Imaging cholangiocarcinoma: CT and MRI techniques.

Cross-sectional imaging plays a crucial role in the detection, diagnosis, staging, and resectability assessment of intra- and extrahepatic cholangiocarcinoma. Despite this vital function, there is a lack of standardized CT and MRI protocol recommendations for imaging cholangiocarcinoma, with substantial differences in image acquisition across institutions and vendor platforms. In this review, we present standardized strategies for the optimal imaging assessment of cholangiocarcinoma including contrast media considerations, patient preparation recommendations, optimal contrast timing, and representative CT and MRI protocols with individual sequence optimization recommendations. Our recommendations are supported by expert opinion from members of the Society of Abdominal Radiology's Disease-Focused Panel (DFP) on Cholangiocarcinoma, encompassing a broad array of institutions and practice patterns.

Temporal Trends in Body Composition and Metabolic Markers Prior to Diagnosis of Pancreatic Ductal Adenocarcinoma.

BACKGROUND & AIMS: Changes in body composition and metabolic factors may serve as biomarkers for the early detection of pancreatic ductal adenocarcinoma (PDAC). The aim of this study was to capture the longitudinal changes in body composition and metabolic factors before diagnosis of PDAC. METHODS: We performed a retrospective cohort study in which all patients (≥18 years) diagnosed with PDAC from 2002 to 2021 were identified. We collected all abdominal computed tomography scans and 10 different blood-based biomarkers up to 36 months before diagnosis. We applied a fully automated abdominal segmentation algorithm previously developed by our group for 3-dimensional quantification of body composition on computed tomography scans. Longitudinal trends of body composition and blood-based biomarkers before PDAC diagnosis were estimated using linear mixed models, compared across different time windows, and visualized using spline regression. RESULTS: We included 1690 patients in body composition analysis, of whom 516 (30.5%) had ≥2 prediagnostic computed tomography scans. For analysis of longitudinal trends of blood-based biomarkers, 3332 individuals were included. As an early manifestation of PDAC, we observed a significant decrease in visceral and subcutaneous adipose tissue (ß = -1.94 [95% confidence interval (CI), -2.39 to -1.48] and ß = -2.59 [95% CI, -3.17 to -2.02]) in area (cm2)/height (m2) per 6 months closer to diagnosis, accompanied by a decrease in serum lipids (eg, low-density lipoprotein [ß = -2.83; 95% CI, -3.31 to -2.34], total cholesterol [ß = -2.69; 95% CI, -3.18 to -2.20], and triglycerides [ß = -1.86; 95% CI, -2.61 to -1.11]), and an increase in blood glucose levels. Loss of muscle tissue and bone volume was predominantly observed in the last 6 months before diagnosis. CONCLUSIONS: This study identified significant alterations in a variety of soft tissue and metabolic markers that occur in the development of PDAC. Early recognition of these metabolic changes may provide an opportunity for early detection.

Primary Carcinoid Tumor of the Ovary: A Case Report With Radiologic and Pathologic Correlation.

Ovarian carcinoid tumors are very rare entities that often mimic other ovarian neoplasms. A case of primary ovarian carcinoid in a 44-year-old woman is presented with emphasis on the magnetic resonance imaging (MRI) features of the tumor and pathologic correlation. Ovarian carcinoid tumors can be variable in their MRI appearance, presumably due to different tumor subtypes and tumor components, thus requiring pathologic diagnosis. It is imperative to accurately diagnose primary ovarian carcinoid tumors, as their prognosis is usually more favorable compared to other malignant ovarian neoplasms.

Cancer Detection Rate and Abnormal Interpretation Rate of Prostate MRI Performed for Clinical Suspicion of Prostate Cancer.

