0.778), and non-irregular morphology (OR = 0.148) as independent predictors (all P < 0.05). The combined model achieved an AUC of 0.772 (sensitivity = 63.2%, specificity = 83.1%). Conclusions We developed a clinically applicable model combining readily available pretreatment clinicopathological and MRI features that effectively stratifies patients by likelihood of achieving rCR after NAC. This tool may facilitate early identification of NAC responders, potentially optimizing treatment strategies and reducing unnecessary chemotherapy exposure. Further validation in prospective, multicenter cohorts is warranted to confirm its generalizability. Neoadjuvant chemotherapy MRI Diagnostic efficacy Breast cancer Radiologic complete response Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Breast cancer (BC) is the second most common cancer worldwide; it poses a serious threat to women's lives and is the leading cause of cancer-related deaths among women ( 1 , 2 ). Neoadjuvant chemotherapy (NAC), a core strategy for the comprehensive treatment of locally progressive BC, significantly improves the tumor downstaging rate by inhibiting tumor proliferative activity, controlling the progression of primary tumors and metastases, and transforming initially unresectable tumors to operable status ( 3 , 4 ). As the clinical use of NAC has become increasingly popular ( 5 , 6 ), the therapeutic response to NAC has been shown to be significantly positively correlated with patient prognosis ( 7 ). Pathological complete response (pCR) is widely recognized as a robust predictor for improved overall survival (OS) and long-term disease-free survival (DFS) in breast cancer patients undergoing NAC. However, the assessment of pCR can only be performed postoperatively. Recent clinical evidence has demonstrated that patients achieving radiologic complete response (rCR) on MRI following NAC treatment similarly exhibit favorable DFS outcomes. Importantly, a comparative study revealed that the DFS benefits in rCR patients were comparable to those achieving pCR, while residual disease was significantly associated with increased risks of early recurrence and mortality( 8 , 9 ). These findings collectively highlight the prognostic value of rCR assessment in predicting patient outcomes. Improved early prediction of radiologic response following neoadjuvant chemotherapy for breast cancer could enhance the accuracy of final outcome prediction, thereby facilitating personalized treatment strategies. Conventional imaging modalities, including mammography and ultrasound, serve as the cornerstone for initial diagnosis and standard practice for monitoring treatment response in breast cancer ( 10 , 11 ). Mammography provides high spatial resolution for evaluating calcifications and architectural distortions, while ultrasound excels in characterizing solid masses and guiding interventions. However, the accuracy of these techniques can be limited by factors such as breast density, operator dependence for ultrasound, and challenges in distinguishing post-therapeutic fibrosis from residual viable tumor ( 12 , 13 ). Although ultrasonography is superior to mammography in the assessment of tumor morphology, its measurement consistency is dependent on operator experience( 12 , 13 ). Compared with mammography and ultrasound, multiparametric MRI, especially dynamic contrast-enhanced MRI (DCE-MRI) combined with diffusion-weighted imaging (DWI), has demonstrated significantly higher accuracy in assessing the extent of the tumor and NAC response ( 14 ). DCE-MRI reflects the state of tumor angiogenesis through quantitative contrast kinetics, and DWI characterizes cell density changes through the apparent diffusion coefficient (ADC); the two methods are synergistic and can be used to comprehensively assess the evolution of tumor biological behavior and thus the response to NAC. Existing evidence suggests that multiparametric MRI demonstrates high predictive efficacy in pCR prediction( 3 , 15 – 18 ), but no consensus has been established on its application in rCR prediction. Notably, although evidence has demonstrated that rCR following NAC is associated with improved prognosis in breast cancer patients( 8 , 9 ), there has been no systematic study of rCR-related predictors, as recent studies have focused on pCR predictors and model construction. Zhang et al. ( 19 ) explored pCR predictors and unexpectedly reported that degree of tumor differentiation, American Joint Committee on Cancer (AJCC) stage I and high Ki-67 expression were independently associated with a rCR, but the study was performed in a single center with a small sample size and did not integrate multiparametric MRI features. While clinicians routinely consider clinicopathological factors like Ki-67 and molecular subtypes when evaluating treatment response, this process often relies on empirical synthesis rather than quantitative integration. Furthermore, the predictive value of pretreatment multiparametric MRI features specifically for rCR—a non-invasive endpoint strongly associated with improved survival outcomes( 9 )—remains underexplored and lacks a comprehensive model. Therefore, this study aimed to develop and validate an integrated predictive model that combines baseline clinicopathological factors with quantitative multiparametric MRI features (DCE-MRI and DWI) to enable early, pre-treatment identification of breast cancer patients most likely to achieve rCR after NAC. The primary innovation of this study lies in the quantitative synthesis of these readily available pre-therapeutic parameters into a single, objective tool, which could potentially aid in personalized patient counseling and stratify patients for future research into response-adapted therapy strategies. Materials and methods Study population The ethics committee of Meizhou People’s Hospital approved this retrospective study (2023-CY-53). This study was conducted in accordance with the Declaration of Helsinki, and the requirement for informed consent was waived. All methods were performed in accordance with the ethical standards of the institutional and/or national research committee and with the Declaration of Helsinki (20). This study collected information on female BC patients who underwent surgery after NAC treatment between January 2016 and September 2023. The inclusion criteria were as follows: (I) patients who underwent NAC before BC surgery, (II) patients who underwent multiparametric MR imaging of the breast before NAC, (III) patients who underwent surgical resection of BC after NAC and whose results of the pathologic efficacy evaluation of NAC were available. The exclusion criteria were as follows: (I) patients with incomplete postoperative pathologic data (n=30), (II) patients who received fewer than 3 courses of NAC (n=4) ,as fewer than 3 cycles may not provide sufficient tumor response assessment and are more likely to represent early discontinuation due to toxicity or progression. (III) patients with occult BC (n=5), (IV) patients with bilateral BC (n=3). A flow chart of the patient inclusion and exclusion criteria is shown in Figure 1. Ultimately, 286 patients fulfilled our inclusion criteria. All the patients were female whose ages ranged from 28−74 years. Patients were divided into an rCR group and a non-rCR group according to their preoperative MRI evaluation (see Group classification criteria below). Clinicopathologic features [including age; family history (defined as a first- or second-degree relative with breast cancer); menstrual status; the levels of carcinoembryonic antigen (CEA), carcinoembryonic antigen 125 (CA-125), and carcinoembryonic antigen 15-3 (CA15-3); human epidermal growth factor receptor 2 (HER-2), progesterone receptor (PR), and estrogen receptor (ER) status; Ki-67 expression; TNM stage; AJCC stage; and molecular subtype of BC] were recorded prior to NAC. Serum tumor marker assessment Pretreatment blood samples for the analysis of CEA, CA-125, and CA15-3 were collected prior to the initiation of any NAC treatment and were the only values used in this analysis. Elevated levels were defined according to our institutional laboratory standards as follows: CEA ≥ 5.0 ng/mL, CA-125 ≥ 35 U/mL, CA15-3 ≥ 35 U/mL. NAC therapeutic protocol Most patients received 4 to 8 cycles of treatment before surgery. In the absence of medical contraindications, all patients received anthracycline-, paclitaxel-, and platinum-based NAC. Trastuzumab and/or patuzumab were used in combination with NAC in HER-2-positive patients. The patients subsequently underwent breast conservation surgery or mastectomy. MRI response evaluation All patients underwent two key MRI examinations: 1) A baseline multiparametric MRI prior to the initiation of NAC, from which all imaging predictors were extracted; and 2) A follow-up MRI after the completion of all NAC cycles and prior to surgery, which was used to evaluate the treatment response and define the radiological endpoint. In this study, rCR was defined as the complete absence of any visible contrast enhancement within the original tumor bed on the post-treatment DCE-MRI sequences, adhering to the concept of complete response in RECIST 1.1. Any residual enhancement, regardless of pattern or degree, was classified as non-rCR for the purposes of this binary outcome analysis. This stringent definition is intentional, as it identifies the patient group with the most profound imaging response. Imaging protocol All breast MRI examinations were conducted using a 3.0T MRI scanner (Siemens, Germany) with patients in the prone position, which allowed the bilateral breasts to naturally fall within a 16-channel phased-array breast coil. DCE-MRI was acquired using a 3D volumetric interpolated breath-hold examination (VIBE) sequence combined with controlled aliasing in parallel imaging results in higher acceleration (CAIPIRINHA). The imaging protocol incorporated time-resolved angiography with interleaved stochastic trajectories (TWIST) for view-sharing and Dixon-based fat suppression (CAIPIRINHA-Dixon-TWIST-VIBE). This sequence employed spoiled gradient-echo (GRE) acquisition with a fast low-angle shot (FLASH) and utilized K-space sharing technology via TWIST. The scanning sequences included T1WI, T2WI, FLAIR, STIR, DCE-MRI and DWI. The DCE-MRI protocol consisted of precontrast T1-weighted VIBE imaging (repetition time/TR=3.78 ms, echo time/TE=1.38 ms, matrix=205×256, FOV=340 mm×340 mm, slice thickness=2 mm, voxel resolution=1.3 mm×1.3 mm×2.0 mm) followed by a dynamic phase using TWIST-VIBE with 34 consecutive acquisitions (TR=6.4 ms, TE=3.3 ms, matrix=288×384, FOV=288 mm×384 mm, slice thickness=2.0 mm, voxel resolution=0.9 mm×0.9 mm×2.0 mm, temporal resolution=8.7 s, flip angle=9°).Gadopentetate dextran was injected at a dosage of 0.1 mmol/kg with an injection rate of 3.0 ml/s. Thirty-four temporal phases were acquired, with the first temporal phase acquired 17.7 seconds after contrast injection and the subsequent single-phase scans spaced at an interval of 8.7 seconds. After contrast agent injection, 20 ml of saline was injected at the same flow rate. DWI was performed using both readout-segmented echo-planar imaging (RS-EPI) and single-shot EPI (SS-EPI) with fat suppression in the transverse plane prior to DCE-MRI. For the final analysis, ADC values derived from the RS-EPI sequence were utilized. This choice was based on its demonstrated superior robustness to susceptibility artifacts and higher spatial resolution compared to SS-EPI, leading to more reliable ADC measurements in our clinical setting.The RS-EPI parameters were TR/TE = 4800/56 ms, FOV = 170 mm × 340 mm, matrix = 96 × 192, flip angle = 180°, bandwidth = 868 Hz, slice thickness = 4.0 mm with 0.8 mm gap, averages = 8, and b-values = 50 and 800 s/mm², while the SS-EPI parameters were TR/TE = 4200/62 ms, FOV = 149 mm × 340 mm, matrix = 100 × 170, bandwidth = 1730 Hz, echo spacing = 0.68 ms, slice thickness = 4.0 mm with 0.8 mm gap, averages = 3, and b-values = 50 and 800 s/mm². Both sequences utilized generalized autocalibrating partially parallel acquisitions (GRAPPA) with an acceleration factor of 2, ensuring complete breast coverage through optimized slice selection. Pathological immunohistochemical assessment ER and PR were evaluated according to the American Society of Clinical Oncology (ASCO)/College of American Pathologists (CAP) guidelines (21). ER and PR were considered positive when they were expressed in ≥1% of cells according to immunohistochemistry (IHC) . HER2 was also evaluated according to the ASCO/CAP guidelines. Only membrane staining of the infiltrating tissue was considered for HER2 scoring. The IHC score for HER2 was categorized as positive (3+), suspicious (2+), or negative (0 or 1+) (22). Tumors with a HER2 IHC score of 3+ did not require further testing, whereas cases with an IHC score of 2+ were further evaluated for HER2 amplification by fluorescence in situ hybridization (FISH). The results of FISH for HER2 were interpreted according to the 2018 ASCO/CAP updated guidelines (23). Luminal A BC was defined as BC that was ER- and/or PR-positive and HER-2-negative with a Ki-67 index <20%. Luminal B BC was defined as BC that was ER- and/or PR-positive and HER-2 positive or negative with a Ki-67 index ≥20%. HER-2-overexpressing BC was defined as ER- and PR-negative and HER-2-positive. Triple-negative breast cancer (TNBC) was defined as BC that was negative for ER, PR and HER-2 (24). For all resected samples, the pathology assessment of NAC efficacy was performed by an experienced pathologist according to the Miller‒Payne grading system. In our study, a pCR was defined as the absence of detectable residual invasive tumor tissue in the breast and the absence of lymph node metastasis, regardless of the presence of residual ductal carcinoma in situ (ypT0/is ypN0). Data processing and collection The images were independently interpreted by two radiologists (a resident and an attending physician), both blinded to the pathological findings. In cases of disagreement in interpretation (>10% measurement difference or diagnostic discordance), a senior radiologist with 16 years of experience was consulted to reach a consensus. Using GE Centricity™ PACS software, tumor evaluation involved: (1) co-registered T2WI/DWI/DCE-MRI sequences identifying target lesions (largest lesion selected if multifocal), with maximum diameter (cm) measured at peak DCE-MRI enhancement using multiplanar reconstruction; (2) ADC analysis where the DCE-MRI slice showing peak enhancement (phase with maximal tumor-to-background contrast) was matched to ADC maps, and three circular ROIs (each ≥10 mm²) systematically sampled the entire solid tumor component (excluding edema) to cover both strongest DCE-MRI enhancement areas and lowest ADC regions while avoiding artifacts (vessels via T2WI flow voids, calcifications via CT, hemorrhage via T1 hyperintensity, necrosis via DCE non-enhancement), with mean ADC (×10⁻³mm²/s) representing tumor heterogeneity; (3) DCE-MRI kinetic analysis using Siemens syngo.via workstation, where the phase with clearest tumor delineation (typically 60-90s post-contrast) was selected for TIC Tool placement of a single ROI in the most homogeneously enhancing solid portion (size-matched to ADC ROIs), generating time-signal intensity curves and enhancement degree measurements. Multiparametric MRI features were acquired before NAC using the ACR BI-RADS MRI lexicon(25) , including the number of lesions (multiple/single), fibroglandular tissue (FGT) type, background parenchymal enhancement (BPE), maximal diameter, degree of enhancement, type of TIC, type of lesion (mass/NME (non-mass enhancement)), margins of the lesion (clear/blurred), morphology (round and oval/irregular), ADC value of the tumor, and normal contralateral breast gland ADC value. All multiparametric MRI features were qualitatively assessed and categorized according to the American College of Radiology Breast Imaging Reporting and Data System (BI-RADS) MRI lexicon(25). This included evaluation of lesion type (mass vs. non-mass enhancement), morphology, margins, internal enhancement characteristics, kinetic curve type, and background parenchymal enhancement. Statistical analysis The data were statistically analyzed using IBM SPSS Statistics 28, and differences were considered significant at P <0.05. Measurement information that conformed to a normal distribution was expressed as the mean ± standard deviation, and an independent samples t test was used for comparisons between the two groups. Count data are expressed as examples, and the chi-square test was used for comparisons between two groups. To identify independent predictors of rCR, we first performed univariable logistic regression analyses for all candidate variables. Variables (maximum tumor diameter, morphology, PR, ER, HER-2 status, Ki-67 index, N stage, and CA15-3 level) with P<0.10 in univariable analyses were included in the subsequent multivariable logistic regression model. All selected variables were entered simultaneously into the multivariable model using the enter method, with statistical significance set at P5 were excluded to ensure model stability. The goodness-of-fit of the final model was evaluated using the Hosmer-Lemeshow test.A predictive model was constructed according to the independent predictors mentioned above. The predictive efficacy of the model was assessed using the receiver operating characteristic (ROC) curves, and the area under the curve (AUC), sensitivity, and specificity of the model were calculated. Results Comparison of clinicopathologic features between the rCR and non-rCR groups The study included 286 breast cancer patients with a mean age of 50.09 ± 9.63 years (range: 28 - 74 years). The majority of patients were premenopausal (51.7%, 148/286), and Luminal B was the predominant molecular subtype (58.0%, 166/286). Most patients had elevated Ki-67 expression (>20%: 79.4%), while HER2-positivity and TNBC accounted for 42.3% (121/286) and 17.5% (50/286), respectively. At diagnosis, 43.4% (124/286) presented with T3/T4 tumors, and 57.7% (165/286) had advanced nodal involvement (N2/N3).Compared with those in the non-rCR group(n=248), the proportions of CA15-3-negative (97.4% vs. 79.8%) and Ki-67 high-expression (97.4% vs. 76.6%) patients in the rCR group(n=38) were significantly greater ( P < 0.01). In addition, significant differences were observed in ER, PR, HER-2, molecular subtype and N stage between the rCR and non-rCR groups ( P < 0.05). Baseline clinicopathologic features of the patients in the rCR and non-rCR groups are presented in Table 1. Table 1 Comparison of clinicopathologic features between the rCR and non-rCR groups Characteristic rCR (n=38) non-rCR (n=248) P Age (y) 51.55±9.41 49.87±9.66 0.316 a Menopausal status 0.104 b Premenopausal 15(39.5%) 133(53.6%) Postmenopausal 23(60.5%) 115(46.4%) Family history of BC 0.051 b No 38(100%) 235(94.8%) Yes 0(0.0%) 13(5.2%) CEA 0.173 b Normal Elevated 34(89.5%) 4(10.5%) 199(80.2%) 49(19.8%) CA-125 0.246 b Normal 34(89.5%) 203(81.9%) Elevated 4(10.5%) 45(18.1%) CA15-3 0.009 b Normal 37(97.4%) 198(79.8%) Elevated 1(2.6%) 50(20.2%) ER 0.025 b Negative Positive 21(55.3%) 17(44.7%) 90(36.3%) 158(63.7%) PR 0.004 b Negative 28(73.7%) 120(48.4%) Positive 10(26.3%) 128(51.6%) HER-2 0.037 b Negative 16(42.1%) 149(60.1%) Positive 22(57.9%) 99(39.9%) Ki-67 0.003 b ≤20% 1(2.6%) 58(23.4%) >20% 37(97.4%) 190(76.6%) Molecular subtypes 0.038 b Luminal A 0(0.0%) 16(6.5%) Luminal B 18(47.4%) 148(59.7%) HER2/neu 11(28.9%) 43(17.3%) TNBC 9(23.7%) 41(16.5%) T stage 0.076 b T1 2(5.3%) 8(3.2%) T2 26(68.4%) 122(49.2%) T3 6(15.8%) 84(33.9%) T4 4(10.5%) 34(13.7%) N stage 0.043 b N0 2(5.3%) 12(4.8%) N1 6(15.8%) 91(36.7%) N2 14(36.8%) 54(21.8%) N3 16(42.1%) 91(36.7%) M stage 0.808 b M0 34(89.5%) 225(90.7%) M1 4(10.5%) 23(9.3%) AJCC stage 0.971 b I 0(0.0%) 0(0.0%) II 7(18.4%) 46(18.5%) II 27(71.1%) 179(72.2%) IV 4(10.5%) 23(9.3%) Note: a is the t value, and b is the χ2 value. *P value <0.05, indicating that the differences were considered statistically significant. Abbreviations: BC, breast cancer ;ER, estrogen receptor; PR, progesterone receptor; HER2, human epidermal growth factor receptor type 2; AJCC, American Joint Committee on Cancer. Comparison of MRI features between the rCR and non-rCR groups before NAC Compared with that in the non-rCR group, the maximum tumor diameter in the rCR group was smaller, the glands were more heterogeneous and dense, and fewer irregularly shaped BCs were present. While these differences were statistically significant ( P < 0.05), the differences in the number of lesions, margins, type of lesion, BPE, degree of enhancement, type of TIC, and ADC value of the tumors between the rCR and non-rCR groups were not statistically significant ( P > 0.05). MRI characteristics of BC patients prior to NAC are shown in Table 2. Table 2 Comparison of MRI features between the rCR and non-rCR groups before NAC Characteristics rCR(n=38) non-rCR(n=248) P Maximum tumor diameter (cm) 3.70±1.85 4.65±1.87 0.004 a Number of lesions 0.287 b Single 20(52.6%) 153(61.7%) Multiple 18(47.4%) 95(38.3%) Margin 0.349 b Clear 1(2.6%) 2(0.8%) Blurred 37(97.4%) 246(99.2%) Type of lesion 0.598 b NME 8(21.1%) 62(25.0%) Mass 30(78.9%) 186(75.0%) Morphology 0.047 b Round/oval 4(10.5%) 7(2.8%) Irregular 34(89.5%) 241(97.2%) FGT 0.044 b Fatty 0(0.0%) 5(2.0%) Sparse 11(28.9%) 109(44.0%) Uneven dense 23(60.6%) 117(47.2%) Dense 4(10.5%) 17(6.9%) BPE 0.359 b Minimal 12(31.6%) 74(29.8%) Mild 11(28.9%) 102(41.1%) Moderate 8(21.1%) 47(19.0%) Marked 7(18.4%) 25(10.1%) Degree of enhancement 0.441 b Mild 0(0.0%) 0(0.0%) Moderate 4(10.5%) 17(6.9%) Marked 34(89.5%) 231(93.1%) Types of TIC 0.946 b Persistent 1(2.6%) 9(3.6%) Plateau 12(31.6%) 79(31.9%) Washout 25(65.8%) 160(64.5%) Tumor ADC value 0.84±0.15 0.83±0.17 0.731 a Note: a is the t value, and b is the χ2 value. *P value <0.05, indicating that the differences were considered statistically significant. Abbreviations: NME, non-mass enhancement; FGT, fibroglandular tissue type; BPE, background parenchymal enhancement; TIC, time‒signal intensity curve. Univariate and multivariate analyses of rCR predictors Univariate analysis revealed that the maximum tumor diameter (OR=0.719), morphology (OR=0.247), N stage(OR=0.396), and the PR (OR=0.335), ER (OR=0.461), and HER-2 (OR=2.069) status as well as the Ki-67 index (OR=11.295) and the CA15-3 level (OR=0.107) were rCR predictors. Further multifactorial analysis revealed that high Ki-67 expression (OR=9.009), elevated CA15-3 (OR=0.098), maximum tumor diameter <3.15 cm (OR=0.778) and irregular morphology (OR=0.148) were independent predictors of an rCR (Table 3). Representative rCR and non-rCR images are detailed in Figures 2 and 3, respectively. Table 3 Univariable and multivariable analyses of rCR predictors Characteristic Univariate analysis Multivariate analysis OR(95% CI) P OR(95% CI) P Maximum tumor Diameter 0.719(0.571 - 0.905) 0.005 0.778(0.619 - 0.978) 0.031 Morphology Round/oval - - - - Irregular 0.247(0.069 - 0.888) 0.032 0.148(0.029 - 0.743) 0.020 PR Negative - - - - Positive 0.335(0.156 - 0.719) 0.005 0.462(0.158 - 1.353) 0.159 ER Negative - - - - Positive 0.461(0.231 - 0.919) 0.028 0.896(0.327 - 2.457) 0.831 HER-2 Negative - - - - Positive 2.069(1.036 - 4.135) 0.039 2.093(0.928 - 4.719) 0.075 N stage N0 N1 N2 N3 - 0.396(0.072 - 2.187) 1.556(0.311 - 7.768) 1.055(0.215 - 5.165) 0.070 - 0.288 0.590 0.947 - 0.304(0.045 - 2.042) 1.631(0.287 - 9.279) 0.764(0.137 - 4.279) 0.055 - 0.221 0.581 0.760 Ki-67 ≤20% - - - - >20% 11.295(1.517 - 84.121) 0.018 9.009(1.121 - 72.380) 0.039 CA15-3 Normal - - - - Elevated 0.107(0.014 - 0.799) 0.029 0.098(0.012 - 0.830) 0.033 Abbreviations: OR, odds ratio; CI, confidence interval. Diagnostic performance of the models in predicting an rCR The joint model combining Ki-67, CA15-3, maximum diameter and morphology predicted the optimal diagnostic efficacy of predicting an rCR (AUC=0.772), followed by the model based on maximum diameter, Ki-67, CA15-3, and morphology (Table 4). The ROC curves of the models are shown in Figure 4. Table 4 Diagnostic performance of parameters in predicting rCR before NAC Parameter AUC(95% CI) Sensitivity Specificity Maximum diameter 0. 684(0.585 - 0.782) 0.766 0.658 Ki-67 0.604 (0. 519 - 0.688) 0.974 0.234 CA15-3 0. 588(0.501 - 0.674) 0.974 0.202 Morphology 0. 539(0. 435 - 0.642) 0.105 0.972 Joint model 0. 772(0. 695 - 0.849) 0.632 0.831 Note: The joint model integrates maximum diameter, Ki-67, CA15-3, and morphology. Discussion The primary goal of this study was to develop and validate an integrated model that leverages routinely available pretreatment data—clinicopathological parameters and multiparametric MRI features—to predict the likelihood of achieving rCR in breast cancer patients undergoing NAC. While experienced clinicians intuitively incorporate factors like Ki-67 and tumor size into their prognostic assessments, our study provides a quantitative framework that systematically combines these variables with advanced MRI characteristics to generate an objective probability score. This approach moves beyond empirical prediction towards a more standardized and potentially more accurate tool for pre-therapeutic stratification. The question of whether rCR can serve as a robust alternative to pCR is central to interpreting our findings. It is crucial to acknowledge that rCR and pCR are not synonymous; discordances exist, primarily due to the inherent limitations of MRI in detecting microscopic residual disease(26). However, a growing body of evidence supports the prognostic value of rCR in its own right. Importantly, as demonstrated by Gampenrieder et al.(9), patients achieving rCR exhibit recurrence-free survival benefits comparable to those achieving pCR, while residual enhancement on MRI is strongly associated with increased risks of early recurrence and mortality. This suggests that while rCR may not perfectly predict pCR, it is a robust imaging biomarker that strongly correlates with superior survival outcomes. Our stringent definition of rCR aims to identify this specific patient subgroup with excellent prognosis. The key advantage of rCR lies in its non-invasive nature, allowing for repeated assessment throughout and at the conclusion of NAC, thus providing critical information for surgical planning and adjuvant therapy discussions long before final pathology is available. Contextualizing our findings within biological and therapeutic heterogeneity is critical. It is well-established that molecular intrinsic subtypes and specific therapeutic regimens are paramount determinants of response to neoadjuvant chemotherapy (NAC). Our findings must be interpreted within this context. Although molecular subtype was not an independent predictor in our final model, the distribution of responses in our cohort (Table 1) aligns with established literature. Consistent with the well-established higher chemosensitivity of these biologically aggressive tumors, we observed a numerical tendency towards higher rCR rates in HER2-positive and triple-negative breast cancers(27, 28).Crucially, the primary aim of our study was to identify baseline, pre-therapeutic predictors, universally available before any treatment decisions are finalized. Consequently, the specific NAC regimen was not included as a model variable, as it is not a predictive feature but a subsequent intervention. The fact that our model—built solely on fundamental tumor characteristics (Ki-67, CA15-3, size, morphology)—retained predictive power across a heterogeneous population receiving standard-of-care treatment is a key finding. It suggests that these baseline features capture a core aspect of tumor biology and chemo-sensitivity that is foundational and transcends specific subtypes or regimens. This does not diminish the importance of subtype or treatment; rather, it positions our model as a complementary tool that provides a robust initial risk stratification based on readily available data. Future research should investigate the integration of our baseline model with therapeutic variables in larger, protocol-driven cohorts to refine predictive accuracy for specific patient subgroups. Accurate prediction of pCR after NAC is crucial for personalized breast cancer management, as it influences surgical planning and adjuvant therapy decisions. However, pCR assessment requires invasive biopsy or surgery, whereas rCR offers a noninvasive alternative. In this study, we screened independent predictors of an rCR by combining clinicopathological parameters and multiparametric MRI features via multivariate logistic regression analysis and constructed a joint prediction model. The results revealed that ER, PR, HER-2, Ki-67, CA15-3, maximum tumor diameter, and lesion morphology were significantly correlated with rCR (all P <0.05) and that high expression of Ki-67 (OR=9.009), elevated CA15-3 (OR=0.098), maximum tumor diameter <3.15 cm (OR=0.778), and irregular morphology (OR=0.148) were identified as independent predictors of an rCR. The joint model constructed on the basis of the above four independent factors demonstrated moderate diagnostic efficacy (AUC=0.772) in predicting rCR, which provides a novel decision-making tool for the noninvasive assessment of the rCR status in the clinic. The level of CA15-3, the most sensitive serum marker for BC, is positively correlated with tumor load and metastatic potential (29-31). In this study, we found that the probability of obtaining an rCR was significantly lower in patients with high CA15-3 expression (OR=0.098), which is similar to the conclusion of Lee et al. (32) and Fujimoto et al.