Factors Associated with Hospital Length of Stay and Adverse Events following Percutaneous Ablation of Lung Tumors.

PURPOSE: To explore the association between risk factors established in the surgical literature and hospital length of stay (HLOS), adverse events, and hospital readmission within 30 days after percutaneous image-guided thermal ablation of lung tumors. MATERIALS AND METHODS: This bi-institutional retrospective cohort study included 131 consecutive adult patients (67 men [51%]; median age, 65 years) with 180 primary or metastatic lung tumors treated in 131 sessions (74 cryoablation and 57 microwave ablation) from 2006 to 2019. Age-adjusted Charlson Comorbidity Index, sex, performance status, smoking status, chronic obstructive pulmonary disease (COPD), primary lung cancer versus pulmonary metastases, number of tumors treated per session, maximum axial tumor diameter, ablation modality, number of pleural punctures, anesthesia type, pulmonary artery-to-aorta ratio, lung densitometry, sarcopenia, and adipopenia were evaluated. Associations between risk factors and outcomes were assessed using univariable and multivariable generalized linear models. RESULTS: In univariable analysis, HLOS was associated with current smoking (incidence rate ratio [IRR], 4.54 [1.23-16.8]; P = .02), COPD (IRR, 3.56 [1.40-9.04]; P = .01), cryoablations with ≥3 pleural punctures (IRR, 3.13 [1.07-9.14]; P = .04), general anesthesia (IRR, 10.8 [4.18-27.8]; P < .001), and sarcopenia (IRR, 2.66 [1.10-6.44]; P = .03). After multivariable adjustment, COPD (IRR, 3.56 [1.57-8.11]; P = .003) and general anesthesia (IRR, 12.1 [4.39-33.5]; P < .001) were the only risk factors associated with longer HLOS. No associations were observed between risk factors and adverse events in multivariable analysis. Tumors treated per session were associated with risk of hospital readmission (P = .03). CONCLUSIONS: Identified preprocedural risk factors from the surgical literature may aid in risk stratification for HLOS after percutaneous ablation of lung tumors, but were not associated with adverse events.

Utility of Noncancerous Chest CT Features for Predicting Overall Survival and Noncancer Death in Patients With Stage I Lung Cancer Treated With Stereotactic Body Radiotherapy.

BACKGROUND. Noncancerous imaging markers can be readily derived from pre-treatment diagnostic and radiotherapy planning chest CT examinations. OBJECTIVE. The purpose of this article was to explore the ability of noncancerous features on chest CT to predict overall survival (OS) and noncancer-related death in patients with stage I lung cancer treated with stereotactic body radiation therapy (SBRT). METHODS. This retrospective study included 282 patients (168 female, 114 male; median age, 75 years) with stage I lung cancer treated with SBRT between January 2009 and June 2017. Pretreatment chest CT was used to quantify coronary artery calcium (CAC) score, pulmonary artery (PA)-to-aorta ratio, emphysema, and body composition in terms of the cross-sectional area and attenuation of skeletal muscle and subcutaneous adipose tissue at the T5, T8, and T10 vertebral levels. Associations of clinical and imaging features with OS were quantified using a multivariable Cox proportional hazards (PH) model. Penalized multivariable Cox PH models to predict OS were constructed using clinical features only and using both clinical and imaging features. The models' discriminatory ability was assessed by constructing time-varying ROC curves and computing AUC at prespecified times. RESULTS. After a median OS of 60.8 months (95% CI, 55.8-68.0), 148 (52.5%) patients had died, including 83 (56.1%) with noncancer deaths. Higher CAC score (11-399: hazard ratio [HR], 1.83 [95% CI, 1.15-2.91], p = .01; ≥ 400: HR, 1.63 [95% CI, 1.01-2.63], p = .04), higher PA-to-aorta ratio (HR, 1.33 [95% CI, 1.16-1.52], p < .001, per 0.1-unit increase), and lower thoracic skeletal muscle index (HR, 0.88 [95% CI, 0.79-0.98], p = .02, per 10-cm2/m2 increase) were independently associated with shorter OS. Discriminatory ability for 5-year OS was greater for the model including clinical and imaging features than for the model including clinical features only (AUC, 0.75 [95% CI, 0.68-0.83] vs 0.61 [95% CI, 0.53-0.70]; p < .01). The model's most important clinical or imaging feature according to mean standardized regression coefficients was the PA-to-aorta ratio. CONCLUSION. In patients undergoing SBRT for stage I lung cancer, higher CAC score, higher PA-to-aorta ratio, and lower thoracic skeletal muscle index independently predicted worse OS. CLINICAL IMPACT. Noncancerous imaging features on chest CT performed before SBRT improve survival prediction compared with clinical features alone.