PURPOSE: To report cancer detection rate (CDR) and abnormal interpretation rate (AIR) in prostate MRI performed for clinical suspicion of prostate cancer (PCa). MATERIALS AND METHODS: This retrospective single-institution, three-center study included patients who underwent MRI for clinical suspicion of PCa between 2017 and 2021. Patients with known PCa were excluded. Patient-level Prostate Imaging-Reporting and Data System (PI-RADS) score was extracted from the radiology report. AIR was defined as number of abnormal MRI (PI-RADS score 3-5) / total number of MRIs. CDR was defined as number of clinically significant PCa (csPCa: Gleason score ≥7) detected at abnormal MRI / total number of MRI. AIR, CDR, and CDR adjusted for pathology confirmation rate were calculated for each of three centers and pre-MRI biopsy status (biopsy-naive and previous negative biopsy). RESULTS: A total of 9,686 examinations (8,643 unique patients) were included. AIR, CDR, and CDR adjusted for pathology confirmation rate were 45.4%, 23.8%, and 27.6% for center I; 47.2%, 20.0%, and 22.8% for center II; and 42.3%, 27.2%, and 30.1% for center III, respectively. Pathology confirmation rate ranged from 81.6% to 88.0% across three centers. AIR and CDR for biopsy-naive patients were 45.5% to 52.6% and 24.2% to 33.5% across three centers, respectively, and those for previous negative biopsy were 27.2% to 39.8% and 11.7% to 14.2% across three centers, respectively. CONCLUSION: We reported CDR and AIR in prostate MRI for clinical suspicion of PCa. CDR needs to be adjusted for pathology confirmation rate and pre-MRI biopsy status for interfacility comparison.

Cancer Detection Rate and Abnormal Interpretation Rate of Prostate MRI in Patients With Low-Grade Cancer.

PURPOSE: The aim of this study was to evaluate the utility of cancer detection rate (CDR) and abnormal interpretation rate (AIR) in prostate MRI for patients with low-grade prostate cancer (PCa). METHODS: This three-center retrospective study included patients who underwent prostate MRI from 2017 to 2021 with known low-grade PCa (Gleason score 6) without prior treatment. Patient-level highest Prostate Imaging Reporting & Data System (PI-RADS®) score and pathologic diagnosis within 1 year after MRI were used to evaluate the diagnostic performance of prostate MRI in detecting clinically significant PCa (csPCa; Gleason score ≥ 7). The metrics AIR, CDR, and CDR adjusted for pathologic confirmation rate were calculated. Radiologist-level AIR-CDR plots were shown. Simulation AIR-CDR lines were created to assess the effects of different diagnostic performances of prostate MRI and the prevalence of csPCa. RESULTS: A total of 3,207 examinations were interpreted by 33 radiologists. Overall AIR, CDR, and CDR adjusted for pathologic confirmation rate at PI-RADS 3 to 5 (PI-RADS 4 and 5) were 51.7% (36.5%), 22.1% (18.8%), and 30.7% (24.6%), respectively. Radiologist-level AIR and CDR at PI-RADS 3 to 5 (PI-RADS 4 and 5) were in the 36.8% to 75.6% (21.9%-57.5%) range and the 16.3%-28.7% (10.9%-26.5%) range, respectively. In the simulation, changing parameters of diagnostic performance or csPCa prevalence shifted the AIR-CDR line. CONCLUSIONS: The authors propose CDR and AIR as performance metrics in prostate MRI and report reference performance values in patients with known low-grade PCa. There was variability in radiologist-level AIR and CDR. Combined use of AIR and CDR could provide meaningful feedback for radiologists to improve their performance by showing relative performance to other radiologists.

A narrative review of imaging for pancreas adenocarcinoma: staging, surgical considerations, and surveillance.

Background and Objective: Pancreas adenocarcinoma is a disease with dire prognosis. Imaging is pivotal to the diagnosis, staging, reassessment, surgical planning, and surveillance of pancreas cancer. The purpose of this paper is to provide the reader an overview of current imaging practices for pancreas adenocarcinoma. Methods: A literature search of original papers and reviews through 2022 was performed using the PubMed database. The most current American College of Radiology Appropriateness Criteria and National Comprehensive Cancer Network guidelines on pancreas cancer imaging were also included. Key Content and Findings: Multidisciplinary team care at a high-volume institution is instrumental to optimal patient management and outcomes. It is therefore important for all team members to be aware of imaging modality options, strengths, and challenges. Additionally, a high-level understanding of imaging findings is useful clinically. This manuscript provides a current overview of imaging modalities used in the identification and assessment of pancreas adenocarcinoma, including ultrasound, computed tomography, magnetic resonance imaging, and positron emission tomography. Imaging findings, including the expected and unexpected, are reviewed to give the novice imager a better understanding. Conclusions: This review provides a current overview of imaging for pancreas adenocarcinoma, including strengths and weakness of various imaging modalities; therefore, providing the reader with a robust resource when considering imaging in the management of this disease.

