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Rationale and Objectives. Early prostate cancer detection and staging from MRI is extremely challenging for both radiologists and deep learning algorithms, but the potential to learn from large and diverse datasets remains a promising avenue to increase their performance within and across institutions. To enable this for prototype-stage algorithms, where the majority of existing research remains, we introduce a flexible federated learning framework for cross-site training, validation, and evaluation of custom deep learning prostate cancer detection algorithms. Materials and Methods. We introduce an abstraction of prostate cancer groundtruth that represents diverse annotation and histopathology data. We maximize use of this groundtruth if and when they are available using UCNet, a custom 3D UNet that enables simultaneous supervision of pixel-wise, region-wise, and gland-wise classification. We leverage these modules to perform cross-site federated training using 1400+ heterogeneous multi-parameteric prostate MRI exams from two University hospitals. Results. We observe a positive result, with significant improvements in cross-site generalization performance with negligible intra-site performance degradation for both lesion segmentation and per-lesion binary classification of clinically-significant prostate cancer. Cross-site lesion segmentation performance intersection-over-union (IoU) improved by 100%, while cross-site lesion classification performance overall accuracy improved by 9.5-14.8%, depending on the optimal checkpoint selected by each site. Conclusion. Federated learning can improve the generalization performance of prostate cancer detection models across institutions while protecting patient health information and institution-specific code and data. However, even more data and participating institutions are likely required to improve the absolute performance of prostate cancer classification models. To enable adoption of federated learning with limited re-engineering of federated components, we open-source our FLtools system at https://federated.ucsf.edu , including examples that can be easily adapted to other medical imaging deep learning projects. [ABSTRACT FROM AUTHOR]

Understanding imaging research experiences, challenges, and strategies for academic radiology departments during and after COVID-19 is critical to prepare for future disruptive events. We summarize key insights and programmatic initiatives at major academic hospitals across the world, based on literature review and meetings of the Radiological Society of North America Vice Chairs of Research (RSNA VCR) group. Through expert discussion and case studies, we provide suggested guidelines to maintain and grow radiology research in the postpandemic era. [ABSTRACT FROM AUTHOR]

Diagnostic radiology (DxR), having had successful serial co-evolutions with imaging equipment and PACS, is faced with another. With a backdrop termed "globotics transition," it should create an IT and informatics infrastructure capable of integrating artificial intelligence (AI) into current critical communication functions of PACS and incorporating functions currently residing in balkanized products. DxR will face the challenge of adopting sustaining and disruptive AI innovations simultaneously. In this co-evolution, a major selection force for AI will be increasing the flow of information and patients; "increasing" means faster flow over larger areas defined by geography and content. Larger content includes a broader spectrum of imaging and nonimaging information streams that facilitate medical decision making. Evolution to faster flow will gravitate toward a hierarchical IT architecture consisting of many small channels feeding into fewer larger channels, something potentially difficult for current PACS. Smartphone-like architecture optimized for communication and integration could provide a large-channel backbone and many smaller feeding channels for basic functions, as well as those needing to innovate rapidly. New, more flexible architectures stimulate market competition in which DxR could act as an artificial selection force to influence development of faster increased flow in current PACS companies, in disruptors such as consolidated AI companies, or in entirely new entrants like Apple or Google. In this co-evolution, DxR should be able to stimulate design of a modern communication medium that increases the flow of information and decreases the time and energy necessary to absorb it, thereby creating even more indispensable clinical value for itself. [ABSTRACT FROM AUTHOR]

Background and objective Probabilistic topic models provide an unsupervised method for analyzing unstructured text. These models discover semantically coherent combinations of words (topics) that could be integrated in a clinical automatic summarization system for primary care physicians performing chart review. However, the human interpretability of topics discovered from clinical reports is unknown. Our objective is to assess the coherence of topics and their ability to represent the contents of clinical reports from a primary care physician's point of view. Methods Three latent Dirichlet allocation models (50 topics, 100 topics, and 150 topics) were fit to a large collection of clinical reports. Topics were manually evaluated by primary care physicians and graduate students. Wilcoxon Signed-Rank Tests for Paired Samples were used to evaluate differences between different topic models, while differences in performance between students and primary care physicians (PCPs) were tested using Mann–Whitney U tests for each of the tasks. Results While the 150-topic model produced the best log likelihood, participants were most accurate at identifying words that did not belong in topics learned by the 100-topic model, suggesting that 100 topics provides better relative granularity of discovered semantic themes for the data set used in this study. Models were comparable in their ability to represent the contents of documents. Primary care physicians significantly outperformed students in both tasks. Conclusion This work establishes a baseline of interpretability for topic models trained with clinical reports, and provides insights on the appropriateness of using topic models for informatics applications. Our results indicate that PCPs find discovered topics more coherent and representative of clinical reports relative to students, warranting further research into their use for automatic summarization. [ABSTRACT FROM AUTHOR]

Rationale and Objectives: The current paradigm of cancer diagnosis involves uncoordinated communication of findings from radiology and pathology to downstream physicians. Discordance between these findings can require additional time from downstream users to resolve, or given incorrect resolution, may adversely impact treatment decisions. To mitigate this problem, we developed a web-based system, called RadPath, for correlating and integrating radiology and pathology reporting. Materials and Methods: RadPath includes interfaces to our institution's clinical information systems, which are used to retrieve reports, images, and test results that are structured into an interactive compendium for a diagnostic patient case. The system includes an editing interface for physicians, allowing for the inclusion of additional clinical data, as well as the ability to retrospectively correlate and contextualize imaging findings following pathology diagnosis. Results: During pilot deployment and testing over the course of 1 year, physicians at our institution have completed 60 RadPath cases, requiring an average of 128 seconds from a radiologist and an average of 93 seconds from a pathologist per case. Several technical and workflow challenges were encountered during development, including interfacing with diverse clinical information systems, automatically structuring report contents, and determining the appropriate physicians to create RadPath summaries. Reaction to RadPath has been positive, with users valuing the system's ability to consolidate diagnostic information. Conclusions: With the increasing complexity of medicine and the movement toward team-based disease management, there is a need for improved clinical communication and information exchange. RadPath provides a platform for generating coherent and correlated diagnostic summaries in cancer diagnosis with minimal additional effort from physicians. [ABSTRACT FROM AUTHOR]

