Single-slice CT Research Articles

Abstract 4136678: Detecting Myosteatosis-Related Low Physical Function in Heart Failure Patients Using Abdominal Computed Tomography and Plasma Amino Acid Profile

Background: Evaluation of sarcopenia requires quantifying appendicular skeletal muscle mass and functional capacity, but resource constraints often limit feasibility. Alterations in muscle quality, especially intramuscular lipid infiltration (i.e., myosteatosis), and systemic metabolic derangements are associated with disability. Hypothesis: Opportunistic CT imaging and plasma amino acid profiling could identify low physical function in patients with heart failure (HF). Methods: We retrospectively examined 220 HF patients (74 ± 13 years old; 59% male) who underwent plasma amino acid profiling, abdominal CT imaging, and dual-energy X-ray absorptiometry (DXA) scan within two months. Myosteatosis was evaluated using single-slice CT imaging at the L3/4 level and quantified as mean muscle attenuation (MMA). Physical function was assessed by the short physical performance battery (SPPB), with low physical function defined as SPPB ≤9. Results: CT-derived single-slice areas of skeletal muscle and adipose tissue were highly correlated with DXA-derived appendicular skeletal muscle mass and total fat mass. Logistic regression analyses revealed that higher age and lower MMA, but not skeletal muscle area, were significantly associated with low physical function independently of NT-proBNP. Subjects with MMA below the median value (32.0 HU for males and 23.7 HU for females) had higher odds of low physical function (adjusted OR 2.04, 95% CI 1.04-3.96). Ten amino acids and the Fischer ratio were significantly correlated with MMA levels. Multivariate regression analyses, adjusted for potential confounders including age, gender, and body mass index, demonstrated that serum albumin and Fischer ratio were independent determinants of MMA levels. When serum albumin and Fischer ratio were dichotomized by their median values (3.6 mg/dL and 3.23, respectively), the prevalence of low physical function was significantly higher in subjects with low albumin and low Fischer ratio, but not in those with low levels of only either parameter, compared to those with high albumin and high Fischer ratio (adjusted OR 2.36, 95% CI 1.02-5.49). Conclusions: Myosteatosis assessed by single-slice abdominal CT imaging is an independent determinant of low physical function in HF patients. Combined with an assessment of serum albumin and Fischer ratio, these simple biomarkers could facilitate the early detection of patients at risk for developing frailty.

DETERMINATION OF LOCAL DIAGNOSTIC REFERENCE LEVEL (LDRL) VALUES OF MSCT IMAGING AND IMAGE NOISE IN CT BRAIN, THORAX, AND ABDOMEN EXAMINATIONS AT DR. SOETRASNO REMBANG HOSPITAL

Multislice CT (MSCT) uses a lower radiation dose per slice than single-slice CT, but patients often receive a higher total dose with MSCT. Therefore, dose control is needed to monitor patients. This study aims to analyze and determine local DRL values and image noise for non-contrast MSCT examinations of the brain, thorax, and abdomen at RSUD dr. Soetrasno Rembang. The method used is a cross-sectional survey with secondary data from 640 patients who underwent MSCT examinations, grouped by age: infants (0-4 years), children (5-14 years), and adults (≥15 years). The analysis showed that several local DRL values for CT Brain, CT Thorax, and CT Abdomen exceeded the national DRL of Indonesia. The DRL value based on CTDIvol for CT Brain is 40.40 mGy across all age groups. The DRL value based on DLP for CT Brain is 1056.4 mGy.cm (infants), 1197 mGy.cm (children), and 1511.5 mGy.cm (adults). For CT Thorax in adults, the DRL value based on CTDIvol is 17.35 mGy, and based on DLP is 981.6 mGy.cm. For CT Abdomen in adults, the DRL value based on CTDIvol is 17.33 mGy, and based on DLP is 1314.4 mGy.cm. The image noise values obtained, namely CT Brain, was 4.43 HU, CT Thorax was 20.88 HU, and CT Abdomen was 10.28 HU, which is not much different from previous research. Keywords: Local Diagnostic Reference Level (LDRL), Multislice CT (MSCT), Image Noise, CT Brain, CT Thorax, CT Abdomen.

Automatic personal identification using a single CT image.