(31) regarding tumor markers and recurrence risk. The underlying mechanism involves the aberrant expression of mucin encoded by the MUC1 gene, the dual role of MUC1-C in mediating antiapoptotic signaling through BCL2A1 activation, and the upregulation of VEGF through stabilizing HIF-1α to promote angiogenesis, which weakens the efficacy of NAC (33-35). Ki-67 is an index of tumor cell proliferation that is associated with the degree of tumor differentiation, invasion, metastasis, and prognosis (36). Studies have shown that the higher the Ki-67 index value, the more active the cells are in the proliferative phase of the tumor, the higher the degree of malignancy, and the stronger their invasive ability, and therefore, tumor progression or metastasis is more likely to occur (37). Although cancer cells with high Ki-67 expression exhibit active proliferation, they are more sensitive to chemotherapy, after which the cancer cells are obviously suppressed, which in turn demonstrates the high efficacy of NAC. Alba et al. (38) conducted a study on 262 BC patients who underwent NAC and reported that a Ki-67 index >50% was an independent predictor of pCR after NAC treatment. Current studies have also shown that high Ki-67 expression can improve the pCR rate after NACT(39, 40), suggesting that cell proliferation is closely related to chemotherapy sensitivity. In our study, high Ki-67 expression (OR=9.009) was an independent predictor of an rCR, which is consistent with previous studies that have suggested a positive association between Ki-67 expression status and response to NAC (41, 42). The possible reasons for this are as follows: (i) cell cycle dependence: paclitaxel/anthracycline drugs target cells in G2/M phase, and the proportion of actively cycling cells is greater in high-Ki-67 tumors; (ii) genomic instability: TP53 mutations are commonly found in high-Ki-67-expressing TNBCs, which leads to defective DNA repair and reduced chemosensitivity (43); (iii) clone selection effect: NAC preferentially removes high-proliferating subpopulations, and residual low-proliferating clones are significantly more resistant (44). Numerous studies have confirmed that tumor size is an influential factor in the efficacy and outcomes of NAC in BC patients (45-47). Derouane et al. (48) demonstrated that larger tumors usually exhibit greater cellular heterogeneity as well as more areas of hypoxia and necrosis, which makes it difficult for chemotherapeutic agents to be evenly distributed and to effectively kill all cancer cells. In addition, larger tumors tend to have higher tumor loads and more complex tumor microenvironments, which increases the difficulty of treatment. Patients with smaller tumors are more likely to achieve complete remission because of their lower cell density, which allows chemotherapeutic agents to penetrate and work more effectively. Gajdos et al. (49) also demonstrated that smaller tumors are more likely to respond to NAC than larger tumors. In our study, a maximum tumor diameter <3.15 cm (OR=0.778) was an independent predictor of an rCR, which suggests that those with smaller tumors were more likely to achieve rCR; this is consistent with the notion that patients with smaller tumors are more likely to achieve complete remission due to their low cellular density and homogeneous vascular distribution. Previous studies have shown that lobulated or burr-like morphology and margins strongly indicate malignancy, with a high positive predictive value (PPV) for malignancy (50), and that the pathological basis of the tumor is associated with an inhomogeneous growth pattern: irregular invasion by tumor cells of the breast ducts or interstitium (51). The irregular morphology may be associated with heterogeneity within the tumor, and such features are more common in malignant lesions (52). Chen et al. (53) showed that pretreatment MRI-based tumor morphologic features may predict the response to NAC in BC patients. In addition, other studies have shown that, compared with irregularly shaped tumors, round and oval tumors are more sensitive to NAC (54). In this study, the results suggested that irregular tumor morphology was an independent negative predictor of an rCR, which is the same as the results of previous studies. This suggests that irregularly shaped tumors are more malignant and that it is more difficult for patients with these tumors to achieve an rCR than those with round/round-like tumors. Accurate screening and identification of patients who can achieve an rCR after treatment with NAC is important for the early treatment and prognosis evaluation of BC patients, and thus a potentially noninvasive, accurate, simple and affordable method is needed to identify patients likely to exhibit an rCR. In this study, we constructed a joint model for predicting rCR based on independent predictors of an rCR (maximal tumor diameter, Ki-67 expression, CA15-3 level, and morphology), and the model was able to better identify patients with an rCR to NAC with an AUC of 0.772. The joint model constructed in this study has several potential advantages if used in clinical applications. First, the model can optimize the management of NAC to a certain degree and allow precise individualized treatment. Second, in terms of treatment strategy optimization, for patients with a high rCR probability, maintaining the original regimen can improve long-term prognosis and provide a decision basis for step-down therapy (reducing the number of chemotherapy cycles); in contrast, for patients with a low rCR probability, strategies can be adjusted in a timely manner, including replacing platinum-enhanced regimens with immune checkpoint inhibitors or targeted drugs to overcome potential drug resistance. Third, in terms of health benefits, the model can reduce myelosuppression and cardiotoxicity events caused by the administration of ineffective chemotherapy, optimize the frequency of imaging-based monitoring, and reduce healthcare costs (compared with the traditional pattern of MRI review every 2 cycles). The rCR rate in our cohort (13.3%) is lower than pCR rates reported in some studies. This discrepancy can be attributed to several factors. Firstly, our study employed a stringent radiological definition of rCR (complete absence of enhancement on DCE-MRI), which aligns with the RECIST 1.1 criteria for complete response but may be less sensitive than pathological assessment in detecting minimal residual disease. Secondly, our patient population included a substantial proportion of cases with advanced disease stages (43.4% T3/T4 tumors and 57.7% with N2/N3 nodal involvement at diagnosis), which are historically associated with lower response rates to NAC. The inherent limitations of MRI, including potential false positives due to post-treatment inflammation or fibrosis that mimics residual enhancement, might also contribute to a more conservative assessment of rCR compared to pathological evaluation. This underscores the challenge of achieving complete imaging response and highlights that rCR and pCR, while correlated, capture different aspects of treatment response.The pronounced imbalance between rCR (13.3%) and non-rCR (86.7%) groups in our cohort reflects the intrinsic low rCR rate to NAC in breast cancer, as established by Zhang et al. (19). While this disparity poses methodological challenges for predictive modeling, it accurately represents real-world clinical populations. Our study implemented a three-pronged strategy to ensure robust conclusions: First, we employed rigorous statistical controls including VIF-restricted variable selection (VIF <5) and Hosmer-Lemeshow goodness-of-fit testing (p=0.965), which maintained model stability despite class imbalance. Second, we deliberately prioritized specificity (83.1%) over sensitivity (63.2%) in model optimization, aligning with the clinical imperative to reliably identify non-responders and avoid overtreatment. This strategic focus was validated by the high negative predictive value (NPV=93.6%), though the modest positive predictive value (PPV=36.4%) underscores the persistent difficulty in confirming rCR within imbalanced datasets. In interpreting our results, several limitations must be considered. These limitations can be categorized into those inherent to the reference standard (MRI) and those related to our study design.First, regarding the reference standard, it is important to acknowledge the inherent limitations of MRI in assessing response to NAC. False negatives can occur due to microscopic residual disease below the detection threshold of MRI or due to non-enhancing residual tumor. False positives can arise from post-treatment inflammation, fibrosis, or granulation tissue that exhibits enhancement, mimicking residual disease(26). Additionally, susceptibility artifacts, for instance from biopsy clips, can occasionally interfere with image interpretation. These inherent limitations of MRI-defined rCR itself contribute to the imperfect performance (AUC < 0.8) of our and any other predictive model based on it.Second, our study has several methodological limitations. The retrospective single-center design may introduce selection bias and inherently limits causal inferences. Patients were treated with different NAC regimens and different numbers of cycles, which may be unpredictable confounding factors. Furthermore, the pronounced class imbalance between rCR and non-rCR groups, while reflective of real-world prevalence, affects the precision of sensitivity estimates and model calibration. Finally, only the rCR and non-rCR groups were analyzed in this study, and subgroup analyses of rPR, rPD, and rSD patients were not performed, which may affect model generalizability. However, our approach provides a methodological framework for handling such imbalanced real-world data: (1) transparent reporting of both rule-out (high NPV) and rule-in (modest PPV) capabilities, (2) clinical prioritization of specificity to guide oncological decision needs, and (3) the use of statistical techniques (VIF restriction, goodness-of-fit testing) robust to prevalence disparities. Future research should build upon these findings. Prospective multicenter studies with larger, balanced cohorts are needed to validate and refine our model. Such studies should incorporate standardized NAC protocols and aim to prospectively validate the proposed predictive algorithm. Beyond this, future efforts should include pre-planned rCR enrichment strategies, external validation across diverse cohorts, and the integration of advanced machine learning techniques (e.g., cost-sensitive neural networks) specifically developed for imbalanced medical data to further enhance predictive performance. Conclusion In conclusion, we developed and validated an integrated model based on pretreatment Ki-67 index, CA15-3 level, tumor diameter, and morphology that can stratify patients by their likelihood of achieving rCR after NAC. Given the established correlation between rCR and favorable survival outcomes(9), this tool provides an objective, quantitative foundation for pre-therapeutic prognostication. Its primary clinical value lies in its ability to rule out non-response with high confidence, potentially sparing patients from the toxicity of ineffective therapy and guiding earlier treatment adaptation. While promising, the clinical utility of this model for guiding personalized treatment decisions requires further validation in large-scale, multicenter, prospective cohorts. Abbreviations Abbreviation Full Term ADC Apparent Diffusion Coefficient AJCC American Joint Committee on Cancer ASCO American Society of Clinical Oncology BPE Background Parenchymal Enhancement BC Breast Cancer CA15-3 Carbohydrate Antigen 15-3 CA125 Carbohydrate Antigen 125 CAIPIRINHA Controlled Aliasing in Parallel Imaging Results in Higher Acceleration CAP College of American Pathologists CEA Carcinoembryonic Antigen DCE-MRI Dynamic Contrast-Enhanced Magnetic Resonance Imaging DFS Disease-Free Survival DWI Diffusion-Weighted Imaging ER Estrogen Receptor FGT Fibroglandular Tissue FISH Fluorescence In Situ Hybridization FLASH Fast Low Angle Shot GRAPPA Generalized Autocalibrating Partially Parallel Acquisitions GRE Gradient Echo HER2 Human Epidermal Growth Factor Receptor 2 IHC Immunohistochemistry Ki-67 Proliferation marker protein MRI Magnetic Resonance Imaging NAC Neoadjuvant Chemotherapy NME Non-Mass Enhancement OS Overall Survival pCR Pathological Complete Response PR Progesterone Receptor rCR Radiologic Complete Response RECIST Response Evaluation Criteria in Solid Tumors ROI Region of Interest RS-EPI Readout-Segmented Echo Planar Imaging SS-EPI Single-Shot Echo Planar Imaging TIC Time-Intensity Curve TNBC Triple-Negative Breast Cancer TWIST Time-resolved angiography With Interleaved Stochastic Trajectories VIBE Volumetric Interpolated Breath-hold Examination VIF Variance Inflation Factor Declarations Competing interests The authors declare that they have no competing interests. Informed consent This retrospective study was approved by the institutional review board of Meizhou People's Hospital. The funding source had no involvement in the study design; in the collection, analysis, and interpretation of data; in the writing of the manuscript; and in the decision to submit the article for publication. Ethical Approval The ethics committee of Meizhou People’s Hospital approved this retrospective study (2023-CY-53). This study was conducted in accordance with the Declaration of Helsinki, and the requirement for informed consent was waived. Funding Meizhou People's Hospital Research Cultivation Project (PY-C2022011). Author Contribution Zhiqi Yang and Xiaofeng Chen conceived and supervised the study. Tianxiang Zhu, Zhuozhi Dai, and Xinwei Zhong were responsible for data acquisition. Bowen Yue and Hao Zhang contributed to methodology and software development. Bowen Yue and Yi Chen performed the formal analysis and drafted the initial manuscript. 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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-8796639","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":602499254,"identity":"72fcd55e-d3f3-434d-b41b-1fcc83d03940","order_by":0,"name":"Bowen Yue","email":"","orcid":"","institution":"Meizhou City People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Bowen","middleName":"","lastName":"Yue","suffix":""},{"id":602499256,"identity":"a5d1257c-87e7-4e53-8fb6-3939139bc154","order_by":1,"name":"Tianxiang Zhu","email":"","orcid":"","institution":"Shantou Central 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11:56:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8796639/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8796639/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104470685,"identity":"294e6612-6b06-4dc8-80d7-34ac68ef51e8","added_by":"auto","created_at":"2026-03-12 07:22:54","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":135199,"visible":true,"origin":"","legend":"\u003cp\u003ePatientinclusion and exclusion flow chart.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8796639/v1/491f23808dd1a1ef2da179bb.png"},{"id":104470703,"identity":"c9bd4b79-fc7c-48f6-84d8-b3a97d38da15","added_by":"auto","created_at":"2026-03-12 07:22:55","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":184131,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea-b\u003c/strong\u003e Representative rCR images. 2a: Baseline T1WI enhancement revealed a round-like tumor with a maximum diameter of approximately2.8 cm. 2b: After 6 courses of NAC, the tumor completely disappeared, and the patient achievedcomplete remission.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8796639/v1/ceb12ab5ff8854026e0e4b36.jpeg"},{"id":104470705,"identity":"bd46d49d-c900-417b-be8b-15e127b18b15","added_by":"auto","created_at":"2026-03-12 07:22:56","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":132353,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea-b\u003c/strong\u003e Representative non-rCR images. 3a: Baseline T1WI enhancement revealed an irregularly shaped tumor with a maximum diameter of approximately6.0 cm. 3b: After 6 courses of NAC, the tumor was significantly reduced in size, and the patient showed partial remission.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8796639/v1/a00e97609e05d72d77a72182.jpeg"},{"id":104470684,"identity":"9f397f0c-2aa8-4ee7-87db-4b4b174524d3","added_by":"auto","created_at":"2026-03-12 07:22:54","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":33245,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operating characteristic curves of the prediction models.