Faculty Bibliography

OBJECTIVE:To compare the image quality of an accelerated single-shot T2-weighted fat-suppressed (FS) MRI of the liver with deep learning-based image reconstruction (DL HASTE-FS) with conventional T2-weighted FS sequence (conventional T2 FS) at 1.5 T. METHODS:One hundred consecutive patients who underwent clinical MRI of the liver at 1.5 T including the conventional T2-weighted fat-suppressed sequence (T2 FS) and accelerated single-shot T2-weighted MRI of the liver with deep learning-based image reconstruction (DL HASTE-FS) were included. Images were reviewed independently by three blinded observers who used a 5-point confidence scale for multiple measures regarding the artifacts and image quality. Descriptive statistics and McNemar's test were used to compare image quality scores and percentage of lesions detected by each sequence, respectively. Intra-class correlation coefficient (ICC) was used to assess consistency in reader scores. RESULTS:Acquisition time for DL HASTE-FS was 51.23 +/ 10.1 s, significantly (p < 0.001) shorter than conventional T2-FS (178.9 ± 85.3 s). DL HASTE-FS received significantly higher scores than conventional T2-FS for strength and homogeneity of fat suppression; sharpness of liver margin; sharpness of intra-hepatic vessel margin; in-plane and through-plane respiratory motion; other ghosting artefacts; liver-fat contrast; and overall image quality (all, p < 0.0001). DL HASTE-FS also received higher scores for lesion conspicuity and sharpness of lesion margin (all, p < .001), without significant difference for liver lesion contrast (p > 0.05). CONCLUSIONS:Accelerated single-shot T2-weighted MRI of the liver with deep learning-based image reconstruction showed superior image quality compared to the conventional T2-weighted fat-suppressed sequence despite a 4-fold reduction in acquisition time. KEY POINTS/CONCLUSIONS:• Conventional fat-suppressed T2-weighted sequence (conventional T2 FS) can take unacceptably long to acquire and is the most commonly repeated sequence in liver MRI due to motion. • DL HASTE-FS demonstrated superior image quality, improved respiratory motion and other ghosting artefacts, and increased lesion conspicuity with comparable liver-to-lesion contrast compared to conventional T2FS sequence. • DL HASTE- FS has the potential to replace conventional T2 FS sequence in routine clinical MRI of the liver, reducing the scan time, and improving the image quality.

OBJECTIVE:The aim of this study was to evaluate the effect of a deep learning based computer-aided diagnosis (DL-CAD) system on radiologists' interpretation accuracy and efficiency in reading biparametric prostate magnetic resonance imaging scans. MATERIALS AND METHODS/METHODS:We selected 100 consecutive prostate magnetic resonance imaging cases from a publicly available data set (PROSTATEx Challenge) with and without histopathologically confirmed prostate cancer. Seven board-certified radiologists were tasked to read each case twice in 2 reading blocks (with and without the assistance of a DL-CAD), with a separation between the 2 reading sessions of at least 2 weeks. Reading tasks were to localize and classify lesions according to Prostate Imaging Reporting and Data System (PI-RADS) v2.0 and to assign a radiologist's level of suspicion score (scale from 1-5 in 0.5 increments; 1, benign; 5, malignant). Ground truth was established by consensus readings of 3 experienced radiologists. The detection performance (receiver operating characteristic curves), variability (Fleiss κ), and average reading time without DL-CAD assistance were evaluated. RESULTS:The average accuracy of radiologists in terms of area under the curve in detecting clinically significant cases (PI-RADS ≥4) was 0.84 (95% confidence interval [CI], 0.79-0.89), whereas the same using DL-CAD was 0.88 (95% CI, 0.83-0.94) with an improvement of 4.4% (95% CI, 1.1%-7.7%; P = 0.010). Interreader concordance (in terms of Fleiss κ) increased from 0.22 to 0.36 (P = 0.003). Accuracy of radiologists in detecting cases with PI-RADS ≥3 was improved by 2.9% (P = 0.10). The median reading time in the unaided/aided scenario was reduced by 21% from 103 to 81 seconds (P < 0.001). CONCLUSIONS:Using a DL-CAD system increased the diagnostic accuracy in detecting highly suspicious prostate lesions and reduced both the interreader variability and the reading time.