Muscle Loss Is Associated with Overall Survival in Patients with Metastatic Colorectal Cancer Independent of Tumor Mutational Status and Weight Loss.

BACKGROUND: Survival in patients with metastatic colorectal cancer (mCRC) has been associated with tumor mutational status, muscle loss, and weight loss. We sought to explore the combined effects of these variables on overall survival. MATERIALS AND METHODS: We performed an observational cohort study, prospectively enrolling patients receiving chemotherapy for mCRC. We retrospectively assessed changes in muscle (using computed tomography) and weight, each dichotomized as >5% or ≤5% loss, at 3, 6, and 12 months after diagnosis of mCRC. We used regression models to assess relationships between tumor mutational status, muscle loss, weight loss, and overall survival. Additionally, we evaluated associations between muscle loss, weight loss, and tumor mutational status. RESULTS: We included 226 patients (mean age 59 ± 13 years, 53% male). Tumor mutational status included 44% wild type, 42% RAS-mutant, and 14% BRAF-mutant. Patients with >5% muscle loss at 3 and 12 months experienced worse survival controlling for mutational status and weight (3 months hazard ratio, 2.66; p < .001; 12 months hazard ratio, 2.10; p = .031). We found an association of >5% muscle loss with BRAF-mutational status at 6 and 12 months. Weight loss was not associated with survival nor mutational status. CONCLUSION: Increased muscle loss at 3 and 12 months may identify patients with mCRC at risk for decreased overall survival, independent of tumor mutational status. Specifically, >5% muscle loss identifies patients within each category of tumor mutational status with decreased overall survival in our sample. Our findings suggest that quantifying muscle loss on serial computed tomography scans may refine survival estimates in patients with mCRC. IMPLICATIONS FOR PRACTICE: In this study of 226 patients with metastatic colorectal cancer, it was found that losing >5% skeletal muscle at 3 and 12 months after the diagnosis of metastatic disease was associated with worse overall survival, independent of tumor mutational status and weight loss. Interestingly, results did not show a significant association between weight loss and overall survival. These findings suggest that muscle quantification on serial computed tomography may refine survival estimates in patients with metastatic colorectal cancer beyond mutational status.

Associations of Skeletal Muscle With Symptom Burden and Clinical Outcomes in Hospitalized Patients With Advanced Cancer.

BACKGROUND: Low muscle mass (quantity) is common in patients with advanced cancer, but little is known about muscle radiodensity (quality). We sought to describe the associations of muscle mass and radiodensity with symptom burden, healthcare use, and survival in hospitalized patients with advanced cancer. METHODS: We prospectively enrolled hospitalized patients with advanced cancer from September 2014 through May 2016. Upon admission, patients reported their physical (Edmonton Symptom Assessment System [ESAS]) and psychological (Patient Health Questionnaire-4 [PHQ-4]) symptoms. We used CT scans performed per routine care within 45 days before enrollment to evaluate muscle mass and radiodensity. We used regression models to examine associations of muscle mass and radiodensity with patients' symptom burden, healthcare use (hospital length of stay and readmissions), and survival. RESULTS: Of 1,121 patients enrolled, 677 had evaluable muscle data on CT (mean age, 62.86 ± 12.95 years; 51.1% female). Older age and female sex were associated with lower muscle mass (age: B, -0.16; P<.001; female: B, -6.89; P<.001) and radiodensity (age: B, -0.33; P<.001; female: B, -1.66; P=.014), and higher BMI was associated with higher muscle mass (B, 0.58; P<.001) and lower radiodensity (B, -0.61; P<.001). Higher muscle mass was significantly associated with improved survival (hazard ratio, 0.97; P<.001). Notably, higher muscle radiodensity was significantly associated with lower ESAS-Physical (B, -0.17; P=.016), ESAS-Total (B, -0.29; P=.002), PHQ-4-Depression (B, -0.03; P=.006), and PHQ-4-Anxiety (B, -0.03; P=.008) symptoms, as well as decreased hospital length of stay (B, -0.07; P=.005), risk of readmission or death in 90 days (odds ratio, 0.97; P<.001), and improved survival (hazard ratio, 0.97; P<.001). CONCLUSIONS: Although muscle mass (quantity) only correlated with survival, we found that muscle radiodensity (quality) was associated with patients' symptoms, healthcare use, and survival. These findings underscore the added importance of assessing muscle quality when seeking to address adverse muscle changes in oncology.

