Faculty Bibliography

PURPOSE/OBJECTIVE:To evaluate the association of patient characteristics, community-level social determinants of health, and cyst risk categories with completion of follow-up recommendations for incidental Pancreatic Cystic Lesions (PCLs). METHODS:We retrospectively identified consecutive patients (2013-2023) whose MRI radiology reports described PCLs. A fine-tuned LLaMA-3.1 8B Instruct large language model was used to extract PCL features. Lesions were classified using the 2017 ACR white paper: Category 1 (low risk), Category 2 (worrisome features), or Category 3 (high-risk stigmata). We recorded demographics and follow-up imaging or endoscopic ultrasound dates. Community-level factors were characterized by the 2020 CDC Social Vulnerability Index (SVI), stratified into quartiles. The primary outcome, "inappropriate follow-up," combined late and no follow-up. Multivariable binomial regression was applied to evaluate associations with inappropriate follow-up. RESULTS:In 7,745 patients (mean age 66.3 years; 4,796 women), 92.9% (7,198/7,745) of cysts were Category 1, 6.4% (498/7,745) were Category 2, and 0.6% (49/7,745) were Category 3. Only 36.3% of patients completed appropriate follow-up, 12.1% were late, and 51.6% were lost to follow-up. Inappropriate follow-up was high in every cyst category: 64.2% in Category 1, 59.4% in Category 2 and 49.0% in Category 3. In multivariable analysis, non-English primary language (RR 1.08; 95% CI, 1.02-1.14) and residing in more vulnerable communities of the 3rd quartiles of the socioeconomic Social Vulnerability Index subcategory (RR 1.07; 95% CI, 1.02-1.12) were associated with inappropriate follow-up. Higher age-adjusted Charlson Comorbidity Index (CCI ≥ 4) (RR .84; 95% CI, .79-.88), CCI 2-3 (RR .84; 95% CI, .79-.88), and higher-risk cysts in patients under 65 years of age (RR .76; 95% CI, .65-.89) were associated with completed follow-up. CONCLUSION/CONCLUSIONS:Follow-up completion for incidental PCLs was low. Factors most consistently associated with follow-up completion were language barriers, residence in socioeconomically vulnerable communities, age-adjusted CCI and higher-risk features among those under 65 years.

PURPOSE/OBJECTIVE:To develop and evaluate a hybrid large language model (LLM)- and rule-based algorithm for tracking pancreatic cystic lesions (PCLs) across serial radiology reports and apply it to a single-center cohort to characterize cyst growth using first-to-last and consecutive comparisons. METHODS:This retrospective study included all abdominal imaging reports interpreted by abdominal radiologists between 2005 and 2024. A validated LLM identified reports describing PCLs. A hybrid algorithm combining LLM-extracted cyst features (size, location) with rule-based logic matched dominant cysts across serial reports. Two blinded radiologists independently annotated cyst matches in a subset of reports. Agreement between algorithm and radiologists was assessed using percent agreement and Cohen's κ. Discrepancies were adjudicated by a third radiologist for error analysis. Absolute size-based growth was assessed according to American College of Radiology (ACR) criteria, whereas cyst growth rate as a worrisome feature was assessed per Kyoto criterion, using both first-to-last and consecutive studies. Predictors of meeting the Kyoto cyst growth rate criterion were analyzed using multivariable Cox regression. RESULTS:In 58 patients with 418 longitudinal reports, agreement with the adjudicated reference standard was 96%. Cohen's κ was 0.81 (95% CI: 0.66-0.91). The overall cohort included 8,176 patients (61.8% female; mean age 67.6 ± 11.7 years) with 8,389 cysts across 28,489 reports over 40.6 ± 28.5 months (annual growth rate = 0.35 mm/year). ACR absolute growth criteria classified a higher proportion of cysts than the Kyoto cyst growth rate criterion using first-to-last examinations (16.3% vs. 7.4%, p < 0.001), whereas Kyoto cyst growth rate criterion identified a higher proportion using consecutive examinations (21.4% vs. 14%, p < 0.001). Larger baseline cyst size predicted meeting the Kyoto cyst growth rate criterion (HR 1.9-4.0; highest HR 4.0, 95% CI 3.08-5.21; p < 0.001). CONCLUSION/CONCLUSIONS:A hybrid LLM-rule-based algorithm enables reliable automated longitudinal tracking of PCLs across serial radiology reports. When applied at scale, cyst growth classification varied according to the guideline framework and interval comparison method used, and baseline cyst size was the strongest predictor of meeting Kyoto growth rate threshold.