Automated Artificial Intelligence Model Trained on a Large Data Set Can Detect Pancreas Cancer on Diagnostic Computed Tomography Scans As Well As Visually Occult Preinvasive Cancer on Prediagnostic Computed Tomography Scans.

BACKGROUND & AIMS: The aims of our case-control study were (1) to develop an automated 3-dimensional (3D) Convolutional Neural Network (CNN) for detection of pancreatic ductal adenocarcinoma (PDA) on diagnostic computed tomography scans (CTs), (2) evaluate its generalizability on multi-institutional public data sets, (3) its utility as a potential screening tool using a simulated cohort with high pretest probability, and (4) its ability to detect visually occult preinvasive cancer on prediagnostic CTs. METHODS: A 3D-CNN classification system was trained using algorithmically generated bounding boxes and pancreatic masks on a curated data set of 696 portal phase diagnostic CTs with PDA and 1080 control images with a nonneoplastic pancreas. The model was evaluated on (1) an intramural hold-out test subset (409 CTs with PDA, 829 controls); (2) a simulated cohort with a case-control distribution that matched the risk of PDA in glycemically defined new-onset diabetes, and Enriching New-Onset Diabetes for Pancreatic Cancer score ≥3; (3) multi-institutional public data sets (194 CTs with PDA, 80 controls), and (4) a cohort of 100 prediagnostic CTs (i.e., CTs incidentally acquired 3-36 months before clinical diagnosis of PDA) without a focal mass, and 134 controls. RESULTS: Of the CTs in the intramural test subset, 798 (64%) were from other hospitals. The model correctly classified 360 CTs (88%) with PDA and 783 control CTs (94%), with a mean accuracy 0.92 (95% CI, 0.91-0.94), area under the receiver operating characteristic (AUROC) curve of 0.97 (95% CI, 0.96-0.98), sensitivity of 0.88 (95% CI, 0.85-0.91), and specificity of 0.95 (95% CI, 0.93-0.96). Activation areas on heat maps overlapped with the tumor in 350 of 360 CTs (97%). Performance was high across tumor stages (sensitivity of 0.80, 0.87, 0.95, and 1.0 on T1 through T4 stages, respectively), comparable for hypodense vs isodense tumors (sensitivity: 0.90 vs 0.82), different age, sex, CT slice thicknesses, and vendors (all P > .05), and generalizable on both the simulated cohort (accuracy, 0.95 [95% 0.94-0.95]; AUROC curve, 0.97 [95% CI, 0.94-0.99]) and public data sets (accuracy, 0.86 [95% CI, 0.82-0.90]; AUROC curve, 0.90 [95% CI, 0.86-0.95]). Despite being exclusively trained on diagnostic CTs with larger tumors, the model could detect occult PDA on prediagnostic CTs (accuracy, 0.84 [95% CI, 0.79-0.88]; AUROC curve, 0.91 [95% CI, 0.86-0.94]; sensitivity, 0.75 [95% CI, 0.67-0.84]; and specificity, 0.90 [95% CI, 0.85-0.95]) at a median 475 days (range, 93-1082 days) before clinical diagnosis. CONCLUSIONS: This automated artificial intelligence model trained on a large and diverse data set shows high accuracy and generalizable performance for detection of PDA on diagnostic CTs as well as for visually occult PDA on prediagnostic CTs. Prospective validation with blood-based biomarkers is warranted to assess the potential for early detection of sporadic PDA in high-risk individuals.

Chemotherapy-associated liver morphological changes in hepatic metastases (CALMCHeM).

