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.

Imaging for Early Detection of Pancreatic Ductal Adenocarcinoma: Updates and Challenges in the Implementation of Screening and Surveillance Programs.

Pancreatic ductal adenocarcinoma (PDA) is one of the most aggressive cancers. It has a poor 5-year survival rate of 12%, partly because most cases are diagnosed at advanced stages, precluding curative surgical resection. Early-stage PDA has significantly better prognoses due to increased potential for curative interventions, making early detection of PDA critically important to improved patient outcomes. We examine current and evolving early detection concepts, screening strategies, diagnostic yields among high-risk individuals, controversies, and limitations of standard-of-care imaging.

Radiomics-based Machine-learning Models Can Detect Pancreatic Cancer on Prediagnostic Computed Tomography Scans at a Substantial Lead Time Before Clinical Diagnosis.

BACKGROUND & AIMS: Our purpose was to detect pancreatic ductal adenocarcinoma (PDAC) at the prediagnostic stage (3-36 months before clinical diagnosis) using radiomics-based machine-learning (ML) models, and to compare performance against radiologists in a case-control study. METHODS: Volumetric pancreas segmentation was performed on prediagnostic computed tomography scans (CTs) (median interval between CT and PDAC diagnosis: 398 days) of 155 patients and an age-matched cohort of 265 subjects with normal pancreas. A total of 88 first-order and gray-level radiomic features were extracted and 34 features were selected through the least absolute shrinkage and selection operator-based feature selection method. The dataset was randomly divided into training (292 CTs: 110 prediagnostic and 182 controls) and test subsets (128 CTs: 45 prediagnostic and 83 controls). Four ML classifiers, k-nearest neighbor (KNN), support vector machine (SVM), random forest (RM), and extreme gradient boosting (XGBoost), were evaluated. Specificity of model with highest accuracy was further validated on an independent internal dataset (n = 176) and the public National Institutes of Health dataset (n = 80). Two radiologists (R4 and R5) independently evaluated the pancreas on a 5-point diagnostic scale. RESULTS: Median (range) time between prediagnostic CTs of the test subset and PDAC diagnosis was 386 (97-1092) days. SVM had the highest sensitivity (mean; 95% confidence interval) (95.5; 85.5-100.0), specificity (90.3; 84.3-91.5), F1-score (89.5; 82.3-91.7), area under the curve (AUC) (0.98; 0.94-0.98), and accuracy (92.2%; 86.7-93.7) for classification of CTs into prediagnostic versus normal. All 3 other ML models, KNN, RF, and XGBoost, had comparable AUCs (0.95, 0.95, and 0.96, respectively). The high specificity of SVM was generalizable to both the independent internal (92.6%) and the National Institutes of Health dataset (96.2%). In contrast, interreader radiologist agreement was only fair (Cohen's kappa 0.3) and their mean AUC (0.66; 0.46-0.86) was lower than each of the 4 ML models (AUCs: 0.95-0.98) (P < .001). Radiologists also recorded false positive indirect findings of PDAC in control subjects (n = 83) (7% R4, 18% R5). CONCLUSIONS: Radiomics-based ML models can detect PDAC from normal pancreas when it is beyond human interrogation capability at a substantial lead time before clinical diagnosis. Prospective validation and integration of such models with complementary fluid-based biomarkers has the potential for PDAC detection at a stage when surgical cure is a possibility.

Artificial intelligence assisted whole organ pancreatic fat estimation on magnetic resonance imaging and correlation with pancreas attenuation on computed tomography.

