OBJECTIVE:To assess the impact of split-bolus (SB) single scan CT on the conspicuity of clear cell renal cell carcinoma (ccRCC) metastases compared with single-bolus injection. METHODS:This retrospective cohort study included consecutive patients with histologically proven metastatic ccRCC who underwent both SB and single-bolus portal venous abdominal CT within 6 months between 2017 and 2022 in a single tertiary center. SB CT utilized BMI-adjusted contrast dose and kVp (80 to 120) with concurrent arterial and portal venous phases. Single bolus CT utilized BMI-adjusted contrast dose at 120 kVp at the portal venous phase. Wilcoxon rank test compared the conspicuity of metastases between the protocols. RESULTS:Of the 47 patients, 80.9% were male, with a mean age of 67±10.4 years and a BMI of 27.1±5.7. There were 48 paired CTs performed with a median interval of 93 days. Contrast dose was 143±27 ml for SB and 108±26 ml for single-bolus ( P <0.001). Sixty-six metastases were analyzed, with an average size of 2 cm: 48.5% in the pancreas, 28.8% in skeletal muscle, and 22.7% in the liver. The median contrast-to-noise ratio (CNR) was higher with SB compared with single-bolus for all metastases (3.0 vs. 1.4), pancreatic metastases (2.7 vs. 1.4), muscle metastases (5.2 vs. 2.0), and liver metastases (2.8 vs. 0.9), all P <0.001. CONCLUSIONS:SB scan provides dramatically higher conspicuity of ccRCC metastases as compared with single-bolus portal venous CT.

Assess the incidence and clinical outcomes of inadvertent bowel sampling with a 17-G coaxial system with an 18-G semi-automatic biopsy needle, omental and mesenteric CT and US-guided biopsy. In this retrospective study, consecutive patients undergoing omental and mesenteric CT and US-guided biopsy with a 17-G introducer, an 18-G semi-automatic biopsy device performed at a single tertiary academic institution between March 1, 2005, and March 1, 2024, were included to assess the incidence and clinical outcomes of inadvertent bowel sampling. Descriptive statistics were used. Among 265 biopsies, there were six cases (6/265, 2.3

Pneumatosis intestinalis on CT presents a diagnostic dilemma, because it could reflect bowel ischemia or benign finding. To determine radiological and clinical features that can predict bowel ischemia in patients with pneumatosis intestinalis on CT. Patients with “pneumatosis” in abdominal CT reports performed between 1/1/2002 and 12/31/2018 were retrospectively included. Pneumatosis intestinalis was confirmed by review of images. Radiological features of pneumatosis, laboratory data, clinical signs and symptoms were collected. Pathologic pneumatosis intestinalis (PPI) was defined as presence of ischemic (viable or dead) bowel on surgery or death during admission or within 30 days of discharge due to ischemia. Univariate statistical analysis was used to identify features associated with PPI, followed by multivariate logistic regression models. A total of 313 consecutive patients with pneumatosis intestinalis (162 (52

To analyze outcomes of non-malignant concordant, discordant, and indeterminate results of CT-guided biopsies determined by standardized radiology-pathology concordance evaluation. In this study, consecutive patients undergoing CT-guided omental and mesenteric biopsy between March 2005 and August 2021 were included. A standardized radiology-pathology concordance workflow was implemented in July 2016, with retrospective concordance assessment applied to earlier cases. Concordance between pathology results and imaging findings was assessed by procedural radiologists. Definitions: concordant, for malignant biopsy results or benign pathology where imaging findings agree; discordant, if pathology results are not congruent with imaging; and indeterminate, if imaging could be explained by pathology, but could also represent malignancy. 222 biopsies were included. Pathology showed non-malignant results in 43/222 (19 Evaluate the impact of routine radiology-pathology assessments of omental and mesenteric biopsy results on patient management and the malignancy rates across different concordance groups. High prevalence of malignancy was seen in discordant 13/24 (54

Routine concordance evaluation between pathology and imaging findings was introduced for CT-guided biopsies. To analyze malignancy rate in concordant, discordant, and indeterminate non-malignant results of CT-guided lung biopsies. Concordance between pathology results and imaging findings of consecutive patients undergoing CT-guided lung biopsy between 7/1/2016 and 9/30/2021 was assessed during routine meetings by procedural radiologists. Concordant was defined as pathology consistent with imaging findings; discordant was used when pathology could not explain imaging findings; indeterminate when pathology could explain imaging findings but there was concern for malignancy. Recommendations for discordant and indeterminate were provided. All the malignant results were concordant. Pathology of repeated biopsy, surgical sample, or follow-up was considered reference standard. Consecutive 828 CT-guided lung biopsies were performed on 795 patients (median age 70 years, IQR 61–77), 423/828 (51 • A routine radiology-pathology concordance evaluation of CT-guided lung biopsies classified 224 non-malignant results as concordant, discordant, or indeterminate. • The percentage of malignancy on follow-up was significantly different in concordant (2 • Time to definitive diagnosis was significantly shorter with repeat biopsy (33 days), compared to imaging follow-up (114 days), p = 0.01.