Significance of Acquisition Parameters for Adipose Tissue Segmentation on CT Images.

OBJECTIVE. CT-based body composition analysis quantifies skeletal muscle and adipose tissue. However, acquisition parameters and quality can vary between CT images obtained for clinical care, which may lead to unreliable measurements and systematic error. The purpose of this study was to estimate the influence of IV contrast medium, tube current-exposure time product, tube potential, and slice thickness on cross-sectional area (CSA) and mean attenuation of subcutaneous (SAT), visceral (VAT), and inter-muscular adipose tissue (IMAT). MATERIALS AND METHODS. We retrospectively analyzed 244 images from 105 patients. We applied semiautomated threshold-based segmentation to CTA, dual-energy CT, and CT images acquired as part of PET examinations. An axial image at the level of the third lumbar vertebral body was extracted from each examination to generate 139 image pairs. Images from each pair were obtained with the same scanner, from the same patient, and during the same examination. Each image pair varied in only one acquisition parameter, which allowed us to estimate the effect of the parameter using one-sample t or median tests and Bland-Altman plots. RESULTS. IV contrast medium application reduced CSA in each adipose tissue compartment, with percentage change ranging from -0.4% (p = .03) to -9.3% (p < .001). Higher tube potential reduced SAT CSA (median percentage change, -4.2%; p < .001) and VAT CSA (median percentage change, -2.8%; p = .001) and increased IMAT CSA (median percentage change, -5.4%; p = .001). Thinner slices increased CSA in the VAT (mean percentage change, 3.0%; p = .005) and IMAT (median percentage change, 17.3%; p < .001) compartments. Lower tube current-exposure time product had a variable effect on CSA (median percentage change, -3.2% for SAT [p < .001], -12.6% for VAT [p = .001], and 58.8% for IMAT [p < .001]). IV contrast medium and higher tube potential increased mean attenuation, with percentage change ranging from 0.8% to 1.7% (p < .05) and from 6.2% to 20.8% (p < .001), respectively. Conversely, thinner slice and lower tube current-exposure time product reduced mean attenuation, with percentage change ranging from -5.4% to -1.0% (p < .001) and from -8.7% to -1.8% (p < .001), respectively. CONCLUSION. Acquisition parameters significantly affect CSA and mean attenuation of adipose tissue. Details of acquisition parameters used for CT-based body composition analysis need to be scrutinized and reported to facilitate interpretation of research studies.

A Multicenter Evaluation of Treatment-Associated Changes in Body Composition in Men with Germ Cell Tumors (GCT) of the Testis: Implications for Adverse Events and Complications.

OBJECTIVES: To characterize changes in body composition following cytotoxic chemotherapy for germ cell carcinoma of the testis (GCT) and quantify associations between body composition metrics and chemotherapy-associated adverse events (AEs) and post-retroperitoneal lymph node dissection (RPLND) complications. MATERIALS AND METHODS: This retrospective multi-center study included 216 men with GCT treated with cytotoxic chemotherapy and/or RPLND (2005-2020). We measured body composition including skeletal muscle (SMI), visceral adipose (VAI,), subcutaneous adipose (SAI), and fat mass (FMI) indices on computed tomography. We quantified chemotherapy-associated changes in body composition and evaluated associations between body composition and incidence of grade 3+ AEs and post-RPLND complications on multivariable logistic regression analyses. RESULTS: 182 men received a median of 3 cycles of cisplatin-based chemotherapy. Following chemotherapy, median change in SMI was -6% (p=<0.0001), while VAI, SAI, and FMI increased by +13% (p=<0.0001), +11% (p=<0.0001), and +6% (p=<0.0001), respectively. 79 patients (43%) experienced at least one grade 3+ AE. A decrease in SMI following chemotherapy was associated with increased risk of grade 3+ AEs (p=0.047). 103 men with a median age of 28.5 years (IQR 23-35.5) underwent RPLND of whom 22 (21.3%) experienced at least one grade 3+ post-RPLND complication. No baseline body composition metrics were associated with post-RPLND complications. CONCLUSIONS: In men with GCT of the testis, chemotherapy was associated with 6% loss of lean muscle mass and gains in adiposity. Lower skeletal muscle was associated with a higher incidence of chemotherapy-associated AEs. Body composition was not associated with the incidence of post-RPLND complications.

