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Machine Learning and Computed Tomography Radiomics to Predict Disease Progression to Upfront Pembrolizumab

Ian Janzen1,2, Cheryl Ho3,4, Barbara Melosky3,4

  • 1Integrative Oncology, BC Cancer Research Institute, 675 West 10th Avenue, Vancouver, BC V5Z Il3, Canada.

Cancers
|January 11, 2025
PubMed
Summary

Predicting non-response to pembrolizumab in advanced non-small cell lung cancer (NSCLC) is crucial. Radiomic features from CT scans can identify patients unlikely to benefit from first-line immunotherapy, enabling earlier treatment adjustments.

Keywords:
immunotherapynon-small- cell lung cancer (NSCLC)peritumoralradiomicstreatment response

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Area of Science:

  • Oncology
  • Radiology
  • Medical Imaging

Background:

  • Pembrolizumab monotherapy is approved for first-line treatment of advanced non-small cell lung cancer (NSCLC) with high PD-L1 expression (≥ 50%) and no EGFR/ALK alterations.
  • A significant proportion of these patients (approximately 55%) do not respond to pembrolizumab, necessitating strategies to identify non-responders early for optimized treatment.
  • Distinguishing non-responders before initiating therapy presents a significant clinical challenge.

Purpose of the Study:

  • To develop and validate a predictive model to identify patients with advanced NSCLC who are unlikely to respond to first-line pembrolizumab monotherapy.
  • To leverage pre-treatment computed tomography (CT) radiomic features and clinical variables for early prediction of treatment response.

Main Methods:

  • A retrospective analysis of two patient cohorts (training set: n = 97; test set: n = 17) treated with pembrolizumab monotherapy.
  • Assessment of treatment response using RECIST 1.1 criteria based on baseline and follow-up CT scans.
  • Development of a logistic regression model incorporating pre-treatment CT radiomic features and clinical variables.

Main Results:

  • The developed logistic regression model demonstrated high predictive accuracy for treatment response (AUC: 0.85 in training set; 0.81 in test set).
  • Radiomic features extracted from the peritumoral region emerged as independent predictors of response.
  • These radiomic features complemented standard CT evaluations and other clinical characteristics in predicting outcomes.

Conclusions:

  • The developed pragmatic model serves as a valuable tool for guiding first-line treatment decisions in NSCLC patients with high PD-L1 expression.
  • This approach has the potential to advance personalized oncology by enabling timely intervention for non-responders.
  • Improved identification of non-responders can lead to more effective disease management and potentially better patient outcomes.