Evaluate a novel algorithm for noise reduction in obese patients using dual-source dual-energy (DE) CT imaging. Seventy-nine patients with contrast-enhanced abdominal imaging (54 women; age: 58 ± 14 years; BMI: 39 ± 5 kg/m2, range: 35–62 kg/m2) from seven DECT (SOMATOM Flash or Force) were retrospectively included (01/2019–12/2020). Image domain data were reconstructed with the standard clinical algorithm (ADMIRE/SAFIRE 2), and denoised with a comparison (ME-NLM) and a test algorithm (rank-sparse kernel regression). Contrast-to-noise ratio (CNR) was calculated. Four blinded readers evaluated the same original and denoised images (0 (worst)–100 (best)) in randomized order for perceived image noise, quality, and their comfort making a diagnosis from a table of 80 options. Comparisons between algorithms were performed using paired t-tests and mixed-effects linear modeling. Average CNR was 5.0 ± 1.9 (original), 31.1 ± 10.3 (comparison; p < 0.001), and 8.9 ± 2.9 (test; p < 0.001). Readers were in good to moderate agreement over perceived image noise (ICC: 0.83), image quality (ICC: 0.71), and diagnostic comfort (ICC: 0.6). Diagnostic accuracy was low across algorithms (accuracy: 66, 63, and 67

To develop and evaluate task-based radiomic features extracted from the mesenteric-portal axis for prediction of survival and response to neoadjuvant therapy in patients with pancreatic ductal adenocarcinoma (PDAC). Consecutive patients with PDAC who underwent surgery after neoadjuvant therapy from two academic hospitals between December 2012 and June 2018 were retrospectively included. Two radiologists performed a volumetric segmentation of PDAC and mesenteric-portal axis (MPA) using a segmentation software on CT scans before (CTtp0) and after (CTtp1) neoadjuvant therapy. Segmentation masks were resampled into uniform 0.625-mm voxels to develop task-based morphologic features (n = 57). These features aimed to assess MPA shape, MPA narrowing, changes in shape and diameter between CTtp0 and CTtp1, and length of MPA segment affected by the tumor. A Kaplan–Meier curve was generated to estimate the survival function. To identify reliable radiomic features associated with survival, a Cox proportional hazards model was used. Features with an ICC ≥ 0.80 were used as candidate variables, with clinical features included a priori. In total, 107 patients (60 men) were included. The median survival time was 895 days (95

Objective To assess success and safety of CT-guided procedures with narrow window access for biopsy. Methods Three hundred ninety-six consecutive patients undergoing abdominal or pelvic CT-guided biopsy or fiducial placement between 01/2015 and 12/2018 were included (183 women, mean age 63 +/- 14 years). Procedures were classified into "wide window" (width of the needle path between structures > 15 mm) and "narrow window" (= 15 mm) based on intraprocedural images. Clinical information, complications, technical and clinical success, and outcomes were collected. The blunt needle approach is preferred by our interventional radiology team for narrow window access. Results There were 323 (81.5%) wide window procedures and 73 ( 18.5%) narrow window procedures with blunt needle approach. The median depth for the narrow window group was greater (97 mm, interquartile range (IQR) 82-113 mm) compared to the wide window group (84 mm, IQR 60-106 mm); p = 0.0017. Technical success was reached in 100% (73/73) of the narrow window and 99.7% (322/323) of the wide window procedures. There was no difference in clinical success rate between the two groups (narrow: 86.4%, 57/ 66; wide: 89.5%, 265/296; p = 0.46). There was no difference in immediate complication rate (narrow: 1.3%, 1/73; wide: 1.2%, 4/323; p = 0.73) or delayed complication rate (narrow: 1.3%, 1/73; wide: 0.6%, 1/323; p = 0.50). Conclusion Narrow window (< 15 mm) access biopsy and fiducial placement with blunt needle approach under CT guidance is safe and successful.

