ReviewFrontiers in oncology2023
Recent advances of pathomics in colorectal cancer diagnosis and prognosis.
Review in Frontiers in oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 citing papers in PubMed.
- Enhancing colorectal cancer diagnosis with the ferroptosis marker GPX4 and serum biomarkers: A retrospective analysis and machine learning approach.Biochemistry and biophysics reports · 2026Article
- Investigating the Molecular Mechanisms of the Anticancer Effects of Eugenol and Cinnamaldehyde Against Colorectal Cancer (CRC) Cells In Vitro.International journal of molecular sciences · 2026Article
- Digital Pathology-Based Comparison of PyRadiomics and HistomicsTK for Nuclei Classification in Melanoma Whole Slide Images.International journal of biomedical imaging · 2026Article
- AI-based histopathology and radiomics fusion for predicting surgical margins in colorectal cancer: improving oncological outcomes through multimodal AI integration.Annals of medicine and surgery (2012) · 2025Article
- Advances in Hereditary Colorectal Cancer: How Precision Medicine Is Changing the Game.Cancers · 2025Review
- Personalized risk stratification in colorectal cancer via PIANOS system.Nature communications · 2025Article
- Deciphering lactate metabolism in colorectal cancer: Prognostic modeling, immune infiltration, and gene mutation insights.World journal of gastroenterology · 2025Article
- Research status and trends of deep learning in colorectal cancer (2011-2023): Bibliometric analysis and visualization.World journal of gastrointestinal oncology · 2025Article
- Predicting the Evolution of Lung Squamous Cell Carcinoma In Situ Using Computational Pathology.Bioengineering (Basel, Switzerland) · 2025Article
- Advancements in pathology: Digital transformation, precision medicine, and beyond.Journal of pathology informatics · 2025Review
- Multiomics-Based Deep Learning Prediction of Prognosis and Therapeutic Response in Patients With Extensive-Stage Small Cell Lung Cancer Receiving Chemoimmunotherapy: A Retrospective Cohort Study.International journal of general medicine · 2025Article
- Deep learning and pathomics analyses predict prognosis of high-grade gliomas.Frontiers in neurology · 2025Article
- Application of Pathomic Features for Differentiating Dysplastic Cells in Patients with Myelodysplastic Syndrome.Bioengineering (Basel, Switzerland) · 2024Article
- Deep learning-based surgical phase recognition in laparoscopic cholecystectomy.Annals of hepato-biliary-pancreatic surgery · 2024Article
- circFOXP1: a potential diagnostic and therapeutic target in human diseases.Frontiers in immunology · 2024Review
- Machine Learning-Based Pathomics Model to Predict the Prognosis in Clear Cell Renal Cell Carcinoma.Technology in cancer research & treatmentArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Colorectal cancer (CRC) is one of the most common malignancies, with the third highest incidence and the second highest mortality in the world. To improve the therapeutic outcome, the risk stratification and prognosis predictions would help guide clinical treatment decisions. Achieving these goals have been facilitated by the fast development of artificial intelligence (AI) -based algorithms using radiological and pathological data, in combination with genomic information. Among them, features extracted from pathological images, termed pathomics, are able to reflect sub-visual characteristics linking to better stratification and prediction of therapeutic responses. In this paper, we review recent advances in pathological image-based algorithms in CRC, focusing on diagnosis of benign and malignant lesions, micro-satellite instability, as well as prediction of neoadjuvant chemoradiotherapy and the prognosis of CRC patients.
Indexed as
Identifiers
What OpenQuestion holds
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.