Machine Learning of Histopathological Images Predicts Recurrences of Resected Pancreatic Ductal Adenocarcinoma With Adjuvant Treatment.

OBJECTIVES: Pancreatic ductal adenocarcinoma is an intractable disease with frequent recurrence after resection and adjuvant therapy. The present study aimed to clarify whether artificial intelligence-assisted analysis of histopathological images can predict recurrence in patients with pancreatic ductal adenocarcinoma who underwent resection and adjuvant chemotherapy with tegafur/5-chloro-2,4-dihydroxypyridine/potassium oxonate. MATERIALS AND METHODS: Eighty-nine patients were enrolled in the study. Machine-learning algorithms were applied to 10-billion-scale pixel data of whole-slide histopathological images to generate key features using multiple deep autoencoders. Areas under the curve were calculated from receiver operating characteristic curves using a support vector machine with key features alone and by combining with clinical data (age and carbohydrate antigen 19-9 and carcinoembryonic antigen levels) for predicting recurrence. Supervised learning with pathological annotations was conducted to determine the significant features for predicting recurrence. RESULTS: Areas under the curves obtained were 0.73 (95% confidence interval, 0.59-0.87) by the histopathological data analysis and 0.84 (95% confidence interval, 0.73-0.94) by the combinatorial analysis of histopathological data and clinical data. Supervised learning model demonstrated that poor tumor differentiation was significantly associated with recurrence. CONCLUSIONS: Results indicate that machine learning with the integration of artificial intelligence-driven evaluation of histopathological images and conventional clinical data provides relevant prognostic information for patients with pancreatic ductal adenocarcinoma.

An infant case of diffuse cerebrospinal lesions and cardiomyopathy caused by a BOLA3 mutation.

INTRODUCTION: Mitochondrial dysfunction results in a wide range of organ disorders through diverse genetic abnormalities. We herein present the detailed clinical course of an infant admitted for extensive, rapidly progressing white matter lesions and hypertrophic cardiomyopathy due to a BOLA3 gene mutation. CASE: A 6-month-old girl with no remarkable family or past medical history until 1 month prior presented with developmental regression and feeding impairment. Ultrasound cardiography and brain magnetic resonance imaging (MRI) respectively disclosed the presence of hypertrophic cardiomyopathy and symmetrical deep white matter lesions. She was transferred to our hospital at age 6 months. High lactate levels in her cerebrospinal fluid suggested mitochondrial dysfunction. Despite vitamin supplementation therapy followed by a ketogenic diet, the patient began exhibiting clusters of myoclonic seizures and respiratory failure. Brain and spinal cord MRI revealed rapid progression of the white matter lesions. She died at 10 months of age. Fibroblasts obtained pre-mortem displayed low mitochondrial respiratory chain complex I and II activity. A homozygous H96R (c. 287 A > G) mutation was identified in the BOLA3 gene. DISCUSSION: No reported case of a homozygous BOLA3 gene mutation has survived past 1 year of life. BOLA3 appears to play a critical role in the electron transport system and production of iron-sulfur clusters that are related to lipid metabolism and enzyme biosynthesis.