Fully automated CT imaging biomarkers for opportunistic prediction of future hip fractures.

OBJECTIVE: Assess automated CT imaging biomarkers in patients who went on to hip fracture, compared with controls. METHODS: In this retrospective case-control study, 6926 total patients underwent initial abdominal CT over a 20-year interval at one institution. A total of 1308 patients (mean age at initial CT, 70.5 ± 12.0 years; 64.4% female) went on to hip fracture (mean time to fracture, 5.2 years); 5618 were controls (mean age 70.3 ± 12.0 years; 61.2% female; mean follow-up interval 7.6 years). Validated fully automated quantitative CT algorithms for trabecular bone attenuation (at L1), skeletal muscle attenuation (at L3), and subcutaneous adipose tissue area (SAT) (at L3) were applied to all scans. Hazard ratios (HRs) comparing highest to lowest risk quartiles and receiver operating characteristic (ROC) curve analysis including area under the curve (AUC) were derived. RESULTS: Hip fracture HRs (95% CI) were 3.18 (2.69-3.76) for low trabecular bone HU, 1.50 (1.28-1.75) for low muscle HU, and 2.18 (1.86-2.56) for low SAT. 10-year ROC AUC values for predicting hip fracture were 0.702, 0.603, and 0.603 for these CT-based biomarkers, respectively. Multivariate combinations of these biomarkers further improved predictive value; the 10-year ROC AUC combining bone/muscle/SAT was 0.733, while combining muscle/SAT was 0.686. CONCLUSION: Opportunistic use of automated CT bone, muscle, and fat measures can identify patients at higher risk for future hip fracture, regardless of the indication for CT imaging. ADVANCES IN KNOWLEDGE: CT data can be leveraged opportunistically for further patient evaluation, with early intervention as needed. These novel AI tools analyse CT data to determine a patient's future hip fracture risk.

Association of Polymorphisms of the IL-17A and IL-17F Genes with Increased Risk of Hypertension and Obesity in Mexican Patients with COVID-19.

Coronavirus disease 2019 (COVID-19) is caused by Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV2). COVID-19 can cause a cytokine release syndrome in which cytokines, including interleukin 17 (IL-17), are massively secreted in response to a specific stimulus. This can contribute to mortality and severe forms of COVID-19. The study aimed to determine the association of SARS-CoV2 infection with the IL-17A rs2275913 and IL-17F rs763780 variants, as well as with the associated comorbidities in COVID-19-positive Mexican patients. The study included 178 patients positive to COVID-19 and 177 COVID-19 negative subjects. For genotyping, the samples were amplified with a TaqMan® probe. There was no association between the AA genotype and A allele of IL-17A variant or the IL-17F C allele with the presence of COVID-19. In regard to comorbidities, a statistically significant association was found between IL-17A rs2275913 AA genotype and hypertension, as well as with the presence of obesity (P = 0.003, OR 23, 95% CI: 2.97-178.092 and P = 0.025, OR 28, 95% CI: 1.52-178.029, respectively) in patients with COVID-19. In conclusion, rs2275913 IL-17A polymorphism in COVID-19 patients seems to confer a higher susceptibility to the presence of hypertension and obesity, increasing the risk of premature cardiovascular disease in this population. However, more studies should be conducted for a better understanding of their relation.

Automated Deep Learning Artificial Intelligence Tool for Spleen Segmentation on CT: Defining Volume-Based Thresholds for Splenomegaly.