BACKGROUND: Fatty pancreas is associated with inflammatory and neoplastic pancreatic diseases. Magnetic resonance imaging (MRI) is the diagnostic modality of choice for measuring pancreatic fat. Measurements typically use regions of interest limited by sampling and variability. We have previously described an artificial intelligence (AI)-aided approach for whole pancreas fat estimation on computed tomography (CT). In this study, we aimed to assess the correlation between whole pancreas MRI proton-density fat fraction (MR-PDFF) and CT attenuation. METHODS: We identified patients without pancreatic disease who underwent both MRI and CT between January 1, 2015 and June 1, 2020. 158 paired MRI and CT scans were available for pancreas segmentation using an iteratively trained convolutional neural network (CNN) with manual correction. Boxplots were generated to visualize slice-by-slice variability in 2D-axial slice MR-PDFF. Correlation between whole pancreas MR-PDFF and age, BMI, hepatic fat and pancreas CT-Hounsfield Unit (CT-HU) was assessed. RESULTS: Mean pancreatic MR-PDFF showed a strong inverse correlation (Spearman -0.755) with mean CT-HU. MR-PDFF was higher in males (25.22 vs 20.87; p = 0.0015) and in subjects with diabetes mellitus (25.95 vs 22.17; p = 0.0324), and was positively correlated with age and BMI. The pancreatic 2D-axial slice-to-slice MR-PDFF variability increased with increasing mean whole pancreas MR-PDFF (Spearman 0.51; p < 0.0001). CONCLUSION: Our study demonstrates a strong inverse correlation between whole pancreas MR-PDFF and CT-HU, indicating that both imaging modalities can be used to assess pancreatic fat. 2D-axial pancreas MR-PDFF is variable across slices, underscoring the need for AI-aided whole-organ measurements for objective and reproducible estimation of pancreatic fat.

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.

FDG-PET Predicts Neoadjuvant Therapy Response and Survival in Borderline Resectable/Locally Advanced Pancreatic Adenocarcinoma.

BACKGROUND: Neoadjuvant therapy (NAT) is used in borderline resectable/locally advanced (BR/LA) pancreatic ductal adenocarcinoma (PDAC). Anatomic imaging (CT/MRI) poorly predicts response, and biochemical (CA 19-9) markers are not useful (nonsecretors/nonelevated) in many patients. Pathologic response highly predicts survival post-NAT, but is only known postoperatively. Because metabolic imaging (FDG-PET) reveals primary tumor viability, this study aimed to evaluate our experience with preoperative FDG-PET in patients with BR/LA PDAC in predicting NAT response and survival. METHODS: We reviewed all patients with resected BR/LA PDAC who underwent NAT with FDG-PET within 60 days of resection. Pre- and post-NAT metabolic (FDG-PET) and biochemical (CA 19-9) responses were dichotomized in addition to pathologic responses. We compared post-NAT metabolic and biochemical responses as preoperative predictors of pathologic responses and recurrence-free survival (RFS) and overall survival (OS). RESULTS: We identified 202 eligible patients. Post-NAT, 58% of patients had optimization of CA 19-9 levels. Major metabolic and pathologic responses were present in 51% and 38% of patients, respectively. Median RFS and OS times were 21 and 48.7 months, respectively. Metabolic response was superior to biochemical response in predicting pathologic response (area under the curve, 0.86 vs 0.75; P<.001). Metabolic response was the only univariate preoperative predictor of OS (odds ratio, 0.25; 95% CI, 0.13-0.40), and was highly correlated (P=.001) with pathologic response as opposed to biochemical response alone. After multivariate adjustment, metabolic response was the single largest independent preoperative predictor (P<.001) for pathologic response (odds ratio, 43.2; 95% CI, 16.9-153.2), RFS (hazard ratio, 0.37; 95% CI, 0.2-0.6), and OS (hazard ratio, 0.21; 95% CI, 0.1-0.4). CONCLUSIONS: Among patients with post-NAT resected BR/LA PDAC, FDG-PET highly predicts pathologic response and survival, superior to biochemical responses alone. Given the poor ability of anatomic imaging or biochemical markers to assess NAT responses in these patients, FDG-PET is a preoperative metric of NAT efficacy, thereby allowing potential therapeutic alterations and surgical treatment decisions. We suggest that FDG-PET should be an adjunct and recommended modality during the NAT phase of care for these patients.

Atypical Metastases in the Abdomen and Pelvis From Biochemically Recurrent Prostate Cancer: 11C-Choline PET/CT With Multimodality Correlation.

