ArticleClinical & experimental metastasis2021
Distinguishing pure histopathological growth patterns of colorectal liver metastases on CT using deep learning and radiomics: a pilot study.
Article in Clinical & experimental metastasis, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Noninvasive molecular imaging signatures of histopathological growth patterns in colorectal cancer liver metastases.Clinical & experimental metastasis · 2026Review
- CT radiomics for survival risk stratification in resectable colorectal liver metastases: a multi-centre study.Scientific reports · 2026Article
- Fully Automatic Artificial Intelligence Liver Anatomy Segmentation in the Management of Colorectal Liver Metastases.Cureus · 2025Article
- Evaluation of a deep-learning segmentation model for patients with colorectal cancer liver metastases (COALA) in the radiological workflow.Insights into imaging · 2025Article
- Applications of Artificial Intelligence for Metastatic Gastrointestinal Cancer: A Systematic Literature Review.Cancers · 2025Review
- Article
- First-Line Therapy in Metastatic, RAS Wild-Type, Left-Sided Colorectal Cancer: Should Everyone Receive Anti-EGFR Therapy?Current oncology reports · 2024Review
- Transcriptomic characterization of the histopathological growth patterns in breast cancer liver metastases.Clinical & experimental metastasis · 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
- Prediction of transformation in the histopathological growth pattern of colorectal liver metastases after chemotherapy using CT-based radiomics.Clinical & experimental metastasis · 2024Article
- Histopathological growth patterns and tumor-infiltrating lymphocytes in breast cancer liver metastases.NPJ breast cancer · 2023Article
- Deep learning models for automatic tumor segmentation and total tumor volume assessment in patients with colorectal liver metastases.European radiology experimental · 2023Article
- Radiomics and machine learning analysis by computed tomography and magnetic resonance imaging in colorectal liver metastases prognostic assessment.La Radiologia medica · 2023Article
- Histopathological growth patterns of neuroendocrine tumor liver metastases.Clinical & experimental metastasis · 2023Article
- Radiomics in colorectal cancer patients.World journal of gastroenterology · 2023Review
- Colorectal liver metastases patients prognostic assessment: prospects and limits of radiomics and radiogenomics.Infectious agents and cancer · 2023Review
- Dynamic radiomics for predicting the efficacy of antiangiogenic therapy in colorectal liver metastases.Frontiers in oncology · 2023Article
- Application of artificial intelligence in diagnosis and treatment of colorectal cancer: A novel Prospect.Frontiers in medicine · 2023Review
- CT, MRI, and radiomics studies of liver metastasis histopathological growth patterns: an up-to-date review.Abdominal radiology (New York) · 2022Review
- Optimization of Preoperative Lymph Node Staging in Patients with Muscle-Invasive Bladder Cancer Using Radiomics on Computed Tomography.Journal of personalized medicine · 2022Article
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12 authors.
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Abstract
Histopathological growth patterns (HGPs) are independent prognosticators for colorectal liver metastases (CRLM). Currently, HGPs are determined postoperatively. In this study, we evaluated radiomics for preoperative prediction of HGPs on computed tomography (CT), and its robustness to segmentation and acquisition variations. Patients with pure HGPs [i.e. 100% desmoplastic (dHGP) or 100% replacement (rHGP)] and a CT-scan who were surgically treated at the Erasmus MC between 2003-2015 were included retrospectively. Each lesion was segmented by three clinicians and a convolutional neural network (CNN). A prediction model was created using 564 radiomics features and a combination of machine learning approaches by training on the clinician's and testing on the unseen CNN segmentations. The intra-class correlation coefficient (ICC) was used to select features robust to segmentation variations; ComBat was used to harmonize for acquisition variations. Evaluation was performed through a 100 × random-split cross-validation. The study included 93 CRLM in 76 patients (48% dHGP; 52% rHGP). Despite substantial differences between the segmentations of the three clinicians and the CNN, the radiomics model had a mean area under the curve of 0.69. ICC-based feature selection or ComBat yielded no improvement. Concluding, the combination of a CNN for segmentation and radiomics for classification has potential for automatically distinguishing dHGPs from rHGP, and is robust to segmentation and acquisition variations. Pending further optimization, including extension to mixed HGPs, our model may serve as a preoperative addition to postoperative HGP assessment, enabling further exploitation of HGPs as a biomarker.
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