ArticleAnnals of surgical oncology2024
Computed Tomography-Based Radiomics with Machine Learning Outperforms Radiologist Assessment in Estimating Colorectal Liver Metastases Pathologic Response After Chemotherapy.
Article in Annals of surgical oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- CT-based radiomics improves survival prediction in colorectal liver metastases: beyond clinical scores.Updates in surgery · 2026Article
- CT radiomics for survival risk stratification in resectable colorectal liver metastases: a multi-centre study.Scientific reports · 2026Article
- Robust multicentre detection and classification of colorectal liver metastases on CT: application of foundation models.NPJ precision oncology · 2026Article
- Margins in Context: How Pathologic Response Reframes Surgical Success in CRLM.Annals of surgical oncology · 2026Article
- Review
- Review
- Multi-Phasic CECT Peritumoral Radiomics Predict Treatment Response to Bevacizumab-Based Chemotherapy in RAS-Mutated Colorectal Liver Metastases.Bioengineering (Basel, Switzerland) · 2026Article
- Artificial intelligence in imaging diagnosis of liver tumors: current status and future prospects.Abdominal radiology (New York) · 2026Review
- Colorectal cancer liver metastases: A radiologic point of view.World journal of gastrointestinal oncology · 2025Article
- Predicting the recurrence risk of liver metastasis from colorectal cancer: a study based on preoperative CT intratumoral and peritumoral radiomics features.Frontiers in oncology · 2025Article
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Authors and funding
17 authors.
Funding
Abstract
objectivesThis study was designed to assess computed tomography (CT)-based radiomics of colorectal liver metastases (CRLM), extracted from posttreatment scans in estimating pathologic treatment response to neoadjuvant therapy, and to compare treatment response estimates between CT-based radiomics and radiological response assessment by using RECIST 1.1 and CT morphologic criteria.
methodsPatients who underwent resection for CRLM from January 2003-December 2012 at a single institution were included. Patients who did not receive preoperative systemic chemotherapy, or without adequate imaging, were excluded. Imaging characteristics were evaluated based on RECIST 1.1 and CT morphologic criteria. A machine-learning model was designed with radiomic features extracted from manually segmented posttreatment CT tumoral and peritumoral regions to identify pathologic responders (≥ 50% response) versus nonresponders. Statistical analysis was performed at the tumor level.
resultsEighty-five patients (median age, 62 years; 55 women) with 95 tumors were included. None of the subjectively evaluated imaging characteristics were associated with pathologic response (p > 0.05). Inter-reader agreement was substantial for RECIST categorical response assessment (K = 0.70) and moderate for CT morphological group response (K = 0.50). In the validation cohort, the machine learning model built with radiomic features obtained an area under the curve (AUC) of 0.87 and outperformed subjective RECIST assessment (AUC = 0.53, p = 0.01) and morphologic assessment (AUC = 0.56, p = 0.02).
conclusionsRadiologist assessment of oligometastatic CRLM after neoadjuvant therapy using RECIST 1.1 and CT morphologic criteria was not associated with pathologic response. In contrast, a machine-learning model based on radiomic features extracted from tumoral and peritumoral regions had high diagnostic performance in assessing responders versus nonresponders.
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