PET with targeted radiotracers has become integral to mapping the location and burden of recurrent disease in patients with biochemical recurrence (BCR) of prostate cancer (PCa). PET with 11C-choline is part of the National Comprehensive Cancer Network and European Association of Urology guidelines for evaluation of BCR. With advances in PET technology, increasing use of targeted radiotracers, and improved survival of patients with BCR because of novel therapeutics, atypical sites of metastases are being increasingly encountered, challenging the conventional view that prostate cancer rarely metastasizes beyond bones or lymph nodes. The purpose of this article is to describe such atypical metastases in the abdomen and pelvis on 11C-choline PET (including metastases to the liver, pancreas, genital tract, urinary tract, peritoneum, abdominal wall, and perineural spread) and to present multimodality imaging features and relevant imaging pitfalls. Given atypical metastases' inconsistent relationship with the serum PSA level and the nonspecific presenting symptoms, atypical metastases are often first detected on imaging. Awareness of their imaging features is important because their detection affects clinical management, patient counseling, prognosis, and clinical trial eligibility. Such awareness is particularly critical because the role of radiologists in the imaging and management of BCR will continue to increase given the expanding regulatory approvals of other targeted and theranostic radiotracers.

Factors Predicting Response, Perioperative Outcomes, and Survival Following Total Neoadjuvant Therapy for Borderline/Locally Advanced Pancreatic Cancer.

OBJECTIVE: To identify predictive factors associated with operative morbidity, mortality, and survival outcomes in patients with borderline resectable (BR) or locally advanced (LA) pancreatic ductal adenocarcinoma (PDAC) undergoing total neoadjuvant therapy (TNT). BACKGROUND: The optimal preoperative treatment sequencing for BR/LA PDA is unknown. TNT, or systemic chemotherapy followed by chemoradiation (CRT), addresses both occult metastases and positive margin risks and thus is a potentially optimal strategy; however, factors predictive of perioperative and survival outcomes are currently undefined. METHODS: We reviewed our experience in BR/LA patients undergoing resection from 2010 to 2017 following TNT assessing operative morbidity, mortality, and survival in order to define outcome predictors and response endpoints. RESULTS: One hundred ninety-four patients underwent resection after TNT, including 123 (63%) BR and 71 (37%) LA PDAC. FOLFIRINOX or gemcitabine along with nab-paclitaxel were used in 165 (85%) and 65 (34%) patients, with 36 (19%) requiring chemotherapeutic switch before long-course CRT and subsequent resection. Radiologic anatomical downstaging was uncommon (28%). En bloc venous and/or arterial resection was required in 125 (65%) patients with 94% of patients achieving R0 margins. The 90-day major morbidity and mortality was 36% and 6.7%, respectively. Excluding operative mortalities, the median, 1-year, 2-year, and 3-year recurrence-free survival (RFS) [overall survival (OS)] rates were 23.5 (58.8) months, 65 (96)%, 48 (78)%, and 32 (62)%, respectively. Radiologic downstaging, vascular resection, and chemotherapy regimen/switch were not associated with survival. Only 3 factors independently associated with prolonged survival, including extended duration (≥6 cycles) chemotherapy, optimal post-chemotherapy CA19-9 response, and major pathologic response. Patients achieving all 3 factors had superior survival outcomes with a survival detriment for each failing factor. In a subset of patients with interval metabolic (PET) imaging after initial chemotherapy, complete metabolic response highly correlated with major pathologic response. CONCLUSION: Our TNT experience in resected BR/LA PDAC revealed high negative margin rates despite low radiologic downstaging. Extended duration chemotherapy with associated biochemical and pathologic responses highly predicted postoperative survival. Potential modifications of initial chemotherapy treatment include extending cycle duration to normalize CA19-9 or achieve complete metabolic response, or consideration of chemotherapeutic switch in order to achieve these factors may improve survival before moving forward with CRT and subsequent resection.

Small bowel radiology.

PURPOSE OF REVIEW: Recent advances in computed tomography (CT), ultrasound (US), magnetic resonance imaging (MRI), and nuclear radiology have improved the diagnosis and characterization of small bowel pathology. Our purpose is to highlight the current status and recent advances in multimodality noninvasive imaging of the small bowel. RECENT FINDINGS: CT and MR enterography are established techniques for small bowel evaluation. Dual-energy CT is a novel technique that has shown promise for the mesenteric ischemia and small bowel bleeding. Advanced US techniques and MRI sequences are being investigated to improve assessment of bowel inflammation, treatment response assessment, motility, and mural fibrosis. Novel radiotracers and scanner technologies have made molecular imaging the new reference standard for small bowel neuroendocrine tumors. Computational image analysis and artificial intelligence (AI) have the potential to augment physician expertise, reduce errors and variability in assessment of the small bowel on imaging. SUMMARY: Advances in translational imaging research coupled with progress in imaging technology have led to a wider adoption of cross-sectional imaging for the evaluation and management of small bowel entities. Ongoing developments in image acquisition and postprocessing techniques, molecular imaging and AI have the strongest potential to transform the care and outcomes of patients with small bowel diseases.