RATIONALE AND OBJECTIVES/OBJECTIVE:Prostate multiparametric magnetic resonance imaging (MRI) is recommended for prostate cancer detection, staging, and surveillance. Incidental bladder lesions are encountered on these studies but remain under-characterized in the literature. The patient characteristics associated with malignancy for these lesions are not well defined. We evaluated the prevalence, histopathologic outcomes, clinical characteristics, and associations with malignancy for incidental bladder lesions on prostate MRI. MATERIALS AND METHODS/METHODS:A retrospective review included 31,241 patients undergoing prostate MRI examinations from January 2013 to January 2023. Imaging reports and medical records were analyzed for incidental bladder lesions, demographic data, clinical symptoms, urinalysis findings, and histopathologic outcomes. Lesions were categorized based on biopsy results or negative clinical follow-up for bladder tumors in chart review. Logistic regression analysis and receiver operating characteristic analyses were performed. RESULTS:Incidental bladder lesions occurred in 0.74% (230/31,241) of examinations, with biopsy-confirmed bladder cancer in 0.11% of patients (34/31,241) or 14.8% (34/230) of cases with lesions. In multivariable analysis, gross hematuria had the strongest association with biopsy-proven bladder cancer (OR 9.26, 95% CI 4.12-20.79, p<0.001). A logistic regression model incorporating age, smoking status, and gross hematuria yielded area under the curve of 0.762 for bladder cancer. CONCLUSION/CONCLUSIONS:Incidental bladder lesions on prostate MRI may represent opportunities for early detection of bladder cancer, but also have potential for harms related to unnecessary procedures. Considering the presence of gross hematuria, possibly stated as part of the MRI referral or patient questionnaire, could improve risk stratification of encountered bladder lesions and early cancer detection.

Ovarian cancer remains the sixth most common cause of cancer mortality in women in the United States and is a leading cause of mortality among patients with gynecologic malignancies. Imaging plays an important role in pretreatment staging of epithelial ovarian cancers, the evaluation of posttreatment response, and follow-up. Accurate pretreatment imaging is integral to determine appropriate first-line therapy. By delineating the extent of disease, imaging can assist decision making regarding the likelihood of optimal primary cytoreduction or need for neoadjuvant chemotherapy when optimal cytoreduction is not felt to be achievable. Contrast-enhanced CT serves as a mainstay modality for the pretreatment assessment of ovarian cancer, with MRI, PET/CT, and, in some instances, PET/MRI used in the pretreatment setting. CT and PET/CT are also integral to assessing response, including in the suspected recurrence setting, with MRI and PET/MRI being used in select cases. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision process support the systematic analysis of the medical literature from peer reviewed journals. Established methodology principles such as Grading of Recommendations Assessment, Development, and Evaluation or GRADE are adapted to evaluate the evidence. The RAND/UCLA Appropriateness Method User Manual provides the methodology to determine the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where peer reviewed literature is lacking or equivocal, experts may be the primary evidentiary source available to formulate a recommendation.

Large language models (LLMs) and multi-modal large language models (MLLMs) represent the cutting-edge in artificial intelligence. This review provides a comprehensive overview of their capabilities and potential impact on radiology. Unlike most existing literature reviews focusing solely on LLMs, this work examines both LLMs and MLLMs, highlighting their potential to support radiology workflows such as report generation, image interpretation, EHR summarization, differential diagnosis generation, and patient education. By streamlining these tasks, LLMs and MLLMs could reduce radiologist workload, improve diagnostic accuracy, support interdisciplinary collaboration, and ultimately enhance patient care. We also discuss key limitations, such as the limited capacity of current MLLMs to interpret 3D medical images and to integrate information from both image and text data, as well as the lack of effective evaluation methods. Ongoing efforts to address these challenges are introduced.

