Machine learning methods in automated detection of CT enterography findings in Crohn's disease: A feasibility study.

PURPOSE: Qualitative findings in Crohn's disease (CD) can be challenging to reliably report and quantify. We evaluated machine learning methodologies to both standardize the detection of common qualitative findings of ileal CD and determine finding spatial localization on CT enterography (CTE). MATERIALS AND METHODS: Subjects with ileal CD and a CTE from a single center retrospective study between 2016 and 2021 were included. 165 CTEs were reviewed by two fellowship-trained abdominal radiologists for the presence and spatial distribution of five qualitative CD findings: mural enhancement, mural stratification, stenosis, wall thickening, and mesenteric fat stranding. A Random Forest (RF) ensemble model using automatically extracted specialist-directed bowel features and an unbiased convolutional neural network (CNN) were developed to predict the presence of qualitative findings. Model performance was assessed using area under the curve (AUC), sensitivity, specificity, accuracy, and kappa agreement statistics. RESULTS: In 165 subjects with 29,895 individual qualitative finding assessments, agreement between radiologists for localization was good to very good (κ = 0.66 to 0.73), except for mesenteric fat stranding (κ = 0.47). RF prediction models had excellent performance, with an overall AUC, sensitivity, specificity of 0.91, 0.81 and 0.85, respectively. RF model and radiologist agreement for localization of CD findings approximated agreement between radiologists (κ = 0.67 to 0.76). Unbiased CNN models without benefit of disease knowledge had very similar performance to RF models which used specialist-defined imaging features. CONCLUSION: Machine learning techniques for CTE image analysis can identify the presence, location, and distribution of qualitative CD findings with similar performance to experienced radiologists.

Committed to Success: A Structured Mentoring Program for Clinically Oriented Physicians.

Objective: To examine impacts of a structured mentorship committee program on academic promotion and participant perceptions because impacts of formal mentorship programs for clinical faculty are unknown. Participants and Methods: This prospective cohort study at a Midwestern Veterans Affairs tertiary care system from December 17, 2019 to December 31, 2022 included clinical track faculty in the Medicine Service below the rank of Clinical Associate Professor. Mentoring meetings (mentee, committee chair, and mentors) were generally held twice annually. All participants were surveyed after each meeting (response rate: 100%). Results: All 23 of 23 (100%) eligible faculty were enrolled as mentees, and 49 distinct meetings occurred. Three (13%) mentees were promoted, and the remaining 20 (87%) continued in the program. Mean scores (SD), scaled 1 (strongly disagree) to 5 (strongly agree), for mentors and mentees were 4.71 (0.51) and 4.80 (0.54) for "effective use of my time"; 4.58 (0.64) and 4.37 (0.49) for "appropriate progress since last meeting"; 4.52 (0.66) and 4.31 (0.64) for "program increased my work satisfaction"; and 4.07 (0.96) and 3.75 (0.92) for "program reduced my work burnout," respectively. Conclusion: Clinically oriented physicians viewed the program positively. It appeared to help junior faculty get promoted and led to improved work satisfaction and reduced burnout.

Local Assessment and Small Bowel Crohn's Disease Severity Scoring using AI.

RATIONALE AND OBJECTIVES: We present a machine learning and computer vision approach for a localized, automated, and standardized scoring of Crohn's disease (CD) severity in the small bowel, overcoming the current limitations of manual measurements CT enterography (CTE) imaging and qualitative assessments, while also considering the complex anatomy and distribution of the disease. MATERIALS AND METHODS: Two radiologists introduced a severity score and evaluated disease severity at 7.5 mm intervals along the curved planar reconstruction of the distal and terminal ileum using 236 CTE scans. A hybrid model, combining deep-learning, 3-D CNN, and Random Forest model, was developed to classify disease severity at each mini-segment. Precision, sensitivity, weighted Cohen's score, and accuracy were evaluated on a 20% hold-out test set. RESULTS: The hybrid model achieved precision and sensitivity ranging from 42.4% to 84.1% for various severity categories (normal, mild, moderate, and severe) on the test set. The model's Cohen's score (κ = 0.83) and accuracy (70.7%) were comparable to the inter-observer agreement between experienced radiologists (κ = 0.87, accuracy = 76.3%). The model accurately predicted disease length, correlated with radiologist-reported disease length (r = 0.83), and accurately identified the portion of total ileum containing moderate-to-severe disease with an accuracy of 91.51%. CONCLUSION: The proposed automated hybrid model offers a standardized, reproducible, and quantitative local assessment of small bowel CD severity and demonstrates its value in CD severity assessment.

