Faculty Bibliography

PURPOSE/OBJECTIVE:To compare the inter-reader agreement of pancreatic adenocarcinoma resectability assessment at pancreatic protocol photon-counting CT (PCCT) with conventional energy-integrating detector CT (EID-CT). METHODS:A retrospective single institution database search identified all contrast-enhanced pancreatic mass protocol abdominal CT performed at an outpatient facility with both a PCCT and EID-CT from 4/11/2022 to 10/30/2022. Patients without pancreatic adenocarcinoma were excluded. Four fellowship-trained abdominal radiologists, blinded to CT type, independently assessed vascular tumor involvement (uninvolved, abuts ≤ 180°, encases > 180°; celiac, superior mesenteric artery (SMA), common hepatic artery (CHA), superior mesenteric vein (SMV), main portal vein), the presence/absence of metastases, overall tumor resectability (resectable, borderline resectable, locally advanced, metastatic), and diagnostic confidence. Fleiss's kappa was used to calculate inter-reader agreement. CTDIvol was recorded. Radiation dose metrics were compared with a two-sample t-test. A p < .05 indicated statistical significance. RESULTS:145 patients (71 men, mean[SD] age: 66[9] years) were included. There was substantial inter-reader agreement, for celiac artery, SMA, and SMV involvement at PCCT (kappa = 0.61-0.69) versus moderate agreement at EID-CT (kappa = 0.56-0.59). CHA had substantial inter-reader agreement at both PCCT (kappa = 0.67) and EIDCT (kappa = 0.70). For metastasis identification, radiologists had substantial inter-reader agreement at PCCT (kappa = 0.78) versus moderate agreement at EID-CT (kappa = 0.56). CTDIvol for PCCT and EID-CT were 16.9[7.4]mGy and 29.8[26.6]mGy, respectively (p < .001). CONCLUSION/CONCLUSIONS:There was substantial inter-reader agreement for involvement of 4/5 major peripancreatic vessels (celiac artery, SMA, CHA, and SMV) at PCCT compared with 2/5 for EID-CT. PCCT also afforded substantial inter-reader agreement for metastasis detection versus moderate agreement at EID-CT with statistically significant radiation dose reduction.

OBJECTIVE:The aim of the study is to evaluate the performance of the ovarian-adnexal reporting and data system magnetic resonance imaging (O-RADS MRI) score and perform individual MRI feature analysis for differentiating between benign and malignant ovarian teratomas. METHODS:In this institutional review board-approved retrospective study, consecutive patients with a pathology-proven fat-containing ovarian mass imaged with contrast-enhanced MRI (1.5T or 3T) from 2013 to 2022 were included. Two blinded radiologists independently evaluated masses per the O-RADS MRI lexicon, including having a "characteristic" or "large" Rokitansky nodule (RN). Additional features analyzed included the following: nodule size/percentage volume relative to total teratoma volume, presence of bulk/intravoxel fat in the nodule, diffusion restriction in the nodule, angular interface, nodule extension through the teratoma border, presence/type of nodule enhancement pattern (solid versus peripheral), and evidence for metastatic disease. An overall O-RADS MRI score was assigned. Patient and lesion features associated with malignancy were evaluated and used to create a malignant teratoma score. χ2, Fisher's exact tests, receiver operating characteristic curve, and κ analysis was performed. RESULTS:One hundred thirty-seven women (median age 34, range 9-84 years) with 123 benign and 14 malignant lesions were included. Mean teratoma size was 7.3 cm (malignant: 14.4 cm, benign: 6.5 cm). 18/123 (14.6%) of benign teratomas were assigned an O-RADS 4 based on the presence of a "large" (11/18) or "noncharacteristic" (12/18) RN. 12/14 malignant nodules occupied >25% of the total teratoma volume (P = 0.09). Features associated with malignancy included the following: age <18 years, an enhancing noncharacteristic RN, teratoma size >12 cm, irregular cystic border, and extralesional extension; these were incorporated into a malignant teratoma score, with a score of 2 or more associated with area under the curve of 0.991 for reviewer 1 and 0.993 for reviewer 2. Peripheral enhancement in a RN was never seen with malignancy (64/123 benign, 0/14 malignant) and would have appropriated downgraded 9/18 overcalled O-RADS 4 benign teratomas. CONCLUSIONS:O-RADS MRI overcalled 15% (18/123) benign teratomas as O-RADS 4 but correctly captured all malignant teratomas. We propose defining a "characteristic" RN as an intravoxel or bulk fat-containing nodule. Observation of a peripheral rim of enhancement in a noncharacteristic RN allowed more accurate prediction of benignity and should be added to the MRI lexicon for improved O-RADS performance.

