External validation of a deep learning CT biomarker to predict first-line immune checkpoint inhibitor monotherapy-associated survival in PD-L1–high metastatic non–small cell lung cancer.

2534 Background: Immune checkpoint inhibitor (ICI) monotherapy is a standard first-line treatment for patients with metastatic non–small cell lung cancer (mNSCLC) with high PD-L1 expression. However, 60% of patients treated with ICI monotherapy progress within 1 year of treatment. Imaging-based biomarkers from routine pretreatment computed tomography (CT) scans may provide a noninvasive approach to refine patient selection and guide treatment decisions. Methods: Enhanced CT Response Score (eCTRS v0.0.2; Sako et al, JCO CCI, 2024) is an imaging-based biomarker that stratifies patient survival among mNSCLC patients receiving ICI monotherapy. eCTRS is derived from pre-treatment CT scans, using deep-learning extracted imaging features, lesion features, and clinical variables of age and sex. eCTRS was previously trained on a diverse, real-world multi-institutional dataset of 1,058 mNSCLC patients. This retrospective study externally validated eCTRS using a deidentified, EHR-derived longitudinal database with imaging from Flatiron Health. Patients with PD-L1–high (PD-L1 tumor proportion score ≥ 50%) mNSCLC without actionable mutations who received first-line ICI monotherapy and had imaging from 12 weeks before to 2 weeks after treatment start were included. Patients were stratified into eCTRS High and eCTRS Low groups using a pre-determined threshold. Survival analyses were conducted by an external, independent group. Kaplan-Meier and Cox proportional hazards analyses evaluated progression-free survival (PFS) and overall survival (OS) stratification. PFS was derived from RECIST 1.1 assessments by a centralized multi-reader radiologist adjudication process, and OS was defined as time from treatment start to death by any cause. Results: 205 patients met all inclusion criteria (median age 72 years, 48% female, 20% non-White). Eighty (39%) patients were classified as eCTRS Low and 125 (61%) as eCTRS High. PFS was improved among eCTRS High patients (Hazard Ratio [HR], 0.71; 95% CI, 0.51–1.00; p=0.048), with median PFS of 231 (95% CI: 133, 350) days versus 88 (95% CI: 57, 179) days in eCTRS Low patients. eCTRS High patients demonstrated significantly improved OS (HR, 0.56; 95% CI, 0.39–0.80; p=0.001), with median OS of 484 (95% CI: 361, NA) days for eCTRS High, versus 155 (95% CI: 75, 295) days for eCTRS Low. Conclusions: A deep learning–based imaging biomarker derived from routinely acquired pretreatment CT imaging identified survival benefit in patients with PD-L1–high metastatic NSCLC treated with first-line ICI monotherapy. These findings suggest that imaging-based biomarkers may serve as complementary, noninvasive tools to identify patients most likely to derive benefit from ICI monotherapy and to support risk-adapted treatment strategies.

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Publication Details

Journal
Journal of Clinical Oncology
Published
2026-05-27
DOI
https://doi.org/10.1200/jco.2026.44.16_suppl.2534
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
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0.00

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article

External validation of a deep learning CT biomarker to predict first-line immune checkpoint inhibitor monotherapy-associated survival in PD-L1–high metastatic non–small cell lung cancer.

Paul M. Novotny, Petr Jordan, Lauren Damato, Ravi B. Parikh et al.
Journal of Clinical Oncology
Radiomics and Machine Learning in Medical Imaging
article

External validation of a deep learning CT biomarker to predict first-line immune checkpoint inhibitor monotherapy-associated survival in PD-L1–high metastatic non–small cell lung cancer.

Paul M. Novotny, Petr Jordan, Lauren Damato, Ravi B. Parikh, Joel Brooks, George R. Simon, Chiharu Sako, Ryan Beasley, Jacqueline Law
article en

Abstract

2534 Background: Immune checkpoint inhibitor (ICI) monotherapy is a standard first-line treatment for patients with metastatic non–small cell lung cancer (mNSCLC) with high PD-L1 expression. However, 60% of patients treated with ICI monotherapy progress within 1 year of treatment. Imaging-based biomarkers from routine pretreatment computed tomography (CT) scans may provide a noninvasive approach to refine patient selection and guide treatment decisions. Methods: Enhanced CT Response Score (eCTRS v0.0.2; Sako et al, JCO CCI, 2024) is an imaging-based biomarker that stratifies patient survival among mNSCLC patients receiving ICI monotherapy. eCTRS is derived from pre-treatment CT scans, using deep-learning extracted imaging features, lesion features, and clinical variables of age and sex. eCTRS was previously trained on a diverse, real-world multi-institutional dataset of 1,058 mNSCLC patients. This retrospective study externally validated eCTRS using a deidentified, EHR-derived longitudinal database with imaging from Flatiron Health. Patients with PD-L1–high (PD-L1 tumor proportion score ≥ 50%) mNSCLC without actionable mutations who received first-line ICI monotherapy and had imaging from 12 weeks before to 2 weeks after treatment start were included. Patients were stratified into eCTRS High and eCTRS Low groups using a pre-determined threshold. Survival analyses were conducted by an external, independent group. Kaplan-Meier and Cox proportional hazards analyses evaluated progression-free survival (PFS) and overall survival (OS) stratification. PFS was derived from RECIST 1.1 assessments by a centralized multi-reader radiologist adjudication process, and OS was defined as time from treatment start to death by any cause. Results: 205 patients met all inclusion criteria (median age 72 years, 48% female, 20% non-White). Eighty (39%) patients were classified as eCTRS Low and 125 (61%) as eCTRS High. PFS was improved among eCTRS High patients (Hazard Ratio [HR], 0.71; 95% CI, 0.51–1.00; p=0.048), with median PFS of 231 (95% CI: 133, 350) days versus 88 (95% CI: 57, 179) days in eCTRS Low patients. eCTRS High patients demonstrated significantly improved OS (HR, 0.56; 95% CI, 0.39–0.80; p=0.001), with median OS of 484 (95% CI: 361, NA) days for eCTRS High, versus 155 (95% CI: 75, 295) days for eCTRS Low. Conclusions: A deep learning–based imaging biomarker derived from routinely acquired pretreatment CT imaging identified survival benefit in patients with PD-L1–high metastatic NSCLC treated with first-line ICI monotherapy. These findings suggest that imaging-based biomarkers may serve as complementary, noninvasive tools to identify patients most likely to derive benefit from ICI monotherapy and to support risk-adapted treatment strategies.

Journal of Clinical OncologyVol. 44(16_suppl)
OhioHealth (US), Emory University (US), Flatiron Health (United States) (US)
National Institutes of Health
Openalex Percentile: Top 6%
Radiomics and Machine Learning in Medical Imaging
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