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8796639/v1/d1b34ef9eba46654e4031fc4.png"},{"id":104835218,"identity":"41d6df33-3cd0-4e2f-a674-6ac882ceb3e4","added_by":"auto","created_at":"2026-03-17 17:42:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1577361,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8796639/v1/d63db75b-a9ec-44c4-b935-d899545d9200.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Early Identification of Breast Cancer Patients Achieving Radiological Complete Response to NAC: A Clinicopathological and MRI-Based Approach","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBreast cancer (BC) is the second most common cancer worldwide; it poses a serious threat to women's lives and is the leading cause of cancer-related deaths among women (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Neoadjuvant chemotherapy (NAC), a core strategy for the comprehensive treatment of locally progressive BC, significantly improves the tumor downstaging rate by inhibiting tumor proliferative activity, controlling the progression of primary tumors and metastases, and transforming initially unresectable tumors to operable status (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). As the clinical use of NAC has become increasingly popular (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e), the therapeutic response to NAC has been shown to be significantly positively correlated with patient prognosis (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Pathological complete response (pCR) is widely recognized as a robust predictor for improved overall survival (OS) and long-term disease-free survival (DFS) in breast cancer patients undergoing NAC. However, the assessment of pCR can only be performed postoperatively. Recent clinical evidence has demonstrated that patients achieving radiologic complete response (rCR) on MRI following NAC treatment similarly exhibit favorable DFS outcomes. Importantly, a comparative study revealed that the DFS benefits in rCR patients were comparable to those achieving pCR, while residual disease was significantly associated with increased risks of early recurrence and mortality(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). These findings collectively highlight the prognostic value of rCR assessment in predicting patient outcomes. Improved early prediction of radiologic response following neoadjuvant chemotherapy for breast cancer could enhance the accuracy of final outcome prediction, thereby facilitating personalized treatment strategies.\u003c/p\u003e \u003cp\u003eConventional imaging modalities, including mammography and ultrasound, serve as the cornerstone for initial diagnosis and standard practice for monitoring treatment response in breast cancer (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Mammography provides high spatial resolution for evaluating calcifications and architectural distortions, while ultrasound excels in characterizing solid masses and guiding interventions. However, the accuracy of these techniques can be limited by factors such as breast density, operator dependence for ultrasound, and challenges in distinguishing post-therapeutic fibrosis from residual viable tumor (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Although ultrasonography is superior to mammography in the assessment of tumor morphology, its measurement consistency is dependent on operator experience(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Compared with mammography and ultrasound, multiparametric MRI, especially dynamic contrast-enhanced MRI (DCE-MRI) combined with diffusion-weighted imaging (DWI), has demonstrated significantly higher accuracy in assessing the extent of the tumor and NAC response (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). DCE-MRI reflects the state of tumor angiogenesis through quantitative contrast kinetics, and DWI characterizes cell density changes through the apparent diffusion coefficient (ADC); the two methods are synergistic and can be used to comprehensively assess the evolution of tumor biological behavior and thus the response to NAC. Existing evidence suggests that multiparametric MRI demonstrates high predictive efficacy in pCR prediction(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan additionalcitationids=\"CR16 CR17\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e), but no consensus has been established on its application in rCR prediction. Notably, although evidence has demonstrated that rCR following NAC is associated with improved prognosis in breast cancer patients(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e), there has been no systematic study of rCR-related predictors, as recent studies have focused on pCR predictors and model construction. Zhang et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e) explored pCR predictors and unexpectedly reported that degree of tumor differentiation, American Joint Committee on Cancer (AJCC) stage I and high Ki-67 expression were independently associated with a rCR, but the study was performed in a single center with a small sample size and did not integrate multiparametric MRI features.\u003c/p\u003e \u003cp\u003eWhile clinicians routinely consider clinicopathological factors like Ki-67 and molecular subtypes when evaluating treatment response, this process often relies on empirical synthesis rather than quantitative integration. Furthermore, the predictive value of pretreatment multiparametric MRI features specifically for rCR\u0026mdash;a non-invasive endpoint strongly associated with improved survival outcomes(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e)\u0026mdash;remains underexplored and lacks a comprehensive model. Therefore, this study aimed to develop and validate an integrated predictive model that combines baseline clinicopathological factors with quantitative multiparametric MRI features (DCE-MRI and DWI) to enable early, pre-treatment identification of breast cancer patients most likely to achieve rCR after NAC. The primary innovation of this study lies in the quantitative synthesis of these readily available pre-therapeutic parameters into a single, objective tool, which could potentially aid in personalized patient counseling and stratify patients for future research into response-adapted therapy strategies.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eStudy population\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe ethics committee of Meizhou People\u0026rsquo;s Hospital approved this retrospective study (2023-CY-53).\u0026nbsp;This study was conducted in accordance with the Declaration of Helsinki, and the requirement for informed consent was waived. All methods were performed in accordance with the ethical standards of the institutional and/or national research committee and with the Declaration of Helsinki (20). This study collected information on female BC patients who underwent surgery after NAC treatment between January 2016 and September 2023. The inclusion criteria were as follows: (I) patients who underwent NAC before BC surgery, (II) patients who underwent multiparametric MR imaging of the breast before NAC, (III) patients who underwent surgical resection of BC after NAC and whose results of the pathologic efficacy evaluation of NAC were available. The exclusion criteria were as follows: (I) patients with incomplete postoperative pathologic data (n=30), (II) patients who received fewer than 3 courses of NAC (n=4)\u0026nbsp;,as fewer than 3 cycles may not provide sufficient tumor response assessment and are more likely to represent early discontinuation due to toxicity or progression. (III) patients with occult BC (n=5), (IV) patients with bilateral BC (n=3). A flow chart of the patient inclusion and exclusion criteria\u0026nbsp;is\u0026nbsp;shown in Figure 1.\u003c/p\u003e\n\u003cp\u003eUltimately, 286 patients fulfilled our inclusion criteria. All the patients were female whose ages ranged from 28\u0026minus;74 years. Patients were divided into an rCR group and a non-rCR group according to their preoperative MRI evaluation (see Group classification criteria below). Clinicopathologic features [including age; family history (defined as a first- or second-degree relative with breast cancer); menstrual status; the levels of carcinoembryonic antigen (CEA), carcinoembryonic antigen 125 (CA-125), and carcinoembryonic antigen 15-3 (CA15-3); human epidermal growth factor receptor 2 (HER-2), progesterone receptor (PR), and estrogen receptor (ER) status; Ki-67 expression; TNM stage; AJCC stage; and molecular subtype of BC] were recorded prior to NAC.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSerum tumor marker assessment\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePretreatment blood samples for the analysis of CEA, CA-125, and CA15-3 were collected prior to the initiation of any NAC treatment and were the only values used in this analysis. Elevated levels were defined according to our institutional laboratory standards as follows: CEA\u0026nbsp;\u0026ge;\u0026nbsp;5.0 ng/mL, CA-125\u0026nbsp;\u0026ge;\u0026nbsp;35 U/mL, CA15-3\u0026nbsp;\u0026ge;\u0026nbsp;35 U/mL.\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eNAC \u003cstrong\u003etherapeutic protocol\u003c/strong\u003e\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eMost patients received 4 to 8 cycles of treatment before surgery. In the absence of medical contraindications, all patients received anthracycline-, paclitaxel-, and platinum-based NAC. Trastuzumab and/or patuzumab were used in combination with NAC in HER-2-positive patients. The patients subsequently underwent breast conservation surgery or mastectomy. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eMRI response evaluation\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll patients underwent two key MRI examinations: 1) A baseline multiparametric MRI prior to the initiation of NAC, from which all imaging predictors were extracted; and 2) A follow-up MRI after the completion of all NAC cycles and prior to surgery, which was used to evaluate the treatment response and define the radiological endpoint. In this study, rCR was defined as the complete absence of any visible contrast enhancement within the original tumor bed on the post-treatment DCE-MRI sequences, adhering to the concept of complete response in RECIST 1.1. Any residual enhancement, regardless of pattern or degree, was classified as non-rCR for the purposes of this binary outcome analysis. This stringent definition is intentional, as it identifies the patient group with the most profound imaging response.\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eImaging protocol\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll breast MRI examinations were conducted using a 3.0T MRI scanner (Siemens, Germany) with patients in the prone position, which allowed the bilateral breasts to naturally fall within a 16-channel phased-array breast coil. DCE-MRI was acquired using a 3D volumetric interpolated breath-hold examination (VIBE) sequence combined with controlled aliasing in parallel imaging results in higher acceleration (CAIPIRINHA). The imaging protocol incorporated time-resolved angiography with interleaved stochastic trajectories (TWIST) for view-sharing and Dixon-based fat suppression (CAIPIRINHA-Dixon-TWIST-VIBE). This sequence employed spoiled gradient-echo (GRE) acquisition with a fast low-angle shot (FLASH) and utilized K-space sharing technology via TWIST.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe scanning sequences included T1WI, T2WI, FLAIR, STIR, DCE-MRI and DWI. The DCE-MRI protocol consisted of precontrast T1-weighted VIBE imaging (repetition time/TR=3.78 ms, echo time/TE=1.38 ms, matrix=205\u0026times;256, FOV=340 mm\u0026times;340 mm, slice thickness=2 mm, voxel resolution=1.3 mm\u0026times;1.3 mm\u0026times;2.0 mm) followed by a dynamic phase using TWIST-VIBE with 34 consecutive acquisitions (TR=6.4 ms, TE=3.3 ms, matrix=288\u0026times;384, FOV=288 mm\u0026times;384 mm, slice thickness=2.0 mm, voxel resolution=0.9 mm\u0026times;0.9 mm\u0026times;2.0 mm, temporal resolution=8.7 s, flip angle=9\u0026deg;).Gadopentetate dextran was injected at a dosage of 0.1 mmol/kg with an injection rate of 3.0 ml/s. Thirty-four temporal phases were acquired, with the first temporal phase acquired 17.7 seconds after contrast injection and the subsequent single-phase scans spaced at an interval of 8.7 seconds. After contrast agent injection, 20 ml of saline was injected at the same flow rate. DWI was performed using both readout-segmented echo-planar imaging (RS-EPI) and single-shot EPI (SS-EPI) with fat suppression in the transverse plane prior to DCE-MRI. For the final analysis, ADC values derived from the RS-EPI sequence were utilized. This choice was based on its demonstrated superior robustness to susceptibility artifacts and higher spatial resolution compared to SS-EPI, leading to more reliable ADC measurements in our clinical setting.The RS-EPI parameters were TR/TE = 4800/56 ms, FOV = 170 mm \u0026times; 340 mm, matrix = 96 \u0026times; 192, flip angle = 180\u0026deg;, bandwidth = 868 Hz, slice thickness = 4.0 mm with 0.8 mm gap, averages = 8, and b-values = 50 and 800 s/mm\u0026sup2;, while the SS-EPI parameters were TR/TE = 4200/62 ms, FOV = 149 mm \u0026times; 340 mm, matrix = 100 \u0026times; 170, bandwidth = 1730 Hz, echo spacing = 0.68 ms, slice thickness = 4.0 mm with 0.8 mm gap, averages = 3, and b-values = 50 and 800 s/mm\u0026sup2;. Both sequences utilized generalized autocalibrating partially parallel acquisitions (GRAPPA) with an acceleration factor of 2, ensuring complete breast coverage through optimized slice selection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003ePathological immunohistochemical assessment\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eER and PR were evaluated according to the American Society of Clinical Oncology (ASCO)/College of American Pathologists (CAP) guidelines (21). ER and PR were considered positive when they were expressed in \u0026ge;1% of cells according to immunohistochemistry (IHC) . HER2 was also evaluated according to the ASCO/CAP guidelines. Only membrane staining of the infiltrating tissue was considered for HER2 scoring. The IHC score for HER2 was categorized as positive (3+), suspicious (2+), or negative (0 or 1+) (22). Tumors with a HER2 IHC score of 3+ did not require further testing, whereas cases with an IHC score of 2+ were further evaluated for HER2 amplification by fluorescence in situ hybridization (FISH). The results of FISH for HER2 were interpreted according to the 2018 ASCO/CAP updated guidelines (23). Luminal A BC was defined as BC that was ER- and/or PR-positive and HER-2-negative with a Ki-67 index \u0026lt;20%. Luminal B BC was defined as BC that was ER- and/or PR-positive and HER-2 positive or negative with a Ki-67 index \u0026ge;20%. HER-2-overexpressing BC was defined as ER- and PR-negative and HER-2-positive. Triple-negative breast cancer (TNBC) was defined as BC that was negative for ER, PR and HER-2 (24). For all resected samples, the pathology assessment of NAC efficacy was performed by an experienced pathologist according to the Miller‒Payne grading system. In our study, a pCR was defined as the absence of detectable residual invasive tumor tissue in the breast and the absence of lymph node metastasis, regardless of the presence of residual ductal carcinoma in situ (ypT0/is ypN0).