Computer vision (CV) mimics human vision, enabling computers to automatically compare radiological images from recent examinations with a large image database for unique identification, crucial in emergency scenarios involving unknown patients or deceased individuals. This study aims to extend a CV-based personal identification method from orthopantomograms (OPGs) to computed tomography (CT) examinations using single CT slices. The study analyzed 819 cranial computed tomography (CCT) examinations from 722 individuals, focusing on single CT slices from six anatomical regions to explore their potential for CV-based personal identification in 69 procedures. CV automatically identifies and describes interesting features in images, which can be recognized in a reference image and then designated as matching points. In this study, the number of matching points was used as an indicator for identification. Across six different regions, identification rates ranged from 41/69 (59%) to 69/69 (100%) across over 700 possible identities. Comparison of images from the same individual achieved higher matching points, averaging 6.32 ± 0.52% (100% represents the maximum possible matching points), while images of different individuals averaged 0.94 ± 0.15%. Reliable matching points are found in the teeth, maxilla, cervical spine, skull bones, and paranasal sinuses, with the maxillary sinuses and ethmoidal cells being particularly suitable for identification due to their abundant matching points. Unambiguous identification of individuals based on a single CT slice is achievable, with maxillary sinus CT slices showing the highest identification rates. However, metal artifacts, especially from dental prosthetics, and various head positions can hinder identification. Radiology possesses a multitude of reference images for a CV database, facilitating automated CV-based personal identification in emergency examinations or cases involving unknown deceased individuals. This enhances patient care and communication with relatives by granting access to medical history. Unknown individuals in radiology or forensics pose challenges, addressed through automatic CV-based identification methods. A single CT slice highlighting the maxillary sinuses is particularly effective for personal identification. Radiology plays a pivotal role in automated personal identification by leveraging its extensive image database.

Epicardial Adipose Tissue and Psoriasis: A Systematic Review and Meta-Analysis.

Background: As a novel biomarker for cardiovascular diseases, epicardial adipose tissue (EAT) has been linked to psoriasis. We conducted an updated systematic review, building upon a previous report on the relationship between EAT and psoriasis. Methods: We searched Medline, Embase, and the Cochrane Central Register of Controlled Trials. The methodological quality of each study was assessed using the Newcastle-Ottawa Scale. The pooled mean difference (MD) or standardized mean difference (SMD) and the corresponding confidence interval (CIs) were calculated. Results: We included 10 studies with 1287 participants. Five of the included studies were of high methodological quality, while the other five were of moderate quality. The pooled data indicated that psoriasis patients had significantly increased EAT compared to individuals in the control group (SMD 1.53, 95% CI 0.61 to 2.45, 9 studies, 1195 participants). The subgroup analysis showed that psoriasis patients had significantly increased EAT thickness compared with the controls (SMD 2.45, 95% CI 0.73 to 4.17, 5 studies, 657 participants). Similarly, EAT area in single-slice CT images was significantly higher in the psoriasis group than in the control group (SMD 0.45, 95% CI 0.14 to 0.76, 2 studies, 195 participants). The EAT volume based on CT images appeared to be higher in the psoriasis group than in the control group, but the difference was not statistically significant (SMD 0.32, 95% CI -0.06 to 0.70, 2 studies, 343 participants). Conclusions: EAT, especially echocardiographic EAT thickness and CT-determined EAT area, was significantly associated with psoriasis, but CT-determined EAT volume was not.

CT-derived body composition and differential association with age, TNM stage and systemic inflammation in patients with colon cancer

Low skeletal muscle index/density (SMI/SMD) is prevalent in cancer, adversely prognostic and associated with tumour stage and the systemic inflammatory response (SIR). Age and SMI/SMD has not been widely studied. The present study analyses the association between age and SMI/SMD after adjustment for other clinicopathological factors. Patients undergoing resectional surgery for TNM Stage I-III disease within the West of Scotland between 2011 and 2014 were identified. A single CT slice was obtained from each patients staging CT scan. SMI and SMD were stratified normal/abnormal. The SIR was stratified using Systemic Inflammatory Grade (SIG). When stratified by age (< 50/50s/60s/70s/80+), 39%/38%/48%/62%/74% and 27%/48%/64%/82%/92% of patients had a low SMI and SMD respectively (both p < 0.001). Older age (OR 1.47, p < 0.001), female sex (OR 1.32, p = 0.032), lower socioeconomic deprivation (OR 1.15, p = 0.004), higher ASA (OR 1.30, p = 0.019), emergency presentation (OR 1.82, p = 0.003), lower BMI (OR 0.67, p < 0.002) and higher SIG (OR 1.23, p < 0.001) were independently associated with low SMI. Older age (OR 2.28, p < 0.001), female sex (OR 1.38, p = 0.038), higher ASA (OR 1.92, p < 0.001), emergency presentation (OR 1.71, p = 0.023), and higher SIG (OR 1.37, p < 0.001) were independently associated with lower SMD. Tumour factors were not independently associated with either SMI/SMD. Age was a major factor associated with low SMI/SMD in patients with colon cancer. Therefore, in these patients it is likely that this represents largely constitutional body composition as opposed to being a disease mediated effect. Adjustment for age is required when considering the cancer mediated effect on SMI/SMD in patients with colon cancer.

