Search

Purpose: To determine the positive predictive value (PPV) for polyps detected at CT colonography (CTC).Methods: Assessment of 739 colorectal lesions >or=6 mm detected prospectively at CTC screening in 479 patients was performed. By-polyp PPV was analyzed according to small (6-9 mm) versus large (>or=10 mm) size; morphology (sessile/pedunculated/flat); diagnostic confidence level (3 = most confident, 1 = least confident); and histology. By-patient PPV was analyzed at various polyp size thresholds.Results: By-polyp PPV for CTC-detected lesions >or=6 mm, 6-9 mm, and >or=10 mm was 91.6% (677/739), 90.1% (410/451), and 92.7% (267/288), respectively (p = 0.4). By-polyp PPV according to sessile, pedunculated, flat, and mass-like morphology was 92.5% (441/477), 96.5% (139/144), 77.7% (73/94), and 97.6% (40/41), respectively (p < 0.0001 for flat versus polypoid morphology). By-polyp PPV according to diagnostic confidence level was 94.7% (554/585) for highest (= level 3), 83.5% (106/127) for intermediate (= level 2), and 63.0% (17/27) for lowest (= level 1) confidence (p < 0.0001 for levels-2/3 versus level-1). By-patient PPV at 6-mm, 8-mm, 10-mm, and 30-mm polyp size thresholds was 92.3% (442/479), 93.0% (306/329), 93.1% (228/245), and 97.4% (38/39), respectively.Conclusion: The overall per-polyp and per-patient PPV for lesions >or=6 mm was 92% for CTC screening. Increased diagnostic confidence and polypoid (non-flat) morphology correlated with a higher PPV, whereas small versus large polyp size had very little effect. [ABSTRACT FROM AUTHOR]

Background: Advanced neoplasia represents the primary target for colorectal-cancer screening and prevention. We compared the diagnostic yield from parallel computed tomographic colonography (CTC) and optical colonoscopy (OC) screening programs. Methods: We compared primary CTC screening in 3120 consecutive adults (mean [+/-SD] age, 57.0+/-7.2 years) with primary OC screening in 3163 consecutive adults (mean age, 58.1+/-7.8 years). The main outcome measures included the detection of advanced neoplasia (advanced adenomas and carcinomas) and the total number of harvested polyps. Referral for polypectomy during OC was offered for all CTC-detected polyps of at least 6 mm in size. Patients with one or two small polyps (6 to 9 mm) also were offered the option of CTC surveillance. During primary OC, nearly all detected polyps were removed, regardless of size, according to established practice guidelines. Results: During CTC and OC screening, 123 and 121 advanced neoplasms were found, including 14 and 4 invasive cancers, respectively. The referral rate for OC in the primary CTC screening group was 7.9% (246 of 3120 patients). Advanced neoplasia was confirmed in 100 of the 3120 patients in the CTC group (3.2%) and in 107 of the 3163 patients in the OC group (3.4%), not including 158 patients with 193 unresected CTC-detected polyps of 6 to 9 mm who were undergoing surveillance. The total numbers of polyps removed in the CTC and OC groups were 561 and 2434, respectively. There were seven colonic perforations in the OC group and none in the CTC group. Conclusions: Primary CTC and OC screening strategies resulted in similar detection rates for advanced neoplasia, although the numbers of polypectomies and complications were considerably smaller in the CTC group. These findings support the use of CTC as a primary screening test before therapeutic OC. [ABSTRACT FROM AUTHOR]

Virtual colonoscopy for colorectal cancer screening sounds attractive: non-invasive, no sedation, and no collection of stool. However, a full bowel preparation is needed and so far its sensitivity and specificity for lesions measuring less than 10 mm diameter have suggested it is not accurate enough to be used for screening. This study involves 1233 average risk asymptomatic adults in three centres who underwent virtual colonoscopy followed by a some day conventional colonoscopy. Multidetector computed tomograph scans were used to generate fast high resolution images, and water soluble and barium contrast materials were used to tag residual fluid and stool.