BACKGROUND:Demand for prostate MRI is increasing, but scan times remain long even in abbreviated biparametric MRIs (bpMRI). Deep learning can be leveraged to accelerate T2-weighted imaging (T2WI). PURPOSE/OBJECTIVE:To compare conventional bpMRIs (CL-bpMRI) with bpMRIs including a deep learning-accelerated T2WI (DL-bpMRI) in diagnosing prostate cancer. STUDY TYPE/METHODS:Retrospective. POPULATION/METHODS:Eighty consecutive men, mean age 66 years (47-84) with suspected prostate cancer or prostate cancer on active surveillance who had a prostate MRI from December 28, 2020 to April 28, 2021 were included. Follow-up included prostate biopsy or stability of prostate-specific antigen (PSA) for 1 year. FIELD STRENGTH AND SEQUENCES/UNASSIGNED:. ASSESSMENT/RESULTS:CL-bpMRI and DL-bpMRI including the same conventional diffusion-weighted imaging (DWI) were presented to three radiologists (blinded to acquisition method) and to a deep learning computer-assisted detection algorithm (DL-CAD). The readers evaluated image quality using a 4-point Likert scale (1 = nondiagnostic, 4 = excellent) and graded lesions using Prostate Imaging Reporting and Data System (PI-RADS) v2.1. DL-CAD identified and assigned lesions of PI-RADS 3 or greater. STATISTICAL TESTS/METHODS:Quality metrics were compared using Wilcoxon signed rank test, and area under the receiver operating characteristic curve (AUC) were compared using Delong's test. SIGNIFICANCE/CONCLUSIONS:P = 0.05. RESULTS:Eighty men were included (age: 66 ± 9 years; 17/80 clinically significant prostate cancer). Overall image quality results by the three readers (CL-T2, DL-T2) are reader 1: 3.72 ± 0.53, 3.89 ± 0.39 (P = 0.99); reader 2: 3.33 ± 0.82, 3.31 ± 0.74 (P = 0.49); reader 3: 3.67 ± 0.63, 3.51 ± 0.62. In the patient-based analysis, the reader results of AUC are (CL-bpMRI, DL-bpMRI): reader 1: 0.77, 0.78 (P = 0.98), reader 2: 0.65, 0.66 (P = 0.99), reader 3: 0.57, 0.60 (P = 0.52). Diagnostic statistics from DL-CAD (CL-bpMRI, DL-bpMRI) are sensitivity (0.71, 0.71, P = 1.00), specificity (0.59, 0.44, P = 0.05), positive predictive value (0.23, 0.24, P = 0.25), negative predictive value (0.88, 0.88, P = 0.48). CONCLUSION/CONCLUSIONS:Deep learning-accelerated T2-weighted imaging may potentially be used to decrease acquisition time for bpMRI. EVIDENCE LEVEL/METHODS:3. TECHNICAL EFFICACY/UNASSIGNED:Stage 2.

Uterine sarcomas are a group of rare uterine tumors comprised of multiple subtypes with different histologic characteristics, prognoses, and imaging appearances. Identification of uterine sarcomas and their differentiation from benign uterine disease on imaging is of critical importance for treatment planning to guide appropriate management and optimize patient outcomes. Herein, we review the spectrum of uterine sarcomas with a focus on the classification of primary sarcoma subtypes and presenting the typical MR imaging appearances.

PURPOSE/OBJECTIVE:To compare terminal ileum (TI) mucosal iodine density obtained at dual-energy CT enterography (DECTE) with conventional CT interpretation and endoscopy in patients with Crohn's disease (CD). MATERIALS AND METHODS/METHODS:) from the distal 2 cm ileum (TI) mucosa obtained using semiautomatic prototype software were compared with endoscopic assessment using Mann Whitney tests. The optimal threshold I% and I were determined from receiver operating curves (ROC). Sensitivity and specificity of conventional interpretation and determined iodine thresholds were compared using McNemar's test. Inter-reader agreement was assessed using kappa. A p < 0.05 indicated statistical significance. RESULTS:was similar for patients with and without endoscopic active inflammation (0.82[0.33]mg/mL and 0.77[0.28]mg/mL, respectively, p = 0.37). Conventional interpretation sensitivity and specificity (R1/R2) were 83.3%/91.7% and 72.7%/54.5%, respectively (all p > 0.05) with moderate inter-reader agreement (Κ = 0.542[95% CI 0.0202-0.088]). CONCLUSION/CONCLUSIONS:Mean normalized iodine density is highly sensitive and specific for endoscopic active inflammation. DECTE could be considered as a surrogate to endoscopy in CD patients. Despite trends towards improved sensitivity and specificity compared with conventional interpretation, future larger studies are needed.

