ArticleJournal of clinical medicine2021
Early Diagnosis of Liver Metastases from Colorectal Cancer through CT Radiomics and Formal Methods: A Pilot Study.
Article in Journal of clinical medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 4 of them syntheses that pooled it.
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Who cites it
39 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Radiomics in precision medicine for colorectal cancer: a bibliometric analysis (2013-2023).Frontiers in oncology · 2024Pooled it
- A Systematic Review and Meta-Analysis of Trifluridine/Tipiracil plus Bevacizumab for the Treatment of Metastatic Colorectal Cancer: Evidence from Real-World Series.Current oncology (Toronto, Ont.) · 2023Pooled it
- Repeatability of radiomics studies in colorectal cancer: a systematic review.BMC gastroenterology · 2023Pooled it
- Role of radiomics in predicting lymph node metastasis in gastric cancer: a systematic review.Frontiers in medicine · 2023Pooled it
- CT-based radiomics improves survival prediction in colorectal liver metastases: beyond clinical scores.Updates in surgery · 2026Article
- Prediction of colorectal cancer liver metastasis through an MRI radiomic model.Scientific reports · 2026Article
- Improving Clinical Diagnostics and Patient Care through Artificial Intelligence and Biosensor Technologies.ACS omega · 2026Review
- Comparison of Radiomic Features from Different MRI Sequences for Predicting Synchronous Liver Metastases after Rectal CancerCurrent medical imaging · 2026Article
- Predicting liver metastasis in colorectal cancer patients using routine biochemical tests enhanced by machine learning.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Article
- Improving early liver metastasis detection in colorectal cancer using a weighted ensemble of ResNet50 and swin transformer: a KHCC study.Frontiers in big data · 2025Article
- Predicting the recurrence risk of liver metastasis from colorectal cancer: a study based on preoperative CT intratumoral and peritumoral radiomics features.Frontiers in oncology · 2025Article
- Radiomics and 256-slice-dual-energy CT in the automated diagnosis of mild acute pancreatitis: the innovation of formal methods and high-resolution CT.La Radiologia medica · 2024Article
- Radiomic Gradient in Peritumoural Tissue of Liver Metastases: A Biomarker for Clinical Practice? Analysing Density, Entropy, and Uniformity Variations with Distance from the Tumour.Diagnostics (Basel, Switzerland) · 2024Article
- Future Perspectives on Radiomics in Acute Liver Injury and Liver Trauma.Journal of personalized medicine · 2024Review
- Quantitative ultrasound (QUS) in the evaluation of liver steatosis: data reliability in different respiratory phases and body positions.La Radiologia medica · 2024Article
- Fully semantic segmentation for rectal cancer based on post-nCRT MRl modality and deep learning framework.BMC cancer · 2024Article
- RoboticHeliyon · 2024Article
- The role of superior hemorrhoidal vein ectasia in the preoperative staging of rectal cancer.Frontiers in oncology · 2024Article
- Colorectal Cancer: Current Updates and Future Perspectives.Journal of clinical medicine · 2023Review
- Pancreatic Ductal Adenocarcinoma: Update of CT-Based Radiomics Applications in the Pre-Surgical Prediction of the Risk of Post-Operative Fistula, Resectability Status and Prognosis.Journal of clinical medicine · 2023Review
Corrections and comments
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Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundLiver metastases are a leading cause of cancer-associated deaths in patients affected by colorectal cancer (CRC). The multidisciplinary strategy to treat CRC is more effective when the radiological diagnosis is accurate and early. Despite the evolving technologies in radiological accuracy, the radiological diagnosis of Colorectal Cancer Liver Metastases (CRCLM) is still a key point. The aim of our study was to define a new patient representation different by Artificial Intelligence models, using Formal Methods (FMs), to help clinicians to predict the presence of liver metastasis when still undetectable using the standard protocols.
methodsWe retrospectively reviewed from 2013 to 2020 the CT scan of nine patients affected by CRC who would develop liver lesions within 4 months and 8 years. Seven patients developed liver metastases after primary staging before any liver surgery, and two patients were enrolled after R0 liver resection. Twenty-one patients were enrolled as the case control group (CCG). Regions of Interest (ROIs) were identified through manual segmentation on the medical images including only liver parenchyma and eventual benign lesions, avoiding major vessels and biliary ducts. Our predictive model was built based on formally verified radiomic features.
resultsThe precision of our methods is 100%, scheduling patients as positive only if they will be affected by CRCLM, showing a 93.3% overall accuracy. Recall was 77.8%.
conclusionFMs can provide an effective early detection of CRCLM before clinical diagnosis only through non-invasive radiomic features even in very heterogeneous and small clinical samples.
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