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Machine learning with textural analysis of longitudinal multiparametric MRI and molecular subtypes accurately predicts pathologic complete response in patients with invasive breast cancer

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  • Aaquib Syed
  • Richard Adam
  • Thomas Ren
  • Jinyu Lu
  • Takouhie Maldjian
  • Tim Q Duong

Abstract

Purpose: To predict pathological complete response (pCR) after neoadjuvant chemotherapy using extreme gradient boosting (XGBoost) with MRI and non-imaging data at multiple treatment timepoints. Material and methods: This retrospective study included breast cancer patients (n = 117) who underwent neoadjuvant chemotherapy. Data types used included tumor ADC values, diffusion-weighted and dynamic-contrast-enhanced MRI at three treatment timepoints, and patient demographics and tumor data. GLCM textural analysis was performed on MRI data. An extreme gradient boosting machine learning algorithm was used to predict pCR. Prediction performance was evaluated using the area under the curve (AUC) of the receiver operating curve along with precision and recall. Results: Prediction using texture features of DWI and DCE images at multiple treatment time points (AUC = 0.871; 95% CI: (0.768, 0.974; p

Suggested Citation

  • Aaquib Syed & Richard Adam & Thomas Ren & Jinyu Lu & Takouhie Maldjian & Tim Q Duong, 2023. "Machine learning with textural analysis of longitudinal multiparametric MRI and molecular subtypes accurately predicts pathologic complete response in patients with invasive breast cancer," PLOS ONE, Public Library of Science, vol. 18(1), pages 1-14, January.
    • Handle: RePEc:plo:pone00:0280320
    DOI: 10.1371/journal.pone.0280320
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