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Related Experiment Video

Updated: Jun 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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Automatic machine learning accurately predicts the efficacy of immunotherapy for patients with inoperable advanced

Siyun Lin1,2, Zhuangxuan Ma3, Yuanshan Yao4

  • 1Huadong Hospital, Fudan University, Department of Thoracic Surgery, Shanghai, China.

Diagnostic and Interventional Radiology (Ankara, Turkey)
|January 16, 2025
PubMed
Summary

Automatic machine learning (autoML) models using computed tomography (CT) radiomics accurately predict immunotherapy response in advanced non-small cell lung cancer (NSCLC). This approach aids in personalized treatment decisions for patients with inoperable NSCLC.

Keywords:
Advanced non-small cell lung cancerautomatic machine learningimmunotherapymodelsradiomics

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Area of Science:

  • Radiomics and Artificial Intelligence in Oncology
  • Medical Imaging and Computational Pathology

Background:

  • Immunotherapy response varies significantly in advanced non-small cell lung cancer (NSCLC).
  • Reliable biomarkers for predicting immunotherapy efficacy in NSCLC are currently lacking.
  • Accurate prediction is crucial for optimizing individualized treatment strategies.

Purpose of the Study:

  • To develop individualized models using automatic machine learning (autoML) for predicting immunotherapy efficacy.
  • To assess the utility of CT-based radiomics features in predicting treatment outcomes for inoperable advanced NSCLC patients.
  • To establish a robust tool for personalized management of NSCLC.

Main Methods:

  • 63 participants with inoperable advanced NSCLC were randomized into training and validation cohorts.
  • Radiomics features were extracted from CT images of the tumor.
  • autoML was employed to generate clinical, radiomics, and fusion models.
  • Model performance was evaluated using multi-class receiver operating characteristic curves.

Main Results:

  • The fusion model achieved an accuracy of 0.84 (AUC 0.89-0.98) in the training cohort.
  • The radiomics model showed the highest accuracy (0.89, AUC 0.98-1.00) in the validation cohort.
  • Radiomics models demonstrated superior prediction in the partial response subgroup.
  • Lower radiomics scores correlated with improved progression-free survival (PFS).

Conclusions:

  • autoML-generated CT-based radiomics models accurately and robustly predict short-term outcomes for NSCLC patients receiving immunotherapy.
  • This approach serves as a powerful tool for assisting in the individualized management of advanced NSCLC.
  • autoML enhances efficiency in feature selection and model construction, offering a rapid, non-invasive method for personalized clinical decisions.