ArticleScientific data2024
Preoperative CT and survival data for patients undergoing resection of colorectal liver metastases.
Article in Scientific data, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 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.
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Who cites it
32 citing papers in PubMed, 39 citations in OpenAlex.
- AI-driven volumetric approach for automatic chemotherapy response assessment in colorectal liver metastases.European radiology · 2026Article
- State of abdominal CT datasets: A critical review of bias, clinical relevance, and real-world applicability.PLOS digital health · 2026Review
- Robust multicentre detection and classification of colorectal liver metastases on CT: application of foundation models.NPJ precision oncology · 2026Article
- A Multi-center Gadolinium-ethoxybenzyl-diethylenetriamine Pentaacetic Acid (Gd-EOB-DTPA) MRI Dataset with Expert Annotations and clinicopathological data.Scientific data · 2026Article
- A Dataset of Abdominal CT with Artery and Vein Segmentations for Colorectal Cancer Surgical Planning.Scientific data · 2026Article
- Application of transformer models in medical image segmentation: a narrative review.Quantitative imaging in medicine and surgery · 2026Review
- Tumor morphology on CT radiomics is largely driven by the local anatomical environment, not the primary tumor type.European radiology experimental · 2026Article
- Liver cancer segmentator: Metadata-guided confidence scoring for reliable segmentation of colorectal liver metastases in CT.Computer methods and programs in biomedicine · 2026Article
- Accuracy and reproducibility of large language model measurements of liver metastases: comparison with radiologist measurements.Japanese journal of radiology · 2026Article
- Self-Supervised Transformer-Based Pipeline for Liver Tumor Segmentation and Type Classification.JCO clinical cancer informatics · 2026Article
- Reducing manual workload in CT and MRI annotation with the Segment Anything Model 2.BMC medical imaging · 2026Article
- Artificial intelligence-driven body composition analysis and its association with survival in patients with colorectal liver metastases.Contemporary oncology (Poznan, Poland) · 2026Article
- Radiomic signatures from baseline CT predict chemotherapy response in unresectable colorectal liver metastases.Journal of medical imaging (Bellingham, Wash.) · 2026Article
- Exploring free radiomics software tools: a multiparametric evaluation for cancer classification.BMC medical imaging · 2025Article
- A Multi-phase CT Dataset for Automated Differential Diagnosis of Liver Tumors.Scientific data · 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
- Gender Medicine in Computed Tomography Radiomics Analysis to Predict Disease Progression in Liver Respectable Colorectal Cancer Patients.Cancer medicine · 2025Article
- Development and validation of an autonomous artificial intelligence agent for clinical decision-making in oncology.Nature cancer · 2025Article
- AI generated annotations for Breast, Brain, Liver, Lungs, and Prostate cancer collections in the National Cancer Institute Imaging Data Commons.Scientific data · 2025Article
- FSS-ULivR: a clinically-inspired few-shot segmentation framework for liver imaging using unified representations and attention mechanisms.Journal of cancer research and clinical oncology · 2025Article
Corrections and comments
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Authors and funding
10 authors at 3 institutions in 2 countries.
Funding
Abstract
The liver is a common site for the development of metastases in colorectal cancer. Treatment selection for patients with colorectal liver metastases (CRLM) is difficult; although hepatic resection will cure a minority of CRLM patients, recurrence is common. Reliable preoperative prediction of recurrence could therefore be a valuable tool for physicians in selecting the best candidates for hepatic resection in the treatment of CRLM. It has been hypothesized that evidence for recurrence could be found via quantitative image analysis on preoperative CT imaging of the future liver remnant before resection. To investigate this hypothesis, we have collected preoperative hepatic CT scans, clinicopathologic data, and recurrence/survival data, from a large, single-institution series of patients (n = 197) who underwent hepatic resection of CRLM. For each patient, we also created segmentations of the liver, vessels, tumors, and future liver remnant. The largest of its kind, this dataset is a resource that may aid in the development of quantitative imaging biomarkers and machine learning models for the prediction of post-resection hepatic recurrence of CRLM.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.