356 The Role of Cancer Cachexia in Influencing Outcomes in Pancreatic &amp; Biliary Malignancy

Abstract Aim To investigate the influence of cancer cachexia on outcomes and clinicopathological variables in pancreatico-biliary malignancies. Method Patients who underwent pancreaticoduodenectomy for malignancy from 2007-2020 at the Glasgow Royal Infirmary were included in this study. Pre-operative single-slice CT scan images were collected for cross sectional analysis of body composition using the semi-automated segmentation programme, SliceOmatic. Body composition measurements were represented as z-scores to account for age and sex. Results Subcutaneous fat radiodensity (SFD) was significantly lower in ampullary adenocarcinoma (-0.17+/-0.95), duodenal adenocarcinoma (-0.03+/-1.08), and cholangiocarcinoma (-0.18+/-1.00) groups compared to the pancreatic cancer group (0.12+/-1.00) (P=0.09). Skeletal muscle index (SMI) was significantly higher in those diagnosed with cholangiocarcinoma compared to all other indications (0.20+/-0.91 vs -0.04+/-1.02, P=0.05). Disease-specific and recurrence-free median survival was lower in those with low SMI compared to those with high SMI for all indications (35 months vs 41 months, 13 months vs 18 months).When comparing those who underwent up-front resection to those who received neoadjuvant therapy, visceral fat index (VFI) was found to be higher (0.06+/-1.00 vs -0.21+/-0.99, P=0.03) and skeletal muscle density was found to be lower (-0.05+/-1.01 vs 0.18+/-0.96, P=0.04). SFD was also found to be significantly lower in those with AJCC stage 1 compared to all other stages (-0.21+/-0.86 vs 0.07+/-1.01, P=0.03). Conclusions Adverse body composition may influence outcomes in pancreatic and biliary malignancy; however, this influence appears to be modest at best. Clinical applications of body composition analysis may be limited to serial monitoring of patients within the context of their treatment pathway.

Sarcopenia is independently associated with poor preoperative physical fitness in patients undergoing colorectal cancer surgery.

Accurate preoperative risk assessment for major colorectal cancer (CRC) surgery remains challenging. Body composition (BC) and cardiopulmonary exercise testing (CPET) can be used to evaluate risk. The relationship between BC and CPET in patients undergoing curative CRC surgery is unclear. Consecutive patients undergoing CPET prior to CRC surgery between 2010 and 2020 were identified between two different UK hospitals. Body composition phenotypes such as sarcopenia, myosteatosis, and visceral obesity were defined using widely accepted thresholds using preoperative single axial slice CT image at L3 vertebrae. Relationships between clinicopathological, BC, and CPET variables were investigated using linear regression analysis. Two hundred eighteen patients with stage I-III CRC were included. The prevalence of sarcopenia, myosteatosis, and visceral obesity was 62%, 33%, and 64%, respectively. The median oxygen uptake at anaerobic threshold (VO2 at AT) was 12.2mL/kg/min (IQR 10.6-14.2), and oxygen uptake at peak exercise (VO2 peak) was 18.8mL/kg/min (IQR 15.4-23). On univariate linear regression analysis, male sex (P<0.001) was positively associated with VO2 at AT. While ASA grade (P<0.001) and BMI (P=0.007) were negatively associated with VO2 at AT, on multivariate linear regression analysis, these variables remained significant (P<0.05). On univariate linear regression analysis, male sex (P<0.001) was positively associated with VO2 peak, whereas age (P<0.001), ASA grade (P<0.001), BMI (P=0.003), sarcopenia (P=0.015), and myosteatosis (P<0.001) were negatively associated with VO2 peak. On multivariate linear regression analysis age (P<0.001), ASA grade (P<0.001), BMI (P<0.001), and sarcopenia (P=0.006) were independently and negatively associated with VO2 peak. The novel finding that sarcopenia is independently associated with reduced VO2 peak performance in CPET supports the supposition that reduced muscle mass relates to poor physical function in CRC patients. Further work should be undertaken to assess whether sarcopenia diagnosed on CT can act as suitable surrogate for CPET to further enhance personalized risk stratification.

