CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison

Large, labeled datasets have driven deep learning methods to achieve expert-level performance on a variety of medical imaging tasks. We present CheXpert, a large dataset that contains 224,316 chest radiographs of 65,240 patients. We design a labeler to automatically detect the presence of 14 observations in radiology reports, capturing uncertainties inherent in radiograph interpretation. We investigate different approaches to using the uncertainty labels for training convolutional neural networks that output the probability of these observations given the available frontal and lateral radiographs. On a validation set of 200 chest radiographic studies which were manually annotated by 3 board-certified radiologists, we find that different uncertainty approaches are useful for different pathologies. We then evaluate our best model on a test set composed of 500 chest radiographic studies annotated by a consensus of 5 board-certified radiologists, and compare the performance of our model to that of 3 additional radiologists in the detection of 5 selected pathologies. On Cardiomegaly, Edema, and Pleural Effusion, the model ROC and PR curves lie above all 3 radiologist operating points. We release the dataset to the public as a standard benchmark to evaluate performance of chest radiograph interpretation models. The dataset is freely available at https://stanfordmlgroup.github.io/competitions/chexpert .Comment: Published in AAAI 201

Technical Challenges in the Clinical Application of Radiomics.

Radiomics is a quantitative approach to medical image analysis targeted at deciphering the morphologic and functional features of a lesion. Radiomic methods can be applied across various malignant conditions to identify tumor phenotype characteristics in the images that correlate with their likelihood of survival, as well as their association with the underlying biology. Identifying this set of characteristic features, called tumor signature, holds tremendous value in predicting the behavior and progression of cancer, which in turn has the potential to predict its response to various therapeutic options. We discuss the technical challenges encountered in the application of radiomics, in terms of methodology, workflow integration, and user experience, that need to be addressed to harness its true potential

Translational Radiomics: Defining the Strategy Pipeline and Considerations for Application-Part 1: From Methodology to Clinical Implementation.

Enterprise imaging has channeled various technological innovations to the field of clinical radiology, ranging from advanced imaging equipment and postacquisition iterative reconstruction tools to image analysis and computer-aided detection tools. More recently, the advancements in the field of quantitative image analysis coupled with machine learning-based data analytics, classification, and integration have ushered us into the era of radiomics, which has tremendous potential in clinical decision support as well as drug discovery. There are important issues to consider to incorporate radiomics as a clinically applicable system and a commercially viable solution. In this two-part series, we offer insights into the development of the translational pipeline for radiomics from methodology to clinical implementation (Part 1) and from that to enterprise development (Part 2)

Federated Learning on Heterogenous Data using Chest CT

Large data have accelerated advances in AI. While it is well known that population differences from genetics, sex, race, diet, and various environmental factors contribute significantly to disease, AI studies in medicine have largely focused on locoregional patient cohorts with less diverse data sources. Such limitation stems from barriers to large-scale data share in medicine and ethical concerns over data privacy. Federated learning (FL) is one potential pathway for AI development that enables learning across hospitals without data share. In this study, we show the results of various FL strategies on one of the largest and most diverse COVID-19 chest CT datasets: 21 participating hospitals across five continents that comprise >10,000 patients with >1 million images. We present three techniques: Fed Averaging (FedAvg), Incremental Institutional Learning (IIL), and Cyclical Incremental Institutional Learning (CIIL). We also propose an FL strategy that leverages synthetically generated data to overcome class imbalances and data size disparities across centers. We show that FL can achieve comparable performance to Centralized Data Sharing (CDS) while maintaining high performance across sites with small, underrepresented data. We investigate the strengths and weaknesses for all technical approaches on this heterogeneous dataset including the robustness to non-Independent and identically distributed (non-IID) diversity of data. We also describe the sources of data heterogeneity such as age, sex, and site locations in the context of FL and show how even among the correctly labeled populations, disparities can arise due to these biases

Drug-eluting stents appear superior to bare metal stents for vein-graft PCI in vessels up to a stent diameter of 4 mm.