Purpose: To investigate the behavior of artificial intelligence (AI) CT-based body composition biomarkers at different virtual monoenergetic imaging (VMI) levels using dual-energy CT (DECT)., Methods: This retrospective study included 88 contrast-enhanced abdominopelvic CTs acquired with rapid-kVp switching DECT. Images were reconstructed into five VMI levels (40, 55, 70, 85, 100 keV). Fully automated algorithms for quantifying CT number (HU) in abdominal fat (subcutaneous and visceral), skeletal muscle, bone, calcium (abdominal Agatston score), and organ size (area or volume) were applied. Biomarker median difference relative to 70 keV and interquartile range were reported by energy level to characterize variation. Linear regression was performed to calibrate non-70 keV data and to estimate their equivalent 70 keV biomarker attenuation values., Results: Relative to 70 keV, absolute median differences in attenuation-based biomarkers (excluding Agatston score) ranged 39-358, 12-102, 5-48, 9-75 HU for 40, 55, 85, 100 keV, respectively. For area-based biomarkers, differences ranged 6-15, 3-4, 2-7, 0-5 cm 2 for 40, 55, 85, 100 keV. For volume-based biomarkers, differences ranged 12-34, 8-68, 12-52, 1-57 cm 3 for 40, 55, 85, 100 keV. Agatston score behavior was more spurious with median differences ranging 70-204 HU. In general, VMI < 70 keV showed more variation in median biomarker measurement than VMI > 70 keV., Conclusion: This study characterized the behavior of a fully automated AI CT biomarker toolkit across varying VMI levels obtained with DECT. The data showed relatively little biomarker value change when measured at or greater than 70 keV. Lower VMI datasets should be avoided due to larger deviations in measured value as compared to 70 keV, a level considered equivalent to conventional 120 kVp exams., Competing Interests: Declarations. Competing interests: GVT: Consultant for GE Healthcare and Canon Medical. JWG: Advisor to RadUnity. SDR: None. TPS: Receives research support from Canon Medical Systems and GE HealthCare, consulting fees from Alara Imaging, Imalogix, Aidoc; royalties from Medical Physics Publishing and royalties related to intellectual property from Qaelum; founder of RadUnity. PJP: Advisor to Nanox-AI, Bracco Diagnostics, and GE HealthCare., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

A spectrum of heterotopic and ectopic splenic conditions may be encountered in clinical practice as incidental asymptomatic detection or symptomatic diagnosis. The radiologist needs to be aware of these conditions and their imaging characteristics to provide a prompt correct diagnosis and avoid misdiagnosis as neoplasm or lymphadenopathy. Having a strong knowledge base of the embryologic development of the spleen improves understanding of the pathophysiologic basis of these conditions. Spleen-specific imaging techniques-such as technetium 99m ( 99m Tc)-labeled denatured erythrocyte scintigraphy, 99m Tc-sulfur colloid liver-spleen scintigraphy, and MRI with ferumoxytol intravenous contrast material-can also be used to confirm the presence or absence of splenic tissue. Heterotopic splenic conditions include splenules and splenogonadal fusion (discontinuous or continuous forms). These heterotopic conditions are caused by incomplete fusion of the splenic primordia (splenule) and abnormal fusion of the gonadal and splenic tissue (splenogonadal fusion). Ectopic splenic conditions arise in patients with a prior splenic injury (splenosis), laxity or maldevelopment of the splenic ligaments (wandering spleen), or heterotaxy syndromes (polysplenia and asplenia). Importantly, these heterotopic and ectopic splenic conditions can also manifest with complications, including vascular torsion and rupture. © RSNA, 2024.