Artificial Intelligence for Quantifying Cumulative Small Bowel Disease Severity on CT-Enterography in Crohn's Disease.

INTRODUCTION: Assessing the cumulative degree of bowel injury in ileal Crohn's disease (CD) is difficult. We aimed to develop machine learning (ML) methodologies for automated estimation of cumulative ileal injury on computed tomography-enterography (CTE) to help predict future bowel surgery. METHODS: Adults with ileal CD using biologic therapy at a tertiary care center underwent ML analysis of CTE scans. Two fellowship-trained radiologists graded bowel injury severity at granular spatial increments along the ileum (1 cm), called mini-segments. ML segmentation methods were trained on radiologist grading with predicted severity and then spatially mapped to the ileum. Cumulative injury was calculated as the sum (S-CIDSS) and mean of severity grades along the ileum. Multivariate models of future small bowel resection were compared with cumulative ileum injury metrics and traditional bowel measures, adjusting for laboratory values, medications, and prior surgery at the time of CTE. RESULTS: In 229 CTE scans, 8,424 mini-segments underwent analysis. Agreement between ML and radiologists injury grading was strong (κ = 0.80, 95% confidence interval 0.79-0.81) and similar to inter-radiologist agreement (κ = 0.87, 95% confidence interval 0.85-0.88). S-CIDSS (46.6 vs 30.4, P = 0.0007) and mean cumulative injury grade scores (1.80 vs 1.42, P < 0.0001) were greater in CD biologic users that went to future surgery. Models using cumulative spatial metrics (area under the curve = 0.76) outperformed models using conventional bowel measures, laboratory values, and medical history (area under the curve = 0.62) for predicting future surgery in biologic users. DISCUSSION: Automated cumulative ileal injury scores show promise for improving prediction of outcomes in small bowel CD. Beyond replicating expert judgment, spatial enterography analysis can augment the personalization of bowel assessment in CD.

Independent Associations of Aortic Calcification with Cirrhosis and Liver Related Mortality in Veterans with Chronic Liver Disease.

INTRODUCTION: Abdominal aortic calcifications (AAC) are incidentally found on medical imaging and useful cardiovascular burden approximations. The Morphomic Aortic Calcification Score (MAC) leverages automated deep learning methods to quantify and score AACs. While associations of AAC and non-alcoholic fatty liver disease (NAFLD) have been described, relationships of AAC with other liver diseases and clinical outcome are sparse. This study's purpose was to evaluate AAC and liver-related death in a cohort of Veterans with chronic liver disease (CLD). METHODS: We utilized the VISN 10 CLD cohort, a regional cohort of Veterans with the three forms of CLD: NAFLD, hepatitis C (HCV), alcohol-associated (ETOH), seen between 2008 and 2014, with abdominal CT scans (n = 3604). Associations between MAC and cirrhosis development, liver decompensation, liver-related death, and overall death were evaluated with Cox proportional hazard models. RESULTS: The full cohort demonstrated strong associations of MAC and cirrhosis after adjustment: HR 2.13 (95% CI 1.63, 2.78), decompensation HR 2.19 (95% CI 1.60, 3.02), liver-related death HR 2.13 (95% CI 1.46, 3.11), and overall death HR 1.47 (95% CI 1.27, 1.71). These associations seemed to be driven by the non-NAFLD groups for decompensation and liver-related death [HR 2.80 (95% CI 1.52, 5.17; HR 2.34 (95% CI 1.14, 4.83), respectively]. DISCUSSION: MAC was strongly and independently associated with cirrhosis, liver decompensation, liver-related death, and overall death. Surprisingly, stratification results demonstrated comparable or stronger associations among those with non-NAFLD etiology. These findings suggest abdominal aortic calcification may predict liver disease severity and clinical outcomes in patients with CLD.

Incorporation of quantitative imaging data using artificial intelligence improves risk prediction in veterans with liver disease.