On June 24th 2022 the US Supreme Court, in a 5-4 decision, overturned Roe v. Wade, the landmark 1973 ruling that established the constitutional right to abortion. We are radiologists and medical physicists, many of whom hold or have held leadership roles in our professional community. We are deeply concerned about this erosion of reproductive choice and bodily autonomy across the many States that will now further restrict or even ban access to abortion. Radiologists are physicians who use medical imaging - such as ultrasound, CT, and MRI - to diagnose and treat disease. We use imaging to monitor pregnancy from start to finish and have perspective on the myriad of issues that can arise, making us qualified to speak on the importance of bodily autonomy and the right to choose whether or not to proceed with a pregnancy. Diagnostic radiologists are experienced in evaluating post-surgical complications and interventional radiologists are thoroughly trained in minimally invasive techniques to stop uncontrolled bleeding and place drains to treat abscesses. "Today, approximately 21 million women around the world obtain unsafe, illegal abortions each year, and complications from these unsafe procedures account for approximately 13% of all maternal deaths, nearly 50,000 annually." 1The American College of Obstetricians and Gynecologists (ACOG). "Facts are important: abortion is healthcare." https://www.acog.org/advocacy/facts-are-important/abortion-is-healthcare#:~:text=ACOG's%20November%202017%20Statement%20of,undue%20interference%20by%20outside%20parties. Accessed May 13, 2022. These are our patients, and their morbidity and mortality is entirely preventable with access to safe and legal abortion. We support our patients, of all genders, in making their own informed decisions about their healthcare and management. We urge lawmakers and policy makers to ensure access to reproductive health including safe, legal abortion to all who need these essential healthcare services. Signed Aditya Karandikar, MD A.J. Mariano, MD Adam A. Dmytriw, MD, MPH, MSc Agnieszka Solberg, MD Alan H. Matsumoto, MD Alda L. Tam, MD Alexandra H. Fairchild, MD Alexia Tatem, MD, MPH Alexie Riofrio, MD Alice Fung, MD Alice Zhou, MD Alison Roth, PhD Allison Gittens, MD Ami A. Shah, MD Amie Y. Lee, MD, FSBI Amina Farooq, MD Amit Chakraborty, MD Amy C. Taylor, MD Amy Killeen, MD Amy L. Kotsenas, MD, FACR Amy Lynn Conners, MD Amy Oliveira, MD Anand Narayan, MD, PhD Andi Senter, MD Andrea A. Birch, MD, FACR Andrew Bruner, MD Aneesa Majid, MD, MBA, FSIR Angela Tong, MD Anika L. McGrath, MD Anjali Malik, MD Ann Leylek Brown, MD Anna Nidecker, MD Anne C. Hoyt, MD Anne Roberts, MD Arjun Patel, MD Arthur Fleischer, MD, FACR, FAIUM, FSRU Asha Sarma, MD Ashley Hastings-Robinson, MD Babak Rejaie, MD Bahar Mansoori, MD Bamidele F. Kammen, MD Benjamin Meyer, MD Beth Vettiyil, MD Beth Zigmund, MD Bindu Avutu, MD, MPH Brian Latimer, MD, PhD Brian Park, MD Brooke Morrell, MD Bruce Curran, MS, ME Cameron Henry, MD Camilo Jaimes, MD Cara Connolly, MD Caroline Robson, MBChB Carolyn C. Meltzer, MD, FACR Carolynn DeBenedectis, MD Cassy L. Cook, MD Catherine Everett, MD, MBA, FACR Catherine H. Phillips, MD Chelsea Dunning, PhD Chelsea Neesham, MD Cheri L. Canon, MD, FACR, FSAR Christian Fauria-Robinson, MD Christie M. Lincoln, MD Christine Dove, MD Christine Glastonbury, MBBS Christine Rehwald, MD Christopher Hess, MD, PhD Christopher Murphy, MD Christy Pomeranz, MD Claudia F.E. Kirsch, MD, PhD Cody Quirk, MD Constantine M. Burgan, MD Courtney Scher, DO Courtney Tomblinson, MD Cristina Fuss, MD Cynthia Santillan, MD Dania Daye, MD, PhD Daniel B. Brown, MD, FSIR Daniel J. Young, MD Daniel Kopans MD Daniel Vargas, MD Dann Martin, MD, MS Darren L. Transue, MD David Thompson, MD David W. Jordan, PhD, FACR, FAAPM Deborah Shatzkes, MD Derek Sun, MD Desiree M. Clement, MD Domenico Mastrodicasa, MD Doris Lin, MD, PhD Edward Lo, MD Elainea Smith, MD Elena Korngold, MD Eleza Golden, MD Elianna L. Goldstein, MD, MS Elizabeth A. Russ, MD Elizabeth England, MD Elizabeth H. Dibble, MD Elizabeth K. Arleo, MD, FACR, FSBI Elizabeth M. Hecht, MD, FSAR Elizabeth Morris, MD Elizabeth P. Maltin, MD, FACR Elizabeth Snyder, MD Emmanuel Carrodeguas, MD Erin A. Cooke, MD Erin Shropshire, MD Erin Simon Schwartz, MD, FACR Etta Pisano, MD Evan Lehrman, MD Faezeh Sodagari, MD Faisal Shah, MD, MBA Florence X. Doo, MD Francesca Rigiroli, MD George K. Vilanilam, MD Geraldine McGinty, MD Gina Landinez, MD Girish Bathla, MD