Quantitative DCE-MRI for prediction of pathological complete response prior to neoadjuvant systemic therapy

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Methods Among 120 randomly selected patients with breast neoplasms, 28 diagnosed with invasive ductal carcinoma underwent NST. All patients underwent at least three MRI examinations: preoperative and before and after NST. Spearman correlation analysis was used to assess the correlation between pCR and Miller–Payne (MP) scores with pharmacokinetic parameters (K trans , K ep , V e , V p ) in the regions of interest (ROI) in the tumour (ROI1), tumoural junction with the normal gland (ROI2), peritumoural region (ROI3), and background parenchymal enhancement; tumour morphological characteristics (type, location, quantity, margin, and maximum diameter); enhancement or shrinkage mode; and residual condition following preoperative MRI. Results A positive correlation was observed between pCR and tumour HER2 expression (r = 0.546); and K ep (r = 0.427) and V e of ROI3 (r = 0.564) (P < 0.05). A negative correlation between pCR, tumour shrinkage pattern (r=-0.506) and residual tumours (r=-0.551) was observed by preoperative MRI (r=-0.551) (P < 0.05). MP associated with progesterone receptor (r=-0.37), HER2 (r = 0.608), and Ki-67 (r = 0.393) expression; tumour shrinkage pattern (r=-0.625); and preoperative MRI residual tumour (r=-0.715) (P < 0.05). Preoperative MRI tumour residual status associated with Ki-67 (r=-0.465) and tumour shrinkage pattern (r = 0.677) (P < 0.05). Conclusions A correlation was observed between DCE-MRI of the peritumoural region prior to NST and pCR. Early MRI evaluation of tumour shrinkage patterns following NST and preoperative tumour residual status showed predictive value for pCR and tumour burden. Dynamic contrast-enhanced magnetic resonance imaging Breast cancer Neoadjuvant systemic therapy Pathological complete response Peritumoural region Response patterns Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction The annual incidence rate of breast cancer is rising, reaching 31% among women. However, due to advancements in early screening and improved treatment options, the overall mortality rate associated with breast cancer is decreasing [1]. The application of neoadjuvant systemic therapy (NST) can improve clinical tumour staging of tumours before surgery, resection, and breast-conserving surgery rates, and reduce the risk of tumour recurrence, providing a new approach to improve patient prognosis[2, 3]. Experts in the field highlight the significance of the pathological complete response (pCR) following NST as a guide for subsequent adjuvant therapy[2]. The presence of residual tumour is an adverse risk factor associated with poor prognosis[4]. Early prediction of poor response to treatment is crucial for timely adjustment of poor response to treatment is crucial for timely adjustment of treatment programmes and mitigating the increased risk of tumour recurrence due to inadequate treatment [5]. Predicting pCR prior to NST may facilitate the selection of a suitable treatment regimen for patients at an earlier stage, and it improves the probability of pCR as well as prognosis. Therefore, the prediction of pCR prior to NST is clinically valuable. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is widely used for preoperative evaluation of breast cancer due to its high sensitivity. DCE-MRI can provide valuable insights into the tumour morphology, dynamics, and perfusion parameters crucial for post-NST pCR prognosis of breast cancer [6]. Yu et al. demonstrated that changes in the transfer constant, K trans , following two NST courses can predict the post-treatment response in tumours [7]. A recent meta-analysis demonstrated that DCE-MRI can be considered an effective method for dynamic monitoring of treatment response following NST in patients with breast cancer patients and shows promising predictive value for pCR [8]. The topic of exploring the correlation between quantitative DCE-MRI-derived tumour characteristics before NST and pCR in patients with breast cancer is crucial, along with the increasing significance of personalized treatment strategies in oncology. An in-depth study of breast cancer in terms of imaging functionality prior to treatment will contribute to a comprehensive understanding of the nature of the tumour and will also advance the exploration of the relationship among breast cancer radiology, histopathology, and clinical prognosis. However, the specific utility of pre-NST DCE-MRI in predicting pCR remains unclear and requires further investigation. The present study aimed to evaluate the correlation between pCR and Miller–Payne (MP) scores with various DCE-MRI-derived pharmacokinetic parameters, imaging characteristics, histological grading, lymph node metastasis (LNM); expression of markers including oestrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), and Ki-67 prior to NST; tumour shrinkage patterns following two NST courses; and residual tumour assessed by preoperative MRI. The study explored the feasibility of MRI in predicting pCR before treatment to provide useful insights for improving the rational formulation of breast cancer treatment and assessment programmes. Materials And Methods Study population This study was approved by the institutional review committee. The requirement to obtain informed patient consent was waived due to the likelihood of traceability. A total of 120 randomly selected female patients, admitted with breast neoplasms between April 2021 and December 2022, were hospitalised for initial MRI and DCE examinations. Basic patient information including age, menopausal status, and clinical tumour staging; biopsy and surgical pathological results; MRI and DCE data were retrospectively collected. The study inclusion criteria were as follows: patients diagnosed with invasive ductal carcinoma (IDC) without a history of breast surgery or other tumours; routine MRI and DCE-MRI scans before treatment; those who received NST and underwent MRI examination after two treatment courses and prior to operation; surgical treatment following NST; complete biopsy and surgical pathology assessments, including immunohistochemical results and LNM; and complete MRI imaging, in line with conventional diagnostic requirements. Patients not meeting the above inclusion criteria were excluded from the study (Fig. 1). MRI acquisition DCE-MRI was performed using a 3.0-T) scanner (Achieva TX, Philips, Netherlands) with a 4-channel breast coil for data acquisition. Patients were placed in a prone position for imaging, and their breasts were naturally suspended within the breast coil. The arms were placed on both sides of the head. Horizontal axis fat suppression, fast-spin echo sequence, T2- and T1-weighted images, and multiphase dynamic enhanced DCE scans were performed separately. DCE sequence used 3D-T1 weighted sequence axial scanning, and T1 mapping images with flip angles (FAs) of 5° and 15° were obtained for the calculation of T1 maps with scan times of 58 s and 52 s, respectively. The other scan parameters were consistent with dynamic enhancement. After 1 min of dynamic enhanced scanning, Gadopentetic Acid Dimeglumine Salt Injection (GDPA) (Magnevist, Bayer AG, Germany) at a 0.1 mmol/kg dosage was administered as an intravenous bolus injection using an MR-compatible power injector (Spectris, Bayer HealthCare, Germany) at a flow rate of 2.0 mL/s, followed by a 20-mL saline flush. Continuous scanning was performed 25 times, with a single and total time of 15.5 s and 7 min 4 s, respectively. DCE parameters were as follows: repetition time (TR)=shortest; echo time (TE)=shortest; flip angle=10°; number of signal average (NSA)=1; layer thickness=4 mm; layer thickness=0 mm; and matrix=340×340. Data analysis Image post-processing DCE permeability analysis was performed by an experienced radiologist with 5 years of experience in breast MR imaging. Specialised quantitative analysis software was used in the Philips workstation. Pharmacokinetic parameters, such as Ktrans, flux rate constant (Kep), extravascular extracellular volume (Ve), and capillary plasma volume (Vp), were obtained by establishing a pharmacokinetic model. Region of interest (ROI) drawing method The maximum dimension of the tumour on a DCE-MRI image, 1 min following GDPA injection (avoiding obvious necrosis, haemorrhage, and cystic changes), and drawing a time-signal intensity curve (TIC), was referred to as ROI1. ROI2 was delineated at the junction of the tumour and normal gland, with an area of 50 mm 2 , and ROI3 was delineated around the tumour, with an area of 30 mm 2 (Fig. 2). DCE-MRI pharmacokinetic parameters were determined separately. Image analysis Background parenchymal enhancement (BPE) and tumour morphological features, including tumour type, location, quantity, margin, maximum diameter, and enhanced morphology, were recorded based on pretreatment MRI images. Tumour shrinkage patterns were recorded based on MRI images after two courses of NST. The preoperative MRI tumour residual status was recorded based on MRI images after NST completion. BPE was divided into four types according to the 2016 American College of Radiology Breast Imaging Reporting and Data System, with the following features: A: almost no enhancement; B: mild background enhancement; C: moderate background enhancement and D: severe background enhancement. The tumour types were classified as enhanced lump or non-lump-enhanced groups; the number of tumours as single or multiple; and tumour margins as clear or indistinct. The maximum tumour diameter was recorded from three directions. Tumour enhancement patterns were classified as homogeneous or heterogeneous. The tumour shrinking pattern was defined as tumour regression on MRI compared to that at baseline after two courses of NST. Tumour regression was classified into 6 categories [9]: 0 (complete imaging response), I (concentric shrinkage), II (fragmentation), III (diffuse contrast enhancement), IV (stable disease), and V (progressive disease). The preoperative MRI residual tumour status was defined as the evaluation of the presence of a tumour based on MRI after treatment completion. Pathological results Results of postoperative pathology and immunohistochemistry from pathological sections were obtained by a professional pathologist, and the breast cancer molecular subtypes, LNM, and MP scores were determined. According to the breast cancer clinical treatment guidelines (2022), breast cancer was classified into four subtypes based on marker expression: Lumina A: ER positive, PR positivity ≥20%, HER2 negative, Ki-67 ≤14%; Lumina B: ER and/or PR positive, HER2 negative, and Ki-67 >14% or low PR expression ≤20%; HER2 overexpression