Predictors of Pathological Complete Response to Neoadjuvant Chemotherapy in Iranian Breast Cancer Patients

TP53 Gene Polymorphism at Codon 72 as a Response Predictor for Neoadjuvant Chemotherapy

Introduction

Breast cancer is the most prevalent cancer in women worldwide, and neoadjuvant chemotherapy is a favored method for achieving pathologic complete response (pCR). The TP53 gene is involved in inducing the response to chemotherapy drugs.

Objectives

The present study sought to correlate polymorphism variants at codon 72 with pCR to neoadjuvant chemotherapy.

Casuistry and Methods

The study was conducted in the state of Sergipe, in northeastern Brazil. A total of 206 patients with a histopathological diagnosis of breast cancer who underwent neoadjuvant chemotherapy from 2019 to 2022 were included. DNA samples were collected for the evaluation of TP53 polymorphism at codon 72. A prospective evaluation of the cases was conducted to verify the surgical pathologic response after chemotherapy; the Response Evaluation Criteria in Solid Tumors (RECIST) were used. The study was approved by the University of São Paulo Ethics and Research Committee.

Results

Of the 168 patients, 44.6% were Arg72Arg, 17.3% were Pro72Pro, and 38.0% were Arg72Pro; pCR was achieved in 21.4% of the patients; 10.1% had progressive disease, 13.7% had stable disease, and 54.2% had a partial pathologic response. The only predictor of pCR in multivariate regression was immunohistochemistry (p < 0.001). In the multivariate analysis, Arg72Pro and Pro72Pro increased the odds of the patient evolving with stable disease. This study was innovative in demonstrating a predictor of stable disease in response to neoadjuvant chemotherapy.

Conclusion

TP53 polymorphism at codon 72 is not a predictor of pCR, but it can be a predictor of stable disease.

Digital image analysis and machine learning-assisted prediction of neoadjuvant chemotherapy response in triple-negative breast cancer

BACKGROUND

Pathological complete response (pCR) is associated with favorable prognosis in patients with triple-negative breast cancer (TNBC). However, only 30-40% of TNBC patients treated with neoadjuvant chemotherapy (NAC) show pCR, while the remaining 60-70% show residual disease (RD). The role of the tumor microenvironment in NAC response in patients with TNBC remains unclear. In this study, we developed a machine learning-based two-step pipeline to distinguish between various histological components in hematoxylin and eosin (H&E)-stained whole slide images (WSIs) of TNBC tissue biopsies and to identify histological features that can predict NAC response.

METHODS

H&E-stained WSIs of treatment-naïve biopsies from 85 patients (51 with pCR and 34 with RD) of the model development cohort and 79 patients (41 with pCR and 38 with RD) of the validation cohort were separated through a stratified eightfold cross-validation strategy for the first step and leave-one-out cross-validation strategy for the second step. A tile-level histology label prediction pipeline and four machine-learning classifiers were used to analyze 468,043 tiles of WSIs. The best-trained classifier used 55 texture features from each tile to produce a probability profile during testing. The predicted histology classes were used to generate a histology classification map of the spatial distributions of different tissue regions. A patient-level NAC response prediction pipeline was trained with features derived from paired histology classification maps. The top graph-based features capturing the relevant spatial information across the different histological classes were provided to the radial basis function kernel support vector machine (rbfSVM) classifier for NAC treatment response prediction.

RESULTS

The tile-level prediction pipeline achieved 86.72% accuracy for histology class classification, while the patient-level pipeline achieved 83.53% NAC response (pCR vs. RD) prediction accuracy of the model development cohort. The model was validated with an independent cohort with tile histology validation accuracy of 83.59% and NAC prediction accuracy of 81.01%. The histological class pairs with the strongest NAC response predictive ability were tumor and tumor tumor-infiltrating lymphocytes for pCR and microvessel density and polyploid giant cancer cells for RD.

CONCLUSION

Our machine learning pipeline can robustly identify clinically relevant histological classes that predict NAC response in TNBC patients and may help guide patient selection for NAC treatment.

