ArticleNPJ digital medicine2018
A principled machine learning framework improves accuracy of stage II colorectal cancer prognosis.
Article in NPJ digital medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 2 of them syntheses that pooled it.
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Who cites it
26 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial intelligence in colorectal surgery: an AI-powered systematic review.Techniques in coloproctology · 2023Pooled it
- Machine Learning Algorithms for Predicting Surgical Outcomes after Colorectal Surgery: A Systematic Review.World journal of surgery · 2022Pooled it
- Examining the gene network and prognostic biomarkers in the onset of colorectal cancer in stool samples using machine learning.Discover oncology · 2026Article
- Machine learning application in colon cancer treatment outcome prediction.Scientific reports · 2026Article
- Deep multimodal state-space fusion of endoscopic-radiomic and clinical data for survival prediction in colorectal cancer.NPJ digital medicine · 2025Article
- The development of an efficient artificial intelligence-based classification approach for colorectal cancer response to radiochemotherapy: deep learning vs. machine learning.Scientific reports · 2025Article
- Few-shot Tumor Bud Segmentation Using Generative Model in Colorectal Carcinoma.Proceedings of SPIE--the International Society for Optical Engineering · 2024Article
- Assessing the clinical utility of tumor invasion proportion of lymph nodes for enhanced risk stratification in N1 colorectal cancer.American journal of cancer research · 2024Article
- Predicting Colorectal Cancer Survival Using Time-to-Event Machine Learning: Retrospective Cohort Study.Journal of medical Internet research · 2023Article
- Down regulation of Cathepsin W is associated with poor prognosis in pancreatic cancer.Scientific reports · 2023Article
- SnapEnsemFS: a snapshot ensembling-based deep feature selection model for colorectal cancer histological analysis.Scientific reports · 2023Article
- Cell graph neural networks enable the precise prediction of patient survival in gastric cancer.NPJ precision oncology · 2022Article
- Development of artificial intelligence technology in diagnosis, treatment, and prognosis of colorectal cancer.World journal of gastrointestinal oncology · 2022Review
- The role of AI technology in prediction, diagnosis and treatment of colorectal cancer.Artificial intelligence review · 2022Article
- Progression-Free Survival Prediction in Patients with Nasopharyngeal Carcinoma after Intensity-Modulated Radiotherapy: Machine Learning vs. Traditional Statistics.Journal of personalized medicine · 2021Article
- Relationship between miR-204 and ANGPTL2 expression and diagnosis, pathological stage, and prognosis in patients with colon cancer.Translational cancer research · 2021Article
- SOX9 is associated with advanced T-stages of clinical stage II colon cancer in young Mexican patients.Oncology letters · 2021Article
- Interpretable survival prediction for colorectal cancer using deep learning.NPJ digital medicine · 2021Article
- Assessment of Immunological Features in Muscle-Invasive Bladder Cancer Prognosis Using Ensemble Learning.Cancers · 2021Article
- Comprehensive insights on pivotal prognostic signature involved in clear cell renal cell carcinoma microenvironment using the ESTIMATE algorithm.Cancer medicine · 2020Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Accurate prognosis is fundamental in planning an appropriate therapy for cancer patients. Consequent to the heterogeneity of the disease, intra- and inter-pathologist variability, and the inherent limitations of current pathological reporting systems, patient outcome varies considerably within similarly staged patient cohorts. This is particularly true when classifying stage II colorectal cancer patients using the current TNM guidelines. The aim of the present work is to address this problem through the use of machine learning. In particular, we introduce a data driven framework which makes use of a large number of diverse types of features, readily collected from immunofluorescence imagery. Its outstanding performance in predicting mortality in stage II patients (AUROC = 0:94), exceeds that of current clinical guidelines such as pT stage (AUROC = 0:65), and is demonstrated on a cohort of 173 colorectal cancer patients.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.