Grace G. Zhu, MD Grace Gwe-Ya Kim, PhD Graham Keir, MD Habib Rahbar, MD Hailey Choi, MD Harmanpreet Bandesha, DO Harrison Lee, MD, MBA Haydee Ojeda-Fournier, MD, FSBI Heather Early, MD Heather Greenwood, MD Ichiro Ikuta, MD, MMSc Irena Dragojevic, PhD J. Hugo Decker MD, PhD James Matthew Kerchberger, MD, MPH Jamie Holtz, MD Jamie Hui, MD Jamie Lee Twist Schroeder, MD, DPhil Jana Ivanidze, MD, PhD Janine T. Katzen, MD Jason Chiang, MD, PhD Jeffers Nguyen, MD Jeffrey D Robinson, MD, MBA, FACR Jeffrey Shyu, MD, MPH, MA Jennifer C. Broder, MD Jennifer Chen, MD Jennifer J. Wan, MD Jennifer Kemp, MD, FACR Jennifer R. Buckley, MD, MBA Jennifer S. Weaver, MD Jesse M. Conyers, MD Jessica B. Robbins, MD Jessica Hayward, MD Jessica R. Leschied, MD Jessica Wen, MD, PhD Jiyon Lee, MD Jocelyn Park, MD Joelle Wazen, MD John Mongan, MD, PhD Jonathan Breslau, MD Jordan Cuskaden, MD Jordan Perchik, MD José Pablo Martínez Barbero, MD, PhD, EDiNR Jubin Jacob, MD Julia Schoen, MD, MS Justin Banaga, MD Kalpana Kanal, PhD, FACR Karla A. Sepulveda, MD Karyn Ledbetter, MD Katarzyna J. Macura, MD, PhD Katherine E. Maturen, MD MS Katherine Frederick-Dyer, MD Kathleen A. Ward, MD, FACR, FAAWR Kathryn McGillen, MD Katia Dodelzon, MD, FSBI Katie M. Davis, DO Kayla Cort, DO Kelly Kisling, PhD Kemi Babagbemi, MD, FACR Kevin C. McGill, MD, MPH Kevin J. Chang, MD, FACR, FSAR Kevin Terashima, MD Khashayar Farsad, MD, PhD Kimberly Feigin, MD Kimberly Kallianos MD Kimberly McFarland, MD Kimberly S. Winsor, MD Kimberly Seifert, MD, MS Kirang Patel, MD Kristin K. Porter, MD, PhD, FSAR Kristin M. Foley, MD Krupa Patel-Lippmann, MD Lacey J. McIntosh, DO Laura Barkley, MD Laura E. Heyneman, MD Laura Padilla, PhD Lauren Groner, DO Lauren M. Harry, MD, MS Lauren M. Ladd, MD Laurie Abrams, MD Leah H. Portnow, MD Leah Schafer, MD Leah Sieck, MD Leonard Morneau, MD Leslie Allen, MD Lindsay Busby, MD, MPH Lisa Kang, MD Lisa Walker, MD Lisa Wang, MD, MBA, MPH Lori Strachowski, MD, FSRU, FAOCR Lucy B. Spalluto, MD, MPH Luyao Shen, MD M Mahesh, MS, PhD, FAAPM, FACR, FACMP, FSCCT, FIOMP M. Victoria Marx, MD Majid Chalian, MD Margaret Fleming, MD, MSc Mariam Moshiri, MD Marianne R. Petruccelli, MD Mark D. Sugi, MD Mark P. Supanich, PhD Marla B.K. Sammer, MD, MHA Mary Tenenbaum, MD Maryellen Sun, MD, FACR, FSAR Masis Isikbay, MD Matthew J. Barkovich, MD Matthew J. Miller, MD Matthew S. Johnson, MD Maya Vella, MD Melika Rezaee, MD Melissa A. Davis, MD, MBA Melissa M. Chen, MD Meredith S. Byers, MD Meridith J. Englander, MD, FSIR, FACR Michael Durst, MD Michael Oumano, PhD Michael S. McCollum, DO Michelle Ouyang, MD Mignonne B. Morrell, MD Mitva Patel, MD Monica J. Wood, MD Morgan P. McBee, MD Nancy J. Fischbein, MD Narasim S. Murthy, MD Nataliya Kovalchuk, PhD Neil Lall, MD Neville Eclov, PhD Nicole Kurzbard Roach, MD Nikhil Madhuripan, MD Nikki S. Ariaratnam, MD Nina S. Vincoff, MD Nishanth Khanna, MD Nishita Kothary, MD, FSIR Noushin Yahyavi-Firouz-Abadi, MD Olga R. Brook, MD Orit A. Glenn, MD Pamela K. Woodard, MD Parag J. Patel, MD, MS Parisa Mazaheri, MD Patricia Rhyner MD, FACR Peter R. Eby, MD, FACR Pradnya Mhatre, MD Preethi Raghu, MD Priyanka Jha, MBBS Rachel F. Gerson, MD Rebecca Milman, PhD Rina Patel, MD Robert L. Gutierrez, MD Robert Marks, MD Robyn Gebhard, MD Rochelle F. Andreotti, MD, FACR, FAIUM, FSRU Rohini Nadgir, MD Rukya Masum, MD Ruth B. Goldstein, MD Ryan Manger, PhD Ryan Woods, MD, MPH Sabala Mandava, MD Samantha G. Harrington, MD, MSc Samir Parikh, MD, FACR Sammy Chu, MD, FRCPC Sandeep S. Arora, MBBS Sandra M. Meyers, PhD Sanjay Prabhu, MBBS Sara Shams, MD, PhD Sarah Nobles, MD Sarah Pittman, MD, FRCPC Sarah Rothan, MD Sejal N. Patel, MD Shabnam Mortazavi, MD, MPH Shalini V. Mukhi, MD Sheila Enamandram, MD, MBA Shelby Payne, MD Shravan Sridhar MD, MS Stephen Stein, MD, FACR Steven P. Poplack, MD Steven W. Hetts, MD, FACR Susan Richardson, PhD Suzanne Shepherd, MD Tarek A. Hijaz, MD Teresa Chapman, MD Theresa Caridi, MD, FSIR Thomas W. Loehfelm, MD, PhD Tiffany L. Chan, MD Tim Jenkins, MD Tina Shiang, MD Titania Juang, PhD Toshimasa J. Clark, MD Uzma Waheed, MD Valeria Potigailo, MD Vasantha Aaron, MD Vinil Shah, MD Virginia Planz, MD Vivek Kalia, MD, MPH Walid Ashmeik, MD Wendy DeMartini, MD William D. Donovan, MD, MPH, FACR William P. Dillon, MD Yasha Gupta, MD Yi Li, MD Yilun Koethe, MD Zachary Hartley-Blossom, MD, MBA Zhen Jane Wang, MD These views reflect the opinions of the authors only and do not equal endorsement from their associated affiliations.