Rationale and Objectives The purpose of this study was to quantify the degree of imaging–histologic discordance in a cohort of patients undergoing computed tomography (CT)–guided lung biopsy for focal lung disease. Materials and Methods A retrospective review was performed of 186 patients who underwent percutaneous lung biopsy of a parenchymal lesion at our institution between January and December 2009. Diagnostic radiology reports of CT or positron emission tomography–CTs performed before biopsy were used to classify the lesion as malignant or benign by five readers. Pathology reports of the biopsied lesions were classified by three readers. Inter-reader agreement and imaging–histologic concordance were quantified using kappa statistics. Discordant benign cases were then revisited to determine downstream effects. Results Inter-reader agreement on report content was substantial or almost perfect with kappas >0.783. Kappas for concordance were as follows: malignant (0.448), primary lung cancer (0.517), metastatic disease to lung (0.449), benign (0.510), and overall agreement (0.381). Of the twelve discordant benign cases that were revisited, four were found to be false negatives, resulting in a delay in diagnosis. Conclusions Our study of imaging–histologic discordance in percutaneous biopsy of lung lesions supports the need for imaging report standardization and improved integration and communication between the fields of radiology and pathology. [ABSTRACT FROM AUTHOR]

Objective: To detail the development of an electronic report that graphically conveys all relevant information from targeted prostate biopsy.Methods: The Urology Integrated Diagnostic Report (Uro-IDR) is based on a published framework (RadPath) which enables the compilation of diagnostic data from urology, radiology, and pathology. Each component of the Uro-IDR is generated by the contributing clinician, is assembled in one document, and provides correlation of the 3 inputs at a glance. Upon completion, the Uro-IDR is automatically linked to the electronic medical record as an interactive file and can also be downloaded for offline sharing as a PDF.Results: At our institution, 1638 individual Uro-IDRs were generated between June 2016 and April 2019. There were 5715 views of these documents via the EMR. The average turnaround time for the creation of an individual report decreased from nearly 8 days at the time of its launch to 2 days after 6 months of use. The average time for report generation was 22 seconds for the pathologist and 69 seconds for the radiologist. An instructive video is linked to this article.Conclusion: The Uro-IDR has proven to be a feasible, efficient, clinically useful form to concisely transmit key information about targeted prostate biopsy to both clinicians and patients. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • A semi-supervised model for histopathological image segmentation. • An EM-based framework to improve semantic segmentation with a large-scale weakly-labeled dataset. • Adaptive biases improve performance by incorporating prior knowledge during EM training. Abstract Automated Gleason grading is an important preliminary step for quantitative histopathological feature extraction. Different from the traditional task of classifying small pre-selected homogeneous regions, semantic segmentation provides pixel-wise Gleason predictions across an entire slide. Deep learning-based segmentation models can automatically learn visual semantics from data, which alleviates the need for feature engineering. However, performance of deep learning models is limited by the scarcity of large-scale fully annotated datasets, which can be both expensive and time-consuming to create. One way to address this problem is to leverage external weakly labeled datasets to augment models trained on the limited data. In this paper, we developed an expectation maximization-based approach constrained by an approximated prior distribution in order to extract useful representations from a large number of weakly labeled images generated from low-magnification annotations. This method was utilized to improve the performance of a model trained on a limited fully annotated dataset. Our semi-supervised approach trained with 135 fully annotated and 1800 weakly annotated tiles achieved a mean Jaccard Index of 49.5% on an independent test set, which was 14% higher than the initial model trained only on the fully annotated dataset. [ABSTRACT FROM AUTHOR]

• A novel intra-domain task-adaptive transfer learning approach for medical imaging datasets is described. • 2D and 3D deep learning models were evaluated to classify acute ischemic stroke onset time. • The proposed models surpass state-of-the-art performance and apply to a broad population of patients. • Results support the value of imaging in determining the use of thrombolytic therapy in unwitnessed stroke. Treatment of acute ischemic strokes (AIS) is largely contingent upon the time since stroke onset (TSS). However, TSS may not be readily available in up to 25% of patients with unwitnessed AIS. Current clinical guidelines for patients with unknown TSS recommend the use of MRI to determine eligibility for thrombolysis, but radiology assessments have high inter-reader variability. In this work, we present deep learning models that leverage MRI diffusion series to classify TSS based on clinically validated thresholds. We propose an intra-domain task-adaptive transfer learning method, which involves training a model on an easier clinical task (stroke detection) and then refining the model with different binary thresholds of TSS. We apply this approach to both 2D and 3D CNN architectures with our top model achieving an ROC-AUC value of 0.74, with a sensitivity of 0.70 and a specificity of 0.81 for classifying TSS < 4.5 h. Our pretrained models achieve better classification metrics than the models trained from scratch, and these metrics exceed those of previously published models applied to our dataset. Furthermore, our pipeline accommodates a more inclusive patient cohort than previous work, as we did not exclude imaging studies based on clinical, demographic, or image processing criteria. When applied to this broad spectrum of patients, our deep learning model achieves an overall accuracy of 75.78% when classifying TSS < 4.5 h, carrying potential therapeutic implications for patients with unknown TSS. [ABSTRACT FROM AUTHOR]