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.

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.

Multilevel Body Composition Analysis on Chest Computed Tomography Predicts Hospital Length of Stay and Complications After Lobectomy for Lung Cancer: A Multicenter Study.

OBJECTIVE: To investigate the impact of thoracic body composition on outcomes after lobectomy for lung cancer. SUMMARY AND BACKGROUND DATA: Preoperative identification of patients at risk for adverse outcomes permits treatment modification. The impact of body composition on lung resection outcomes has not been investigated in a multicenter setting. METHODS: A total of 958 consecutive patients undergoing lobectomy for lung cancer at 3 centers from 2014 to 2017 were retrospectively analyzed. Muscle and adipose tissue cross-sectional area at the fifth, eighth, and tenth thoracic vertebral body was quantified. Prospectively collected outcomes from a national database were abstracted to characterize the association between sums of muscle and adipose tissue and hospital length of stay (LOS), number of any postoperative complications, and number of respiratory postoperative complications using multivariate regression. A priori determined covariates were forced expiratory volume in 1 second and diffusion capacity of the lungs for carbon monoxide predicted, age, sex, body mass index, race, surgical approach, smoking status, Zubrod and American Society of Anesthesiologists scores. RESULTS: Mean patient age was 67 years, body mass index 27.4 kg/m2 and 65% had stage i disease. Sixty-three percent underwent minimally invasive lobectomy. Median LOS was 4 days and 34% of patients experienced complications. Muscle (using 30 cm2 increments) was an independent predictor of LOS (adjusted coefficient 0.972; P = 0.002), any postoperative complications (odds ratio 0.897; P = 0.007) and postoperative respiratory complications (odds ratio 0.860; P = 0.010). Sarcopenic obesity was also associated with LOS and adverse outcomes. CONCLUSIONS: Body composition on preoperative chest computed tomography is an independent predictor of LOS and postoperative complications after lobectomy for lung cancer.

Reducing Time and Patient Radiation of Computed Tomography-guided Thoracic Needle Biopsies With Single-rotation Axial Acquisitions: An Alternative to "CT Fluoroscopy".

PURPOSE: To investigate the effect on procedure time and patient radiation indices of replacing helical acquisitions for needle guidance during thoracic needle biopsy (TNB) with intermittent single-rotation axial acquisitions. MATERIALS AND METHODS: This retrospective intervention study included 215 consecutive TNBs performed by a single operator from 2014 to 2018. Characteristics of patients, lesions, and procedures were compared between TNBs guided only by helical acquisitions initiated in the control room (helical group, n=141) and TNBs guided in part by intermittent single-rotation axial computed tomography controlled by foot pedal (single-rotation group, n=74). Procedure time and patient radiation indices were primary outcomes, complications, and radiologist radiation dose were secondary outcomes. RESULTS: Patient, lesion, and procedural characteristics did not differ between helical and single-rotation groups. Use of single-rotation axial acquisitions decreased procedure time by 10.5 minutes (95% confidence interval [CI]: 8.2-12.8 min) or 27% (95% CI: 22%-32%; P<0.001). Patient dose in cumulative volume computed tomography dose index decreased by 23% (95% CI: 12%-33%) or 8 mGy (95% CI: 4.3-31.6 mGy; P=0.01). Dose-length product decreased by 50% (95% CI: 40%-60%) or 270 mGy cm (95% CI: 195-345 mGy cm; P<0.001). No operator radiation exposure was detected. Rate of diagnostic result, pneumothorax, hemoptysis, and hemorrhage did not differ between groups. CONCLUSIONS: Replacing helical acquisitions with intermittent single-rotation axial acquisitions significantly decreases TNB procedure time and patient radiation indices without adversely affecting diagnostic rate, procedural complications, or operator radiation dose.

Sarcopenia on preoperative chest computed tomography predicts cancer-specific and all-cause mortality following pneumonectomy for lung cancer: A multicenter analysis.