Background: Multiple commercial and open-source software applications are available for texture analysis. Nonstandard techniques can cause undesirable variability that impedes result reproducibility and limits clinical utility. Objective: The purpose of this study is to measure agreement of texture metrics extracted by 6 software packages. Methods: This retrospective study included 40 renal cell carcinomas with contrast-enhanced CT from The Cancer Genome Atlas and Imaging Archive. Images were analyzed by 7 readers at 6 sites. Each reader used 1 of 6 software packages to extract commonly studied texture features. Inter and intra-reader agreement for segmentation was assessed with intra-class correlation coefficients. First-order (available in 6 packages) and second-order (available in 3 packages) texture features were compared between software pairs using Pearson correlation. Results: Inter- and intra-reader agreement was excellent (ICC 0.93-1). First-order feature correlations were strong (r>0.8, p<0.001) between 75% (21/28) of software pairs for mean and standard deviation, 48% (10/21) for entropy, 29% (8/28) for skewness, and 25% (7/28) for kurtosis. Of 15 second-order features, only co-occurrence matrix correlation, grey-level non-uniformity, and run-length non-uniformity showed strong correlation between software packages (0.90-1, p<0.001). Conclusion: Variability in first and second order texture features was common across software configurations and produced inconsistent results. Standardized algorithms and reporting methods are needed before texture data can be reliably used for clinical applications. Clinical Impact: It is important to be aware of variability related to texture software processing and configuration when reporting and comparing outputs.

PURPOSE/OBJECTIVE:To determine whether multi-parametric magnetic resonance imaging (mpMRI) can reliably predict proximity of prostate cancer to the prostatic urethra in a contemporary series of men undergoing radical prostatectomy (RP) at two academic centers. METHODS:Clinical characteristics of consecutive men undergoing pre-operative mpMRI prior to RP and whole-mount axial serial step-sectioned pathology examination at two academic centers between Jun 2016 and Oct 2018 were analyzed retrospectively. Every tumor was characterized by its pathologic minimum distance to the prostatic urethral lumen (pMDUL). Only the cancer closest to the urethra represented the prostatic urethral index lesion. The radiologic minimum distance of the index lesion to the prostatic urethral lumen was measured and noted as ≤ 5 mm versus  > 5 mm. The sensitivity, specificity, positive and negative predicting values (PPV and NPV) and area under the receivers operating characteristics curve (AUC) were calculated for performance of mpMRI for predicting pMDUL ≤ 5 mm. RESULTS:Of the 163 surgical specimens examined, 112 (69%) exhibited a pMDUL ≤ 5 mm. These men had significantly higher grade group (GG) and advanced pathological and clinical stage. The rates of high PI-RADS score and presence of gross extracapsular extension were also significantly greater for the group with pMDUL ≤ 5 mm. The AUC, sensitivity, specificity, PPV, and NPV were 0.641, 51.8, 76.5, 82.9, and 42.4%, respectively, for mpMRI to predict pMDUL < 5 mm. CONCLUSIONS:Nearly 70% of men undergoing RP present with tumor within 5 mm of the prostatic urethra. These tumors present higher risk characteristics, and mpMRI exhibited moderate performance and high PPV in their pre-operative detection. Physicians performing partial gland ablation should take these results into consideration during treatment selection and planning.

PURPOSE/OBJECTIVE:With clinical volumes decreased, radiologists volunteered to participate virtually in daily clinical rounds and provide communication between frontline physicians and patients with coronavirus disease 2019 (COVID-19) and their families affected by restrictive hospital visitation policies. The purpose of this survey-based assessment was to demonstrate the beneficial effects of radiologist engagement during this pandemic and potentially in future crises if needed. METHODS:After the program's completion, a survey consisting of 13 multiple-choice and open-ended questions was distributed to the 69 radiologists who volunteered for a minimum of 7 days. The survey focused on how the experience would change future practice, the nature of interaction with medical students, and the motivation for volunteering. The electronic medical record system identified the patients who tested positive for or were suspected of having COVID-19 and the number of notes documenting family communication. RESULTS:In all, 69 radiologists signed or cosigned 7,027 notes. Of the 69 radiologists, 60 (87.0%) responded to the survey. All found the experience increased their understanding of COVID-19 and its effect on the health care system. Overall, 59.6% agreed that participation would result in future change in communication with patients and their families. Nearly all (98.1%) who worked with medical students agreed that their experience with medical students was rewarding. A majority (82.7%) chose to participate as a way to provide service to the patient population. CONCLUSION/CONCLUSIONS:This program provided support to frontline inpatient teams while also positively affecting the radiologist participants. If a similar situation arises in the future, this communication tool could be redeployed, especially with the collaboration of medical students.

