ArticleBMC cancer2022
Deep-learning model for predicting the survival of rectal adenocarcinoma patients based on a surveillance, epidemiology, and end results analysis.
Article in BMC cancer, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 3 of them syntheses that pooled it.
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Who cites it
31 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- A Systematic Review on Machine Learning Techniques for Survival Analysis in Cancer.Cancer medicine · 2025Pooled it
- Comparison of machine learning methods versus traditional Cox regression for survival prediction in cancer using real-world data: a systematic literature review and meta-analysis.BMC medical research methodology · 2025Pooled it
- Advancements in Image-Based Analyses for Morphology and Staging of Colon Cancer: A Comprehensive Review.BioMed research international · 2025Pooled it
- An interpretable survival benefit analytics framework for optimizing cancer treatment decision-making.Medical & biological engineering & computing · 2026Article
- N-glycan signatures of immunoglobulins-depleted serum proteins in colorectal cancer.Glycoconjugate journal · 2026Article
- Metastatic patterns, prognostic factors, and deep learning model development in primary gastrointestinal melanoma: a retrospective cohort analysis.Translational cancer research · 2026Article
- A deep-learning approach to predict mortality in very-low-birth-weight infants treated in the intensive care units using DeepSurv.Frontiers in pediatrics · 2026Article
- Preoperative prediction of rectal Cancer staging combining MRI deep transfer learning, radiomics features, and clinical factors: accurate differentiation from stage T2 to T3.BMC gastroenterology · 2024Article
- Review
- Case-Base Neural Network: Survival analysis with time-varying, higher-order interactions.Machine learning with applications · 2024Article
- Different lymph node staging systems for predicting the prognosis of colorectal neuroendocrine neoplasms.World journal of gastrointestinal oncology · 2024Article
- Artificial intelligence-assisted ultrasound imaging in hemophilia: research, development, and evaluation of hemarthrosis and synovitis detection.Research and practice in thrombosis and haemostasis · 2024Article
- Unified deep learning models for enhanced lung cancer prediction with ResNet-50-101 and EfficientNet-B3 using DICOM images.BMC medical imaging · 2024Article
- Article
- Predicting cutaneous malignant melanoma patients' survival using deep learning: a retrospective cohort study.Journal of cancer research and clinical oncology · 2023Article
- Deep-learning-based survival prediction of patients with lower limb melanoma.Discover oncology · 2023Article
- Application of machine learning in predicting survival outcomes involving real-world data: a scoping review.BMC medical research methodology · 2023Article
- A deep learning-based model (DeepMPM) to help predict survival in patients with malignant pleural mesothelioma.Translational cancer research · 2023Article
- How to use the Surveillance, Epidemiology, and End Results (SEER) data: research design and methodology.Military Medical Research · 2023Review
- Artificial intelligence based system for predicting permanent stoma after sphincter saving operations.Scientific reports · 2023Article
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4 authors.
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Abstract
backgroundWe collected information on patients with rectal adenocarcinoma in the United States from the Surveillance, Epidemiology, and EndResults (SEER) database. We used this information to establish a model that combined deep learning with a multilayer neural network (the DeepSurv model) for predicting the survival rate of patients with rectal adenocarcinoma.
methodsWe collected patients with rectal adenocarcinoma in the United States and older than 20 yearswho had been added to the SEER database from 2004 to 2015. We divided these patients into training and test cohortsat a ratio of 7:3. The training cohort was used to develop a seven-layer neural network based on the analysis method established by Katzman and colleagues to construct a DeepSurv prediction model. We then used the C-index and calibration plots to evaluate the prediction performance of the DeepSurv model.
resultsThe 49,275 patients with rectal adenocarcinoma included in the study were randomly divided into the training cohort (70%, n = 34,492) and the test cohort (30%, n = 14,783). There were no statistically significant differences in clinical characteristics between the two cohorts (p > 0.05). We applied Cox proportional-hazards regression to the data in the training cohort, which showed that age, sex, marital status, tumor grade, surgery status, and chemotherapy status were significant factors influencing survival (p < 0.05). Using the training cohort to construct the DeepSurv model resulted in a C-index of the model of 0.824, while using the test cohort to verify the DeepSurv model yielded a C-index of 0.821. Thesevalues show that the prediction effect of the DeepSurv model for the test-cohort patients was highly consistent with the prediction resultsfor the training-cohort patients.
conclusionThe DeepSurv prediction model of the seven-layer neural network that we have established can accurately predict the survival rateand time of rectal adenocarcinoma patients.
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