Meta-analysis confirms achieving pathological complete response after neoadjuvant chemotherapy predicts favourable prognosis for breast cancer patients

Adjuvant Chemotherapy May be Waived for Breast Cancer Nonresponders to Neoadjuvant Chemotherapy: A Population-Based Large Cohort Study

PURPOSE

This study aimed to evaluate the efficacy of adjuvant chemotherapy (AC) in breast cancer patients who did not respond to neoadjuvant chemotherapy (NAC) following surgery.

METHOD

A retrospective analysis was performed using a large, population-based cohort to identify breast cancer patients who underwent radical surgery following NAC without achieving a response. Kaplan-Meier analysis and Cox regression models were employed to assess clinical outcomes and prognostic factors. Propensity score matching (PSM) was applied to compare outcomes between patients receiving AC vs. those who did not, followed by subgroup analyses.

RESULTS

A total of 1866 patients were included, of whom 1030 received postoperative AC. The median follow-up time was 68.0 months. Patients receiving AC had a median overall survival (OS) of 124.0 months, compared to 93.0 months for those not receiving AC. However, multivariate analysis indicated that receiving postoperative AC was not an independent prognostic factor. Furthermore, PSM analysis indicated no improvement in long-term survival for patients receiving postoperative AC compared to those not receiving it. Subgroup analysis further supported these findings, revealing no significant differences in OS between AC and Non-AC cohorts across various subgroups.

CONCLUSION

These findings suggest that breast cancer patients unresponsive to NAC may derive limited benefit from subsequent AC. Therefore, the decision to administer AC should be carefully considered, and alternative therapeutic strategies should be explored for these patients.

Development of bioengineered 3D patient derived breast cancer organoid model focusing dynamic fibroblast-stem cell reciprocity

Three-dimensional (3D) models, such as tumor spheroids and organoids, are increasingly developed by integrating tissue engineering, regenerative medicine, and personalized therapy strategies. These advanced 3Din-vitromodels are not merely endpoint-driven but also offer the flexibility to be customized or modulated according to specific disease parameters. Unlike traditional 2D monolayer cultures, which inadequately capture the complexities of solid tumors, 3D co-culture systems provide a more accurate representation of the tumor microenvironment. This includes critical interactions with mesenchymal stem/stromal cells (MSCs) and induced pluripotent stem cells (iPSCs), which significantly modulate cancer cell behavior and therapeutic responses. Most of the findings from the co-culture of Michigan Cancer Foundation-7 breast cancer cells and MSC showed the formation of monolayers. Although changes in the plasticity of MSCs and iPSCs caused by other cells and extracellular matrix (ECM) have been extensively researched, the effect of MSCs on cancer stem cell (CSC) aggressiveness is still controversial and contradictory among different research communities. Some researchers have argued that CSCs proliferate more, while others have proposed that cancer spread occurs through dormancy. This highlights the need for further investigation into how these interactions shape cancer aggressiveness. The objective of this review is to explore changes in cancer cell behavior within a 3D microenvironment enriched with MSCs, iPSCs, and ECM components. By describing various MSC and iPSC-derived 3D breast cancer models that replicate tumor biology, we aim to elucidate potential therapeutic targets for breast cancer. A particular focus of this review is the Transwell system, which facilitates understanding how MSCs and iPSCs affect critical processes such as migration, invasion, and angiogenesis. The gradient formed between the two chambers is based on diffusion, as seen in the human body. Once optimized, this Transwell model can serve as a high-throughput screening platform for evaluating various anticancer agents. In the future, primary cell-based and patient-derived 3D organoid models hold promise for advancing personalized medicine and accelerating drug development processes.

Predicting ipsilateral supraclavicular lymph node pathological complete response: nomogram based on the inflammatory markers

Background

The prediction of ISLN pCR after neoadjuvant chemotherapy (NAC) based on inflammatory markers and its prognostic value have rarely been investigated.

Methods

Patients diagnosed with ISLN-involved breast cancer who received NAC in West China Hospital between September 2009 and December 2020 were enrolled in the derivation cohort for model construction and survival analysis, and patients with the same criteria between January 2021 and July 2024 were involved in validation cohort for external validation. After randomly dividing patients into training and testing groups at 7:3 ratio, a nomogram predicting ISLN pCR was constructed based on logistic regression in training group. Internal validation was performed in the testing group and external validation was performed in the independent validation cohort. The ROC curves were applied to validate the accuracy of the model. Survival analysis was performed using Kaplan-Meier plots.

Results

A total of 120 eligible patients were involved in the derivation cohort to establish the nomogram (84 patients in training group and 36 patients in testing group), and 45 patients were involved in the independent validation cohort for external validation of the nomogram. Pretreatment NLR and hormone receptor (HR) status, as well as preoperative SII, CEA, CA15-3 and anti-HER2 therapy were included in the nomogram predicting ISLN pCR. The AUC were 0.906 (95% CI 0.837-0.975, P<0.001), 0.888 (95% CI 0.751-1.000, P<0.001) and 0.828 (95% CI 0.703-0.953, P< 0.001) in training, testing groups and the validation cohort respectively. ISLN pCR was significantly associated with better prognosis (all P<0.05).

Conclusion

Inflammatory factors combined with tumor makers, hormone receptor status and anti-HER2 therapy could predict ISLN pCR effectively, which was significantly associated with improved survival outcomes.

The Role of the NOLUS Score in Predicting pCR and iDFS in HR-positive HER2-negative Early Breast Cancer Patients who Received Neoadjuvant Chemotherapy

Background/Aim

Breast cancer remains a significant health challenge, with neoadjuvant chemotherapy (NACT) improving clinical outcomes in certain subtypes. However, the role of NACT in hormone receptor-positive, HER2-negative (HR+/HER2-) breast cancer is unclear due to various outcomes and generally low rates of pathologic complete response (pCR). This study introduces the Non-Luminal Disease Score (NOLUS) as a potential predictive tool for assessing the response to NACT in these cases.

Patients and Methods

We retrospectively assessed patients diagnosed with locally advanced HR+/HER2- breast cancer who received NACT at our institution from 2009 to 2023. The study explored the association between NOLUS and pCR rates. NOLUS was calculated as positive or negative based on the percentage of estrogen receptor, progesterone receptor, and Ki-67 in tumor cells. We also investigated the correlation between pCR and invasive disease-free survival (iDFS), and examined NOLUS positivity across different age groups.

Results

A total of 149 patients met the inclusion criteria. NOLUS-positive patients exhibited a significantly higher pCR rate of 33.33% compared to 10.4% in NOLUS-negative patients (p=0.0031). With a median follow-up of 2.47 years, NOLUS-positive patients who achieved pCR had a 100% iDFS rate, mirroring the pCR versus residual disease patterns seen in triple-negative patients. NOLUS positivity was observed in 20.43% of patients aged 22-50, compared to 8.93% in those over 50, though this difference was not statistically significant.

Conclusion

NOLUS exhibits potential in predicting pCR in HR+/HER2- breast cancer, serving as a cost-effective substitute for genomic tests.

Pathologic Complete Response After Neoadjuvant Chemotherapy in Breast Cancer Patients Treated With Mastectomy: Indications for Treatment and Oncological Outcomes

Objective

The aim of this study was to evaluate the clinical outcomes of breast cancer (BC) patients treated with neoadjuvant chemotherapy (NAC) followed by mastectomy, focusing on cases achieving pathologic complete response (pCR). The implications of residual ductal carcinoma in situ (DCIS) on prognosis and survival were examined.

Materials and Methods

A retrospective cohort study included BC patients treated with NAC followed by mastectomy at the breast unit of IRCCS Humanitas Research Hospital between March 2010 and October 2021. Patients were sub-grouped into two: Those with residual DCIS (ypTis) and those with complete response without residual tumor (ypT0). Key variables such as demographics, tumor characteristics, treatment regimens, and survival outcomes were analyzed.

Results

Of 681 patients treated with NAC, 175 achieved pCR, with 60 undergoing mastectomy. Among these 60 patients, 24 had residual DCIS (ypTis) while 36 had no residual invasive or in situ disease (ypT0). Patients with ypTis had higher rates of multifocal disease (62.5% vs. 27.8%, p = 0.006) and stage III disease (37.5% vs. 11.1%, p = 0.046). Triple-negative breast cancer was more prevalent in the ypT0 group (55.6% vs. 20.8%, p = 0.005). During a mean follow-up of 47 months, 11 patients experienced recurrence, with no significant differences in disease-free survival (DFS) and overall survival (OS) between the groups (p = 0.781, p = 0.963, respectively).

Conclusion

Residual DCIS after NAC did not significantly impact DFS or OS compared to complete pathologic response without residual DCIS. This study underscores the need for further research to refine pCR definitions and improve NAC's prognostic and therapeutic roles in BC management.

Peritumoral Adipose Tissue Features Derived from [18F]fluoro-2-deoxy-2-d-glucose Positron Emission Tomography/Computed Tomography as Predictors for Response to Neoadjuvant Chemotherapy in Breast Cancer Patients

This study investigated whether the textural features of peritumoral adipose tissue (AT) on [18F]fluoro-2-deoxy-2-d-glucose (FDG) positron emission tomography/computed tomography (PET/CT) can predict the pathological response to neoadjuvant chemotherapy (NAC) and progression-free survival (PFS) in breast cancer patients. We retrospectively enrolled 147 female breast cancer patients who underwent staging FDG PET/CT and completed NAC and underwent curative surgery. We extracted 10 first-order features, 6 gray-level co-occurrence matrix (GLCM) features, and 3 neighborhood gray-level difference matrix (NGLDM) features of peritumoral AT and evaluated the predictive value of those imaging features for pathological complete response (pCR) and PFS. The results of our study demonstrated that GLCM homogeneity showed the highest predictability for pCR among the peritumoral AT imaging features in the receiver operating characteristic curve analysis. In multivariate logistic regression analysis, the mean standardized uptake value (SUV), 50th percentile SUV, 75th percentile SUV, SUV histogram entropy, GLCM entropy, and GLCM homogeneity of the peritumoral AT were independent predictors for pCR. In multivariate survival analysis, SUV histogram entropy and GLCM correlation of peritumoral AT were independent predictors of PFS. Textural features of peritumoral AT on FDG PET/CT could be potential imaging biomarkers for predicting the response to NAC and disease progression in breast cancer patients.