type (HER2+): immunohistochemical staining for 3+ or 2+ with fluorescent in situ hybridization detection for gene amplification; triple-negative: ER, PR, HER2 negative. ER and PR were divided into high (>10%) or low (≤10%) expression group; Ki-67 was divided into high (>20%) or low (≤20%) expression group; and HER2 and LNM were divided into positive or negative groups. According to the MP scoring system, the level of cell abundance of tumour residuals after NST was classified into five grades: Grade 1 (G1): infiltrating cancer cells show no changes or only a few cancer cells show changes, and the overall number of cancer cells remains unchanged; Grade 2 (G2): infiltrating cancer cell numbers are slightly reduced, but the total number is still high, and the reduction of cancer cells does not exceed 30%; Grade 3 (G3): infiltrating cancer cell numbers range from 30% to 90%; Grade 4 (G4): infiltrating cancer cell numbers are significantly decreased by over 90%, with only scattered small clusters or individual cancer cells remaining; Grade 5 (G5): no infiltrating cancer cells in the tumour bed, but in situ ductal carcinoma may exist. Residual cancer burden was divided into high- and low-degree groups. pCR was defined as the complete disappearance of intramuscular cancer cells in the breast; although in situ cancer components maybe present, there were no tumour cells in the axillary lymph nodes [10]. Statistical analysis The data were assessed by testing normality, and data conforming to the normal distribution were expressed as . Data that did conform to normal distribution were expressed as the median. Relevance of pCR, MP, basic patient information, pathology, MRI characteristics of the tumours, and DCE-MRI pharmacokinetic parameters were assessed using Spearman’s correlation test. All statistical analyses were conducted using the SPSS version 25 statistical software package (SPSS Inc., Chicago, IL, USA) and GraphPad Prism version 8.0 (GraphPad Software, Boston, Massachusetts USA). P<0.05 was considered statistically significant. Results Patient characteristics Clinical details and tumour characteristics of the patients are summarised in Table 1 . The study included 28 participants (mean age: 51 ± 8 years, range 32–65 years), including one patients with bilateral breast cancer and the rest with unilateral breast cancer. All patients had cancers corresponding to the following specific molecular subtypes: Lumina A: 2 cases (2%), Lumina B: 8 cases (27%), triple-negative: 2 cases (7%), HER2+: 17 cases (59%), pCR: 9 cases (31%), and no pCR: 20 cases (69%). The NST plan consisted of all patients undergoing neoadjuvant chemotherapy, and HER2 + patients receiving targeted therapy containing pertuzumab with or without trastuzumab. Table 1 Patient and tumour characteristics between pCR and non-pCR. Characteristic Study Sample (n = 29) Pathologic Response pCR(n = 9) Non-pCR(n = 20) P Value Age 1 51 ± 8 58 ± 8 50 ± 9 0.432 Menopausal status 0.979 Premenopausal 16(55) 5(56) 11(55) Postmenopausal 13(45) 4(44) 9(45) Clinical tumor stage 0.116 T2 24(83) 6(67) 18(90) T3 or T4 5(17) 3(33) 2(10) Clinical nodal stage 0.385 N0 5(17) 1(10) 4(21) N1, N2, or N3 24(83) 9(90) 15(79) Grade 0.236 II 16(55) 3(33) 13(65) III or IV 13(45) 6(67) 7(35) ER 0.454 ≤ 10% 13(45) 5(56) 8(40) > 10% 16(55) 4(44) 12(60) PR 0.258 ≤ 10% 18(62) 7(78) 11(55) > 10% 11(38) 2(22) 9(45) Ki67 0.258 ≤ 20% 11(38) 2(22) 9(45) > 20% 18(62) 7(78) 11(55) HER2+ 17(59) 9(100) 8(40) 0.001 Lumina A 2(7) 0(0) 2(10) 0343 Lumina B 8(27) 0(0) 8(40) 0.026* Three Negative 2(7) 0(0) 2(10) 0.343 Residual cancer burden 0.000** Low(G5 or G4) 12(41) 9(100) 3(15) High(G1 to G3) 17(59) 0(0) 17(85) Lymph node metastasis 0.000** Yes 20(69) 5(56) 15(75) 0.312 No 9(31) 4(44) 5(25) 1 Data presented as mean ± standard deviation. *P < 0.05, **P < 0.01 Factors influencing pCR and MP Pretreatment MR characteristics and post-treatment MR characteristics are summarised in Table 2 . pCR correlated with HER2 status (r = 0.564), tumour shrinkage pattern (r=-0.506), tumour residual status on preoperative MRI (r=-0.551), Kep (r = 0.546), and Ve (r = 0.427) of ROI3 in the peritumoural region (P < 0.05), as shown in Fig. 3 . No correlation was observed between pCR and ER, PR status, BPE, tumour morphological characteristics, enhancement mode, TIC, pharmacokinetic parameters of ROI1 and ROI2, histological grading, or LNM (P > 0.05). MP scores correlated with PR (r=-0.376), HER2 status (r = 0.608), Ki-67 (r = 0.393), tumour shrinkage pattern (r=-0.625), and tumour residual status on preoperative MRI (r=-0.715) (P 0.05). Table 2 Comparison of pretreatment MRI characteristics and post-treatment MRI characteristics between pathological complete response (pCR) and non-pCR. Characteristic Study Sample (n = 29) Pathologic Response pCR(n = 9) Non-pCR(n = 20) P Value BPE 0.072 A or B 21(72) 5(56) 16(80) C or D 8(28) 4(44) 4(20) Size 1 38.3(28.5,46.9) 43.7(32.7,56.6) 34(28.4,44.025) 0.192 Side 0.571 Right 17(59) 6(67) 11(55) Left 12(41) 3(33) 9(45) Lesion type 0.369 mass 19(66) 7(78) 12(60) nonmass 10(34) 2(22) 8(40) Enhancement 0.395 Homogeneous 4(14) 2(22) 2(10) Heterogeneous 25(86) 7(78) 18(90) Number of lesions 0.203 Single 11(38) 5(56) 6(30) Multiple 18(62) 4(44) 14(70) Margin 0.642 Clear 11(38) 4(44) 7(35) Unclear 18(62) 5(56) 13(65) Shrinkage pattern a 0.005** 0 7(24) 5(56) 2(10) 1 11(38) 3(33) 8(40) 2 6(21) 1(11) 5(25) 3 2(7) 0(0) 2(10) 4 3(10) 0(0) 3(15) 5 0(0) 0(0) 0(0) Tumour regression b 0.002** Yes 18(62) 2(22) 16(80) No 11(38) 7(78) 4(20) TIC 0.170 I 3(10) 2(22) 1(5) II 24(83) 7(78) 17(85) III 2(7) 0(0) 2(10) BPE = Background parenchymal enhancement, TIC = time-signal intensity curve. 1 Data presented as median (P25, P75). a Shrinkage pattern represents the shrinkage pattern on MRI of tumours after two courses of NST, b Tumour regression represents the presence of the tumour evaluated using MRI after treatment. **P < 0.01 Factors influencing tumour residual status on preoperative MRI Preoperative MRI tumour residual status correlated with Ki-67 expression (r=-0.465) and tumour shrinkage pattern (r = 0.677) (P < 0.05). No correlation was observed between preoperative MRI tumour residual status and ER, PR, HER2 status, BPE, tumour morphological characteristics, enhancement mode, TIC, DCE-MRI pharmacokinetic parameters, histological grading, or LNM, (P > 0.05). Discussion Our study suggests that peritumoural DCE-MRI contributes to the prediction of pCR before NST. Our results showed that pCR positively correlated with Kep and Ve in ROI3 in the peritumoural region. This could account for the fact that patients who achieved pCR after NST had higher Kep and Ve in the peritumoural region, indicating higher vascular permeability and a larger intracellular intravascular gap in the region. Researchers discovered that certain patients with breast cancer developed oedema in the peritumoural region, and studies found that peritumoural oedema (PE) was linked to tumour invasive characteristics, such as large tumour volume, lymphovascular invasion (LVI), high Ki-67 index, poor prognosis, low distant metastasis-free survival, and overall survival [ 11 – 15 ]. Hyaluronan (HA) is an important glycosaminoglycan present in the pericellular and extracellular matrix; HA can facilitate tissue hydration. Both inflammatory disease development and malignant tumour progression are associated with increased HA. Kettunen et al. reported a positive correlation between the peritumoural-tumour-apparent diffusion coefficient (ADC) ratio, axillary lymph node positivity, and HA accumulation, suggesting that the peritumoural-tumour-ADC ratio may be an imaging indicator to identify invasion and prognosis of breast cancer [ 16 ]. Pekoz et al. [ 17 ] reported that the development of oedema surrounding the breast cancer was related to LVI on MRI in 105 patients with invasive breast cancer, prior to NST. Park et al. [ 18 ] reported that patients with PE have higher LVI levels and vascular fibrosis and suggested that PE is related to the tumour microenvironment. A study conducted by Musall et al. [ 19 ] employing quantitative ADC measurements to assess PE characteristics and predict treatment response based on a multivariate model analysis in 108 patients with triple-negative breast cancer reported that the PE of both fat-inclusive (area under the curve [AUC] = 0.66) and fat-exclusive peritumoural regions (AUC = 0.64) could predict pCR following NST with low performance. Due to the rapid development of radiomics in recent years, there has been a considerable increase in research focusing on radiomics within peritumoural regions of breast cancer. A study by Braman et al. [ 20 ] suggested that characteristics of the peritumoural region (9–12 mm from the tumour) can improve distinction of the HER2 status (maximum AUC: 0.85; 95% confidence interval [CI]: 0.79–0.90). The researchers also reported a significant correlation between the radiomics characteristics of the peritumoural region (0–3 mm) and density of tumour infiltrating peripheral lymphocytes (r = 0.57; 95% CI: 0.39–0.75; P = 0.002). The proportion of tumour-infiltrating lymphocytes has been reported to be an independent predictor of pCR in breast cancer [ 21 ]. Moreover, local tumour invasion and changes in the tumour microenvironment influence tumour metastasis and poor prognosis [ 4 , 22 ]. Therefore, metabolic changes in the peritumoural region may affect the therapeutic efficacy and prognosis of tumours. While the intratumoural radiomic characteristics of HER2 + alone may not distinguish pCR (AUC = 0.66; 95% CI: 0.43–0.88; P = 0.08), a combination with intratumoural and peritumoural features helps successful pCR prediction (maximum AUC = 0.80; 95% CI: 0.61–0.98; P = 0.003); with an accuracy, sensitivity, and specificity of 79%, 94%, and 58%, respectively [ 20 ]. Li et al. [ 23 ] constructed a model based on pre-NST MRI of the tumour and peritumoural volumes to identify pCR. Their study showed that while the tumour and peritumoural volume of interest radiomics classifiers independently had the potential to predict pCR, a radiomics model based on the tumour (AUC = 0.92) combined with the peritumoural (AUC = 0.98) volume of interest could further improve the prediction accuracy. Based on pre-NST DCE images combined with intratumoural and peritumoural radiomics methods, Braman et al. [ 24 ] demonstrated that pCR could be successfully predicted with or without receptor binding. Although all the above studies demonstrated the value of peritumoural region prior to NST and its potential for the successful prediction of pCR using imaging, these studies did not use DCE-MRI pharmacokinetic parameters for quantitative evaluation. DCE-MRI is a non-invasive diagnostic method that has been developed in recent