Related Experiment Video
Updated: Feb 8, 2026
Isolation of Circulating Tumor Cells in an Orthotopic Mouse Model of Colorectal Cancer
Published on: July 18, 2017
Interpretable deep learning model of circulating genomics for quantitative survival prediction in advanced non-small
Yu Wang1, Yi-Tong Li1, Ming-Hao Wang2
1Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China.
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.
More Related Videos
10:29Semi-automatic PD-L1 Characterization and Enumeration of Circulating Tumor Cells from Non-small Cell Lung Cancer Patients by Immunofluorescence
Published on: August 14, 2019
06:46Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
Published on: September 27, 2024
Related Concept Videos
01:21Cancer Survival Analysis
02:02Genomics
02:27Predicting Molecular Geometry
01:18Survival Curves
The Kaplan-Meier estimator is the most common method for constructing survival curves. This...
01:19Survival Tree
Building a Survival Tree
Constructing a...
01:22Interpreting R Charts
An R chart plots the range of subsets of measurements collected from a process. Each point on the chart represents the range—defined as the difference between the maximum and minimum...