Develop and Validate a Nomogram Combining Contrast-Enhanced Spectral Mammography Deep Learning with Clinical-Pathological Features to Predict Neoadjuvant Chemotherapy Response in Patients with ER-Positive/HER2-Negative Breast Cancer

RATIONALE AND OBJECTIVES

To develop and validate a nomogram that combines contrast-enhanced spectral mammography (CESM) deep learning with clinical-pathological features to predict neoadjuvant chemotherapy (NAC) response (either low Miller Payne (MP-L) grades 1-2 or high MP (MP-H) grades 3-5) in patients with ER-positive/HER2-negative breast cancer.

MATERIALS AND METHODS

In this retrospective study, 265 breast cancer patients were randomly allocated into training and test sets (used for models training and testing, respectively) at a 4:1 ratio. Deep learning models, based on the pre-trained ResNet34 model and initially fine-tuned for identifying breast cancer, were trained using low-energy and subtracted CESM images. The predicted results served as deep learning features for the deep learning-based model. Clinical-pathological features, including age, progesterone receptor (PR) status, estrogen receptor (ER) status, Ki67 expression levels, and neutrophil-to-lymphocyte ratio, were used for the clinical model. All these features contributed to the nomogram. Feature selection was performed through univariate analysis. Logistic regression models were developed and chosen using a stepwise selection method. The deep learning-based and clinical models, along with the nomogram, were evaluated using precision-recall curves, receiver operating characteristic (ROC) curves, specificity, recall, accuracy, negative predictive value, positive predictive value (PPV), balanced accuracy, F1-score, and decision curve analysis (DCA).

RESULTS

The nomogram demonstrated considerable predictive ability, with higher area under the ROC curve (0.95, P < 0.05), accuracy (0.94), specificity (0.98), PPV (0.89), and precision (0.89) compared to the deep learning-based and clinical models. In DCA, the nomogram showed substantial clinical value in assisting breast cancer treatment decisions, exhibiting a higher net benefit than the other models.

CONCLUSION

The nomogram, integrating CESM deep learning with clinical-pathological features, proved valuable for predicting NAC response in patients with ER-positive/HER2-negative breast cancer. Nomogram outperformed deep learning-based and clinical models.

Efficacy and safety of different regimens of neoadjuvant therapy in patients with hormone receptor-positive, her2-negative breast cancer: a network meta-analysis

Introduction

The objective of this systematic review and network meta-analysis (NMA) is to assess the effectiveness and safety of various neoadjuvant treatment protocols in individuals diagnosed with hormone receptor-positive, her2 negative(HR+/HER2-) breast cancer.

Materials and methods

A systematic search was conducted in four databases (Medline, Embase, Web of Science, and CENTRAL) from the inception of the databases to January 16, 2024, to identify randomized controlled trials (RCTs) to various neoadjuvant therapy options in patients diagnosed with hormone receptor-positive, HER2-negative breast cancer. A network meta-analysis was conducted to evaluate pathological complete response (pCR).

Results

There were 17 randomized controlled trials (RCTs) included in the analysis. These trials examined 16 different treatment regimens and involved a total of 5752 participants. The analysis revealed that the six most effective neoadjuvant treatment regimens for HR+/HER2- breast cancer were: CT(A)+olaparib (82.5%), CT(A)+nivolumab (76.5%), Com (74.9%), CT (72.1%), Mono+eribulin (72.0%), and CT(A)+pembrolizumab (70.4%).Paired meta-analysis for pathological complete response (pCR) found no statistically significant distinction between treatment regimens that included both anthracycline and immunosuppressants and regimens that relied solely on anthracycline chemotherapy(OR:1.14, 95%ci 0.79-1.64, I2 = 71%, P=0.50). Similarly, there was no significant difference between platinum-based chemotherapy and anthracycline-basedchemotherapy(OR:1.37, 95%ci 0.53- 3.56, I2 = 11%, P=0.52). With regards to safety, adverse effects of grade 3-5 were observed, which included haematological toxicity, gastrointestinal reactions, skin and mucous membrane reactions, neuropathy, hepatotoxicity, and cardiac disorders.

Conclusions

The CT(A)+Olaparib and CT(A)+nivolumab groups demonstrated superior efficacy in neoadjuvant therapy for HR+/HER2- breast cancer. Furthermore, it is crucial to focus on effectively managing the adverse effects of the treatment plan to enhance patient's ability to tolerate it. Given the constraints of the current research, additional well-executed and suitable RCTs are necessary to validate the findings of this investigation. Although pCR is valuable in assessing the effect of neoadjuvant therapy in some cases, prognostic prediction and efficacy assessment in patients with HR+/HER2- breast cancer should be based on a combination of broader clinical and biological characteristics.

Systematic review registration

PROSPERO https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42024534539, CRD42024501740.

Respective contribution of baseline clinical data, tumour metabolism and tumour blood-flow in predicting pCR after neoadjuvant chemotherapy in HER2 and Triple Negative breast cancer

BACKGROUND

The aim of this study is to investigate the added value of combining tumour blood flow (BF) and metabolism parameters, including texture features, with clinical parameters to predict, at baseline, the pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in patients with newly diagnosed breast cancer (BC).

METHODS

One hundred and twenty-eight BC patients underwent a 18F-FDG PET/CT before any treatment. Tumour BF and metabolism parameters were extracted from first-pass dynamic and delayed PET images, respectively. Standard and texture features were extracted from BF and metabolic images. Prediction of pCR was performed using logistic regression, random forest and support vector classification algorithms. Models were built using clinical (C), clinical and metabolic (C+M) and clinical, metabolic and tumour BF (C+M+BF) information combined. Algorithms were trained on 80% of the dataset and tested on the remaining 20%. Univariate and multivariate features selections were carried out on the training dataset. A total of 50 shuffle splits were performed. The analysis was carried out on the whole dataset (HER2 and Triple Negative (TN)), and separately in HER2 (N=76) and TN (N=52) tumours.

RESULTS

In the whole dataset, the highest classification performances were observed for C+M models, significantly (p-value<0.01) higher than C models and better than C+M+BF models (mean balanced accuracy of 0.66, 0.61, and 0.64 respectively). For HER2 tumours, equal performances were noted for C and C+M models, with performances higher than C+M+BF models (mean balanced accuracy of 0.64, and 0.61 respectively). Regarding TN tumours, the best classification results were reported for C+M models, with better performances than C and C+M+BF models but not significantly (mean balanced accuracy of 0.65, 0.63, and 0.62 respectively).

CONCLUSION

Baseline clinical data combined with global and texture tumour metabolism parameters assessed by 18F-FDG PET/CT provide a better prediction of pCR after NAC in patients with BC compared to clinical parameters alone for TN, and HER2 and TN tumours together. In contrast, adding BF parameters to the models did not improve prediction, regardless of the tumour subgroup analysed.

Assessment of the Predictive Role of Ki-67 in Breast Cancer Patients' Responses to Neoadjuvant Chemotherapy

OBJECTIVE

Neoadjuvant chemotherapy (NAC) in breast cancer (BC) is being considered for a broader range of cases, including locally advanced tumors and situations where downstaging could reduce extensive surgery. Several trials have explored predictive markers of pathological complete response (pCR). The role of Ki-67 as a predictor of pCR has been demonstrated in studies. However, the cut-off remains vague, given the lack of standardization of measurement methods. The aim of our study was to evaluate the predictive value of Ki-67 in response to NAC and to identify the cut-off values that exhibit the strongest correlation with best response.

MATERIALS AND METHODS

This retrospective study included 187 patients who had undergone surgery following NAC for BC at the CHU Souss Massa of Agadir between January 2020 and January 2023. Logistic regression was used to assess the correlation between Ki-67 and patients' characteristics. Optimal Ki-67 cutoff was identified by receiver operating characteristic curve. Kaplan-Meier curves were used to assess disease-free survival (DFS), and survival comparisons were assessed with the log-rank test.

RESULTS

The median age was 51.8±10.7 years and 51.4% of tumors were smaller than 5 cm. Node invasion was found in 55.4%. Luminal B subtype was found in 49.7%, followed by human epidermal growth factor receptor-2 (HER-2)-positive in 27.4%, triple-negative in 14.3% and Luminal A in 8.6%. pCR occurred in 40% of patients overall. Subgroup analysis revealed a significant association between pCR and tumor size (p<0.001), lymph node involvement (p<0.001), grade 2 (p<0.001), vascular invasion (p<0.001), and positive HER-2 status (p = 0.022). In statistical analysis, pathological responses were improved in patients with Ki-67 >35% (p<0.001). DFS was 98.8% at 12 months. No statistical difference was found in DFS according to Ki-67 values and pCR status.

CONCLUSION

Our results indicate that Ki-67 is a predictive marker for response in the neoadjuvant setting in BC patients. Our study showed that a Ki-67 cut-off >35% predicts a better pCR rate in response to NAC. However, this cutoff value remains controversial due to the absence of a standard method of measurement, with inter- and intra-observer variability. It would be necessary to validate this cutoff in other studies.

Implementation of the Targeted Axillary Dissection Procedure in Clinically Node-Positive Breast Cancer: A Retrospective Analysis

BACKGROUND

The targeted axillary dissection (TAD) procedure is used in clinically positive lymph node (cN+) breast cancer to assess whether pathological complete response (pCR) is achieved after neoadjuvant systemic therapy (NST) to decide on de-escalation of axillary lymph node dissection (ALND). In this study, we review the implementation of the TAD procedure in a large regional breast cancer center.

METHODS

All TAD procedures between 2016 and 2022 were reviewed. The TAD procedure consists of marking pre-NST the largest suspected metastatic lymph node(s) using a radioactive I-125 seed. During surgery, the marked node was excised together with a sentinel node procedure. Axillary therapy (ALND, axillary radiotherapy, or nothing) recommendations were based on the amount of suspected positive axillary lymph nodes (ALNs < 4 or ≥ 4) pre-NST and if pCR was achieved after NST.

RESULTS

A total of 312 TAD procedures were successfully performed in 309 patients. In 134 (43%) cases, pCR of the TAD lymph nodes were achieved. Per treatment protocol, 43 cases (14%) did not receive any axillary treatment, 218 cases (70%) received adjuvant axillary radiotherapy, and 51 cases (16%) underwent an ALND. During a median follow-up of 2.8 years, 46 patients (14%) developed recurrence, of which 11 patients (3.5%) had axillary recurrence.

