ArticleCancers2020
Diffusion-Weighted MRI and Diffusion Kurtosis Imaging to Detect RAS Mutation in Colorectal Liver Metastasis.
Article in Cancers, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers.
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Who cites it
44 citing papers in PubMed, 49 citations in OpenAlex.
- Apparent diffusion coefficient and kurtosis parameters show early response to anti-angiogenic therapy in patients with colorectal liver metastases.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026Observational
- Toward multimodal integration of colorectal cancer and chronic kidney disease: transcriptomic modeling as a framework for the SIRIO study "Spatial radiomics and transcriptomics to the discovery of the cross-link between colon cancer and chronic kidney disease".Radiology and oncology · 2026Article
- Integrated radiomics and machine learning approach for ras mutation status prediction in colorectal liver metastases.La Radiologia medica · 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
- Improved treatment of colorectal liver metastases by early response evaluation and regimen adjustment: a prospective study of clinical functional MR-based modeling.Cancer biology & medicine · 2025Article
- Image Analysis as tool for Predicting Colorectal Cancer Molecular Alterations: A Scoping Review.Molecular imaging and radionuclide therapy · 2025Article
- Differentiation of early-stage endometrial carcinoma from benign endometrial lesions: a comparative study of six diffusion models.Quantitative imaging in medicine and surgery · 2025Article
- Machine learning and radiomics analysis by computed tomography in colorectal liver metastases patients for RAS mutational status prediction.La Radiologia medica · 2024Article
- A multicentric study of radiomics and artificial intelligence analysis on contrast-enhanced mammography to identify different histotypes of breast cancer.La Radiologia medica · 2024Article
- Radiomics and machine learning analysis of liver magnetic resonance imaging for prediction and early detection of tumor response in colorectal liver metastases.Korean journal of clinical oncology · 2024Article
- A comparative study of mono-exponential and advanced diffusion-weighted imaging in differentiating stage IA endometrial carcinoma from benign endometrial lesions.Journal of cancer research and clinical oncology · 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
- Machine Learning and Radiomics Analysis for Tumor Budding Prediction in Colorectal Liver Metastases Magnetic Resonance Imaging Assessment.Diagnostics (Basel, Switzerland) · 2024Article
- Radiomics and artificial intelligence analysis by T2-weighted imaging and dynamic contrast-enhanced magnetic resonance imaging to predict Breast Cancer Histological Outcome.La Radiologia medica · 2023Article
- Radiomics and machine learning analysis by computed tomography and magnetic resonance imaging in colorectal liver metastases prognostic assessment.La Radiologia medica · 2023Article
- Prognostic Assessment of Gastropancreatic Neuroendocrine Neoplasm: Prospects and Limits of Radiomics.Diagnostics (Basel, Switzerland) · 2023Review
- Artificial intelligence and radiation effects on brain tissue in glioblastoma patient: preliminary data using a quantitative tool.La Radiologia medica · 2023Article
- Colorectal liver metastases patients prognostic assessment: prospects and limits of radiomics and radiogenomics.Infectious agents and cancer · 2023Review
- Recent Advances in Ultrasound Breast Imaging: From Industry to Clinical Practice.Diagnostics (Basel, Switzerland) · 2023Review
Corrections and comments
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Authors and funding
11 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesTo detect RAS mutation in colorectal liver metastasis by Diffusion-Weighted Magnetic Resonance Imaging (DWI-MRI) - and Diffusion Kurtosis imaging (DKI)-derived parameters.
methodsIn total, 106 liver metastasis (60 metastases with RAS mutation) in 52 patients were included in this retrospective study. Diffusion and perfusion parameters were derived by DWI (apparent diffusion coefficient (ADC), basal signal (S0), pseudo-diffusion coefficient (DP), perfusion fraction (FP) and tissue diffusivity (DT)) and DKI data (mean of diffusion coefficient (MD) and mean of diffusional Kurtosis (MK)). Wilcoxon-Mann-Whitney U tests for non-parametric variables and receiver operating characteristic (ROC) analyses were calculated with area under ROC curve (AUC). Moreover, pattern recognition approaches (linear classifier, support vector machine, k-nearest neighbours, decision tree), with features selection methods and a leave-one-out cross validation approach, were considered.
resultsA significant discrimination between the group with RAS mutation and the group without RAS mutation was obtained by the standard deviation value of MK (MK STD), by the mean value of MD, and by that of FP. The best results were reached by MK STD with an AUC of 0.80 (sensitivity of 72%, specificity of 85%, accuracy of 79%) using a cut-off of 203.90 × 10
conclusionsDiffusion-Weighted imaging and Diffusion Kurtosis imaging could be used to detect the RAS mutation in liver metastasis. The standard deviation value of MK and the mean value of MD were the more accurate parameters in the RAS mutation detection, with an AUC of 0.80.
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