BACKGROUND: Research trials have shown improved short-term outcome with drug-eluting stents (DES) over bare metal stents (BMS) in saphenous vein graft (SVG) percutaneous coronary intervention (PCI), primarily by reducing target vessel revascularization (TVR) for in-stent restenosis. We compared the outcomes in patients undergoing SVG stent implantation treated with DES or BMS. In exploratory analyses we investigated the influence of stent generation and diameter. METHODS: Data were obtained from a prospective database of 657 patients who underwent PCI for SVG lesions between 2003 and 2011. A total of 344 patients had PCI with BMS and 313 with DES. Propensity scores were developed based on 15 observed baseline covariates in a logistic regression model with stent type as the dependent variable. The nearest-neighbour-matching algorithm with Greedy 5-1 Digit Matching was used to produce two patient cohorts of 313 patients each. We assessed major adverse cardiac events (MACE) out to a median of 3.3 years (interquartile range: 2.1-4.1). MACE was defined as all-cause mortality, myocardial infarction (MI), TVR and stroke. RESULTS: There was a significant difference in MACE between the two groups in favour of DES (17.9% DES vs. 31.2% BMS group; p = 0.0017) over the 5-year follow-up period. MACE was driven by increased TVR in the BMS group. There was no difference in death, MI or stroke. Adjusted Cox analysis confirmed a decreased risk of MACE for DES compared with BMS 0.75 (95% confidence interval (CI) 0.52-0.94), with no difference in the hazard of all-cause mortality (hazard ratio: 1.08; 95% CI: 0.77-1.68). However, when looking at stent diameters greater than 4 mm, no difference was seen in MACE rates between BMS and DES. CONCLUSIONS: Overall in our cohort of patients who had PCI for SVG disease, DES use resulted in lower MACE rates compared with BMS over a 5-year follow-up period; however, for stent diameters over 4 mm no difference in MACE rates was seen

Concierge and Second-Opinion Radiology: Review of Current Practices

Radiology\u27s core assets include the production, interpretation, and distribution of quality imaging studies. Second-opinion services and concierge practices in radiology aim to augment traditional services by providing patient-centered and physician-centered care, respectively. Patient centeredness enhances patients\u27 understanding and comfort with their radiology tests and procedures and allows them to make better decisions about their health care. As the fee-for-service paradigm shifts to value-based care models, radiology practices have begun to diversify imaging service delivery and communication to coincide with the American College of Radiology Imaging 3.0 campaign. Physician-centered consultation allows for communication of evidence-based guidelines to assist referring physicians and other providers in making the most appropriate imaging or treatment decision for a specific clinical condition. There are disparate practice models and payment schema for the various second-opinion and concierge practices. This review article explores the current state and payment models of second-opinion and concierge practices in radiology. This review also includes a discussion on the benefits, roadblocks, and ethical issues that surround these novel types of practices

Datafish Multiphase Data Mining Technique to Match Multiple Mutually Inclusive Independent Variables in Large PACS Databases

Retrospective data mining has tremendous potential in research but is time and labor intensive. Current data mining software contains many advanced search features but is limited in its ability to identify patients who meet multiple complex independent search criteria. Simple keyword and Boolean search techniques are ineffective when more complex searches are required, or when a search for multiple mutually inclusive variables becomes important. This is particularly true when trying to identify patients with a set of specific radiologic findings or proximity in time across multiple different imaging modalities. Another challenge that arises in retrospective data mining is that much variation still exists in how image findings are described in radiology reports. We present an algorithmic approach to solve this problem and describe a specific use case scenario in which we applied our technique to a real-world data set in order to identify patients who matched several independent variables in our institution\u27s picture archiving and communication systems (PACS) database

Large intra-thoracic desmoid tumor with airway compression: A case report and review of the literature

Intra-thoracic desmoid tumors are exceedingly rare tumors in the pediatric population, and can present with compression of cardiopulmonary structures and respiratory compromise. Surgical and anesthetic management of these tumors are challenging. We report the case of a 14-year-old male who presented with dyspnea, found to have a large intra-thoracic mass that was critically compressing his trachea. We provide a review of the literature of desmoid tumors and discuss the management of intra-thoracic masses

Evaluating the Effect of Unstructured Clinical Information on Clinical Decision Support Appropriateness Ratings

OBJECTIVE: To determine the appropriateness rating (AR) of advanced inpatient imaging requests that were not rated by prospective, point-of-care clinical decision support (CDS) using computerized provider order entry. MATERIALS AND METHODS: During 30-day baseline and intervention periods, CDS generated an AR for advanced inpatient imaging requests (nuclear medicine, CT, and MRI) using provider-selected structured indications from pull-down menus in the computerized provider order entry portal. The AR was only displayed during the intervention, and providers were required to acknowledge the AR to finalize the request. Subsequently, the unstructured free text information accompanying all requests was reviewed, and the AR was revised when possible. The percentage of unrated requests and the overall AR, before and after radiologist review, were compared between periods and by provider type. RESULTS: CDS software prospectively generated an AR for only 25.4% and 28.4% of baseline and intervention imaging requests, respectively; however, radiologist review generated an AR for 82.4% and 93.6% of the same requests. During the respective periods, the percentage of baseline and intervention imaging requests considered appropriate was 18.7% and 22.9% by prospective CDS software rating and increased to 82.4% and 88.7% with radiologist review. CONCLUSION: Despite limited effective use of CDS software, the percentage of requests containing additional, relevant clinical information increased, and the majority of requests had overall high appropriateness when reviewed by a radiologist. Additional work is needed to improve the amount and quality of clinical information available to CDS software and to facilitate the entry of this information by appropriate end users