SynthesisAbdominal radiology (New York)2025
Machine learning and deep learning models for preoperative detection of lymph node metastasis in colorectal cancer: a systematic review and meta-analysis.
Synthesis in Abdominal radiology (New York), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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Who cites it
5 citing papers in PubMed.
- CT-based Node-RADS for metastatic lymph node detection in colon cancer and influence of microsatellite instability.Abdominal radiology (New York) · 2026Article
- Machine learning-based preoperative classification of colorectal cancer stage using systemic inflammatory and nutritional biomarkers.BMC gastroenterology · 2026Article
- Chinese clinical practice guidelines for super minimally invasive surgery of digestive tract tumors.Journal of translational internal medicine · 2025Article
- Development and validation of an AI-augmented deep learning model for survival prediction in de novo metastatic colorectal cancer.Discover oncology · 2025Article
- PTEN and HES1 Gene Expression Alteration in Breast Cancer: Any Association with Tumor Histomorphological Features or Invasive Behavior?Asian Pacific journal of cancer prevention : APJCP · 2025Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo evaluate the diagnostic performance of Machine Learning (ML) and Deep Learning (DL) models for predicting preoperative Lymph Node Metastasis (LNM) in Colorectal Cancer (CRC) patients.
methodsA systematic review and meta-analysis were conducted following PRISMA-DTA and AMSTAR-2 guidelines. We searched PubMed, Web of Science, Embase, and Cochrane Library databases until February 16, 2024. Study quality and risk of bias were assessed using the QUADAS-2 tool. Data were analyzed using STATA v18, applying random-effects models to all analyses.
resultsTwelve studies involving 8321 patients were included, with most published in 2021-2024 (9/12). The pooled AUC of ML models for predicting LNM in CRC patients was 0.87 (95% CI: 0.82-0.91, I
conclusionML models demonstrate strong potential for preoperative LNM staging and treatment planning in CRC, potentially reducing the need for additional surgeries and related health and financial burdens. Further prospective multicenter studies, with standardized reporting of algorithms, modality parameters, and LNM staging, are needed to validate these findings.
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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.