years to evaluate the tumour microenvironment. This technique reflects the blood flow characteristics, microvascular permeability, vascular density, tissue oxygen concentration, and tissue metabolic levels. Liu et al. [ 25 ] used a multiple comparison method to analyse the relationship between DCE-MRI and prognostic factors in 151 patients with invasive ductal carcinoma [ 25 ]. The study identified Ktrans as the best predictive indicator of histologic grade, LNM, ER, PR, and Ki-67 expression. Kep and Ve can both predict Ki-67 expression, indicating that DCE-MRI is valuable for predicting tumour invasiveness and prognosis. Previous studies have confirmed the significance of DCE-MRI in predicting the post-NST response, and Ktrans is reported to have a high sensitivity and specificity in predicting pCR [ 26 ]. A retrospective study analysing DCE-MRI images of 84 patients with locally advanced breast cancer who received NST, reported that the maximum tumour diameter and Ktrans could predict pCR both before and following treatment; Ve could predict pCR following treatment, among which the AUC predicted by the DCE-MRI dynamic parameters was between 0.66 and 0.79 [ 27 ]. Our results showed that there was no correlation between the DCE-MRI parameters of tumours and pCR, which may be attributed to the heterogeneity of breast cancer, relatively small sample size, and differences in research methods and samples. Further research will be required to gain better insight into these aspects. However, the results of this study suggested that alterations in pretreatment DCE-MRI-derived parameters in the peritumoural region can inform therapeutic decision-making and help to further optimize the choice of tumour treatment regimen. The results from our study showed that pCR was associated with tumour shrinkage patterns after NST, and these results are consistent with the findings of Heacock et al. [ 28 ], who reported a correlation of pCR after NST with concentric tumour shrinkage in HER2 + breast cancer. Kim et al. [ 29 ] reported that concentric tumour shrinkage was more common in an MP 3–5 pathological response group, following tumour treatment. However, studies by Reis et al. [ 9 ] suggest that fragmentation was a better treatment response, and there was a moderate correlation between MRI findings and the pathological maximum diameter [ 9 ]. The study also indicated that standardisation of tumour shrinkage models could, therefore, provide valuable information for clinical practice. Our results suggested a correlation between preoperative MRI residual tumour status and tumour shrinkage patterns, consistent with the findings of Park et al. [ 30 ]. They studied the MRI features of 109 patients with preoperative DCE-MRI and NST and found that size of the tumour with centripetal contraction after NST showed a smaller difference between MRI and pathology findings. Compared with other subtypes, ER positive/HER2 negative breast cancer MRI measurements tended to underestimate the size of the residual tumour; however, the size of the residual tumour on MRI was highly consistent with the pathology (intraclass correlation coefficient = 0.808, P < 0.001). Our results showing a positive correlation between pCR, MP, and preoperative MRI tumour residual status are consistent with these findings. These findings suggest the potential of early MRI evaluation in predicting treatment outcomes. In our study, patients in the HER2- group did not achieve pCR, which may be related to the low response rate after NST in patients with hormone receptor-positive malignancies [ 4 , 27 ]. A case of non-pCR is presented in Fig. 4 . In addition, there was only a small number of TNBC patients with triple-negative breast cancer in our study. Ki-67 is a nuclear antigen associated with cell proliferation and an important clinical marker for the classification, prognosis, and treatment of breast cancer [ 31 , 32 ]. Luminal A breast cancer with lower Ki-67 expression has a better prognosis and better therapeutic effect. Research has shown that a high Ki-67 proliferation index (> 14%) is related to poor histologic grades, and Ki-67 is also a recognised marker of tumour invasion and proliferation [ 6 ]. High Ki-67 expression has been shown to predict pCR prior to NST, but it is also associated with a higher risk of recurrence and a poorer prognosis [ 31 ]. This is consistent with our results showing a positive correlation between MP score and Ki-67 and a negative correlation between MRI tumour residue and Ki-67. This was a retrospective study, and a selection bias may have existed. The number of patients included in this study (n = 28) was relatively small, and larger validation studies are required to confirm our results. In addition, the HER2- group included no patients with pCR after surgery, which resulted in uneven data distribution; however, this may also reflect a better therapeutic effect of neoadjuvant chemotherapy combined with targeted therapy. Further studies with a larger sample size will help focus on research related to the early prediction of tumour treatment efficacy by combining DCE-MRI with histopathology. Conclusions Tumour occurrence and development is a systematic process of change, and changes in the microenvironment of the tumour and peritumoural region are valuable for tumour progression, invasion, infiltration and metastasis. Our study indicates that pretreatment DCE-MRI of the peritumoural region has value in predicting pCR. Therefore, attention should be paid to the significance of the peritumoural region in breast cancer invasion and prognosis. DCE-MRI detects these changes in a non-invasive manner prior to treatment, it is crucial for informing treatment decisions, improving prognostic accuracy, or advancing the understanding of breast cancer pathophysiology. The shrinkage pattern of the tumour after NST and the tumour residual status on preoperative MRI has important value in predicting pCR, contributing valuable insights for refining treatment strategies and improving patient prognoses. However, our findings should be considered preliminary and require further prospective investigation in studies with a larger patient cohort. Abbreviations DCE-MRI, dynamic contrast-enhanced magnetic resonance imaging; NST, neoadjuvant systemic therapy; pCR, pathological complete response; MP, Miller–Payne; ROI, region of interest; LNM, lymph node metastasis; ER, oestrogen receptor; PR, progesterone receptor; HER2, human epidermal growth factor receptor 2; IDC, invasive ductal carcinoma; K trans, transfer constant; T2WI, T2 weighted image; T1WI, T1 weighted image; FA, flip angle; GDPA, Gadopentetic Acid Dimeglumine Salt; Kep, flux rate constant; Ve, extravascular extracellular volume; Vp, capillary plasma volume; TIC, time-signal intensity curve; BPE, background parenchymal enhancement; Breast Imaging Reporting and Data System (BI-RADS); TN, triple-negative; LVI, lymphovascular invasion; HA, Hyaluronan; ADC, apparent diffusion coefficient; TNBC, triple-negative breast cancer; AUC, area under the curve; CI, confidence interval; HG, histologic grade Declarations Declaration of interest: The authors disclose no conflicts of interest. 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Scott Med J 68:121–128. https://doi.org/10.1177/00369330231174230 Park NJ-Y, Jeong JY, Park JY et al (2021) Peritumoral edema in breast cancer at preoperative MRI: an interpretative study with histopathological review toward understanding tumor microenvironment. Sci Rep 11:12992. https://doi.org/10.1038/s41598-021-92283-z Musall BC, Adrada BE, Candelaria RP et al (2022) Quantitative Apparent Diffusion Coefficients From Peritumoral Regions as Early Predictors of Response to Neoadjuvant Systemic Therapy in Triple-Negative Breast Cancer. J Magn Reson Imaging 56:1901–1909. https://doi.org/10.1002/jmri.28219 Braman N, Prasanna P, Whitney J et al (2019) Association of Peritumoral Radiomics With Tumor Biology and Pathologic Response to Preoperative Targeted Therapy for HER2 (ERBB2)–Positive Breast Cancer. JAMA Netw Open 2:e192561. https://doi.org/10.1001/jamanetworkopen.2019.2561 Gong C, Lin Q, Cen Y et al (2022) The ratio of PD1 + CD8 + T cells in stromal area of tumor tissue is associated with the effect of neoadjuvant chemotherapy in HER2 negative breast cancer patients. J Clin Oncol 40:e12626–e12626. https://doi.org/10.1200/JCO.2022.40.16_suppl.e12626 De Visser KE, Joyce JA (2023) The evolving tumor microenvironment: From cancer initiation to metastatic outgrowth. Cancer Cell 41:374–403. https://doi.org/10.1016/j.ccell.2023.02.016 Li C, Lu N, He Z et al (2022) A Noninvasive Tool Based on Magnetic Resonance Imaging Radiomics for the Preoperative Prediction of Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer. Ann Surg Oncol 29:7685–7693. https://doi.org/10.1245/s10434-022-12034-w Braman NM, Etesami M, Prasanna P et al (2017) Intratumoral and peritumoral radiomics for the pretreatment prediction of pathological complete response to neoadjuvant chemotherapy based on breast DCE-MRI. Breast Cancer Res 19:57. https://doi.org/10.1186/s13058-017-0846-1 Liu F, Wang M, Li H (2018) Role of perfusion parameters on DCE-MRI and ADC values on DWMRI for invasive ductal carcinoma at 3.0 Tesla. World J Surg Oncol 16:239. https://doi.org/10.1186/s12957-018-1538-8 Marinovich ML, Sardanelli F, Ciatto S et al (2012) Early prediction of pathologic response to neoadjuvant therapy in breast cancer: Systematic review of the accuracy of MRI. Breast 21:669–677. https://doi.org/10.1016/j.breast.2012.07.006 Drisis S, Metens T, Ignatiadis M et al (2016) Quantitative DCE-MRI for prediction of pathological complete response following neoadjuvant treatment for locally advanced breast cancer: the impact of breast cancer subtypes on the diagnostic accuracy. Eur Radiol 26:1474–1484. https://doi.org/10.1007/s00330-015-3948-0 Heacock L, Lewin A, Ayoola A et al (2020) Dynamic Contrast-Enhanced MRI Evaluation of Pathologic Complete Response in Human Epidermal Growth Factor Receptor 2 (HER2)-Positive Breast Cancer After HER2-Targeted Therapy. Acad Radiol 27:e87–e93. https://doi.org/10.1016/j.acra.2019.07.011 Kim TH, Kang DK, Yim H et al (2012) Magnetic Resonance Imaging Patterns of Tumor Regression After Neoadjuvant Chemotherapy in Breast Cancer Patients: Correlation With Pathological Response Grading System Based on Tumor Cellularity. J Comput Assist Tomogr 36. https://doi.org/10.1097/RCT.0b013e318246abf3 Park JY, Kim YS, Lee SE (2022) Breast MRI for Evaluating Residual Tumor Size Following NeoadjuvantChemotherapy: Clinicopathologic Factors and MRI Imaging Features Affectingits Accuracy. Curr Med Imaging Former Curr Med Imaging Reviews 18:876–882. https://doi.org/10.2174/1573405617666211117141057 Li C, Song L, Yin J (2021) Intratumoral and Peritumoral Radiomics Based on Functional Parametric Maps from Breast DCE-MRI for Prediction of HER-2 and Ki-67 Status. J Magn Reson Imaging 54:703–714. https://doi.org/10.1002/jmri.27651 Halvorsen OJ, Haukaas SA, Akslen LA Combined Loss of PTEN and p27 Expression Is Associated with Tumor Cell Proliferation by Ki-67 and Increased Risk of Recurrent Disease in Localized Prostate Cancer. Clin Cancer Res 9:1474–1479. https://doi.org/10.1002/jmri.27651 Statements & Declarations Additional Declarations No competing interests reported. 