ArticleAnnals of surgical oncology2021
Differences in Liver Parenchyma are Measurable with CT Radiomics at Initial Colon Resection in Patients that Develop Hepatic Metastases from Stage II/III Colon Cancer.
Article in Annals of surgical oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Gut microbiota-driven pre-metastatic niche formation in colorectal cancer liver metastasis: mechanisms and translational significance.Gut pathogens · 2026Review
- Radiomics-based artificial intelligence (AI) models in colorectal cancer (CRC) diagnosis, metastasis detection, prognosis, and treatment response prediction.Abdominal radiology (New York) · 2026Review
- Machine learning-based radiomics models for the prediction of metachronous liver metastases in patients with colorectal cancer: A multimodal study.Oncology letters · 2025Article
- Understanding pre-metastatic niche formation: implications for colorectal cancer liver metastasis.Journal of translational medicine · 2025Review
- Assessing the Significance and Progression of Too Small to Characterize Lesions in Hepatic CT Scans of Patients With a Cancer History: A Two-Center Cross-Sectional Study.Health science reports · 2025Article
- Radiomics in sporadic microsatellite instable, mismatch repair deficient and Lynch syndrome-associated pancreatic ductal adenocarcinoma: a pilot study.Frontiers in oncology · 2025Article
- Heterogenous liver parenchymal enhancement in CT is a favorable prognosis of HCC after hepatic resection.American journal of cancer research · 2024Article
- Radiomics and machine learning analysis by computed tomography and magnetic resonance imaging in colorectal liver metastases prognostic assessment.La Radiologia medica · 2023Article
- CT radiomics analysis of primary colon cancer patients with or without liver metastases: a correlative study with [Abdominal radiology (New York) · 2023Article
- Colorectal liver metastases patients prognostic assessment: prospects and limits of radiomics and radiogenomics.Infectious agents and cancer · 2023Review
- Radiomics artificial intelligence modelling for prediction of local control for colorectal liver metastases treated with radiotherapy.Physics and imaging in radiation oncology · 2022Article
- Machine learning radiomics can predict early liver recurrence after resection of intrahepatic cholangiocarcinoma.HPB : the official journal of the International Hepato Pancreato Biliary Association · 2022Article
- The Hepatic Pre-Metastatic Niche.Cancers · 2022Review
- Current challenges in metastasis research and future innovation for clinical translation.Clinical & experimental metastasis · 2022Article
- Predicting cancer outcomes with radiomics and artificial intelligence in radiology.Nature reviews. Clinical oncology · 2022Review
- Review
- Contrast Administration Impacts CT-Based Radiomics of Colorectal Liver Metastases and Non-Tumoral Liver Parenchyma Revealing the "Radiological" Tumour Microenvironment.Diagnostics (Basel, Switzerland) · 2021Article
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Authors and funding
15 authors.
Funding
Abstract
backgroundCurrently, there are no methods to identify patients with an increased risk of liver metastases to guide patient selection for liver-directed therapies. We tried to determine whether quantitative image features (radiomics) of the liver obtained from preoperative staging CT scans at the time of initial colon resection differ in patients that subsequently develop liver metastases, extrahepatic metastases, or demonstrate prolonged disease-free survival.
methodsPatients who underwent resection of stage II/III colon cancer from 2004 to 2012 with available preoperative CT scans were included in this single-institution, retrospective case-control study. Patients were grouped by initial recurrence patterns: liver recurrence, extrahepatic recurrence, or no evidence of disease at 5 years. Radiomic features of the liver parenchyma extracted from CT images were compared across groups.
resultsThe cohort consisted of 120 patients divided evenly between three recurrence groups, with an equal number of stage II and III patients in each group. After adjusting for multiple comparisons, 44 of 254 (17%) imaging features displayed different distributions across the three patient groups (p < 0.05), with the clearest distinction between those with liver recurrence and no evidence of disease. Increased heterogeneity in the liver parenchyma by radiomic analysis was protective of liver metastases.
conclusionsCT radiomics is a promising tool to identify patients at high risk of developing liver metastases and is worthy of further investigation and validation.
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