Spaced radiology: encouraging durable memory using spaced testing in pediatric radiology.

Applied memory research in the field of cognitive and educational psychology has generated a large body of data to support the use of spacing and testing to promote long-term or durable memory. Despite the consensus of this scientific community, most learners, including radiology residents, do not utilize these tools for learning new information. We present a discussion of these parallel and synergistic learning techniques and their incorporation into a software platform, called Spaced Radiology, which we created for teaching radiology residents. Specifically, this software uses these evidence-based strategies to teach pediatric radiology through a flashcard deck system.

Creation and Curation of the Society of Imaging Informatics in Medicine Hackathon Dataset.

In order to support innovation, the Society of Imaging Informatics in Medicine (SIIM) elected to create a collaborative computing experience called a "hackathon." The SIIM Hackathon has always consisted of two components, the event itself and the infrastructure and resources provided to the participants. In 2014, SIIM provided a collection of servers to participants during the annual meeting. After initial server setup, it was clear that clinical and imaging "test" data were also needed in order to create useful applications. We outline the goals, thought process, and execution behind the creation and maintenance of the clinical and imaging data used to create DICOM and FHIR Hackathon resources.

Focused Decision Support: a Data Mining Tool to Query the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial Dataset and Guide Screening Management for the Individual Patient.

The Prostate, Lung, Colorectal, and Ovarian Cancer (PLCO) Screening Trial enrolled ~155,000 participants to determine whether certain screening exams reduced mortality from prostate, lung, colorectal, and ovarian cancer. Repurposing the data provides an unparalleled resource for matching patients with the outcomes of demographically or diagnostically comparable patients. A web-based application was developed to query this subset of patient information against a given patient's demographics and risk factors. Analysis of the matched data yields outcome information which can then be used to guide management decisions and imaging software. Prognostic information is also estimated via the proportion of matched patients that progress to cancer. The US Preventative Services Task Force provides screening recommendations for cancers of the breast, colorectal tract, and lungs. There is wide variability in adherence of clinicians to these guidelines and others published by the Fleischner Society and various cancer organizations. Data mining the PLCO dataset for clinical decision support can optimize the use of limited healthcare resources, focusing screening on patients for whom the benefit to risk ratio is the greatest and most efficacious. A data driven, personalized approach to cancer screening maximizes the economic and clinical efficacy and enables early identification of patients in which the course of disease can be improved. Our dynamic decision support system utilizes a subset of the PLCO dataset as a reference model to determine imaging and testing appropriateness while offering prognostic information for various cancers.

Constructing a Computer-Aided Differential Diagnosis Engine from Open-Source APIs.

This paper describes the design and implementation of an application that parses and analyzes radiology report text to provide a radiologic differential diagnosis. The system was constructed using a combination of freely available web-based APIs and originally developed during the Society for Imaging Informatics in Medicine (SIIM) 2014 Hackathon. Continued development has refined and increased the accuracy of the algorithm. This project demonstrates the power and possibilities of combining existing technologies to solve unique problems as well as the stimulus of the hackathon setting to spur innovation.

Data-driven decision support for radiologists: re-using the National Lung Screening Trial dataset for pulmonary nodule management.

Real-time mining of large research trial datasets enables development of case-based clinical decision support tools. Several applicable research datasets exist including the National Lung Screening Trial (NLST), a dataset unparalleled in size and scope for studying population-based lung cancer screening. Using these data, a clinical decision support tool was developed which matches patient demographics and lung nodule characteristics to a cohort of similar patients. The NLST dataset was converted into Structured Query Language (SQL) tables hosted on a web server, and a web-based JavaScript application was developed which performs real-time queries. JavaScript is used for both the server-side and client-side language, allowing for rapid development of a robust client interface and server-side data layer. Real-time data mining of user-specified patient cohorts achieved a rapid return of cohort cancer statistics and lung nodule distribution information. This system demonstrates the potential of individualized real-time data mining using large high-quality clinical trial datasets to drive evidence-based clinical decision-making.

Using Standardized Lexicons for Report Template Validation with LexMap, a Web-based Application.