BACKGROUND. Splenomegaly historically has been assessed on imaging by use of potentially inaccurate linear measurements. Prior work tested a deep learning artificial intelligence (AI) tool that automatically segments the spleen to determine splenic volume. OBJECTIVE. The purpose of this study is to apply the deep learning AI tool in a large screening population to establish volume-based splenomegaly thresholds. METHODS. This retrospective study included a primary (screening) sample of 8901 patients (4235 men, 4666 women; mean age, 56 ± 10 [SD] years) who underwent CT colonoscopy (n = 7736) or renal donor CT (n = 1165) from April 2004 to January 2017 and a secondary sample of 104 patients (62 men, 42 women; mean age, 56 ± 8 years) with end-stage liver disease who underwent contrast-enhanced CT performed as part of evaluation for potential liver transplant from January 2011 to May 2013. The automated deep learning AI tool was used for spleen segmentation, to determine splenic volumes. Two radiologists independently reviewed a subset of segmentations. Weight-based volume thresholds for splenomegaly were derived using regression analysis. Performance of linear measurements was assessed. Frequency of splenomegaly in the secondary sample was determined using weight-based volumetric thresholds. RESULTS. In the primary sample, both observers confirmed splenectomy in 20 patients with an automated splenic volume of 0 mL; confirmed incomplete splenic coverage in 28 patients with a tool output error; and confirmed adequate segmentation in 21 patients with low volume (< 50 mL), 49 patients with high volume (> 600 mL), and 200 additional randomly selected patients. In 8853 patients included in analysis of splenic volumes (i.e., excluding a value of 0 mL or error values), the mean automated splenic volume was 216 ± 100 [SD] mL. The weight-based volumetric threshold (expressed in milliliters) for splenomegaly was calculated as (3.01 × weight [expressed as kilograms]) + 127; for weight greater than 125 kg, the splenomegaly threshold was constant (503 mL). Sensitivity and specificity for volume-defined splenomegaly were 13% and 100%, respectively, at a true craniocaudal length of 13 cm, and 78% and 88% for a maximum 3D length of 13 cm. In the secondary sample, both observers identified segmentation failure in one patient. The mean automated splenic volume in the 103 remaining patients was 796 ± 457 mL; 84% (87/103) of patients met the weight-based volume-defined splenomegaly threshold. CONCLUSION. We derived a weight-based volumetric threshold for splenomegaly using an automated AI-based tool. CLINICAL IMPACT. The AI tool could facilitate large-scale opportunistic screening for splenomegaly.

Utility of Fully Automated Body Composition Measures on Pretreatment Abdominal CT for Predicting Survival in Patients With Colorectal Cancer.

BACKGROUND. CT examinations contain opportunistic body composition data with potential prognostic utility. Previous studies have primarily used manual or semiautomated tools to evaluate body composition in patients with colorectal cancer (CRC). OBJECTIVE. The purpose of this article is to assess the utility of fully automated body composition measures derived from pretreatment CT examinations in predicting survival in patients with CRC. METHODS. This retrospective study included 1766 patients (mean age, 63.7 ± 14.4 [SD] years; 862 men, 904 women) diagnosed with CRC between January 2001 and September 2020 who underwent pretreatment abdominal CT. A panel of fully automated artificial intelligence-based algorithms was applied to portal venous phase images to quantify skeletal muscle attenuation at the L3 lumbar level, visceral adipose tissue (VAT) area and subcutaneous adipose tissue (SAT) area at L3, and abdominal aorta Agatston score (aortic calcium). The electronic health record was reviewed to identify patients who died of any cause (n = 848). ROC analyses and logistic regression analyses were used to identify predictors of survival, with attention to highest- and lowest-risk quartiles. RESULTS. Patients who died, compared with patients who survived, had lower median muscle attenuation (19.2 vs 26.2 HU, p < .001), SAT area (168.4 cm2 vs 197.6 cm2, p < .001), and aortic calcium (620 vs 182, p < .001). Measures with highest 5-year AUCs for predicting survival in patients without (n = 1303) and with (n = 463) metastatic disease were muscle attenuation (0.666 and 0.701, respectively) and aortic calcium (0.677 and 0.689, respectively). A combination of muscle attenuation, SAT area, and aortic calcium yielded 5-year AUCs of 0.758 and 0.732 in patients without and with metastases, respectively. Risk of death was increased (p < .05) in patients in the lowest quartile for muscle attenuation (hazard ratio [HR] = 1.55) and SAT area (HR = 1.81) and in the highest quartile for aortic calcium (HR = 1.37) and decreased (p < .05) in patients in the highest quartile for VAT area (HR = 0.79) and SAT area (HR = 0.76). In 423 patients with available BMI, BMI did not significantly predict death (p = .75). CONCLUSION. Fully automated CT-based body composition measures including muscle attenuation, SAT area, and aortic calcium predict survival in patients with CRC. CLINICAL IMPACT. Routine pretreatment body composition evaluation could improve initial risk stratification of patients with CRC.

Improved CT-based Osteoporosis Assessment with a Fully Automated Deep Learning Tool.