Quality gaps in public pancreas imaging datasets: Implications & challenges for AI applications.

OBJECTIVE: Quality gaps in medical imaging datasets lead to profound errors in experiments. Our objective was to characterize such quality gaps in public pancreas imaging datasets (PPIDs), to evaluate their impact on previously published studies, and to provide post-hoc labels and segmentations as a value-add for these PPIDs. METHODS: We scored the available PPIDs on the medical imaging data readiness (MIDaR) scale, and evaluated for associated metadata, image quality, acquisition phase, etiology of pancreas lesion, sources of confounders, and biases. Studies utilizing these PPIDs were evaluated for awareness of and any impact of quality gaps on their results. Volumetric pancreatic adenocarcinoma (PDA) segmentations were performed for non-annotated CTs by a junior radiologist (R1) and reviewed by a senior radiologist (R3). RESULTS: We found three PPIDs with 560 CTs and six MRIs. NIH dataset of normal pancreas CTs (PCT) (n = 80 CTs) had optimal image quality and met MIDaR A criteria but parts of pancreas have been excluded in the provided segmentations. TCIA-PDA (n = 60 CTs; 6 MRIs) and MSD(n = 420 CTs) datasets categorized to MIDaR B due to incomplete annotations, limited metadata, and insufficient documentation. Substantial proportion of CTs from TCIA-PDA and MSD datasets were found unsuitable for AI due to biliary stents [TCIA-PDA:10 (17%); MSD:112 (27%)] or other factors (non-portal venous phase, suboptimal image quality, non-PDA etiology, or post-treatment status) [TCIA-PDA:5 (8.5%); MSD:156 (37.1%)]. These quality gaps were not accounted for in any of the 25 studies that have used these PPIDs (NIH-PCT:20; MSD:1; both: 4). PDA segmentations were done by R1 in 91 eligible CTs (TCIA-PDA:42; MSD:49). Of these, corrections were made by R3 in 16 CTs (18%) (TCIA-PDA:4; MSD:12) [mean (standard deviation) Dice: 0.72(0.21) and 0.63(0.23) respectively]. CONCLUSION: Substantial quality gaps, sources of bias, and high proportion of CTs unsuitable for AI characterize the available limited PPIDs. Published studies on these PPIDs do not account for these quality gaps. We complement these PPIDs through post-hoc labels and segmentations for public release on the TCIA portal. Collaborative efforts leading to large, well-curated PPIDs supported by adequate documentation are critically needed to translate the promise of AI to clinical practice.

Borderline Resectable and Locally Advanced Pancreatic Cancer: FDG PET/MRI and CT Tumor Metrics for Assessment of Pathologic Response to Neoadjuvant Therapy and Prediction of Survival.