The Role of Ultrasound Features in Predicting the Breast Cancer Response to Neoadjuvant Chemotherapy

Background Neoadjuvant chemotherapy (NACT) has become the standard of care for locally advanced breast cancer. This study investigates whether baseline ultrasound features can predict complete pathological response (pCR) after NACT. Methods This retrospective study was approved by the Institutional Review Board of King Abdulaziz University Hospital, Jeddah, Saudi Arabia, with a waiver of informed consent. Records of female patients aged over 18 years with locally advanced breast cancer treated with NACT from 2018 to 2020 were reviewed. Baseline ultrasound parameters were assessed, including posterior effect, echo pattern, margin, and maximum lesion diameter. Tumor grade and immunophenotype were documented from the core biopsy. pCR was defined as the absence of invasive residual disease in the breast and axilla. Univariate and multivariate analyses assessed the association between ultrasound features and pathological response. Results A total of 110 breast cancer cases were analyzed: 36 (32.7%) were estrogen receptor (ER)-positive/human epidermal growth factor 2 (HER-2) negative, 49 (44.5%) were HER-2 positive, and 25 (22.7%) were triple-negative (TN). A pCR was achieved in 20 (18%) of cancers. Lesion diameter was significantly different between pCR and non-pCR groups, 28.5 ± 12 mm versus 39 ± 18 mm, respectively, with an area under the curve (AUC) of 0.7, a confidence interval (CI) of 0.55-0.81, and a p-value of 0.01. No significant association was observed between ultrasound features, tumor grade, and immunophenotype with pCR. Conclusion Ultrasound features could not predict pCR. A smaller tumor diameter was the only significant factor associated with pCR. Further prospective studies combining imaging features from different modalities are needed to explore the potential of varying imaging features in predicting post-NACT pathological response more comprehensively.

Predicting neoadjuvant treatment response in triple-negative breast cancer using machine learning

Background

Neoadjuvant chemotherapy (NAC) is the standard treatment for early-stage triple negative breast cancer (TNBC). The primary endpoint of NAC is a pathological complete response (pCR). NAC results in pCR in only 30%â€"40% of TNBC patients. Tumor-infiltrating lymphocytes (TILs), Ki67 and phosphohistone H3 (pH3) are a few known biomarkers to predict NAC response. Currently, systematic evaluation of the combined value of these biomarkers in predicting NAC response is lacking. In this study, the predictive value of markers derived from H&E and IHC stained biopsy tissue was comprehensively evaluated using a supervised machine learning (ML)-based approach. Identifying predictive biomarkers could help guide therapeutic decisions by enabling precise stratification of TNBC patients into responders and partial or non-responders.

Methods

Serial sections from core needle biopsies (n=76) were stained with H&E, and immunohistochemically for the Ki67 and pH3 markers, followed by whole slide image (WSI) generation. The resulting WSI triplets were co-registered with H&E WSIs serving as the reference. Separate mask region-based CNN (MRCNN) models were trained with annotated H&E, Ki67 and pH3 images for detecting tumor cells, stromal and intratumoral TILs (sTILs and tTILs), Ki67 + , and pH3 + cells. Top image patches with a high density of cells of interest were identified as hotspots. Best classifiers for NAC response prediction were identified by training multiple ML models, and evaluating their performance by accuracy, area under curve, and confusion matrix analyses.

Results

Highest prediction accuracy was achieved when hotspot regions were identified by tTIL counts and each hotspot was represented by measures of tTILs, sTILs, tumor cells, Ki67 + , and pH3 + features. Regardless of the hotspot selection metric, a complementary use of multiple histological features (tTILs, sTILs) and molecular biomarkers (Ki67 and pH3) resulted in top ranked performance at the patient level.

Conclusions

Overall, our results emphasize that prediction models for NAC response should be based on biomarkers in combination rather than in isolation. Our study provides compelling evidence to support the use of ML-based models to predict NAC response in patients with TNBC.

Tumor characteristics, therapy, and prognosis in young breast cancer patients ≤ 35 years

PURPOSES

Young breast cancer patients aged 35 years and younger are a small group of women who tend to present at high-risk form of the disease. More analysis of the data on tumor characteristics, treatment, and survival is necessary to help improving treatment and outcome.

METHODS

In this retrospective study, we compared the clinical and tumor characteristics, the treatments, and the survival of 257 women aged ≤ 35 years, with 6566 women aged 50-69 years. We used a registry-based data of patients with invasive, non-metastatic breast cancer diagnosed between 2000 and 2015.

