Radiomics-Based Prediction of Future Portal Vein Tumor Infiltration in Patients with HCC—A Proof-of-Concept Study

Abstract

Portal vein infiltration (PVI) is a typical complication of HCC. Once diagnosed, it leads to classification as BCLC C with an enormous impact on patient management, as systemic therapies are henceforth recommended. Our aim was to investigate whether radiomics analysis using imaging at initial diagnosis can predict the occurrence of PVI in the course of disease. Between 2008 and 2018, we retrospectively identified 44 patients with HCC and an in-house, multiphase CT scan at initial diagnosis who presented without CT-detectable PVI but developed it in the course of disease. Accounting for size and number of lesions, growth type, arterial enhancement pattern, Child–Pugh stage, AFP levels, and subsequent therapy, we matched 44 patients with HCC who did not develop PVI to those developing PVI in the course of disease (follow-up ended December 2021). After segmentation of the tumor at initial diagnosis and texture analysis, we used LASSO regression to find radiomics features suitable for PVI detection in this matched set. Using an 80:20 split between training and holdout validation dataset, 17 radiomics features remained in the fitted model. Applying the model to the holdout validation dataset, sensitivity to detect occurrence of PVI was 0.78 and specificity was 0.78. Radiomics feature extraction had the ability to detect aggressive HCC morphology likely to result in future PVI. An additional radiomics evaluation at initial diagnosis might be a useful tool to identify patients with HCC at risk for PVI during follow-up benefiting from a closer surveillance.

Feasibility Study of Computed Tomographic Radiomics Model for the Prediction of Early and Intermediate Stage Hepatocellular Carcinoma Using BCLC Staging

“…Xun et al 13 combined clinical and radiomic-based characteristics to construct a model for predicting HCC microvascular invasion (MVI) and patient prognosis and the model demonstrated good detection efficiency. Stoehr et al 14 found that radiomics analysis using imaging at initial diagnosis might be a useful tool for identifying patients with HCC who are at risk for portal vein infiltration during follow-up, and that would benefit from closer monitoring. The 2 studies prove that radiomics analysis is useful for predicting both MVI and macrovascular invasion of HCC which determine the patient's prognosis.…”

Estimability study on the age of toddlers’ gait development based on gait parameters

“…An estimation model of walking age using five gait parameters was created by multiple regression analysis. The walking data of 97 participants were randomly divided into training data (80%, N = 78) and test data (20%, N = 19) 23 , 24 . Table 4 shows the characteristics of the training and test datasets.…”