Following rigorous feature selection, 2 independent models were developed using machine learning and tested on a temporal validation cohort (n=45). Model performance was benchmarked against clinicopathological models and nuclear medicine physicians.
Following rigorous feature selection, 2 independent models were developed using machine learning and tested on a temporal validation cohort (n=45).
Model performance was benchmarked against clinicopathological models and nuclear medicine physicians.
Critically, we found no significant correlation between the radiomic signatures of LNM and PD-L1, which validates our 2-model approach.
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Following rigorous feature selection, 2 independent models were developed using machine learning and tested on a temporal validation cohort (n=45). Model performance was benchmarked against clinicopathological models and nuclear medicine physicians. Critically, we found no significant correlation between the radiomic signatures of LNM and PD-L1, which validates our 2-model approach.
Preoperative stratification for non–small cell lung cancer (NSCLC) necessitates separate evaluations of lymph node metastasis (LNM) to guide surgical decisions and of programmed death-ligand 1 (PD-L1) expression to inform immunotherapy. The model for PD-L1 expression (PT-IPT-SVM) achieved an area under the curve of 0.776 (95% CI 0.641‐0.911) in the temporal validation cohort, with a sensitivity of 0.800 (95% CI 0.609‐0.991) and a specificity of 0.650 (95% CI 0.401‐0.899). Decision curve analysis confirmed the clinical utility of both models.
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