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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-3987208","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":274872775,"identity":"1bf22fa8-5913-4a1e-80e6-99fb62b3ed65","order_by":0,"name":"Xingrui Wang","email":"","orcid":"","institution":"Zhongshan People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xingrui","middleName":"","lastName":"Wang","suffix":""},{"id":274872776,"identity":"15e6946b-0226-4565-93cc-8462f83d042e","order_by":1,"name":"Xuehong Xiao","email":"","orcid":"","institution":"Zhongshan People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xuehong","middleName":"","lastName":"Xiao","suffix":""},{"id":274872777,"identity":"05064774-4a29-4f1d-97a1-40c803524f2e","order_by":2,"name":"Ang Yang","email":"","orcid":"","institution":"Zhongshan People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ang","middleName":"","lastName":"Yang","suffix":""},{"id":274872778,"identity":"74ff476b-e8be-40e3-8827-a477b18af460","order_by":3,"name":"Shuyan Zeng","email":"","orcid":"","institution":"Zhongshan People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Shuyan","middleName":"","lastName":"Zeng","suffix":""},{"id":274872779,"identity":"827e209d-9515-433d-b4c3-efef7ce78e58","order_by":4,"name":"Wenxi Chen","email":"","orcid":"","institution":"Zhongshan People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Wenxi","middleName":"","lastName":"Chen","suffix":""},{"id":274872780,"identity":"bf2cca78-7d5a-494f-a99b-f9b17f89ac48","order_by":5,"name":"Yi Chen","email":"","orcid":"","institution":"Zhongshan People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yi","middleName":"","lastName":"Chen","suffix":""},{"id":274872781,"identity":"9306b522-22ff-4e90-8614-76e4df211b8f","order_by":6,"name":"Shien Cui","email":"","orcid":"","institution":"Zhongshan People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Shien","middleName":"","lastName":"Cui","suffix":""},{"id":274872782,"identity":"24bed379-0de3-4332-a200-1faced112ff8","order_by":7,"name":"Zhihua Huang","email":"","orcid":"","institution":"Zhongshan People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zhihua","middleName":"","lastName":"Huang","suffix":""},{"id":274872783,"identity":"3409b268-d614-4d62-b2db-10e603b63936","order_by":8,"name":"Yumei Zeng","email":"","orcid":"","institution":"Zhongshan People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yumei","middleName":"","lastName":"Zeng","suffix":""},{"id":274872784,"identity":"6f8ebf64-bb55-4fd4-93e2-9ce45eb3d9db","order_by":9,"name":"Xiaoxing Huang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAy0lEQVRIiWNgGAWjYBAC+/PNxz984LGRY2NvIFbPjWNpjDNk0oz5eA4Qq+VAjhkzj83hRDmJBCJ1MDacMXvAk5OWwCb5eOMNhhqbaIJamJnbyg0kztjksUmnFVswHEvLbSCkhY3h8AYJw560YjbpHDMJxobDhLXwMCQYSCT+O5zYJnmGSC0SDClmEgd4gFokeIjUYiBxLNmwgSfNmI0H6JcEYvxiwN988PEfYFTKtx/eeONDjQ1hLag2JpCiHKKFVB2jYBSMglEwMgAAo7494xAQhIgAAAAASUVORK5CYII=","orcid":"","institution":"Zhongshan People's Hospital","correspondingAuthor":true,"prefix":"","firstName":"Xiaoxing","middleName":"","lastName":"Huang","suffix":""}],"badges":[],"createdAt":"2024-02-25 07:33:45","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3987208/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3987208/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":51775203,"identity":"5996836a-0707-40f1-a49f-3fa44d55c75f","added_by":"auto","created_at":"2024-02-28 20:36:00","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":60172,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart for study inclusion\u003c/p\u003e","description":"","filename":"Fig11.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3987208/v1/64330e2ed7d394adb8ef3adf.jpg"},{"id":51774651,"identity":"00134628-7649-43f8-bc27-7fb35ed75b7b","added_by":"auto","created_at":"2024-02-28 20:28:00","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":201453,"visible":true,"origin":"","legend":"\u003cp\u003eDCE-MRI analysis and pathology A 50-year-old woman with left breast invasive ductal carcinoma (grade II) and ER-, PR-, HER2 3+ and Ki-67 50%. A: Dynamic enhancement image 1 min after GDPA injection; B–E: pharmacokinetic parameter maps for Ktrans, Kep, Ve and Vp, obtained by establishing a pharmacokinetic model; F–G: enhancement images after two treatment courses and prior to operation; H: surgical pathological image. ROI1: Horizontal region of maximum tumour diameter; ROI2: the junction of the tumour and normal gland, with an area of 50 mm2; ROI3: around the tumour, with an area of 30 mm2. The pathology of unilateral simple mastectomy and axillary anterior sentinel lymph node biopsy revealed that G5 of MP without sentinel lymph node metastasis.\u003c/p\u003e","description":"","filename":"Fig21.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3987208/v1/981956e25a39be3ea82383c0.jpg"},{"id":51774648,"identity":"21851894-fea6-40ec-accd-7cd3bdcf9b7b","added_by":"auto","created_at":"2024-02-28 20:28:00","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":37430,"visible":true,"origin":"","legend":"\u003cp\u003eDCE-MRI analysis and pathology A 59-year-old woman with left breast invasive ductal carcinoma (grade II) and ER+, PR+, HER2 1+ and Ki-67 10%. A: Dynamic enhancement image 1 min after GDPA injection; B–E: the pharmacokinetic parameter maps for Ktrans, Kep, Ve and Vp, obtained by establishing a pharmacokinetic model; F–G: enhancement images after two treatment courses and prior to operation; H: surgical pathological images. ROI1: Horizontal region of maximum tumour diameter; ROI2: the junction of the tumour and normal gland, with an area of 50 mm2; ROI3: around the tumour, with an area of 30 mm2. The pathology of unilateral simple mastectomy and axillary anterior sentinel lymph node biopsy revealed that G3 of MP with sentinel lymph node metastasis.\u003c/p\u003e","description":"","filename":"Fig3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3987208/v1/137ac0a693af786f793e77d1.jpg"},{"id":51774650,"identity":"0f95cb8e-f137-445f-b136-65e7cca48f04","added_by":"auto","created_at":"2024-02-28 20:28:00","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":203024,"visible":true,"origin":"","legend":"\u003cp\u003eComparisons of pharmacokinetic parameters between pCR and non-pCR Kep (r=0.546) and Ve (r=0.427) in ROI3 were significantly different between pCR and non-pCR in the peritumoural region. Spearman correlation was used. *P\u0026lt;0.05\u003c/p\u003e","description":"","filename":"Fig4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3987208/v1/47641b39f2a95a749128f7c6.jpg"},{"id":52111708,"identity":"00d400de-e8aa-41dc-ad95-5b23328db872","added_by":"auto","created_at":"2024-03-06 23:08:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":680574,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3987208/v1/ce658d88-582a-436c-a3f0-8fec6c71cd94.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Quantitative DCE-MRI for prediction of pathological complete response prior to neoadjuvant systemic therapy","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe annual incidence rate of breast cancer is rising, reaching 31% among women. However, due to advancements in early screening and improved treatment options, the overall mortality rate associated with breast cancer is decreasing\u0026nbsp;[1]. The application of neoadjuvant systemic therapy (NST) can improve clinical tumour staging of tumours before surgery, resection, and breast-conserving surgery rates, and reduce the risk of tumour recurrence, providing a new approach to improve patient prognosis[2, 3]. Experts in the field highlight the significance of the pathological complete response (pCR) following NST as a guide for subsequent adjuvant therapy[2]. The presence of residual tumour is an adverse risk factor associated with poor prognosis[4]. Early prediction of poor response to treatment is crucial for timely adjustment of poor response to treatment is crucial for timely adjustment of treatment programmes and mitigating the increased risk of tumour recurrence due to inadequate treatment\u0026nbsp;[5]. Predicting pCR prior to NST may facilitate the selection of a suitable treatment regimen for patients at an earlier stage, and it improves the probability of pCR as well as prognosis. Therefore, the prediction of pCR prior to NST is clinically valuable.\u003c/p\u003e\n\u003cp\u003eDynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is widely used for preoperative evaluation of breast cancer due to its high sensitivity. DCE-MRI can provide valuable insights into the tumour morphology, dynamics, and perfusion parameters crucial for post-NST pCR prognosis of breast cancer\u0026nbsp;[6]. Yu et al. demonstrated that changes in the transfer constant, K\u003csub\u003etrans\u003c/sub\u003e,\u0026nbsp;following two NST courses can predict the post-treatment response in tumours\u0026nbsp;[7]. A recent meta-analysis demonstrated that DCE-MRI can be considered an effective method for dynamic monitoring of treatment response following NST in patients with breast cancer patients and shows promising predictive value for pCR\u0026nbsp;[8].\u0026nbsp;The topic of exploring the correlation between quantitative DCE-MRI-derived tumour characteristics before NST and pCR in patients with breast cancer is crucial, along with the increasing significance of personalized treatment strategies in oncology. An in-depth study of breast cancer in terms of imaging functionality prior to treatment will contribute to a comprehensive understanding of the nature of the tumour and will also advance the exploration of the relationship among breast cancer radiology, histopathology, and clinical prognosis.\u0026nbsp;However, the specific utility of pre-NST DCE-MRI in predicting pCR remains unclear and requires further investigation.\u003c/p\u003e\n\u003cp\u003eThe present study aimed to evaluate the correlation between pCR and Miller\u0026ndash;Payne (MP) scores with various DCE-MRI-derived pharmacokinetic parameters, imaging characteristics, histological grading, lymph node metastasis (LNM); expression of markers including oestrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), and Ki-67 prior to NST; tumour shrinkage patterns following two NST courses; and residual tumour assessed by preoperative MRI. The study explored the feasibility of MRI in predicting pCR before treatment to provide useful insights for improving the rational formulation of breast cancer treatment and assessment programmes.