Work

The Value of Machine Learning-based Radiomics Model Characterized by PET Imaging with 68Ga-FAPI in Assessing Microvascular Invasion of Hepatocellular Carcinoma

Year: 2024

Type: article

Abstract: RATIONALE AND OBJECTIVES: ) in semi-automatic delineation methods on the predictions of the model. METHODS: . Extracted shape features, first-, second- and higher-order features. Eight PET radiomics models for predicting MVI were constructed and tested. RESULTS: In the testing set, the logistic regression (LR) model achieved the highest AUC values for three groups of 30%, 50%, and 60%, with values of 0.785, 0.896, and 0.859, respectively, while the random forest (RF) model in 40% group obtained the highest AUC value of 0.815. The LR model in 50% group and the extreme gradient boosting (XGBoost) model in 60% group achieved the highest accuracy, each at 87.5%. The highest sensitivity was observed in the support vector machine (SVM) model in 30% group, at 100%. CONCLUSION: in semi-automatic delineation methods exert a degree of influence on performance of the radiomics model.

Source: Academic Radiology

DOI: 10.1016/j.acra.2024.11.034

Authors: Rongqin Fan, Xueqin Long, Xiaoliang Chen, Yanmei Wang, Demei Chen, Rui Zhou

Institutions: Chongqing University, Chongqing Cancer Hospital, GenomiCare (China), Shanghai Superconductor Technology Co., Ltd. (China)

Language: English

Topic: Radiomics and Machine Learning in Medical Imaging

Subfield: Radiology, Nuclear Medicine and Imaging

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: hybrid

Estimated APC paid: $3,510