Development and Implementation of a Multisite Registry Using Structured Templates for Actionable Findings in the Kidney.

PURPOSE: The aim of this study was to scale structured report templates categorizing actionable renal findings across health systems and create a centralized registry of patient and report data. METHODS: In January 2017, three academic radiology departments agreed to prospectively include identical structured templates categorizing the malignant likelihood of renal findings in ≥90% of all adult ultrasound, MRI, and CT reports, a new approach for two sites. Between November 20, 2017, and September 30, 2019, deidentified HL7 report data were transmitted to a centralized ACR registry. An automated algorithm extracted categories. Radiologists were requested to addend reports with missing or incomplete templates after the first month. Separately, each site submitted patient sociodemographic and clinical data 12 months before and at least 3 months after enrollment. RESULTS: A total of 164,982 eligible radiology reports were transmitted to the registry; 4,159 (2.5%) were excluded because of missing categories or radiologist names. The final cohort included 160,823 examinations on 102,619 unique patients. Mean template use before and after addendum requests was 99.3% and 99.9% at SITE1, 86.5% and 94.6% at SITE2, and 91.4% and 96.0% at SITE3. Matching patient sociodemographic and clinical data were obtained on 96.9% of reports from SITE1, 94.2% from SITE2, and 96.0% from SITE3. Regulatory, cultural, and technology barriers to the creation of a multisite registry were identified. CONCLUSIONS: Barriers to the adoption of unified structured report templates for actionable kidney findings can be addressed. Deidentified report and patient data can be securely transmitted to an external registry. These data can facilitate the collection of diverse evidence-based population imaging outcomes.

Artificial intelligence development in pediatric body magnetic resonance imaging: best ideas to adapt from adults.

Emerging manifestations of artificial intelligence (AI) have featured prominently in virtually all industries and facets of our lives. Within the radiology literature, AI has shown great promise in improving and augmenting radiologist workflow. In pediatric imaging, while greatest AI inroads have been made in musculoskeletal radiographs, there are certainly opportunities within thoracoabdominal MRI for AI to add significant value. In this paper, we briefly review non-interpretive and interpretive data science, with emphasis on potential avenues for advancement in pediatric body MRI based on similar work in adults. The discussion focuses on MRI image optimization, abdominal organ segmentation, and osseous lesion detection encountered during body MRI in children.

A succinct rating scale for radiology report quality.

CONTEXT: Poorly written radiology reports are common among residents and are a significant challenge for radiology education. While training may improve report quality, a professionally developed reliable and valid scale to measure report quality does not exist. OBJECTIVES: To develop a measurement tool for report quality, the quality of report scale, with rigorous validation through empirical data. METHODS: A research team of an experienced psychometrician and six senior radiologists conducted qualitative and quantitative studies. Five items were identified for the quality of report scale, each measuring a distinct aspect of report quality. Two dedicated training sessions were designed and implemented to help residents generate high-quality reports. In a blinded fashion, the quality of report scale was applied to 804 randomly selected reports issued before (n = 403) and after (n = 401) training. Full-scale psychometrical assessments were implemented onto the quality of report scale's item- and scale-scores from the reports. The quality of report scale scores were correlated with report professionalism and attendings' preference and were compared pre-/post-training. RESULTS: The quality of report scale showed sound psychometrical properties, with high validity and reliability. Reports with higher quality of report scale score were more professional and preferable by attendings. Training improved the quality of report scale score, empirically validating the quality of report scale further. CONCLUSION: While succinct and practitioner friendly, the quality of report scale is a reliable and valid measure of radiology report quality and has the potential to be easily adapted to other fields such as pathology, where similar training would be beneficial.

Imaging of unusual perineal masses.

OBJECTIVE: The purpose of this article is to describe the radiologic features of unusual tumors that occur in the perineum. CONCLUSION: The perineal space is often overlooked because of the infrequency of abnormalities. Accurate image interpretation and visualization of extent of pathology is important for proper management. Trauma and infectious diseases occur in the acute setting, whereas tumors are common in the chronic setting. Cross-sectional imaging plays a crucial role in depicting perineal anatomy and evaluating the extent of disease.