Ovarian cancer remains low in prevalence but has the highest mortality of all gynecologic malignancies. Population-based screening for ovarian cancer remains a topic of interest in contemporary practice, given that the majority of cancers encountered are high-grade aggressive malignancies, for which favorable survival is encountered in the setting of early-stage disease. This document summarizes a review of the available data from randomized and observational trials that have evaluated the role of imaging for ovarian cancer screening in average-risk and high-risk patients. When considering screening using pelvic ultrasound in average-risk patients, we found insufficient published evidence to recommend ovarian cancer screening. Randomized controlled trials have not demonstrated a mortality benefit in this setting. Screening with pelvic ultrasound may be appropriate for select patients at high risk, although the existing data remain limited as large, randomized trials have not been performed in this setting. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision process support the systematic analysis of the medical literature from peer reviewed journals. Established methodology principles such as Grading of Recommendations Assessment, Development, and Evaluation or GRADE are adapted to evaluate the evidence. The RAND/UCLA Appropriateness Method User Manual provides the methodology to determine the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where peer reviewed literature is lacking or equivocal, experts may be the primary evidentiary source available to formulate a recommendation.

Early detection is crucial for improving survival rates of pancreatic ductal adenocarcinoma (PDA), yet current diagnostic methods can often fail at this stage. Recently, there has been significant interest in improving risk stratification and developing imaging biomarkers, through novel imaging techniques, and most notably, artificial intelligence (AI) technology. This review provides an overview of these advancements, with a focus on deep learning methods for early detection of PDA.

PURPOSE/OBJECTIVE:Though prior studies have proven CTC's efficacy in outpatients, its utility in the inpatient setting has not been studied. We evaluated the efficacy of a modified CTC protocol in the inpatient setting, primarily for patients awaiting organ transplantation. METHODS:This retrospective study compared a group of inpatient CTCs from 2019 to 2021 and a randomly selected, age-matched 2:1 control group of outpatient CTCs. Both groups were assessed based on established criteria from literature. RESULTS:10 % (63/652) of CTCs were performed in the inpatient setting, of which 29 were excluded, yielding 34 inpatient cases. 90 % (589/652) of CTCs were performed in the outpatient setting, from which 68 randomly selected, age-matched patients were selected as controls. Significantly more (24 %, 8/34) inpatients expired due to extracolonic causes (vs. 1 %, 1/68 outpatients, p < 0.05). 62 % (21/34) of inpatient CTCs were reported as diagnostic (vs. 74 %, 50/68 outpatient, p = 0.22). Significantly more inpatients (12 %, 4/34) than outpatients (1 %, 1/68) were unable to tolerate two imaging positions (p = 0.02). Subsequent colonoscopy was performed in 24 % (8/34) of inpatients, revealing pathologies including colonic polyps and non-bleeding ulcers. Inpatient CTCs had lower average quality scores, significant for one reviewer (p = 0.009-0.054). Inpatients had a larger number of segments with: >25 % residual fluid (1.22-1.28 inpatients vs. 0.60-0.73 outpatients, p = 0.003-0.026) and inadequate fluid tagging (1.10 inpatients vs. 0.49 outpatients, p = 0.046-0.0501). Distention was not significantly different between groups (p = 0.317-0.410). CONCLUSION/CONCLUSIONS:Quality of inpatient CTC was inferior to outpatient CTCs across several metrics. 24 % undergoing inpatient CTC died of extracolonic causes within 22 months, and most did not have findings warranting intervention, questioning the value of this difficult exam in this patient population. Routine CT may be sufficient to exclude large or metastatic colonic lesions precluding transplant.

Acute and chronic bowel pathologies can often be mistaken for manifestations of inflammatory bowel disease (IBD), and there are many entities with imaging and clinical features that overlap with IBD, making diagnosis difficult. We describe multiple inflammatory, infectious, neoplastic, and vascular entities with imaging and clinical features that may mimic IBD, and highlight differentiating features to assist in diagnosis.