RESULTS

Young women showed lower rate of hormone receptor (HR) positivity. Their tumors were more often HER2-positive, which showed lower rate of differentiation and higher rate of Ki-67 expression compared to their older counterparts. Women aged 35 years and younger were more likely to undergo neoadjuvant therapy and mastectomy. Endocrine therapy was underrepresented in young patients. 5-Year disease-free survival (DFS) was significantly lower in the younger patient group (81.7% vs. 91.3%, p < 0.001), while 5-year overall survival (OS) was not impaired (91.4% vs. 91.1%, p = 0.847).

CONCLUSION

The unfavorable disease-free survival in the group of younger patients might be explained by their unfavorable tumor characteristics. The surgical treatment appears to be more aggressive in young breast cancer patients and is more frequently combined with chemotherapy and immunotherapy, either in a neoadjuvant or in an adjuvant setting.

NUSAP1 and PCLAF (KIA0101) Downregulation by Neoadjuvant Therapy is Associated with Better Therapeutic Outcomes and Survival in Breast Cancer

Purpose. To evaluate whether changes in genomic expression that occur beginning with breast cancer (BC) diagnosis and through to tumor resection after neoadjuvant chemotherapy (NCT) reveal biomarkers that can help predict therapeutic response and survival. Materials and Methods. We determined gene expression profiles based on microarrays in tumor samples from 39 BC patients who showed pathologic complete response (pCR) or therapeutic failure (non-pCR) after NCT (cyclophosphamide-doxorubicin/epirubicin). Based on unsupervised clustering of gene expression, together with functional enrichment analyses of differentially expressed genes, we selected NUSAP1, PCLAF, MME, and DST. We evaluated the NCT response and the expression of these four genes in BC histologic subtypes. In addition, we study the presence of tumor-infiltrating lymphocytes. Finally, we analyze the correlation between NUSAP1 and PCLAF against disease-free survival (DFS) and overall survival (OS). Results. A signature of 43 differentially expressed genes discriminated pCR from non-pCR patients (|fold change >2|, false discovery rate <0.05) only in biopsies taken after surgery. Patients achieving pCR showed downregulation of NUSAP1 and PCLAF in tumor tissues and increased DFS and OS, while overexpression of these genes correlated with poor therapeutic response and OS. These genes are involved in the regulation of mitotic division. Conclusions. The downregulation of NUSAP1 and PCLAF after NCT is associated with the tumor response to chemotherapy and patient survival.

Predicting lymphovascular invasion in clinically node-negative breast cancer detected by abbreviated magnetic resonance imaging: Transfer learning vs. radiomics

Objective

To compare the performance of abbreviated breast magnetic resonance imaging (AB-MRI)-based transfer learning (TL) algorithm and radionics analysis for lymphovascular invasion (LVI) prediction in patients with clinically node-negative invasive breast cancer (IBC).

Methods

Between November 2017 and October 2020, 233 clinically node-negative IBCs detected by AB-MRI were retrospectively enrolled. One hundred thirty IBCs from center 1 (37 LVI-positive and 93 LVI-negative) were assigned as the training cohort and 103 from center 2 (25 LVI-positive and 78 LVI-negative) as the validation cohort. Based on AB-MRI, a TL signature (TLS) and a radiomics signature (RS) were built with the least absolute shrinkage and selection operator (LASSO) logistic regression. Their diagnostic performances were validated and compared using areas under the receiver operating curve (AUCs), net reclassification improvement (NRI), integrated discrimination improvement (IDI), decision curve analysis (DCA), and stratification analysis. A convolutional filter visualization technique was used to map the response areas of LVI on the AB-MRI.

Results

In the validation cohort, compared with RS, the TLS showed better capability in discriminating LVI-positive from LVI-negative lesions (AUC: 0.852 vs. 0.726, p < 0.001; IDI = 0.092, p < 0.001; NRI = 0.554, p < 0.001). The diagnostic performance of TLS was not affected by the menstrual state, molecular subtype, or contrast agent type (all p > 0.05). Moreover, DCA showed that the TLS added more net benefit than RS for clinical utility.

Conclusions

An AB-MRI-based TLS was superior to RS for preoperative LVI prediction in patients with clinically node-negative IBC.

Identification of tumor biomarkers for pathological complete response to neoadjuvant treatment in locally advanced breast cancer

BACKGROUND

Therapeutic response predictors like age, nodal status, and tumor grade and markers, like ER/PR, HER2, and Ki67, are not reliable in predicting the response to a specific form of chemotherapy. The current study aims to identify and validate reliable markers that can predict pathological complete response (pCR) in fluorouracil, epirubicin, and cyclophosphamide (FEC)-based neoadjuvant therapy with (NACT/RT) and without concurrent radiation (NACT).

