ArticleCancer imaging : the official publication of the International Cancer Imaging Society2023
Tumor classification of gastrointestinal liver metastases using CT-based radiomics and deep learning.
Article in Cancer imaging : the official publication of the International Cancer Imaging Society, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed, 32 citations in OpenAlex.
- Liver metastases beyond classical imaging findings.Abdominal radiology (New York) · 2026Review
- Machine learning and deep learning models for predicting colorectal cancer metastases: A comprehensive review.European journal of radiology open · 2026Review
- Robust multicentre detection and classification of colorectal liver metastases on CT: application of foundation models.NPJ precision oncology · 2026Article
- The diagnostic value of enhanced CT radiomics and deep learning in differentiating pediatric peripheral neuroblastoma from ganglioneuroblastoma.BMC cancer · 2026Article
- Prognostic potential of radiomics evaluation of lung artery thrombus for pulmonary embolism patients.International journal of computer assisted radiology and surgery · 2026Article
- Fully automated 3D multi-modal deep learning model for preoperative T-stage prediction of colorectal cancer usingEuropean journal of nuclear medicine and molecular imaging · 2026Article
- Preoperative evaluation of C2 pedicle screw placement using a deep learning model: Development and validation study.PloS one · 2026Article
- Radiomics Signature of Aging Myocardium in Cardiac Photon-Counting Computed Tomography.Diagnostics (Basel, Switzerland) · 2025Article
- Radiomics-driven spectral profiling of six kidney stone types with monoenergetic CT reconstructions in photon-counting CT.European radiology · 2025Article
- An optimized deep learning model based on transperineal ultrasound images for precision diagnosis of female stress urinary incontinence.Frontiers in medicine · 2025Article
- Textural heterogeneity of liver lesions in CT imaging - comparison of colorectal and pancreatic metastases.Abdominal radiology (New York) · 2024Article
- Differentiation of granulomatous nodules with lobulation and spiculation signs from solid lung adenocarcinomas using a CT deep learning model.BMC cancer · 2024Article
- Machine Learning and Radiomics Analysis for Tumor Budding Prediction in Colorectal Liver Metastases Magnetic Resonance Imaging Assessment.Diagnostics (Basel, Switzerland) · 2024Article
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Authors and funding
12 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesThe goal of this study is to demonstrate the performance of radiomics and CNN-based classifiers in determining the primary origin of gastrointestinal liver metastases for visually indistinguishable lesions.
methodsIn this retrospective, IRB-approved study, 31 pancreatic cancer patients with 861 lesions (median age [IQR]: 65.39 [56.87, 75.08], 48.4% male) and 47 colorectal cancer patients with 435 lesions (median age [IQR]: 65.79 [56.99, 74.62], 63.8% male) were enrolled. A pretrained nnU-Net performed automated segmentation of 1296 liver lesions. Radiomics features for each lesion were extracted using pyradiomics. The performance of several radiomics-based machine-learning classifiers was investigated for the lesions and compared to an image-based deep-learning approach using a DenseNet-121. The performance was evaluated by AUC/ROC analysis.
resultsThe radiomics-based K-nearest neighbor classifier showed the best performance on an independent test set with AUC values of 0.87 and an accuracy of 0.67. In comparison, the image-based DenseNet-121-classifier reached an AUC of 0.80 and an accuracy of 0.83.
conclusionsCT-based radiomics and deep learning can distinguish the etiology of liver metastases from gastrointestinal primary tumors. Compared to deep learning, radiomics based models showed a varying generalizability in distinguishing liver metastases from colorectal cancer and pancreatic adenocarcinoma.
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