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April 20, 2026

Machine learning models for predicting microvascular invasion in hepatocellular carcinoma with three-dimensional whole-lesion ¹⁸F-FDG PET radiomics.

Hong Yang D Pang Shilai Zhang Zhi Yang Meishe Gan Linlin Wei Ning Li Hongjiao Wei Guoyou Xiao Hai Liao

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Key Points

The study aims to predict microvascular invasion in hepatocellular carcinoma using machine learning models and radiomics data from PET imaging.
Developed machine learning models utilizing three-dimensional whole-lesion radiomics features.
Evaluated the performance of these models in predicting microvascular invasion.
The logistic regression model exhibited the best performance among the tested models.
The model shows potential for noninvasive preoperative assessment of microvascular invasion.

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Implication

Modeling shows promise for noninvasive microvascular invasion assessment in hepatocellular carcinoma, suggesting improved preoperative evaluations.

Abstract

The LR model demonstrates the best overall performance and shows promise for noninvasive preoperative MVI assessment.

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Cite This Study

Yang et al. (2026) studied this question.

synapsesocial.com/papers/69e8661d6e0dea528ddea93a https://doi.org/10.1967/s002449912942

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