Prognostic power assessment of clinical parameters to predict neoadjuvant response therapy in HER2‐positive breast cancer patients: A machine learning approach

Abstract

BackgroundAbout 15%–20% of breast cancer (BC) cases is classified as Human Epidermal growth factor Receptor type 2 (HER2) positive. The Neoadjuvant chemotherapy (NAC) was initially introduced for locally advanced and inflammatory BC patients to allow a less extensive surgical resection, whereas now it represents the current standard for early‐stage and operable BC. However, only 20%–40% of patients achieve pathologic complete response (pCR). According to the results of practice‐changing clinical trials, the addition of trastuzumab to NAC brings improvements to pCR, and recently, the use of pertuzumab plus trastuzumab has registered further statistically significant and clinically meaningful improvements in terms of pCR. The goal of our work is to propose a machine learning model to predict the pCR to NAC in HER2‐positive patients based on a subset of clinical features.MethodFirst, we evaluated the significant association of clinical features with pCR on the retrospectively collected data referred to 67 patients afferent to Istituto Tumori “Giovanni Paolo II.” Then, we performed a feature selection procedure to identify a subset of features to be used for training a machine learning‐based classification algorithm. As a result, pCR to NAC was associated with ER status, Pgr status, and HER2 score.ResultsThe machine learning model trained on a subgroup of essential features reached an AUC of 73.27% (72.44%–73.66%) and an accuracy of 71.67% (71.64%–73.13%). According to our results, the clinical features alone are not enough to define a support system useful for clinical pathway.ConclusionOur results seem worthy of further investigation in large validation studies and this work could be the basis of future study that will also involve radiomics analysis of biomedical images.

Early Breast Cancer Risk Assessment: Integrating Histopathology with Artificial Intelligence

“…The predictive efficacy of the model was pinpointed by comparison to HER2 expression, which was found less predictive [123]. On the other hand, an a empt to apply an ML model to predict the pathologic complete response (pCR) to neoadjuvant therapy in HER2+ BC patients based on a subset of clinical features only has demonstrated that clinical features alone are inadequate for defining a useful support system in clinical pathways [124].…”

“…Although the model relies on publicly available data and lacks extensive real-world validation, ongoing AI model development holds promise for providing convenient and accurate assistance in predicting cancer risks [123]. It is emphasized that these analyses necessitate scalable algorithms for large patient cohorts and addressing latent confounders, to achieve optimization tools from deep learning [124].…”