BACKGROUND. Imaging biomarkers of response to neoadjuvant therapy (NAT) for pancreatic ductal adenocarcinoma (PDA) are needed to optimize treatment decisions and long-term outcomes. OBJECTIVE. The purpose of this study was to investigate metrics from PET/MRI and CT to assess pathologic response of PDA to NAT and to predict overall survival (OS). METHODS. This retrospective study included 44 patients with 18F-FDG-avid borderline resectable or locally advanced PDA on pretreatment PET/MRI who also underwent post-NAT PET/MRI before surgery between August 2016 and February 2019. Carbohydrate antigen 19-9 (CA 19-9) level, metabolic metrics from PET/MRI, and morphologic metrics from CT (n = 34) were compared between pathologic responders (College of American Pathologists scores 0 and 1) and nonresponders (scores 2 and 3). AUCs were measured for metrics significantly associated with pathologic response. Relation to OS was evaluated with Cox proportional hazards models. RESULTS. Among 44 patients (22 men, 22 women; mean age, 62 ± 11.6 years), 19 (43%) were responders, and 25 (57%) were nonresponders. Median OS was 24 months (range, 6-42 months). Before treatment, responders and nonresponders did not differ in CA 19-9 level, metabolic metrics, or CT metrics (p > .05). After treatment, responders and nonresponders differed in complete metabolic response (CMR) (responders, 89% [17/19]; nonresponders, 40% [10/25]; p = .04], mean change in SUVmax (ΔSUVmax; responders, -70% ± 13%; nonresponders, -37% ± 42%; p < .001), mean change in SUVmax corrected to serum glucose level (ΔSUVgluc) (responders, -74% ± 12%; nonresponders, -30% ± 58%; p < .001), RECIST response on CT (responders, 93% [13/14]; nonresponders, 50% [10/20]; p = .02)], and mean change in tumor volume on CT (ΔTvol) (responders, -85% ± 21%; nonresponders, 57% ± 400%; p < .001). The AUC of CMR for pathologic response was 0.75; ΔSUVmax, 0.83; ΔSUVgluc, 0.87; RECIST, 0.71; and ΔTvol 0.86. The AUCs of bivariable PET/MRI and CT models were 0.83 (CMR and ΔSUVmax), 0.87 (CMR and ΔSUVgluc), and 0.87 (RECIST and ΔTvol). OS was associated with CMR (p = .03), ΔSUVmax (p = .003), ΔSUVgluc (p = .003), and RECIST (p = .046). CONCLUSION. Unlike CA 19-9 level, changes in metabolic metrics from PET/MRI and morphologic metrics from CT after NAT were associated with pathologic response and OS in patients with PDA, warranting prospective validation. CLINICAL IMPACT. Imaging metrics associated with pathologic response and OS in PDA could help guide clinical management and outcomes for patients with PDA who undergo emergency therapeutic interventions.

Computerized tomography scan in pre-diagnostic pancreatic ductal adenocarcinoma: Stages of progression and potential benefits of early intervention: A retrospective study.

BACKGROUND: The frequency, nature and timeline of changes on thin-slice (≤3 mm) multi-detector computerized tomography (CT) scans in the pre-diagnostic phase of pancreatic ductal adenocarcinoma (PDAC) are unknown. It is unclear if identifying imaging changes in this phase will improve PDAC survival beyond lead time. METHODS: From a cohort of 128 subjects (Cohort A) with CT scans done 3-36 months before diagnosis of PDAC we developed a CTgram defining CT Stages (CTS) I through IV in the radiological progression of pre-diagnostic PDAC. We constructed Cohort B of PDAC resected at CTS I and II and compared survival in CTS I and II in Cohort A (n = 22 each; control natural history cohort) vs Cohort B (n = 33 and 72, respectively; early interception cohort). RESULTS: CTs were abnormal in 16% and 85% at 24-36 and 3-6 months respectively, before PDAC diagnosis. The PDAC CTgram stages, findings and median lead times (months) to clinical diagnosis were: CTS I: Abrupt duct cut-off/duct dilatation (-12.8); CTS II: Low density mass confined to pancreas (-9.5), CTS III: Peri-pancreatic infiltration (-5.8), CTS IV: Distant metastases (only at diagnosis). PDAC survival was better in cohort B than in cohort A despite inclusion of lead time in Cohort A: CTS I (36 vs 17.2 months, p = 0.03), CTS II (35.2 vs 15.3 months, p = 0.04). CONCLUSION: Starting 12-18 months before PDAC diagnosis, progressive and increasingly frequent changes occur on CT scans. Resection of PDAC at the time of pre-diagnostic CT changes is likely to provide survival benefit beyond lead time.

Clinical PET/MRI: 2018 Update.

OBJECTIVE: The purpose of this article is to provide an update on clinical PET/MRI, including current and developing clinical indications and technical developments. CONCLUSION: PET/MRI is evolving rapidly, transitioning from a predominant research focus to exciting clinical practice. Key technical obstacles have been overcome, and further technical advances promise to herald significant advancements in image quality. Further optimization of protocols to address challenges posed by this hybrid modality will ensure the long-term success of PET/MRI.

