Cancer Medicine (Nov 2023)

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

  • Annarita Fanizzi,
  • Agnese Latorre,
  • Domenica Antonia Bavaro,
  • Samantha Bove,
  • Maria Colomba Comes,
  • Erika Francesca Di Benedetto,
  • Federico Fadda,
  • Daniele La Forgia,
  • Francesco Giotta,
  • Gennaro Palmiotti,
  • Nicole Petruzzellis,
  • Lucia Rinaldi,
  • Alessandro Rizzo,
  • Vito Lorusso,
  • Raffaella Massafra

DOI
https://doi.org/10.1002/cam4.6512
Journal volume & issue
Vol. 12, no. 22
pp. 20663 – 20669

Abstract

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Abstract Background About 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. Method First, 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. Results The 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. Conclusion Our 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.

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