Editorial Comment: Does Washout CT Still Have a Role for Characterization of Adrenal Incidentalomas?Francesca Rigiroli, MD1Audio Available | Share

PURPOSE:Manual measurement of body composition on computed tomography (CT) is time-consuming, limiting its clinical use. We validate a software program, Automatic Body composition Analyzer using Computed tomography image Segmentation (ABACS), for the automated measurement of body composition by comparing its performance to manual segmentation in a cohort of patients with bladder cancer.METHOD:We performed a retrospective analysis of 285 patients treated for bladder cancer at the Duke University Health System from 1996 to 2017. Abdominal CT images were manually segmented at L3 using Slice-O-Matic. Automated segmentation was performed with ABACS on the same L3-level images. Measures of interest were skeletal muscle (SM) area, subcutaneous adipose tissue (SAT) area, and visceral adipose tissue (VAT) area. SM index, SAT index, and VAT index were calculated by dividing component areas by patient height2 (m2). Patients were dichotomized as sarcopenic, having excessive subcutaneous fat, or having excessive visceral fat using published cut-off values. Agreement between manual and automated segmentation was assessed using the Pearson product-moment correlation coefficient (PPMCC), the interclass correlation coefficient (ICC3), and the kappa statistic (κ).RESULTS:There was strong agreement between manual and automatic segmentation, with PPMCCs > 0.90 and ICC3s > 0.90 for SM, SAT, and VAT areas. Categorization of patients as sarcopenic (κ = 0.73), having excessive subcutaneous fat (κ = 0.88), or having excessive visceral fat (κ = 0.90) displayed high agreement between methods.CONCLUSIONS:Automated segmentation of body composition measures on CT using ABACS performs similarly to manual analysis and may expedite data collection in body composition research.

Background: Pericardial adipose tissue (PAT) is associated with adverse cardiovascular outcomes in those with and without established heart failure (HF). However, it is not known whether PAT is associated with adverse outcomes in patients with end-stage HF undergoing left ventricular assist device (LVAD) implantation. This study aimed to evaluate the associations between PAT and LVAD-associated outcomes. Methods and Results: We retrospectively measured computed tomography-derived PAT volumes in 77 consecutive adults who had available chest CT imaging prior to HeartMate 3 LVAD surgery between October 2015 and March 2019 at Duke University Hospital. Study groups were divided into above-median (>219 cm3) and below-median (<219 cm3) PAT volume. Those with above-median PAT had a higher proportion of atrial fibrillation, chronic kidney disease and ischemic cardiomyopathy. Groups with abovemedian vs below-median PAT had similar Kaplan-Meier incidence rates over 2 years for (1) composite allcause mortality, redo-LVAD surgery and cardiac transplantation (35.9 vs 32.2%; log-rank P = 0.65) and (2) composite incident hospitalizations for HF, gastrointestinal bleeding, LVAD-related infection, and stroke (61.5 vs 60.5%; log-rank P = 0.67). Conclusions: In patients with end-stage HF undergoing LVAD therapy, PAT is not associated with worse 2-year LVAD-related outcomes. The significance of regional adiposity vs obesity in LVAD patients warrants further investigation. (J Cardiac Fail 2022;28:149-153)