BACKGROUND AND AIMS: Utilization of electronic health records data to derive predictive indexes such as the electronic Child-Turcotte-Pugh (eCTP) Score can have significant utility in health care delivery. Within the records, CT scans contain phenotypic data which have significant prognostic value. However, data extractions have not traditionally been applied to imaging data. In this study, we used artificial intelligence to automate biomarker extraction from CT scans and examined the value of these features in improving risk prediction in patients with liver disease. APPROACH AND RESULTS: Using a regional liver disease cohort from the Veterans Health System, we retrieved administrative, laboratory, and clinical data for Veterans who had CT scans performed for any clinical indication between 2008 and 2014. Imaging biomarkers were automatically derived using the analytic morphomics platform. In all, 4614 patients were included. We found that the eCTP Score had a Concordance index of 0.64 for the prediction of overall mortality while the imaging-based model alone or with eCTP Score performed significantly better [Concordance index of 0.72 and 0.73 ( p <0.001)]. For the subset of patients without hepatic decompensation at baseline (n=4452), the Concordance index for predicting future decompensation was 0.67, 0.79, and 0.80 for eCTP Score, imaging alone, or combined, respectively. CONCLUSIONS: This proof of concept demonstrates that the potential of utilizing automated extraction of imaging features within CT scans either alone or in conjunction with classic health data can improve risk prediction in patients with chronic liver disease.

Using Artificial Intelligence to Predict Cirrhosis From Computed Tomography Scans.

INTRODUCTION: Undiagnosed cirrhosis remains a significant problem. In this study, we developed and tested an automated liver segmentation tool to predict the presence of cirrhosis in a population of patients with paired liver biopsy and computed tomography (CT) scans. METHODS: We used a cohort of 1,590 CT scans within the Morphomics database to train an automated liver segmentation model using 3D-U-Net and Google's DeeplLabv3+. Imaging features were then automatically calculated from an external test cohort of patients with chronic liver disease who had a paired liver biopsy and CT within 6 months of each other in January 2004-2012. Using gradient boosting decision trees, we developed multivariate models to predict the presence of histologic cirrhosis and evaluated with 5-fold cross-validated c-statistic. RESULTS: Our cohort had 351 patients; 96 patients had cirrhosis. Of the total cohort, 72 were postliver transplant. Both fibrosis (FIB)-4 and liver morphomics alone performed equally well with area under the receiving operating characteristics of 0.76 (95% confidence interval 0.70-0.81) and 0.71 (95% confidence interval 0.65-0.76), respectively ( P = 0.2). However, the combination of liver morphomics with laboratory values or liver morphomics with laboratory and demographic data resulted in significant improved performance with area under the receiving operating characteristics of 0.84 (0.80-0.89) and 0.85 (0.81-0.90), respectively, compared with FIB-4 alone ( P < 0.001). In a subgroup analysis, we also examined performance in patients without liver transplantation and saw similar augmentation of FIB-4. DISCUSSION: This proof-of-principle study demonstrates that automatically extracted features within CT scans can be combined with classic electronic medical record data to improve the prediction of cirrhosis in patients with liver disease. This tool may be used in both pretransplant and posttransplant patients and has the potential to improve our ability to detect undiagnosed cirrhosis.

Reference distributions of aortic calcification and association with Framingham risk score.

Evidence supporting aortic calcification as a leverageable cardiovascular risk factor is rapidly growing. Given aortic calcification's potential as a clinical correlate, we assessed granular vertebral-indexed calcification measurements of the abdominal aorta in a well curated reference population. We evaluated the relationship of aortic calcification measurements with Framingham risk scores. After exclusion, 4073 participants from the Reference Analytic Morphomic Population with varying vertebral levels were included. The percent of the aortic wall calcified was used to assess calcification burden at the L1-L4 levels. Descriptive statistics of participants, sex-specific vertebral indexed calcification measurements, relational plots, and relevant associations are reported. Mean aortic attenuation was higher in female than male participants. Overall, mean aortic calcium was higher with reference to inferior abdominal aortic measurements and demonstrated significant differences across all abdominal levels [L3 Area (mm[Formula: see text]): Females 6.34 (sd 16.60), Males 6.23 (sd 17.21); L3 Volume (mm[Formula: see text]): Females 178.90 (sd 474.19), Males 195.80 (sd 547.36); Wall Calcification (%): Females (L4) 6.97 (sd 16.03), Males (L3) 5.46 (13.80)]. Participants with elevated calcification had significantly higher Framingham risk scores compared to participants with normal calcification scores. Opportunistically measuring aortic calcification may inform further cardiovascular risk assessment and enhance cardiovascular event surveillance efforts.

Validity of Administrative Claims and Electronic Health Registry Data From a Single Practice for Eye Health Surveillance.

