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Nat Cancer ; 3(10): 1151-1164, 2022 10.
Artigo em Inglês | MEDLINE | ID: mdl-36038778

RESUMO

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.


Assuntos
Carcinoma Pulmonar de Células não Pequenas , Neoplasias Pulmonares , Radiologia , Humanos , Carcinoma Pulmonar de Células não Pequenas/diagnóstico por imagem , Neoplasias Pulmonares/diagnóstico por imagem , Receptor de Morte Celular Programada 1/uso terapêutico , Genômica
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