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eData processing and collection\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe images were independently interpreted by two radiologists (a resident and an attending physician), both blinded to the pathological findings. In cases of disagreement in interpretation (\u0026gt;10% measurement difference or diagnostic discordance), a senior radiologist with 16 years of experience was consulted to reach a consensus.\u0026nbsp;Using GE Centricity\u0026trade; PACS software, tumor evaluation involved: (1) co-registered T2WI/DWI/DCE-MRI sequences identifying target lesions (largest lesion selected if multifocal), with maximum diameter (cm) measured at peak DCE-MRI enhancement using multiplanar reconstruction; (2) ADC analysis where the DCE-MRI slice showing peak enhancement (phase with maximal tumor-to-background contrast) was matched to ADC maps, and three circular ROIs (each\u0026nbsp;\u0026ge;10 mm\u0026sup2;) systematically sampled the entire solid tumor component (excluding edema) to cover both strongest DCE-MRI enhancement areas and lowest ADC regions while avoiding artifacts (vessels via T2WI flow voids, calcifications via CT, hemorrhage via T1 hyperintensity, necrosis via DCE non-enhancement), with mean ADC (\u0026times;10⁻\u0026sup3;mm\u0026sup2;/s) representing tumor heterogeneity; (3) DCE-MRI kinetic analysis using Siemens syngo.via workstation, where the phase with clearest tumor delineation (typically 60-90s post-contrast) was selected for TIC Tool placement of a single ROI in the most homogeneously enhancing solid portion (size-matched to ADC ROIs), generating time-signal intensity curves and enhancement degree measurements.\u003c/p\u003e\n\u003cp\u003eMultiparametric MRI features were acquired before NAC using the ACR BI-RADS MRI lexicon(25) , including the number of lesions (multiple/single), fibroglandular tissue (FGT) type, background parenchymal enhancement (BPE), maximal diameter, degree of enhancement, type of TIC, type of lesion (mass/NME (non-mass enhancement)), margins of the lesion (clear/blurred), morphology (round and oval/irregular), ADC value of the tumor, and normal contralateral breast gland ADC value.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll multiparametric MRI features were qualitatively assessed and categorized according to the American College of Radiology Breast Imaging Reporting and Data System (BI-RADS) MRI lexicon(25). This included evaluation of lesion type (mass vs. non-mass enhancement), morphology, margins, internal enhancement characteristics, kinetic curve type, and background parenchymal enhancement.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eStatistical analysis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data were statistically analyzed using IBM SPSS Statistics 28, and differences were considered significant at \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05. Measurement information that conformed to a normal distribution was expressed as the mean \u0026plusmn; standard deviation, and an independent samples t test was used for comparisons between the two groups. Count data are expressed as examples, and the chi-square test was used for comparisons between two groups. To identify independent predictors of rCR, we first performed univariable logistic regression analyses for all candidate variables. Variables (maximum tumor diameter, morphology, PR, ER, HER-2 status, Ki-67 index, N stage, and CA15-3 level) with P\u0026lt;0.10 in univariable analyses were included in the subsequent multivariable logistic regression model. All selected variables were entered simultaneously into the multivariable model using the enter method, with statistical significance set at P\u0026lt;0.05 for retention in the final model.Multicollinearity was assessed using variance inflation factors (VIF), and variables with VIF \u0026gt;5 were excluded to ensure model stability. The goodness-of-fit of the final model was evaluated using the Hosmer-Lemeshow test.A predictive model was constructed according to the independent predictors mentioned above. The predictive efficacy of the model was assessed using the receiver operating characteristic (ROC) curves, and the area under the curve (AUC), sensitivity, and specificity of the model were calculated.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eComparison of clinicopathologic features between\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003ethe\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003erCR and non-rCR groups\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study included 286 breast cancer patients with a mean age of 50.09 \u0026plusmn; 9.63 years (range: 28\u003cstrong\u003e-\u003c/strong\u003e74 years). The majority of patients were premenopausal (51.7%, 148/286), and Luminal B was the predominant molecular subtype (58.0%, 166/286). Most patients had elevated Ki-67 expression (\u0026gt;20%: 79.4%), while HER2-positivity and TNBC accounted for 42.3% (121/286) and 17.5% (50/286), respectively. At diagnosis, 43.4% (124/286) presented with T3/T4 tumors, and 57.7% (165/286) had advanced nodal involvement (N2/N3).Compared with those in the non-rCR group(n=248), the proportions of CA15-3-negative (97.4% vs. 79.8%) and Ki-67 high-expression (97.4% vs. 76.6%) patients in the rCR group(n=38) \u0026nbsp;were significantly greater (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01). In addition, significant differences were observed in ER, PR, HER-2, molecular subtype and N stage between the rCR and non-rCR groups (\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.05). Baseline clinicopathologic features of the patients in the rCR and non-rCR groups are presented in Table 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u0026nbsp;\u003c/strong\u003eComparison of clinicopathologic features between the rCR and non-rCR groups\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"97%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003eCharacteristic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003erCR (n=38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003enon-rCR (n=248)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003eAge (y)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\u0026nbsp; \u0026nbsp; 51.55\u0026plusmn;9.41\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e49.87\u0026plusmn;9.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e0.316\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003eMenopausal status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e0.104\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003ePremenopausal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e15(39.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e133(53.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003ePostmenopausal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e23(60.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e115(46.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003eFamily history of BC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e0.051\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e38(100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e235(94.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e0(0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e13(5.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003eCEA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e0.173\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e\u0026nbsp; Normal\u003c/p\u003e\n \u003cp\u003eElevated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e34(89.5%)\u003c/p\u003e\n \u003cp\u003e4(10.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e199(80.2%)\u003c/p\u003e\n \u003cp\u003e49(19.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003eCA-125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e0.246\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e34(89.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e203(81.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003eElevated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e4(10.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e45(18.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003eCA15-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e0.009\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e37(97.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e198(79.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003eElevated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e1(2.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e50(20.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003eER\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e0.025\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e\u0026nbsp; Negative\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; Positive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e21(55.3%)\u003c/p\u003e\n \u003cp\u003e17(44.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e90(36.3%)\u003c/p\u003e\n \u003cp\u003e158(63.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003ePR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e0.004\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e\u0026nbsp; Negative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e28(73.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e120(48.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e10(26.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e128(51.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003eHER-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e0.037\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e16(42.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e149(60.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e22(57.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e99(39.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003eKi-67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e0.003\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e\u0026le;20%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e1(2.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e58(23.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026gt;20%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e37(97.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e190(76.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003eMolecular subtypes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e0.038\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003eLuminal A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e0(0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e16(6.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003eLuminal B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e18(47.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e148(59.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003eHER2/neu\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e11(28.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e43(17.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003eTNBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e9(23.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e41(16.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003eT stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e0.076\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003eT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e2(5.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e8(3.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003eT2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e26(68.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e122(49.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003eT3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e6(15.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e84(33.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003eT4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e4(10.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e34(13.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003eN stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e0.043\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003eN0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e2(5.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e12(4.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003eN1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e6(15.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e91(36.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003eN2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e14(36.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e54(21.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003eN3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e16(42.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e91(36.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003eM stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e0.808\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003eM0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e34(89.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e225(90.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003eM1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e4(10.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e23(9.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003eAJCC stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e0.971\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e0(0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e0(0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003eII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e7(18.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e46(18.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003eII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e27(71.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e179(72.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003eIV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e4(10.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e23(9.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: a is the t value, and b is the \u0026chi;2 value. *P value \u0026lt;0.05, indicating that the differences were considered statistically significant. Abbreviations: BC,\u003cstrong\u003e\u0026nbsp;breast cancer\u003c/strong\u003e;ER, estrogen receptor; PR, progesterone receptor; HER2, human epidermal growth factor receptor type 2; AJCC, American Joint Committee on Cancer.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eComparison of MRI features between\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003ethe\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003erCR and non-rCR groups before NAC\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCompared with that in the non-rCR group, the maximum tumor diameter in the rCR group was smaller, the glands were more heterogeneous and dense, and fewer irregularly shaped BCs were present. While these differences were statistically significant (\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.05), the differences in the number of lesions, margins, type of lesion, BPE, degree of enhancement, type of TIC, and ADC value of the tumors between the rCR and non-rCR groups were not statistically significant (\u003cem\u003eP\u003c/em\u003e \u0026gt; 0.05). MRI characteristics of BC patients prior to NAC are shown in Table 2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u0026nbsp;\u003c/strong\u003eComparison of MRI features between the rCR and non-rCR groups\u0026nbsp;before NAC\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"97%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003erCR(n=38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003enon-rCR(n=248)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003eMaximum tumor\u003c/p\u003e\n \u003cp\u003ediameter (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e3.70\u0026plusmn;1.