Purpose: To develop, test, and validate a deep learning (DL) tool that improves upon a previous feature-based CT image processing bone mineral density (BMD) algorithm and compare it against the manual reference standard. Materials and Methods: This single-center, retrospective, Health Insurance Portability and Accountability Act-compliant study included manual L1 trabecular Hounsfield unit measurements from abdominal CT scans in 11 035 patients (mean age, 58 years ± 12 [SD]; 6311 women) as the reference standard. Automated level selection and L1 trabecular region of interest (ROI) placement were then performed in this CT cohort with both a previously validated feature-based image processing tool and a new DL tool. Overall technical success rates and agreement with the manual reference standard were assessed. Results: The overall success rate of the DL tool in this heterogeneous patient cohort was significantly higher than that of the older image processing BMD algorithm (99.3% vs 89.4%, P < .001). Using this DL tool, the closest median Hounsfield unit values for single-, three-, and seven-slice vertebral ROIs were within 5% of the manual reference standard Hounsfield unit values in 35.1%, 56.9%, and 85.8% of scans; within 10% in 56.6%, 75.6%, and 92.9% of scans; and within 25% in 76.5%, 89.3%, and 97.1% of scans, respectively. Trade-offs in sensitivity and specificity for osteoporosis assessment were observed from the single-slice approach (sensitivity, 39.4%; specificity, 98.3%) to the minimum value of the multislice approach (for seven contiguous slices; sensitivity, 71.3% and specificity, 94.6%). Conclusion: The new DL BMD tool demonstrated a higher success rate than the older feature-based image processing tool, and its outputs can be targeted for higher specificity or sensitivity for osteoporosis assessment.Keywords: CT, CT-Quantitative, Abdomen/GI, Skeletal-Axial, Spine, Deep Learning, Machine Learning Supplemental material is available for this article. © RSNA, 2022.

Computed Tomography and Magnetic Resonance Imaging Overlay in the Spine for Surgical Planning: A Technical Report.

BACKGROUND: Computed tomography (CT) and magnetic resonance imaging (MRI) studies are used separately for surgical planning of spine surgery. Advanced techniques exist for creating CT-MR fusion images, but at this time these techniques are not easily accessible for large-scale use. TECHNIQUE: We propose a simple graphical technique for CT-MR image overlay, for use in the surgical planning of spinal decompression and guidance of intraoperative resection. The proposed technique involves overlaying a single cross-section from anatomically comparable MRI and CT studies on any software with basic image editing functions. RESULTS: We demonstrate CT-MR fusion images of 8 patients of the senior author in which the technique was used. We found that it can also be referenced intraoperatively for navigation. CONCLUSIONS: Compared to other techniques, our proposed method can be easily implemented by clinicians to create simple CT-MRI fusion images that can be useful for preoperative planning and intraoperative navigation.

Deep Learning CT-based Quantitative Visualization Tool for Liver Volume Estimation: Defining Normal and Hepatomegaly.

Background Imaging assessment for hepatomegaly is not well defined and currently uses suboptimal, unidimensional measures. Liver volume provides a more direct measure for organ enlargement. Purpose To determine organ volume and to establish thresholds for hepatomegaly with use of a validated deep learning artificial intelligence tool that automatically segments the liver. Materials and Methods In this retrospective study, liver volumes were successfully derived with use of a deep learning tool for asymptomatic outpatient adults who underwent multidetector CT for colorectal cancer screening (unenhanced) or renal donor evaluation (contrast-enhanced) at a single medical center between April 2004 and December 2016. The performance of the craniocaudal and maximal three-dimensional (3D) linear measures was assessed. The manual liver volume results were compared with the automated results in a subset of renal donors in which the entire liver was included at both precontrast and postcontrast CT. Unenhanced liver volumes were standardized to a postcontrast equivalent, reflecting a correction of 3.6%. Linear regression analysis was performed to assess the major patient-specific determinant or determinants of liver volume among age, sex, height, weight, and body surface area. Results A total of 3065 patients (mean age ± standard deviation, 54 years ± 12; 1639 women) underwent multidetector CT for colorectal screening (n = 1960) or renal donor evaluation (n = 1105). The mean standardized automated liver volume ± standard deviation was 1533 mL ± 375 and demonstrated a normal distribution. Patient weight was the major determinant of liver volume and demonstrated a linear relationship. From this result, a linear weight-based upper limit of normal hepatomegaly threshold volume was derived: hepatomegaly (mL) = 14.0 × (weight [kg]) + 979. A craniocaudal threshold of 19 cm was 71% sensitive (49 of 69 patients) and 86% specific (887 of 1030 patients) for hepatomegaly, and a maximal 3D linear threshold of 24 cm was 78% sensitive (54 of 69) and 66% specific (678 of 1030). In the subset of 189 patients, the median difference in hepatic volume between the deep learning tool and the semiautomated or manual method was 2.3% (38 mL). Conclusion A simple weight-based threshold for hepatomegaly derived by using a fully automated CT-based liver volume segmentation based on deep learning provided an objective and more accurate assessment of liver size than linear measures. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Sosna in this issue.

Fully Automated Deep Learning Tool for Sarcopenia Assessment on CT: L1 Versus L3 Vertebral Level Muscle Measurements for Opportunistic Prediction of Adverse Clinical Outcomes.

