Neoadjuvant chemotherapy in patients with breast cancer: Does response in the breast predict axillary node response?

ACR Appropriateness Criteria® Monitoring Response to Neoadjuvant Systemic Therapy for Breast Cancer: 2022 Update

Imaging plays a vital role in managing patients undergoing neoadjuvant chemotherapy, as treatment decisions rely heavily on accurate assessment of response to therapy. This document provides evidence-based guidelines for imaging breast cancer before, during, and after initiation of neoadjuvant chemotherapy. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision process support the systematic analysis of the medical literature from peer reviewed journals. Established methodology principles such as Grading of Recommendations Assessment, Development, and Evaluation or GRADE are adapted to evaluate the evidence. The RAND/UCLA Appropriateness Method User Manual provides the methodology to determine the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where peer reviewed literature is lacking or equivocal, experts may be the primary evidentiary source available to formulate a recommendation.

Application of triple evaluation method in predicting the efficacy of neoadjuvant therapy for breast cancer

OBJECTIVE

To analyze the factors related to the efficacy of neoadjuvant therapy for breast cancer and find appropriate evaluation methods for evaluating the efficacy of neoadjuvant therapy METHODS: A total of 143 patients with breast cancer treated by neoadjuvant chemotherapy at Baotou Cancer Hospital were retrospectively analyzed. The chemotherapy regimen was mainly paclitaxel combined with carboplatin for 1 week, docetaxel combined with carboplatin for 3 weeks, and was replaced with epirubicin combined with cyclophosphamide after evaluation of disease progression. All HER2-positive patients were treated with simultaneous targeted therapy, including trastuzumab single-target therapy and trastuzumab combined with pertuzumab double-target therapy. Combined with physical examination, color Doppler ultrasound, and magnetic resonance imaging (MRI), a systematic evaluation system was initially established-the "triple evaluation method." A baseline evaluation was conducted before treatment. The efficacy was evaluated by physical examination and color Doppler every cycle, and the efficacy was evaluated by physical examination, color Doppler, and MRI every two cycles.

RESULTS

The increase in ultrasonic blood flow after treatment could affect the efficacy of monitoring. The presence of two preoperative time-signal intensity curves is a therapeutically effective protective factor for inflow. The triple evaluation determined by physical examination, color Doppler ultrasound, and MRI in determining clinical efficacy is consistent with the effectiveness of the pathological gold standard.

CONCLUSION

The therapeutic effect of neoadjuvant therapy can be better evaluated by combining clinical physical examination, color ultrasound, and nuclear magnetic resonance evaluation. The three methods complement each other to avoid the insufficient evaluation of a single method, which is convenient for most prefecty-level hospitals. Additionally, this method is simple, feasible, and suitable for promotion.

Deep learning radiomics of ultrasonography for comprehensively predicting tumor and axillary lymph node status after neoadjuvant chemotherapy in breast cancer patients: A multicenter study

BACKGROUND

Neoadjuvant chemotherapy (NAC) can downstage tumors and axillary lymph nodes in breast cancer (BC) patients. However, tumors and axillary response to NAC are not parallel and vary among patients. This study aims to explore the feasibility of deep learning radiomics nomogram (DLRN) for independently predicting the status of tumors and lymph node metastasis (LNM) after NAC.

METHODS

In total, 484 BC patients who completed NAC from two hospitals (H1: 297 patients in the training cohort and 99 patients in the validation cohort; H2: 88 patients in the test cohort) were retrospectively enrolled. The authors developed two deep learning radiomics (DLR) models for personalized prediction of the tumor pathologic complete response (PCR) to NAC (DLR-PCR) and the LNM status (DLR-LNM) after NAC based on pre-NAC and after-NAC ultrasonography images. Furthermore, they proposed two DLRNs (DLRN-PCR and DLRN-LNM) for two different tasks based on the clinical characteristics and DLR scores, which were generated from both DLR-PCR and DLR-LNM.

RESULTS

In the validation and test cohorts, DLRN-PCR exhibited areas under the receiver operating characteristic curves (AUCs) of 0.903 and 0.896 with sensitivities of 91.2% and 75.0%, respectively. DLRN-LNM achieved AUCs of 0.853 and 0.863, specificities of 82.0% and 81.8%, and negative predictive values of 81.3% and 87.2% in the validation and test cohorts, respectively. The two DLRN models achieved satisfactory predictive performance based on different BC subtypes.