CONCLUSIONS

Introduction of the TAD procedure has resulted in a reduction of 84% of previously indicated ALNDs. Moreover, 18% of cases did not receive adjuvant axillary radiotherapy. These data show that implementation of de-escalation axillary treatment with the TAD procedure appeared to be successful.

18F-FDG Dedicated Breast PET Complementary to Breast MRI for Evaluating Early Response to Neoadjuvant Chemotherapy

Purpose To compare quantitative measures of tumor metabolism and perfusion using fluorine 18 (18F) fluorodeoxyglucose (FDG) dedicated breast PET (dbPET) and breast dynamic contrast-enhanced (DCE) MRI during early treatment with neoadjuvant chemotherapy (NAC). Materials and Methods Prospectively collected DCE MRI and 18F-FDG dbPET examinations were analyzed at baseline (T0) and after 3 weeks (T1) of NAC in 20 participants with 22 invasive breast cancers. FDG dbPET-derived standardized uptake value (SUV), metabolic tumor volume, and total lesion glycolysis (TLG) and MRI-derived percent enhancement (PE), signal enhancement ratio (SER), and functional tumor volume (FTV) were calculated at both time points. Differences between FDG dbPET and MRI parameters were evaluated after stratifying by receptor status, Ki-67 index, and residual cancer burden. Parameters were compared using Wilcoxon signed rank and Mann-Whitney U tests. Results High Ki-67 tumors had higher baseline SUVmean (difference, 5.1; P = .01) and SUVpeak (difference, 5.5; P = .04). At T1, decreases were observed in FDG dbPET measures (pseudo-median difference T0 minus T1 value [95% CI]) of SUVmax (-6.2 [-10.2, -2.6]; P < .001), SUVmean (-2.6 [-4.9, -1.3]; P < .001), SUVpeak (-4.2 [-6.9, -2.3]; P < .001), and TLG (-29.1 mL3 [-71.4, -6.8]; P = .005) and MRI measures of SERpeak (-1.0 [-1.3, -0.2]; P = .02) and FTV (-11.6 mL3 [-22.2, -1.7]; P = .009). Relative to nonresponsive tumors, responsive tumors showed a difference (95% CI) in percent change in SUVmax of -34.3% (-55.9%, 1.5%; P = .06) and in PEpeak of -42.4% (95% CI: -110.5%, 8.5%; P = .08). Conclusion 18F-FDG dbPET was sensitive to early changes during NAC and provided complementary information to DCE MRI that may be useful for treatment response evaluation. Keywords: Breast, PET, Dynamic Contrast-enhanced MRI Clinical trial registration no. NCT01042379 Supplemental material is available for this article. © RSNA, 2024.

Residual pure intralymphatic carcinoma component only (lymphovascular tumor emboli without invasive carcinoma) after neoadjuvant chemotherapy is associated with poor outcome: Not pathologic complete response

Residual pure intralymphatic carcinoma (PIC) component only after neoadjuvant chemotherapy (NAC) is lymphovascular tumor emboli without invasive carcinoma and extremely rare form of residual tumor after NAC. Although several studies have been published, the prognostic influence of residual PIC component only had not been fully evaluated. This study aims to evaluate the clinicopathologic features and the prognostic value of residual PIC component only. We reviewed the 251 patients with no residual invasive carcinoma in breast after NAC and found 12 patients with residual PIC component only after NAC. Five cases were triple negative, 6 were HER2 positive, and 1 was estrogen receptor positive and HER2 negative. The extent of PIC component ranged from 0.18 to 50.00 mm. The detailed microscopic PIC component findings did not significantly correlate with regional lymph node metastasis, local recurrence, or distant metastasis (p > 0.05). In multivariate survival analysis, the presence of lymph node metastasis and pretreatment ki-67 labeling index more than 50 % was statistically associated with greater risk of relapse [Cox proportional hazards ratio (HR) = 3.236, 95 % confidence interval (CI), 1.461-7.280, p = 0.004; HR = 3.046, 95 % CI, 1.421-6.529, p = 0.004, respectively) and residual PIC component only tended to be associated with greater risk of relapse (HR = 2.378, 95 % CI, 0.853-6.631; p = 0.098), but not reached to statistically significance. In patients without lymph node metastasis, the presence of residual PIC component only was associated with worse disease-free survival (p = 0.004). Although the number of published studies still limited, residual residual PIC component only after NAC is associated with poor outcome, and it should not be considered as pathological complete response.

Predictive value of stromal tumor-infiltrating lymphocytes in patients with breast cancer treated with neoadjuvant chemotherapy: A meta-analysis

BACKGROUND

The predictive value of tumor-infiltrating lymphocytes (TILs) in response to neoadjuvant chemotherapy (NAC) for breast cancer (BC) has received increasing attention. Here, a meta-analysis was conducted to evaluate the correlation between the expression of stromal TILs and pathological complete response (pCR) after NAC in BC patients.

METHODS

The PubMed, Embase, Cochrane Library, and Web of Science databases were searched online by using a combination of keywords and free words to screen literature on the expression of stromal TILs and pCR after NAC in patients with BC. The data were extracted and evaluated for quality. Relative risk (RR) was used to evaluate the relationship between the expression of stromal TILs before NAC and pCR in BC patients. Meta-analysis was performed with Review Manager 5.3 and STATA 14.0 software.

RESULTS

Eleven studies involving 6039 BC patients were included in the meta-analysis. The results showed a generally high expression of stromal TILs in BC patients, and the pCR rate after NAC in BC patients with a high expression of stromal TILs was significantly higher than that in BC patients with a low expression of stromal TILs [RR = 1.83, 95% confidence interval (CI): 1.69-1.97]. Subgroup analysis based on the molecular subtypes of BC showed that the pCR rate was significantly higher in patients with a high expression of stromal TILs in hormone receptor (HR)-positive BC [RR = 3.23, 95% CI: 2.43-4.30], human epidermal growth factor receptor 2 (HER-2)-positive BC [RR = 1.41, 95% CI: 1.25-1.60], and triple-negative BC [RR = 1.70, 95% CI: 1.53-1.90] than in those with a low expression of stromal TILs. Subgroup analysis based on expression threshold showed that the pCR rate was higher in patients with a high expression of stromal TILs than in patients with a low expression of stromal TILs at different expression thresholds (10% [RR = 1.99, 95% CI: 1.55-2.55], 20%/30% [RR = 1.57, 95% CI: 1.37-1.81], 50%/60% [RR = 1.91, 95% CI: 1.73-2.11].

CONCLUSION

TILs can be used as a predictor of pCR after NAC in patients with BC, and the appropriate high expression threshold of stromal TILs should be selected as the predictive value according to the molecular subtype of BC.

Long-Term Outcomes and Predictors of Response in Breast Cancer Patients with Advanced Nodal Involvement

BACKGROUND

Advanced nodal disease is associated with poor prognosis. However, modern neoadjuvant systemic therapy (NST) regimens have resulted in higher pathologic complete response (pCR) rates, which are associated with improved survival. We sought to assess contemporary outcomes in patients with advanced nodal involvement and response to NST.

STUDY DESIGN

We conducted a single-institution, retrospective study of 521 patients with cN2-3 primary nonmetastatic breast cancer treated with NST followed by surgery and radiation from 2012 to 2018. Descriptive statistics, multivariate Cox regression, and Kaplan-Meier analyses were performed.

RESULTS

The mean age was 50.5 years, and median follow-up was 61 (4.7 to 197) months. The majority of patients had hormone receptor-positive (HR+)/HER2-negative tumors (HER2-; n = 242, 47.8%). Most were cT2 (n = 243; 46.6%) or cT3 (n = 139; 26.7%) and 73.3% (n = 382) had cN3 disease. Rate of axillary pCR was 34.2%, and breast and axillary pCR was 19.4% (n = 101). Event-free survival (EFS) at 5 years was 75.1% (95% CI, 0.71 to 0.79). Rate of locoregional recurrence was 6.7%; distant metastatic rate was 29.4%. Axillary pCR with or without breast pCR was significantly associated with longer EFS (p = 0.001). Achieving breast/axillary pCR was an independent predictor of improved EFS (hazard ratio 0.22, p < 0.0001). Having triple-negative disease was associated with worse EFS (hazard ratio 1.74, p = 0.008).

CONCLUSIONS

In a high-risk cohort of patients with cN2-3 disease, trimodality therapy was effective in achieving durable EFS. Approximately one-third of patients achieved axillary pCR, which was associated with improved survival. Further studies are needed to accurately determine axillary response in cN2-3 breast cancer after NST in order to develop de-escalation strategies to reduce morbidity associated with axillary surgery.

18F-FDG PET/CT-based deep learning radiomics predicts 5-years disease-free survival after failure to achieve pathologic complete response to neoadjuvant chemotherapy in breast cancer

BACKGROUND

This study aimed to assess whether a combined model incorporating radiomic and depth features extracted from PET/CT can predict disease-free survival (DFS) in patients who failed to achieve pathologic complete response (pCR) after neoadjuvant chemotherapy.

RESULTS

This study retrospectively included one hundred and five non-pCR patients. After a median follow-up of 71 months, 15 and 7 patients experienced recurrence and death, respectively. The primary tumor volume underwent feature extraction, yielding a total of 3644 radiomic features and 4096 depth features. The modeling procedure employed Cox regression for feature selection and utilized Cox proportional-hazards models to make predictions on DFS. Time-dependent receiver operating characteristic (ROC) curves and the area under the ROC curve (AUC) were utilized to evaluate and compare the predictive performance of different models. 2 clinical features (RCB, cT), 4 radiomic features, and 7 depth features were significant predictors of DFS and were included to develop models. The integrated model incorporating RCB, cT, and radiomic and depth features extracted from PET/CT images exhibited the highest accuracy for predicting 5-year DFS in the training (AUC 0.943) and the validation cohort (AUC 0.938).

CONCLUSION

The integrated model combining radiomic and depth features extracted from PET/CT images can accurately predict 5-year DFS in non-pCR patients. It can help identify patients with a high risk of recurrence and strengthen adjuvant therapy to improve survival.

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.

Early Prediction of Response to Neoadjuvant Chemotherapy Using Quantitative Parameters on Automated Breast Ultrasound Combined with Contrast-Enhanced Ultrasound in Breast Cancer

OBJECTIVE

This prospective study was aimed at evaluating the role of automated breast ultrasound (ABUS) and contrast-enhanced ultrasound (CEUS) in the early prediction of treatment response to neoadjuvant chemotherapy (NAC) in patients with breast cancer.