BACKGROUND. Sarcopenia is associated with adverse clinical outcomes. CT-based skeletal muscle measurements for sarcopenia assessment are most commonly performed at the L3 vertebral level. OBJECTIVE. The purpose of this article is to compare the utility of fully automated deep learning CT-based muscle quantitation at the L1 versus L3 level for predicting future hip fractures and death. METHODS. This retrospective study included 9223 asymptomatic adults (mean age, 57 ± 8 [SD] years; 4071 men, 5152 women) who underwent unenhanced low-dose abdominal CT. A previously validated fully automated deep learning tool was used to assess muscle for myosteatosis (by mean attenuation) and myopenia (by cross-sectional area) at the L1 and L3 levels. Performance for predicting hip fractures and death was compared between L1 and L3 measures. Performance for predicting hip fractures and death was also evaluated using the established clinical risk scores from the fracture risk assessment tool (FRAX) and Framingham risk score (FRS), respectively. RESULTS. Median clinical follow-up interval after CT was 8.8 years (interquartile range, 5.1-11.6 years), yielding hip fractures and death in 219 (2.4%) and 549 (6.0%) patients, respectively. L1-level and L3-level muscle attenuation measurements were not different in 2-, 5-, or 10-year AUC for hip fracture (p = .18-.98) or death (p = .19-.95). For hip fracture, 5-year AUCs for L1-level muscle attenuation, L3-level muscle attenuation, and FRAX score were 0.717, 0.709, and 0.708, respectively. For death, 5-year AUCs for L1-level muscle attenuation, L3-level muscle attenuation, and FRS were 0.737, 0.721, and 0.688, respectively. Lowest quartile hazard ratios (HRs) for hip fracture were 2.20 (L1 attenuation), 2.45 (L3 attenuation), and 2.53 (FRAX score), and for death were 3.25 (L1 attenuation), 3.58 (L3 attenuation), and 2.82 (FRS). CT-based muscle cross-sectional area measurements at L1 and L3 were less predictive for hip fracture and death (5-year AUC ≤ 0.571; HR ≤ 1.56). CONCLUSION. Automated CT-based measurements of muscle attenuation for myosteatosis at the L1 level compare favorably with previously established L3-level measurements and clinical risk scores for predicting hip fracture and death. Assessment for myopenia was less predictive of outcomes at both levels. CLINICAL IMPACT. Alternative use of the L1 rather than L3 level for CT-based muscle measurements allows sarcopenia assessment using both chest and abdominal CT scans, greatly increasing the potential yield of opportunistic CT screening.

A Primer on the Use of Artificial Intelligence in Spine Surgery.

DESIGN: This was a narrative review. PURPOSE: Summarize artificial intelligence (AI) fundamentals as well as current and potential future uses in spine surgery. SUMMARY OF BACKGROUND DATA: Although considered futuristic, the field of AI has already had a profound impact on many industries, including health care. Its ability to recognize patterns and self-correct to improve over time mimics human cognitive function, but on a much larger scale. METHODS: Review of literature on AI fundamentals and uses in spine pathology. RESULTS: Machine learning (ML), a subset of AI, increases in hierarchy of complexity from classic ML to unsupervised ML to deep leaning, where Language Processing and Computer Vision are possible. AI-based tools have been developed to segment spinal structures, acquire basic spinal measurements, and even identify pathology such as tumor or degeneration. AI algorithms could have use in guiding clinical management through treatment selection, patient-specific prognostication, and even has the potential to power neuroprosthetic devices after spinal cord injury. CONCLUSION: While the use of AI has pitfalls and should be adopted with caution, future use is promising in the field of spine surgery and medicine as a whole. LEVEL OF EVIDENCE: Level IV.

Ultrasound-Guided Omental Biopsy: Diagnostic Yield and Association With CT Features Based on a Single-Institution 18-Year Series.