OBJECTIVES/OBJECTIVE:To assess the inter-reader variability in response assessment for HCC treated with radioembolization (TARE) compared with 3D quantitative criteria (qEASL); and to evaluate their role in prediction of pathological necrosis and clinical outcomes. MATERIALS AND METHODS/METHODS:57 patients with 77 HCCs who underwent TARE were included. Five radiologists recorded multiple imaging features and assigned mRECIST/LIRADS Treatment Response (TR) categories on post-treatment MRI at 4-6 weeks and 6-9 months after TARE. qEASL categories were assigned by a separate reader. Inter-reader variability between LIRADS TR/mRECIST/qEASL were evaluated and hazards regression was used in predicting clinical outcomes. RESULTS:Inter-reader agreement was fair for mRECIST (K = 0.43 and 0.34 at first and second follow-up respectively); moderate for LIRADS TR (K = 0.48 and 0.53 at first and second follow-up respectively). Inter-criterion agreement was moderate to substantial (r = 0.41-0.65 and r = 0.54-0.60 at first and second follow-up) for mRECIST-qEASL. LIRADS TR correlated well with qEASL for all readers at both follow-ups (K = 0.45-0.78; K = 0.39-0.77 for first and second follow-up). qEASL was the most accurate in predicting Tumor-Free Survival (TFS) on first (HR 2.23 [1.44-3.46], p < 0.001) and second (HR 1.69 [1.15-2.48], p = 0.008) follow-up. LIRADS TR was the most accurate in predicting histopathological necrosis (8 patients underwent liver transplantation and 1 patient underwent tumor resection during the period of the study). CONCLUSIONS:HCC response assessment following TARE is challenging, resulting in poor to moderate inter-reader agreement for mRECIST, and moderate inter-reader agreement for LIRADS TR response assessment criteria. qEASL outperformed mRECIST criteria for early identification of responders and predicting TFS, suggesting an advantage in volumetric tumor response assessment. LIRADS TR outperformed other criteria in predicting pathological necrosis.

Endometriosis is a common gynecologic disorder characterized by the presence of ectopic endometrial tissue outside the endometrial cavity. Magnetic Resonance Imaging (MRI) has become a mainstay for diagnosis and staging of this disease. In the literature, significant heterogeneity exists in the descriptions of imaging findings and anatomic sites of involvement. The Society of Abdominal Radiology's Endometriosis Disease-Focused Panel presents this consensus document to establish an MRI lexicon for endometriosis MRI evaluation and anatomic localization.

Endometriosis is a common entity causing chronic pain and infertility in women. The gold standard method for diagnosis is diagnostic laparoscopy, which is invasive and costly. MRI has shown promise in its ability to diagnose endometriosis and its efficacy for preoperative planning. The Society of Abdominal Radiology established a Disease-Focused Panel (DFP) to improve patient care for patients with endometriosis. In this article, the DFP performs a literature review and uses its own experience to provide technical recommendations on optimizing MRI Pelvis for the evaluation of endometriosis.

The role of the radiologist in the diagnosis and management of patients with endometriosis is increasing. Improvement in MRI imaging techniques has improved detection rate of subtle manifestations of endometriosis by radiologists. Therefore, the role of imaging in the diagnosis and follow-up after treatment is also likely to increase. Knowledge of new medical management pathways used in treating patients with endometriosis-related pain is important. The knowledge of various medication regimens will allow radiologists to continue to evaluate baseline disease, and to potentially assess for imaging response/stability to these medications. This article will review the current medical therapies in use in the management of endometriosis-related pain and describe potential imaging-related findings expected with these therapies.