METHODS

Forty-three patients with pathologically confirmed invasive breast cancer treated with NAC were included. The standard for evaluation of response to NAC was based on surgery within 21 d of completing treatment. The patients were classified as having a pathological complete response (pCR) and a non-pCR. All patients underwent CEUS and ABUS 1 wk before receiving NAC and after two treatment cycles. The rising time (RT), time to peak (TTP), peak intensity (PI), wash-in slope (WIS) and wash-in area under the curve (Wi-AUC) were measured on the CEUS images before and after NAC. The maximum tumor diameters in the coronal and sagittal planes were measured on ABUS, and the tumor volume (V) was calculated. The difference (∆) in each parameter between the two treatment time points was compared. Binary logistic regression analysis was used to identify the predictive value of each parameter.

RESULTS

∆V, ∆TTP and ∆PI were independent predictors of pCR. The CEUS-ABUS model achieved the highest AUC (0.950), followed by those based on CEUS (0.918) and ABUS (0.891) alone.

CONCLUSION

The CEUS-ABUS model could be used clinically to optimize the treatment of patients with breast cancer.

Comprehensive Profiling of Cancer-Associated Cells in the Blood of Breast Cancer Patients Undergoing Neoadjuvant Chemotherapy to Predict Pathological Complete Response

Neoadjuvant chemotherapy (NAC) can affect pathological complete response (pCR) in breast cancers; the resection that follows identifies patients with residual disease who are then offered second-line therapies. Circulating tumor cells (CTCs) and cancer-associated macrophage-like cells (CAMLs) in the blood can be used as potential biomarkers for predicting pCR before resection. CTCs are of epithelial origin that undergo epithelial-to-mesenchymal transition to become more motile and invasive, thereby leading to invasive mesenchymal cells that seed in distant organs, causing metastasis. Additionally, CAMLs in the blood of cancer patients are reported to either engulf or aid the transport of cancer cells to distant organs. To study these rare cancer-associated cells, we conducted a preliminary study where we collected blood from patients treated with NAC after obtaining their written and informed consent. Blood was collected before, during, and after NAC, and Labyrinth microfluidic technology was used to isolate CTCs and CAMLs. Demographic, tumor marker, and treatment response data were collected. Non-parametric tests were used to compare pCR and non-pCR groups. Univariate and multivariate models were used where CTCs and CAMLs were analyzed for predicting pCR. Sixty-three samples from 21 patients were analyzed. The median(IQR) pre-NAC total and mesenchymal CTC count/5 mL was lower in the pCR vs. non-pCR group [1(3.5) vs. 5(5.75); p = 0.096], [0 vs. 2.5(7.5); p = 0.084], respectively. The median(IQR) post-NAC CAML count/5 mL was higher in the pCR vs. non-pCR group [15(6) vs. 6(4.5); p = 0.004]. The pCR group was more likely to have >10 CAMLs post-NAC vs. non-pCR group [7(100%) vs. 3(21.4%); p = 0.001]. In a multivariate logistic regression model predicting pCR, CAML count was positively associated with the log-odds of pCR [OR = 1.49(1.01, 2.18); p = 0.041], while CTCs showed a negative trend [Odds Ratio (OR) = 0.44(0.18, 1.06); p = 0.068]. In conclusion, increased CAMLs in circulation after treatment combined with lowered CTCs was associated with pCR.

Radiomics of contrast-enhanced spectral mammography for prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer

BACKGROUND

Neoadjuvant chemotherapy (NAC) has been regarded as one of the standard treatments for patients with locally advanced breast cancer. No previous study has investigated the feasibility of using a contrast-enhanced spectral mammography (CESM)-based radiomics nomogram to predict pathological complete response (pCR) after NAC.

OBJECTIVE

To develop and validate a CESM-based radiomics nomogram to predict pCR after NAC in breast cancer.

METHODS

A total of 118 patients were enrolled, which are divided into a training dataset including 82 patients (with 21 pCR and 61 non-pCR) and a testing dataset of 36 patients (with 9 pCR and 27 non-pCR). The tumor regions of interest (ROIs) were manually segmented by two radiologists on the low-energy and recombined images and radiomics features were extracted. Intraclass correlation coefficients (ICCs) were used to assess the intra- and inter-observer agreements of ROI features extraction. In the training set, the variance threshold, SelectKBest method, and least absolute shrinkage and selection operator regression were used to select the optimal radiomics features. Radiomics signature was calculated through a linear combination of selected features. A radiomics nomogram containing radiomics signature score (Rad-score) and clinical risk factors was developed. The receiver operating characteristic (ROC) curve and calibration curve were used to evaluate prediction performance of the radiomics nomogram, and decision curve analysis (DCA) was used to evaluate the clinical usefulness of the radiomics nomogram.

RESULTS

The intra- and inter- observer ICCs were 0.769-0.815 and 0.786-0.853, respectively. Thirteen radiomics features were selected to calculate Rad-score. The radiomics nomogram containing Rad-score and clinical risk factor showed an encouraging calibration and discrimination performance with area under the ROC curves of 0.906 (95% confidence interval (CI): 0.840-0.966) in the training dataset and 0.790 (95% CI: 0.554-0.952) in the test dataset.

CONCLUSIONS

The CESM-based radiomics nomogram had good prediction performance for pCR after NAC in breast cancer; therefore, it has a good clinical application prospect.

Assessing the Correlation of Rate of Pathological Complete Response and Outcome in Post Neoadjuvant Chemotherapy Setting and Molecular Subtypes of Breast Cancer

Background Neoadjuvant chemotherapy (NAC) is being widely used in treating breast cancer (BC). This study aimed to analyze the correlation between clinicopathological features, immunohistochemistry (IHC)-based molecular subtypes, and the pathological response to NAC and its relationship with disease-free survival (DFS) and overall survival (OS). Materials and methods A retrospective analysis of 211 breast cancer patients who received NAC between 2008 and 2018 was performed. Tumors were classified by IHC into luminal A, luminal B, human epidermal growth factor receptor 2 (HER2)-enriched, and triple-negative subtypes. The chi-square test was used to assess the association between pathological response and clinicopathological parameters. Cox regression analysis was used to assess factors related to DFS and OS. Results Post NAC, 19.4% of patients achieved a pathologic complete response (pCR). Estrogen receptor (ER), progesterone receptor (PR), HER2 (p<0.001, 0.005, and 0.02), Ki67 (p=0.03), molecular subtypes (p<0.001), T stage (p=0.04), and N stage (p=0.01) were significantly associated with pathological response. The rate of pCR was highest among HER2-enriched and triple-negative tumors (45.2% and 28%, respectively) with OR=0.13 and p<0.001 for the HER2-enriched subtype. Patients with pCR were 61% less likely to develop metastasis (adjusted hazard ratio [aHR]=0.39, p=0.06, 95% CI=0.14-1.06) and were significantly associated with better OS (aHR=0.07, p=0.02, 95% CI=0.01-0.61). Patients who were ≤40 years old (aHR=2.1, p=0.01), with T4 (aHR=3.4, p=0.02), grade 3 (aHR=2.5, p=0.01), and node-positive disease (HR=2.24, p=0.02) were at an increased risk of developing metastasis. High Ki67 was found to be significantly associated with better DFS (p=0.006). Conclusion HER2-enriched and triple-negative BC were associated with a higher rate of pCR. Patients with pCR had significantly better DFS and OS. Younger age, advanced stage, higher grade, and lymph node involvement were risk factors for metastasis.

Neoadjuvant pegylated liposomal doxorubicin- and epirubicin-based combination therapy regimens for early breast cancer: a multicenter retrospective case-control study

PURPOSE

This study aimed to compare the effectiveness and safety of pegylated liposomal doxorubicin (PLD)-based and epirubicin-based combination therapy regimen as neoadjuvant therapy for early breast cancer.

METHODS

Patients with stage I-III breast cancer who underwent neoadjuvant therapy followed by surgery between January 2018 and December 2019 were retrospectively reviewed. The primary outcome was pathological complete response (pCR) rate. The secondary outcome was radiologic complete response (rCR) rate. Outcomes were compared between treatment groups PLD-cyclophosphamide followed by docetaxel (LC-T group) or epirubicin-cyclophosphamide followed by docetaxel (EC-T group), using both propensity-score matched (matched) and unmatched data.

RESULTS

Data were analyzed from patients who received neoadjuvant LC-T (n = 178) or EC-T (n = 181) treatment. The overall pCR rate and rCR rate were higher in the LC-T group compared with the EC-T group (unmatched pCR: 25.3% vs. 15.5%, p = 0.026; rCR: 14.7% vs. 6.7%, p = 0.016; matched pCR: 26.9% vs. 16.1%, p = 0.034; rCR: 15.5% vs. 7.4%, p = 0.044). Analysis by molecular subtype showed that compared with EC-T treatment, LC-T treatment achieved significantly greater pCR rate in triple-negative subtype and greater rCR rate in Her2 (+) subtype.

CONCLUSIONS

Neoadjuvant PLD-based therapy may be a potential option for patients with early-stage breast cancer. The current results warrant further investigation.

Multiparametric MRI radiomics fusion for predicting the response and shrinkage pattern to neoadjuvant chemotherapy in breast cancer

Purpose

During neoadjuvant chemotherapy (NACT), breast tumor morphological and vascular characteristics are usually changed. This study aimed to evaluate the tumor shrinkage pattern and response to NACT by preoperative multiparametric magnetic resonance imaging (MRI), including dynamic contrast-enhanced MRI (DCE-MRI), diffuse weighted imaging (DWI) and T2 weighted imaging (T2WI).

Method

In this retrospective analysis, female patients with unilateral unifocal primary breast cancer were included for predicting tumor pathologic/clinical response to NACT (n=216, development set, n=151 and validation set, n=65) and for discriminating the tumor concentric shrinkage (CS) pattern from the others (n=193; development set, n=135 and validation set, n=58). Radiomic features (n=102) of first-order statistical, morphological and textural features were calculated on tumors from the multiparametric MRI. Single- and multiparametric image-based features were assessed separately and were further combined to feed into a random forest-based predictive model. The predictive model was trained in the testing set and assessed on the testing dataset with an area under the curve (AUC). Molecular subtype information and radiomic features were fused to enhance the predictive performance.

Results

The DCE-MRI-based model showed higher performance (AUCs of 0.919, 0.830 and 0.825 for tumor pathologic response, clinical response and tumor shrinkage patterns, respectively) than either the T2WI or the ADC image-based model. An increased prediction performance was achieved by a model with multiparametric MRI radiomic feature fusion.