\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003ch2\u003eStudy population\u003c/h2\u003e\n\u003cp\u003eThis study was approved by the institutional review committee. The requirement to obtain informed patient consent was waived due to the likelihood of traceability. A total of 120 randomly selected female patients, admitted with breast neoplasms between April 2021 and December 2022, were hospitalised for initial MRI and DCE examinations. Basic patient information including age, menopausal status, and clinical tumour staging; biopsy and surgical pathological results; MRI and DCE data were retrospectively collected.\u003c/p\u003e\n\u003cp\u003eThe study inclusion criteria were as follows: patients diagnosed with invasive ductal carcinoma (IDC) without a history of breast surgery or other tumours; routine MRI and DCE-MRI scans before treatment; those who received NST and underwent MRI examination after two treatment courses and prior to operation; surgical treatment following NST; complete biopsy and surgical pathology assessments, including immunohistochemical results and LNM; and complete MRI imaging, in line with conventional diagnostic requirements. Patients not meeting the above inclusion criteria were excluded from the study (Fig. 1).\u003c/p\u003e\n\u003ch2\u003eMRI acquisition\u003c/h2\u003e\n\u003cp\u003eDCE-MRI was performed using a 3.0-T) scanner (Achieva TX, Philips, Netherlands) with a 4-channel breast coil for data acquisition. Patients were placed in a prone position for imaging, and their breasts were naturally suspended within the breast coil. The arms were placed on both sides of the head. Horizontal axis fat suppression, fast-spin echo sequence, T2- and T1-weighted images, and multiphase dynamic enhanced DCE scans were performed separately.\u0026nbsp;DCE sequence used 3D-T1 weighted sequence axial scanning, and T1 mapping images with flip angles (FAs) of 5\u0026deg; and 15\u0026deg; were obtained for the calculation of T1 maps with scan times of 58 s and 52 s, respectively. The other scan parameters were consistent with dynamic enhancement. After 1 min of dynamic enhanced scanning, Gadopentetic Acid Dimeglumine Salt Injection (GDPA)\u0026nbsp;(Magnevist, Bayer AG, Germany) at a 0.1 mmol/kg dosage was administered as an intravenous bolus injection using an MR-compatible power injector (Spectris, Bayer HealthCare, Germany) at a flow rate of 2.0 mL/s, followed by a 20-mL saline flush. Continuous scanning was performed 25 times, with a single and total time of 15.5 s and 7 min 4 s, respectively. DCE parameters were as follows: repetition time (TR)=shortest; echo time (TE)=shortest; flip angle=10\u0026deg;; number of signal average (NSA)=1; layer thickness=4 mm; layer thickness=0 mm; and matrix=340\u0026times;340.\u003c/p\u003e\n\u003ch2\u003eData analysis\u003c/h2\u003e\n\u003cp\u003eImage post-processing\u003c/p\u003e\n\u003cp\u003eDCE permeability analysis was performed by an experienced radiologist with 5 years of experience in breast MR imaging. Specialised quantitative analysis software was used in the Philips workstation. Pharmacokinetic parameters, such as Ktrans, flux rate constant (Kep), extravascular extracellular volume (Ve), and capillary plasma volume (Vp), were obtained by establishing a pharmacokinetic model.\u003c/p\u003e\n\u003cp\u003eRegion of interest (ROI) drawing method\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe maximum dimension of the tumour on a DCE-MRI image, 1 min following GDPA injection (avoiding obvious necrosis, haemorrhage, and cystic changes), and drawing a time-signal intensity curve (TIC), was referred to as ROI1. ROI2 was delineated at the junction of the tumour and normal gland, with an area of 50 mm\u003csup\u003e2\u003c/sup\u003e,\u003csup\u003e\u0026nbsp;\u003c/sup\u003eand ROI3 was delineated around the tumour, with an area of 30 mm\u003csup\u003e2\u0026nbsp;\u003c/sup\u003e(Fig. 2). DCE-MRI pharmacokinetic parameters were determined separately.\u003c/p\u003e\n\u003cp\u003eImage analysis\u003c/p\u003e\n\u003cp\u003eBackground parenchymal enhancement (BPE) and tumour morphological features, including tumour type, location, quantity, margin, maximum diameter, and enhanced morphology, were recorded based on pretreatment MRI images. Tumour shrinkage patterns were recorded based on MRI images after two courses of NST. The preoperative MRI tumour residual status was recorded based on MRI images after NST completion.\u003c/p\u003e\n\u003cp\u003eBPE was divided into four types according to the 2016 American College of Radiology Breast Imaging Reporting and Data System, with the following features: A: almost no enhancement; B: mild background enhancement; C: moderate background enhancement and D: severe background enhancement. The tumour types were classified as enhanced lump or non-lump-enhanced groups; the number of tumours as single or multiple; and tumour margins as clear or indistinct. The maximum tumour diameter was recorded from three directions. Tumour enhancement patterns were classified as homogeneous or heterogeneous. The tumour shrinking pattern was defined as tumour regression on MRI compared to that at baseline after two courses of NST. Tumour regression was classified into 6 categories [9]: 0 (complete imaging response), I (concentric shrinkage), II (fragmentation), III (diffuse contrast enhancement), IV (stable disease), and V (progressive disease). The preoperative MRI residual tumour status was defined as the evaluation of the presence of a tumour based on MRI after treatment completion.\u003c/p\u003e\n\u003ch2\u003ePathological results\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eResults of postoperative pathology and immunohistochemistry from pathological sections were obtained by a professional pathologist, and the breast cancer molecular subtypes, LNM, and MP scores were determined.\u003c/p\u003e\n\u003cp\u003eAccording to the breast cancer clinical treatment guidelines (2022), breast cancer was classified into four subtypes based on marker expression: Lumina A: ER positive, PR positivity \u0026ge;20%, HER2 negative, Ki-67 \u0026le;14%; Lumina B: ER and/or PR positive, HER2 negative, and Ki-67 \u0026gt;14% or low PR expression \u0026le;20%; HER2 overexpression type (HER2+): immunohistochemical staining for 3+ or 2+ with fluorescent in situ hybridization detection for gene amplification; triple-negative: ER, PR, HER2 negative.\u003c/p\u003e\n\u003cp\u003eER and PR were divided into high (\u0026gt;10%) or low (\u0026le;10%) expression group; Ki-67 was divided into high (\u0026gt;20%) or low (\u0026le;20%) expression group; and HER2 and LNM were divided into positive or negative groups.\u003c/p\u003e\n\u003cp\u003eAccording to the MP scoring system, the level of cell abundance of tumour residuals after NST was classified into five grades: Grade 1 (G1): infiltrating cancer cells show no changes or only a few cancer cells show changes, and the overall number of cancer cells remains unchanged; Grade 2 (G2): infiltrating cancer cell numbers are slightly reduced, but the total number is still high, and the reduction of cancer cells does not exceed 30%; Grade 3 (G3): infiltrating cancer cell numbers range from 30% to 90%; Grade 4 (G4): infiltrating cancer cell numbers are significantly decreased by over 90%, with only scattered small clusters or individual cancer cells remaining; Grade 5 (G5): no infiltrating cancer cells in the tumour bed, but in situ ductal carcinoma may exist. Residual cancer burden was divided into high- and low-degree groups.\u003c/p\u003e\n\u003cp\u003epCR was defined as the complete disappearance of intramuscular cancer cells in the breast; although in situ cancer components maybe present, there were no tumour cells in the axillary lymph nodes [10].\u003c/p\u003e\n\u003ch2\u003eStatistical analysis\u003c/h2\u003e\n\u003cp\u003eThe data were assessed by testing normality, and data conforming to the normal distribution were expressed as \u003cimg width=\"33\" src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAEIAAAAqCAIAAACIkRqIAAAAAXNSR0IArs4c6QAAAAlwSFlzAAAdhwAAHYcBj+XxZQAAAi1JREFUaEPtVztygzAQhZwFu8j4BHACnD5tOijtA+QQUJrObSo3gROEE3hcRNxFWf2wwGYCrGBsRmrs0WhX+97u2xUupdR5/vXy/BAYAgvjkfJos2GzYZwBW1TGKUU4tNlAkGfc1GbDOKUIh3Nmo0oD13XjAhFul2kNo4jhit5rklgQ8GoY4QGe7L3XIUTcOYHpnEU1QfjK5eJgVGnMFOi6QVppvFWF2G7uTkhst2sWighRriAI4lR0DCEHkvh+Qvgv7PG/fOWR8hrlvYXTdVA4H+koj0RkuQyN5NybdCdgwBnpvYGDgWCQFEgkEASMNr2Ke8W4zMY1QME/gOKWI6nTstiv/K75v0+VgNEdzo3EV6/M4HyKP/ZOQh6lsXrrDYORbYO00JVbk3SDXgLXBIKspdocUVRCvjJoP6q1q3x3Ntzoc+f1K4iZTnm7H5A1E3qZ7VftztmmWvWm/6p1RIpw2VAXEtGz9H5KaTMb8HjbnhPeysoLmYnmgdd44eHI66v8+q5losMo4hXI+rgLuZ6y0xRP0YExw3H2Lm69RL23d6UT6e+FHWMTmiUi8wEEKEJ2q18BFmZnY64PjwRlQS7lDaVsz3E266t67w5qbdoYGn3yqTB8iqsGFanxTUEcbWlQRx6DKa4eIGquCx219kcoG9lwoT1Bf/K1KvKh4zajpS5cgsr5Yxgv7qFulNZhn8T4z4CFZMNqw2gVIp0tpKgsDGQdGDW32TBKJ9LZH3zB9KrIzXsvAAAAAElFTkSuQmCC\" alt=\"image\"\u003e. Data that did conform to normal distribution were expressed as the median. Relevance of pCR, MP, basic patient information, pathology, MRI characteristics of the tumours, and DCE-MRI pharmacokinetic parameters were assessed using Spearman\u0026rsquo;s correlation test. All statistical analyses were conducted using the SPSS version 25 statistical software package (SPSS Inc., Chicago, IL, USA) and GraphPad Prism version 8.0 (GraphPad Software, Boston, Massachusetts USA). P\u0026lt;0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003ePatient characteristics\u003c/h2\u003e \u003cp\u003eClinical details and tumour characteristics of the patients are summarised in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The study included 28 participants (mean age: 51\u0026thinsp;\u0026plusmn;\u0026thinsp;8 years, range 32\u0026ndash;65 years), including one patients with bilateral breast cancer and the rest with unilateral breast cancer. All patients had cancers corresponding to the following specific molecular subtypes: Lumina A: 2 cases (2%), Lumina B: 8 cases (27%), triple-negative: 2 cases (7%), HER2+: 17 cases (59%), pCR: 9 cases (31%), and no pCR: 20 cases (69%).\u003c/p\u003e \u003cp\u003eThe NST plan consisted of all patients undergoing neoadjuvant chemotherapy, and HER2\u0026thinsp;+\u0026thinsp;patients receiving targeted therapy containing pertuzumab with or without trastuzumab.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePatient and tumour characteristics between pCR and non-pCR.