To compare the image quality and hepatic metastasis detection of low-dose deep learning image reconstruction (DLIR) with full-dose filtered back projection (FBP)/iterative reconstruction (IR). A contrast-detail phantom consisting of low-contrast objects was scanned at five CT dose index levels (10, 6, 3, 2, and 1 mGy). A total of 154 participants with 305 hepatic lesions who underwent abdominal CT were enrolled in a prospective non-inferiority trial with a three-arm design based on phantom results. Data sets with full dosage (13.6 mGy) and low dosages (9.5, 6.8, or 4.1 mGy) were acquired from two consecutive portal venous acquisitions, respectively. All images were reconstructed with FBP (reference), IR (control), and DLIR (test). Eleven readers evaluated phantom data sets for object detectability using a two-alternative forced-choice approach. Non-inferiority analyses were performed to interpret the differences in image quality and metastasis detection of low-dose DLIR relative to full-dose FBP/IR. The phantom experiment showed the dose reduction potential from DLIR was up to 57 • Radiation dose levels for DLIR can be reduced to 50 • The reduction of radiation by 70

Objective: To assess the diagnostic performance and reader confidence in determining the resectability of pancreatic cancer at computed tomography (CT) using a new deep learning image reconstruction (DLIR) algorithm. Methods: A retrospective review was conduct of on forty-seven patients with pathologically confirmed pancreatic cancers who underwent baseline multiphasic contrast-enhanced CT scan. Image data sets were reconstructed using filtered back projection (FBP), hybrid model-based adaptive statistical iterative reconstruction (ASiR-V) 60 %, and DLIR "TrueFidelity" at low(L), medium(M), and high strength levels(H). Four board-certified abdominal radiologists reviewed the CT images and classified cancers as resectable, borderline resectable, or unresectable. Diagnostic performance and reader confidence for categorizing the resectability of pancreatic cancer were evaluated based on the reference standards, and the interreader agreement was assessed using Fleiss k statistics. Results: For prediction of margin-negative resections(ie, R0), the average area under the receiver operating characteristic curve was significantly higher with DLIR-H (0.91; 95 % confidence interval [CI]: 0.79, 0.98) than FBP (0.75; 95 % CI:0.60, 0.86) and ASiR-V (0.81; 95 % CI:0.67, 0.91) (p = 0.030 and 0.023 respectively). Reader confidence scores were significantly better using DLIR compared to FBP and ASiR-V 60 % and increased linearly with the increase of DLIR strength level (all p < 0.001). Among the image reconstructions, DLIR-H showed the highest interreader agreement in the resectability classification and lowest subject variability in the reader confidence. Conclusions: The DLIR-H algorithm may improve the diagnostic performance and reader confidence in the CT assignment of the local resectability of pancreatic cancer while reducing the interreader variability.

Purpose: To assess the impact of radiology review for discordance between pathology results from computed tomography (CT)-guided biopsies versus imaging findings performed before a biopsy. Materials and Methods: In this retrospective review, which is compliant with the Health Insurance Portability and Accountability Act and approved by the institutional review board, 926 consecutive CT-guided biopsies performed between January 2015 and December 2017 were included. In total, 453 patients were presented in radiology review meetings (prospective group), and the results were classified as concordant or discordant. Results from the remaining 473 patients not presented at the radiology review meetings were retrospectively classified. Times to reintervention and to definitive diagnosis were obtained for discordant cases; of these, 49 (11%) of the 453 patients were in the prospective group and 55 (12%) of the 473 patients in the retrospective group. Results: Pathology results from CT-guided biopsies were discordant with imaging in 11% (104/926) of the cases, with 57% (59/104) of these cases proving to be malignant. In discordant cases, reintervention with biopsy and surgery yielded a shorter time to definitive diagnosis (28 and 14 days, respectively) than an imaging follow-up (78 days) (P < .001). The median time to diagnosis was 41 days in the prospective group and 56 days in the retrospective group (P = .46). When radiologists evaluated the concordance between pathology and imaging findings and recommended a repeat biopsy for the discordant cases, more biopsies were performed (50% [11/22] vs 13% [4/31]; P = .005). Conclusions: Eleven percent of CT-guided biopsies yielded pathology results that were discordant with imaging findings, with 57% of these proving to be malignant on further workup.