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e4.65\u0026plusmn;1.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e0.004\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003eNumber of lesions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e0.287\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003eSingle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e20(52.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e153(61.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003eMultiple\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e18(47.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e95(38.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003eMargin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e0.349\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003eClear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e1(2.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e2(0.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003eBlurred\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e37(97.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e246(99.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003eType of lesion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e0.598\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003eNME\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e8(21.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e62(25.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003eMass\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e30(78.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e186(75.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003eMorphology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e0.047\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003eRound/oval\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e4(10.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e7(2.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003eIrregular\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e34(89.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e241(97.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003eFGT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e0.044\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003eFatty\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e0(0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e5(2.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003eSparse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e11(28.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e109(44.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003eUneven dense\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e23(60.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e117(47.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003eDense\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e4(10.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e17(6.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003eBPE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e0.359\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003eMinimal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e12(31.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e74(29.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003eMild\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e11(28.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e102(41.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e8(21.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e47(19.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003eMarked\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e7(18.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e25(10.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003eDegree of enhancement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e0.441\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003eMild\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e0(0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e0(0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e4(10.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e17(6.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003eMarked\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e34(89.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e231(93.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003eTypes of TIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e0.946\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003ePersistent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e1(2.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e9(3.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003ePlateau\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e12(31.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e79(31.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003eWashout\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e25(65.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e160(64.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003eTumor ADC value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e0.84\u0026plusmn;0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e0.83\u0026plusmn;0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e0.731\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: a is the t value, and b is the \u0026chi;2 value. *P value \u0026lt;0.05, indicating that the differences were considered statistically significant. Abbreviations: NME, non-mass enhancement; FGT, fibroglandular tissue type; BPE, background parenchymal enhancement; TIC, time‒signal intensity curve.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eUnivariate and multivariate analyses of rCR predictors\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUnivariate analysis revealed that the maximum tumor diameter (OR=0.719), morphology (OR=0.247), N stage(OR=0.396), and the PR (OR=0.335), ER (OR=0.461), and HER-2 (OR=2.069) status as well as the Ki-67 index (OR=11.295) and the CA15-3 level (OR=0.107) were rCR predictors. Further multifactorial analysis revealed that high Ki-67 expression (OR=9.009), elevated CA15-3 (OR=0.098), maximum tumor diameter \u0026lt;3.15 cm (OR=0.778) and irregular morphology (OR=0.148) were independent predictors of an rCR (Table 3). Representative rCR and non-rCR images are detailed in Figures 2 and 3, respectively.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e Univariable and multivariable analyses of rCR predictors\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eCharacteristic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003eUnivariate analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003eMultivariate analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003eOR(95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003eOR(95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eMaximum tumor\u003c/p\u003e\n \u003cp\u003eDiameter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003e0.719(0.571\u003cstrong\u003e-\u003c/strong\u003e0.905)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e0.778(0.619\u003cstrong\u003e-\u003c/strong\u003e0.978)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eMorphology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u0026nbsp; Round/oval\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u0026nbsp; Irregular\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003e0.247(0.069\u003cstrong\u003e-\u003c/strong\u003e0.888)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e0.148(0.029\u003cstrong\u003e-\u003c/strong\u003e0.743)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003ePR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003e0.335(0.156\u003cstrong\u003e-\u003c/strong\u003e0.719)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e0.462(0.158\u003cstrong\u003e-\u003c/strong\u003e1.353)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.159\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eER\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u0026nbsp; Negative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u0026nbsp; Positive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003e0.461(0.231\u003cstrong\u003e-\u003c/strong\u003e0.919)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e0.896(0.327\u003cstrong\u003e-\u003c/strong\u003e2.457)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.831\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eHER-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003e2.069(1.036\u003cstrong\u003e-\u003c/strong\u003e4.135)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e2.093(0.928\u003cstrong\u003e-\u003c/strong\u003e4.719)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.075\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eN stage\u003c/p\u003e\n \u003cp\u003eN0\u003c/p\u003e\n \u003cp\u003eN1\u003c/p\u003e\n \u003cp\u003eN2\u003c/p\u003e\n \u003cp\u003eN3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003cp\u003e0.396(0.072\u003cstrong\u003e-\u003c/strong\u003e2.187)\u003c/p\u003e\n \u003cp\u003e1.556(0.311\u003cstrong\u003e-\u003c/strong\u003e7.768)\u003c/p\u003e\n \u003cp\u003e1.055(0.215\u003cstrong\u003e-\u003c/strong\u003e5.165)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.070\u003c/p\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003cp\u003e0.288\u003c/p\u003e\n \u003cp\u003e0.590\u003c/p\u003e\n \u003cp\u003e0.947\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003cp\u003e0.304(0.045\u003cstrong\u003e-\u003c/strong\u003e2.042)\u003c/p\u003e\n \u003cp\u003e1.631(0.287\u003cstrong\u003e-\u003c/strong\u003e9.279)\u003c/p\u003e\n \u003cp\u003e0.764(0.137\u003cstrong\u003e-\u003c/strong\u003e4.279)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.055\u003c/p\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003cp\u003e0.221\u003c/p\u003e\n \u003cp\u003e0.581\u003c/p\u003e\n \u003cp\u003e0.760\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eKi-67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u0026le;20%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u0026gt;20%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003e11.295(1.517\u003cstrong\u003e-\u003c/strong\u003e84.121)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e9.009(1.121\u003cstrong\u003e-\u003c/strong\u003e72.380)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eCA15-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eElevated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003e0.107(0.014\u003cstrong\u003e-\u003c/strong\u003e0.799)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e0.098(0.012\u003cstrong\u003e-\u003c/strong\u003e0.830)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: OR, odds ratio; CI, confidence interval.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eDiagnostic performance of\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003ethe\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003emodels in predicting\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003ean\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003erCR\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe joint model combining Ki-67, CA15-3, maximum diameter and morphology predicted the optimal diagnostic efficacy of predicting an rCR (AUC=0.772), followed by the model based on maximum diameter, Ki-67, CA15-3, and morphology (Table 4). The ROC curves of the models are shown in Figure 4.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eDiagnostic performance of parameters in predicting rCR before NAC\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"585\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eParameter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 225px;\"\u003e\n \u003cp\u003eAUC(95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eSensitivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eSpecificity\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eMaximum diameter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 225px;\"\u003e\n \u003cp\u003e0. 684(0.585\u003cstrong\u003e-\u003c/strong\u003e0.782)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e0.766\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e0.658\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eKi-67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 225px;\"\u003e\n \u003cp\u003e0.604 (0. 519\u003cstrong\u003e-\u003c/strong\u003e0.688)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e0.974\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e0.234\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eCA15-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 225px;\"\u003e\n \u003cp\u003e0. 588(0.501\u003cstrong\u003e-\u003c/strong\u003e0.674)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e0.974\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e0.202\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eMorphology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 225px;\"\u003e\n \u003cp\u003e0. 539(0. 435\u003cstrong\u003e-\u003c/strong\u003e0.642)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e0.105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e0.972\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eJoint model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 225px;\"\u003e\n \u003cp\u003e0. 772(0. 695\u003cstrong\u003e-\u003c/strong\u003e0.849)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e0.632\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e0.831\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: The joint model integrates maximum diameter, Ki-67, CA15-3, and morphology.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe primary goal of this study was to develop and validate an integrated model that leverages routinely available pretreatment data\u0026mdash;clinicopathological parameters and multiparametric MRI features\u0026mdash;to predict the likelihood of achieving rCR in breast cancer patients undergoing NAC. While experienced clinicians intuitively incorporate factors like Ki-67 and tumor size into their prognostic assessments, our study provides a quantitative framework that systematically combines these variables with advanced MRI characteristics to generate an objective probability score. This approach moves beyond empirical prediction towards a more standardized and potentially more accurate tool for pre-therapeutic stratification.\u003c/p\u003e\n\u003cp\u003eThe question of whether rCR can serve as a robust alternative to pCR is central to interpreting our findings. It is crucial to acknowledge that rCR and pCR are not synonymous; discordances exist, primarily due to the inherent limitations of MRI in detecting microscopic residual disease(26). However, a growing body of evidence supports the prognostic value of rCR in its own right. Importantly, as demonstrated by Gampenrieder et al.