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eStudy Sample\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;29)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003ePathologic Response\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003epCR(n\u0026thinsp;=\u0026thinsp;9)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNon-pCR(n\u0026thinsp;=\u0026thinsp;20)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51\u0026thinsp;\u0026plusmn;\u0026thinsp;8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58\u0026thinsp;\u0026plusmn;\u0026thinsp;8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50\u0026thinsp;\u0026plusmn;\u0026thinsp;9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.432\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMenopausal status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.979\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePremenopausal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16(55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5(56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11(55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePostmenopausal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13(45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4(44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9(45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical tumor stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.116\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24(83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6(67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18(90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3 or T4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5(17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3(33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2(10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical nodal stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.385\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5(17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4(21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN1, N2, or N3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24(83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9(90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15(79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.236\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16(55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3(33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e 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colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.258\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;10%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18(62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7(78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11(55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;10%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11(38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2(22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9(45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKi67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.258\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;20%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11(38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2(22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9(45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;20%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18(62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7(78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11(55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHER2+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17(59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9(100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8(40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLumina A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2(7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2(10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0343\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLumina B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8(27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8(40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.026*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThree Negative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2(7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2(10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.343\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidual cancer burden\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow(G5 or G4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12(41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9(100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3(15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh(G1 to G3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17(59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17(85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymph node metastasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20(69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5(56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15(75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.312\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9(31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4(44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5(25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003csup\u003e1\u003c/sup\u003e Data presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation. *P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **P\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eFactors influencing pCR and MP\u003c/h3\u003e\n\u003cp\u003ePretreatment MR characteristics and post-treatment MR characteristics are summarised in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. pCR correlated with HER2 status (r\u0026thinsp;=\u0026thinsp;0.564), tumour shrinkage pattern (r=-0.506), tumour residual status on preoperative MRI (r=-0.551), Kep (r\u0026thinsp;=\u0026thinsp;0.546), and Ve (r\u0026thinsp;=\u0026thinsp;0.427) of ROI3 in the peritumoural region (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eNo correlation was observed between pCR and ER, PR status, BPE, tumour morphological characteristics, enhancement mode, TIC, pharmacokinetic parameters of ROI1 and ROI2, histological grading, or LNM (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eMP scores correlated with PR (r=-0.376), HER2 status (r\u0026thinsp;=\u0026thinsp;0.608), Ki-67 (r\u0026thinsp;=\u0026thinsp;0.393), tumour shrinkage pattern (r=-0.625), and tumour residual status on preoperative MRI (r=-0.715) (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eNo correlation was observed between MP and ER status, BPE, tumour morphological characteristics, enhancement mode, TIC, DCE pharmacokinetic parameters, histological grade, or LNM (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of pretreatment MRI characteristics and post-treatment MRI characteristics between pathological complete response (pCR) and non-pCR.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eStudy Sample\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;29)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003ePathologic Response\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003epCR(n\u0026thinsp;=\u0026thinsp;9)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNon-pCR(n\u0026thinsp;=\u0026thinsp;20)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBPE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA or B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21(72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5(56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16(80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC or D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8(28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4(44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4(20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSize\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38.3(28.5,46.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43.7(32.7,56.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34(28.4,44.025)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.192\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.571\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17(59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6(67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11(55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeft\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12(41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3(33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9(45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLesion type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.369\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emass\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19(66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7(78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12(60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enonmass\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10(34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2(22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8(40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnhancement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.395\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHomogeneous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4(14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2(22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2(10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeterogeneous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25(86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7(78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18(90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of lesions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.203\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11(38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5(56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6(30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMultiple\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18(62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4(44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14(70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMargin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.642\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11(38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4(44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7(35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnclear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18(62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5(56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13(65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShrinkage pattern\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.005**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7(24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5(56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2(10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11(38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3(33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8(40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6(21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5(25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2(7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2(10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3(10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3(15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumour regression\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18(62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2(22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16(80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11(38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7(78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4(20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTIC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.170\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3(10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2(22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1(5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24(83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7(78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17(85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2(7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2(10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eBPE\u0026thinsp;=\u0026thinsp;Background parenchymal enhancement, TIC\u0026thinsp;=\u0026thinsp;time-signal intensity curve. \u003csup\u003e1\u003c/sup\u003e Data presented as median (P25, P75). \u003csup\u003ea\u003c/sup\u003e Shrinkage pattern represents the shrinkage pattern on MRI of tumours after two courses of NST, \u003csup\u003eb\u003c/sup\u003e Tumour regression represents the presence of the tumour evaluated using MRI after treatment. **P\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eFactors influencing tumour residual status on preoperative MRI\u003c/h3\u003e\n\u003cp\u003ePreoperative MRI tumour residual status correlated with Ki-67 expression (r=-0.465) and tumour shrinkage pattern (r\u0026thinsp;=\u0026thinsp;0.677) (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). No correlation was observed between preoperative MRI tumour residual status and ER, PR, HER2 status, BPE, tumour morphological characteristics, enhancement mode, TIC, DCE-MRI pharmacokinetic parameters, histological grading, or LNM, (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur study suggests that peritumoural DCE-MRI contributes to the prediction of pCR before NST. Our results showed that pCR positively correlated with Kep and Ve in ROI3 in the peritumoural region. This could account for the fact that patients who achieved pCR after NST had higher Kep and Ve in the peritumoural region, indicating higher vascular permeability and a larger intracellular intravascular gap in the region. Researchers discovered that certain patients with breast cancer developed oedema in the peritumoural region, and studies found that peritumoural oedema (PE) was linked to tumour invasive characteristics, such as large tumour volume, lymphovascular invasion (LVI), high Ki-67 index, poor prognosis, low distant metastasis-free survival, and overall survival [\u003cspan additionalcitationids=\"CR12 CR13 CR14\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Hyaluronan (HA) is an important glycosaminoglycan present in the pericellular and extracellular matrix; HA can facilitate tissue hydration. Both inflammatory disease development and malignant tumour progression are associated with increased HA. Kettunen et al. reported a positive correlation between the peritumoural-tumour-apparent diffusion coefficient (ADC) ratio, axillary lymph node positivity, and HA accumulation, suggesting that the peritumoural-tumour-ADC ratio may be an imaging indicator to identify invasion and prognosis of breast cancer [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Pekoz et al. [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] reported that the development of oedema surrounding the breast cancer was related to LVI on MRI in 105 patients with invasive breast cancer, prior to NST. Park et al. [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] reported that patients with PE have higher LVI levels and vascular fibrosis and suggested that PE is related to the tumour microenvironment. A study conducted by Musall et al. [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] employing quantitative ADC measurements to assess PE characteristics and predict treatment response based on a multivariate model analysis in 108 patients with triple-negative breast cancer reported that the PE of both fat-inclusive (area under the curve [AUC]\u0026thinsp;=\u0026thinsp;0.66) and fat-exclusive peritumoural regions (AUC\u0026thinsp;=\u0026thinsp;0.64) could predict pCR following NST with low performance. Due to the rapid development of radiomics in recent years, there has been a considerable increase in research focusing on radiomics within peritumoural regions of breast cancer. A study by Braman et al. [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] suggested that characteristics of the peritumoural region (9\u0026ndash;12 mm from the tumour) can improve distinction of the HER2 status (maximum AUC: 0.85; 95% confidence interval [CI]: 0.79\u0026ndash;0.90). The researchers also reported a significant correlation between the radiomics characteristics of the peritumoural region (0\u0026ndash;3 mm) and density of tumour infiltrating peripheral lymphocytes (r\u0026thinsp;=\u0026thinsp;0.57; 95% CI: 0.39\u0026ndash;0.75; P\u0026thinsp;=\u0026thinsp;0.002). The proportion of tumour-infiltrating lymphocytes has been reported to be an independent predictor of pCR in breast cancer [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Moreover, local tumour invasion and changes in the tumour microenvironment influence tumour metastasis and poor prognosis [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Therefore, metabolic changes in the peritumoural region may affect the therapeutic efficacy and prognosis of tumours. While the intratumoural radiomic characteristics of HER2\u0026thinsp;+\u0026thinsp;alone may not distinguish pCR (AUC\u0026thinsp;=\u0026thinsp;0.66; 95% CI: 0.43\u0026ndash;0.88; P\u0026thinsp;=\u0026thinsp;0.08), a combination with intratumoural and peritumoural features helps successful pCR prediction (maximum AUC\u0026thinsp;=\u0026thinsp;0.80; 95% CI: 0.61\u0026ndash;0.98; P\u0026thinsp;=\u0026thinsp;0.003); with an accuracy, sensitivity, and specificity of 79%, 94%, and 58%, respectively [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Li et al. [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] constructed a model based on pre-NST MRI of the tumour and peritumoural volumes to identify pCR. Their study showed that while the tumour and peritumoural volume of interest radiomics classifiers independently had the potential to predict pCR, a radiomics model based on the tumour (AUC\u0026thinsp;=\u0026thinsp;0.92) combined with the peritumoural (AUC\u0026thinsp;=\u0026thinsp;0.98) volume of interest could further improve the prediction accuracy. Based on pre-NST DCE images combined with intratumoural and peritumoural radiomics methods, Braman et al. [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] demonstrated that pCR could be successfully predicted with or without receptor binding. Although all the above studies demonstrated the value of peritumoural region prior to NST and its potential for the successful prediction of pCR using imaging, these studies did not use DCE-MRI pharmacokinetic parameters for quantitative evaluation.\u003c/p\u003e \u003cp\u003eDCE-MRI is a non-invasive diagnostic method that has been developed in recent years to evaluate the tumour microenvironment. This technique reflects the blood flow characteristics, microvascular permeability, vascular density, tissue oxygen concentration, and tissue metabolic levels. Liu et al. [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] used a multiple comparison method to analyse the relationship between DCE-MRI and prognostic factors in 151 patients with invasive ductal carcinoma [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The study identified Ktrans as the best predictive indicator of histologic grade, LNM, ER, PR, and Ki-67 expression. Kep and Ve can both predict Ki-67 expression, indicating that DCE-MRI is valuable for predicting tumour invasiveness and prognosis. Previous studies have confirmed the significance of DCE-MRI in predicting the post-NST response, and Ktrans is reported to have a high sensitivity and specificity in predicting pCR [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. A retrospective study analysing DCE-MRI images of 84 patients with locally advanced breast cancer who received NST, reported that the maximum tumour diameter and Ktrans could predict pCR both before and following treatment; Ve could predict pCR following treatment, among which the AUC predicted by the DCE-MRI dynamic parameters was between 0.66 and 0.79 [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Our results showed that there was no correlation between the DCE-MRI parameters of tumours and pCR, which may be attributed to the heterogeneity of breast cancer, relatively small sample size, and differences in research methods and samples. Further research will be required to gain better insight into these aspects. However, the results of this study suggested that alterations in pretreatment DCE-MRI-derived parameters in the peritumoural region can inform therapeutic decision-making and help to further optimize the choice of tumour treatment regimen.