11C-Choline PET/CT for Detection and Localization of Parathyroid Adenomas.

OBJECTIVE: The purpose of this study is to determine the efficacy of 11C-choline PET/CT for the detection of parathyroid adenomas by retrospectively reviewing a large patient population. MATERIALS AND METHODS: In this single-institution retrospective study, 7088 11C-choline PET/CT scans performed of 2933 men with prostate cancer from January 2005 through February 2016 were evaluated. Patients with suspected parathyroid adenomas were identified through a review of the electronic medical record and relevant imaging. Patient demographics, laboratory results, and lesion characteristics were noted. Pathologically proven parathyroid adenomas and lesions in patients with imaging or laboratory findings consistent with the diagnosis were considered positive. RESULTS: Thirteen men (mean [± SD] age, 72 ± 7 years) with pathologically or laboratory-proven parathyroid adenomas were identified. All had abnormally elevated serum calcium and parathyroid hormone levels. All adenomas were tracer avid on 11C-choline PET/CT (maximum standardized uptake value, 5.6 ± 3.0), with activity averaging 4.2 times that of the blood pool and 2.1 times that of the adjacent thyroid. One case of an ectopic adenoma was identified. Of the six pathologically confirmed cases, none displayed high-grade features such as capsular, vascular, or adjacent tissue invasion. Three additional patients with possible parathyroid adenomas at 11C-choline PET/CT were ultimately found to have thyroid lesions on the basis of tissue diagnosis; however, none of these patients had abnormal calcium or parathyroid hormone levels. CONCLUSION: In our patient population, 11C-choline PET/CT identified parathyroid adenomas with high specificity. Prospective investigation is warranted to validate this result and delineate the utility of 11C-choline PET/CT relative to other modalities.

CT-guided transgluteal biopsy for systematic sampling of the prostate in patients without rectal access: a 13-year single-center experience.

OBJECTIVES: The purpose of our study was to examine the safety and diagnostic utility of transgluteal CT-guided prostate biopsy for prostate sampling in patients without rectal access. METHODS: Seventy-three biopsies were performed in 65 patients over a 13-year period (2002-2015). Mean prostate-specific antigen (PSA) at biopsy was 7.8 ng/mL (range 0.37-31.5). Electronic medical records were reviewed for procedural details and complications. Mean PSA and number of cores in malignant and benign cohorts were compared with Student's t test. RESULTS: Technical success rate was 97.3% (71/73; mean cores 8, range 3-28). Of these, 43.6% (31/71) yielded malignancy (mean Gleason score 7, range 6-10) and 56.3% (40/71) yielded benign tissue. The only complication was an asymptomatic periprostatic hematoma (1/73; 1.4%). In 14 patients who underwent surgery, Gleason scores were concordant in 71.4% (10/14) and discordant in 28.6% (4/14; Gleason 6 on biopsy but Gleason 7 on surgical specimen). Mean effective radiation dose was 18.5 mSv (median 15.0, range 4.4-86.2). There was no significant difference in either mean PSA (p = 0.06) or number of core specimens (p = 0.33) between malignant and benign cohorts. CONCLUSION: CT-guided transgluteal prostate biopsy is highly safe and reliable for the detection of prostate cancer in men without rectal access. KEY POINTS: • Prostate cancer detection in men without rectal access is challenging. • CT-guided transgluteal prostate biopsy is safe and effective in these patients. • CT-guided biopsy may be particularly effective in diagnosing high-grade prostate cancer. • Unilateral CT-guided biopsy may be effective in patients with focal lesions. • The radiation exposure with this technique is acceptable.

Image Noise, CNR, and Detectability of Low-Contrast, Low-Attenuation Liver Lesions in a Phantom: Effects of Radiation Exposure, Phantom Size, Integrated Circuit Detector, and Iterative Reconstruction.