Conclusions

All these results demonstrated that multiparametric MRI features and their information fusion could be of important clinical value for the preoperative prediction of treatment response and shrinkage pattern.

Organoids from patient biopsy samples can predict the response of BC patients to neoadjuvant chemotherapy

PROPOSE

Neoadjuvant chemotherapy has been widely used in locally advanced and inflammatory breast cancer. Generally, complete pathological response after neoadjuvant chemotherapy treatment predicts survival. Studies have shown that patient-derived organoids can be used in cancer research and drug development. Therefore, we aimed to generate a living organoid biobank from biopsy samples to predict the response of patients to neoadjuvant chemotherapy.

METHOD

We generated a living organoid biobank from locally advanced breast cancer patients receiving neoadjuvant chemotherapy. When the patient received neoadjuvant chemotherapy, the organoids were treated with similar drugs, thereby simulating the situation of the patient receiving treatment.

RESULT

We successfully constructed organoids from breast cancer biopsies, demonstrating that organoids can be generated from a small sample of tissue. The phenotype of breast cancer organoid often agreed with the original breast cancer according to the blinded histopathological analysis of H&E stain tissue and organoid sections. In addition, our data confirm that the patient's response to chemotherapy closely matches the organoids' response to drugs.

CONCLUSION

Our data indicate that patient-derived organoids can be used to predict the clinical response of breast cancer patients to neoadjuvant chemotherapy in vitro and to screen drugs that have different effects on different patients. Key messageComplete pathological response (pCR) after adjuvant chemotherapy can predict, survival, therefore, predicting patient response to neoadjuvant chemotherapy is critical.Patient-derived organoids (PDOs) matched the original tumour in terms of histopathology, hormone receptor levels and HER2 receptor status.Patient-derived organoids can predict the responsiveness of patient to neoadjuvant chemotherapy.

Staging Breast Cancer with MRI, the T. A Key Role in the Neoadjuvant Setting

Breast cancer (BC) is the most common cancer among women worldwide. Neoadjuvant chemotherapy (NACT) indications have expanded from inoperable locally advanced to early-stage breast cancer. Achieving a pathological complete response (pCR) has been proven to be an excellent prognostic marker leading to better disease-free survival (DFS) and overall survival (OS). Although diagnostic accuracy of MRI has been shown repeatedly to be superior to conventional methods in assessing the extent of breast disease there are still controversies regarding the indication of MRI in this setting. We intended to review the complex literature concerning the tumor size in staging, response and surgical planning in patients with early breast cancer receiving NACT, in order to clarify the role of MRI. Morphological and functional MRI techniques are making headway in the assessment of the tumor size in the staging, residual tumor assessment and prediction of response. Radiomics and radiogenomics MRI applications in the setting of the prediction of response to NACT in breast cancer are continuously increasing. Tailored therapy strategies allow considerations of treatment de-escalation in excellent responders and avoiding or at least postponing breast surgery in selected patients.

SDF-1 expression and tumor-infiltrating lymphocytes identify clinical subtypes of triple-negative breast cancer with different responses to neoadjuvant chemotherapy and survival

Background

In this study, we investigated the prediction and prognostic value of SDF-1 for triple-negative breast cancer (TNBC) patients who underwent neoadjuvant chemotherapy (NAC) following standard radical surgery.

Methods

A total of 303 TNBC patients were included in this study. The NAC regimen was weekly paclitaxel plus carboplatin (PC) for all patients. SDF-1 and CXCR4 expression were measured at baseline and surgery via enzyme-linked immunosorbent assay (ELISA) and immunohistochemistry (IHC), respectively. Correlations between variables and treatment response were studied, and Cox proportional hazards regression analysis was implemented for prognostic evaluation.

Results

Of the 303 patients, 103 (34.0%) experienced pathological complete response (pCR) after completion of NAC. Serum SDF-1 expression before NAC was significantly correlated with the abundance of TILs. A higher pCR rate was more likely to be observed in patients with lower serum SDF-1 levels before NAC (P=0.001, OR=0.997, 95% CI: 0.996-0.999) and higher levels of TILs (P=0.005). In the multivariate survival model for nonpCR patients, serum SDF-1 expression at surgery served as an independent prognostic value for survival (high level, HR=1.980, 95% CI: 1.170-3.350, low level was used as a reference; P=0.011). Additionally, the predictive and prognostic value of serum SDF-1 expression was significant in patients with high abundance of TILs but not in patients with low abundance of TILs.

Conclusions

This study contributes to the clarification of the value of serum SDF-1 to predict pCR and survival for TNBC patients who underwent NAC. This new serum marker, together with TILs, might help identify clinical subtypes of TNBC with different treatment responses and survival and play an important role in tailoring and modifying the NAC strategy for advanced TNBCs in the future.

Novel Radiomic Measurements of Tumor-Associated Vasculature Morphology on Clinical Imaging as a Biomarker of Treatment Response in Multiple Cancers

PURPOSE

The tumor-associated vasculature (TAV) differs from healthy blood vessels by its convolutedness, leakiness, and chaotic architecture, and these attributes facilitate the creation of a treatment-resistant tumor microenvironment. Measurable differences in these attributes might also help stratify patients by likely benefit of systemic therapy (e.g., chemotherapy). In this work, we present a new category of computational image-based biomarkers called quantitative tumor-associated vasculature (QuanTAV) features, and demonstrate their ability to predict response and survival across multiple cancer types, imaging modalities, and treatment regimens involving chemotherapy.

EXPERIMENTAL DESIGN

We isolated tumor vasculature and extracted mathematical measurements of twistedness and organization from routine pretreatment radiology (CT or contrast-enhanced MRI) of a total of 558 patients, who received one of four first-line chemotherapy-based therapeutic intervention strategies for breast (n = 371) or non-small cell lung cancer (NSCLC, n = 187).

RESULTS

Across four chemotherapy-based treatment strategies, classifiers of QuanTAV measurements significantly (P < 0.05) predicted response in held out testing cohorts alone (AUC = 0.63-0.71) and increased AUC by 0.06-0.12 when added to models of significant clinical variables alone. Similarly, we derived QuanTAV risk scores that were prognostic of recurrence-free survival in treatment cohorts who received surgery following chemotherapy for breast cancer [P = 0.0022; HR = 1.25; 95% confidence interval (CI), 1.08-1.44; concordance index (C-index) = 0.66] and chemoradiation for NSCLC (P = 0.039; HR = 1.28; 95% CI, 1.01-1.62; C-index = 0.66). From vessel-based risk scores, we further derived categorical QuanTAV high/low risk groups that were independently prognostic among all treatment groups, including patients with NSCLC who received chemotherapy only (P = 0.034; HR = 2.29; 95% CI, 1.07-4.94; C-index = 0.62). QuanTAV response and risk scores were independent of clinicopathologic risk factors and matched or exceeded models of clinical variables including posttreatment response.

CONCLUSIONS

Across these domains, we observed an association of vascular morphology on CT and MRI-as captured by metrics of vessel curvature, torsion, and organizational heterogeneity-and treatment outcome. Our findings suggest the potential of shape and structure of the TAV in developing prognostic and predictive biomarkers for multiple cancers and different treatment strategies.

Hormone Receptor and HER2 Status Switch in Non-pCR Breast Cancer Specimens after Neoadjuvant Therapy

Introduction

This project aimed to identify the frequency of a switch of hormone receptor (HR) and/or HER2 status after neoadjuvant chemotherapy (NAC) for early breast cancer.

Methods

Tumor samples from patients without pathological complete response (non-pCR) were evaluated. Pathological complete response (pCR) was defined as no invasive tumor in breast and lymph nodes (ypT0/is ypN0). HR and HER2 status determined before NAC was compared with the corresponding receptor status determined in the surgical specimen after NAC.

Results

245 consecutive patients with primary invasive breast cancer, treated with NAC with/without targeted therapy between January 1, 2016 and December 31, 2019, at the LMU Breast Center, Munich, Germany, were identified. In 128 patients (52%), surgery revealed non-pCR after completed NAC. In 35 cases (27%), a switch of either HR and/or HER2 status between the initial biopsy and the surgical specimen was detected. Twenty cases had a switch in HR status, while 15 cases had a switch in HER2 status.

Conclusion

In a substantial number (27%) of non-pCR cases, a switch in biomarker status after completed neoadjuvant treatment was detected. These results are consistent with prior evidence. Yet, routine reevaluation of HR and HER2 status is not recommended in guidelines so far. Future research needs to address the impact of HR and HER2 status switch on therapy adaptation and on subsequent patient outcome. Particularly, in view of the recent therapy advances, it will be critical to evaluate whether individualization of treatment concepts based on the biology of the non-pCR specimens is preferable to the initial therapy concept based on the pathology at primary diagnosis.

Does Residual Cancer Burden Predict Local Recurrence After Neoadjuvant Chemotherapy?

BACKGROUND

The extent of residual disease after neoadjuvant chemotherapy (NAC) can be quantified by the Residual Cancer Burden (RCB), a prognostic tool used to estimate survival outcomes in breast cancer. This study investigated the association between RCB and locoregional recurrence (LRR).

METHODS

The study reviewed 532 women with breast cancer who underwent NAC between 2010 and 2016. Relapse in the ipsilateral breast, skin/subcutis at the surgical site, chest wall, pectoralis, or regional lymph nodes defined an LRR. The LRR cumulative incidence (LRCI) was estimated using the Fine and Gray competing-risks model, with death and distant recurrence defined as competing events. The association of LRCI with prognostic variables was evaluated.

RESULTS

Overall, 5.5% of the patients experienced an LRR after a median follow-up period of 65 months. The 5-year LRCI rates by RCB were as follows: RCB-0 (0.9%), RCB-1 (3.2%), RCB-2 (6.0%), and RCB-3 (12.9%). In the univariable analysis, LRCI varied significantly by RCB (p = 0.010). The multivariable analysis showed a significant association of LRCI with increasing RCB, and the patients with hormone receptor-positive (HR+)/human epidermal growth factor receptor 2-negative (HER2-) phenotype were at lower risk for LRR than those with HER2+ and triple-negative cancers (p < 0.032). The patients with RCB-3 were at a higher risk for local relapse than those with RCB-0 (hazard ratio, 13.78; confidence interval, 2.25-84.45; p = 0.04). Type of operation (p = 0.04) and use of adjuvant radiation (p = 0.046) were statistically significant in the multivariable model.