BACKGROUND. The greater omentum can serve as a useful target for percutaneous biopsy; in clinical practice, CT is commonly used for biopsy guidance. OBJECTIVE. The purpose of this study was to evaluate the diagnostic yield of percutaneous ultrasound (US)-guided omental biopsy and to explore the association of the diagnostic yield with prebiopsy diagnostic CT findings. METHODS. This retrospective study included 163 patients (120 women and 43 men; mean age, 65 ± 12 [SD] years; mean body mass index [BMI], 28.9 ± 7.9) who underwent US-guided omental biopsy between 2002 and 2020 at a single institution at which US served as the first-line modality for omental biopsy guidance. Biopsies were performed by abdominal radiologists without dedicated interventional radiology fellowship training. Postbiopsy clinical follow-up and imaging follow-up were reviewed to establish the ultimate diagnosis for each patient. Omental biopsies were characterized as diagnostic or nondiagnostic relative to the ultimate diagnosis. Associations were explored between diagnostic yield and findings on prebiopsy CT and biopsy US. RESULTS. US-guided omental biopsy was performed using an 18-gauge core needle biopsy technique in 156 patients and fine-needle aspiration in seven patients. The mean number of biopsy passes was 2.5 ± 1.0, and mean omental thickness near the biopsy site on CT was 2.6 ± 1.2 cm. On prebiopsy diagnostic CT, omental disease appeared infiltrative in 127 (78%) patients versus mass-forming in 36 (22%) and appeared hypoechoic in 105 (64%) patients versus iso- to hyperechoic in 58 (36%). The ultimate diagnosis was malignant tumor in 154 (95%) patients (most commonly, gynecologic tumors in 82 patients [high-grade serous adenocarcinoma in 56] and gastrointestinal tumors in 45 patients) and a benign finding in nine (6%) patients. The omental biopsy was diagnostic relative to the ultimate diagnosis in 155 (95%) patients. A diagnostic versus nondiagnostic biopsy was not associated (p > .05) with age, BMI, number of biopsy passes, or omental target thickness or attenuation. A total of 94% (120/127) of US-guided omental biopsies of infiltrative cases and 97% (35/36) of biopsies of mass-forming cases were diagnostic (p = .50). A total of 96% (102/106) of US-guided omental biopsies of hypoechoic cases and 93% (53/57) of biopsies of iso- to hyperechoic cases were diagnostic (p = .36). No complications occurred. CONCLUSION. US-guided biopsy of omental disease suspected on CT is safe and effective for tissue diagnosis. Although omental disease commonly appears on US as diffuse infiltrative thickening without a discrete target, sampling based on prebiopsy CT landmarks is diagnostic in most cases. CLINICAL IMPACT. US should be considered the first-line modality for omental biopsy guidance when feasible.

Opportunistic Screening at Abdominal CT: Use of Automated Body Composition Biomarkers for Added Cardiometabolic Value.

Abdominal CT is a frequently performed imaging examination for a wide variety of clinical indications. In addition to the immediate reason for scanning, each CT examination contains robust additional data on body composition that generally go unused in routine clinical practice. There is now growing interest in harnessing this additional information. Prime examples of cardiometabolic information include measurement of bone mineral density for osteoporosis screening, quantification of aortic calcium for assessment of cardiovascular risk, quantification of visceral fat for evaluation of metabolic syndrome, assessment of muscle bulk and density for diagnosis of sarcopenia, and quantification of liver fat for assessment of hepatic steatosis. All of these relevant biometric measures can now be fully automated through the use of artificial intelligence algorithms, which provide rapid and objective assessment and allow large-scale population-based screening. Initial investigations into these measures of body composition have demonstrated promising performance for prediction of future adverse events that matches or exceeds the best available clinical prediction models, particularly when these CT-based measures are used in combination. In this review, the concept of CT-based opportunistic screening is discussed, and an overview of the various automated biomarkers that can be derived from essentially all abdominal CT examinations is provided, drawing heavily on the authors' experience. As radiology transitions from a volume-based to a value-based practice, opportunistic screening represents a promising example of adding value to services that are already provided. If the potentially high added value of these objective CT-based automated measures is ultimately confirmed in subsequent investigations, this opportunistic screening approach could be considered for intentional CT-based screening. ©RSNA, 2021.

Intestinal malrotation in adults: prevalence and findings based on CT colonography.

OBJECTIVES: Intestinal malrotation is largely a pediatric diagnosis, but initial detection can be made in adulthood. CT colonography (CTC) provides an ideal means for estimating prevalence. Our purpose was to evaluate the prevalence and imaging findings of intestinal malrotation in asymptomatic adults at CTC screening, as well as incomplete optical colonoscopy (OC) referral. METHODS: The CTC database of a single academic institution was searched for cases of intestinal malrotation (developmental nonrotation). Prevalence was estimated from 11,176 adults undergoing CTC. Demographic, clinical, imaging (CTC and other abdominal exams), and surgical data were reviewed. RESULTS: 27 cases of malrotation were confirmed (mean age 62 ± 9 years; 15 M/12F), including 17 from the CTC screening cohort (0.17% prevalence) and 10 from incomplete OC (0.75% prevalence; p < 0.001). Most cases (59%; 16/27) were initially diagnosed at CTC. In 67% (12/18); the presence of malrotation was missed on at least one relevant abdominal imaging examination. At least 22% (6/27) had a history of unexplained, chronic intermittent abdominal pain. At CTC, the SMA-SMV relationship was normal in only 11% (3/27). The ileocecal valve was located in the RLQ in only 22% (6/27). Two patients (7%) had associated findings of heterotaxy (polysplenia). CONCLUSIONS: The prevalence of intestinal malrotation was four times greater for patients referred from incomplete OC compared with primary screening CTC, likely related to anatomic challenges at endoscopy. Malrotation was frequently missed at other abdominal imaging examinations. CTC can uncover unexpected cases of malrotation in adults, which may be relevant in terms of potential for future complications.