Importance: Diagnostic information from administrative claims and electronic health record (EHR) data may serve as an important resource for surveillance of vision and eye health, but the accuracy and validity of these sources are unknown. Objective: To estimate the accuracy of diagnosis codes in administrative claims and EHRs compared to retrospective medical record review. Design, Setting, and Participants: This cross-sectional study compared the presence and prevalence of eye disorders based on diagnostic codes in EHR and claims records vs clinical medical record review at University of Washington-affiliated ophthalmology or optometry clinics from May 2018 to April 2020. Patients 16 years and older with an eye examination in the previous 2 years were included, oversampled for diagnosed major eye diseases and visual acuity loss. Exposures: Patients were assigned to vision and eye health condition categories based on diagnosis codes present in their billing claims history and EHR using the diagnostic case definitions of the US Centers for Disease Control and Prevention Vision and Eye Health Surveillance System (VEHSS) as well as clinical assessment based on retrospective medical record review. Main Outcome and Measures: Accuracy was measured as area under the receiver operating characteristic curve (AUC) of claims and EHR-based diagnostic coding vs retrospective review of clinical assessments and treatment plans. Results: Among 669 participants (mean [range] age, 66.1 [16-99] years; 357 [53.4%] female), identification of diseases in billing claims and EHR data using VEHSS case definitions was accurate for diabetic retinopathy (claims AUC, 0.94; 95% CI, 0.91-0.98; EHR AUC, 0.97; 95% CI, 0.95-0.99), glaucoma (claims AUC, 0.90; 95% CI, 0.88-0.93; EHR AUC, 0.93; 95% CI, 0.90-0.95), age-related macular degeneration (claims AUC, 0.87; 95% CI, 0.83-0.92; EHR AUC, 0.96; 95% CI, 0.94-0.98), and cataracts (claims AUC, 0.82; 95% CI, 0.79-0.86; EHR AUC, 0.91; 95% CI, 0.89-0.93). However, several condition categories showed low validity with AUCs below 0.7, including diagnosed disorders of refraction and accommodation (claims AUC, 0.54; 95% CI, 0.49-0.60; EHR AUC, 0.61; 95% CI, 0.56-0.67), diagnosed blindness and low vision (claims AUC, 0.56; 95% CI, 0.53-0.58; EHR AUC, 0.57; 95% CI, 0.54-0.59), and orbital and external diseases (claims AUC, 0.63; 95% CI, 0.57-0.69; EHR AUC, 0.65; 95% CI, 0.59-0.70). Conclusion and Relevance: In this cross-sectional study of current and recent ophthalmology patients with high rates of eye disorders and vision loss, identification of major vision-threatening eye disorders based on diagnosis codes in claims and EHR records was accurate. However, vision loss, refractive error, and other broadly defined or lower-risk disorder categories were less accurately identified by diagnosis codes in claims and EHR data.

Comparing Telephone Survey Responses to Best-Corrected Visual Acuity to Estimate the Accuracy of Identifying Vision Loss: Validation Study.

BACKGROUND: Self-reported questions on blindness and vision problems are collected in many national surveys. Recently released surveillance estimates on the prevalence of vision loss used self-reported data to predict variation in the prevalence of objectively measured acuity loss among population groups for whom examination data are not available. However, the validity of self-reported measures to predict prevalence and disparities in visual acuity has not been established. OBJECTIVE: This study aimed to estimate the diagnostic accuracy of self-reported vision loss measures compared to best-corrected visual acuity (BCVA), inform the design and selection of questions for future data collection, and identify the concordance between self-reported vision and measured acuity at the population level to support ongoing surveillance efforts. METHODS: We calculated accuracy and correlation between self-reported visual function versus BCVA at the individual and population level among patients from the University of Washington ophthalmology or optometry clinics with a prior eye examination, randomly oversampled for visual acuity loss or diagnosed eye diseases. Self-reported visual function was collected via telephone survey. BCVA was determined based on retrospective chart review. Diagnostic accuracy of questions at the person level was measured based on the area under the receiver operator curve (AUC), whereas population-level accuracy was determined based on correlation. RESULTS: The survey question, "Are you blind or do you have serious difficulty seeing, even when wearing glasses?" had the highest accuracy for identifying patients with blindness (BCVA ≤20/200; AUC=0.797). The highest accuracy for detecting any vision loss (BCVA <20/40) was achieved by responses of "fair," "poor," or "very poor" to the question, "At the present time, would you say your eyesight, with glasses or contact lenses if you wear them, is excellent, good, fair, poor, or very poor" (AUC=0.716). At the population level, the relative relationship between prevalence based on survey questions and BCVA remained stable for most demographic groups, with the only exceptions being groups with small sample sizes, and these differences were generally not significant. CONCLUSIONS: Although survey questions are not considered to be sufficiently accurate to be used as a diagnostic test at the individual level, we did find relatively high levels of accuracy for some questions. At the population level, we found that the relative prevalence of the 2 most accurate survey questions were highly correlated with the prevalence of measured visual acuity loss among nearly all demographic groups. The results of this study suggest that self-reported vision questions fielded in national surveys are likely to yield an accurate and stable signal of vision loss across different population groups, although the actual measure of prevalence from these questions is not directly analogous to that of BCVA.