Abstract PO1-11-11: A Novel Body Composition Risk Score (B-Score) and Overall Survival among Patients with Early-Stage Breast Cancer

Abstract Background: Measurements (amount, distribution, and radiodensity) of muscle and adipose tissue were reported to be individually associated with overall survival in breast cancer survivors, but they were not typically combined to develop an overall risk score. Such a score can identify patients at high risk of death and prioritize patients in need of lifestyle interventions. Objectives: We developed a novel body composition risk score (B-Score) by combining multiple tissue measurements. Methods: We included 3105 patients with stage II or III breast cancer (diagnosed from 2000 to 2013) at Kaiser Permanente Northern California and Dana Farber Cancer Institute. From CT scans at diagnosis, we used the third lumber vertebrae as the landmark to assess areas (cm2) and radiodensity (HU) of muscle and adipose tissue. We divided the area by height2 (m2) to derive the index and measured the average HU of the tissue area as the radiodensity, which acts as a marker of tissue 'quality' to be indicative of lipid content, inflammation, and angiogenesis. Out of all tissue measurements, we considered skeletal muscle index (SMI), subcutaneous adipose tissue index (SATI) and SAT radiodensity as they were independent predictors for overall survival. Each measurement was dichotomized using optimal stratification, with low SMI (&amp;lt; 40.1cm2/m2), high SATI (≥75.7cm2/m2), and high SAT radiodensity (≥ -97.2HU) considered risk factors. We calculated B-Score as the sum of these factors and estimated its association with overall survival using Cox proportional hazards regression with adjustment for clinicopathologic factors. Results: Mean (standard deviation) age at diagnosis was 53.9 (11.8) years, 2161 (70.1%) were Non-Hispanic White, 1880 (60.5%) were stage II, and 919 (29.6%) died over a median follow-up of 10.7 years. Most patients (60.6%) had only one body composition risk factor (B-Score = 1). Compared to those with no body composition risk factors (B-Score = 0), the risk of death increased with more body composition risk factors: the adjusted hazard ratios were 1.10 (95% CI: 0.85, 1.42), 1.47 (95% CI: 1.12, 1.92), and 2.11 (95% CI: 1.26, 3.53) for B-Scores of 1, 2, and 3, respectively (Ptrend &amp;lt; 0.001). Conclusions: More unfavorable body composition characteristics were associated with increased risks of overall mortality in a dose-response manner. Considering body composition measurements together as a composite score (B-Score) may prove a useful tool to identify patients at high risk of death following breast cancer diagnosis. Table. Adjusted Associations of the Body Composition Risk Score (B-Score) with Overall Mortality Among Patients with Early-Stage Breast Cancer Abbreviations: HR, hazard ratio. a Areas (cm2) and radiodensity (HU) of skeletal muscle and subcutaneous adipose tissue (SAT) were extracted from the single axial CT slice at the third lumbar vertebra. The body composition risk score was defined as the sum of three risk factors: low skeletal muscle index (SMI; &amp;lt;40.1cm2/m2), high SAT index (SATI; ≥75.7cm2/m2), and high SAT radiodensity (≥ -97.2HU). b Adjusted for age (years), race and ethnicity (Asian, Black, Hispanic, Non-Hispanic White, and Others), stage (II, III), ER (ER+, ER-), PR (PR+, PR-), HER2 (HER2+, HER2-, unknown), smoking (current, former, never), Charlson comorbidity index (0, 1-2, ≥3), and BMI (&amp;lt;18.5, 28.5-24.9, 25-29.9, and ≥30 kg/m2). For the trend test, B-Score was considered as a continuous variable with adjustment for the above covariates. Citation Format: En Cheng, Bette Caan, Wendy Chen, Carla Prado, Elizabeth Cespedes Feliciano. A Novel Body Composition Risk Score (B-Score) and Overall Survival among Patients with Early-Stage Breast Cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO1-11-11.

Voxel-wise body composition analysis using image registration of a three-slice CT imaging protocol: methodology and proof-of-concept studies

