JCO clinical cancer informatics · 2026
Modeling the Pretest Probability of Identifying Druggable Mutations in Lung Cancer Using Nationwide Comprehensive Genomic Profiling Data.
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Ikushima Hiroaki, Watanabe Kousuke, Shinozaki-Ushiku Aya, Kodera Satoshi, Takeda Norihiko, Oda Katsutoshi, Kage Hidenori
Аннотация
PURPOSE: Comprehensive genomic profiling (CGP) is a key strategy in precision medicine for lung cancer, yet its clinical implementation remains limited, partly because of the uncertainty in identifying druggable mutations in individual patients. In this study, we investigated the potential of an artificial intelligence (AI)-based tool to predict the probability of identifying druggable mutations before CGP (pretest probability).
METHODS: We developed an eXtreme Gradient Boosting (XGBoost) prediction model trained on pre-CGP clinical variables from 3,470 patients with lung cancer (June 2019-November 2023) to estimate the probability of identifying druggable mutations. The key predictors were identified using explainable artificial intelligence (XAI) analysis. The refined model was deployed as a web application and evaluated in a temporally independent test cohort of 1,307 patients (December 2023-November 2024), with Brier score as the primary end point.
RESULTS: The prediction model achieved an area under the receiver operating characteristic curve (AUROC) of 0.85 (95% CI, 0.82 to 0.89) in the overall validation cohort and 0.79 (95% CI, 0.74 to 0.84) in patients for whom a driver mutation had not been identified through companion diagnostic testing. The XAI analysis identified sex, smoking history, histology, and metastatic sites as important predictors. Even among patients who underwent tissue CGP, bone (P = .011) and lung (P < .001) metastases were significantly associated with a higher druggable mutation detection rate. The deployed model achieved Brier scores of 0.19 in the overall independent test cohort and 0.16 in patients for whom a driver mutation had not been identified through companion diagnostic testing.
CONCLUSION: These findings indicate that an AI-based tool using pre-CGP clinical data may support broader CGP implementation and improve access to targeted therapies.
METHODS: We developed an eXtreme Gradient Boosting (XGBoost) prediction model trained on pre-CGP clinical variables from 3,470 patients with lung cancer (June 2019-November 2023) to estimate the probability of identifying druggable mutations. The key predictors were identified using explainable artificial intelligence (XAI) analysis. The refined model was deployed as a web application and evaluated in a temporally independent test cohort of 1,307 patients (December 2023-November 2024), with Brier score as the primary end point.
RESULTS: The prediction model achieved an area under the receiver operating characteristic curve (AUROC) of 0.85 (95% CI, 0.82 to 0.89) in the overall validation cohort and 0.79 (95% CI, 0.74 to 0.84) in patients for whom a driver mutation had not been identified through companion diagnostic testing. The XAI analysis identified sex, smoking history, histology, and metastatic sites as important predictors. Even among patients who underwent tissue CGP, bone (P = .011) and lung (P < .001) metastases were significantly associated with a higher druggable mutation detection rate. The deployed model achieved Brier scores of 0.19 in the overall independent test cohort and 0.16 in patients for whom a driver mutation had not been identified through companion diagnostic testing.
CONCLUSION: These findings indicate that an AI-based tool using pre-CGP clinical data may support broader CGP implementation and improve access to targeted therapies.