Automated assessment of longitudinal biomarker changes at abdominal CT: correlation with subsequent cardiovascular events in an asymptomatic adult screening cohort.

BACKGROUND: Cardiovascular (CV) disease is a major public health concern, and automated methods can potentially capture relevant longitudinal changes on CT for opportunistic CV screening purposes. METHODS: Fully-automated and validated algorithms that quantify abdominal fat, muscle, bone, liver, and aortic calcium were retrospectively applied to a longitudinal adult screening cohort undergoing serial non-contrast CT examination between 2005 and 2016. Downstream major adverse events (MI/CVA/CHF/death) were identified via algorithmic EHR search. Logistic regression, ROC curve, and Cox survival analyses assessed for associations between changes in CT variables and adverse events. RESULTS: Final cohort included 1949 adults (942 M/1007F; mean age, 56.2 ± 6.2 years at initial CT). Mean interval between CT scans was 5.8 ± 2.0 years. Mean clinical follow-up interval from initial CT was 10.4 ± 2.7 years. Major CV events occurred after follow-up CT in 230 total subjects (11.8%). Mean change in aortic calcium Agatston score was significantly higher in CV(+) cohort (591.6 ± 1095.3 vs. 261.1 ± 764.3), as was annualized Agatston change (120.5 ± 263.6 vs. 46.7 ± 143.9) (p < 0.001 for both). 5-year area under the ROC curve (AUC) for Agatston change was 0.611. Hazard ratio for Agatston score change > 500 was 2.8 (95% CI 1.5-4.0) relative to < 500. Agatston score change was the only significant univariate CT biomarker in the survival analysis. Changes in fat and bone measures added no meaningful prediction. CONCLUSION: Interval change in automated CT-based abdominal aortic calcium load represents a promising predictive longitudinal tool for assessing cardiovascular and mortality risks. Changes in other body composition measures were less predictive of adverse events.

CT imaging review of uncommon peritoneal-based neoplasms: beyond carcinomatosis.

Pathologic involvement of the peritoneum can result from a wide variety of conditions, including both neoplastic and non-neoplastic entities. Neoplastic involvement of the peritoneal ligaments, mesenteries, and spaces from malignant spread of epithelial cancers, termed peritoneal carcinomatosis, is frequently encountered at CT evaluation. However, a host of other more unusual benign and malignant neoplasms can manifest with peritoneal disease, including both primary and secondary peritoneal processes, many of which can closely mimic peritoneal carcinomatosis at CT. In this review, we discuss a wide array of unusual peritoneal-based neoplasms that can resemble the more common peritoneal carcinomatosis. Beyond reviewing the salient features for each of these entities, particular emphasis is placed on any specific clinical and CT imaging clues that may allow the interpreting radiologist to appropriately narrow the differential diagnosis and, in some cases, make an imaging-specific diagnosis.

Morphometric analysis of cervical interlaminar space for posterior surgical approach and decompression.

PURPOSE: The purpose of this study is to provide a morphometric description of the bony margins of the interlaminar spaces by level in the cervical spine for guidance of safe posterior cervical surgical dissection and decompression. We also aim to describe the impact of increasing static cervical lordosis on the overlap between the lamina. METHODS: Morphometric measurements of the interlaminar space were performed on 100 consecutive cervical spine CT scans of patients ranging in age from 18 to 50 years were selected. Three raters performed measurements of the interlaminar height measured using two techniques (true interlaminar height and surgical interlaminar height), and interlaminar width from C2-C3 to C7-T1. RESULTS: In total, 100 patients were included. The true interlaminar height was greatest at C2-3, C3-4, C4-5 (5.2 ± 1.4-1.8 mm) and smallest at C6-7 (4.4 ± 1.3 mm). Surgical interlaminar height was greatest at C3-4 (4.2 ± 1.7) and smallest at C6-7 (3.0 ± 1.3 mm). The widest interlaminar space was observed at C3-4 (27.1 ± 2.1 mm) and most narrow at C7-T1 (20.9 ± 2.4 mm). Following multivariate regression, male gender was associated with greater interlaminar widths at each cervical level between C4 and T1 (Table 2). While greater patient height was associated with larger interlaminar height (true and surgical) and width at C2-3 and C4-5, weight was not independently associated with the interlaminar measurements. Increasing C2-C7 lordosis was significantly associated with decreasing true and surgical interlaminar heights at all levels except C7-T1, but was not associated with differences between interlaminar width. CONCLUSION: The study provides a morphometric analysis of interlaminar anatomy in the cervical spine. Surgeons can apply this information in their pre-operative plan to safely approach the posterior cervical spine.

