ArticleScientific reports2020
DeepBTS: Prediction of Recurrence-free Survival of Non-small Cell Lung Cancer Using a Time-binned Deep Neural Network.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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24 citing papers in PubMed.
- Development and external validation of a machine learning-based Cox model for predicting in-hospital survival in classic heatstroke: a multicenter retrospective study.Scientific reports · 2026Article
- Artificial Intelligence in Lung Cancer: From Early Detection to Personalized Therapy.Current oncology reports · 2026Review
- Application of deep learning in prognostic prediction of non-small cell lung cancer.Frontiers in cell and developmental biology · 2026Review
- Effectiveness of Artificial Intelligence Models in Predicting Lung Cancer Recurrence: A Gene Biomarker-Driven Review.Cancers · 2025Review
- Opportunities and challenges in lung cancer care in the era of large language models and vision language models.Translational lung cancer research · 2025Review
- Machine learning for predicting the prognosis of patients with thymoma and thymic carcinoma.Journal of thoracic disease · 2025Article
- Multimodal integration strategies for clinical application in oncology.Frontiers in pharmacology · 2025Review
- Deep contrastive learning for predicting cancer prognosis using gene expression values.Briefings in bioinformatics · 2024Article
- Predicting mortality and recurrence in colorectal cancer: Comparative assessment of predictive models.Heliyon · 2024Article
- A new model using deep learning to predict recurrence after surgical resection of lung adenocarcinoma.Scientific reports · 2024Article
- Autoencoder-based multimodal prediction of non-small cell lung cancer survival.Scientific reports · 2023Article
- Machine Learning-Assisted Recurrence Prediction for Patients With Early-Stage Non-Small-Cell Lung Cancer.JCO clinical cancer informatics · 2023Article
- Deep learning model integrating positron emission tomography and clinical data for prognosis prediction in non-small cell lung cancer patients.BMC bioinformatics · 2023Article
- A CT-based transfer learning approach to predict NSCLC recurrence: The added-value of peritumoral region.PloS one · 2023Article
- AI/ML advances in non-small cell lung cancer biomarker discovery.Frontiers in oncology · 2023Review
- Gross tumour volume radiomics for prognostication of recurrence & death following radical radiotherapy for NSCLC.NPJ precision oncology · 2022Article
- Use of deep learning to predict postoperative recurrence of lung adenocarcinoma from preoperative CT.International journal of computer assisted radiology and surgery · 2022Article
- Deep Learning with Multimodal Integration for Predicting Recurrence in Patients with Non-Small Cell Lung Cancer.Sensors (Basel, Switzerland) · 2022Article
- Harnessing multimodal data integration to advance precision oncology.Nature reviews. Cancer · 2022Review
- Neural Networks for Survival Prediction in Medicine Using Prognostic Factors: A Review and Critical Appraisal.Computational and mathematical methods in medicine · 2022Review
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14 authors.
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Abstract
Accurate prediction of non-small cell lung cancer (NSCLC) prognosis after surgery remains challenging. The Cox proportional hazard (PH) model is widely used, however, there are some limitations associated with it. In this study, we developed novel neural network models called binned time survival analysis (DeepBTS) models using 30 clinico-pathological features of surgically resected NSCLC patients (training cohort, n = 1,022; external validation cohort, n = 298). We employed the root-mean-square error (in the supervised learning model, s- DeepBTS) or negative log-likelihood (in the semi-unsupervised learning model, su-DeepBTS) as the loss function. The su-DeepBTS algorithm achieved better performance (C-index = 0.7306; AUC = 0.7677) than the other models (Cox PH: C-index = 0.7048 and AUC = 0.7390; s-DeepBTS: C-index = 0.7126 and AUC = 0.7420). The top 14 features were selected using su-DeepBTS model as a selector and could distinguish the low- and high-risk groups in the training cohort (p = 1.86 × 10
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