BackgroundComputed tomography (CT) is an imaging modality commonly used for studies of internal body structures and very useful for detailed studies of body composition. The aim of this study was to develop and evaluate a fully automatic image registration framework for inter-subject CT slice registration. The aim was also to use the results, in a set of proof-of-concept studies, for voxel-wise statistical body composition analysis (Imiomics) of correlations between imaging and non-imaging data.MethodsThe current study utilized three single-slice CT images of the liver, abdomen, and thigh from two large cohort studies, SCAPIS and IGT. The image registration method developed and evaluated used both CT images together with image-derived tissue and organ segmentation masks. To evaluate the performance of the registration method, a set of baseline 3-single-slice CT images (from 2780 subjects including 8285 slices) from the SCAPIS and IGT cohorts were registered. Vector magnitude and intensity magnitude error indicating inverse consistency were used for evaluation. Image registration results were further used for voxel-wise analysis of associations between the CT images (as represented by tissue volume from Hounsfield unit and Jacobian determinant) and various explicit measurements of various tissues, fat depots, and organs collected in both cohort studies.ResultsOur findings demonstrated that the key organs and anatomical structures were registered appropriately. The evaluation parameters of inverse consistency, such as vector magnitude and intensity magnitude error, were on average less than 3 mm and 50 Hounsfield units. The registration followed by Imiomics analysis enabled the examination of associations between various explicit measurements (liver, spleen, abdominal muscle, visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), thigh SAT, intermuscular adipose tissue (IMAT), and thigh muscle) and the voxel-wise image information.ConclusionThe developed and evaluated framework allows accurate image registrations of the collected three single-slice CT images and enables detailed voxel-wise studies of associations between body composition and associated diseases and risk factors.

ECR 2024 Abstract Highlights

The following highlights spotlight selected abstracts presented at the European Congress of Radiology (ECR) 2024. Covering a range of topics, from deep learning applications in neurological disorders to using a single cranial CT slice for identifying deceased individuals, these highlights present the latest cutting-edge developments in radiology.

Estimating Body Weight From Measurements From Different Single-Slice Computed Tomography Levels: An Evaluation of Total Cross-Sectional Body Area Measurements and Deep Learning.

This study aimed to evaluate the correlation between the estimated body weight obtained from 2 easy-to-perform methods and the actual body weight at different computed tomography (CT) levels and determine the best reference site for estimating body weight. A total of 862 patients from a public database of whole-body positron emission tomography/CT studies were retrospectively analyzed. Two methods for estimating body weight at 10 single-slice CT levels were evaluated: a linear regression model using total cross-sectional body area and a deep learning-based model. The accuracy of body weight estimation was evaluated using the mean absolute error (MAE), root mean square error (RMSE), and Spearman rank correlation coefficient ( ρ ). In the linear regression models, the estimated body weight at the T5 level correlated best with the actual body weight (MAE, 5.39 kg; RMSE, 7.01 kg; ρ = 0.912). The deep learning-based models showed the best accuracy at the L5 level (MAE, 6.72 kg; RMSE, 8.82 kg; ρ = 0.865). Although both methods were feasible for estimating body weight at different single-slice CT levels, the linear regression model using total cross-sectional body area at the T5 level as an input variable was the most favorable method for single-slice CT analysis for estimating body weight.

CT-derived Epicardial Adipose Tissue Inflammation Predicts Outcome in Patients Undergoing Transcatheter Aortic Valve Replacement.

Inflammatory changes in epicardial (EAT) and pericardial adipose tissue (PAT) are associated with increased overall cardiovascular risk. Using routine, preinterventional cardiac CT data, we examined the predictive value of quantity and quality of EAT and PAT for outcome after transcatheter aortic valve replacement (TAVR). Cardiac CT data of 1197 patients who underwent TAVR at the in-house heart center between 2011 and 2020 were retrospectively analyzed. The amount and density of EAT and PAT were quantified from single-slice CT images at the level of the aortic valve. Using established risk scores and known independent risk factors, a clinical benchmark model (BMI, Chronic kidney disease stage, EuroSCORE 2, STS Prom, year of intervention) for outcome prediction (2-year mortality) after TAVR was established. Subsequently, we tested whether the additional inclusion of area and density values of EAT and PAT in the clinical benchmark model improved prediction. For this purpose, the cohort was divided into a training (n=798) and a test cohort (n=399). Within the 2-year follow-up, 264 patients died. In the training cohort, particularly the addition of EAT density to the clinical benchmark model showed a significant association with outcome (hazard ratio 1.04, 95% CI: 1.01-1.07; P =0.013). In the test cohort, the outcome prediction of the clinical benchmark model was also significantly improved with the inclusion of EAT density (c-statistic: 0.589 vs. 0.628; P =0.026). EAT density as a surrogate marker of EAT inflammation was associated with 2-year mortality after TAVR and may improve outcome prediction independent of established risk parameters.

