ResearchConnect.info: An Interactive Web-Based Platform for Building Academic Collaborations.

RATIONALE AND OBJECTIVES: Radiology is a rapidly evolving field that benefits from continuous innovation and research participation among trainees. Traditional methods for involving residents in research are often inefficient and limited, usually due to the absence of a standardized approach to identifying available research projects. A centralized online platform can enhance networking and offer equal opportunities for all residents. MATERIALS AND METHODS: Research Connect is an online platform built with PHP, SQL, and JavaScript. Features include project and collaboration listing as well as advertisement of project openings to medical/undergraduate students, residents, and fellows. The automated system maintains project data and sends notifications for new research opportunities when they meet user preference criteria. Both pre- and post-launch surveys were used to assess the platform's efficacy. RESULTS: Before the introduction of Research Connect, 69% of respondents used informal conversations as their primary method of discovering research opportunities. One year after its launch, Research Connect had 141 active users, comprising 63 residents and 41 faculty members, along with 85 projects encompassing various radiology subspecialties. The platform received a median satisfaction rating of 4 on a 1-5 scale, with 54% of users successfully locating projects of interest through the platform. CONCLUSION: Research Connect addresses the need for a standardized method and centralized platform with active research projects and is designed for scalability. Feedback suggests it has increased the visibility and accessibility of radiology research, promoting greater trainee involvement and academic collaboration.

Interrater Agreement of BT-RADS for Evaluation of Follow-up MRI in Patients with Treated Primary Brain Tumor.

BACKGROUND AND PURPOSE: The Brain Tumor Reporting and Data System (BT-RADS) is a structured radiology reporting algorithm that was introduced to provide uniformity in posttreatment primary brain tumor follow-up and reporting, but its interrater reliability (IRR) assessment has not been widely studied. Our goal is to evaluate the IRR among neuroradiologists and radiology residents in the use of BT-RADS. MATERIALS AND METHODS: This retrospective study reviewed 103 consecutive MR studies in 98 adult patients previously diagnosed with and treated for primary brain tumor (January 2019 to February 2019). Six readers with varied experience (4 neuroradiologists and 2 radiology residents) independently evaluated each case and assigned a BT-RADS score. Readers were blinded to the original score reports and the reports from other readers. Cases in which at least 1 neuroradiologist scored differently were subjected to consensus scoring. After the study, a post hoc reference score was also assigned by 2 readers by using future imaging and clinical information previously unavailable to readers. The interrater reliabilities were assessed by using the Gwet AC2 index with ordinal weights and percent agreement. RESULTS: Of the 98 patients evaluated (median age, 53 years; interquartile range, 41-66 years), 53% were men. The most common tumor type was astrocytoma (77%) of which 56% were grade 4 glioblastoma. Gwet index for interrater reliability among all 6 readers was 0.83 (95% CI: 0.78-0.87). The Gwet index for the neuroradiologists' group (0.84 [95% CI: 0.79-0.89]) was not statistically different from that for the residents' group (0.79 [95% CI: 0.72-0.86]) (χ2 = 0.85; P = .36). All 4 neuroradiologists agreed on the same BT-RADS score in 57 of the 103 studies, 3 neuroradiologists agreed in 21 of the 103 studies, and 2 neuroradiologists agreed in 21 of the 103 studies. Percent agreement between neuroradiologist blinded scores and post hoc reference scores ranged from 41%-52%. CONCLUSIONS: A very good interrater agreement was found when tumor reports were interpreted by independent blinded readers by using BT-RADS criteria. Further study is needed to determine if this high overall agreement can translate into greater consistency in clinical care.

Performance and clinical utility of an artificial intelligence-enabled tool for pulmonary embolism detection.

PURPOSE: Diagnosing pulmonary embolism (PE) is still challenging due to other conditions that can mimic its appearance, leading to incomplete or delayed management and several inter-observer variabilities. This study evaluated the performance and clinical utility of an artificial intelligence (AI)-based application designed to assist clinicians in the detection of PE on CT pulmonary angiography (CTPA). PATIENTS AND METHODS: CTPAs from 230 US cities acquired on 57 scanner models from 6 different vendors were retrospectively collected. Three US board certified expert radiologists defined the ground truth by majority agreement. The same cases were analyzed by CINA-PE, an AI-driven algorithm capable of detecting and highlighting suspected PE locations. The algorithm's performance at a per-case and per-finding level was evaluated. Furthermore, cases with PE not mentioned in the clinical report but correctly detected by the algorithm were analyzed. RESULTS: A total of 1204 CTPAs (mean age 62.1 years ± 16.6[SD], 44.4 % female, 14.9 % positive) were included in the study. Per-case sensitivity and specificity were 93.9 % (95%CI: 89.3 %-96.9 %) and 94.8 % (95%CI: 93.3 %-96.1 %), respectively. Per-finding positive predictive value was 89.5 % (95%CI: 86.7 %-91.9 %). Among the 196 positive cases, 29 (15.6 %) were not mentioned in the clinical report. The algorithm detected 22/29 (76 %) of these cases, leading to a reduction in the miss rate from 15.6 % to 3.8 % (7/186). CONCLUSIONS: The AI-based application may improve diagnostic accuracy in detecting PE and enhance patient outcomes through timely intervention. Integrating AI tools in clinical workflows can reduce missed or delayed diagnoses, and positively impact healthcare delivery and patient care.

The Utility of Spectroscopic MRI in Stereotactic Biopsy and Radiotherapy Guidance in Newly Diagnosed Glioblastoma.

Current diagnostic and therapeutic approaches for gliomas have limitations hindering survival outcomes. We propose spectroscopic magnetic resonance imaging as an adjunct to standard MRI to bridge these gaps. Spectroscopic MRI is a volumetric MRI technique capable of identifying tumor infiltration based on its elevated choline (Cho) and decreased N-acetylaspartate (NAA). We present the clinical translatability of spectroscopic imaging with a Cho/NAA ≥ 5x threshold for delineating a biopsy target in a patient diagnosed with non-enhancing glioma. Then, we describe the relationship between the undertreated tumor detected with metabolite imaging and overall survival (OS) from a pilot study of newly diagnosed GBM patients treated with belinostat and chemoradiation. Each cohort (control and belinostat) were split into subgroups using the median difference between pre-radiotherapy Cho/NAA ≥ 2x and the treated T1-weighted contrast-enhanced (T1w-CE) volume. We used the Kaplan-Meier estimator to calculate median OS for each subgroup. The median OS was 14.4 months when the difference between Cho/NAA ≥ 2x and T1w-CE volumes was higher than the median compared with 34.3 months when this difference was lower than the median. The T1w-CE volumes were similar in both subgroups. We find that patients who had lower volumes of undertreated tumors detected via spectroscopy had better survival outcomes.