Metabolic abnormalities, liver and body fat in American versus Chinese patients with non-alcoholic fatty liver disease.

Background and Aim: Non-alcoholic fatty liver disease (NAFLD) is common in the United States and China. We compared prevalence of metabolic syndrome (MS), hepatic steatosis and fibrosis, and quantity and quality of body fat between American versus Chinese patients with NAFLD. Methods: NAFLD patients were prospectively recruited from the University of Michigan Health System (UMHS) in the United States and Peking University Health Sciences Center (PUHSC) in China. All patients had baseline computed tomography (CT), laboratory tests and Fibroscan® controlled attenuation parameter (CAP) and liver stiffness measurement (LSM). Comparisons were made for overall cohorts and matched cohorts (matched for sex, age, and body mass index [BMI] category). Logistic regression was performed to identify independent predictors of moderate and severe steatosis and lack of advanced fibrosis. Results: One-hundred and one American and One-hundred and sixty Chinese patients were included. UMHS patients were older, with higher prevalence of MS, had higher LSM and CAP scores, and more fat in liver, visceral, subcutaneous, and muscle compartments than PUHSC patients. Differences in LSM, visceral fat Hounsfield unit, and subcutaneous fat area (SFA) persisted in the matched cohort. NAFLD patients with MS had significantly higher LSM, and more fat in liver, visceral, subcutaneous and muscle compartments than those without. Moderate or severe steatosis was independently associated with MS, visceral fat quality, and SFA, while the absence of advanced fibrosis was associated with Asian race and not having MS. Conclusion: American patients with NAFLD had more liver fibrosis than Chinese patients despite having better quality visceral fat and after matching for age, sex, and BMI category.

AGA Clinical Practice Guideline on Systemic Therapy for Hepatocellular Carcinoma.

BACKGROUND & AIMS: Hepatocellular carcinoma (HCC), the most common primary liver cancer, remains a deadly cancer, with an incidence that has tripled in the United States since 1980. In recent years, new systemic therapies for HCC have been approved and a critical assessment of the existing data is necessary to balance benefits and harms and inform the development of evidence-based guidelines. METHODS: The American Gastroenterological Association formed a multidisciplinary group consisting of a Technical Review Panel and a Guideline Panel. The Technical Review Panel prioritized clinical questions and outcomes according to their importance for clinicians and patients and conducted an evidence review of systemic therapies in patients with advanced-stage HCC. The Grading of Recommendations Assessment, Development and Evaluation framework was used to assess evidence. The Guideline Panel reviewed the evidence and used the Evidence-to-Decision Framework to develop recommendations. RESULTS: The Panel reviewed the evidence, summarized in the Technical Review, for the following medications approved by the US Food and Drug Administration for HCC: first-line therapies: bevacizumab+atezolizumab, sorafenib, and lenvatinib; second-line therapies: cabozantinib, pembrolizumab, ramucirumab, and regorafenib; and other agents: bevacizumab, nivolumab, and nivolumab+ipilimumab. CONCLUSIONS: The Panel agreed on 11 recommendations focused on systemic therapy for HCC in patients who are not eligible for locoregional therapy or resection, those with metastatic disease and preserved liver function, those with poor liver function, and those on systemic therapy as adjuvant therapy.

Systematic review: development of a consensus code set to identify cirrhosis in electronic health records.

