Multimodal integration of radiology, pathology and genomics for prediction of response to PD-(L)1 blockade in patients with non-small cell lung cancer

TLDR

Immunotherapy is used to treat almost all patients with advanced NSCLC, but robust predictive biomarkers are difficult to identify. The study aims to assess whether integrating radiology, pathology, and genomics can predict response to PD‑(L)1 blockade in advanced NSCLC patients. The authors extracted patient‑level features from CT scans, PD‑L1 IHC slides, and genomic data using expert annotations, then combined them with machine‑learning to build a risk prediction model. The multimodal model achieved AUC 0.80 (95 % CI 0.74–0.86), outperforming tumor mutational burden (AUC 0.61) and PD‑L1 IHC score (AUC 0.73), demonstrating improved prediction of immunotherapy response.

Abstract

Immunotherapy is used to treat almost all patients with advanced non-small cell lung cancer (NSCLC); however, identifying robust predictive biomarkers remains challenging. Here we show the predictive capacity of integrating medical imaging, histopathologic and genomic features to predict immunotherapy response using a cohort of 247 patients with advanced NSCLC with multimodal baseline data obtained during diagnostic clinical workup, including computed tomography scan images, digitized programmed death ligand-1 immunohistochemistry slides and known outcomes to immunotherapy. Using domain expert annotations, we developed a computational workflow to extract patient-level features and used a machine-learning approach to integrate multimodal features into a risk prediction model. Our multimodal model (area under the curve (AUC) = 0.80, 95% confidence interval (CI) 0.74-0.86) outperformed unimodal measures, including tumor mutational burden (AUC = 0.61, 95% CI 0.52-0.70) and programmed death ligand-1 immunohistochemistry score (AUC = 0.73, 95% CI 0.65-0.81). Our study therefore provides a quantitative rationale for using multimodal features to improve prediction of immunotherapy response in patients with NSCLC using expert-guided machine learning.