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 .

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Context:Histopathological characterization of colorectal polyps is critical for determining the risk of colorectal cancer and future rates of surveillance for patients. However, this characterization is a challenging task and suffers from significant inter- and intra-observer variability.Aims:We built an automatic image analysis method that can accurately classify different types of colorectal polyps on whole-slide images to help pathologists with this characterization and diagnosis.Setting and Design:Our method is based on deep-learning techniques, which rely on numerous levels of abstraction for data representation and have shown state-of-the-art results for various image analysis tasks.Subjects and Methods:Our method covers five common types of polyps (i.e., hyperplastic, sessile serrated, traditional serrated, tubular, and tubulovillous/villous) that are included in the US Multisociety Task Force guidelines for colorectal cancer risk assessment and surveillance. We developed multiple deep-learning approaches by leveraging a dataset of 2074 crop images, which were annotated by multiple domain expert pathologists as reference standards.Statistical Analysis:We evaluated our method on an independent test set of 239 whole-slide images and measured standard machine-learning evaluation metrics of accuracy, precision, recall, and F1 score and their 95% confidence intervals.Results:Our evaluation shows that our method with residual network architecture achieves the best performance for classification of colorectal polyps on whole-slide images (overall accuracy: 93.0%, 95% confidence interval: 89.0%–95.9%).Conclusions:Our method can reduce the cognitive burden on pathologists and improve their efficacy in histopathological characterization of colorectal polyps and in subsequent risk assessment and follow-up recommendations.

Objectives-The radiology report is the most important source of clinical imaging information. It documents critical information about the patient's health and the radiologist's interpretation of medical findings. It also communicates information to the referring physicians and records that information for future clinical and research use. Although efforts to structure some radiology report information through predefined templates are beginning to bear fruit, a large portion of radiology report information is entered in free text. The free text format is a major obstacle for rapid extraction and subsequent use of information by clinicians, researchers, and healthcare information systems. This difficulty is due to the ambiguity and subtlety of natural language, complexity of described images, and variations among different radiologists and healthcare organizations. As a result, radiology reports are used only once by the clinician who ordered the study and rarely are used again for research and data mining. In this work, machine learning techniques and a large multi-institutional radiology report repository are used to extract the semantics of the radiology report and overcome the barriers to the re-use of radiology report information in clinical research and other healthcare applications.Material and methods-We describe a machine learning system to annotate radiology reports and extract report contents according to an information model. This information model covers the majority of clinically significant contents in radiology reports and is applicable to a wide variety of radiology study types. Our automated approach uses discriminative sequence classifiers for named-entity recognition to extract and organize clinically significant terms and phrases consistent with the information model. We evaluated our information extraction system on 150 radiology reports from three major healthcare organizations and compared its results to a commonly used non-machine learning information extraction method. We also evaluated the generalizability of our approach across different organizations by training and testing our system on data from different organizations.Results-Our results show the efficacy of our machine learning approach in extracting the information model's elements (10-fold cross-validation average performance: precision: 87%, recall: 84%, F1 score: 85%) and its superiority and generalizability compared to the common nonmachine learning approach (p-value < 0.05). HHS Public Access Author Manuscript Author ManuscriptAuthor Manuscript Author ManuscriptConclusions-Our machine learning information extraction approach provides an effective automatic method to annotate and extract clinically significant information from a large collection of free text radiology reports. This information extraction system can help clinicians better understand the radiology reports and prioritize their review process. In addition, the extracted information can be used by researchers to link radiology reports to information from ...