BACKGROUND: Mortality risk prediction in patients undergoing pneumonectomy for non-small cell lung cancer (NSCLC) remains imperfect. Here, we aimed to assess whether sarcopenia on routine chest computed tomography (CT) independently predicts worse cancer-specific (CSS) and overall survival (OS) following pneumonectomy for NSCLC. METHODS: We included consecutive adults undergoing standard or carinal pneumonectomy for NSCLC at Massachusetts General Hospital and Heidelberg University from 2010 to 2018. We measured muscle cross-sectional area (CSA) on CT at thoracic vertebral levels T8, T10, and T12 within 90 days prior to surgery. Sarcopenia was defined as T10 muscle CSA less than two standard deviations below the mean in healthy controls. We adjusted time-to-event analyses for age, body mass index, Charlson Comorbidity Index, forced expiratory volume in 1 second in % predicted, induction therapy, sex, smoking status, tumor stage, side of pneumonectomy, and institution. RESULTS: Three hundred and sixty-seven patients (67.4% male, median age 62 years, 16.9% early-stage) underwent predominantly standard pneumonectomy (89.6%) for stage IIIA NSCLC (45.5%) and squamous cell histology (58%). Sarcopenia was present in 104 of 367 patients (28.3%). Ninety-day all-cause mortality was 7.1% (26/367). After a median follow-up of 20.5 months (IQR, 9.2-46.9), 183 of 367 patients (49.9%) had died. One hundred and thirty-three (72.7%) of these deaths were due to lung cancer. Sarcopenia was associated with shorter CSS (HR 1.7, p = 0.008) and OS (HR 1.7, p = 0.003). CONCLUSIONS: This transatlantic multicenter study confirms that sarcopenia on preoperative chest CT is an independent risk factor for CSS and OS following pneumonectomy for NSCLC.

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.

Percutaneous Cryoablation: Safety and Efficacy for Pain Palliation of Metastases to Pleura and Chest Wall.

PURPOSE: To assess safety and efficacy of percutaneous cryoablation for pain palliation of metastases to pleura and chest wall. MATERIALS AND METHODS: This retrospective single-center cohort study included 22 patients (27% female, mean age 63 y ± 11.4) who underwent 25 cryoablation procedures for pain palliation of 39 symptomatic metastases measuring 5.1 cm ± 1.9 (range, 2.0-8.0 cm) in pleura and chest wall between June 2012 and December 2017. Pain intensity was assessed using a numerical scale (0-10 points). Statistical tests t test, χ2, and Wilcoxon signed rank were performed. RESULTS: Patients were followed for a median of 4.1 months (interquartile range [IQR], 2.3-10.1; range, 0.1-36.7 mo) before death or loss to follow-up. Following cryoablation, pain intensity decreased significantly by a median of 4.5 points (IQR, 2.8-6; range, 0-10 points; P = .0002 points, Wilcoxon signed rank). Pain relief of at least 3 points was documented following 18 of 20 procedures. Pain relief occurred within a median of 1 day following cryoablation (IQR, 1-2; range, 1-4 d) and lasted for a median of 5 weeks (IQR, 3-17; range, 1-34 wk). Systemic opioid requirements decreased in 11 of 22 patients (50%) by an average of 56% ± 34. Difference in morphine milligram equivalents was not significant (P = .73, Wilcoxon signed rank). No procedure-related complications occurred despite previous radiation of 7 tumors. Of 25 procedures, 22 (88%) were performed on an outpatient basis. CONCLUSIONS: Percutaneous cryoablation for metastases to pleura and chest wall can safely provide significant pain relief within days following a single session.

Computed Tomography-based Body Composition Analysis and Its Role in Lung Cancer Care.

Body composition analysis, also referred to as analytic morphomics, morphomics, or morphometry, describes the measurement of imaging biomarkers of body composition such as muscle and adipose tissue, most commonly on computed tomography (CT) images. A growing body of literature supports the use of such metrics derived from routinely acquired CT images for risk prediction in various patient populations, including those with lung cancer. Metrics include cross-sectional area and attenuation of skeletal muscle and subcutaneous, visceral, and intermuscular adipose tissue. The purpose of this review is to provide an overview of the concepts, definitions, assessment tools, segmentation techniques and associated pitfalls, interpretation of those measurements on chest and abdomen CT, and a discussion of reported outcomes associated with body composition metrics in patients with early-stage and advanced lung cancer.