CONCLUSIONS

The proposed DLRN models have the potential to accurately predict the tumor PCR and LNM status after NAC.

PLAIN LANGUAGE SUMMARY

In this study, we proposed two deep learning radiomics nomogram models based on pre-neoadjuvant chemotherapy (NAC) and preoperative ultrasonography images for independently predicting the status of tumor and axillary lymph node (ALN) after NAC. A more comprehensive assessment of the patient's condition after NAC can be achieved by predicting the status of the tumor and ALN separately. Our model can potentially provide a noninvasive and personalized method to offer decision support for organ preservation and avoidance of excessive surgery.

Prediction of axillary lymph node pathological complete response to neoadjuvant therapy using nomogram and machine learning methods

Purpose

To determine the feasibility of predicting the rate of an axillary lymph node pathological complete response (apCR) using nomogram and machine learning methods.

Methods

A total of 247 patients with early breast cancer (eBC), who underwent neoadjuvant therapy (NAT) were included retrospectively. We compared pre- and post-NAT ultrasound information and calculated the maximum diameter change of the primary lesion (MDCPL): [(pre-NAT maximum diameter of primary lesion – post-NAT maximum diameter of preoperative primary lesion)/pre-NAT maximum diameter of primary lesion] and described the lymph node score (LNS) (1): unclear border (2), irregular morphology (3), absence of hilum (4), visible vascularity (5), cortical thickness, and (6) aspect ratio <2. Each description counted as 1 point. Logistic regression analyses were used to assess apCR independent predictors to create nomogram. The area under the curve (AUC) of the receiver operating characteristic curve as well as calibration curves were employed to assess the nomogram’s performance. In machine learning, data were trained and validated by random forest (RF) following Pycharm software and five-fold cross-validation analysis.

Results

The mean age of enrolled patients was 50.4 ± 10.2 years. MDCPL (odds ratio [OR], 1.013; 95% confidence interval [CI], 1.002–1.024; p=0.018), LNS changes (pre-NAT LNS – post-NAT LNS; OR, 2.790; 95% CI, 1.190–6.544; p=0.018), N stage (OR, 0.496; 95% CI, 0.269–0.915; p=0.025), and HER2 status (OR, 2.244; 95% CI, 1.147–4.392; p=0.018) were independent predictors of apCR. The AUCs of the nomogram were 0.74 (95% CI, 0.68–0.81) and 0.76 (95% CI, 0.63–0.90) for training and validation sets, respectively. In RF model, the maximum diameter of the primary lesion, axillary lymph node, and LNS in each cycle, estrogen receptor status, progesterone receptor status, HER2, Ki67, and T and N stages were included in the training set. The final validation set had an AUC value of 0.85 (95% CI, 0.74–0.87).

Conclusion

Both nomogram and machine learning methods can predict apCR well. Nomogram is simple and practical, and shows high operability. Machine learning makes better use of a patient’s clinicopathological information. These prediction models can assist surgeons in deciding on a reasonable strategy for axillary surgery.

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

Background:

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

Methods:

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

Results:

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

Conclusion:

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

Assessment of axillary node status by ultrasound after neoadjuvant chemotherapy in patients with clinically node-positive breast cancer according to breast cancer subtype

The use of sentinel node biopsy (SNB) following neoadjuvant chemotherapy (NAC) for patients with cN1 breast cancer is controversial. Improvements of negative predictive value (NPV) by axillary ultrasound (AUS), which corresponds to the accurate prediction rate of node-negative status after NAC, would lead to decreased FNR of SNB following NAC. In this study, we retrospectively investigated the accurate prediction rate of NPV by AUS after NAC in patients with cytologically node-positive breast cancer treated between January 2012 and December 2016. Of 279 eligible patients, the NPV was 49.2% in all patients, but varied significantly by tumor subtype (p < 0.001) and tumor response determined by magnetic resonance imaging (MRI) (p = 0.0003). Of the 23 patients with clinically node negative (ycN0) by AUS and clinical complete response in primary lesion by MRI, the NPV was 100% in patients with HR±/HER2+ or HR-/HER2- breast cancer. In conclusion, regarding FNR reduction post-NAC, it will be of clinical value to take tumor subtype and primary tumor response using MRI into account to identify patients for SNB after NAC.