(9), patients achieving rCR exhibit recurrence-free survival benefits comparable to those achieving pCR, while residual enhancement on MRI is strongly associated with increased risks of early recurrence and mortality. This suggests that while rCR may not perfectly predict pCR, it is a robust imaging biomarker that strongly correlates with superior survival outcomes. Our stringent definition of rCR aims to identify this specific patient subgroup with excellent prognosis. The key advantage of rCR lies in its non-invasive nature, allowing for repeated assessment throughout and at the conclusion of NAC, thus providing critical information for surgical planning and adjuvant therapy discussions long before final pathology is available.\u003c/p\u003e\n\u003cp\u003eContextualizing our findings within biological and therapeutic heterogeneity is critical. It is well-established that molecular intrinsic subtypes and specific therapeutic regimens are paramount determinants of response to neoadjuvant chemotherapy (NAC). Our findings must be interpreted within this context. Although molecular subtype was not an independent predictor in our final model, the distribution of responses in our cohort (Table 1) aligns with established literature. Consistent with the well-established higher chemosensitivity of these biologically aggressive tumors, we observed a numerical tendency towards higher rCR rates in HER2-positive and triple-negative breast cancers(27, 28).Crucially, the primary aim of our study was to identify baseline, pre-therapeutic predictors, universally available before any treatment decisions are finalized. Consequently, the specific NAC regimen was not included as a model variable, as it is not a predictive feature but a subsequent intervention. The fact that our model\u0026mdash;built solely on fundamental tumor characteristics (Ki-67, CA15-3, size, morphology)\u0026mdash;retained predictive power across a heterogeneous population receiving standard-of-care treatment is a key finding. It suggests that these baseline features capture a core aspect of tumor biology and chemo-sensitivity that is foundational and transcends specific subtypes or regimens.\u003c/p\u003e\n\u003cp\u003eThis does not diminish the importance of subtype or treatment; rather, it positions our model as a complementary tool that provides a robust initial risk stratification based on readily available data. Future research should investigate the integration of our baseline model with therapeutic variables in larger, protocol-driven cohorts to refine predictive accuracy for specific patient subgroups.\u003c/p\u003e\n\u003cp\u003eAccurate prediction of pCR after NAC is crucial for personalized breast cancer management, as it influences surgical planning and adjuvant therapy decisions. However, pCR assessment requires invasive biopsy or surgery, whereas rCR offers a noninvasive alternative. In this study, we screened independent predictors of an rCR by combining clinicopathological parameters and multiparametric MRI features via multivariate logistic regression analysis and constructed a joint prediction model. The results revealed that ER, PR, HER-2, Ki-67, CA15-3, maximum tumor diameter, and lesion morphology were significantly correlated with rCR (all \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05) and that high expression of Ki-67 (OR=9.009), elevated CA15-3 (OR=0.098), maximum tumor diameter \u0026lt;3.15 cm (OR=0.778), and irregular morphology (OR=0.148) were identified as independent predictors of an rCR. The joint model constructed on the basis of the above four independent factors demonstrated moderate diagnostic efficacy (AUC=0.772) in predicting rCR, which provides a novel decision-making tool for the noninvasive assessment of the rCR status in the clinic.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe level of CA15-3, the most sensitive serum marker for BC, is positively correlated with tumor load and metastatic potential (29-31). In this study, we found that the probability of obtaining an rCR was significantly lower in patients with high CA15-3 expression (OR=0.098), which is similar to the conclusion of Lee et al. (32)\u0026nbsp;and Fujimoto et al.(31)\u0026nbsp;regarding tumor markers and recurrence risk. The underlying mechanism involves the aberrant expression of mucin encoded by the MUC1 gene, the dual role of MUC1-C in mediating\u0026nbsp;antiapoptotic\u0026nbsp;signaling through BCL2A1 activation, and the upregulation of VEGF through stabilizing HIF-1\u0026alpha; to promote angiogenesis, which\u0026nbsp;weakens\u0026nbsp;the efficacy of NAC\u0026nbsp;(33-35).\u003c/p\u003e\n\u003cp\u003eKi-67 is an index of tumor cell proliferation that is associated with the degree of tumor differentiation, invasion, metastasis, and prognosis (36). Studies have shown that the higher the Ki-67 index value, the more active the cells are in the proliferative phase of the tumor, the higher the degree of malignancy, and the stronger their invasive ability, and therefore, tumor progression or metastasis is more likely to occur (37). Although cancer cells with high Ki-67 expression exhibit active proliferation, they are more sensitive to chemotherapy, after which the cancer cells are obviously suppressed, which in turn demonstrates the high efficacy of NAC. Alba et al. (38) conducted a study on 262 BC patients who underwent NAC and reported that a Ki-67 index \u0026gt;50% was an independent predictor of pCR after NAC treatment.\u0026nbsp;Current studies have also shown that high Ki-67 expression can improve the pCR rate after NACT(39, 40), suggesting that cell proliferation is closely related to chemotherapy sensitivity.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn our study, high Ki-67 expression (OR=9.009) was an independent predictor of an rCR, which is consistent with previous studies that have suggested a positive association between Ki-67 expression status and response to NAC (41, 42). The possible reasons for this are as follows: (i) cell cycle dependence: paclitaxel/anthracycline drugs target cells in G2/M phase, and the proportion of actively cycling cells is greater in high-Ki-67 tumors; (ii) genomic instability: TP53 mutations are commonly found in high-Ki-67-expressing TNBCs, which leads to defective DNA repair and reduced chemosensitivity (43); (iii) clone selection effect: NAC preferentially removes high-proliferating subpopulations, and residual low-proliferating clones are significantly more resistant (44).\u003c/p\u003e\n\u003cp\u003eNumerous studies have confirmed that tumor size is an influential factor in the efficacy and outcomes of NAC in BC patients (45-47). Derouane et al. (48) demonstrated that larger tumors usually exhibit greater cellular heterogeneity as well as more areas of hypoxia and necrosis, which makes it difficult for chemotherapeutic agents to be evenly distributed and to effectively kill all cancer cells. In addition, larger tumors tend to have higher tumor loads and more complex tumor microenvironments, which increases the difficulty of treatment. Patients with smaller tumors are more likely to achieve complete remission because of their lower cell density, which allows chemotherapeutic agents to penetrate and work more effectively. Gajdos et al. (49) also demonstrated that smaller tumors are more likely to respond to NAC than larger tumors. In our study, a maximum tumor diameter \u0026lt;3.15 cm (OR=0.778) was an independent predictor of an rCR, which suggests that those with smaller tumors were more likely to achieve rCR; this is consistent with the notion that patients with smaller tumors are more likely to achieve complete remission due to their low cellular density and homogeneous vascular distribution.\u003c/p\u003e\n\u003cp\u003ePrevious studies have shown that lobulated or burr-like morphology and margins strongly indicate malignancy, with a high positive predictive value (PPV) for malignancy (50), and that the pathological basis of the tumor is associated with an inhomogeneous growth pattern: irregular invasion by tumor cells of the breast ducts or interstitium (51). The irregular morphology may be associated with heterogeneity within the tumor, and such features are more common in malignant lesions (52). Chen et al. (53) showed that pretreatment MRI-based tumor morphologic features may predict the response to NAC in BC patients. In addition, other studies have shown that, compared with irregularly shaped tumors, round and oval tumors are more sensitive to NAC (54). In this study, the results suggested that irregular tumor morphology was an independent negative predictor of an rCR, which is the same as the results of previous studies. This suggests that irregularly shaped tumors are more malignant and that it is more difficult for patients with these tumors to achieve an rCR than those with round/round-like tumors.\u003c/p\u003e\n\u003cp\u003eAccurate screening and identification of patients who can achieve an rCR after treatment with NAC is important for the early treatment and prognosis evaluation of BC patients, and thus a potentially noninvasive, accurate, simple and affordable method is needed to identify patients likely to exhibit an rCR. In this study, we constructed a joint model for predicting rCR based on independent predictors of an rCR (maximal tumor diameter, Ki-67 expression, CA15-3 level, and morphology), and the model was able to better identify patients with an rCR to NAC with an AUC of 0.772. The joint model constructed in this study has several potential advantages if used in clinical applications. First, the model can optimize the management of NAC to a certain degree and allow precise individualized treatment. Second, in terms of treatment strategy optimization, for patients with a high rCR probability, maintaining the original regimen can improve long-term prognosis and provide a decision basis for step-down therapy (reducing the number of chemotherapy cycles); in contrast, for patients with a low rCR probability, strategies can be adjusted in a timely manner, including replacing platinum-enhanced regimens with immune checkpoint inhibitors or targeted drugs to overcome potential drug resistance. Third, in terms of health benefits, the model can reduce myelosuppression and cardiotoxicity events caused by the administration of ineffective chemotherapy, optimize the frequency of imaging-based monitoring, and reduce healthcare costs (compared with the traditional pattern of MRI review every 2 cycles).\u003c/p\u003e\n\u003cp\u003eThe rCR rate in our cohort (13.3%) is lower than pCR rates reported in some studies. This discrepancy can be attributed to several factors. Firstly, our study employed a stringent radiological definition of rCR (complete absence of enhancement on DCE-MRI), which aligns with the RECIST 1.1 criteria for complete response but may be less sensitive than pathological assessment in detecting minimal residual disease. Secondly, our patient population included a substantial proportion of cases with advanced disease stages (43.4% T3/T4 tumors and 57.7% with N2/N3 nodal involvement at diagnosis), which are historically associated with lower response rates to NAC. The inherent limitations of MRI, including potential false positives due to post-treatment inflammation or fibrosis that mimics residual enhancement, might also contribute to a more conservative assessment of rCR compared to pathological evaluation. This underscores the challenge of achieving complete imaging response and highlights that rCR and pCR, while correlated, capture different aspects of treatment response.The pronounced imbalance between rCR (13.3%) and non-rCR (86.7%) groups in our cohort reflects the intrinsic low rCR rate to NAC in breast cancer, as established by Zhang et al. (19). While this disparity poses methodological challenges for predictive modeling, it accurately represents real-world clinical populations. Our study implemented a three-pronged strategy to ensure robust conclusions:\u0026nbsp;First, we employed rigorous statistical controls including VIF-restricted variable selection (VIF \u0026lt;5) and Hosmer-Lemeshow goodness-of-fit testing (p=0.965), which maintained model stability despite class imbalance. Second, we deliberately prioritized specificity (83.1%) over sensitivity (63.2%) in model optimization, aligning with the clinical imperative to reliably identify non-responders and avoid overtreatment. This strategic focus was validated by the high negative predictive value (NPV=93.6%), though the modest positive predictive value (PPV=36.4%) underscores the persistent difficulty in confirming rCR within imbalanced datasets.\u003c/p\u003e\n\u003cp\u003eIn interpreting our results, several limitations must be considered. These limitations can be categorized into those inherent to the reference standard (MRI) and those related to our study design.First, regarding the reference standard, it is important to acknowledge the inherent limitations of MRI in assessing response to NAC. False negatives can occur due to microscopic residual disease below the detection threshold of MRI or due to non-enhancing residual tumor. False positives can arise from post-treatment inflammation, fibrosis, or granulation tissue that exhibits enhancement, mimicking residual disease(26). Additionally, susceptibility artifacts, for instance from biopsy clips, can occasionally interfere with image interpretation. These inherent limitations of MRI-defined rCR itself contribute to the imperfect performance (AUC \u0026lt; 0.8) of our and any other predictive model based on it.Second, our study has several methodological limitations. The retrospective single-center design may introduce selection bias and inherently limits causal inferences. Patients were treated with different NAC regimens and different numbers of cycles, which may be unpredictable confounding factors. Furthermore, the pronounced class imbalance between rCR and non-rCR groups, while reflective of real-world prevalence, affects the precision of sensitivity estimates and model calibration. Finally, only the rCR and non-rCR groups were analyzed in this study, and subgroup analyses of rPR, rPD, and rSD patients were not performed, which may affect model generalizability.\u003c/p\u003e\n\u003cp\u003eHowever, our approach provides a methodological framework for handling such imbalanced real-world data: (1) transparent reporting of both rule-out (high NPV) and rule-in (modest PPV) capabilities, (2) clinical prioritization of specificity to guide oncological decision needs, and (3) the use of statistical techniques (VIF restriction, goodness-of-fit testing) robust to prevalence disparities.\u003c/p\u003e\n\u003cp\u003eFuture research should build upon these findings. Prospective multicenter studies with larger, balanced cohorts are needed to validate and refine our model. Such studies should incorporate standardized NAC protocols and aim to prospectively validate the proposed predictive algorithm. Beyond this, future efforts should include pre-planned rCR enrichment strategies, external validation across diverse cohorts, and the integration of advanced machine learning techniques (e.g., cost-sensitive neural networks) specifically developed for imbalanced medical data to further enhance predictive performance.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, we developed and validated an integrated model based on pretreatment Ki-67 index, CA15-3 level, tumor diameter, and morphology that can stratify patients by their likelihood of achieving rCR after NAC. Given the established correlation between rCR and favorable survival outcomes(9), this tool provides an objective, quantitative foundation for pre-therapeutic prognostication. Its primary clinical value lies in its ability to rule out non-response with high confidence, potentially sparing patients from the toxicity of ineffective therapy and guiding earlier treatment adaptation. While promising, the clinical utility of this model for guiding personalized treatment decisions requires further validation in large-scale, multicenter, prospective cohorts.