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

Abstract

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.

MRI Liver Imaging Integrated with Texture Analysis in Native Liver Survivor Patients with Biliary Atresia after Kasai Portoenterostomy: Correlation with Medical Outcome after Surgical Treatment

“…For MRI quantitative analysis, liver and spleen volumes were measured by an abdominal radiologist with 15 years of experience in hepatobiliary imaging using a semiautomatic method with OsiriX ® version 3.3 software (Geneva, Switzerland) [ 17 , 18 ]. In detail, liver and spleen contours were manually traced at different levels on T2-weighted images with the closed polygon selection tool; then, the remaining boundaries were automatically outlined using the Grow Region (2D/3D Segmentation) tool.…”

Artificial Intelligence for Personalized Genetics and New Drug Development: Benefits and Cautions

“…A very promising technological advancement for the future of health care is predictive analytics [44][45][46][47], used to predict outcomes, events, and behaviors that will or may occur in the future. This technology has the potential to significantly transform health care systems in several countries, creating strong tools to identify and address heath threats, improve patient outcomes, and reduce the cost of medical care.…”

Variability of Care and Access to Transplantation for Children with Biliary Atresia Who Need a Liver Replacement

“…Schneider et al, for example, suggested that failure to normalize serum bilirubin within three months after Kasai should elicit prompt evaluation for LT as extended native liver survival is exceptional among these patients [50]. Moreover, though a "Kasai success predicting score" remains far from reality, other groups support more research in that direction as part of BA management-a strategy that would allow early primary transplantation [47,54,[57][58][59][60][61].…”