An enormous amount of data exists in unstructured diagnostic and interventional radiology reports. Free text or non-standardized terminologies limit the ability to parse, extract, and analyze these report data elements. Medical lexicons and ontologies contain standardized terms for relevant concepts including disease entities, radiographic technique, and findings. The use of standardized terms offers the potential to improve reporting consistency and facilitate computer analysis. The purpose of this project was to implement an interface to aid in the creation of standards-compliant reporting templates for use in interventional radiology. Non-standardized procedure report text was analyzed and referenced to RadLex, SNOMED-CT, and LOINC. Using JavaScript, a web application was developed which determined whether exact terms or synonyms in reports existed within these three reference resources. The NCBO BioPortal Annotator web service was used to map terms, and output from this application was used to create an interactive annotated version of the original report. The application was successfully used to analyze and modify five distinct reports for the Society of Interventional Radiology's standardized reporting project.

Interrater Reliability of Cervical Neural Foraminal Stenosis Using Traditional and Splayed Reconstructions: Analysis of One Hundred Scans.

OBJECTIVE: The oblique sagittal orientation of the cervical neural foramina hinders the evaluation of cervical neural foraminal stenosis (CNFS) on traditional axial and sagittal slices. Traditional image reconstruction techniques to generate oblique slices provide only a view of the foramina unilaterally. We present a simple technique for generating splayed slices that show the bilateral neuroforamina simultaneously and assess its reliability compared with traditional axial windows. METHODS: Cervical computed tomography (CT) scans from 100 patients were retrospectively collected and de-identified. The axial slices were reformatted into a curved reformat with the plane of the reformat extending across the bilateral neuroforamina. The foramina along the C2-T1 vertebral levels were assessed by 4 neuroradiologists using the axial and splayed slices. The intrarater agreement across the axial and splayed slices for a given foramen and the interrater agreement for the axial and splayed slices individually were calculated using the Cohen κ statistic. RESULTS: Interrater agreement was overall higher for the splayed slices (κ = 0.25) compared with the axial slices (κ = 0.20). The splayed slices were more likely to have fair agreement across raters compared with the axial slices. Intrarater agreement between the axial and splayed slices was poorer for residents compared with fellows. CONCLUSIONS: Splayed reconstructions showing the bilateral neuroforamina en face can be readily generated from axial CT imaging. These splayed reconstructions can improve the consistency of CNFS evaluation compared with traditional CT slices and should be considered in the workup of CNFS, particularly for less experienced readers.

Postoperative Pharynx and Larynx.

Cancers of the pharynx and larynx are treated using a combination of chemotherapeutic, radiation, and surgical techniques, depending on the cancer type, biology, location, and stage, as well as patient and other factors. When imaging in the postsurgical setting, the knowledge of the type of tumor, preoperative appearance, and type of surgery performed is essential for accurate interpretation. Surgical anatomic changes, surgical implants/devices, and potential postsurgical complications must be differentiated from suspected recurrent tumors.

Predictors of Free Flap Volume Loss in Nonosseous Reconstruction of Head and Neck Oncologic Defects.

OBJECTIVES: Free tissue reconstruction of the head and neck must be initially overcorrected due to expected postoperative free flap volume loss, which can be accelerated by adjuvant radiation therapy. In this study, we aim to identify patient and treatment-specific factors that may significantly contribute to this phenomenon and translate these characteristics into a predictive model for expected percent free flap volume loss in a given patient. METHODS: Patients with a history of oral cavity and/or oropharyngeal cancer who underwent nonosseous free flap reconstruction were reviewed between January 2009 and November 2018 at a tertiary care center. Demographics/characteristics, total radiation dose, radiation fractionation (RF), and pre/postradiation free flap volume as evaluated by computed tomography imaging were collected. Free flap volume was measured by a fellowship-trained neuroradiologist in all cases. Only patients receiving adjuvant radiotherapy with available pre/postradiation imaging were included. Multivariable linear regression modeling for prediction of free flap volume loss was performed with optimization via stepwise elimination. RESULTS: Thirty patients were included for analysis. Mean flap volume loss was 42.7% ± 17.4%. The model predicted flap volume loss in a significant fashion (P = .004, R2 = 0.49) with a mean magnitude of error of 9.8% ± 7.5%. Age (ß = 0.01, P = .003) and RF (ß = -0.01, P = .009) were individual predictors of flap volume loss. CONCLUSIONS: Our model predicts percent free flap volume loss in a significant fashion. Age and RF are individual predictors of free flap volume loss, the latter being a novel finding that is also modifiable through hyperfractionation radiotherapy schedules.