Opportunistic CT-derived analysis of fat and muscle tissue composition predicts mortality in patients with cardiogenic shock

Prognosis estimation in patients with cardiogenic shock (CS) is important to guide clinical decision making. Aim of this study was to investigate the predictive value of opportunistic CT-derived body composition analysis in CS patients. Amount and density of fat and muscle tissue of 152 CS patients were quantified from single-slice CT images at the level of the intervertebral disc space L3/L4. Multivariable Cox regression and Kaplan–Meier survival analyses were performed to evaluate the predictive value of opportunistically CT-derived body composition parameters on the primary endpoint of 30-day mortality. Within the 30-day follow-up, 90/152 (59.2%) patients died. On multivariable analyses, lactate (Hazard Ratio 1.10 [95% Confidence Interval 1.04–1.17]; p = 0.002) and patient age (HR 1.04 [95% CI 1.01–1.07], p = 0.017) as clinical prognosticators, as well as visceral adipose tissue (VAT) area (HR 1.004 [95% CI 1.002–1.007]; p = 0.001) and skeletal muscle (SM) area (HR 0.987 [95% CI 0.975–0.999]; p = 0.043) as imaging biomarkers remained as independent predictors of 30-day mortality. Kaplan–Meier survival analyses showed significantly increased 30-day mortality in patients with higher VAT area (p = 0.015) and lower SM area (p = 0.035). CT-derived VAT and SM area are independent predictors of dismal outcomes in CS patients and have the potential to emerge as new imaging biomarkers available from routine diagnostic CT.

Whole-lesion iodine map histogram analysis versus single-slice spectral CT parameters for determining novel International Association for the Study of Lung Cancer grade of invasive non-mucinous pulmonary adenocarcinomas

PurposeThe purpose of this study was to evaluate and compare the performances of whole-lesion iodine map histogram analysis to those of single-slice spectral computed tomography (CT) parameters in discriminating between low-to-moderate grade invasive non-mucinous pulmonary adenocarcinoma (INMA) and high-grade INMA according to the novel International Association for the Study of Lung Cancer grading system of INMA. Materials and methodsSixty-one patients with INMA (34 with low-to-moderate grade [i.e., grade I and grade II] and 27 with high grade [i.e., grade III]) were evaluated with spectral CT. There were 28 men and 33 women, with a mean age of 56.4 ± 10.5 (standard deviation) years (range: 29–78 years). The whole-lesion iodine map histogram parameters (mean, standard deviation, variance, skewness, kurtosis, entropy, and 1st, 10th, 25th, 50th, 75th, 90th, and 99th percentile) were measured for each INMA. In other sessions, by placing regions of interest at representative levels of the tumor and normalizing them, spectral CT parameters (iodine concentration and normalized iodine concentration) were obtained. Discriminating capabilities of spectral CT and histogram parameters were assessed and compared using area under the ROC curve (AUC) and logistic regression models. ResultsThe 1st, 10th, and 25th percentiles of the iodine map histogram analysis, and iodine concentration and normalized iodine concentration of single-slice spectral CT parameters were significantly different between high-grade and low-to-moderate grade INMAs (P < 0.001 to P = 0.002). The 1st percentile of histogram parameters (AUC, 0.84; 95% confidence interval [CI]: 0.73–0.92) and iodine concentration (AUC, 0.78; 95% CI: 0.66–0.88) from single-slice spectral CT parameters had the best performance for discriminating between high-grade and low-to-moderate grade INMAs. At ROC curve analysis no significant differences in AUC were found between histogram parameters (AUC = 0.86; 95% CI: 0.74–0.93) and spectral CT parameters (AUC = 0.81; 95% CI: 0.74–0.93) (P = 0.60). ConclusionBoth whole-lesion iodine map histogram analysis and single-slice spectral CT parameters help discriminate between low-to-moderate grade and high-grade INMAs according to the novel International Association for the Study of Lung Cancer grading system, with no differences in diagnostic performances.

Identification of Differences in Body Composition Measures Using 3D-Derived Artificial Intelligence from Multiple CT Scans across the L3 Vertebra Compared to a Single Mid-Point L3 CT Scan.

Body composition analysis in colorectal cancer (CRC) typically utilises a single 2D-abdominal axial CT slice taken at the mid-L3 level. The use of artificial intelligence (AI) allows for analysis of the entire L3 vertebra (non-mid-L3 and mid-L3). The goal of this study was to determine if the use of an AI approach offered any additional information on capturing body composition measures. A total of 2203 axial CT slices of the entire L3 level (4-46 slices were available per patient) were retrospectively collected from 203 CRC patients treated at Western Health, Melbourne (97 males; 47.8%). A pretrained artificial intelligence (AI) model was used to segment muscle, visceral adipose tissue (VAT), and subcutaneous adipose tissue (SAT) on these slices. The difference in body composition measures between mid-L3 and non-mid-L3 scans was compared for each patient, and for males and females separately. Body composition measures derived from non-mid-L3 scans exhibited a median range of 0.85% to 6.28% (average percent difference) when compared to the use of a single mid-L3 scan. Significant variation in the VAT surface area (p = 0.02) was observed in females compared to males, whereas male patients exhibited a greater variation in SAT surface area (p < 0.001) and radiodensity (p = 0.007). Significant differences in various body composition measures were observed when comparing non-mid-L3 slices to only the mid-L3 slice. Researchers should be aware that considering only the use of a single midpoint L3 CT scan slice will impact the estimate of body composition measurements.