Percentile-based averaging and skeletal muscle gauge improve body composition analysis: validation at multiple vertebral levels.

BACKGROUND: Skeletal muscle metrics on computed tomography (CT) correlate with clinical and patient-reported outcomes. We hypothesize that aggregating skeletal muscle measurements from multiple vertebral levels and skeletal muscle gauge (SMG) better predict outcomes than skeletal muscle radioattenuation (SMRA) or -index (SMI) at a single vertebral level. METHODS: We performed a secondary analysis of prospectively collected clinical (overall survival, hospital readmission, time to unplanned hospital readmission or death, and readmission or death within 90 days) and patient-reported outcomes (physical and psychological symptom burden captured as Edmonton Symptom Assessment Scale and Patient Health Questionnaire) of patients with advanced cancer who experienced an unplanned admission to Massachusetts General Hospital from 2014 to 2016. First, we assessed the correlation of skeletal muscle cross-sectional area, SMRA, SMI, and SMG at one or more of the following thoracic (T) or lumbar (L) vertebral levels: T5, T8, T10, and L3 on CT scans obtained ≤50 days before index assessment. Second, we aggregated measurements across all available vertebral levels using percentile-based averaging (PBA) to create the average percentile. Third, we constructed one regression model adjusted for age, sex, sociodemographic factors, cancer type, body mass index, and intravenous contrast for each combination of (i) vertebral level and average percentile, (ii) muscle metrics (SMRA, SMI, & SMG), and (iii) clinical and patient-reported outcomes. Fourth, we compared the performance of vertebral levels and muscle metrics by ranking otherwise identical models by concordance statistic, number of included patients, coefficient of determination, and significance of muscle metric. RESULTS: We included 846 patients (mean age: 63.5 ± 12.9 years, 50.5% males) with advanced cancer [predominantly gastrointestinal (32.9%) or lung (18.9%)]. The correlation of muscle measurements between vertebral levels ranged from 0.71 to 0.84 for SMRA and 0.67 to 0.81 for SMI. The correlation of individual levels with the average percentile was 0.90-0.93 for SMRA and 0.86-0.92 for SMI. The intrapatient correlation of SMRA with SMI was 0.21-0.40. PBA allowed for inclusion of 8-47% more patients than any single-level analysis. PBA outperformed single-level analyses across all comparisons with average ranks 2.6, 2.9, and 1.6 for concordance statistic, coefficient of determination, and significance (range 1-5, µ = 3), respectively. On average, SMG outperformed SMRA and SMI across outcomes and vertebral levels: the average rank of SMG was 1.4, 1.4, and 1.4 for concordance statistic, coefficient of determination, and significance (range 1-3, µ = 2), respectively. CONCLUSIONS: Multivertebral level skeletal muscle analyses using PBA and SMG independently and additively outperform analyses using individual levels and SMRA or SMI.

A Fully Automated Deep Learning Pipeline for Multi-Vertebral Level Quantification and Characterization of Muscle and Adipose Tissue on Chest CT Scans.

Body composition on chest CT scans encompasses a set of important imaging biomarkers. This study developed and validated a fully automated analysis pipeline for multi-vertebral level assessment of muscle and adipose tissue on routine chest CT scans. This study retrospectively trained two convolutional neural networks on 629 chest CT scans from 629 patients (55% women; mean age, 67 years ± 10 [standard deviation]) obtained between 2014 and 2017 prior to lobectomy for primary lung cancer at three institutions. A slice-selection network was developed to identify an axial image at the level of the fifth, eighth, and 10th thoracic vertebral bodies. A segmentation network (U-Net) was trained to segment muscle and adipose tissue on an axial image. Radiologist-guided manual-level selection and segmentation generated ground truth. The authors then assessed the predictive performance of their approach for cross-sectional area (CSA) (in centimeters squared) and attenuation (in Hounsfield units) on an independent test set. For the pipeline, median absolute error and intraclass correlation coefficients for both tissues were 3.6% (interquartile range, 1.3%-7.0%) and 0.959-0.998 for the CSA and 1.0 HU (interquartile range, 0.0-2.0 HU) and 0.95-0.99 for median attenuation. This study demonstrates accurate and reliable fully automated multi-vertebral level quantification and characterization of muscle and adipose tissue on routine chest CT scans. Keywords: Skeletal Muscle, Adipose Tissue, CT, Chest, Body Composition Analysis, Convolutional Neural Network (CNN), Supervised Learning Supplemental material is available for this article. © RSNA, 2022.

