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<p>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 (<i>n</i> = 463), evaluated in an internal validation cohort (<i>n</i> = 109) and validated in an external validation cohort (<i>n</i> = 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.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年2月5日

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  • 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

摘要

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.