Low Thoracic Skeletal Muscle Area Predicts Morbidity After Pneumonectomy for Lung Cancer.

BACKGROUND: Sarcopenia represented by low psoas muscle area is associated with increased hospital length of stay (LOS), postoperative complications, and mortality. We studied whether thoracic skeletal muscle area (TSMA) derived from computed tomography (CT) predicts morbidity after pneumonectomy for lung cancer. METHODS: Consecutive patients who underwent pneumonectomy for lung cancer from 2005 to 2017 were retrospectively analyzed. TSMA was defined as the sum of muscle area at the level of the eighth and the 12th thoracic vertebral bodies on preoperative CT. Patients were stratified into sex-specific TSMA quartiles for univariate time-to-event analyses. The effect of continuous TSMA measurements on operative complications, hospital and intensive care unit (ICU) LOS, discharge disposition, and hospital readmission within 90 days was estimated using multivariable models adjusted for age, sex, body mass index, forced expiratory volume in 1 second, Zubrod score, and pneumonectomy type. RESULTS: Standard (n = 102, 78.5%) or high-risk (n = 28, 21.5%; extrapleural: n = 3, 2.3%; carinal: n = 9, 6.9%; completion: n = 16, 12.3%) pneumonectomy was performed in 130 patients (60.8 ± 10.6 years; 43.1% women). Major complications occurred in 33.1% (n = 43 of 130) and readmission in 17.7% (n = 23 of 130) of patients. In multivariable models, patients with high TSMA experienced fewer overall (odds ratio [OR], 0.87; P = .04) and cardiopulmonary (OR, 0.86; P = .04) complications, and fewer readmissions (OR, 0.78; P = .01). Associations with ICU LOS (hazard ratio, 1.08; P = .051) and hospital LOS (hazard ratio, 1.05; P = .18) did not reach significance. CONCLUSIONS: TSMA predicts adverse outcome after pneumonectomy for lung cancer. This marker, readily derived from standard chest CT, identifies patients at increased risk for postoperative complications and may help select patients appropriate for focused rehabilitation before pneumonectomy.

Incidence, aetiology and outcomes of major postoperative haemorrhage after pulmonary lobectomy.

OBJECTIVES: Post-lobectomy bleeding is uncommon and rarely studied. In this study, we aimed to determine the incidence of post-lobectomy haemorrhage and compare the outcomes of reoperation and non-operative management. METHODS: We conducted a single-institution review of lobectomy cases from 2009 to 2018. The patients were divided into two groups based on the treatment for postoperative bleeding: reoperation or transfusion of packed red blood cells with observation. Transfusion correcting intraoperative blood loss was excluded. One or more criteria defined postoperative bleeding: (i) drop in haematocrit ≥10 or (ii) frank, sustained chest tube bleeding with or without associated hypotension. Covariates included demographics, comorbidities and operative characteristics. Outcomes were operative mortality, complications, length of hospital stay and readmission within 30 days. RESULTS: Following 1960 lobectomies (92% malignant disease, 8% non-malignant), haemorrhage occurred in 42 cases (2.1%), leading to reoperation in 27 (1.4%), and non-operative management in 15 (0.8%). The median time to reoperation was 17 h. No source of bleeding was identified in 44% of re-explorations. Patients with postoperative haemorrhage were more often male (64.3% vs 41.2%; P < 0.01) and more likely to have preoperative anaemia (45.2% vs 26.5%; P = 0.01), prior median sternotomy (14.3% vs 6.0%; P = 0.04), an infectious indication (7.1% vs 1.8%; P = 0.01) and operative adhesiolysis (45.2% vs 25.8%; P = 0.01). Compared with non-operative management, reoperation was associated with fewer units of packed red blood cells transfusion (0.4 vs 1.9; P < 0.001), while complication rates were similar and 30-day mortality was absent in either group. CONCLUSIONS: Haemorrhage after lobectomy is associated with multiple risk factors. Reoperation may avoid transfusion. A prospective study should optimize timing and selection of operative and non-operative management.