Atherosclerotic Plaque Burden on Abdominal CT: Automated Assessment With Deep Learning on Noncontrast and Contrast-enhanced Scans.

BACKGROUND: Abdominal aortic atherosclerotic plaque burden may have clinical significance but manual measurement is time-consuming and impractical. PURPOSE: To perform external validation on an automated atherosclerotic plaque detector for noncontrast and postcontrast abdominal CT. MATERIALS AND METHODS: The training data consisted of 114 noncontrast CT scans and 23 postcontrast CT urography scans. The testing data set consisted of 922 CT colonography (CTC) scans, and 1207 paired noncontrast and postcontrast CT scans from renal donors from a second institution. Reference standard data included manual plaque segmentations in the 137 training scans and manual plaque burden measurements in the 922 CTC scans. The total Agatston score and group (0-3) was determined using fully-automated deep learning software. Performance was assessed by measures of agreement, linear regression, and paired evaluations. RESULTS: On CTC scans, automated Agatston scoring correlated highly with manual assessment (R2 = 0.94). On paired renal donor CT scans, automated Agatston scoring on postcontrast CT correlated highly with noncontrast CT (R2 = 0.95). When plaque burden was expressed as a group score, there was excellent agreement for both the CTC (weighted kappa 0.80 ± 0.01 [95% confidence interval: 0.78-0.83]) and renal donor (0.83 ± 0.02 [0.79-0.86]) assessments. CONCLUSION: Fully automated detection, segmentation, and scoring of abdominal aortic atherosclerotic plaques on both pre- and post-contrast CT was validated and may have application for population-based studies.

Liver Steatosis Categorization on Contrast-Enhanced CT Using a Fully Automated Deep Learning Volumetric Segmentation Tool: Evaluation in 1204 Healthy Adults Using Unenhanced CT as a Reference Standard.

BACKGROUND. Hepatic attenuation at unenhanced CT is linearly correlated with the MRI proton density fat fraction (PDFF). Liver fat quantification at contrast-enhanced CT is more challenging. OBJECTIVE. The purpose of this article is to evaluate liver steatosis categorization on contrast-enhanced CT using a fully automated deep learning volumetric hepatosplenic segmentation algorithm and unenhanced CT as the reference standard. METHODS. A fully automated volumetric hepatosplenic segmentation algorithm using 3D convolutional neural networks was applied to unenhanced and contrast-enhanced series from a sample of 1204 healthy adults (mean age, 45.2 years; 726 women, 478 men) undergoing CT evaluation for renal donation. The mean volumetric attenuation was computed from all designated liver and spleen voxels. PDFF was estimated from unenhanced CT attenuation and served as the reference standard. Contrast-enhanced attenuations were evaluated for detecting PDFF thresholds of 5% (mild steatosis, 10% and 15% (moderate steatosis); PDFF less than 5% was considered normal. RESULTS. Using unenhanced CT as reference, estimated PDFF was ≥ 5% (mild steatosis), ≥ 10%, and ≥ 15% (moderate steatosis) in 50.1% (n = 603), 12.5% (n = 151) and 4.8% (n = 58) of patients, respectively. ROC AUC values for predicting PDFF thresholds of 5%, 10%, and 15% using contrast-enhanced liver attenuation were 0.669, 0.854, and 0.962, respectively, and using contrast-enhanced liver-spleen attenuation difference were 0.662, 0.866, and 0.986, respectively. A total of 96.8% (90/93) of patients with contrast-enhanced liver attenuation less than 90 HU had steatosis (PDFF ≥ 5%); this threshold of less than 90 HU achieved sensitivity of 75.9% and specificity of 95.7% for moderate steatosis (PDFF ≥ 15%). Liver attenuation less than 100 HU achieved sensitivity of 34.0% and specificity of 94.2% for any steatosis (PDFF ≥ 5%). A total of 93.8% (30/32) of patients with contrast-enhanced liver-spleen attenuation difference 10 HU or less had moderate steatosis (PDFF ≥ 15%); a liver-spleen difference less than 5 HU achieved sensitivity of 91.4% and specificity of 95.0% for moderate steatosis. Liver-spleen difference less than 10 HU achieved sensitivity of 29.5% and specificity of 95.5% for any steatosis (PDFF ≥ 5%). CONCLUSION. Contrast-enhanced volumetric hepatosplenic attenuation derived using a fully automated deep learning CT tool may allow objective categoric assessment of hepatic steatosis. Accuracy was better for moderate than mild steatosis. Further confirmation using different scanning protocols and vendors is warranted. CLINICAL IMPACT. If these results are confirmed in independent patient samples, this automated approach could prove useful for both individualized and population-based steatosis assessment.

