Same same but different: A Web-based deep learning application revealed classifying features for the histopathologic distinction of cortical malformations.

OBJECTIVE: The microscopic review of hematoxylin-eosin-stained images of focal cortical dysplasia type IIb and cortical tuber of tuberous sclerosis complex remains challenging. Both entities are distinct subtypes of human malformations of cortical development that share histopathological features consisting of neuronal dyslamination with dysmorphic neurons and balloon cells. We trained a convolutional neural network (CNN) to classify both entities and visualize the results. Additionally, we propose a new Web-based deep learning application as proof of concept of how deep learning could enter the pathologic routine. METHODS: A digital processing pipeline was developed for a series of 56 cases of focal cortical dysplasia type IIb and cortical tuber of tuberous sclerosis complex to obtain 4000 regions of interest and 200 000 subsamples with different zoom and rotation angles to train a neural network. Guided gradient-weighted class activation maps (Guided Grad-CAMs) were generated to visualize morphological features used by the CNN to distinguish both entities. RESULTS: Our best-performing network achieved 91% accuracy and 0.88 area under the receiver operating characteristic curve at the tile level for an unseen test set. Novel histopathologic patterns were found through the visualized Guided Grad-CAMs. These patterns were assembled into a classification score to augment decision-making in routine histopathology workup. This score was successfully validated by 11 expert neuropathologists and 12 nonexperts, boosting nonexperts to expert level performance. SIGNIFICANCE: Our newly developed Web application combines the visualization of whole slide images with the possibility of deep learning-aided classification between focal cortical dysplasia IIb and tuberous sclerosis complex. This approach will help to introduce deep learning applications and visualization for the histopathologic diagnosis of rare and difficult-to-classify brain lesions.

Clinical and Patient-Reported Outcomes of Direct-Acting Antivirals for the Treatment of Chronic Hepatitis C Among Patients on Opioid Agonist Treatment: A Real-world Prospective Cohort Study.

BACKGROUND: Patient-reported outcomes (PROs) can help to reduce uncertainties about hepatitis C virus (HCV) treatment with direct-acting antivirals (DAAs) among people who inject drugs and increase treatment uptake in this high-risk group. Besides clinical data, this study analyzed for the first time PROs in a real-world sample of patients on opioid agonist treatment (OAT) and HCV treatment with DAAs. METHODS: HCV treatment data including virological response, adherence, safety, and PROs of 328 German patients on OAT were analyzed in a pragmatic prospective cohort study conducted from 2016 to 2018. Clinical effectiveness was defined as sustained virological response (SVR) at week 12 after end of treatment and calculated in per-protocol (PP) and intention-to-treat (ITT) analyses. Changes over time in PROs on health-related quality of life, physical and mental health, functioning, medication tolerability, fatigue, concentration, and memory were analyzed by repeated-measures analyses of variances (ANOVAs). RESULTS: We found high adherence and treatment completion rates, a low number of mainly mild adverse events, and high SVR rates (PP: 97.5% [n = 285]; ITT: 84.5% [n = 328]). Missing SVR data in the ITT sample were mainly caused by patients lost to follow-up after treatment completion. Most PROs showed statistically significant but modest improvements over time, with more pronounced improvements in highly impaired patients. CONCLUSIONS: This real-world study confirms that DAA treatment among OAT patients is feasible, safe, and effective. PROs show that all patients, but particularly those with higher somatic, mental, and social burden, benefit from DAA treatment.