ArticleCancer medicine2023
Comparison of deep learning-based recurrence-free survival with random survival forest and Cox proportional hazard models in Stage-I NSCLC patients.
Article in Cancer medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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9 citing papers in PubMed.
- Machine learning-based early survival prediction in early-onset hepatocellular carcinoma: a SEER-based multi-model comparative study.Translational cancer research · 2026Article
- Integrating Blood-Based Immune-Inflammation Biomarkers into Artificial Intelligence-Driven Prognostic Models in Oncology.International journal of molecular sciences · 2026Review
- Outcome Risk Modeling for Disability-Free Longevity: Comparison of Random Forest and Random Survival Forest Methods.medRxiv : the preprint server for health sciences · 2026Article
- Machine Learning Models for Disease-Free Survival Analysis after Liver Resection for Hepatocellular Carcinoma: A Multicentric French Collaborative Study.Liver cancer · 2026Article
- Application of deep learning in prognostic prediction of non-small cell lung cancer.Frontiers in cell and developmental biology · 2026Review
- Survival Prediction in Stomach Cancer with Deep Learning: Unveiling Model Decisions with LIME and SHAP.Asian Pacific journal of cancer prevention : APJCP · 2025Article
- Combining lymph node ratio to develop prognostic models for postoperative gastric neuroendocrine neoplasm patients.World journal of gastrointestinal oncology · 2024Article
- Deep Learning-based Time-to-event Analysis of Depression and Asthma using the All of Us Research Program.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
- Comparison of deep learning-based recurrence-free survival with random survival forest and Cox proportional hazard models in Stage-I NSCLC patients.Cancer medicine · 2023Article
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6 authors.
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Abstract
backgroundThe curative treatment for Stage I non-small cell lung cancer (NSCLC) is surgical resection. Even for Stage I patients, the probability of recurrence after curative treatment is around 20%.
methodsIn this retrospective study, we included 268 operated Stage I NSCLC patients between January 2008 and June 2018 to analyze the prognostic factors (pathological stage, histological type, number of sampled mediastinal lymph node stations, type of resection, SUVmax of the lesion) that may affect relapse with three different methods, Cox proportional hazard (CoxPH), random survival forest (RSF), DeepSurv, and to compare the performance of these methods with Harrell's C-index. The dataset was randomly split into two sets, training and test sets.
resultsIn the training set, DeepSurv showed the best performance among the three models, the C-index of the training set was 0.832, followed by RSF (0.675) and CoxPH (0.672). In the test set, RSF showed the best performance among the three models, followed by DeepSurv with 0.677 and CoxPH methods with 0.625.
conclusionIn conclusion, machine-learning techniques can be useful in predicting recurrence for lung cancer and guide clinicians both in choosing the adjuvant treatment options and best follow-up programs.
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