MATERIALS AND METHODS

Tandem mass tag (TMT) quantitative liquid chromatography-tandem mass spectrometry (LC-MS/MS) was used to identify differentially expressed proteins from core needle breast biopsy between pCR (n = 4) and no-pCR (n = 4). Immunoblotting of shortlisted proteins with the tissue lysates confirmed the differential expression of the markers. Further, immunohistochemistry (IHC) was performed on formalin-fixed paraffin-embedded sections of treatment-naive core needle biopsies. In the NACT, 29 pCR and 130 no-pCR and in NACT/RT, 32 pCR and 71 no-pCR were used.

RESULTS

733 and 807 proteins were identified in NACT and NACT/RT groups, respectively. Ten proteins were shortlisted for validation as potential pCR-predictive markers. THBS1, TNC, and DCN were significantly overexpressed in no-pCR in both the groups. In NACT, CPA3 was significantly upregulated in the no-pCR. In NACT/RT, HnRNPAB was significantly upregulated and HMGB1 significantly downregulated in the no-pCR. HMGB1 was the only marker to show prognostic significance.

CONCLUSION

Quantitative proteomics followed by IHC identified and validated potential biomarkers for predicting patient response to therapy. These markers can be used, following larger-scale validation, in combination with routine histological analysis providing vital indications of treatment response.

Predictors of axillary node response in node-positive patients undergoing neoadjuvant chemotherapy for breast cancer

Background:

The ability to accurately predict which patients will achieve a pathologic complete response (pCR) after neoadjuvant chemotherapy could help identify those who could safely be spared the potential morbidity of axillary lymph node dissection. We performed a retrospective analysis of a cohort of clinically node-positive patients managed with neoadjuvant chemotherapy with the goal of identifying predictors of axillary pCR.

Methods:

Eligible patients were aged 18 years or older, had clinical T1–T4, N1–N3, M0 breast cancer and received neoadjuvant chemotherapy followed by surgical axillary lymph node staging between 2001 and 2017 at Misericordia Hospital, Edmonton, Alberta. Patient data, including tumour characteristics, details of neoadjuvant chemotherapy, imaging results before and after neoadjuvant chemotherapy, and final pathologic analysis, were collected from the appropriate provincial electronic data repositories. We summarized the data using descriptive statistics. We characterized associations between clinical/tumour characteristics and pCR using univariate and multivariate regression analysis.

Results:

Of the 323 patients included in the study, 130 (40.2%) achieved axillary pCR. Absence of residual disease in the breast was associated with axillary pCR (odds ratio 6.74, 95% confidence interval 2.89–15.67). HER2-positive, triple-negative and ER-positive/PR-negative/HER2-negative tumours were significantly associated with a pCR on univariate analysis; the association trended toward significance on multivariate analysis.

Conclusion:

Our findings support the routine use of neoadjuvant chemotherapy and sentinel lymph node biopsy in patients with an absence of residual disease in the breast, and potentially in those with HER2-positive or triple-negative subtypes, and highlight the ER-positive/PR-negative biomarker subtype as a potential predictor of nodal response.

Pathological responses and survival outcomes in patients with locally advanced breast cancer after neoadjuvant chemotherapy: a single-institute experience

BACKGROUND

Pathological complete response (pCR) is a surrogate for the efficacy of neoadjuvant chemotherapy (NCT) in locally advanced breast cancer (LABC). We analyzed the predictive clinical factors for pathological responses and survival outcomes in a cohort of Egyptian patients.

METHODS

We evaluated the medical records of patients with breast cancer who received NCT in our academic institute. Survival curves were estimated with the Kaplan-Meier method. Cox proportional models were used for multiple regression analysis.

RESULTS

Our cohort included 368 patients with a median age of 48 years (range 21-70). The median follow-up time was 3 years. The clinical tumor stage (T3-4) represented 58%, with 80% having positive axillary nodes. The luminal subgroup prevailed by 68%. The objective response rate (ORR) reached 78%, and 16% of patients achieved pCR. The clinical node stage and optimal chemotherapy were associated with higher ORR (p = 0.035 and p = 0.001, respectively). Predictors of pCR were clinical T-stage (p = 0.026), high Ki-67 index > 20 (p = 0.05), and receiving optimal chemotherapy (p = 0.014). The estimated 3-year disease free-survival (DFS) was 53%. Receptor status, achieving ORR, and pCR were associated with better DFS with hazard ratios of 0.56, p = 0.008; 0.38, p = 0.04; and 0.28, p = 0.007, respectively.