Purpose To assess image noise, contrast-to-noise ratio (CNR) and detectability of low-contrast, low-attenuation liver lesions in a semianthropomorphic phantom by using either a discrete circuit (DC) detector and filtered back projection (FBP) or an integrated circuit (IC) detector and iterative reconstruction (IR) with changes in radiation exposure and phantom size. Materials and Methods An anthropomorphic phantom without or with a 5-cm-thick fat-mimicking ring (widths, 30 and 40 cm) containing liver inserts with four spherical lesions was scanned with five exposure settings on each of two computed tomography scanners, one equipped with a DC detector and the other with an IC detector. Images from the DC and IC detector scanners were reconstructed with FBP and IR, respectively. Image noise and lesion CNR were measured. Four radiologists evaluated lesion presence on a five-point diagnostic confidence scale. Data analyses included receiver operating characteristic (ROC) curve analysis and noninferiority analysis. Results The combination of IC and IR significantly reduced image noise (P < .001) (with the greatest reduction in the 40-cm phantom and at lower exposures) and improved lesion CNR (P < .001). There was no significant difference in area under the ROC curve between detector-reconstruction combinations at fixed exposure for either phantom. Reader accuracy with IC-IR was noninferior at 50% (100 mAs [effective]) and 25% (300 mAs [effective]) exposure reduction for the 30- and 40-cm phantoms, respectively (adjusted P < .001 and .04 respectively). IC-IR improved readers' confidence in the presence of a lesion (P = .029) independent of phantom size or exposure level. Conclusion IC-IR improved objective image quality and lesion detection confidence but did not result in superior diagnostic accuracy when compared with DC-FBP. Moderate exposure reductions maintained comparable diagnostic accuracy for both detector-reconstruction combinations. Lesion detection in the 40-cm phantom was inferior at smaller exposure reduction than in the 30-cm phantom. (©) RSNA, 2016 Online supplemental material is available for this article.

Diagnostic Accuracy of CT Enterography for Active Inflammatory Terminal Ileal Crohn Disease: Comparison of Full-Dose and Half-Dose Images Reconstructed with FBP and Half-Dose Images with SAFIRE.

Purpose To compare the diagnostic accuracy and image quality of computed tomographic (CT) enterographic images obtained at half dose and reconstructed with filtered back projection (FBP) and sinogram-affirmed iterative reconstruction (SAFIRE) with those of full-dose CT enterographic images reconstructed with FBP for active inflammatory terminal or neoterminal ileal Crohn disease. Materials and Methods This retrospective study was compliant with HIPAA and approved by the institutional review board. The requirement to obtain informed consent was waived. Ninety subjects (45 with active terminal ileal Crohn disease and 45 without Crohn disease) underwent CT enterography with a dual-source CT unit. The reference standard for confirmation of active Crohn disease was active terminal ileal Crohn disease based on ileocolonoscopy or established Crohn disease and imaging features of active terminal ileal Crohn disease. Data from both tubes were reconstructed with FBP (100% exposure); data from the primary tube (50% exposure) were reconstructed with FBP and SAFIRE strengths 3 and 4, yielding four datasets per CT enterographic examination. The mean volume CT dose index (CTDIvol) and size-specific dose estimate (SSDE) at full dose were 13.1 mGy (median, 7.36 mGy) and 15.9 mGy (median, 13.06 mGy), respectively, and those at half dose were 6.55 mGy (median, 3.68 mGy) and 7.95 mGy (median, 6.5 mGy). Images were subjectively evaluated by eight radiologists for quality and diagnostic confidence for Crohn disease. Areas under the receiver operating characteristic curves (AUCs) were estimated, and the multireader, multicase analysis of variance method was used to compare reconstruction methods on the basis of a noninferiority margin of 0.05. Results The mean AUCs with half-dose scans (FBP, 0.908; SAFIRE 3, 0.935; SAFIRE 4, 0.924) were noninferior to the mean AUC with full-dose FBP scans (0.908; P < .003). The proportion of images with inferior quality was significantly higher with all half-dose reconstructions than with full-dose FBP (mean proportion: 0.117 for half-dose FBP, 0.054 for half-dose SAFIRE 3, 0.054 for half-dose SAFIRE 4, and 0.017 for full-dose FBP; P < .001). Conclusion The diagnostic accuracy of half-dose CT enterography with FBP and SAFIRE is statistically noninferior to that of full-dose CT enterography for active inflammatory terminal ileal Crohn disease, despite an inferior subjective image quality. (©) RSNA, 2016 Online supplemental material is available for this article.

CT-Guided Transgluteal Biopsy for Systematic Random Sampling of the Prostate in Patients Without Rectal Access.