Differential inflammatory response dynamics in normal lung following stereotactic body radiation therapy with protons versus photons.

BACKGROUND AND PURPOSE: To compare time-dependent changes in lung parenchyma of early-stage non-small cell lung carcinoma (NSCLC) patients after stereotactic body radiation therapy with protons (SBPT) or photons (SBRT). MATERIALS AND METHOD: We retrospectively identified NSCLC patients treated with SBPT and matched each one with a SBRT patient by patient, tumor, and treatment characteristics. Lung parenchyma on serial post-treatment chest computer tomography (CT) scans was deformably registered with the treatment plan to analyze lung density changes as function of dose, quantified by Houndsfield Unit (HU)/Gy. A thoracic radiologist also evaluated the CTs using an established grading system. RESULTS: We matched 23 SBPT/SBRT pairs, including 5 patients treated with both modalities (internally matched cohort). Normal lung response following SBPT significantly increased in the early time period (CTs acquired <6 months, median 3 months) post-treatment, and then did not change significantly in the later time period (CTs acquired 6-14 months, median 9 months). For SBRT, the normal lung response was similar to SBPT in the early time period, but then increased significantly from the early to the late time period (p = 0.007). These differences were most pronounced in sensitive (response >6 HU/Gy) patients and in the internally matched cohort. However, there was no significant difference in the maximum observed response in the entire cohort over all time periods, median 3.4 [IQR, 1.0-5.4] HU/Gy (SBPT) versus 2.5 [1.6-5.2] HU/Gy (SBRT). Qualitative radiological evaluation was highly correlated with the quantitative analysis (p < 0.0001). CONCLUSION: While there was no significant difference in maximum response after SBPT versus SBRT, dose-defined lung inflammation occurred earlier after proton irradiation. Further investigation is warranted into the mechanisms of inflammation and therapeutic consequences after proton versus photon irradiation.

Preoperative thoracic muscle area on computed tomography predicts long-term survival following pneumonectomy for lung cancer.

OBJECTIVES: To assess the prognostic role of thoracic muscle as quantified on preoperative computed tomography (CT) for the estimation of overall survival (OS) following pneumonectomy. METHODS: Muscle cross-sectional area (CSA) at the level of the fifth (T5) and eighth (T8) thoracic vertebra was measured on CT scans of consecutive patients with lung cancer prior to pneumonectomy. We stratified patients into high and low muscle groups using the gender-specific median of muscle CSA as separator and estimated associations of muscle CSA and OS using the Kaplan-Meier analysis. Multivariable logistic regression adjusted for body mass index, Charlson comorbidity index (includes age), forced expiratory volume in the first second as a % of predicted, sex, race, smoking status, tumour stage and prior lung cancer treatment was performed. RESULTS: A total of 128 patients were included (61.0 ± 10.6 years of age, mean body mass index of 26.9 kg/m2, 55.5% men). The T8 level showed fewer artefacts and strong correlation with the T5 level (Pearson's rho = 0.904). T8 CSA was therefore used for subsequent analyses. Mean T8 CSA was 118.5 cm2 (median 115.3 cm2) in men and 75.2 cm2 (median 74.0 cm2) in women. During a median follow-up of 23.6 months (interquartile range 39.3), 65 patients (50.8%) died, of whom 41 were in the low muscle group. The Kaplan-Meier analysis showed significantly longer OS in the high muscle group (log-rank P = 0.02). Multivariable analysis showed an independent association of muscle CSA and OS (P = 0.02) with a hazard ratio of 0.80 (confidence interval 0.67-0.98) per 10-cm2 increment. CONCLUSIONS: Thoracic muscle is independently associated with long-term overall survival following pneumonectomy for lung cancer and may contribute to refined survival estimates in this population. IRB PROTOCOL: Protocol #2017P000650, approved 21 April 2017.