BACKGROUND: Electronic health records (EHRs) collate longitudinal data that can be used to facilitate large-scale research in patients with cirrhosis. However, there is no consensus code set to define the presence of cirrhosis in EHR. This systematic review aims to evaluate the validity of diagnostic coding in cirrhosis and to synthesise a comprehensive set of ICD-10 codes for future EHR research. METHOD: MEDLINE and EMBASE databases were searched for studies that used EHR to identify cirrhosis and cirrhosis-related complications. Validated code sets were summarised, and the performance characteristics were extracted. Citation analysis was done to inform development of a consensus code set. This was then validated in a cohort of patients. RESULTS: One thousand six hundred twenty-six records were screened, and 18 studies were identified. The positive predictive value (PPV) was the most frequently reported statistical estimate and was ≥80% in 17/18 studies. Citation analyses showed continued variation in those used in contemporary research practice. Nine codes were identified as those most frequently used in the literature and these formed the consensus code set. This was validated in diverse patient populations from Europe and North America and showed high PPV (83%-89%) and greater sensitivity for the identification of cirrhosis than the most often used code set in the recent literature. CONCLUSION: There is variation in code sets used to identify cirrhosis in contemporary research practice. A consensus set has been developed and validated, showing improved performance, and is proposed to align EHR study designs in cirrhosis to facilitate international collaboration and comparisons.

Healthy US population reference values for CT visceral fat measurements and the impact of IV contrast, HU range, and spinal levels.

Measurements of visceral adipose tissue cross-sectional area and radiation attenuation from computed tomography (CT) scans provide useful information about risk and mortality. However, scan protocols vary, encompassing differing vertebra levels and utilizing differing phases of contrast enhancement. Furthermore, fat measurements have been extracted from CT using different Hounsfield Unit (HU) ranges. To our knowledge, there have been no large studies of healthy cohorts that reported reference values for visceral fat area and radiation attenuation at multiple vertebra levels, for different contrast phases, and using different fat HU ranges. Two-phase CT scans from 1,677 healthy, adult kidney donors (age 18-65) between 1999 and 2017, previously studied to determine healthy reference values for skeletal muscle measures, were utilized. Visceral adipose tissue cross-sectional area (VFA) and radiation attenuation (VFRA) measures were quantified using axial slices at T10 through L4 vertebra levels. T-tests were used to compare males and females, while paired t-tests were conducted to determine the effect (magnitude and direction) of (a) contrast enhancement and (b) different fat HU ranges on each fat measure at each vertebra level. We report the means, standard deviations, and effect sizes of contrast enhancement and fat HU range. Male and female VFA and VFRA were significantly different at all vertebra levels in both contrast and non-contrast scans. Peak VFA was observed at L4 in females and L2 in males, while peak VFRA was observed at L1 in both females and males. In general, non-contrast scans showed significantly greater VFA and VFRA compared to contrast scans. The average paired difference due to contrast ranged from 1.6 to - 8% (VFA) and 3.2 to - 3.0% (VFRA) of the non-contrast value. HU range showed much greater differences in VFA and VFRA than contrast. The average paired differences due to HU range ranged from - 5.3 to 22.2% (VFA) and - 5.9 to 13.6% (VFRA) in non-contrast scans, and - 4.4 to 20.2% (VFA) and - 4.1 to 12.6% (VFRA) in contrast scans. The - 190 to - 30 HU range showed the largest differences in both VFA (10.8% to 22.2%) and VFRA (7.6% to 13.6%) compared to the reference range (- 205 to - 51 HU). Incidentally, we found that differences in lung inflation result in very large differences in visceral fat measures, particularly in the thoracic region. We assessed the independent effects of contrast presence and fat HU ranges on visceral fat cross-sectional area and mean radiation attenuation, finding significant differences particularly between different fat HU ranges. These results demonstrate that CT measurements of visceral fat area and radiation attenuation are strongly dependent upon contrast presence, fat HU range, sex, breath cycle, and vertebra level of measurement. We quantified contrast and non-contrast reference values separately for males and females, using different fat HU ranges, for lumbar and thoracic CT visceral fat measures at multiple vertebra levels in a healthy adult US population.

Muscle mass affects paclitaxel systemic exposure and may inform personalized paclitaxel dosing.

