ArticleCancers2022
Identification of CT Imaging Phenotypes of Colorectal Liver Metastases from Radiomics Signatures-Towards Assessment of Interlesional Tumor Heterogeneity.
Article in Cancers, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Noninvasive molecular imaging signatures of histopathological growth patterns in colorectal cancer liver metastases.Clinical & experimental metastasis · 2026Review
- Prediction of colorectal cancer liver metastasis through an MRI radiomic model.Scientific reports · 2026Article
- CRLM-GAN: a feature-constrained GAN-based deep learning framework for multi-parametric MRI-based segmentation of colorectal liver metastases before and after chemotherapy.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Textural heterogeneity of liver lesions in CT imaging - comparison of colorectal and pancreatic metastases.Abdominal radiology (New York) · 2024Article
- Radiomics models to predict bone marrow metastasis of neuroblastoma using CT.Cancer innovation · 2024Article
- Computed tomography imaging phenotypes of hepatoblastoma identified from radiomics signatures are associated with the efficacy of neoadjuvant chemotherapy.Pediatric radiology · 2024Article
- Comparison of diagnostic accuracy of radiomics parameter maps and standard reconstruction for the detection of liver lesions in computed tomography.Frontiers in oncology · 2024Article
- Radiomics and machine learning analysis by computed tomography and magnetic resonance imaging in colorectal liver metastases prognostic assessment.La Radiologia medica · 2023Article
- Tumor classification of gastrointestinal liver metastases using CT-based radiomics and deep learning.Cancer imaging : the official publication of the International Cancer Imaging Society · 2023Article
- Colorectal liver metastases patients prognostic assessment: prospects and limits of radiomics and radiogenomics.Infectious agents and cancer · 2023Review
- Article
- Collinearity and Dimensionality Reduction in Radiomics: Effect of Preprocessing Parameters in Hypertrophic Cardiomyopathy Magnetic Resonance T1 and T2 Mapping.Bioengineering (Basel, Switzerland) · 2023Article
- Histology of metastatic colorectal cancer in a lymph node.PloS one · 2023Article
- Disappearing Colorectal Liver Metastases: Do We Really Need a Ghostbuster?Healthcare (Basel, Switzerland) · 2022Review
- The Potential and Emerging Role of Quantitative Imaging Biomarkers for Cancer Characterization.Cancers · 2022Review
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10 authors.
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Abstract
(1) Background: Tumoral heterogeneity (TH) is a major challenge in the treatment of metastatic colorectal cancer (mCRC) and is associated with inferior response. Therefore, the identification of TH would be beneficial for treatment planning. TH can be assessed by identifying genetic alterations. In this work, a radiomics-based approach for assessment of TH in colorectal liver metastases (CRLM) in CT scans is demonstrated. (2) Methods: In this retrospective study, CRLM of mCRC were segmented and radiomics features extracted using pyradiomics. Unsupervised k-means clustering was applied to features and lesions. Feature redundancy was evaluated by principal component analysis and reduced by Pearson correlation coefficient cutoff. Feature selection was conducted by LASSO regression and visual analysis of the clusters by radiologists. (3) Results: A total of 47 patients’ (36% female, median age 64) CTs with 261 lesions were included. Five clusters were identified, and the categories small disseminated (n = 31), heterogeneous (n = 105), homogeneous (n = 64), mixed (n = 59), and very large type (n = 2) were assigned based on visual characteristics. Further statistical analysis showed correlation (p < 0.01) of clusters with sex, primary location, T- and N-status, and mutational status. Feature reduction and selection resulted in the identification of four features as a final set for cluster definition. (4) Conclusions: Radiomics features can characterize TH in liver metastases of mCRC in CT scans, and may be suitable for a better pretherapeutic classification of liver lesion phenotypes.
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