Visceral obesity determined by CT as a predictor of short-term postoperative complications in patients with ovarian cancer.

To explore the association between visceral obesity and short-term postoperative complications in patients with advanced ovarian cancer undergoing cytoreductive surgery. The medical records of patients with advanced epithelial ovarian cancer were reviewed. The visceral fat area, subcutaneous fat area and total fat area at the L3/4 level were measured on a preoperative single-slice CT scan. The receiver operating characteristic (ROC) curve was used to calculate the optimal cutoff value for the visceral fat area. The relationship between the visceral fat area and the characteristics of ovarian cancer patients were analyzed. Univariable and multivariable logistic regression analyses were performed to investigate relationship between perioperative characteristics and short-term complications. According to the ROC curve, the best cutoff value of the VFA was 93 cm2. Of the 130 patients, 53.8% (70/130) had visceral obesity. Patients with visceral obesity were older than those with nonvisceral obesity (58.4years old vs. 52.1years old, p < 0.001). The proportion of patients with hypertension was higher (35.7 vs. 13.3%, p = 0.003). The total fat area and subcutaneous fat area were larger in patients with visceral obesity (294.3 ± 75.5 vs. 176.2 ± 68.7, p < 0.001; 158.9 ± 54.7 vs. 121.7 ± 52.6, p < 0.001). Compared with patients in the nonvisceral obese group, patients in the visceral obese group were more likely to have postoperative fever (21/70 30.0% vs. 8/60 1.25%, p = 0.023), leading to a longer length of hospital stay (21days vs. 17days, p = 0.009). The time from surgery to adjuvant chemotherapy for patients with visceral obesity was shorter (24days vs. 19days, p = 0.037). Multivariate analysis showed that visceral obesity (OR = 6.451, p < 0.001) and operation time (OR = 1.006, p < 0.001) were independent predictors of postoperative complications. Visceral obesity is an important risk factor for short-term postoperative complications in patients with advanced ovarian cancer undergoing cytoreductive surgery.

Strong impact of sarcopenic state defined by skeletal muscle mass index on postoperative complication of Crohn's disease patients

BackgroundMalnutrition impacts the clinical course of Crohn's disease; however, there is little evidence of its influence on perioperative adverse events. We assessed whether nutritional indicators are associated with postoperative complications in surgical treatment of Crohn's disease. Methods137 patients with Crohn's disease who underwent surgical treatment between January 2011 and December 2020 were included. Skeletal muscle index was calculated by a single CT slice. We analyzed the risk factors for adverse events. Results37 % of patients had postoperative complications. Adverse events occurred more frequently in patients with high serum C-reactive protein, low serum albumin, prognostic nutritional index <38.3, skeletal muscle index <38.9 cm2/m2, abdominoperineal resection, long surgical duration, and mass hemorrhage. Among patients with skeletal muscle index <38.9 cm2/m2, patients who experienced adverse events had higher visceral fat index compared with those who did not (0.85 vs. 0.45, P = 0.04). Multivariate analysis revealed that skeletal muscle index <38.9 cm2/m2 and low serum albumin were the independent risk factors for postoperative complications (Odds ratio, 2.85; 95 % confidence interval, 1.13–7.16; P = 0.03, 2.62; 1.09–6.26; P = 0.03, respectively). Separated by sex, low serum albumin (<3.5 and <2.8 g/dL, male and female, respectively) and skeletal muscle index (<38.9 and <36.6 cm2/m2, male and female, respectively) were statistically related to postoperative complications. ConclusionsSkeletal muscle index is the most useful nutritional predictor of postoperative complications in Crohn's disease patients among other nutritional indices. We believe that these patients are at high risk of postoperative complications and need appropriate nutritional support in the perioperative period.

Can AI-based body composition assessment outperform body surface area in predicting dose-limiting toxicities for colonic cancer patients on chemotherapy?