Background The value of dual-energy computed tomography (DECT)-based radiomics in renal lesions is unknown. Purpose To develop DECT-based radiomic models and assess their incremental values in comparison to conventional measurements for differentiating enhancing from non-enhancing small renal lesions. Material and Methods A total of 349 patients with 519 small renal lesions (390 non-enhancing, 129 enhancing) who underwent contrast-enhanced nephrographic phase DECT examinations between June 2013 and January 2020 on multiple DECT platforms were retrospectively recruited. Cohort A included all lesions, while cohort B included Bosniak II–IV and solid enhancing renal lesions. Radiomic models were built with features selected by the least absolute shrinkage and selection operator regression (LASSO). ROC analyses were performed to compare the diagnostic accuracy among conventional and radiomic models for predicting enhancing renal lesions. Results The individual iodine concentration (IC), normalized IC, mean attenuation on 75-keV images, radiomic model of iodine images, 75-keV images and a combined model integrating all the above-mentioned features all demonstrated high AUCs for predicting renal lesion enhancement in cohort A (AUCs = 0.934–0.979) as well as in the test dataset (AUCs = 0.892–0.962) of cohort B (P values with Bonferroni correction >0.003). The AUC (0.864) of mean attenuation on 75-keV images was significantly lower than those of other models (all P values ≤0.001) except the radiomic model of 75-keV images (P = 0.038) in the training dataset of cohort B. Conclusion No incremental value was found by adding radiomic and machine learning analyses to iodine images for differentiating enhancing from non-enhancing renal lesions.

Background Current imaging methods for prediction of complete margin resection (R0) in patients with pancreatic ductal adenocarcinoma (PDAC) are not reliable. Purpose To investigate whether tumor-related and perivascular CT radiomic features improve preoperative assessment of arterial involvement in patients with surgically proven PDAC. Materials and Methods This retrospective study included consecutive patients with PDAC who underwent surgery after preoperative CT between 2012 and 2019. A three-dimensional segmentation of PDAC and perivascular tissue surrounding the superior mesenteric artery (SMA) was performed on preoperative CT images with radiomic features extracted to characterize morphology, intensity, texture, and task-based spatial information. The reference standard was the pathologic SMA margin status of the surgical sample: SMA involved (tumor cells ≤1 mm from margin) versus SMA not involved (tumor cells >1 mm from margin). The preoperative assessment of SMA involvement by a fellowship-trained radiologist in multidisciplinary consensus was the comparison. High reproducibility (intraclass correlation coefficient, 0.7) and the Kolmogorov-Smirnov test were used to select features included in the logistic regression model. Results A total of 194 patients (median age, 66 years; interquartile range, 60-71 years; age range, 36-85 years; 99 men) were evaluated. Aside from surgery, 148 patients underwent neoadjuvant therapy. A total of 141 patients' samples did not involve SMA, whereas 53 involved SMA. A total of 1695 CT radiomic features were extracted. The model with five features (maximum hugging angle, maximum diameter, logarithm robust mean absolute deviation, minimum distance, square gray level co-occurrence matrix correlation) showed a better performance compared with the radiologist assessment (model vs radiologist area under the curve, 0.71 [95% CI: 0.62, 0.79] vs 0.54 [95% CI: 0.50, 0.59]; P < .001). The model showed a sensitivity of 62% (33 of 53 patients) (95% CI: 51, 77) and a specificity of 77% (108 of 141 patients) (95% CI: 60, 84). Conclusion A model based on tumor-related and perivascular CT radiomic features improved the detection of superior mesenteric artery involvement in patients with pancreatic ductal adenocarcinoma. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Do and Kambadakone in this issue.

