Machine Learning Evaluation of Biliary Atresia Patients to Predict Long-Term Outcome after the Kasai Procedure

Kasai portoenterostomy (KP) represents the first-line treatment for biliary atresia (BA). The purpose was to compare the accuracy of quantitative parameters extracted from laboratory tests, US imaging, and MR imaging studies using machine learning (ML) algorithms to predict the long-term medical outcome in native liver survivor BA patients after KP. Twenty-four patients were evaluated according to clinical and laboratory data at initial evaluation (median follow-up = 9.7 years) after KP as having ideal (n = 15) or non-ideal (n = 9) medical outcomes. Patients were re-evaluated after an additional 4 years and classified in group 1 (n = 12) as stable and group 2 (n = 12) as non-stable in the disease course. Laboratory and quantitative imaging parameters were merged to test ML algorithms. Total and direct bilirubin (TB and DB), as laboratory parameters, and US stiffness, as an imaging parameter, were the only statistically significant parameters between the groups. The best algorithm in terms of accuracy, sensitivity, specificity, and AUCROC was naive Bayes algorithm, selecting only laboratory parameters (TB and DB). This preliminary ML analysis confirms the fundamental role of TB and DB values in predicting the long-term medical outcome for BA patients after KP, even though their values may be within the normal range. Physicians should be alert when TB and DB values change slightly

Prediction of 2-[18F]FDG PET-CT SUVmax for Adrenal Mass Characterization: A CT Radiomics Feasibility Study

Simple Summary Adrenal masses represent a common incidental finding at imaging exams such as computed tomography (CT) and magnetic resonance imaging performed for unrelated reasons. Encompassing both benign and malignant entities, these lesions can prove challenging to classify. 2-[F-18]FDG PET-CT is a recognized imaging modality for the characterization of indeterminate adrenal masses, but it is an expensive imaging modality and involves radiation exposure. To reduce the number of required scans and identify a less invasive potential alternative, we investigated whether CT radiomics could be used to predict the diagnostic parameter obtained using 2-[F-18]FDG PET-CT (namely SUVmax). However, in our retrospective cohort of 179 adrenal masses and 150 PET-CT scans (of which 66 without iodine contrast injection), no correlation was found between the radiomics synthetic value (RadSV) and 2-[F-18]FDG PET-CT SUVmax. This preliminary finding suggests that it might not be possible to use CT radiomics to reduce 2-[F-18]FDG PET-CT referrals, confirming the role as problem solving tool of this imaging modality. Background: Indeterminate adrenal masses (AM) pose a diagnostic challenge, and 2-[F-18]FDG PET-CT serves as a problem-solving tool. Aim of this study was to investigate whether CT radiomics features could be used to predict the 2-[F-18]FDG SUVmax of AM. Methods: Patients with AM on 2-[F-18]FDG PET-CT scan were grouped based on iodine contrast injection as CT contrast-enhanced (CE) or CT unenhanced (NCE). Two-dimensional segmentations of AM were manually obtained by multiple operators on CT images. Image resampling and discretization (bin number = 16) were performed. 919 features were calculated using PyRadiomics. After scaling, unstable, redundant, and low variance features were discarded. Using linear regression and the Uniform Manifold Approximation and Projection technique, a CT radiomics synthetic value (RadSV) was obtained. The correlation between CT RadSV and 2-[F-18]FDG SUVmax was assessed with Pearson test. Results: A total of 725 patients underwent PET-CT from April 2020 to April 2021. In 150 (21%) patients, a total of 179 AM (29 bilateral) were detected. Group CE consisted of 84 patients with 108 AM (size = 18.1 & PLUSMN; 4.9 mm) and Group NCE of 66 patients with 71 AM (size = 18.5 & PLUSMN; 3.8 mm). In both groups, 39 features were selected. No statisticallyf significant correlation between CT RadSV and 2-[F-18]FDG SUVmax was found (Group CE, r = 0.18 and p = 0.058; Group NCE, r = 0.13 and p = 0.27). Conclusions: It might not be feasible to predict 2-[F-18]FDG SUVmax of AM using CT RadSV. Its role as a problem-solving tool for indeterminate AM remains fundamental