Rationale and Objectives: Pancreas segmentation accuracy at CT is critical for the identification of pancreatic pathologies and is essential for the development of imaging biomarkers. Our objective was to benchmark the performance of five high-performing pancreas segmentation models across multiple metrics stratified by scan and patient/pancreatic characteristics that may affect segmentation performance., Materials and Methods: In this retrospective study, PubMed and ArXiv searches were conducted to identify pancreas segmentation models which were then evaluated on a set of annotated imaging datasets. Results (Dice score, Hausdorff distance [HD], average surface distance [ASD]) were stratified by contrast status and quartiles of peri-pancreatic attenuation (5 mm region around pancreas). Multivariate regression was performed to identify imaging characteristics and biomarkers (n = 9) that were significantly associated with Dice score., Results: Five pancreas segmentation models were identified: Abdomen Atlas [AAUNet, AASwin, trained on 8448 scans], TotalSegmentator [TS, 1204 scans], nnUNetv1 [MSD-nnUNet, 282 scans], and a U-Net based model for predicting diabetes [DM-UNet, 427 scans]. These were evaluated on 352 CT scans (30 females, 25 males, 297 sex unknown; age 58 ± 7 years [ ± 1 SD], 327 age unknown) from 2000-2023. Overall, TS, AAUNet, and AASwin were the best performers, Dice= 80 ± 11%, 79 ± 16%, and 77 ± 18%, respectively (pairwise Sidak test not-significantly different). AASwin and MSD-nnUNet performed worse (for all metrics) on non-contrast scans (vs contrast, P < .001). The worst performer was DM-UNet (Dice=67 ± 16%). All algorithms except TS showed lower Dice scores with increasing peri-pancreatic attenuation (P < .01). Multivariate regression showed non-contrast scans, (P < .001; MSD-nnUNet), smaller pancreatic length (P = .005, MSD-nnUNet), and height (P = .003, DM-UNet) were associated with lower Dice scores., Conclusion: The convolutional neural network-based models trained on a diverse set of scans performed best (TS, AAUnet, and AASwin). TS performed equivalently to AAUnet and AASwin with only 13% of the training set size (8488 vs 1204 scans). Though trained on the same dataset, a transformer network (AASwin) had poorer performance on non-contrast scans whereas its convolutional network counterpart (AAUNet) did not. This study highlights how aggregate assessment metrics of pancreatic segmentation algorithms seen in other literature are not enough to capture differential performance across common patient and scanning characteristics in clinical populations., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Abhinav Suri reports financial support was provided by National Institutes of Health and reports a relationship with Springer Nature that includes royalties. Perry Pickhardt reports relationship with General Electric Company that includes consulting or advisory, Bracco Imaging SpA that includes consulting or advisory, Zebra Technologies Corp that includes consulting or advisory, Elucent that includes equity or stocks, SHINE that includes equity or stocks. Ronald Summers reports a relationship with PingAn that includes a collaborative grant, patent royalties and/or software licenses from iCAD, Philips, ScanMed, Philips, ScanMed, PingAn, Massachusetts General Brigham, and Translation Holdings; member of the Academic Radiology, Journal of Medical Imaging, and Radiology: AI Editorial Boards. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Published by Elsevier Inc.)