RATIONALE AND OBJECTIVES/OBJECTIVE:To date, no clinically useful classification system has been developed for reliably differentiating mucinous cystic neoplasm (MCN) from a benign hepatic cyst (BHC) in the liver. The objective was to use machine learning and a multi-center study design to develop and assess the performance of a novel classification system for predicting whether a hepatic cystic lesion represents MCN or BHC. MATERIALS AND METHODS/METHODS:A multi-center cohort study identified 154 surgically resected hepatic cystic lesions in 154 subjects which were pathologic confirmed as MCN (43) or BHC (111). Readers at each institution recorded seven pre-determined imaging features previously identified as potential differentiating features from prior publications. The contribution of each of these features to differentiating MCN from BHC was assessed by machine learning to develop an optimal classification system. RESULTS:Although several of the assessed imaging features demonstrated statistical significance, only 3 imaging features were found by machine learning to significantly contribute to a potential classification system: (1) solid enhancing nodule (2) all septations arising from an external macro-lobulation (3) whether the lesion was solitary or one of multiple cystic liver lesions. The optimal classification system had only four categories and correctly identified 144/154 lesion (93.5%). CONCLUSION/CONCLUSIONS:This multi-center follow-up study was able to use machine learning to develop a highly accurate classification system for differentiation of hepatic MCN from BHC, which could be readily applied to clinical practice.

BACKGROUND:Early diagnosis and treatment of prostate cancer (PCa) can be curative; however, prostate-specific antigen is a suboptimal screening test for clinically significant PCa. While prostate magnetic resonance imaging (MRI) has demonstrated value for the diagnosis of PCa, the acquisition time is too long for a first-line screening modality. PURPOSE/OBJECTIVE:To accelerate prostate MRI exams, utilizing a variational network (VN) for image reconstruction. STUDY TYPE/METHODS:Retrospective. SUBJECTS/METHODS:One hundred and thirteen subjects (train/val/test: 70/13/30) undergoing prostate MRI. FIELD STRENGTH/SEQUENCE/UNASSIGNED:3.0 T; a T2 turbo spin echo (TSE) T2-weighted image (T2WI) sequence in axial and coronal planes, and axial echo-planar diffusion-weighted imaging (DWI). ASSESSMENT/RESULTS:, and apparent diffusion coefficient map-according to the Prostate Imaging Reporting and Data System (PI-RADS v2.1), for both VN and standard reconstructions. Accuracy of PI-RADS ≥3 for clinically significant cancer was computed. Projected scan time of the retrospectively under-sampled biparametric exam was also computed. STATISTICAL TESTS/UNASSIGNED:One-sided Wilcoxon signed-rank test was used for comparison of image quality. Sensitivity, specificity, positive predictive value, and negative predictive value were calculated for lesion detection and grading. Generalized estimating equation with cluster effect was used to compare differences between standard and VN bp-MRI. A P-value of <0.05 was considered statistically significant. RESULTS:(Reader 1: 3.20 ± 0.70 (Standard), 3.40 ± 0.75 (VN) P = 0.98; Reader 2: 2.85 ± 0.81 (Standard), 3.00 ± 0.79 (VN) P = 0.93; Reader 3: 4.45 ± 0.72 (Standard), 4.05 ± 0.69 (VN) P = 0.02; Reader 4: 4.50 ± 0.69 (Standard), 4.45 ± 0.76 (VN) P = 0.50). In the lesion evaluation study, there was no significant difference in the number of PI-RADS ≥3 lesions identified on standard vs. VN bp-MRI (P = 0.92, 0.59, 0.87) with similar sensitivity and specificity for clinically significant cancer. The average scan time of the standard clinical biparametric exam was 11.8 minutes, and this was projected to be 3.2 minutes for the accelerated exam. DATA CONCLUSION/UNASSIGNED:Diagnostic accelerated biparametric prostate MRI exams can be performed using deep learning methods in <4 minutes, potentially enabling rapid screening prostate MRI. LEVEL OF EVIDENCE/METHODS:3 TECHNICAL EFFICACY: Stage 5.