CONCLUSIONS

Luminal tumors still draw benefit from neoadjuvant chemotherapy in terms of clinical response and breast conservative surgery. Treatment escalation to those who did not achieve pCR requires more investigation, given a higher recurrence rate in real-world experience.

Extracellular Vesicle-Associated miRNAs and Chemoresistance: A Systematic Review

Simple Summary

There is an urgent need for a non-invasive, specific biomarker to identify patients at risk of chemoresistance, which it is the ability of cancer cells to escape the effect of chemotherapy drugs. Extracellular vesicles contain an abundance of miRNAs that demonstrate expression across a range of cancers including breast cancer, renal cell carcinoma, lung cancer, multiple myeloma, and lymphoma. Interestingly, miRNAs encapsulated within extracellular vesicles (EVs) including exosomes display an association with chemoresistance. Here, we performed a systematic revision to evaluate the association between miRNAs within EVs and chemotherapy resistance. The summarized graphical abstract indicates that several exosome-derived miRNAs involved in chemotherapy resistance can be found among different types of cancers, such as colorectal, ovarian, breast, and lung cancer, and lymphoma.

Abstract

Cancer is a leading public health issue globally, and diagnosis is often associated with poor outcomes and reduced patient survival. One of the major contributors to the fatality resultant of cancer is the development of resistance to chemotherapy, known as chemoresistance. Furthermore, there are limitations in our ability to identify patients that will respond to therapy, versus patients that will develop relapse, and display limited or no response to treatment. This often leads to patients being subjected to multiple futile treatment cycles, and results in a reduction in their quality of life. Therefore, there is an urgent clinical need to develop tools to identify patients at risk of chemoresistance, and recent literature has suggested that small extracellular vesicles, known as exosomes, may be a vital source of information. Extracellular vesicles (EV) are membrane bound vesicles, involved in cell-cell communication, through the transfer of their cargo, which includes proteins, lipids, and miRNAs. A defined exploration strategy was performed in this systematic review in order to provide a compilation of key EV miRNAs which may be predictive of chemoresistance. We searched the PubMed, Science Direct, and Scopus databases using the following keywords: Extracellular vesicles OR exosomes OR EVs AND miRNA AND Chemotherapy OR Chemoresistance OR Cancer Recurrence from 2010 to 2020. We found 31 articles that reported key EV-associated miRNAs involved in cancer recurrence related to chemoresistance. Interestingly, multiple studies of the same tumor type identified different microRNAs, and few studies identified the same ones. Specifically, miR-21, miR-222, and miR-155 displayed roles in response to chemotherapy, and were found to be common in colorectal cancer, ovarian cancer, breast cancer, and diffuse large B cell lymphoma patients (DLBCL). miR-21 and miR-222 were found to favour the development of chemoresistance, whereas miR-155 exhibited a contrasting role, depending on the type of primary tumor. Whilst high levels of miR-155 were found to correlate with chemotherapy resistance in DLBCL, it was found to be predictive of an effective response towards chemotherapy in breast cancer. Thus, further research regarding the roles of these miRNAs would be beneficial in terms of designing novel tools to counteract the progression of cancer in a not-to-distant future.

Expression of therapy-induced senescence markers in breast cancer samples upon incomplete response to neoadjuvant chemotherapy

Senescence is a cell stress response induced by replicative, oxidative, oncogenic, and genotoxic stresses. Tumor cells undergo senescence in response to several cancer therapeutics in vitro (Therapy-Induced Senescence, TIS), including agents utilized as neoadjuvant chemotherapy (NAC) in the treatment of invasive breast cancer. TIS has been proposed to contribute to adverse therapy outcomes including relapse. However, there is limited evidence on the induction of senescence in response to NAC in clinical cancer and its contribution to disease outcomes. In this work, the expression of three senescence-associated markers (p21CIP1, H3K9Me3 (histone H3 lysine 9 trimethylation), and Lamin B1) was investigated in breast cancer samples that developed partial or incomplete pathological response to NAC (n=37). Accordingly, 40.54% of all samples showed marker expression consistent with a senescence-like phenotype, while the remainders were either negative or inconclusive for senescence (2.70 and 56.8%, respectively). Moreover, analysis of core-needle biopsies revealed minimal changes in p21CIP1 and H3K9Me3, but significant changes in Lamin B1 expression levels following NAC, highlighting a more predictive role of Lamin B1 in senescence detection. However, our analysis did not establish an association between TIS and cancer relapse as only three patients (8.1%) with a senescence-like profile developed short-term recurrent disease. Our analysis indicates that identification of TIS in tumor samples requires large-scale transcriptomic and protein marker analyses and extended clinical follow-up. Better understanding of in vivo senescence should elucidate its contribution to therapy outcomes and pave the way for the utilization of senolytic approaches as potential adjuvant cancer therapy.