Can one stop nucleic acid sampling (OSNA) predict nodal positivity following neoadjuvant chemotherapy? A prospective cohort study of 293 patients

Until recently, axillary node clearance had long been the standard of care in patients with axillary node-positive disease. One stop nucleic acid sampling (OSNA) has been used to guide intraoperative decision-making regarding suitability for axillary node clearance (ANC). The aim of this study is to evaluate the use of OSNA following neoadjuvant chemotherapy (NACT) and whether it can predict lymph node burden in ANC. A single center, prospective cohort study was performed on 297 patients having OSNA between 2016 and 2019. Patients were sub-classified according to node positivity at diagnosis and those treated with NACT and outcomes included copy number and lymph node harvest. Axillary complete pathological response was observed in 24/36 patients (67%) following NACT. 14/16 patients (87%) having axillary node clearance had axillary node disease limited to 4 nodes. OSNA copy numbers were significantly higher in patients showing disease progression following NACT. Overall, 73% of patients with lymph node positivity at diagnosis could be successfully treated with a combination of NACT and lymph node excision of four nodes. De-escalating axillary surgical treatment to resection of four nodes following NACT may be effective in balancing oncological resection and limiting treatment morbidity. ONSA can correctly identify patients experiencing disease progression who would benefit from traditional three-level ANC.

The implications of a pathological complete response of the primary tumour after neoadjuvant chemotherapy for breast cancer on axillary surgery

BACKGROUND

Management of the node-positive axilla after neoadjuvant chemotherapy is controversial. The aim of this study is to predict the group of patients who may require a less invasive approach for axillary management. One possible group are patients with pathological complete response of the primary after chemotherapy.

RESULTS

A unicentral retrospective cohort study including all breast cancer patients with axillary node metastases at presentation who received neoadjuvant chemotherapy resulting in pathological complete response. Pathological complete response in the axillary lymph nodes was recorded. A correlation between the response in the primary tumour and the lymph nodes was assessed. A subgroup analysis was conducted for different biological groups. Complete response was seen in the axillary nodes in 80.5% of patients. Patients with lobular cancer were less likely to show a similar response in the axilla as the primary tumour (p = 0.077). A higher incidence of axillary response was observed in HER2-positive tumours (p = 0.082). All patients with grade 3 tumours achieved complete response in the axilla (p = 0.094). Patients with negative or weak positive hormone receptor status had a significantly higher rate of complete response in the axilla compared to strongly positive hormone receptor status (OR, 7.8; 95% CI, 1.7-34.5; p = 0.007).

CONCLUSION

A less invasive axillary surgery may be safely recommended in selected group of node-positive patients after neoadjuvant chemotherapy when the primary tumour shows complete response. This group may include HER2-positive, ER-negative and grade 3 tumours. Less response is expected in ER-positive and lobular carcinoma even with complete response in the primary.

Early progression of breast cancer during neoadjuvant chemotherapy may predict poorer prognoses

Background: In Finland, breast cancers treated with neoadjuvant chemotherapy (NACT) are usually locally advanced and/or have an inflammatory phenotype. We evaluated early NACT responses in breast tumours and lymph nodes and their correlation with survival.Material and methods: We collected a retrospective dataset of 145 patients with very high-risk but non-metastasised breast cancers that were treated with NACT in a Finnish University Hospital between September 2013 and January 2019. The patients underwent magnetic resonance imaging (MRI) scans before beginning NACT and after every second NACT cycle thereafter.Results: The total pathological complete response rate was only 10.7% and breast cancer-specific survival (BCSS) at 24 months was 93.0%. The 2-year breast cancer-specific survival (BCSS) rate was 93.0%, but this varied from 86.5% for the triple-negative subtype to 100.0% for the luminal A-like subtype. Enlargement of the malignant axillary lymph nodes during the first two NACT cycles was associated with poor BCSS rates in HER2-negative patients (p = .00003 in the univariate analysis; hazard ratio = 26.3; 95% confidence interval = 2.66-259.6; p = .005 in the multivariate analysis). Furthermore, progression in the combined diameters of the breast tumours and axillary lymph nodes during the period between a patient's pre-treatment MRI and her MRI after two NACT cycles was also correlated with worse BCSS rates in both univariate and multivariate analyses.Conclusions: An early MRI assessment after two NACT cycles, specifically of the tumour's axillary lymph nodes, has the potential to predict short-term BCSS in patients with locally advanced HER2-negative breast cancers.