\u003c/p\u003e \u003cp\u003eThe results from our study showed that pCR was associated with tumour shrinkage patterns after NST, and these results are consistent with the findings of Heacock et al. [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], who reported a correlation of pCR after NST with concentric tumour shrinkage in HER2\u0026thinsp;+\u0026thinsp;breast cancer. Kim et al. [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] reported that concentric tumour shrinkage was more common in an MP 3\u0026ndash;5 pathological response group, following tumour treatment. However, studies by Reis et al. [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] suggest that fragmentation was a better treatment response, and there was a moderate correlation between MRI findings and the pathological maximum diameter [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The study also indicated that standardisation of tumour shrinkage models could, therefore, provide valuable information for clinical practice. Our results suggested a correlation between preoperative MRI residual tumour status and tumour shrinkage patterns, consistent with the findings of Park et al. [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. They studied the MRI features of 109 patients with preoperative DCE-MRI and NST and found that size of the tumour with centripetal contraction after NST showed a smaller difference between MRI and pathology findings. Compared with other subtypes, ER positive/HER2 negative breast cancer MRI measurements tended to underestimate the size of the residual tumour; however, the size of the residual tumour on MRI was highly consistent with the pathology (intraclass correlation coefficient\u0026thinsp;=\u0026thinsp;0.808, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Our results showing a positive correlation between pCR, MP, and preoperative MRI tumour residual status are consistent with these findings. These findings suggest the potential of early MRI evaluation in predicting treatment outcomes.\u003c/p\u003e \u003cp\u003eIn our study, patients in the HER2- group did not achieve pCR, which may be related to the low response rate after NST in patients with hormone receptor-positive malignancies [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. A case of non-pCR is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. In addition, there was only a small number of TNBC patients with triple-negative breast cancer in our study. Ki-67 is a nuclear antigen associated with cell proliferation and an important clinical marker for the classification, prognosis, and treatment of breast cancer [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Luminal A breast cancer with lower Ki-67 expression has a better prognosis and better therapeutic effect. Research has shown that a high Ki-67 proliferation index (\u0026gt;\u0026thinsp;14%) is related to poor histologic grades, and Ki-67 is also a recognised marker of tumour invasion and proliferation [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. High Ki-67 expression has been shown to predict pCR prior to NST, but it is also associated with a higher risk of recurrence and a poorer prognosis [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. This is consistent with our results showing a positive correlation between MP score and Ki-67 and a negative correlation between MRI tumour residue and Ki-67.\u003c/p\u003e \u003cp\u003eThis was a retrospective study, and a selection bias may have existed. The number of patients included in this study (n\u0026thinsp;=\u0026thinsp;28) was relatively small, and larger validation studies are required to confirm our results. In addition, the HER2- group included no patients with pCR after surgery, which resulted in uneven data distribution; however, this may also reflect a better therapeutic effect of neoadjuvant chemotherapy combined with targeted therapy. Further studies with a larger sample size will help focus on research related to the early prediction of tumour treatment efficacy by combining DCE-MRI with histopathology.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eTumour occurrence and development is a systematic process of change, and changes in the microenvironment of the tumour and peritumoural region are valuable for tumour progression, invasion, infiltration and metastasis. Our study indicates that pretreatment DCE-MRI of the peritumoural region has value in predicting pCR. Therefore, attention should be paid to the significance of the peritumoural region in breast cancer invasion and prognosis. DCE-MRI detects these changes in a non-invasive manner prior to treatment, it is crucial for informing treatment decisions, improving prognostic accuracy, or advancing the understanding of breast cancer pathophysiology. The shrinkage pattern of the tumour after NST and the tumour residual status on preoperative MRI has important value in predicting pCR, contributing valuable insights for refining treatment strategies and improving patient prognoses. However, our findings should be considered preliminary and require further prospective investigation in studies with a larger patient cohort.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eDCE-MRI, dynamic contrast-enhanced magnetic resonance imaging; NST, neoadjuvant systemic therapy; pCR, pathological complete response; MP, Miller\u0026ndash;Payne;\u0026nbsp;ROI, region of interest;\u0026nbsp;LNM, lymph node metastasis;\u0026nbsp;ER, oestrogen receptor; PR, progesterone receptor; HER2, human epidermal growth factor receptor 2; IDC, invasive ductal carcinoma;\u0026nbsp;K\u003csub\u003etrans,\u003c/sub\u003e transfer constant; T2WI, T2 weighted image; T1WI, T1 weighted image; FA, flip angle; GDPA, Gadopentetic Acid Dimeglumine Salt; Kep, flux rate constant; Ve, extravascular extracellular volume; Vp, capillary plasma volume; TIC, time-signal intensity curve; BPE, background parenchymal enhancement; Breast Imaging Reporting and Data System (BI-RADS); TN, triple-negative; LVI, lymphovascular invasion; HA, Hyaluronan; ADC, apparent diffusion coefficient; TNBC, triple-negative breast cancer; AUC, area under the curve; CI, confidence interval; HG, histologic grade\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eDeclaration of interest:\u003c/h2\u003e \u003cp\u003eThe authors disclose no conflicts of interest.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eEthics approval:\u003c/h2\u003e \u003cp\u003eThis study was approved by the institutional review committee. The requirement to obtain informed patient consent was waived due to the likelihood of traceability.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eH.XX. provided methodology and funding acquisition; H.XX. and X.XH. were in charge of supervision, and project administration; W.XR. provided data curation, writing main manuscript text and figures visualization; Y.A. provided software support; Z.SY. and C.WX. offered data collection; C.Y. , C.SE. , H.ZH. and Z.YM. provided data support.\u003c/p\u003e\u003ch2\u003eData availability statements:\u003c/h2\u003e \u003cp\u003eAll relevant data are within the paper.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSiegel RL, Miller KD, Wagle NS, Jemal A (2023) Cancer statistics, 2023. 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Clin Cancer Res 9:1474\u0026ndash;1479. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/jmri.27651\u003c/span\u003e\u003cspan address=\"10.1002/jmri.27651\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStatements \u0026amp; Declarations\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Dynamic contrast-enhanced magnetic resonance imaging, Breast cancer, Neoadjuvant systemic therapy, Pathological complete response, Peritumoural region, Response patterns","lastPublishedDoi":"10.21203/rs.3.rs-3987208/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3987208/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eTo explore the correlation between quantitative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI)-derived tumour characteristics prior to neoadjuvant systemic therapy (NST) and pathological complete response (pCR) in patients with breast cancer patients.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eAmong 120 randomly selected patients with breast neoplasms, 28 diagnosed with invasive ductal carcinoma underwent NST. All patients underwent at least three MRI examinations: preoperative and before and after NST. Spearman correlation analysis was used to assess the correlation between pCR and Miller\u0026ndash;Payne (MP) scores with pharmacokinetic parameters (K\u003csub\u003etrans\u003c/sub\u003e, K\u003csub\u003eep\u003c/sub\u003e, V\u003csub\u003ee\u003c/sub\u003e, V\u003csub\u003ep\u003c/sub\u003e) in the regions of interest (ROI) in the tumour (ROI1), tumoural junction with the normal gland (ROI2), peritumoural region (ROI3), and background parenchymal enhancement; tumour morphological characteristics (type, location, quantity, margin, and maximum diameter); enhancement or shrinkage mode; and residual condition following preoperative MRI.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA positive correlation was observed between pCR and tumour HER2 expression (r\u0026thinsp;=\u0026thinsp;0.546); and K\u003csub\u003eep\u003c/sub\u003e (r\u0026thinsp;=\u0026thinsp;0.427) and V\u003csub\u003ee\u003c/sub\u003e of ROI3 (r\u0026thinsp;=\u0026thinsp;0.564) (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). A negative correlation between pCR, tumour shrinkage pattern (r=-0.506) and residual tumours (r=-0.551) was observed by preoperative MRI (r=-0.551) (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). MP associated with progesterone receptor (r=-0.37), HER2 (r\u0026thinsp;=\u0026thinsp;0.608), and Ki-67 (r\u0026thinsp;=\u0026thinsp;0.393) expression; tumour shrinkage pattern (r=-0.625); and preoperative MRI residual tumour (r=-0.715) (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Preoperative MRI tumour residual status associated with Ki-67 (r=-0.465) and tumour shrinkage pattern (r\u0026thinsp;=\u0026thinsp;0.677) (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eA correlation was observed between DCE-MRI of the peritumoural region prior to NST and pCR. Early MRI evaluation of tumour shrinkage patterns following NST and preoperative tumour residual status showed predictive value for pCR and tumour burden.\u003c/p\u003e","manuscriptTitle":"Quantitative DCE-MRI for prediction of pathological complete response prior to neoadjuvant systemic therapy","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-28 20:27:55","doi":"10.21203/rs.3.rs-3987208/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"55ec6c7b-e6c6-42db-a66e-966da16a84f8","owner":[],"postedDate":"February 28th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-03-06T23:00:04+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-28 20:27:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3987208","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3987208","identity":"rs-3987208","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}