Computed Tomography-Based Radiomic Analysis for Preoperatively Predicting the Macrovesicular Steatosis Grade in Cadaveric Donor Liver Transplantation

Abstract

This study is aimed at determining the ability of computed tomography- (CT-) based radiomic analysis to distinguish between grade 0/1 and grade 2/3 macrovesicular steatosis (MaS) in cadaveric donor liver transplantation cases. Preoperative noncontrast-enhanced CT images of 150 patients with biopsy-confirmed MaS were analyzed retrospectively; these patients were classified into the low-grade MaS ( n = 100 , grade 0 or 1) and high-grade MaS ( n = 50 , grade 2 or 3) groups. Three-dimensional spherical regions of interest of 40 pixel (2.5 cm) in diameter were placed in the right anterior and left lateral segments of the liver. Thereafter, 300 regions of interest (ROIs) were segmented and randomly assigned to the training and testing groups at a ratio of 7 : 3. A total of 402 radiomic features were extracted from each ROI. For MaS classification, a radiomic model was established using multivariate logistic regression analysis. Clinical data, including age, sex, and liver function, were collected to establish the clinical model at the patient level. The performances of the radiomic and clinical models, i.e., the diagnostic discrimination, calibration, and clinical utilities, were evaluated. The radiomic model, with seven selected features, depicted a good discrimination with an area under the receiver operating characteristic curve (AUC) of 0.907 (95% confidence interval (CI): 0.869–0.940) in the training cohort and 0.906 (95% CI: 0.843–0.959) in the testing cohort. The calibration curve revealed good agreement between the predicted and observed probabilities in the training and testing cohorts (both P > 0.05 in the H-L test). Decision curve analysis revealed that the radiomic model was more beneficial than the treat-all or treat-none schemes for predicting the MaS grade. Alanine transaminase and gamma-glutamyl transferase were used for building the clinical model, and the AUC was 0.784 in the total cohort. The CT-based radiomic model outperforming the conventional clinical model could provide an important reference for MaS grading in cadaveric liver donors.

Radiobioinformatics: A novel bridge between basic research and clinical practice for clinical decision support in diffuse liver diseases

“…A Ding et al. [66] study showed that CT‐based radiomic models were more accurate than conventional clinical models and served as a valuable reference for macrosteatosis (MaS) grading in cadaveric liver donors.…”

“…A deep learning and morphological operation-based method was proposed by Huo et al [65] to estimate liver attenuation in peripheral regions of interest to further reduce vessel effects. A Ding et al [66] study showed that CT-based radiomic models were more accurate than conventional clinical models and served as a valuable reference for macrosteatosis (MaS) grading in cadaveric liver donors. evaluating liver fibrosis stages.…”

Determination of reproducible radiomic features for diagnosis of fatty liver disease in a crab-eating macaque model

“…Radiomics analyses objectively quantify visual information in images by extracting high-throughput quantitative features and converting encrypted medical imaging into minable numerical data, thus providing information on the shape, signal intensity, and texture of the target organ or region of interest. Radiomics plays a pivotal role in the diagnosis and grading of several liver diseases, for which most efforts have focused on hepatic malignancies and liver diffuse diseases, such as fatty liver and liver fibrosis [1][2][3][4]. However, translation of radiomic research into clinical decision-making is challenging because of the uncertainty in radiomic-based models [5].…”

Repeatability of computed tomography liver radiomic features in a nonhuman primate model of diet-induced steatosis

“…For the liver, radiomics has been used to characterize a variety of conditions, from hepatic malignancies to diffuse liver diseases encompassing a range of pathological manifestations. [1][2][3][4][5] Central tendency first-order radiomic features derived from unenhanced computed tomography (CT) images, such as region mean or median, have been used to discriminate moderate from severe liver steatosis, [6][7][8][9][10][11] defined as intrahepatic accumulation of fats equivalent to at least 5% of the whole organ weight or within hepatocytes. 12 Second-order features were found to be useful in characterizing milder liver disease, an important step to allow for earlier intervention.…”

“…Radiomics analyses are gaining increasing interest as quantitative readouts for unbiased image-classification tasks, toward the improved discrimination of disease phenotypes and evaluation of novel therapeutic regimens. For the liver, radiomics has been used to characterize a variety of conditions, from hepatic malignancies to diffuse liver diseases encompassing a range of pathological manifestations 1 – 5 Central tendency first-order radiomic features derived from unenhanced computed tomography (CT) images, such as region mean or median, have been used to discriminate moderate from severe liver steatosis, 6 – 11 defined as intrahepatic accumulation of fats equivalent to at least 5% of the whole organ weight or within hepatocytes 12 .…”