Changes in CT Radiomic Features Associated with Lymphocyte Distribution Predict Overall Survival and Response to Immunotherapy in Non–Small Cell Lung Cancer

Abstract

No predictive biomarkers can robustly identify patients with non–small cell lung cancer (NSCLC) who will benefit from immune checkpoint inhibitor (ICI) therapies. Here, in a machine learning setting, we compared changes (“delta”) in the radiomic texture (DelRADx) of CT patterns both within and outside tumor nodules before and after two to three cycles of ICI therapy. We found that DelRADx patterns could predict response to ICI therapy and overall survival (OS) for patients with NSCLC. We retrospectively analyzed data acquired from 139 patients with NSCLC at two institutions, who were divided into a discovery set (D1 = 50) and two independent validation sets (D2 = 62, D3 = 27). Intranodular and perinodular texture descriptors were extracted, and the relative differences were computed. A linear discriminant analysis (LDA) classifier was trained with 8 DelRADx features to predict RECIST-derived response. Association of delta-radiomic risk score (DRS) with OS was determined. The association of DelRADx features with tumor-infiltrating lymphocyte (TIL) density on the diagnostic biopsies (n = 36) was also evaluated. The LDA classifier yielded an AUC of 0.88 ± 0.08 in distinguishing responders from nonresponders in D1, and 0.85 and 0.81 in D2 and D3. DRS was associated with OS [HR: 1.64; 95% confidence interval (CI), 1.22–2.21; P = 0.0011; C-index = 0.72). Peritumoral Gabor features were associated with the density of TILs on diagnostic biopsy samples. Our results show that DelRADx could be used to identify early functional responses in patients with NSCLC.

Document Details

Document Type
Pub Defense Publication
Publication Date
Jan 01, 2020
Source ID
10.1158/2326-6066.cir-19-0476

Entities

People

  • Amit Gupta
  • Anant Madabhushi
  • Germán Corredor
  • Kaustav Bera
  • Mehdi Alilou
  • Michael Feldman
  • Mohammadhadi Khorrami
  • Pingfu Fu
  • Pradnya Patil
  • Prateek Prasanna
  • Priya D. Velu
  • Rajat Thawani
  • Vamsidhar Velcheti

Organizations

  • Case Western Reserve University
  • Cleveland Clinic
  • Hospital of the University of Pennsylvania
  • Maimonides Medical Center (Jewish)
  • NYU Langone Health
  • National Cancer Institute
  • National Center for Research Resources
  • UH Cleveland Medical Center
  • Weill Cornell Medicine

Tags

Fields of Study

  • Biology
  • Medicine

Readers

  • Neural Network Machine Learning.
  • Oncology
  • Oncology and Biomarker-Based Cancer Detection.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • Biotechnology
  • Biotechnology - Cancer Biotech