Multiomics Machine Learning to Predict Neoadjuvant Chemotherapy Outcome and Relapse of Breast Cancer

TL;DR

This study uses machine learning with multiomics data to predict how breast cancer patients respond to chemotherapy and their risk of recurrence.

Contribution

A stacked-ensemble model combining MRI and clinicopathologic data improves prediction of chemotherapy response and recurrence in breast cancer.

Findings

The MO-integrated model achieved an AUC of 0.917 for predicting pathological complete response.

The elastic-net Cox model achieved a concordance index of 0.78 for 3-year disease-free survival prediction.

Combining MRI radiomics with clinical data outperformed individual models in predicting outcomes.

Abstract

Objective: The aim of this study was to investigate multiomics (MO) integration with stacked-ensemble learning for predicting neoadjuvant chemotherapy (NAC) response and recurrence risk in breast cancer (BC). Impact Statement: This study demonstrates that a stacked-ensemble learning model integrating clinicopathologic and magnetic resonance imaging (MRI)-based intratumoral heterogeneity biomarkers effectively predicts NAC response and postoperative recurrence risk in BC patients. These findings underscore MO and machine learning’s potential to optimize clinical decision-making. Introduction: Selecting BC patients who will benefit from NAC remains challenging. Methods: We retrospectively analyzed 124 BC patients receiving NAC (3 to 8 cycles) prior to mastectomy. Two radiomics signatures—RadSET and RadSITH—were derived from pre-NAC high-resolution dynamic MRI to track entire-tumor and intratumoral heterogeneous characteristics, respectively. These signatures were integrated with clinicopathologic indicators using stacked-ensemble learning algorithms to predict pathological complete response (pCR) and 3-year disease-free survival (DFS). Results: Among the 124 patients, the pCR rate was 26.6%. For pCR prediction, RadSITH and RadSET yielded areas under the curve (AUCs) of 0.798 and 0.770, respectively. The MO-integrated model, combining RadSITH, RadSET, clinical N stage, and molecular subtype, achieved a significantly higher AUC (0.917; 95% confidence interval [CI], 0.860 to 0.958; P < 0.05) than individual models. Postoperative recurrence occurred in 13.6% of patients. The elastic-net Cox model achieved a DFS concordance index of 0.78 (95% CI, 0.72 to 0.83) using pre-NAC variables (MO-predicted pCR, Response Evaluation Criteria in Solid Tumors response, RadSITH), and 0.81 (95% CI, 0.76 to 0.92) with post-NAC variables (pathologic grade, pCR status, pT stage, and pN stage). Conclusion: The MO integration with stacked-ensemble learning effectively predicts NAC response and recurrence risk in BC.

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