PURPOSE: To review imaging findings in chemotherapy-associated liver morphological changes in hepatic metastases (CALMCHeM) on computed tomography (CT)/magnetic resonance imaging (MRI) and its association with tumor burden. METHODS: We performed a retrospective chart review to identify patients with hepatic metastases who received chemotherapy and subsequent follow-up imaging where CT or MRI showed morphological changes in the liver. The morphological changes searched for were nodularity, capsular retraction, hypodense fibrotic bands, lobulated outline, atrophy or hypertrophy of segments or lobes, widened fissures, and one or more features of portal hypertension (splenomegaly/venous collaterals/ascites). The inclusion criteria were as follows: a) no known chronic liver disease; b) availability of CT or MRI images before chemotherapy that showed no morphological signs of chronic liver disease; c) at least one follow-up CT or MRI image demonstrating CALMCHeM after chemotherapy. Two radiologists in consensus graded the initial hepatic metastases tumor burden according to number (≤10 and >10), lobe distribution (single or both lobes), and liver parenchyma volume affected (<50%, or ≥50%). Imaging features after treatment were graded according to a pre-defined qualitative assessment scale of "normal," "mild," "moderate," or "severe." Descriptive statistics were performed with binary groups based on the number, lobar distribution, type, and volume of the liver affected. Chi-square and t-tests were used for comparative statistics. The Cox proportional hazard model was used to determine the association between severe CALMCHeM changes and age, sex, tumor burden, and primary carcinoma type. RESULTS: A total of 219 patients met the inclusion criteria. The most common primaries were from breast (58.4%), colorectal (14.2%), and neuroendocrine (11.0%) carcinomas. Hepatic metastases were discrete in 54.8% of cases, confluent in 38.8%, and diffuse in 6.4%. The number of metastases was >10 in 64.4% of patients. The volume of liver involved was <50% in 79.8% and ≥50% in 20.2% of cases. The severity of CALMCHeM at the first imaging follow-up was associated with a larger number of metastases (P = 0.002) and volume of the liver affected (P = 0.015). The severity of CALMCHeM had progressed to moderate to severe changes in 85.9% of patients, and 72.5% of patients had one or more features of portal hypertension at the last follow-up. The most common features at the final follow-up were nodularity (95.0%), capsular retraction (93.4%), atrophy (66.2%), and ascites (65.7%). The Cox proportional hazard model showed metastases affected ≥50% of the liver (P = 0.033), and the female gender (P = 0.004) was independently associated with severe CALMCHeM. CONCLUSION: CALMCHeM can be observed with a wide variety of malignancies, is progressive in severity, and the severity correlates with the initial metastatic liver disease burden.

Bounding box-based 3D AI model for user-guided volumetric segmentation of pancreatic ductal adenocarcinoma on standard-of-care CTs.

OBJECTIVES: To develop a bounding-box-based 3D convolutional neural network (CNN) for user-guided volumetric pancreas ductal adenocarcinoma (PDA) segmentation. METHODS: Reference segmentations were obtained on CTs (2006-2020) of treatment-naïve PDA. Images were algorithmically cropped using a tumor-centered bounding box for training a 3D nnUNet-based-CNN. Three radiologists independently segmented tumors on test subset, which were combined with reference segmentations using STAPLE to derive composite segmentations. Generalizability was evaluated on Cancer Imaging Archive (TCIA) (n = 41) and Medical Segmentation Decathlon (MSD) (n = 152) datasets. RESULTS: Total 1151 patients [667 males; age:65.3 ± 10.2 years; T1:34, T2:477, T3:237, T4:403; mean (range) tumor diameter:4.34 (1.1-12.6)-cm] were randomly divided between training/validation (n = 921) and test subsets (n = 230; 75% from other institutions). Model had a high DSC (mean ± SD) against reference segmentations (0.84 ± 0.06), which was comparable to its DSC against composite segmentations (0.84 ± 0.11, p = 0.52). Model-predicted versus reference tumor volumes were comparable (mean ± SD) (29.1 ± 42.2-cc versus 27.1 ± 32.9-cc, p = 0.69, CCC = 0.93). Inter-reader variability was high (mean DSC 0.69 ± 0.16), especially for smaller and isodense tumors. Conversely, model's high performance was comparable between tumor stages, volumes and densities (p > 0.05). Model was resilient to different tumor locations, status of pancreatic/biliary ducts, pancreatic atrophy, CT vendors and slice thicknesses, as well as to the epicenter and dimensions of the bounding-box (p > 0.05). Performance was generalizable on MSD (DSC:0.82 ± 0.06) and TCIA datasets (DSC:0.84 ± 0.08). CONCLUSION: A computationally efficient bounding box-based AI model developed on a large and diverse dataset shows high accuracy, generalizability, and robustness to clinically encountered variations for user-guided volumetric PDA segmentation including for small and isodense tumors. CLINICAL RELEVANCE: AI-driven bounding box-based user-guided PDA segmentation offers a discovery tool for image-based multi-omics models for applications such as risk-stratification, treatment response assessment, and prognostication, which are urgently needed to customize treatment strategies to the unique biological profile of each patient's tumor.