Gold standard chemotherapy dosage is based on body surface area (BSA); however many patients experience dose-limiting toxicities (DLT). We aimed to evaluate the effectiveness of BSA, two-dimensional (2D) and three-dimensional (3D) body composition (BC) measurements derived from Lumbar 3 vertebra (L3) computed tomography (CT) slices, in predicting DLT in colon cancer patients. 203 patients (60.87 ± 12.42 years; 97 males, 47.8%) receiving adjuvant chemotherapy (Oxaliplatin and/or 5-Fluorouracil) were retrospectively evaluated. An artificial intelligence segmentation model was used to extract 2D and 3D body composition measurements from each patients' single mid-L3 CT slice as well as multiple-L3 CT scans to produce a 3D BC report. DLT was defined as any incidence of dose reduction or discontinuation due to chemotherapy toxicities. A receiver operating characteristic (ROC) analysis was performed on BSA and individual body composition measurements to demonstrate their predictive performance. A total of 120 (59.1%) patients experienced DLT. Age and BSA did not vary significantly between DLT and non-DLT group. Females were significantly more likely to experience DLT (p = 4.9 × 10-3). In all patients, the predictive effectiveness of 2D body composition measurements (females: AUC = 0.50-0.54; males: AUC = 0.50-0.61) was equivalent to that of BSA (females: AUC = 0.49; males: AUC = 0.58). The L3 3D skeletal muscle volume was the most predictive indicator of DLT (AUC of 0.66 in females and 0.64 in males). Compared to BSA and 2D body composition measurements, 3D L3 body composition measurements had greater potential to predict DLT in CRC patients receiving chemotherapy and this was sex dependent.

Mean Heart Dose Prediction Using Parameters of Single-Slice Computed Tomography and Body Mass Index: Machine Learning Approach for Radiotherapy of Left-Sided Breast Cancer of Asian Patients.

Deep inspiration breath-hold (DIBH) is an excellent technique to reduce the incidental radiation received by the heart during radiotherapy in patients with breast cancer. However, DIBH is costly and time-consuming for patients and radiotherapy staff. In Asian countries, the use of DIBH is restricted due to the limited number of patients with a high mean heart dose (MHD) and the shortage of radiotherapy personnel and equipment compared to that in the USA. This study aimed to develop, evaluate, and compare the performance of ten machine learning algorithms for predicting MHD using a patient's body mass index and single-slice CT parameters to identify patients who may not require DIBH. Machine learning models were built and tested using a dataset containing 207 patients with left-sided breast cancer who were treated with field-in-field radiotherapy with free breathing. The average MHD was 251 cGy. Stratified repeated four-fold cross-validation was used to build models using 165 training data. The models were compared internally using their average performance metrics: F2 score, AUC, recall, accuracy, Cohen's kappa, and Matthews correlation coefficient. The final performance evaluation for each model was further externally analyzed using 42 unseen test data. The performance of each model was evaluated as a binary classifier by setting the cut-off value of MHD ≥ 300 cGy. The deep neural network (DNN) achieved the highest F2 score (78.9%). Most models successfully classified all patients with high MHD as true positive. This study indicates that the ten models, especially the DNN, might have the potential to identify patients who may not require DIBH.

Single slice thigh CT muscle group segmentation with domain adaptation and self-training.

Thigh muscle group segmentation is important for assessing muscle anatomy, metabolic disease, and aging. Many efforts have been put into quantifying muscle tissues with magnetic resonance (MR) imaging, including manual annotation of individual muscles. However, leveraging publicly available annotations in MR images to achieve muscle group segmentation on single-slice computed tomography (CT) thigh images is challenging. We propose an unsupervised domain adaptation pipeline with self-training to transfer labels from three-dimensional MR to single CT slices. First, we transform the image appearance from MR to CT with CycleGAN and feed the synthesized CT images to a segmenter simultaneously. Single CT slices are divided into hard and easy cohorts based on the entropy of pseudo-labels predicted by the segmenter. After refining easy cohort pseudo-labels based on anatomical assumption, self-training with easy and hard splits is applied to fine-tune the segmenter. On 152 withheld single CT thigh images, the proposed pipeline achieved a mean Dice of 0.888 (0.041) across all muscle groups, including gracilis, hamstrings, quadriceps femoris, and sartorius muscle. To our best knowledge, this is the first pipeline to achieve domain adaptation from MR to CT for thigh images. The proposed pipeline effectively and robustly extracts muscle groups on two-dimensional single-slice CT thigh images. The container is available for public use in GitHub repository available at: https://github.com/MASILab/DA_CT_muscle_seg.