ArticleKorean journal of clinical oncology2024
Radiomics and machine learning analysis of liver magnetic resonance imaging for prediction and early detection of tumor response in colorectal liver metastases.
Article in Korean journal of clinical oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- A Retrospective Study on Predicting Ki-67 Expression in Esophageal Cancer Patients Based on Delta Radiomics.Journal of imaging · 2026Article
- Advances in the Therapeutic Landscape of Hepatocellular Carcinoma: Current Strategies and Future Perspectives.Cancers · 2026Review
- Association between induced organ atrophy assessed by artificial intelligence-generated automatic segmentation and efficacy of bevacizumab in combination with chemotherapy in metastatic colorectal cancer.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Computed Tomography-Based Habitat Analysis for Prognostic Stratification in Colorectal Liver Metastases.Cancer innovation · 2025Article
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Authors and funding
5 authors.
Funding
Abstract
purposeThe aim of this study was to demonstrate the effectiveness of a machine learning-based radiomics model for distinguishing tumor response and overall survival in patients with unresectable colorectal liver metastases (CRLM) treated with targeted biological therapy.
methodsWe prospectively recruited 17 patients with unresectable liver metastases of colorectal cancer, who had been given targeted biological therapy as the first line of treatment. All patients underwent liver magnetic resonance imaging (MRI) three times up until 8 weeks after chemotherapy. We evaluated the diagnostic performance of machine learning-based radiomics model in tumor response of liver MRI compared with the guidelines for the Response Evaluation Criteria in Solid Tumors. Overall survival was evaluated using the Kaplan-Meier analysis and compared to the Cox proportional hazard ratios following univariate and multivariate analyses.
resultsPerformance measurement of the trained model through metrics showed the accuracy of the machine learning model to be 76.5%, and the area under the receiver operating characteristic curve was 0.857 (95% confidence interval [CI], 0.605-0.976; P < 0.001). For the patients classified as non-progressing or progressing by the radiomics model, the median overall survival was 17.5 months (95% CI, 12.8-22.2), and 14.8 months (95% CI, 14.2-15.4), respectively (P = 0.431, log-rank test).
conclusionMachine learning-based radiomics models could have the potential to predict tumor response in patients with unresectable CRLM treated with biologic therapy.
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