CONCLUSIONS

The study results demonstrate a significant association between LRCI and increasing RCB, although distant recurrence is a substantial driver of disease outcomes. Future prospective studies should examine the role of RCB in clinical decisions regarding indications for adjuvant therapy.

Preoperative Breast Magnetic Resonance Imaging as a Predictor of Response to Neoadjuvant Chemotherapy

Introduction:

The ability to accurately predict pathologic complete response (pCR) after neoadjuvant chemotherapy (NAC) in breast cancer would improve patient selection for specific treatment strategies, would provide important information for patients to aid in the treatment selection process, and could potentially avoid the need for more extensive surgery. The diagnostic performance of magnetic resonance imaging (MRI) in predicting pCR has previously been studied, with mixed results. Magnetic resonance imaging performance may also be influenced by tumour and patient factors.

Methods:

Eighty-seven breast cancer patients who underwent NAC were studied. Pre-NAC and post-NAC MRI findings were compared with pathologic findings postsurgical excision. The impact of patient and tumour characteristics on MRI accuracy was evaluated.

Results:

The mean (SD) age of participants was 48.7 (10.3) years. The rate of pCR based on post-NAC MRI was 19.5% overall (19/87). The sensitivity, specificity, positive predictive value (PPV), negative predictive value, and accuracy in predicting pCR were 52.9%, 77.1%, 36.0%, 87.1%, and 72.4%, respectively. Positive predictive value was the highest in nonluminal versus Luminal A disease (45.0% vs 25.0%, P < .001), with higher rates of false positivity in nonluminal subtypes (P = .002). Tumour grade, T category, and histological subtype were all independent predictors of MRI accuracy regarding post-NAC tumour size.

Conclusion:

Magnetic resonance imaging alone is insufficient to accurately predict pCR in breast cancer patients post-NAC. Magnetic resonance imaging predictions of pCR are more accurate in nonluminal subtypes. Tumour grade, T category, and histological subtype should be considered when evaluating post-NAC tumour sizes.

Estrogen Receptor and Claudin-6 Might Play Vital Roles for Long-Term Prognosis in Patients With Luminal A Breast Cancer Who Underwent Neoadjuvant Chemotherapy

Purpose

It is well-known that the pathological complete response (pCR) rate in patients with luminal A cancer (LAC) is lower than those of other subtypes of breast cancer. The phenotype of cancer often alters after neoadjuvant chemotherapy (NAC) which may be related to hypoxia, and the latter might induce the drift of the estrogen receptor (ER). The phenotype drift in local advanced LAC after NAC might influence the long-term prognosis.

Methods

The oxygen concentration of cancer tissues during NAC was recorded and analyzed (n = 43). The expression of ER and claudin-6 was detected in pre- and post-NAC specimens.

Results

NAC might induce the cycling intracanceral hypoxia, and the pattern was related to NAC response. The median follow-up time was 61 months. Most of the patients (67%) with stable or increased ER and claudin-6 expression exhibited perfect prognosis (DFS = 100%, 61 months). About 20% of patients with decreased claudin-6 would undergo the poor prognosis (DFS = 22.2%, 61 months). The contrasting prognosis (100% vs. 22.2%) had nothing to do with the response of NAC in the above patients. Only 13% patients had stable claudin-6 and decreased ER, whose prognosis might relate to the response of NAC.

Conclusion

NAC might induce cycling intracanceral hypoxia to promote the phenotype drift in local advanced LAC, and the changes in ER and claudin-6 after NAC would determine the long-term prognosis.

Correlation Between Immune-Related Genes and Tumor-Infiltrating Immune Cells With the Efficacy of Neoadjuvant Chemotherapy for Breast Cancer

Background: In the absence of targeted therapy or clear clinically relevant biomarkers, neoadjuvant chemotherapy (NAC) is still the standard neoadjuvant systemic therapy for breast cancer. Among the many biomarkers predicting the efficacy of NAC, immune-related biomarkers, such as immune-related genes and tumor-infiltrating lymphocytes (TILs), play a key role.

Methods: We analyzed gene expression from several datasets in the Gene Expression Omnibus (GEO) database and evaluated the relative proportion of immune cells using the CIBERSORT method. In addition, mIHC/IF detection was performed on clinical surgical specimens of triple-negative breast cancer patients after NAC.

Results: We obtained seven immune-related genes, namely, CXCL1, CXCL9, CXCL10, CXCL11, IDO1, IFNG, and ORM1 with higher expression in the pathological complete response (pCR) group than in the non-pCR group. In the pCR group, the levels of M1 and γδT macrophages were higher, while those of the M2 macrophages and mast cells were lower. After NAC, the proportions of M1, γδT cells, and resting CD4 memory T cells were increased, while the proportions of natural killer cells and dendritic cells were decreased with downregulated immune-related genes. The results of mIHC/IF detection and the prognostic information of corresponding clinical surgical specimens showed the correlation of proportions of natural killer cells, CD8-positive T cells, and macrophages with different disease-free survival outcomes.

Conclusion: The immune-related genes and immune cells of different subtypes in the tumor microenvironment are correlated with the response to NAC in breast cancer, and the interaction between TILs and NAC highlights the significance of combining NAC with immunotherapy to achieve better clinical benefits.

The association between body mass index and pathological complete response in neoadjuvant-treated breast cancer patients

BACKGROUND

Obesity seems to be associated with a poorer response to adjuvant chemotherapy in breast cancer (BC); however, associations in the neoadjuvant chemotherapy (NACT) setting and according to menopausal status are less studied. This study aims to investigate the association between pretreatment body mass index (BMI) and pathological complete response (pCR) following NACT in BC according to menopausal and estrogen receptor (ER) status.

MATERIAL AND METHODS

The study cohort consisted of 491 patients receiving NACT in 2005-2019. Based on pre-NACT patient and tumor characteristics, the association between BMI and achieving pCR was analyzed using logistic regression models (crude and adjusted models (age, tumor size, and node status)) with stratification by menopausal and ER status.

RESULTS

In the overall cohort, being overweight (BMI ≥25) compared by being normal-weight (BMI <25), increased the odds of accomplishing pCR by 15%. However, based on the 95% confidence interval (CI) the data were compatible with associations within the range of a decrease of 30% to an increase of 89%. Stratification according to menopausal status also showed no strong association: the odds ratio (OR) of accomplishing pCR in overweight premenopausal patients compared with normal-weight premenopausal patients was 1.76 (95% CI 0.88-3.55), whereas for postmenopausal patients the corresponding OR was 0.71 (95% CI 0.35-1.46).

DISCUSSION

In a NACT BC cohort of 491 patients, we found no evidence of high BMI as a predictive factor of accomplishing pCR, neither in the whole cohort nor stratified by menopausal status. Given the limited precision in our results, larger studies are needed before considering BMI in clinical decision-making regarding NACT or not.

Advances in Imaging in Evaluating the Efficacy of Neoadjuvant Chemotherapy for Breast Cancer

Neoadjuvant chemotherapy (NAC) is increasingly widely used in breast cancer treatment, and accurate evaluation of its response provides essential information for treatment and prognosis. Thus, the imaging tools used to quantify the disease response are critical in evaluating and managing patients treated with NAC. We discussed the recent progress, advantages, and disadvantages of common imaging methods in assessing the efficacy of NAC for breast cancer.

The Radiological Response Rate Pattern Is Associated With Recurrence Free Survival in Breast Cancer Patients Undergoing Neoadjuvant Chemotherapy

PURPOSE

The aim of this study was to evaluate the radiological response rate patterns during neoadjuvant chemotherapy (NAC) in patients with breast cancer.

METHODS

Patients who underwent NAC with two specific chemotherapy regimens (doxorubicin with cyclophosphamide or doxorubicin with docetaxel) and who underwent a response evaluation every two cycles were included in the study. The initial response ratio was defined as the ratio of the largest tumor diameter at diagnosis to that after two cycles of NAC. The latter response ratio was defined as the ratio between the tumor size after two cycles and that after four cycles of NAC. The radiological response rate pattern was divided into three groups: the fast-to-slow response group (F-S group, initial response ratio > latter response ratio + 20%), slow-to-fast response group (S-F group, latter response ratio > initial response ratio + 20%), and constant response group (less than 20% difference between the initial and latter response ratios).

RESULTS

In total, 177 patients were included in the analysis. Forty-two (23.9%) patients were categorized into the F-S group, 26 (14.8%) into the S-F group, and 108 (61.2%) into the constant group. Clinicopathologic factors did not differ according to radiologic response rate patterns. The median follow-up period was 50 months (range, 3-112) months. In the univariate analysis, the F-S group had a significantly worse recurrence-free survival than the S-F and constant groups (hazard ratio [HR], 3.63; 95% confidence interval [CI], 1.05-12.46; p = 0.041). The F-S group also presented with significantly worse survival than the S-F group in the multivariate analysis (HR, 3.45; 95% CI, 1.00-11.89; p = 0.049).

CONCLUSION

The F-S group had a poorer survival rate than the S-F group. Radiological response rate patterns may be useful for accurate prognostic assessments, especially when considering post-neoadjuvant therapy.

Multiparametric 18F-FDG PET/MRI-Based Radiomics for Prediction of Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer

BACKGROUND

The aim of this study was to assess whether multiparametric ¹⁸F-FDG PET/MRI-based radiomics analysis is able to predict pathological complete response in breast cancer patients and hence potentially enhance pretherapeutic patient stratification.

METHODS

A total of 73 female patients (mean age 49 years; range 27-77 years) with newly diagnosed, therapy-naive breast cancer underwent simultaneous ¹⁸F-FDG PET/MRI and were included in this retrospective study. All PET/MRI datasets were imported to dedicated software (ITK-SNAP v. 3.6.0) for lesion annotation using a semi-automated method. Pretreatment biopsy specimens were used to determine tumor histology, tumor and nuclear grades, and immunohistochemical status. Histopathological results from surgical tumor specimens were used as the reference standard to distinguish between complete pathological response (pCR) and noncomplete pathological response. An elastic net was employed to select the most important radiomic features prior to model development. Sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were calculated for each model.

