Predicting colorectal polyp recurrence using time-to-event analysis of medical records

Identifying patient characteristics that influence the rate of colorectal polyp recurrence can provide important insights into which patients are at higher risk for recurrence. We used natural language processing to extract polyp morphological characteristics from 953 polyp-presenting patients' electronic medical records. We used subsequent colonoscopy reports to examine how the time to polyp recurrence (731 patients experienced recurrence) is influenced by these characteristics as well as anthropometric features using Kaplan-Meier curves, Cox proportional hazards modeling, and random survival forest models. We found that the rate of recurrence differed significantly by polyp size, number, and location and patient smoking status. Additionally, right-sided colon polyps increased recurrence risk by 30% compared to left-sided polyps. History of tobacco use increased polyp recurrence risk by 20% compared to never-users. A random survival forest model showed an AUC of 0.65 and identified several other predictive variables, which can inform development of personalized polyp surveillance plans.

Deep neural networks for automated classification of colorectal polyps on histopathology slides: A multi-institutional evaluation

Histological classification of colorectal polyps plays a critical role in both screening for colorectal cancer and care of affected patients. In this study, we developed a deep neural network for classification of four major colorectal polyp types on digitized histopathology slides and compared its performance to local pathologists' diagnoses at the point-of-care retrieved from corresponding pathology labs. We evaluated the deep neural network on an internal dataset of 157 histopathology slides from the Dartmouth-Hitchcock Medical Center (DHMC) in New Hampshire, as well as an external dataset of 513 histopathology slides from 24 different institutions spanning 13 states in the United States. For the internal evaluation, the deep neural network had a mean accuracy of 93.5% (95% CI 89.6%-97.4%), compared with local pathologists' accuracy of 91.4% (95% CI 87.0%-95.8%). On the external test set, the deep neural network achieved an accuracy of 85.7% (95% CI 82.7%-88.7%), significantly outperforming the accuracy of local pathologists at 80.9% (95% CI 77.5%-84.3%, p<0.05) at the point-of-care. If confirmed in clinical settings, our model could assist pathologists by improving the diagnostic efficiency, reproducibility, and accuracy of colorectal cancer screenings.

Pathologist-level classification of histologic patterns on resected lung adenocarcinoma slides with deep neural networks

Classification of histologic patterns in lung adenocarcinoma is critical for determining tumor grade and treatment for patients. However, this task is often challenging due to the heterogeneous nature of lung adenocarcinoma and the subjective criteria for evaluation. In this study, we propose a deep learning model that automatically classifies the histologic patterns of lung adenocarcinoma on surgical resection slides. Our model uses a convolutional neural network to identify regions of neoplastic cells, then aggregates those classifications to infer predominant and minor histologic patterns for any given whole-slide image. We evaluated our model on an independent set of 143 whole-slide images. It achieved a kappa score of 0.525 and an agreement of 66.6% with three pathologists for classifying the predominant patterns, slightly higher than the inter-pathologist kappa score of 0.485 and agreement of 62.7% on this test set. All evaluation metrics for our model and the three pathologists were within 95% confidence intervals of agreement. If confirmed in clinical practice, our model can assist pathologists in improving classification of lung adenocarcinoma patterns by automatically pre-screening and highlighting cancerous regions prior to review. Our approach can be generalized to any whole-slide image classification task, and code is made publicly available at https://github.com/BMIRDS/deepslide.

Automated detection of celiac disease on duodenal biopsy slides: a deep learning approach

Celiac disease prevalence and diagnosis have increased substantially in recent years. The current gold standard for celiac disease confirmation is visual examination of duodenal mucosal biopsies. An accurate computer-aided biopsy analysis system using deep learning can help pathologists diagnose celiac disease more efficiently. In this study, we trained a deep learning model to detect celiac disease on duodenal biopsy images. Our model uses a state-of-the-art residual convolutional neural network to evaluate patches of duodenal tissue and then aggregates those predictions for whole-slide classification. We tested the model on an independent set of 212 images and evaluated its classification results against reference standards established by pathologists. Our model identified celiac disease, normal tissue, and nonspecific duodenitis with accuracies of 95.3%, 91.0%, and 89.2%, respectively. The area under the receiver operating characteristic curve was greater than 0.95 for all classes. We have developed an automated biopsy analysis system that achieves high performance in detecting celiac disease on biopsy slides. Our system can highlight areas of interest and provide preliminary classification of duodenal biopsies prior to review by pathologists. This technology has great potential for improving the accuracy and efficiency of celiac disease diagnosis.

EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks

We present EDA: easy data augmentation techniques for boosting performance on text classification tasks. EDA consists of four simple but powerful operations: synonym replacement, random insertion, random swap, and random deletion. On five text classification tasks, we show that EDA improves performance for both convolutional and recurrent neural networks. EDA demonstrates particularly strong results for smaller datasets; on average, across five datasets, training with EDA while using only 50% of the available training set achieved the same accuracy as normal training with all available data. We also performed extensive ablation studies and suggest parameters for practical use.