Fully automated CT imaging biomarkers of bone, muscle, and fat: correcting for the effect of intravenous contrast.

PURPOSE: Fully automated CT-based algorithms for quantifying bone, muscle, and fat have been validated for unenhanced abdominal scans. The purpose of this study was to determine and correct for the effect of intravenous (IV) contrast on these automated body composition measures. MATERIALS AND METHODS: Initial study cohort consisted of 1211 healthy adults (mean age, 45.2 years; 733 women) undergoing abdominal CT for potential renal donation. Multiphasic CT protocol consisted of pre-contrast, arterial, and parenchymal phases. Fully automated CT-based algorithms for quantifying bone mineral density (BMD, L1 trabecular HU), muscle area and density (L3-level MA and M-HU), and fat (visceral/subcutaneous (V/S) fat ratio) were applied to pre-contrast and parenchymal phases. Effect of IV contrast upon these body composition measures was analyzed. Square of the Pearson correlation coefficient (r2) was generated for each comparison. RESULTS: Mean changes (± SD) in L1 BMD, L3-level MA and M-HU, and V/S fat ratio were 26.7 ± 27.2 HU, 2.9 ± 10.2 cm2, 18.8 ± 6.0 HU, - 0.1 ± 0.2, respectively. Good linear correlation between pre- and post-contrast values was observed for all automated measures: BMD (pre = 0.87 × post; r2 = 0.72), MA (pre = 0.98 × post; r2 = 0.92), M-HU (pre = 0.75 × post + 5.7; r2 = 0.75), and V/S (pre = 1.11 × post; r2 = 0.94); p < 0.001 for all r2 values. There were no significant trends according to patient age or gender that required further correction. CONCLUSION: Fully automated quantitative tissue measures of bone, muscle, and fat at contrast-enhanced abdominal CT can be correlated with non-contrast equivalents using simple, linear relationships. These findings will facilitate evaluation of mixed CT cohorts involving larger patient populations and could greatly expand the potential for opportunistic screening.

Timely Evaluation and Management of Primary Hyperparathyroidism in Patients With Kidney Stones.

BACKGROUND: Nephrolithiasis is a classic, treatable manifestation of primary hyperparathyroidism (PHPT). We examined predictors of kidney stone formation in PHPT patients and determined how efficiently the diagnosis of PHPT is made in patients whose initial presentation is with stones. MATERIALS AND METHODS: We performed a retrospective analysis of surgically treated PHPT patients, comparing 247 patients who were kidney stone formers and 1047 patients with no kidney stones. We also analyzed 51 stone-forming patients whose stone evaluation and treatment were completed within our health system before PHPT diagnosis. RESULTS: Stone-forming patients had higher 24-h urinary calcium (342 versus 304 mg/d, P = 0.005), higher alkaline phosphatase (92 versus 85 IU/L, P = 0.012), and were more likely to be normocalcemic (26.6% versus 16.9%, P = 0.001). Surprisingly, 47.3% of stone formers had normal urinary calcium levels (<300 mg/d). Of the 51 stone-forming patients treated at our institution, serum calcium was measured within 6 mo of stone diagnosis in 37 (72.5%) patients. Only 16 (31.4%) of these patients had elevated calcium levels, and only 10 (62.5%) of these 16 had a serum parathyroid hormone ordered within the following 3 mo. These patients had a significantly shorter time from their first stone to surgical treatment compared to other patients (median 8.5 versus 49.1 mo, P = 0.001). CONCLUSIONS: Elevated serum and urinary calcium levels are not evaluated in the majority of PHPT patients presenting with kidney stones. In nephrolithiasis patients, provider consideration of PHPT with prompt serum calcium and parathyroid hormone evaluation significantly reduces time to treatment.