An imageomics and multi-network based deep learning model for risk assessment of liver transplantation for hepatocellular cancer.

INTRODUCTION: Liver transplantation (LT) is an effective treatment for hepatocellular carcinoma (HCC), the most common type of primary liver cancer. Patients with small HCC (<5 cm) are given priority over others for transplantation due to clinical allocation policies based on tumor size. Attempting to shift from the prevalent paradigm that successful transplantation and longer disease-free survival can only be achieved in patients with small HCC to expanding the transplantation option to patients with HCC of the highest tumor burden (>5 cm), we developed a convergent artificial intelligence (AI) model that combines transient clinical data with quantitative histologic and radiomic features for more objective risk assessment of liver transplantation for HCC patients. METHODS: Patients who received a LT for HCC between 2008-2019 were eligible for inclusion in the analysis. All patients with post-LT recurrence were included, and those without recurrence were randomly selected for inclusion in the deep learning model. Pre- and post-transplant magnetic resonance imaging (MRI) scans and reports were compressed using CapsNet networks and natural language processing, respectively, as input for a multiple feature radial basis function network. We applied a histological image analysis algorithm to detect pathologic areas of interest from explant tissue of patients who recurred. The multilayer perceptron was designed as a feed-forward, supervised neural network topology, with the final assessment of recurrence risk. We used area under the curve (AUC) and F-1 score to assess the predictability of different network combinations. RESULTS: A total of 109 patients were included (87 in the training group, 22 in the testing group), of which 20 were positive for cancer recurrence. Seven models (AUC; F-1 score) were generated, including clinical features only (0.55; 0.52), magnetic resonance imaging (MRI) only (0.64; 0.61), pathological images only (0.64; 0.61), MRI plus pathology (0.68; 0.65), MRI plus clinical (0.78, 0.75), pathology plus clinical (0.77; 0.73), and a combination of clinical, MRI, and pathology features (0.87; 0.84). The final combined model showed 80 % recall and 89 % precision. The total accuracy of the implemented model was 82 %. CONCLUSION: We validated that the deep learning model combining clinical features and multi-scale histopathologic and radiomic image features can be used to discover risk factors for recurrence beyond tumor size and biomarker analysis. Such a predictive, convergent AI model has the potential to alter the LT allocation system for HCC patients and expand the transplantation treatment option to patients with HCC of the highest tumor burden.

Chilled Potatoes Decrease Postprandial Glucose, Insulin, and Glucose-dependent Insulinotropic Peptide Compared to Boiled Potatoes in Females with Elevated Fasting Glucose and Insulin.

Resistant starch (RS) has been shown to improve postprandial glycemia and insulin sensitivity in adults with metabolic syndrome. RS is found naturally in potatoes, where the amount varies based on cooking method and serving temperature. Thirty females with a mean BMI of 32.8 ± 3.7 kg/m2, fasting glucose of 110.5 mg/dL, and insulin of 10.3 µIU/L, completed this randomized, crossover study. A quantity of 250 g of boiled (low RS) and baked then chilled (high RS) russet potatoes were consumed on two separate occasions. Glycemic (glucose and insulin) and incretin response, subjective satiety, and dietary intake were measured. Results showed that the chilled potato elicited significant reductions at 15 and 30 min in glucose (4.8% and 9.2%), insulin (25.8% and 22.6%), and glucose-dependent insulinotropic peptide (GIP) (41.1% and 37.6%), respectively. The area under the curve for insulin and GIP were significantly lower after the chilled potato, but no differences were seen in glucose, glucagon-like peptide-1, and peptide YY, or overall subjective satiety. A higher carbohydrate and glycemic index but lower fat diet was consumed 48-hours following the chilled potato than the boiled potato. This study demonstrates that consuming chilled potatoes higher in RS can positively impact the glycemic response in females with elevated fasting glucose and insulin.