Pancreatic Cystic Lesions: Next Generation of Radiologic Assessment.

Pancreatic cystic lesions are frequently identified on cross-sectional imaging. As many of these are presumed branch-duct intraductal papillary mucinous neoplasms, these lesions generate much anxiety for the patients and clinicians, often necessitating long-term follow-up imaging and even unnecessary surgical resections. However, the incidence of pancreatic cancer is overall low for patients with incidental pancreatic cystic lesions. Radiomics and deep learning are advanced tools of imaging analysis that have attracted much attention in addressing this unmet need, however, current publications on this topic show limited success and large-scale research is needed.

Clinical Implementation of an Artificial Intelligence Algorithm for Magnetic Resonance-Derived Measurement of Total Kidney Volume.

OBJECTIVE: To evaluate the performance of an internally developed and previously validated artificial intelligence (AI) algorithm for magnetic resonance (MR)-derived total kidney volume (TKV) in autosomal dominant polycystic kidney disease (ADPKD) when implemented in clinical practice. PATIENTS AND METHODS: The study included adult patients with ADPKD seen by a nephrologist at our institution between November 2019 and January 2021 and undergoing an MR imaging examination as part of standard clinical care. Thirty-three nephrologists ordered MR imaging, requesting AI-based TKV calculation for 170 cases in these 161 unique patients. We tracked implementation and performance of the algorithm over 1 year. A radiologist and a radiology technologist reviewed all cases (N=170) for quality and accuracy. Manual editing of algorithm output occurred at radiology or radiology technologist discretion. Performance was assessed by comparing AI-based and manually edited segmentations via measures of similarity and dissimilarity to ensure expected performance. We analyzed ADPKD severity class assignment of algorithm-derived vs manually edited TKV to assess impact. RESULTS: Clinical implementation was successful. Artificial intelligence algorithm-based segmentation showed high levels of agreement and was noninferior to interobserver variability and other methods for determining TKV. Of manually edited cases (n=84), the AI-algorithm TKV output showed a small mean volume difference of -3.3%. Agreement for disease class between AI-based and manually edited segmentation was high (five cases differed). CONCLUSION: Performance of an AI algorithm in real-life clinical practice can be preserved if there is careful development and validation and if the implementation environment closely matches the development conditions.

Preoperative Analysis of Venous Anatomy Before Deep Inferior Epigastric Perforator Free-Flap Breast Reconstruction Using Ferumoxytol-enhanced Magnetic Resonance Angiography.