Objective: To assess the diagnostic performance of post-contrast CT for predicting moderate hepatic steatosis in an older adult cohort undergoing a uniform CT protocol, utilizing hepatic and splenic attenuation values., Materials and Methods: A total of 1676 adults (mean age, 68.4 ± 10.2 years; 1045M/631F) underwent a CT urothelial protocol that included unenhanced, portal venous, and 10-min delayed phases through the liver and spleen. Automated hepatosplenic segmentation for attenuation values (in HU) was performed using a validated deep-learning tool. Unenhanced liver attenuation < 40.0 HU, corresponding to > 15% MRI-based proton density fat, served as the reference standard for moderate steatosis., Results: The prevalence of moderate or severe steatosis was 12.9% (216/1676). The diagnostic performance of portal venous liver HU in predicting moderate hepatic steatosis (AUROC = 0.943) was significantly better than the liver-spleen HU difference (AUROC = 0.814) (p < 0.001). Portal venous phase liver thresholds of 80 and 90 HU had a sensitivity/specificity for moderate steatosis of 85.6%/89.6%, and 94.9%/74.7%, respectively, whereas a liver-spleen difference of -40 HU and -10 HU had a sensitivity/specificity of 43.5%/90.0% and 92.1%/52.5%, respectively. Furthermore, livers with moderate-severe steatosis demonstrated significantly less post-contrast enhancement (mean, 35.7 HU vs 47.3 HU; p < 0.001)., Conclusion: Moderate steatosis can be reliably diagnosed on standard portal venous phase CT using liver attenuation values alone. Consideration of splenic attenuation appears to add little value. Moderate steatosis not only has intrinsically lower pre-contrast liver attenuation values (< 40 HU), but also enhances less, typically resulting in post-contrast liver attenuation values of 80 HU or less., Clinical Relevance Statement: Moderate steatosis can be reliably diagnosed on post-contrast CT using liver attenuation values alone. Livers with at least moderate steatosis enhance less than those with mild or no steatosis, which combines with the lower intrinsic attenuation to improve detection., Key Points: The liver-spleen attenuation difference is frequently utilized in routine practice but appears to have performance limitations. The liver-spleen attenuation difference is less effective than liver attenuation for moderate steatosis. Moderate and severe steatosis can be identified on standard portal venous phase CT using liver attenuation alone., (© 2024. The Author(s), under exclusive licence to European Society of Radiology.)

Rationale and Objectives: In the United States, cirrhosis was the 12th leading cause of death in 2016. Despite end-stage cirrhosis being irreversible, earlier stages of hepatic fibrosis can be reversed via early diagnosis and intervention. The objective is to investigate the utility of a fully automated technique to measure liver surface nodularity (LSN) for staging hepatic fibrosis (stages F0-F4)., Materials and Methods: In this retrospective study, a dataset consisting of patients with multiple etiologies of liver disease collected at Institution-A (METAVIR F0-F4, 2000-2016) was used. The LSN was automatically measured in contrast-enhanced CT volumes and compared against scores from a manual tool. Area under the receiver operating characteristics curve (AUC) was used to distinguish between clinically significant fibrosis (≥ F2), advanced fibrosis (≥F3), and end-stage cirrhosis (F4)., Results: The study sample had 480 patients (304 men, 176 women, mean age, 49±9). Automatically derived LSN scores progressively increased with the fibrosis stage: F0 (1.64 [mean]±1.13 [standard deviation]), F1 (2.16±2.39), F2 (2.17±2.55), F3 (2.23±2.52), and F4 (4.21±2.94). For discriminating significant fibrosis (≥F2), advanced fibrosis (≥F3), and cirrhosis (F4), the automated tool achieved ROC AUCs of 73.9%, 82.5%, and 87.8% respectively. The sensitivity and specificity for significant fibrosis (nodularity threshold 1.51) was 85.2% and 73.3%, advanced fibrosis (nodularity threshold 1.73) was 84.2% and 79.5%, and cirrhosis (nodularity threshold 2.18) was 86.5% and 79.5%. Statistical tests revealed that the automated LSN scores distinguished patients with advanced fibrosis (p<.001) and cirrhosis (p<.001)., Conclusion: The fully automated LSN measurement retained its predictive power for distinguishing between advanced fibrosis and cirrhosis. The clinical impact is that the fully automated LSN measurement may be useful for early interventions and population-based studies. It can automatically predict the fibrosis stage in ∼45 s in comparison to the ∼2 min needed to manually measure the LSN in a CT volume., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Perry J Pickhardt reports a relationship with Bracco that includes: consulting or advisory. Perry J Pickhardt reports a relationship with Nanox that includes: consulting or advisory. Perry J Pickhardt reports a relationship with GE Healthcare that includes: consulting or advisory. Ronald M. Summers reports a relationship with iCAD that includes: consulting or advisory and funding grants. Ronald M. Summers reports a relationship with Philips that includes: consulting or advisory and funding grants. Ronald M. Summers reports a relationship with ScanMed that includes: consulting or advisory and funding grants. Ronald M. Summers reports a relationship with PingAn that includes: consulting or advisory and funding grants. Ronald M. Summers reports a relationship with Translation Holdings that includes: consulting or advisory and funding grants. Ronald M. Summers reports a relationship with MGB that includes: consulting or advisory and funding grants. R.M.S is a member of the editorial board at Radiology: AI If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Published by Elsevier Inc.)

