ArticleLa Radiologia medica2022
Radiomics and machine learning analysis based on magnetic resonance imaging in the assessment of liver mucinous colorectal metastases.
Article in La Radiologia medica, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers.
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Who cites it
50 citing papers in PubMed.
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- Interpretable intratumoral and peritumoral radiomics using SHAP for predicting microsatellite status in gastric adenocarcinoma: a dual-center study.Frontiers in oncology · 2026Article
- Radiologists and treatment response assessment: conventional and functional radiological criteria for predicting therapeutic response in virus-related and non-virus-related cancers.Infectious agents and cancer · 2025Review
- Review
- Visual Perception and Pre-Attentive Attributes in Oncological Data Visualisation.Bioengineering (Basel, Switzerland) · 2025Review
- Dual-Time-Point Radiomics for Prognosis Prediction in Colorectal Liver Metastasis Treated with Neoadjuvant Therapy Before Radical Resection: A Two-Center Study.Annals of surgical oncology · 2025Article
- A Systematic Review of Disappearing Colorectal Liver Metastases: Resection or No Resection?Journal of clinical medicine · 2025Review
- All You Need to Know About TACE: A Comprehensive Review of Indications, Techniques, Efficacy, Limits, and Technical Advancement.Journal of clinical medicine · 2025Review
- CT-based radiomics integrated model for brain metastases in stage III/IV ALK-positive lung adenocarcinoma patients.Frontiers in oncology · 2025Article
- MRI management of focal liver lesions: what a beginner cannot fail to know.Frontiers in oncology · 2025Review
- Radiomics in radiology: What the radiologist needs to know about technical aspects and clinical impact.La Radiologia medica · 2024Review
- Recent trends in AI applications for pelvic MRI: a comprehensive review.La Radiologia medica · 2024Review
- Machine learning and radiomics analysis by computed tomography in colorectal liver metastases patients for RAS mutational status prediction.La Radiologia medica · 2024Article
- A combinatorial MRI sequence-based radiomics model for preoperative prediction of microsatellite instability status in rectal cancer.Scientific reports · 2024Article
- Machine learning-based radiomics analysis in predicting RAS mutational status using magnetic resonance imaging.La Radiologia medica · 2024Article
- Scientific Status Quo of Small Renal Lesions: Diagnostic Assessment and Radiomics.Journal of clinical medicine · 2024Review
- An Informative Review of Radiomics Studies on Cancer Imaging: The Main Findings, Challenges and Limitations of the Methodologies.Current oncology (Toronto, Ont.) · 2024Review
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17 authors.
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Abstract
purposeThe purpose of this study is to evaluate the Radiomics and Machine Learning Analysis based on MRI in the assessment of Liver Mucinous Colorectal Metastases.Query
methodsThe cohort of patients included a training set (121 cases) and an external validation set (30 cases) with colorectal liver metastases with pathological proof and MRI study enrolled in this approved study retrospectively. About 851 radiomics features were extracted as median values by means of the PyRadiomics tool on volume on interest segmented manually by two expert radiologists. Univariate analysis, linear regression modelling and pattern recognition methods were used as statistical and classification procedures.
resultsThe best results at univariate analysis were reached by the wavelet_LLH_glcm_JointEntropy extracted by T2W SPACE sequence with accuracy of 92%. Linear regression model increased the performance obtained respect to the univariate analysis. The best results were obtained by a linear regression model of 15 significant features extracted by the T2W SPACE sequence with accuracy of 94%, a sensitivity of 92% and a specificity of 95%. The best classifier among the tested pattern recognition approaches was k-nearest neighbours (KNN); however, KNN achieved lower precision than the best linear regression model.
conclusionsRadiomics metrics allow the mucinous subtype lesion characterization, in order to obtain a more personalized approach. We demonstrated that the best performance was obtained by T2-W extracted textural metrics.
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