BACKGROUND: Venous congestion after deep inferior epigastric artery perforator (DIEP) flap breast reconstruction is a complication that may be partially attributable to variations in venous abdominal wall anatomy. In previous work, we have shown that ferumoxytol may be used as a bloodpool contrast agent to perform high-resolution venous imaging. Our current aim was to use this technology to perform a detailed analysis of the venous anatomy among patients undergoing DIEP flap breast reconstruction. METHODS: All patients undergoing DIEP flap reconstruction with preoperative ferumoxytol-enhanced magnetic resonance angiography (FE-MRA) were retrospectively reviewed. A detailed anatomic analysis of each abdominal wall on FE-MRAwas performed before review of operative findings. Statistical analysis was used to determine venous characteristics associated with superficial inferior epigastric vein (SIEV) augmentation. RESULTS: From 2012 to 2016, 59 patients underwent preoperative FE-MRA. This resulted in imaging for 118 hemiabdomen and 99 flaps. Superficial-deep communication was identified in 117 of 118 hemiabdomen. Fifty (93%) of 59 patients had greater than 1-mm venous communication of the superficial system across midline. Reconstructed breasts were based on dominant medial row perforators in 82 (83%) of 99 flaps. The mean diameters of the SIEVand dominant venous perforator were 3.8 and 2.8mm, respectively. Anatomic characteristics associated with SIEVaugmentation included SIEVdiameter ( P = 0.01), dominant perforator diameter ( P = 0.04), and the ratio between these 2 variables ( P = 0.001). CONCLUSIONS: Ferumoxytol-enhanced magnetic resonance angiography provides excellent imaging of the venous system. Anatomic characteristics such as the diameter of the SIEVand the diameter of the dominant perforator may be useful in determining which flaps require venous augmentation using the SIEV.

Postoperative surveillance of pancreatic ductal adenocarcinoma (PDAC) recurrence: practice pattern on standardized imaging and reporting from the society of abdominal radiology disease focus panel on PDAC.

PURPOSE: Surgical resection is the only potential curative treatment for patients with pancreatic ductal adenocarcinoma (PDAC), but unfortunately most patients recur within 5 years of surgery. This article aims to assess the practice patterns across major academic institutions and develop consensus recommendations for postoperative imaging and interpretation in patients with PDAC. METHODS: The consensus recommendations for postoperative imaging surveillance following PDAC resection were developed using the Delphi method. Members of the Society of Abdominal Radiology (SAR) PDAC Disease Focused Panel (DFP) underwent three rounds of surveys followed by live webinar group discussions to develop consensus recommendations. RESULTS: Significant variations currently exist in the postoperative surveillance of PDAC, even among academic institutions. Differentiating common postoperative inflammatory and fibrotic changes from tumor recurrence remains a diagnostic challenge, and there is no reliable size threshold or growth rate of imaging findings that can provide differentiation. A new liver lesion or peritoneal nodule should be considered suspicious for tumor recurrence, and the imaging features should be interpreted in the appropriate clinical context (e.g., CA 19-9, clinical presentation, pathologic staging). CONCLUSION: Postoperative imaging following PDAC resection is challenging to interpret due to the presence of confounding postoperative inflammatory changes. A standardized reporting template for locoregional findings and report impression may improve communication of relaying risk of recurrence with referring providers, which merits validation in future studies.

Radiomics Boosts Deep Learning Model for IPMN Classification.

Intraductal Papillary Mucinous Neoplasm (IPMN) cysts are pre-malignant pancreas lesions, and they can progress into pancreatic cancer. Therefore, detecting and stratifying their risk level is of ultimate importance for effective treatment planning and disease control. However, this is a highly challenging task because of the diverse and irregular shape, texture, and size of the IPMN cysts as well as the pancreas. In this study, we propose a novel computer-aided diagnosis pipeline for IPMN risk classification from multi-contrast MRI scans. Our proposed analysis framework includes an efficient volumetric self-adapting segmentation strategy for pancreas delineation, followed by a newly designed deep learning-based classification scheme with a radiomics-based predictive approach. We test our proposed decision-fusion model in multi-center data sets of 246 multi-contrast MRI scans and obtain superior performance to the state of the art (SOTA) in this field. Our ablation studies demonstrate the significance of both radiomics and deep learning modules for achieving the new SOTA performance compared to international guidelines and published studies (81.9% vs 61.3% in accuracy). Our findings have important implications for clinical decision-making. In a series of rigorous experiments on multi-center data sets (246 MRI scans from five centers), we achieved unprecedented performance (81.9% accuracy). The code is available upon publication.