RESULTS

The best results in terms of AUCs and NPV for predicting complete pathological response in the entire cohort were obtained by the combination of all MR sequences and PET (0.8 and 79.5%, respectively), and no significant differences from the other models were observed. In further subgroup analyses, combining all MR and PET data, the best AUC (0.94) for predicting complete pathologic response was obtained in the HR+/HER2- group. No difference between results with/without the inclusion of PET characteristics was observed in the TN/HER2+ group, each leading to an AUC of 0.92 for all MR and all MR + PET datasets.

CONCLUSION

¹⁸F-FDG PET/MRI enables comprehensive high-quality radiomics analysis for the prediction of pCR in breast cancer patients, especially in those with HR+/HER2- receptor status.

Clinical Significance of Breast Cancer Molecular Subtypes and Ki67 Expression as a Predictive Value for Pathological Complete Response following Neoadjuvant Chemotherapy: Experience from a Tertiary Care Center in Lebanon

Introduction

Breast cancer is considered nowadays the most prevalent cancer worldwide. The molecular era has successfully divided breast cancer into subtypes based on the various hormonal receptors. These molecular subtypes play a major role in determining the neoadjuvant chemotherapy to be administered. It was noted that the use of neoadjuvant chemotherapy was associated with higher achievement of pathological complete response. The aim of the study was to determine the predictive role of breast cancer subtypes in the efficacy and prognosis of neoadjuvant chemotherapy regimens.

Methods

Combining dose dense anthracycline-based, regular dose anthracycline-based, and nonanthracycline-based chemotherapy, we observed data from 87 patients with breast cancer who received surgery after administration of neoadjuvant chemotherapy at our institution between January 2015 and July 2018. The patients were classified into luminal A, luminal B, HER2 overexpression, and triple negative breast cancer as well as low Ki67 (≤14%) and high Ki67 (>14%) expression groups using immunohistochemistry. Pathologic complete response was the only neoadjuvant chemotherapy outcome parameter. To evaluate variables associated with pathologic complete response, we used univariate analyses followed by multivariate logistic regression.

Results

87 patients with breast cancer were classified into different subtypes according to the 12^th St. Gallen International Breast Cancer Conference. The response rate to neoadjuvant chemotherapy was significantly different (p = 0.046) between the subgroups. There were significant correlations between pathological complete response (pCR) and ER status (p < 0.0001), HER2 (p = 0.013), molecular subtypes (p = 0.018), T stage (p = 0.024), N stage before chemotherapy (p = 0.04), and type of chemotherapy (p = 0.029). Luminal B type patients had the lowest pCR, followed by luminal A type patients.

Conclusion

Evaluating molecular subtype's significance in breast cancer prognosis warrants additional studies in our region with extensive data about patient-specific neoadjuvant chemotherapy regimens. Our study was able to reproduce results complementary to those present in the literature in other outcomes.

Computer-aided classification of MRI for pathological complete response to neoadjuvant chemotherapy in breast cancer

Background: To determine suitable optimal classifiers and examine the general applicability of computer-aided classification to compare the differences between a computer-aided system and radiologists in predicting pathological complete response (pCR) from patients with breast cancer receiving neoadjuvant chemotherapy. Methods: We analyzed a total of 455 masses and used the U-Net network and ResNet to execute MRI segmentation and pCR classification. The diagnostic performance of radiologists, the computer-aided system and a combination of radiologists and computer-aided system were compared using receiver operating characteristic curve analysis. Results: The combination of radiologists and computer-aided system had the best performance for predicting pCR with an area under the curve (AUC) value of 0.899, significantly higher than that of radiologists alone (AUC: 0.700) and computer-aided system alone (AUC: 0.835). Conclusion: An automated classification system is feasible to predict the pCR to neoadjuvant chemotherapy in patients with breast cancer and can complement MRI.

Molecular Biomarkers Predict Pathological Complete Response of Neoadjuvant Chemotherapy in Breast Cancer Patients: Review

Simple Summary

Breast cancer is the most common cancer in women worldwide. Although many studies have aimed to understand the genetic basis of breast cancer, leading to increasingly accurate diagnoses, only a few molecular biomarkers are used in clinical practice to predict response to therapy. Current studies aim to develop more personalized therapies to decrease the adverse effects of chemotherapy. Personalized medicine not only requires clinical, but also molecular characterization of tumors, which allows the use of more effective drugs for each patient. The aim of this study was to identify potential molecular biomarkers that can predict the response to therapy after neoadjuvant chemotherapy in patients with breast cancer. In this review, we summarize genomic, transcriptomic, and proteomic biomarkers that can help predict the response to therapy.

Abstract

Neoadjuvant chemotherapy (NAC) is often used to treat locally advanced disease for tumor downstaging, thus improving the chances of breast-conserving surgery. From the NAC response, it is possible to obtain prognostic information as patients may reach a pathological complete response (pCR). Those who do might have significant advantages in terms of survival rates. Breast cancer (BC) is a heterogeneous disease that requires personalized treatment strategies. The development of targeted therapies depends on identifying biomarkers that can be used to assess treatment efficacy as well as the discovery of new and more accurate therapeutic agents. With the development of new “OMICS” technologies, i.e., genomics, transcriptomics, and proteomics, among others, the discovery of new biomarkers is increasingly being used in the context of clinical practice, bringing us closer to personalized management of BC treatment. The aim of this review is to compile the main biomarkers that predict pCR in BC after NAC.

A Practical Predictive Model Based on Ultrasound Imaging and Clinical Indices for Estimation of Response to Neoadjuvant Chemotherapy in Patients with Breast Cancer

Purpose

Clinical responses of neoadjuvant chemotherapy (NACT) are associated with prognosis in patients with breast cancer. The selection of suitable variables for the prediction of clinical responses remains controversial. Herein, we developed a predictive model based on ultrasound imaging and clinical indices to identify patients most likely to benefit from NACT.

Patients and Methods

We recruited a total of 225 consecutive patients who underwent NACT followed by surgery and axillary lymph node dissection at the Sixth Hospital of Ning Bo City of Zhe Jiang Province between January 1, 2018, and March 31, 2021. All patients had been diagnosed with breast cancer following the clinical examination. First, we created a training cohort of patients who underwent NACT+surgery (N=180) to develop a nomogram. We then validated the performance of the nomogram in a validation cohort of patients who underwent NACT+ surgery (N=45). Multivariate logistic regression was then used to identify independent risk factors that were associated with the response to NACT; these were then incorporated into the nomogram.

Results

Multivariate logistic regression analysis identified several significant differences as to clinical responses of NACT, including neutrophil–lymphocyte ratio (NLR), body mass index (BMI), pulsatility index (PI), resistance index (RI), blood flow, Ki67, histological type, molecular subtyping, and tumor size. The performance of the nomogram score exhibited a robust C-index of 0.89 (95% confidence interval [CI]: 0.83 to 0.95) in the training cohort and a high C-index of 0.87 (95% CI: 0.81 to 0.93) in the validation cohort. Clinical impact curves showed that the nomogram had a good predictive ability.

Conclusion

We successfully established an accurate and optimized nomogram incorporated ultrasound imaging and clinical indices that could be used preoperatively to predict clinical responses of NACT. This model can be used to evaluate the risk of clinical responses to NACT and therefore facilitate the choice of personalized therapy.

Analysis of mammograms using artificial intelligence to predict response to neoadjuvant chemotherapy in breast cancer patients: proof of concept

Objectives

In this proof of concept study, a deep learning–based method for automatic analysis of digital mammograms (DM) as a tool to aid in assessment of neoadjuvant chemotherapy (NACT) treatment response in breast cancer (BC) was examined.

Methods

Baseline DM from 453 patients receiving NACT between 2005 and 2019 were included in the study cohort. A deep learning system, using the aforementioned baseline DM, was developed to predict pathological complete response (pCR) in the surgical specimen after completion of NACT. Two image patches, one extracted around the detected tumour and the other from the corresponding position in the reference image, were fed into a classification network. For training and validation, 1485 images obtained from 400 patients were used, and the model was ultimately applied to a test set consisting of 53 patients.

Results

A total of 95 patients (21%) achieved pCR. The median patient age was 52.5 years (interquartile range 43.7–62.1), and 255 (56%) were premenopausal. The artificial intelligence (AI) model predicted the pCR as represented by the area under the curve of 0.71 (95% confidence interval 0.53–0.90; p = 0.035). The sensitivity was 46% at a fixed specificity of 90%.

Conclusions

Our study describes an AI platform using baseline DM to predict BC patients’ responses to NACT. The initial AI performance indicated the potential to aid in clinical decision-making. In order to continue exploring the clinical utility of AI in predicting responses to NACT for BC, further research, including refining the methodology and a larger sample size, is warranted.

Key Points

• We aimed to answer the following question: Prior to initiation of neoadjuvant chemotherapy, can artificial intelligence (AI) applied to digital mammograms (DM) predict breast tumour response?

• DMs contain information that AI can make use of for predicting pathological complete (pCR) response after neoadjuvant chemotherapy for breast cancer.

• By developing an AI system designed to focus on relevant parts of the DM, fully automatic pCR prediction can be done well enough to potentially aid in clinical decision-making.

Can axillary surgery be omitted in patients with breast pathologic complete response after neoadjuvant systemic therapy for breast cancer? A real-world retrospective study in China

PURPOSE

Studies show that axillary surgery can be potentially omitted in certain breast cancer patients who achieve breast pathologic complete response (pCR) after neoadjuvant systemic therapy (NST). However, potential differences between the ypT0 and ypTis subgroups remain to be explored. Furthermore, whether axillary surgery can be omitted in patients with clinically assessed positive axillary lymph nodes (cN+) remains unknown. This study was to evaluate the status of axillary lymph nodes for patients who achieved breast pCR after NST in the real-world study.

METHODS

This retrospective cohort study included 258 patients with early or locally advanced breast cancer who underwent breast and axillary surgery after NST. Clinical and pathologic data were compared between patients with breast pCR (ypT0/is) and those without breast pCR.

RESULTS

The rate of breast pCR after NST was 27.1% (70/258). Among the patients with initial cN0, the rate of axillary pCR was similar between the breast pCR and breast non-pCR groups (100% vs. 85.7%, P = 0.1543). Among those with breast pCR, the rate of axillary pCR was 100% in both the ypT0 and ypTis subgroups. Furthermore, among those with initial cN+, the rate of axillary pCR was higher in the breast pCR group than in the breast non-pCR group (82.7% vs. 22.9%, P < 0.0001). Among the patients with breast pCR, the rate of axillary pCR was higher in the ypT0 subgroup than in the ypTis subgroup (94.3% vs. 58.8%, P = 0.0034).

