Triphasic CT Radiomics Model for Preoperative Prediction of Hepatocellular Carcinoma Pathological Grading
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View abstract on PubMed
Summary
This summary is machine-generated.This study developed a triphasic CT radiomics model to predict hepatocellular carcinoma (HCC) pathological markers. The model noninvasively enhances preoperative diagnosis and prognostic evaluation for HCC.
Area Of Science
- Radiology
- Oncology
- Medical Imaging
Background
- Hepatocellular carcinoma (HCC) diagnosis and prognosis rely on accurate pathological markers.
- Noninvasive methods for predicting these markers are crucial for clinical decision-making.
Purpose Of The Study
- To develop and validate a triphasic CT-based radiomics model for predicting multiple critical pathological markers in HCC.
- To assess the model's performance in grading Edmondson-Steiner (Ed), Microvascular invasion (MVI), and Satellite nodule (SN).
Main Methods
- A retrospective study of 174 HCC patients (187 lesions) using 2264 radiomic features from arterial, venous, and delayed phase CT images.
- Feature selection using mRMR, SelectKBest, and LASSO algorithms.
- Development of single-phase and triphasic fusion models using logistic regression and SVM classifiers.
Main Results
- The triphasic fusion model demonstrated superior performance in predicting Ed, MVI, and SN grading.
- Achieved high AUCs in both testing (0.890-0.829) and validation (0.836-0.810) datasets.
- Fusion model outperformed individual single-phase models.
Conclusions
- The triphasic CT radiomics model offers a noninvasive tool for preoperative prediction of HCC pathological grading.
- Enhances diagnostic accuracy for clinical decision-making and prognostic evaluation.
- Facilitates improved patient management strategies for HCC.
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