DCD Liver Grafts Can Safely Be Used for Recipients With Grade I-II Portal Vein Thrombosis: A Multicenter Analysis.

With donation after circulatory death (DCD) liver transplantation (LT), the goal of the recipient implantation procedure is to minimize surgical complexity to avoid a tenuous environment for an already marginal graft. The presence of portal vein thrombosis (PVT) at the time of LT adds surgical complexity, yet' to date, no studies have investigated the utilization of DCD liver grafts for patients with PVT. Methods: All DCD LT performed at Mayo Clinic-Florida, Mayo Clinic-Arizona, and Mayo Clinic-Rochester from 2006 to 2020 were reviewed (N = 771). Patients with PVT at the time of transplant were graded using Yerdel classification. A 1:3 propensity match between patients with PVT and those without PVT was performed. Results: A total of 91 (11.8%) patients with PVT undergoing DCD LT were identified. Grade I PVT was present in 62.6% of patients, grade II PVT in 27.5%, grade III in 8.8%, and grade 4 in 1.1%. At the time of LT, thromboendovenectomy was performed in 89 cases (97.8%). There was no difference in the rates of early allograft dysfunction (43.2% versus 52.4%; P = 0.13) or primary nonfunction (1.1% versus 1.1%; P = 0.41) between the DCD PVT and DCD without PVT groups, respectively. The rate of ischemic cholangiopathy was not significantly different between the DCD PVT (11.0%) and DCD without PVT groups (10.6%; P = 0.92). Graft (P = 0.58) and patient survival (P = 0.08) were similar between the 2 groups. Graft survival at 1-, 3-, and 5-y was 89.9%, 84.5%, and 79.3% in the DCD PVT group. Conclusions: In appropriately selected recipients with grades I-II PVT, DCD liver grafts can be utilized safely with excellent outcomes.

Prevalence, features, and explanations of missed and misinterpreted pancreatic cancer on imaging: a matched case-control study.

PURPOSE: To characterize the prevalence of missed pancreatic masses and pancreatic ductal adenocarcinoma (PDAC)-related findings on CT and MRI between pre-diagnostic patients and healthy individuals. MATERIALS AND METHODS: Patients diagnosed with PDAC (2010-2016) were retrospectively reviewed for abdominal CT- or MRI-examinations 1 month-3 years prior to their diagnosis, and subsequently matched to controls in a 1:4 ratio. Two blinded radiologists scored each imaging exam on the presence of a pancreatic mass and secondary features of PDAC. Additionally, original radiology reports were graded based on the revised RADPEER criteria. RESULTS: The cohort of 595 PDAC patients contained 60 patients with a pre-diagnostic CT and 27 with an MRI. A pancreatic mass was suspected in hindsight on CT in 51.7% and 50% of cases and in 1.3% and 0.9% of controls by reviewer 1 (p < .001) and reviewer 2 (p < .001), respectively. On MRI, a mass was suspected in 70.4% and 55.6% of cases and 2.9% and 0% of the controls by reviewer 1 (p < .001) and reviewer 2 (p < .001), respectively. Pancreatic duct dilation, duct interruption, focal atrophy, and features of acute pancreatitis is strongly associated with PDAC (p < .001). In cases, a RADPEER-score of 2 or 3 was assigned to 56.3% of the CT-reports and 71.4% of MRI-reports. CONCLUSION: Radiological features as pancreatic duct dilation and interruption, and focal atrophy are common first signs of PDAC and are often missed or unrecognized. Further investigation with dedicated pancreas imaging is warranted in patients with PDAC-related radiological findings.

Focal Nodular Hyperplasia and Focal Nodular Hyperplasia-like Lesions.

