ArticleBMC bioinformatics2020
Liver imaging features by convolutional neural network to predict the metachronous liver metastasis in stage I-III colorectal cancer patients based on preoperative abdominal CT scan.
Article in BMC bioinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 4 of them syntheses that pooled it.
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Who cites it
29 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Artificial intelligence for the prediction of synchronous and metachronous liver metastasis in colorectal cancer patients: a systematic review and meta-analysis.Abdominal radiology (New York) · 2026Pooled it
- Pooled it
- A systematic review on deep learning-based automated cancer diagnosis models.Journal of cellular and molecular medicine · 2024Pooled it
- Machine Learning Algorithms for Predicting Surgical Outcomes after Colorectal Surgery: A Systematic Review.World journal of surgery · 2022Pooled it
- CT radiomics for noninvasive prediction of histologic differentiation in gastric cancer.Medical physics · 2026Article
- Multimodal prediction of metachronous liver metastasis in stage I-III colorectal cancer patients: multicenter cohort study employing machine learning.Abdominal radiology (New York) · 2026Article
- Machine learning for predicting liver metastasis in colorectal cancer.Discover oncology · 2026Article
- Machine learning and deep learning models for predicting colorectal cancer metastases: A comprehensive review.European journal of radiology open · 2026Review
- Prediction of colorectal cancer liver metastasis through an MRI radiomic model.Scientific reports · 2026Article
- Artificial intelligence and computational prediction models for risk stratification, treatment response, and outcomes in colorectal cancer: a narrative review.Frontiers in oncology · 2026Review
- Explainable multi-modal radiomics for early prediction of liver metastasis in rectal cancer: a multicentric study.Insights into imaging · 2025Article
- Developing a multiomics data-based mathematical model to predict colorectal cancer recurrence and metastasis.BMC medical informatics and decision making · 2025Article
- Applications of Artificial Intelligence for Metastatic Gastrointestinal Cancer: A Systematic Literature Review.Cancers · 2025Review
- Diagnostic accuracy of artificial intelligence based on imaging data for predicting distant metastasis of colorectal cancer: a systematic review and meta-analysis.Frontiers in oncology · 2025Article
- Organotropic metastasis in colorectal cancer: integrating molecular pathways with therapeutic opportunities.Frontiers in immunology · 2025Review
- A pelvis MR transformer-based deep learning model for predicting lung metastases risk in patients with rectal cancer.Frontiers in oncology · 2025Article
- MRI T2WI-based radiomics combined with KRAS gene mutation constructed models for predicting liver metastasis in rectal cancer.BMC medical imaging · 2024Article
- Prognostication of colorectal cancer liver metastasis by CE-based radiomics and machine learning.Translational oncology · 2024Article
- Predictive modeling algorithms for liver metastasis in colorectal cancer: A systematic review of the current literature.Annals of hepato-biliary-pancreatic surgery · 2024Review
- Artificial Intelligence to Early Predict Liver Metastases in Patients with Colorectal Cancer: Current Status and Future Prospectives.Life (Basel, Switzerland) · 2023Review
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6 authors.
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Abstract
backgroundIntroducing deep learning approach to medical images has rendered a large amount of un-decoded information into usage in clinical research. But mostly, it has been focusing on the performance of the prediction modeling for disease-related entity, but not on the clinical implication of the feature itself. Here we analyzed liver imaging features of abdominal CT images collected from 2019 patients with stage I - III colorectal cancer (CRC) using convolutional neural network (CNN) to elucidate its clinical implication in oncological perspectives.
resultsCNN generated imaging features from the liver parenchyma. Dimension reduction was done for the features by principal component analysis. We designed multiple prediction models for 5-year metachronous liver metastasis (5YLM) using combinations of clinical variables (age, sex, T stage, N stage) and top principal components (PCs), with logistic regression classification. The model using "1
conclusionThe imaging features combined with clinical information improved the performance compared to the standardized prediction model using only clinical information. The liver imaging features generated by CNN may have the potential to predict liver metastasis. These results suggest that even though there were no liver metastasis during the primary colectomy, the features of liver imaging can impose characteristics that could be predictive for metachronous liver metastasis.
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