AIMS: Patients with low muscle mass have increased risk of paclitaxel-induced peripheral neuropathy, which is dependent on systemic paclitaxel exposure. Dose optimization may be feasible through the secondary use of radiologic data for body composition. The objective of this study was to interrogate morphomic parameters as predictors of paclitaxel pharmacokinetics to identify alternative dosing strategies that may improve treatment outcomes. METHODS: This was a secondary analysis of female patients with breast cancer scheduled to receive 80 mg/m2 weekly paclitaxel infusions. Paclitaxel was measured at the end of initial infusion to estimate maximum concentration (Cmax ). Computed tomography (CT) scans were used to measure 29 body composition features for inclusion in pharmacokinetic modelling. Monte Carlo simulations were performed to identify infusion durations that limit the probability of exceeding Cmax > 2885 ng/mL, which was selected based on prior work linking this to an unacceptable risk of peripheral neuropathy. RESULTS: Thirty-nine patients were included in the analysis. The optimal model was a two-compartment pharmacokinetic model with T11 skeletal muscle area as a covariate of paclitaxel volume of distribution (Vd). Simulations suggest that extending infusion of the standard paclitaxel dose from 1 hour to 2 and 3 hours in patients who have skeletal muscle area 4907-7080 mm2 and <4907 mm2 , respectively, would limit risk of Cmax > 2885 ng/mL to <50%, consequently reducing neuropathy, while marginally increasing overall systemic paclitaxel exposure. CONCLUSION: Extending paclitaxel infusion duration in ~25% of patients who have low skeletal muscle area is predicted to reduce peripheral neuropathy while maintaining systemic exposure, suggesting that personalizing paclitaxel dosing based on body composition may improve treatment outcomes.

Effect of sarcopenia on survival in patients with cirrhosis: A meta-analysis.

BACKGROUND & AIMS: The association between sarcopenia and prognosis in patients with cirrhosis remains to be determined. In this study, we aimed to quantify the association between sarcopenia and the risk of mortality in patients with cirrhosis, stratified by sex, underlying liver disease etiology, and severity of hepatic dysfunction. METHODS: PubMed, Web of Science, EMBASE, and major scientific conference sessions were searched without language restriction through 13 January 2021 with an additional manual search of bibliographies of relevant articles. Cohort studies of ≥100 patients with cirrhosis and ≥12 months of follow-up that evaluated the association between sarcopenia, muscle mass and the risk of mortality were included. RESULTS: Twenty-two studies involving 6,965 patients with cirrhosis were included. The pooled prevalence of sarcopenia in patients with cirrhosis was 37.5% overall (95% CI 32.4%-42.8%), and was higher in male patients, those with alcohol-associated liver disease, those with Child-Pugh grade C cirrhosis, and when sarcopenia was defined by L3-SMI (third lumbar-skeletal muscle index). Sarcopenia was associated with an increased risk of mortality in patients with cirrhosis (adjusted hazard ratio [aHR] 2.30, 95% CI 2.01-2.63), with similar findings in a sensitivity analysis of patients with cirrhosis without hepatocellular carcinoma (aHR 2.35, 95% CI 1.95-2.83) and in subgroups stratified by sex, liver disease etiology, and severity of hepatic dysfunction. The association between quantitative muscle mass index and mortality further supports the association between sarcopenia and poor prognosis (aHR 0.95, 95% CI 0.93-0.98). There was no significant heterogeneity in any of our analyses. CONCLUSIONS: Sarcopenia was highly and independently associated with higher risk of mortality in patients with cirrhosis. LAY SUMMARY: The prevalence of sarcopenia and its association with death in patients with cirrhosis remain unclear. This meta-analysis indicated that sarcopenia affected about one-third of patients with cirrhosis and up to 50% of patients with alcohol-related liver disease or Child-Pugh class C cirrhosis. Sarcopenia was independently associated with an ∼2-fold higher risk of mortality in patients with cirrhosis. The mortality rate increased with greater severity or longer durations of sarcopenia. Increasing awareness about the importance of sarcopenia in patients with cirrhosis among stakeholders must be prioritized.

A correction score to compare aortic calcification in contrast enhanced and non-contrast measurements from computed tomography scans.

BACKGROUND: Aortic wall calcification shows strong promise as a cardiovascular risk factor. While useful for visual enhancement of vascular tissue, enhancement creates heterogeneity between scans with and without contrast. We evaluated the relationship between aortic calcification in routine abdominal computed tomography scans (CT) with and without contrast. METHODS: Inclusion was limited to those with abdominal CT-scans with and without contrast enhancement within 120 days. Analytic Morphomics, a semi-automated computational image processing system, was used to provide standardized, granular, anatomically indexed measurements of aortic wall calcification from abdominal CT-scans. Aortic calcification area (ACA) and aortic wall calcification percent (ACP) and were the outcomes of interest. Multiple linear regression was used to evaluate the relationship of aortic measurements. Models were further controlled for age and sex. Stratification of measurements by vertebral level was also performed. RESULTS: A positive association was observed for non-contrast calcification in ACP ß 0.74 (95% CI 0.72, 0.76) and ACA ß 0.44 (95% 0.43, 0.45). Stratified results demonstrated the highest coefficient of determination at L2 for percent and L3 for area models [R2 0.91 (ACP) 0.74 (ACA)]. Adjusted lumber-level associations between non-contrast and contrast measurements ranged from (ß 0.69-0.82) in ACP and (ß 0.37-0.54) in ACA. CONCLUSION: A straightforward correction score for comparison of abdominal aortic calcification measurements in contrast-enhanced and non-contrast scans is discussed. Correction of aortic calcification from CT scans can reduce scan heterogeneity and will be instrumental in creating larger cardiovascular cohorts as well as cardiovascular risk surveillance programs.

