ArticleEuropean radiology experimental2023
Deep learning models for automatic tumor segmentation and total tumor volume assessment in patients with colorectal liver metastases.
Article in European radiology experimental, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT02162563 (Treatment Strategies in Colorectal Cancer Patients With Initially Unresectable Liver-only Metastases CAIRO5 a Randomized Phase 3 Study of the Dutch Colorectal Cancer Group), which is not on this map. Cited by 16 papers.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Treatment Strategies in Colorectal Cancer Patients With Initially Unresectable Liver-only Metastases CAIRO5 a Randomized Phase 3 Study of the Dutch Colorectal Cancer Group (DCCG)
Who cites it
16 citing papers in PubMed.
- Automated model-based lesion tracking in CT: colorectal liver metastases as a use case for development and performance analysis.European radiology experimental · 2026Article
- A Real-World Evaluation of Failure Detection for Liver CT Segmentation.medRxiv : the preprint server for health sciences · 2026Article
- Robust multicentre detection and classification of colorectal liver metastases on CT: application of foundation models.NPJ precision oncology · 2026Article
- Advances in Artificial Intelligence-Based Liver-Related Semantic Segmentation Techniques and Applications Using CT Imaging.Cancer medicine · 2026Review
- Pediatric Personalized Deep Learning Models for Segmentation of Hepatoblastoma at CT and MRI.Radiology. Imaging cancer · 2026Article
- Foundation model-guided multi-view semi-supervised CT segmentation of liver tumors in resource-constrained settings.NPJ digital medicine · 2025Article
- 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
- Precision in Liver Surgery: A Comparative Analysis of Volumetry Techniques.Annals of surgical oncology · 2025Article
- Revolutionizing gastroenterology and hepatology with artificial intelligence: From precision diagnosis to equitable healthcare through interdisciplinary practice.World journal of gastroenterology · 2025Review
- 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
- A CT-based deep learning-driven tool for automatic liver tumor detection and delineation in patients with cancer.Cell reports. Medicine · 2025Article
- Automated AI-based segmentation of canine hepatic focal lesions from CT studies.Frontiers in veterinary science · 2025Article
- Development and external evaluation of a self-learning auto-segmentation model for Colorectal Cancer Liver Metastases Assessment (COALA).Insights into imaging · 2024Article
- Deep learning-based segmentation of kidneys and renal cysts on T2-weighted MRI from patients with autosomal dominant polycystic kidney disease.European radiology experimental · 2024Article
- Artificial intelligence techniques in liver cancer.Frontiers in oncology · 2024Review
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Authors and funding
35 authors.
Funding
Abstract
backgroundWe developed models for tumor segmentation to automate the assessment of total tumor volume (TTV) in patients with colorectal liver metastases (CRLM).
methodsIn this prospective cohort study, pre- and post-systemic treatment computed tomography (CT) scans of 259 patients with initially unresectable CRLM of the CAIRO5 trial (NCT02162563) were included. In total, 595 CT scans comprising 8,959 CRLM were divided into training (73%), validation (6.5%), and test sets (21%). Deep learning models were trained with ground truth segmentations of the liver and CRLM. TTV was calculated based on the CRLM segmentations. An external validation cohort was included, comprising 72 preoperative CT scans of patients with 112 resectable CRLM. Image segmentation evaluation metrics and intraclass correlation coefficient (ICC) were calculated.
resultsIn the test set (122 CT scans), the autosegmentation models showed a global Dice similarity coefficient (DSC) of 0.96 (liver) and 0.86 (CRLM). The corresponding median per-case DSC was 0.96 (interquartile range [IQR] 0.95-0.96) and 0.80 (IQR 0.67-0.87). For tumor segmentation, the intersection-over-union, precision, and recall were 0.75, 0.89, and 0.84, respectively. An excellent agreement was observed between the reference and automatically computed TTV for the test set (ICC 0.98) and external validation cohort (ICC 0.98). In the external validation, the global DSC was 0.82 and the median per-case DSC was 0.60 (IQR 0.29-0.76) for tumor segmentation.
conclusionsDeep learning autosegmentation models were able to segment the liver and CRLM automatically and accurately in patients with initially unresectable CRLM, enabling automatic TTV assessment in such patients. RELEVANCE STATEMENT: Automatic segmentation enables the assessment of total tumor volume in patients with colorectal liver metastases, with a high potential of decreasing radiologist's workload and increasing accuracy and consistency. KEY POINTS: • Tumor response evaluation is time-consuming, manually performed, and ignores total tumor volume. • Automatic models can accurately segment tumors in patients with colorectal liver metastases. • Total tumor volume can be accurately calculated based on automatic segmentations.
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