Objectives: To evaluate the utility of CT-based abdominal fat measures for predicting the risk of death and cardiometabolic disease in an asymptomatic adult screening population., Methods: Fully automated AI tools quantifying abdominal adipose tissue (L3 level visceral [VAT] and subcutaneous [SAT] fat area, visceral-to-subcutaneous fat ratio [VSR], VAT attenuation), muscle attenuation (L3 level), and liver attenuation were applied to non-contrast CT scans in asymptomatic adults undergoing CT colonography (CTC). Longitudinal follow-up documented subsequent deaths, cardiovascular events, and diabetes. ROC and time-to-event analyses were performed to generate AUCs and hazard ratios (HR) binned by octile., Results: A total of 9223 adults (mean age, 57 years; 4071:5152 M:F) underwent screening CTC from April 2004 to December 2016. 549 patients died on follow-up (median, nine years). Fat measures outperformed BMI for predicting mortality risk-5-year AUCs for muscle attenuation, VSR, and BMI were 0.721, 0.661, and 0.499, respectively. Higher visceral, muscle, and liver fat were associated with increased mortality risk-VSR > 1.53, HR = 3.1; muscle attenuation < 15 HU, HR = 5.4; liver attenuation < 45 HU, HR = 2.3. Higher VAT area and VSR were associated with increased cardiovascular event and diabetes risk-VSR > 1.59, HR = 2.6 for cardiovascular event; VAT area > 291 cm 2 , HR = 6.3 for diabetes (p < 0.001). A U-shaped association was observed for SAT with a higher risk of death for very low and very high SAT., Conclusion: Fully automated CT-based measures of abdominal fat are predictive of mortality and cardiometabolic disease risk in asymptomatic adults and uncover trends that are not reflected in anthropomorphic measures., Clinical Relevance Statement: Fully automated CT-based measures of abdominal fat soundly outperform anthropometric measures for mortality and cardiometabolic risk prediction in asymptomatic patients., Key Points: Abdominal fat depots associated with metabolic dysregulation and cardiovascular disease can be derived from abdominal CT. Fully automated AI body composition tools can measure factors associated with increased mortality and cardiometabolic risk. CT-based abdominal fat measures uncover trends in mortality and cardiometabolic risk not captured by BMI in asymptomatic outpatients., Competing Interests: Compliance with ethical standards. Guarantor: The scientific guarantor of this publication is M.H.L., MD. Conflict of interest: The authors of this manuscript declare relationships with the following companies: Dr. Pickhardt serves as an advisor to Bracco and Nanox; Dr. Summers receives royalties from iCAD, PingAn, Philips, Translation Holdings, and ScanMed and research support from PingAn and NVIDIA. Statistics and biometry: One of the authors, Mr. Zea, has significant statistical expertise. Informed consent: Written informed consent was waived by the Institutional Review Board. Ethical approval: Institutional Review Board approval was obtained. Study subjects or cohorts overlap: Some study subjects or cohorts have been previously reported in prior studies comparing the predictive value of our panel of fully automated CT-based measures with clinical tools for predicting cardiometabolic risk. Importantly, however, we feel our work is unique and innovative in the following ways: we uncover trends in abdominal fat deposition and ectopic fat deposition associated with increased mortality and cardiometabolic disease risk, which can be readily derived from existing CT data for additional value-added “opportunistic” screening. Our fully automated AI tools address a need and have the potential to improve standardization and methodological heterogeneity and decrease barriers to clinical implementation. The “U-shaped” relationship we observed for survival and subcutaneous fat provides new insight into the so-called “obesity paradox,” whereby more favorable outcomes are observed in overweight and obese patients. Methodology: Retrospective Observational cohort study Performed at one institution, (© 2024. The Author(s), under exclusive licence to European Society of Radiology.)