CONCLUSION

Axillary surgery may potentially be omitted in patients with initial cN0 who achieve breast pCR (ypT0/is), and may also be considered for omission in patients with initial cN+ who achieve ypT0 (not ypTis).

Multicontrast MRI-based radiomics for the prediction of pathological complete response to neoadjuvant chemotherapy in patients with early triple negative breast cancer

INTRODUCTION

To assess pre-therapeutic MRI-based radiomic analysis to predict the pathological complete response to neoadjuvant chemotherapy (NAC) in women with early triple negative breast cancer (TN).

MATERIALS AND METHODS

This monocentric retrospective study included 75 TN female patients with MRI (T1-weighted, T2-weighted, diffusion-weighted and dynamic contrast enhancement images) performed before NAC. For each patient, the tumor(s) and the parenchyma were independently segmented and analyzed with radiomic analysis to extract shape, size, and texture features. Several sets of features were realized based on the 4 different sequence images. Performances of 4 classifiers (random forest, multilayer perceptron, support vector machine (SVM) with linear or quadratic kernel) were compared based on pathological complete response (defined on the excised tissues), on 100 draws with 75% as training set and 25% as test.

RESULTS

The combination of features extracted from different MR images improved the classifier performance (more precisely, the features from T1W, T2W and DWI). The SVM with quadratic kernel showed the best performance with a mean AUC of 0.83, a sensitivity of 0.85 and a specificity of 0.75 in the test set.

CONCLUSION

MRI-based radiomics may be relevant to predict NAC response in TN cancer. Our results promote the use of multi-contrast MRI sources for radiomics, providing enrich source of information to enhance model generalization.

Prediction of Tumor Shrinkage Pattern to Neoadjuvant Chemotherapy Using a Multiparametric MRI-Based Machine Learning Model in Patients With Breast Cancer

Aim: After neoadjuvant chemotherapy (NACT), tumor shrinkage pattern is a more reasonable outcome to decide a possible breast-conserving surgery (BCS) than pathological complete response (pCR). The aim of this article was to establish a machine learning model combining radiomics features from multiparametric MRI (mpMRI) and clinicopathologic characteristics, for early prediction of tumor shrinkage pattern prior to NACT in breast cancer.

Materials and Methods: This study included 199 patients with breast cancer who successfully completed NACT and underwent following breast surgery. For each patient, 4,198 radiomics features were extracted from the segmented 3D regions of interest (ROI) in mpMRI sequences such as T1-weighted dynamic contrast-enhanced imaging (T1-DCE), fat-suppressed T2-weighted imaging (T2WI), and apparent diffusion coefficient (ADC) map. The feature selection and supervised machine learning algorithms were used to identify the predictors correlated with tumor shrinkage pattern as follows: (1) reducing the feature dimension by using ANOVA and the least absolute shrinkage and selection operator (LASSO) with 10-fold cross-validation, (2) splitting the dataset into a training dataset and testing dataset, and constructing prediction models using 12 classification algorithms, and (3) assessing the model performance through an area under the curve (AUC), accuracy, sensitivity, and specificity. We also compared the most discriminative model in different molecular subtypes of breast cancer.

Results: The Multilayer Perception (MLP) neural network achieved higher AUC and accuracy than other classifiers. The radiomics model achieved a mean AUC of 0.975 (accuracy = 0.912) on the training dataset and 0.900 (accuracy = 0.828) on the testing dataset with 30-round 6-fold cross-validation. When incorporating clinicopathologic characteristics, the mean AUC was 0.985 (accuracy = 0.930) on the training dataset and 0.939 (accuracy = 0.870) on the testing dataset. The model further achieved good AUC on the testing dataset with 30-round 5-fold cross-validation in three molecular subtypes of breast cancer as following: (1) HR+/HER2–: 0.901 (accuracy = 0.816), (2) HER2+: 0.940 (accuracy = 0.865), and (3) TN: 0.837 (accuracy = 0.811).

Conclusions: It is feasible that our machine learning model combining radiomics features and clinical characteristics could provide a potential tool to predict tumor shrinkage patterns prior to NACT. Our prediction model will be valuable in guiding NACT and surgical treatment in breast cancer.

Tumor Sphericity Predicts Response in Neoadjuvant Chemotherapy for Invasive Breast Cancer

This retrospective study examined magnetic resonance imaging (MRI)–derived tumor sphericity (SPH) as a quantitative measure of breast tumor morphology, and investigated the association between SPH and reader-assessed morphological pattern (MP). In addition, association of SPH with pathologic complete response was evaluated in patients enrolled in an adaptively randomized clinical trial designed to rapidly identify new agents for breast cancer. All patients underwent MRI examinations at multiple time points during the treatment. SPH values from pretreatment (T0) and early-treatment (T1) were investigated in this study. MP on T0 dynamic contrast-enhanced MRI was ranked from 1 to 5 in 220 patients. Mean SPH values decreased with the increased order of MP. SPH was higher in patients with pathologic complete response than in patients without (difference at T0: 0.04, 95% confidence interval [CI]: 0.02–0.05, P < .001; difference at T1: 0.03, 95% CI: 0.02–0.04, P < .001). The area under the receiver operating characteristic curve was estimated as 0.61 (95% CI, 0.57–0.65) at T0 and 0.58 (95% CI, 0.55–0.62) at T1. When the analysis was performed by cancer subtype defined by hormone receptor (HR) and human epidermal growth factor receptor 2 (HER2) status, highest area under the receiver operating characteristic curve were observed in HR−/HER2+: 0.67 (95% CI, 0.54–0.80) at T0, and 0.63 (95% CI, 0.51–0.76) at T1. Tumor SPH showed promise to quantify MRI MPs and as a biomarker for predicting treatment outcome at pre- or early-treatment time points.

Correlation Between Preoperative Radiological and Postoperative Pathological Tumor Size in Patients With HER2+ Breast Cancer After Neoadjuvant Chemotherapy Plus Trastuzumab and Pertuzumab

BACKGROUND

Neoadjuvant chemotherapy (NAC) in combination with anti-HER2 treatment is standard of care in patients with early HER2 positive breast cancer. Preoperative radiological evaluation is mandatory for defining the extent of surgery. In this study, we evaluated the correlation between preoperative radiological and postoperative pathological tumor size in early HER2 positive patients after neoadjuvant chemotherapy in combination with trastuzumab and pertuzumab. In a patient population with HER2 positive breast cancer, who received neoadjuvant chemotherapy and anti-HER2 treatment, the correlation between preoperative radiological and postoperative pathological tumor size was performed. Concordance of radiological and pathological tumor size was found in 55.7%, leading to more extensive breast surgery as required in 7 cases and to the underestimation of 6 neoplastic lesions before surgery, respectively.

PATIENTS AND METHODS

Seventy early HER2 positive breast cancer patients were included and retrospectively analysed. All preoperative radiological assessments as well as the tumor board decision on surgical extent and pathological evaluation were completed at the Medical University of Vienna. Preoperative radiological assessment of tumor size and lymph node status were compared with final histopathological findings. The correlation between different radiological modalities regarding tumor size was investigated.

RESULTS

Concordance of radiological and pathological tumor size was found in 55.7 % (50% by sonography and 66.7% by MRI, respectively) of patients with a nonsignificant correlation of r = 0.31 (P = .08). Of the 39 patients with pathologic complete remission (pCR), 16 were also classified as radiological complete response (rCR) while 23 of those showed a radiological stable disease or partial response. In 6 patients, radiological assessment showed a CR but invasive cancer with a tumor size range from 7 to 36 mm was found in histopathological examination. Neither menopausal status (P= .69) nor BMI (P = .60) and age (P = .50) had an impact on the correlation between radiological and histopathological tumor size. Regarding lymph node status, a statistically significant association and clinically relevant correlation between radiological and histopathological evaluation was found (r = 0.66, P < .001).

CONCLUSION

Concordance between radiology and histopathology was low regarding tumor size after NAC in combination with trastuzumab and pertuzumab, but significant regarding lymph node status.

Is pathological complete response predictable after neoadjuvant chemotherapy in breast cancer? A single institution's retrospective experience

BACKGROUND

Pathological complete response (pCR), in breast cancers, after neoadjuvant chemotherapy is linked to improved survival. Determining complete response to chemotherapy prior to surgery has remained elusive even using a combination of pathological factors and imaging modalities, making surgery still a necessity.

METHODS

A retrospective analysis was performed from a single institution from 2013 to 2018. Breast cancer patients treated with neoadjuvant chemotherapy with pre- and post-chemotherapy magnetic resonance imaging (MRI) were included. Patients receiving other neoadjuvant modalities were excluded. Imaging characteristics, including response to chemotherapy and pathological factors, were recorded.

RESULTS

Analysis showed 134 patients were identified with 40/134 (29.9%) noted to have radiological complete response and 34/134 (25.6%) had pCR. The positive predictive value for MRI to detect pCR was greatest for oestrogen receptor (ER) negative and human epidermal growth factor receptor 2 (HER2) negative tumours at 81.8% and worst for ER+ HER2- tumours at 25%. The negative predictive value was greatest for ER+ HER2- tumours at 93.9% and worst for ER- HER2- tumours at 77.4%.

CONCLUSION

MRI after neoadjuvant chemotherapy for breast cancer even combined with tumour factors is not an accurate predictor of pCR.

New horizons in imaging and surgical assessment of breast cancer lymph node metastasis

Axillary nodal status is one of the most important prognostic factors in breast cancer. While sentinel lymph node biopsy (SLNB) is a safe and validated procedure for clinically node-negative patients, axillary management of clinically node-positive patients has been more controversial. Patients with clinically detected axillary metastases often benefit from neoadjuvant chemotherapy (NAC). Those who convert to node-negative disease following NAC are important to identify, since they can often be spared significant morbidity from axillary dissection. SLNB has shown widely varying false-negative rates (FNR) but with the use of dual mapping and surgical biopsy of 3 or more nodes, it is considered an acceptable method to stage the axilla in clinically node-positive patients who receive NAC. Various methods including targeted axillary dissection (TAD) have been shown to decrease the FNR of SLNB. We will review appropriate methods to identify a metastatic node and subsequent ultrasound-guided biopsy with tissue marking techniques. We underscore key points in monitoring axillary response, techniques to accurately localize the biopsied and clipped known metastatic node for surgical excision and the effect of various methods in reducing the FNR of SLNB, including the emerging concept of TAD on patient care.