Addition of carboplatin-gemcitabine as second-line neoadjuvant chemotherapy in non-responsive locally advanced breast cancer patients to standard neoadjuvant chemotherapy and evaluation of factors affecting response: a randomized controlled trial

Background

Neoadjuvant chemotherapy (NACT) is the prime approach to the management of locally advanced breast cancer (LABC). Influenced by different factors such as pathologic tumor characteristics, hormone receptor status, HER2 and proliferation marker expressions, response to therapy cannot be easily predicted. Pathologic complete response (pCR) has been considered as an endpoint to NACT; however, pCR rates have been unsatisfactory in such patients. In this randomized trial, we studied the efficacy of carboplatin/gemcitabine as second-line NACT while evaluating the impact of different factors affecting response.

Methods

In this randomized controlled trial, 52 clinically non-responsive (confirmed by palpation and/or ultrasonography) LABC patients to 4 cycles of doxorubicin/cyclophosphamide followed by 4 cycles of paclitaxel ± trastuzumab were randomly allocated to two groups. “Control” group underwent breast surgery and were further evaluated for pCR (ypT0/is ypN0). “Intervention” group received 2 cycles of carboplatin/gemcitabine and patients were further evaluated for pCR following surgery.

Results

In a total of 52 patients, pCR rate was 30.7%. pCR and response rate in lymph nodes were higher in carboplatin/gemcitabine recipients (32% vs 29.7 and 44% vs 40.7% respectively), however differences were insignificant. In both the “intervention” group and total study population, most pCR cases were of the hormone receptor (HR)+/HER2+ subtype (87.5% and 75% respectively). HER2 positivity, ki67 expression, lower extent of ER positivity, higher tumor grade and tumor-infiltrating lymphocyte (TIL) lead to higher pCR rates. Adverse events following addition of carboplatin/gemcitabine were mostly hematologic and none required hospitalization. Anemia was the most common grade 3 adverse event observed. No grade 4 toxicity was evident.

Conclusion

Although the proposed carboplatin/gemcitabine combination could not improve pCR rates as expected, probability of immune activation following use of carboplatin in achieving response to NACT may be considered. Accounting for the highest number of pCR cases in the “intervention” group, the HR+/HER2+ subtype with high TILs may be considered as most responsive to the proposed regimen in this study. It is noteworthy that the proposed combination imposed minimal toxicity.

Trial registration

This trial was prospectively registered in IRCT.ir (IRCT2017100136491N1). Date of registration: 19 November 2017.

Prediction of Pathologic Complete Response by Ultrasonography and Magnetic Resonance Imaging After Neoadjuvant Chemotherapy in Patients with Breast Cancer

Purpose

To compare the diagnostic performance for pathologic complete response (pCR) in breast cancer after neoadjuvant chemotherapy (NAC) between ultrasound (US) and magnetic resonance imaging (MRI).

Patients and Methods

A total of 1,219 breast cancer patients with 1,232 tumors who accepted US and/or MRI examination after NAC and before breast surgery were included. The diagnostic performance of US, MRI, and US plus MRI in predicting pCR was compared.

Results

The sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) of US for pCR were 36.2%, 90.2%, 71.0%, 67.3%, and 71.9%, respectively, while for MRI they were 44.4%, 92.9%, 75.6%, 77.7%, and 75.0%, respectively. The combination of US and MRI had increased specificity (98.0%) and PPV (86.8%), decreased sensitivity (22.5%) and NPV (68.8%), but similar accuracy (70.5%) in comparison with US or MRI alone. The prediction of pCR by imaging differed in different histological, molecular subtypes and primary tumor size.

Conclusion

Neither US nor MRI could predict a pCR with sufficient accuracy. The combination of US and MRI could not predict a pCR reliably either. The explanation of imaging for pCR should take into account histological, molecular subtypes, and primary tumor size.