OBJECTIVE: The objective of our study was to review our experience with CT-guided transgluteal prostate biopsy in patients without rectal access. MATERIALS AND METHODS: Twenty-one CT-guided transgluteal prostate biopsy procedures were performed in 16 men (mean age, 68 years; age range, 60-78 years) who were under conscious sedation. The mean prostate-specific antigen (PSA) value was 11.4 ng/mL (range, 2.3-39.4 ng/mL). Six had seven prior unsuccessful transperineal or transurethral biopsies. Biopsy results, complications, sedation time, and radiation dose were recorded. The mean PSA values and number of core specimens were compared between patients with malignant results and patients with nonmalignant results using the Student t test. RESULTS: The average procedural sedation time was 50.6 minutes (range, 15-90 minutes) (n = 20), and the mean effective radiation dose was 8.2 mSv (median, 6.6 mSv; range 3.6-19.3 mSv) (n = 13). Twenty of the 21 (95%) procedures were technically successful. The only complication was a single episode of gross hematuria and penile pain in one patient, which resolved spontaneously. Of 20 successful biopsies, 8 (40%) yielded adenocarcinoma (Gleason score: mean, 8; range, 7-9). Twelve biopsies yielded nonmalignant results (60%): high-grade prostatic intraepithelial neoplasia (n = 3) or benign prostatic tissue with or without inflammation (n = 9). Three patients had carcinoma diagnosed on subsequent biopsies (second biopsy, n = 2 patients; third biopsy, n = 1 patient). A malignant biopsy result was not significantly associated with the number of core specimens (p = 0.3) or the mean PSA value (p = 0.1). CONCLUSION: CT-guided transgluteal prostate biopsy is a safe and reliable technique for the systematic random sampling of the prostate in patients without a rectal access. In patients with initial negative biopsy results, repeat biopsy should be considered if there is a persistent rise in the PSA value.

Effect of reduced radiation exposure and iterative reconstruction on detection of low-contrast low-attenuation lesions in an anthropomorphic liver phantom: an 18-reader study.

PURPOSE: To measure the effect of reduced radiation exposure on low-contrast low-attenuation liver lesion detection in an anthropomorphic abdominal phantom by using filtered back projection (FBP) and sinogram-affirmed iterative reconstruction. MATERIALS AND METHODS: Eighteen radiologists blinded to phantom and study design interpreted randomized image data sets that contained 36 spherical simulated liver lesions of three sizes and three attenuation differences (5-mm diameter: 12, 18, and 24 HU less than the 90-HU background attenuation of the simulated liver insert; 10- and 15-mm diameter: 6, 12, and 18 HU less than the 90-HU background attenuation) scanned with four discrete exposure settings and reconstructed by using FBP and sinogram-affirmed iterative reconstruction. Response assessment included region-level lesion presence or absence on a five-point diagnostic confidence scale. Statistical evaluation included multireader multicase receiver operating characteristic curve analysis, with nonparametric methods and noninferiority analysis at a margin of -0.10. RESULTS: Pooled accuracy at 75% exposure for both FBP and sinogram-affirmed iterative reconstruction was noninferior to 100% exposure (P = .002 and P < .001, respectively). Subsequent exposure reductions resulted in a significant decrease in accuracy. When the smallest (5-mm-diameter) lesions were excluded from analysis, sinogram-affirmed iterative reconstruction was superior to FBP at 100% exposure (P = .011), and sinogram-affirmed iterative reconstruction at 25% and 50% exposure reduction was noninferior to FBP at 100% exposure (P ≤ .013). Reader confidence was greater with sinogram-affirmed iterative reconstruction than with FBP for 10- and 15-mm lesions (2.94 vs 2.76 and 3.62 vs 3.52, respectively). CONCLUSION: In this low-contrast low-attenuation liver lesion model, a 25% exposure reduction maintained noninferior diagnostic accuracy. However, detection was inferior with each subsequent exposure reduction, regardless of reconstruction method. Sinogram-affirmed iterative reconstruction and FBP performed equally well at modest exposure reduction (25%-50%). Readers had higher confidence levels with sinogram-affirmed iterative reconstruction for the 10- and 15-mm lesions.