ArticleScientific reports2018
Deep learning based tissue analysis predicts outcome in colorectal cancer.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 261 papers, 7 of them syntheses that pooled it.
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Who cites it
261 citing papers in PubMed, 7 syntheses or guidelines pooled it.
- Transforming histologic assessment: artificial intelligence in cancer diagnosis and personalized treatment.British journal of cancer · 2025Pooled it
- Use of artificial intelligence for the prediction of lymph node metastases in early-stage colorectal cancer: systematic review.BJS open · 2024Pooled it
- AI predicting recurrence in non-muscle-invasive bladder cancer: systematic review with study strengths and weaknesses.Frontiers in oncology · 2024Pooled it
- Application of Artificial Intelligence in the diagnosis and treatment of colorectal cancer: a bibliometric analysis, 2004-2023.Frontiers in oncology · 2024Pooled it
- Critical Analysis of the Current Medical Image-Based Processing Techniques for Automatic Disease Evaluation: Systematic Literature Review.Sensors (Basel, Switzerland) · 2022Pooled it
- Visualizing knowledge evolution trends and research hotspots of artificial intelligence in colorectal cancer: A bibliometric analysis.Frontiers in oncology · 2022Pooled it
- Accuracy of computer-aided image analysis in the diagnosis of odontogenic cysts: A systematic review.Medicina oral, patologia oral y cirugia bucal · 2021Pooled it
- Prediction of lymph node metastasis in early colorectal cancer based on histologic images by artificial intelligence.Scientific reports · 2022Trial
- Development of a Computational Histology Artificial Intelligence-Powered Prognostic Biomarker in Colorectal Cancer in The Cancer Genome Atlas.World journal of oncology · 2026Article
- V7-DiVA: a feature-based Deep MIL colorectal whole-slide histopathology research platform with blinded external image-input validation.Journal of translational medicine · 2026Article
- AI-Guided Ferroptosis Biomarker Discovery and Routine Laboratory-Based Machine Learning for Predicting Nodal Metastasis in Colon Cancer.Metabolites · 2026Article
- SurvGRN: a multi-feature fusion framework for bladder cancer survival prediction.International journal of surgery (London, England) · 2026Article
- Cancer in transition: discovery of tumor-intrinsic transcriptional programs shaping the immune and microenvironmental landscape.Biomarker research · 2026Review
- An interpretable weighted ensemble based on routinely collected clinical data for the accurate prediction of axillary lymph node metastasis.Quantitative imaging in medicine and surgery · 2026Article
- Integrating artificial intelligence (AI) into colorectal cancer reporting.The Journal of pathology · 2026Review
- Uncovering Hidden Prognostic Patterns in Colorectal Cancer Histology Using Unsupervised Learning: A Computational Pathology Study.Bioengineering (Basel, Switzerland) · 2026Article
- Interpretable machine learning for survival prediction and risk stratification in elderly patients with breast cancer after breast-conserving surgery.Gland surgery · 2026Article
- A Deep Learning Approach for Mandibular Condyle Segmentation on Ultrasonography.Journal of imaging informatics in medicine · 2026Article
- Artificial intelligence in digital pathology diagnosis and analysis: technologies, challenges, and future prospects.Military Medical Research · 2026Review
- EfficientNetB5-Based Deep Learning for Automated Cancer Detection in Tissue Microarray Images.Journal of Cancer · 2026Article
201 more citing papers are in PubMed but not listed here.
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Image-based machine learning and deep learning in particular has recently shown expert-level accuracy in medical image classification. In this study, we combine convolutional and recurrent architectures to train a deep network to predict colorectal cancer outcome based on images of tumour tissue samples. The novelty of our approach is that we directly predict patient outcome, without any intermediate tissue classification. We evaluate a set of digitized haematoxylin-eosin-stained tumour tissue microarray (TMA) samples from 420 colorectal cancer patients with clinicopathological and outcome data available. The results show that deep learning-based outcome prediction with only small tissue areas as input outperforms (hazard ratio 2.3; CI 95% 1.79-3.03; AUC 0.69) visual histological assessment performed by human experts on both TMA spot (HR 1.67; CI 95% 1.28-2.19; AUC 0.58) and whole-slide level (HR 1.65; CI 95% 1.30-2.15; AUC 0.57) in the stratification into low- and high-risk patients. Our results suggest that state-of-the-art deep learning techniques can extract more prognostic information from the tissue morphology of colorectal cancer than an experienced human observer.
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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.