Focal nodular hyperplasia (FNH) is a benign lesion occurring in a background of normal liver. FNH is seen most commonly in young women and can often be accurately diagnosed at imaging, including CT, MRI, or contrast-enhanced US. In the normal liver, FNH frequently must be differentiated from hepatocellular adenoma, which although benign, is managed differently because of the risks of hemorrhage and malignant transformation. When lesions that are histologically identical to FNH occur in a background of abnormal liver, they are termed FNH-like lesions. These lesions can be a source of diagnostic confusion and must be differentiated from malignancies. Radiologists' familiarity with the imaging appearance of FNH-like lesions and knowledge of the conditions that predispose a patient to their formation are critical to minimizing the risks of unnecessary intervention for these lesions, which are rarely symptomatic and carry no risk for malignant transformation. FNH is thought to form secondary to an underlying vascular disturbance, a theory supported by the predilection for formation of FNH-like lesions in patients with a variety of hepatic vascular abnormalities. These include abnormalities of hepatic outflow such as Budd-Chiari syndrome, abnormalities of hepatic inflow such as congenital absence of the portal vein, and hepatic microvascular disturbances, such as those that occur after exposure to certain chemotherapeutic agents. Familiarity with the imaging appearances of these varied conditions and knowledge of their association with formation of FNH-like lesions allow radiologists to identify with confidence these benign lesions that require no intervention. Online supplemental material is available for this article. ©RSNA, 2022.

Extraskeletal Ewing Sarcoma from Head to Toe: Multimodality Imaging Review.

Extraskeletal Ewing sarcoma (EES) is a rare subtype in the Ewing sarcoma family of tumors (ESFT), which also includes Ewing sarcoma of bone (ESB) and, more recently, primitive neuroectodermal tumors. Although these tumors often have different manifestations, they are grouped on the basis of common genetic translocation and diagnosis from specific molecular and immunohistochemical features. While the large majority of ESFT cases occur in children and in bones, approximately 25% originate outside the skeleton as EES. Importantly, in the adult population these extraskeletal tumors are more common than ESB. Imaging findings of EES tumors are generally nonspecific, with some variation based on location and the tissues involved. A large tumor with central necrosis that does not cross the midline is typical. Despite often nonspecific findings, imaging plays an important role in the evaluation and management of ESFT, with MRI frequently the preferred imaging modality for primary tumor assessment and local staging. Chest CT and fluorine 18 fluorodeoxyglucose PET/CT are most sensitive for detecting lung and other distant or nodal metastases. Management often involves chemotherapy with local surgical excision, when possible. A multidisciplinary treatment approach should be used given the propensity for large tumor size and local invasion, which can make resection difficult. Despite limited data, outcomes are similar to those of other ESFT cases, with 5-year survival exceeding 80%. However, with metastatic disease, the long-term prognosis is poor. ©RSNA, 2022.

Impact of measurement method on interobserver variability of apparent diffusion coefficient of lesions in prostate MRI.

PURPOSE: To compare the inter-observer variability of apparent diffusion coefficient (ADC) values of prostate lesions measured by 2D-region of interest (ROI) with and without specific measurement instruction. METHODS: Forty lesions in 40 patients who underwent prostate MR followed by targeted prostate biopsy were evaluated. A multi-reader study (10 readers) was performed to assess the agreement of ADC values between 2D-ROI without specific instruction and 2D-ROI with specific instruction to place a 9-pixel size 2D-ROI covering the lowest ADC area. The computer script generated multiple overlapping 9-pixel 2D-ROIs within a 3D-ROI encompassing the entire lesion placed by a single reader. The lowest mean ADC values from each 2D-small-ROI were used as reference values. Inter-observer agreement was assessed using the Bland-Altman plot. Intraclass correlation coefficient (ICC) was assessed between ADC values measured by 10 readers and the computer-calculated reference values. RESULTS: Ten lesions were benign, 6 were Gleason score 6 prostate carcinoma (PCa), and 24 were clinically significant PCa. The mean±SD ADC reference value by 9-pixel-ROI was 733 ± 186 (10-6 mm2/s). The 95% limits of agreement of ADC values among readers were better with specific instruction (±112) than those without (±205). ICC between reader-measured ADC values and computer-calculated reference values ranged from 0.736-0.949 with specific instruction and 0.349-0.919 without specific instruction. CONCLUSION: Interobserver agreement of ADC values can be improved by indicating a measurement method (use of a specific ROI size covering the lowest ADC area).