Imaging for Treated Aneurysms (Including Clipping, Coiling, Stents, Flow Diverters).

Intracranial aneurysms are common in the adult population and carry a risk of rupture leading to catastrophic subarachnoid hemorrhage. Treatment of aneurysms has evolved significantly, with the introduction of new techniques and devices for minimally invasive and endovascular approaches. Follow-up imaging after aneurysm treatment is standard of care to monitor for recurrence or other complications, and the preferred imaging modality and schedule for follow-up are areas of active research. The modality and follow-up schedule should be tailored to treatment technique, aneurysm characteristics, and patient factors.

Reshaping Radiology Precall Preparation: Integrating a Cloud-Based PACS Viewer Into a Flipped Classroom Model.

Preparing residents for the on-call experience in Radiology is one of the most important aspects of education within a training program. Traditionally, this preparation has occurred via a combination of case conferences and didactic lectures by program faculty, daily teaching at the workstation, and precall assessments. Recently, a blended curricular model referred to as the flipped classroom has generated a lot of attention within the realm of graduate medical education. We applied this technique to resident precall education in the subspecialty of Neuroradiology, and surveyed the participants about their perceptions of the course. The structure, implementation, and web-based platform used to create the flipped classroom experience is described herein.

Integration of a Zero-footprint Cloud-based Picture Archiving and Communication System with Customizable Forms for Radiology Research and Education.

RATIONALE AND OBJECTIVES: The purpose of this study was to integrate web-based forms with a zero-footprint cloud-based Picture Archiving and Communication Systems (PACS) to create a tool of potential benefit to radiology research and education. MATERIALS AND METHODS: Web-based forms were created with a front-end and back-end architecture utilizing common programming languages including Vue.js, Node.js and MongoDB, and integrated into an existing zero-footprint cloud-based PACS. RESULTS: The web-based forms application can be accessed in any modern internet browser on desktop or mobile devices and allows the creation of customizable forms consisting of a variety of questions types. Each form can be linked to an individual DICOM examination or a collection of DICOM examinations. CONCLUSIONS: Several uses are demonstrated through a series of case studies, including implementation of a research platform for multi-reader multi-case (MRMC) studies and other imaging research, and creation of an online Objective Structure Clinical Examination (OSCE) and an educational case file.

Personalizing lung cancer risk prediction and imaging follow-up recommendations using the National Lung Screening Trial dataset.

OBJECTIVE: To demonstrate a data-driven method for personalizing lung cancer risk prediction using a large clinical dataset. MATERIALS AND METHODS: An algorithm was used to categorize nodules found in the first screening year of the National Lung Screening Trial as malignant or nonmalignant. Risk of malignancy for nodules was calculated based on size criteria according to the Fleischner Society recommendations from 2005, along with the additional discriminators of pack-years smoking history, sex, and nodule location. Imaging follow-up recommendations were assigned according to Fleischner size category malignancy risk. RESULTS: Nodule size correlated with malignancy risk as predicted by the Fleischner Society recommendations. With the additional discriminators of smoking history, sex, and nodule location, significant risk stratification was observed. For example, men with ≥60 pack-years smoking history and upper lobe nodules measuring >4 and ≤6 mm demonstrated significantly increased risk of malignancy at 12.4% compared to the mean of 3.81% for similarly sized nodules (P < .0001). Based on personalized malignancy risk, 54% of nodules >4 and ≤6 mm were reclassified to longer-term follow-up than recommended by Fleischner. Twenty-seven percent of nodules ≤4 mm were reclassified to shorter-term follow-up. DISCUSSION: Using available clinical datasets such as the National Lung Screening Trial in conjunction with locally collected datasets can help clinicians provide more personalized malignancy risk predictions and follow-up recommendations. CONCLUSION: By incorporating 3 demographic data points, the risk of lung nodule malignancy within the Fleischner categories can be considerably stratified and more personalized follow-up recommendations can be made.