HomeRadiology: Imaging CancerVol. 3, No. 5 PreviousNext Research HighlightsFree AccessRadioactive Particle Implantation Combined with Chemotherapy for Treatment of Pancreatic AdenocarcinomaFrancesca RigiroliFrancesca RigiroliFrancesca RigiroliPublished Online:Sep 17 2021https://doi.org/10.1148/rycan.2021219020MoreSectionsPDF ToolsImage ViewerAdd to favoritesCiteTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinked In Take-Away Points■ Major Focus: To determine the efficacy of iodine 125 (125I) radioactive particle implantation plus regional arterial chemotherapy perfusion versus regional arterial chemotherapy perfusion therapy alone for treatment of patients with advanced pancreatic ductal adenocarcinoma.■ Key Result: This retrospective study showed significantly improved survival and overall clinical benefit of 125I radioactive particle implantation plus regional arterial chemotherapy.■ Impact: This preliminary study supports further evaluation of combined 125I radioactive particle implantation plus regional arterial chemotherapy to improve outcomes in advanced stage pancreatic ductal adenocarcinoma.Pancreatic ductal adenocarcinoma is often diagnosed at an advanced stage of disease, causing an extremely low survival rate at 1 year. While regional arterial infusion chemotherapy, obtained through catheterization of gastroduodenal, superior mesenteric, or splenic arteries, increases regional drug concentrations, responses remain low. Recent studies suggest that combining this therapy with 125I radioactive particle implantation improves inhibition of tumor growth and pain management.This retrospective study compared patients treated with 125I radioactive particle implantation plus arterial infusion chemotherapy (n = 11) versus those treated only with arterial infusion chemotherapy (n = 12). Treatment with combined 125I radioactive particle implantation plus regional arterial chemotherapy significantly improved radiologic response to therapy (73% vs 42%, P < .05) and 6-month and 9-month survival (91.7% and 50% vs 63.6% and 18.2%, respectively; P < .05) as compared with regional arterial chemotherapy alone. Combined therapy also reduced pain and improved overall quality of life to a greater extent.This study describing benefits of a “double” interventional radiologic approach suggests that development of upgraded materials and novel interventional techniques can improve treatment outcomes in pancreatic ductal adenocarcinoma. Several limits may affect the applicability of this approach, including the retrospective nature, small sample size, and preliminary application only to patients with advanced-stage disease. A future prospective study is needed to confirm these results.Highlighted ArticleYang L, Li C, Wang Z, et al. The clinical efficacy of computed tomography-guided 125I particle implantation combined with arterial infusion chemotherapy in the treatment of pancreatic cancer. J Cancer Res Ther 2021;17(3):720–725. doi: doi.org/10.4103/jcrt.jcrt_563_20Highlighted Article1. Yang L, Li C, Wang Z, et al. The clinical efficacy of computed tomography-guided 125I particle implantation combined with arterial infusion chemotherapy in the treatment of pancreatic cancer. J Cancer Res Ther 2021;17(3):720–725.doi: doi.org/10.4103/jcrt.jcrt_563_20 Crossref, Medline, Google ScholarArticle HistoryPublished online: Sept 17 2021 FiguresReferencesRelatedDetailsRecommended Articles Tumor-Vessel Relationships in Pancreatic Ductal Adenocarcinoma at Multidetector CT: Different Classification Systems and Their Influence on Treatment PlanningRadioGraphics2016Volume: 37Issue: 1pp. 93-112Preoperative CT Classification of the Resectability of Pancreatic Cancer: Interobserver AgreementRadiology2019Volume: 293Issue: 2pp. 343-349CT Radiomic Features of Superior Mesenteric Artery Involvement in Pancreatic Ductal Adenocarcinoma: A Pilot StudyRadiology2021Volume: 301Issue: 3pp. 610-622Pancreatic Adenocarcinoma Staging in the Era of Preoperative Chemotherapy and Radiation TherapyRadiology2018Volume: 287Issue: 2pp. 374-390Imaging-based Risk Scores for Treatment Selection in Early Pancreatic Cancer: A Step Forward for Tailored TreatmentRadiology2020Volume: 296Issue: 3pp. 552-553See More RSNA Education Exhibits Pancreatic Cancer: Early Detection, Diagnosis and StagingDigital Posters2020Beyond Surgical Resectability: Pearls and Pitfalls in Preoperative Imaging for PancreatoduodenectomyDigital Posters2020Missed Imaging Findings of Pancreatic Ductal Adenocarcinoma: Lessons Learned from Difficult Cases  Digital Posters2020 RSNA Case Collection Superior Mesenteric Artery SyndromeRSNA Case Collection2021Primary pancreatic lymphoma RSNA Case Collection2020Flipped hepatic Artery Infusion Pump ReservoirRSNA Case Collection2022 Vol. 3, No. 5 Metrics Altmetric Score PDF download

Background Differentiate malignant from benign enhancing foci on breast magnetic resonance imaging (MRI) through radiomic signature. Methods Forty-five enhancing foci in 45 patients were included in this retrospective study, with needle biopsy or imaging follow-up serving as a reference standard. There were 12 malignant and 33 benign lesions. Eight benign lesions confirmed by over 5-year negative follow-up and 15 malignant histopathologically confirmed lesions were added to the dataset to provide reference cases to the machine learning analysis. All MRI examinations were performed with a 1.5-T scanner. One three-dimensional T1-weighted unenhanced sequence was acquired, followed by four dynamic sequences after intravenous injection of 0.1 mmol/kg of gadobenate dimeglumine. Enhancing foci were segmented by an expert breast radiologist, over 200 radiomic features were extracted, and an evolutionary machine learning method (“training with input selection and testing”) was applied. For each classifier, sensitivity, specificity and accuracy were calculated as point estimates and 95% confidence intervals (CIs). Results A k -nearest neighbour classifier based on 35 selected features was identified as the best performing machine learning approach. Considering both the 45 enhancing foci and the 23 additional cases, this classifier showed a sensitivity of 27/27 (100%, 95% CI 87–100%), a specificity of 37/41 (90%, 95% CI 77–97%), and an accuracy of 64/68 (94%, 95% CI 86–98%). Conclusion This preliminary study showed the feasibility of a radiomic approach for the characterisation of enhancing foci on breast MRI.