Diabetes mellitus and metabolic syndrome are closely linked with visceral body composition, but clinical assessment is limited to external measurements and laboratory values including hemoglobin A1c (HbA1c). Modern deep learning and AI algorithms allow automated extraction of biomarkers for organ size, density, and body composition from routine computed tomography (CT) exams. Comparing visceral CT biomarkers across groups with differing glycemic control revealed significant, progressive CT biomarker changes with increasing HbA1c. For example, in the unenhanced female cohort, mean changes between normal and poorly-controlled diabetes showed: 53% increase in visceral adipose tissue area, 22% increase in kidney volume, 24% increase in liver volume, 6% decrease in liver density (hepatic steatosis), 16% increase in skeletal muscle area, and 21% decrease in skeletal muscle density (myosteatosis) (all p < 0.001). The multisystem changes of metabolic syndrome can be objectively and retrospectively measured using automated CT biomarkers, with implications for diabetes, metabolic syndrome, and GLP-1 agonists., (© 2024. The Author(s).)

Background: The long-acting glucagon-like peptide-1 receptor agonist semaglutide is used to treat type 2 diabetes or obesity in adults. Clinical trials have observed associations of semaglutide with weight loss, improved diabetic control, and cardiovascular risk reduction. Objective: To evaluate intrapatient changes in body composition after initiation of semaglutide therapy by applying an automated suite of CT-based artificial intelligence (AI) body composition tools. Methods: This retrospective study included adult patients with semaglutide treatment who underwent abdominopelvic CT both within 5 years before and within 5 years after semaglutide initiation, between January 2016 and November 2023. An automated suite of previously validated CT-based AI body composition tools was applied to pre-semaglutide and post-semaglutide scans to quantify visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) area, skeletal muscle area and attenuation, intermuscular adipose tissue (IMAT) area, liver volume and attenuation, and trabecular bone mineral density (BMD). Patients with ≥5-kg weight loss and ≥5-kg weight gain between scans were compared. Results: The study included 241 patients (mean age, 60.4±12.4 years; 151 women, 90 men). In the weight-loss group (n=67), the post-semaglutide scan, versus pre-semaglutide scan, showed decrease in VAT area (341.1 vs 309.4 cm 2 , p<.001), SAT area (371.4 vs 410.7 cm 2 , p<.001), muscle area (179.2 vs 193.0, p<.001), and liver volume (2379.0 vs 2578 HU, p=.009), and increase in liver attenuation (74.5 vs 67.6 HU, p=.03). In the weight-gain group (n=48), the post-semaglutide scan, versus pre-semaglutide scan, showed increase in VAT area (334.0 vs 312.8, p=.002), SAT area (485.8 vs 488.8 cm 2 , p=.01), and IMAT area (48.4 vs 37.6, p=.009), and decrease in muscle attenuation (5.9 vs 13.1, p<.001). Other comparisons were not significant (p>.05). Conclusion: Patients using semaglutide who lost versus gained weight demonstrated distinct patterns of changes in CT-based body composition measures. Those with weight loss exhibited overall favorable shifts in measures related to cardiometabolic risk. Muscle attenuation decrease in those with weight gain is consistent with decreased muscle quality. Clinical Impact: Automated CT-based AI tools provide biomarkers of body composition changes in patients using semaglutide beyond that which is evident by standard clinical measures.