Trial reportScientific reports2022
Prediction of lymph node metastasis in early colorectal cancer based on histologic images by artificial intelligence.
Trial report in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 3 of them syntheses that pooled it.
What it found
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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
22 citing papers in PubMed, 3 syntheses or guidelines pooled it, 49 citations in OpenAlex.
- Transforming histologic assessment: artificial intelligence in cancer diagnosis and personalized treatment.British journal of cancer · 2025Pooled it
- Efficacy of a whole slide image-based prediction model for lymph node metastasis in T1 colorectal cancer: A systematic review.Journal of gastroenterology and hepatology · 2024Pooled it
- Use of artificial intelligence for the prediction of lymph node metastases in early-stage colorectal cancer: systematic review.BJS open · 2024Pooled it
- Pathology-derived clinical micro-architectural diagnostics of tumour-microbiome interactions in colorectal cancer.Journal of translational medicine · 2026Article
- Predicting Metastatic Potential from Histopathological Whole Slide Images of Primary Tumors in Head and Neck Squamous Cell Carcinoma Using Attention-Based Deep Learning and Signaling Pathway Alterations.Head and neck pathology · 2026Article
- Optimizing lymph node harvest in laparoscopic colorectal cancer: predictors and station-specific lymph node submission solutions.Annals of coloproctology · 2026Article
- Circulating tumor DNA methylation-based method for noninvasive detection and stage stratification of colorectal tumor.Clinical epigenetics · 2026Article
- Article
- Identification of clinical and pathological risk factors for lymph-node metastasis in T1 stage colon cancer: a population-based study.Updates in surgery · 2025Article
- A novel artificial intelligence approach to the prediction of lymph node metastasis using whole-slide imaging in patients with T1 colorectal cancer.Surgical endoscopy · 2025Article
- CRCFound: A Colorectal Cancer CT Image Foundation Model Based on Self-Supervised Learning.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- The Gut Microbiota and Colorectal Cancer: Understanding the Link and Exploring Therapeutic Interventions.Biology · 2025Review
- Utilizing tumor deposit count as a stratification criterion in revising TNM staging system for patients with colorectal cancer: a nomogram review study.Frontiers in oncology · 2025Article
- The risk factors of lymph node metastasis in early colorectal cancer: a predictive nomogram and risk assessment.International journal of colorectal disease · 2024Article
- Accuracy Goals in Predicting Preoperative Lymph Node Metastasis for T1 Colorectal Cancer Resected Endoscopically.Gut and liver · 2024Review
- Colorectal cancer prognosis based on dietary pattern using synthetic minority oversampling technique with K-nearest neighbors approach.Scientific reports · 2024Article
- Article
- Construction and validation of artificial intelligence pathomics models for predicting pathological staging in colorectal cancer: Using multimodal data and clinical variables.Cancer medicine · 2024Observational
- Quantitative analysis of prion disease using an AI-powered digital pathology framework.Scientific reports · 2023Article
- Endoscopic Treatment of T1 Colorectal Cancer.Cancers · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Risk evaluation of lymph node metastasis (LNM) for endoscopically resected submucosal invasive (T1) colorectal cancers (CRC) is critical for determining therapeutic strategies, but interobserver variability for histologic evaluation remains a major problem. To address this issue, we developed a machine-learning model for predicting LNM of T1 CRC without histologic assessment. A total of 783 consecutive T1 CRC cases were randomly split into 548 training and 235 validation cases. First, we trained convolutional neural networks (CNN) to extract cancer tile images from whole-slide images, then re-labeled these cancer tiles with LNM status for re-training. Statistical parameters of the tile images based on the probability of primary endpoints were assembled to predict LNM in cases with a random forest algorithm, and defined its predictive value as random forest score. We evaluated the performance of case-based prediction models for both training and validation datasets with area under the receiver operating characteristic curves (AUC). The accuracy for classifying cancer tiles was 0.980. Among cancer tiles, the accuracy for classifying tiles that were LNM-positive or LNM-negative was 0.740. The AUCs of the prediction models in the training and validation sets were 0.971 and 0.760, respectively. CNN judged the LNM probability by considering histologic tumor grade.
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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.