Changes in skeletal muscle and adipose tissue during cytotoxic chemotherapy for testicular germ cell carcinoma and associations with adverse events.

OBJECTIVES: To quantify changes in body composition during cytotoxic chemotherapy for germ cell carcinoma of the testis (GCT) and evaluate associations between change in skeletal muscle and adipose tissue and chemotherapy-associated adverse events. MATERIALS AND METHODS: This retrospective single-institution study evaluated men with GCT treated with cytotoxic chemotherapy from 2005 to 2018. We measured skeletal muscle index (SMI [cm2/m2]), skeletal muscle density (SMD [Hounsfield Units (HU)]), skeletal muscle gauge (SMG [cm²*HU/m²]), fat mass index (FMI [kg/m2]), visceral adipose index (VAI [cm2/m2]), and subcutaneous adipose index (SAI [cm2/m2]) on axial computed tomography images at the level of the third lumbar vertebra within 75 days before and after chemotherapy. Chemotherapy-associated adverse events (AE) were graded based on the Common Terminology Criteria for Adverse Events (CTCAE v5.0.) Changes in body composition were quantified. Predictors of change in body composition were evaluated with multivariable linear regression. Associations between baseline or change in body composition and AEs were estimated with multivariable logistic regression adjusting for age, comorbidity, performance status, stage, and number/type of chemotherapy cycles. RESULTS: 141 patients (median age, 30 years [IQR 25-39]) including 86 patients (61%) with non-seminomatous GCT were included. Patients received a median of 3 cycles of cisplatin-based chemotherapy, and 124 patients (88%) completed planned chemotherapy. Median observed changes in SMI, SMD, and SMG were -6% (P<0.0001), -2% (P=0.07), and -7% (P<0.0001), respectively, while FMI increased 5.3% (P<0.0001). Overall, 120 patients (85%) experienced at least one AE including one or more ≥grade 3 AE in 57 patients (48%). Decrease in SMI (OR: 0.89, P=0.02), decrease in SMG (OR: 0.88, P=0.01,) and post-chemotherapy SMG (OR: 0.94, P=0.05) were independently associated with higher incidence of AEs, while pre-chemotherapy skeletal muscle parameters and post-chemotherapy SMI and SMD were not associated with AEs (P>0.05 for all). Preoperative adipose tissue or change in adiposity was not associated with incidence of AEs. CONCLUSIONS: In men with GCT receiving cytotoxic chemotherapy, a decrease in skeletal muscle mass and quality during chemotherapy were associated with a higher incidence of chemotherapy-associated AEs. Adipose tissue was not associated with the incidence of AEs.

Machine Learning in Body Composition Analysis.

Body composition analysis (BCA) generates objective anthropometric data that can inform prognostication and treatment decisions across a wide variety of urologic conditions. A patient's body composition, specifically muscle and adipose tissue mass, may be characterized via segmentation of cross-sectional images (computed tomography, magnetic resonance imaging) obtained as part of routine clinical care. Unfortunately, conventional semi-automated segmentation techniques are time- and resource-intensive, precluding translation into clinical practice. Machine learning (ML) offers the potential to automate and scale rapid and accurate BCA. To date, ML for BCA has relied on algorithms called convolutional neural networks designed to detect and analyze images in ways similar to human neuronal connections. This mini review provides a clinically oriented overview of ML and its use in BCA. We address current limitations and future directions for translating ML and BCA into clinical practice. PATIENT SUMMARY: Body composition analysis is the measurement of muscle and fat in your body based on analysis of computed tomography or magnetic resonance imaging scans. We discuss the use of machine learning to automate body composition analysis. The information provided can be used to guide shared decision-making and to help in identifying the best therapy option.