ReviewDiagnostics (Basel, Switzerland)2022
Deep Learning on Histopathological Images for Colorectal Cancer Diagnosis: A Systematic Review.
Review in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers, 1 of them a synthesis that pooled it.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
42 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Virtual histopathology methods in medical imaging - a systematic review.BMC medical imaging · 2024Pooled it
- AI-Powered TAT-Net Hyperspectral Imaging for Rapid and Accurate Differentiation of Sinonasal Inverted Papilloma and Malignancy.World journal of otorhinolaryngology - head and neck surgery · 2026Article
- COOT-CNN: joint architecture and training-strategy optimization for binary colorectal histology patch classification.Scientific reports · 2026Article
- Review
- Immunohistochemistry (IHC) Versus Genomic Profiling in Cancer: Roles in Precision Medicine.Cureus · 2026Review
- Machine learning and deep learning models for predicting colorectal cancer metastases: A comprehensive review.European journal of radiology open · 2026Review
- Pathology Foundation Models: Evolution, Current Landscape, Challenges and Opportunities from a Technical and Clinical Perspective.Bioengineering (Basel, Switzerland) · 2026Review
- Ensemble of Handcrafted and Learned Features for Colorectal Cancer Classification.Journal of imaging informatics in medicine · 2026Article
- Integrating histopathology and immune marker analysis for machine learning-based colorectal cancer prognostics.Diagnostic pathology · 2026Article
- Next-Generation Sequencing-Based Detection ofGenes · 2026Article
- Weakly supervised colorectal gland segmentation through self-supervised learning and attention-based pseudo-labeling.Scientific reports · 2026Article
- Colorectal Lesion diagnosis using transformer and deep learning with multiscale feature interface.PloS one · 2026Article
- Cell-in-Cell Structures in Colorectal Cancer: A Proposed Assessment Method and Correlation with Established Poor Prognostic Factors.Journal of personalized medicine · 2025Article
- Deep Learning for Pathology: YOLOv8 with EigenCAM for Reliable Colorectal Cancer Diagnostics.Bioengineering (Basel, Switzerland) · 2025Article
- Efficient deep neural networks for cancer detection on histopathology combining attention and image downsampling.Scientific reports · 2025Article
- Impact of the COVID-19 Pandemic on Colorectal Cancer Surgery: Surgical Outcomes and Tumor Characteristics in a Multicenter Retrospective Cohort.Journal of clinical medicine · 2025Article
- Artificial intelligence in healthcare and medicine: clinical applications, therapeutic advances, and future perspectives.European journal of medical research · 2025Review
- Systematic review and meta-analysis of deep learning for MSI-H in colorectal cancer whole slide images.NPJ digital medicine · 2025Article
- Accurate colorectal cancer detection using a random hinge exponential distribution coupled attention network on pathological images.Abdominal radiology (New York) · 2025Article
- Diagnosis of colorectal cancer using residual transformer with mixed attention and explainable AI.PloS one · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Colorectal cancer (CRC) is the second most common cancer in women and the third most common in men, with an increasing incidence. Pathology diagnosis complemented with prognostic and predictive biomarker information is the first step for personalized treatment. The increased diagnostic load in the pathology laboratory, combined with the reported intra- and inter-variability in the assessment of biomarkers, has prompted the quest for reliable machine-based methods to be incorporated into the routine practice. Recently, Artificial Intelligence (AI) has made significant progress in the medical field, showing potential for clinical applications. Herein, we aim to systematically review the current research on AI in CRC image analysis. In histopathology, algorithms based on Deep Learning (DL) have the potential to assist in diagnosis, predict clinically relevant molecular phenotypes and microsatellite instability, identify histological features related to prognosis and correlated to metastasis, and assess the specific components of the tumor microenvironment.
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Registered trials
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.