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 319px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAbbreviation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 319px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFull Term\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 319px;\"\u003e\n \u003cp\u003eADC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 319px;\"\u003e\n \u003cp\u003eApparent Diffusion Coefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 319px;\"\u003e\n \u003cp\u003eAJCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 319px;\"\u003e\n \u003cp\u003eAmerican Joint Committee on Cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 319px;\"\u003e\n \u003cp\u003eASCO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 319px;\"\u003e\n \u003cp\u003eAmerican Society of Clinical Oncology\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 319px;\"\u003e\n \u003cp\u003eBPE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 319px;\"\u003e\n \u003cp\u003eBackground Parenchymal Enhancement\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 319px;\"\u003e\n \u003cp\u003eBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 319px;\"\u003e\n \u003cp\u003eBreast Cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 319px;\"\u003e\n \u003cp\u003eCA15-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 319px;\"\u003e\n \u003cp\u003eCarbohydrate Antigen 15-3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 319px;\"\u003e\n \u003cp\u003eCA125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 319px;\"\u003e\n \u003cp\u003eCarbohydrate Antigen 125\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 319px;\"\u003e\n \u003cp\u003eCAIPIRINHA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 319px;\"\u003e\n \u003cp\u003eControlled Aliasing in Parallel Imaging Results in Higher Acceleration\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 319px;\"\u003e\n \u003cp\u003eCAP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 319px;\"\u003e\n \u003cp\u003eCollege of American Pathologists\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 319px;\"\u003e\n \u003cp\u003eCEA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 319px;\"\u003e\n \u003cp\u003eCarcinoembryonic Antigen\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 319px;\"\u003e\n \u003cp\u003eDCE-MRI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 319px;\"\u003e\n \u003cp\u003eDynamic Contrast-Enhanced Magnetic Resonance Imaging\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 319px;\"\u003e\n \u003cp\u003eDFS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 319px;\"\u003e\n \u003cp\u003eDisease-Free Survival\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 319px;\"\u003e\n \u003cp\u003eDWI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 319px;\"\u003e\n \u003cp\u003eDiffusion-Weighted Imaging\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 319px;\"\u003e\n \u003cp\u003eER\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 319px;\"\u003e\n \u003cp\u003eEstrogen Receptor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 319px;\"\u003e\n \u003cp\u003eFGT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 319px;\"\u003e\n \u003cp\u003eFibroglandular Tissue\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 319px;\"\u003e\n \u003cp\u003eFISH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 319px;\"\u003e\n \u003cp\u003eFluorescence In Situ Hybridization\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 319px;\"\u003e\n \u003cp\u003eFLASH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 319px;\"\u003e\n \u003cp\u003eFast Low Angle Shot\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 319px;\"\u003e\n \u003cp\u003eGRAPPA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 319px;\"\u003e\n \u003cp\u003eGeneralized Autocalibrating Partially Parallel Acquisitions\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 319px;\"\u003e\n \u003cp\u003eGRE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 319px;\"\u003e\n \u003cp\u003eGradient Echo\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 319px;\"\u003e\n \u003cp\u003eHER2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 319px;\"\u003e\n \u003cp\u003eHuman Epidermal Growth Factor Receptor 2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 319px;\"\u003e\n \u003cp\u003eIHC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 319px;\"\u003e\n \u003cp\u003eImmunohistochemistry\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 319px;\"\u003e\n \u003cp\u003eKi-67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 319px;\"\u003e\n \u003cp\u003eProliferation marker protein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 319px;\"\u003e\n \u003cp\u003eMRI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 319px;\"\u003e\n \u003cp\u003eMagnetic Resonance Imaging\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 319px;\"\u003e\n \u003cp\u003eNAC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 319px;\"\u003e\n \u003cp\u003eNeoadjuvant Chemotherapy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 319px;\"\u003e\n \u003cp\u003eNME\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 319px;\"\u003e\n \u003cp\u003eNon-Mass Enhancement\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 319px;\"\u003e\n \u003cp\u003eOS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 319px;\"\u003e\n \u003cp\u003eOverall Survival\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 319px;\"\u003e\n \u003cp\u003epCR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 319px;\"\u003e\n \u003cp\u003ePathological Complete Response\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 319px;\"\u003e\n \u003cp\u003ePR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 319px;\"\u003e\n \u003cp\u003eProgesterone Receptor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 319px;\"\u003e\n \u003cp\u003erCR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 319px;\"\u003e\n \u003cp\u003eRadiologic Complete Response\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 319px;\"\u003e\n \u003cp\u003eRECIST\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 319px;\"\u003e\n \u003cp\u003eResponse Evaluation Criteria in Solid Tumors\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 319px;\"\u003e\n \u003cp\u003eROI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 319px;\"\u003e\n \u003cp\u003eRegion of Interest\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 319px;\"\u003e\n \u003cp\u003eRS-EPI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 319px;\"\u003e\n \u003cp\u003eReadout-Segmented Echo Planar Imaging\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 319px;\"\u003e\n \u003cp\u003eSS-EPI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 319px;\"\u003e\n \u003cp\u003eSingle-Shot Echo Planar Imaging\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 319px;\"\u003e\n \u003cp\u003eTIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 319px;\"\u003e\n \u003cp\u003eTime-Intensity Curve\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 319px;\"\u003e\n \u003cp\u003eTNBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 319px;\"\u003e\n \u003cp\u003eTriple-Negative Breast Cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 319px;\"\u003e\n \u003cp\u003eTWIST\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 319px;\"\u003e\n \u003cp\u003eTime-resolved angiography With Interleaved Stochastic Trajectories\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 319px;\"\u003e\n \u003cp\u003eVIBE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 319px;\"\u003e\n \u003cp\u003eVolumetric Interpolated Breath-hold Examination\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 319px;\"\u003e\n \u003cp\u003eVIF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 319px;\"\u003e\n \u003cp\u003eVariance Inflation Factor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003ch2\u003eInformed consent\u003c/h2\u003e\n\u003cp\u003eThis retrospective study was approved by the institutional review board of Meizhou People\u0026apos;s Hospital. The funding source had no involvement in the study design; in the collection, analysis, and interpretation of data; in the writing of the manuscript; and in the decision to submit the article for publication.\u003c/p\u003e\n\u003ch2\u003eEthical Approval\u003c/h2\u003e\n\u003cp\u003eThe ethics committee of Meizhou People\u0026rsquo;s Hospital approved this retrospective study (2023-CY-53). This study was conducted in accordance with the Declaration of Helsinki, and the requirement for informed consent was waived.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eMeizhou People\u0026apos;s Hospital Research Cultivation Project (PY-C2022011).\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eZhiqi Yang and Xiaofeng Chen conceived and supervised the study. Tianxiang Zhu, Zhuozhi Dai, and Xinwei Zhong were responsible for data acquisition. Bowen Yue and Hao Zhang contributed to methodology and software development. Bowen Yue and Yi Chen performed the formal analysis and drafted the initial manuscript. Xiaohong Chen, Zhiqi Yang, and Xiaofeng Chen critically reviewed and edited the manuscript. All authors reviewed the results and approved the final version.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eThe data cohorts used and/or analyzed in the present study are available from the corresponding authors upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBray F, Laversanne M, Sung H, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 2024;74:229-263.\u003c/li\u003e\n\u003cli\u003eWaks AG, Winer EP. Breast cancer treatment: a review. JAMA 2019;321:288-300.\u003c/li\u003e\n\u003cli\u003ePesapane F, Agazzi GM, Rotili A, et al. 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AJR Am J Roentgenol 2009;193:994-1000.\u003c/li\u003e\n\u003cli\u003eHanby AM, walker C. Tavassoli FA, Devilee P: pathology and genetics: tumours of the breast and female genital organs. WHO classification of tumours series - volume IV. Lyon, France: IARC Press. Breast Cancer Research 2004;6:133.\u003c/li\u003e\n\u003cli\u003eTabar L, Vitak B, Chen HH, Yen MF, Duffy SW, Smith RA. Beyond randomized controlled trials: organized mammographic screening substantially reduces breast carcinoma mortality. Cancer 2001;91:1724-1731.\u003c/li\u003e\n\u003cli\u003eChen P, Wang C, Lu R, et al. Multivariable models based on baseline imaging features and clinicopathological characteristics to predict breast pathologic response after neoadjuvant chemotherapy in patients with breast cancer. Breast Care (Basel) 2022;17:306-315.\u003c/li\u003e\n\u003cli\u003eMichishita S, Kim SJ, Shimazu K, et al. 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Breast 2015;24:159-165.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-imaging","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmim","sideBox":"Learn more about [BMC Medical Imaging](http://bmcmedimaging.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmim/default.aspx","title":"BMC Medical Imaging","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Neoadjuvant chemotherapy, MRI, Diagnostic efficacy, Breast cancer, Radiologic complete response","lastPublishedDoi":"10.21203/rs.3.rs-8796639/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8796639/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eRadiologic complete response (rCR) after neoadjuvant chemotherapy (NAC) is increasingly recognized as a prognostic indicator in breast cancer, yet predictive models integrating baseline clinicopathological and multiparametric MRI features remain underdeveloped. We aimed to develop and validate a combined model for pretreatment prediction of rCR.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis retrospective study analyzed 286 consecutive breast cancer patients who received NAC between 2016 and 2023. rCR was strictly defined as complete absence of enhancement on post-treatment DCE-MRI per RECIST 1.1 criteria. Pretreatment clinicopathological variables (including ER, PR, HER2 status, Ki-67 index, and serum CA15-3 level) and multiparametric MRI characteristics (tumor size, morphology, enhancement kinetics, and ADC values) were evaluated. Variable selection was performed using multivariable logistic regression with variance inflation factor restriction (VIF\u0026thinsp;\u0026lt;\u0026thinsp;5) to identify independent predictors. Model performance was assessed via receiver operating characteristic (ROC) analysis.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eSignificant differences in ER/PR/HER2 status, Ki-67, CA15-3, tumor diameter, and morphology were observed between rCR (13.3%, 38/286) and non-rCR groups (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Multivariate analysis identified high Ki-67 (OR\u0026thinsp;=\u0026thinsp;9.009), low CA15-3 (OR\u0026thinsp;=\u0026thinsp;0.098), tumor diameter\u0026thinsp;\u0026lt;\u0026thinsp;3.15 cm (OR\u0026thinsp;=\u0026thinsp;0.778), and non-irregular morphology (OR\u0026thinsp;=\u0026thinsp;0.148) as independent predictors (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The combined model achieved an AUC of 0.772 (sensitivity\u0026thinsp;=\u0026thinsp;63.2%, specificity\u0026thinsp;=\u0026thinsp;83.1%).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003e We developed a clinically applicable model combining readily available pretreatment clinicopathological and MRI features that effectively stratifies patients by likelihood of achieving rCR after NAC. This tool may facilitate early identification of NAC responders, potentially optimizing treatment strategies and reducing unnecessary chemotherapy exposure. Further validation in prospective, multicenter cohorts is warranted to confirm its generalizability.\u003c/p\u003e","manuscriptTitle":"Early Identification of Breast Cancer Patients Achieving Radiological Complete Response to NAC: A Clinicopathological and MRI-Based Approach","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-12 07:22:43","doi":"10.21203/rs.3.rs-8796639/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-03-05T16:54:59+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-10T04:54:14+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-09T11:23:40+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-09T11:18:16+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Imaging","date":"2026-02-05T11:06:33+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-imaging","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmim","sideBox":"Learn more about [BMC Medical Imaging](http://bmcmedimaging.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmim/default.aspx","title":"BMC Medical Imaging","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"27f21bbd-1ffd-47f8-adac-f68c9c6380a6","owner":[],"postedDate":"March 12th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-12T07:22:44+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-12 07:22:43","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8796639","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8796639","identity":"rs-8796639","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}