ArticleQuantitative imaging in medicine and surgery2025
A nomogram based on a combination of preoperative multimodal imaging and serological signatures for predicting microvascular invasion of hepatocellular carcinoma: an independent external validation study.
Article in Quantitative imaging in medicine and surgery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Microvascular invasion (MVI) is a key determinant of recurrence and survival in hepatocellular carcinoma (HCC), yet it remains difficult to assess preoperatively. Robust, noninvasive tools integrating multimodal imaging and serological features are needed to inform surgical planning. This study aimed to establish and validate a reliable diagnostic model based on a combination of multimodal imaging and serological signatures for predicting MVI in HCC. Methods: This retrospective study included 179 patients with HCC treated at the Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, from 2015 to 2022. Univariable logistic regression was used for initial screening; variables with P<0.10 entered the multivariable logistic regression with backward stepwise selection. A nomogram was then constructed and internally (bootstrap) and externally validated to investigate the associations of MVI features based on multimodal imaging and serological signatures. A nomogram was developed and was externally validated in an independent cohort from Zhongshan Hospital, Fudan University (Institution 2) (n=80). Results: Logistic regression analysis was performed to identify seven parameters related to MVI, including tumor number, diameter, Adler blood flow grading, visualized macrovascular around the tumor, arterial peritumoral enhancement, nonenhancing area, and non-nodular type tumor. Based on these seven parameters, a final combined model was developed. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value in predicting MVI were 88.0% and 84.0%, 72% and 84.0%, 94.0% and 84.0%, 81.0% and 82.0%, 91% and 86.0% in the modeling and validation cohorts, respectively. Conclusions: A reliable combined model based on multimodal imaging and serological signatures effectively predicts preoperative MVI in patients with HCC and may assist clinicians in determining the optimal intervention and achieving greater precision and individualization of treatment.
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