Interpretable deep learning model of circulating genomics for quantitative survival prediction in advanced non-small cell lung cancer.
[PURPOSE] Accurate quantitative survival prediction in advanced non-small cell lung cancer (NSCLC) remains an unmet clinical need.
- p-value P < 0.001
APA
Wang Y, Li YT, et al. (2026). Interpretable deep learning model of circulating genomics for quantitative survival prediction in advanced non-small cell lung cancer.. Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico. https://doi.org/10.1007/s12094-026-04220-z
MLA
Wang Y, et al.. "Interpretable deep learning model of circulating genomics for quantitative survival prediction in advanced non-small cell lung cancer.." Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico, 2026.
PMID
41649698
Abstract
[PURPOSE] Accurate quantitative survival prediction in advanced non-small cell lung cancer (NSCLC) remains an unmet clinical need. While liquid biopsy is widely used, single circulating tumor DNA (ctDNA) shows limited predictive power. We developed an interpretable deep-learning model to quantitatively predict outcomes.
[METHODS/PATIENTS] We integrated data from 1373 advanced NSCLC patients profiled by two ultra-deep ctDNA sequencing assays (MSK-ACCESS and ctDx Lung). Features associated with overall survival (OS) were incorporated into a deep-learning network (DeepSurv), which estimates time-to-event survival probabilities. Model performance was evaluated by time-dependent area under the curve (AUC). SHapley Additive exPlanations (SHAP) were employed to interpret model output.
[RESULTS] A total of 1373 patients were analyzed, with 1012 using MSK-ACCESS (discovery) and 361 using ctDx Lung (validation). Among over 40 clinicopathological features, ctDNA status, cell-free DNA (cfDNA) concentration, age, blood-based TP53, EGFR, PIK3CA, ARID1A, STK11 and MET mutations significantly predicted OS. In ctDNA-positive patients, TP53/PIK3CA/ARID1A/STK11/MET-mutated patients had significantly inferior OS compared with wildtype patients (P < 0.001). Using above variables, DeepSurv was trained and tested in the MSK-ACCESS cohort (12-month AUC = 0.75), outperforming single cfDNA (AUC = 0.66) or ctDNA (AUC = 0.59), and externally validated in the ctDx Lung cohort. Compared with high-risk patients, DeepSurv-identified low-risk patients had significantly longer OS in both discovery (12-month OS 87.8% vs 53.8%, HR 0.32, P < 0.001) and validation cohorts (73.2% vs 48.4%, HR 0.42, P < 0.001). SHAP revealed TP53 and cfDNA concentration > 4.8 ng/mL had the most important contributions.
[CONCLUSIONS] The interpretable DeepSurv model, integrating multimodal features, enables quantitative survival prediction and risk stratification in advanced NSCLC, facilitating personalized decision-making.
[METHODS/PATIENTS] We integrated data from 1373 advanced NSCLC patients profiled by two ultra-deep ctDNA sequencing assays (MSK-ACCESS and ctDx Lung). Features associated with overall survival (OS) were incorporated into a deep-learning network (DeepSurv), which estimates time-to-event survival probabilities. Model performance was evaluated by time-dependent area under the curve (AUC). SHapley Additive exPlanations (SHAP) were employed to interpret model output.
[RESULTS] A total of 1373 patients were analyzed, with 1012 using MSK-ACCESS (discovery) and 361 using ctDx Lung (validation). Among over 40 clinicopathological features, ctDNA status, cell-free DNA (cfDNA) concentration, age, blood-based TP53, EGFR, PIK3CA, ARID1A, STK11 and MET mutations significantly predicted OS. In ctDNA-positive patients, TP53/PIK3CA/ARID1A/STK11/MET-mutated patients had significantly inferior OS compared with wildtype patients (P < 0.001). Using above variables, DeepSurv was trained and tested in the MSK-ACCESS cohort (12-month AUC = 0.75), outperforming single cfDNA (AUC = 0.66) or ctDNA (AUC = 0.59), and externally validated in the ctDx Lung cohort. Compared with high-risk patients, DeepSurv-identified low-risk patients had significantly longer OS in both discovery (12-month OS 87.8% vs 53.8%, HR 0.32, P < 0.001) and validation cohorts (73.2% vs 48.4%, HR 0.42, P < 0.001). SHAP revealed TP53 and cfDNA concentration > 4.8 ng/mL had the most important contributions.
[CONCLUSIONS] The interpretable DeepSurv model, integrating multimodal features, enables quantitative survival prediction and risk stratification in advanced NSCLC, facilitating personalized decision-making.
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