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Related Experiment Video

Updated: Oct 2, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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Development of Gene Expression-Based Random Forest Model for Predicting Neoadjuvant Chemotherapy Response in

Seongyong Park1, Gwansu Yi1

  • 1Korean Advanced Institute of Science and Technology, Daejeon 34141, Korea.

Cancers
|February 25, 2022
PubMed
Summary

Predicting neoadjuvant chemotherapy response in triple negative breast cancer is now more accurate. An 86-gene random forest model reliably predicts pathological complete response, improving patient survival insights.

Keywords:
machine learning (ML)neoadjuvant chemotherapy (NAC)pathological complete response (pCR)predictive biomarkerrandom forest (RF)residual disease (RD)triple negative breast cancer (TNBC)

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Area of Science:

  • Oncology
  • Genomics
  • Biostatistics

Background:

  • Predicting neoadjuvant chemotherapy (NAC) response in triple-negative breast cancer (TNBC) is crucial for patient survival but remains challenging.
  • Accurate prediction of chemosensitivity is vital for optimizing treatment strategies in TNBC.

Purpose of the Study:

  • To develop and validate an 86-gene random forest (RF) classifier for predicting NAC response (pCR or RD) in TNBC patients.
  • To assess the model's performance in predicting pathological complete response (pCR) and its correlation with clinical outcomes and chemosensitivity.

Main Methods:

  • Development of an 86-gene-based random forest classifier.
  • Evaluation of model performance using Receiver Operating Characteristic (ROC) and Precision Recall (PR) curves (AUROC=0.891, AUPRC=0.829).
  • Correlation analysis with distance recurrence-free survival (DRFS) and cyclophosphamide sensitivity in TNBC cell lines; functional enrichment analysis of the 86 genes.

Main Results:

  • The RF model achieved high accuracy in predicting pCR (AUROC=0.891, AUPRC=0.829).
  • At >90% specificity, the model demonstrated superior sensitivity (69.2%) compared to existing models (36.9%).
  • Predicted pCR status correlated with improved DRFS and showed significant correlation with cyclophosphamide sensitivity in TNBC cell lines (SRCC=0.697, p=0.031).

Conclusions:

  • The developed 86-gene RF model offers a reliable method for predicting NAC response in TNBC.
  • The model's predictions are associated with patient survival and in vitro chemosensitivity, potentially guiding treatment decisions.
  • The 86 genes are linked to DNA repair and cell cycle mechanisms, offering insights into TNBC chemosensitivity.