Adapted time-varying covariates Cox model for predicting future cirrhosis development performs well in a large hepatitis C cohort.

BACKGROUND: Patients with hepatitis C virus (HCV) frequently remain at risk for cirrhosis after sustained virologic response (SVR). Existing cirrhosis predictive models for HCV do not account for dynamic antiviral treatment status and are limited by fixed laboratory covariates and short follow up time. Advanced fibrosis assessment modalities, such as transient elastography, remain inaccessible in many settings. Improved cirrhosis predictive models are needed. METHODS: We developed a laboratory-based model to predict progression of liver disease after SVR. This prediction model used a time-varying covariates Cox model adapted to utilize longitudinal laboratory data and to account for antiretroviral treatment. Individuals were included if they had a history of detectable HCV RNA and at least 2 AST-to-platelet ratio index (APRI) scores available in the national Veterans Health Administration from 2000 to 2015, Observation time extended through January 2019. We excluded individuals with preexisting cirrhosis. Covariates included baseline patient characteristics and 16 time-varying laboratory predictors. SVR, defined as permanently undetectable HCV RNA after antiviral treatment, was modeled as a step function of time. Cirrhosis development was defined as two consecutive APRI scores > 2. We predicted cirrhosis development at 1-, 3-, and 5-years follow-up. RESULTS: In a national sample of HCV patients (n = 182,772) with a mean follow-up of 6.32 years, 42% (n = 76,854) achieved SVR before 2016 and 16.2% (n = 29,566) subsequently developed cirrhosis. The model demonstrated good discrimination for predicting cirrhosis across all combinations of laboratory data windows and cirrhosis prediction intervals. AUROCs ranged from 0.781 to 0.815, with moderate sensitivity 0.703-0.749 and specificity 0.723-0.767. CONCLUSION: A novel adaptation of time-varying covariates Cox modeling technique using longitudinal laboratory values and dynamic antiviral treatment status accurately predicts cirrhosis development at 1-, 3-, and 5-years among patients with HCV, with and without SVR. It improves upon earlier cirrhosis predictive models and has many potential population-based applications, especially in settings without transient elastography available.

Automated Measurements of Body Composition in Abdominal CT Scans Using Artificial Intelligence Can Predict Mortality in Patients With Cirrhosis.

Body composition measures derived from already available electronic medical records (computed tomography [CT] scans) can have significant value, but automation of measurements is needed for clinical implementation. We sought to use artificial intelligence to develop an automated method to measure body composition and test the algorithm on a clinical cohort to predict mortality. We constructed a deep learning algorithm using Google's DeepLabv3+ on a cohort of de-identified CT scans (n = 12,067). To test for the accuracy and clinical usefulness of the algorithm, we used a unique cohort of prospectively followed patients with cirrhosis (n = 238) who had CT scans performed. To assess model performance, we used the confusion matrix and calculated the mean accuracy of 0.977 ± 0.02 (0.975 ± 0.018 for the training and test sets, respectively). To assess for spatial overlap, we measured the mean intersection over union and mean boundary contour scores and found excellent overlap between the manual and automated methods with mean scores of 0.954 ± 0.030, 0.987 ± 0.009, and 0.948 ± 0.039 (0.983 ± 0.013 for the training and test set, respectively). Using these automated measurements, we found that body composition features were predictive of mortality in patients with cirrhosis. On multivariate analysis, the addition of body composition measures significantly improved prediction of mortality for patients with cirrhosis over Model for End-Stage Liver Disease alone (P < 0.001). Conclusion: The measurement of body composition can be automated using artificial intelligence and add significant value for incidental CTs performed for other clinical indications. This is proof of concept that this methodology could allow for wider implementation into the clinical arena.