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Accessible model predicts response in hormone receptor positive HER2 negative breast cancer receiving neoadjuvant chemotherapy

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Title: Accessible model predicts response in hormone receptor positive HER2 negative breast cancer receiving neoadjuvant chemotherapy
Authors: Luca Mastrantoni, Giovanna Garufi, Giulia Giordano, Noemi Maliziola, Elena Di Monte, Giorgia Arcuri, Valentina Frescura, Angelachiara Rotondi, Armando Orlandi, Luisa Carbognin, Antonella Palazzo, Federica Miglietta, Letizia Pontolillo, Alessandra Fabi, Lorenzo Gerratana, Sergio Pannunzio, Ida Paris, Sara Pilotto, Fabio Marazzi, Antonio Franco, Gianluca Franceschini, Maria Vittoria Dieci, Roberta Mazzeo, Fabio Puglisi, Valentina Guarneri, Michele Milella, Giovanni Scambia, Diana Giannarelli, Giampaolo Tortora, Emilio Bria
Source: npj Breast Cancer, Vol 11, Iss 1, Pp 1-13 (2025)
Publisher Information: Nature Portfolio, 2025.
Publication Year: 2025
Collection: LCC:Neoplasms. Tumors. Oncology. Including cancer and carcinogens
Subject Terms: Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
More Details: Abstract Hormone receptor-positive/HER2-negative breast cancer (BC) is the most common subtype of BC and typically occurs as an early, operable disease. In patients receiving neoadjuvant chemotherapy (NACT), pathological complete response (pCR) is rare and multiple efforts have been made to predict disease recurrence. We developed a framework to predict pCR using clinicopathological characteristics widely available at diagnosis. The machine learning (ML) models were trained to predict pCR (n = 463), evaluated in an internal validation cohort (n = 109) and validated in an external validation cohort (n = 151). The best model was an Elastic Net, which achieved an area under the curve (AUC) of respectively 0.86 and 0.81. Our results highlight how simpler models using few input variables can be as valuable as more complex ML architectures. Our model is freely available and can be used to enhance the stratification of BC patients receiving NACT, providing a framework for the development of risk-adapted clinical trials.
Document Type: article
File Description: electronic resource
Language: English
ISSN: 2374-4677
Relation: https://doaj.org/toc/2374-4677
DOI: 10.1038/s41523-025-00727-w
Access URL: https://doaj.org/article/071d2ee3b6c24d3ca0260f479dba6d52
Accession Number: edsdoj.071d2ee3b6c24d3ca0260f479dba6d52
Database: Directory of Open Access Journals
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  Data: Abstract Hormone receptor-positive/HER2-negative breast cancer (BC) is the most common subtype of BC and typically occurs as an early, operable disease. In patients receiving neoadjuvant chemotherapy (NACT), pathological complete response (pCR) is rare and multiple efforts have been made to predict disease recurrence. We developed a framework to predict pCR using clinicopathological characteristics widely available at diagnosis. The machine learning (ML) models were trained to predict pCR (n = 463), evaluated in an internal validation cohort (n = 109) and validated in an external validation cohort (n = 151). The best model was an Elastic Net, which achieved an area under the curve (AUC) of respectively 0.86 and 0.81. Our results highlight how simpler models using few input variables can be as valuable as more complex ML architectures. Our model is freely available and can be used to enhance the stratification of BC patients receiving NACT, providing a framework for the development of risk-adapted clinical trials.
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