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

Sarcopenia, sarcopenic obesity and the clinical outcome of the older inpatients with COVID-19 infection: a prospective observational study

OBJECTIVE

We aimed to investigate the impact of sarcopenia and sarcopenic obesity (SO) on the clinical outcome in older patients with COVID-19 infection and chronic disease.

METHODS

We prospectively collected data from patients admitted to Huadong Hospital for COVID-19 infection between November 1, 2022, and January 31, 2023. These patients were included from a previously established comprehensive geriatric assessment (CGA) cohort. We collected information on their pre-admission condition regarding sarcopenia, SO, and malnutrition, as well as their medical treatment. The primary endpoint was the incidence of intubation, while secondary endpoints included in-hospital mortality rates. We then utilized Kaplan-Meier (K-M) survival curves and the log-rank tests to compare the clinical outcomes related to intubation or death, assessing the impact of sarcopenia and SO on patient clinical outcomes.

RESULTS

A total of 113 patients (age 89.6 ± 7.0 years) were included in the study. Among them, 51 patients had sarcopenia and 39 had SO prior to hospitalization. Intubation was required for 6 patients without sarcopenia (9.7%) and for 18 sarcopenia patients (35.3%), with 16 of these being SO patients (41%). Mortality occurred in 2 patients without sarcopenia (3.3%) and in 13 sarcopenia patients (25.5%), of which 11 were SO patients (28%). Upon further analysis, patients with SO exhibited significantly elevated risks for both intubation (Hazard Ratio [HR] 7.43, 95% Confidence Interval [CI] 1.26-43.90, P < 0.001) and mortality (HR 6.54, 95% CI 1.09-39.38, P < 0.001) after adjusting for confounding factors.

CONCLUSIONS

The prevalence of sarcopenia or SO was high among senior inpatients, and both conditions were found to have a significant negative impact on the clinical outcomes of COVID-19 infection. Therefore, it is essential to regularly assess and intervene in these conditions at the earliest stage possible.

Preoperative exercise training decreases complications of minimally invasive lung cancer surgery: A randomized controlled trial

OBJECTIVE

Limited evidence exists regarding the efficacy of preoperative exercise in reducing short-term complications after minimally invasive surgery in patients with non-small cell lung cancer. This study aims to investigate the impact of preoperative exercise on short-term complications after minimally invasive lung resection.

METHODS

In this prospective, open-label, randomized (1:1) controlled trial at Xiangya Hospital, China (September 2020 to February 2022), patients were randomly assigned to a preoperative exercise group with 16-day alternate supervised exercise or a control group. The primary outcome assessed was short-term postoperative complications, with a follow-up period of 30 days postsurgery.

RESULTS

A total of 124 patients were recruited (preoperative exercise group n = 62; control n = 62). Finally, 101 patients (preoperative exercise group; n = 51 and control; n = 50) with a median age of 56 years (interquartile range, 50-62 years) completed the study. Compared with the control group, the preoperative exercise group showed fewer postoperative complications (preoperative exercise 3/51 vs control 10/50; odds ratio, 0.17; 95% CI, 0.04-0.86; P = .03) and shorter hospital stays (mean difference, -2; 95% CI, -3 to -1; P = .01). Preoperative exercise significantly improved depression, stress, functional capacity, and quality of life (all P < .05) before surgery. Furthermore, preoperative exercise demonstrated a significantly lower minimum blood pressure during surgery and lower increases in body temperature on day 2 after surgery, neutrophil-to-lymphocyte ratio, and neutrophil count after surgery (all P < .05). Exploratory research on lung tissue RNA sequencing (5 in each group) showed downregulation of the tumor necrosis factor signaling pathway in the preoperative exercise group compared with the control group.

CONCLUSIONS

Preoperative exercise training decreased short-term postoperative complications in patients with non-small cell lung cancer.

Age-related Muscle Fat Infiltration in Lung Screening Participants: Impact of Smoking Cessation

Rationale

Skeletal muscle fat infiltration progresses with aging and is worsened among individuals with a history of cigarette smoking. Many negative impacts of smoking on muscles are likely reversible with smoking cessation.

Objectives

To determine if the progression of skeletal muscle fat infiltration with aging is altered by smoking cessation among lung cancer screening participants.

Methods

This was a secondary analysis based on the National Lung Screening Trial. Skeletal muscle attenuation in Hounsfield unit (HU) was derived from the baseline and follow-up low-dose CT scans using a previously validated artificial intelligence algorithm. Lower attenuation indicates greater fatty infiltration. Linear mixed-effects models were constructed to evaluate the associations between smoking status and the muscle attenuation trajectory.

Measurements and Main Results

Of 19,019 included participants (age: 61 years, 5 [SD]; 11,290 males), 8,971 (47.2%) were actively smoking cigarettes. Accounting for body mass index, pack-years, percent emphysema, and other confounding factors, actively smoking predicted a lower attenuation in both males (β0 =-0.88 HU, P<.001) and females (β0 =-0.69 HU, P<.001), and an accelerated muscle attenuation decline-rate in males (β1=-0.08 HU/y, P<.05). Age-stratified analyses indicated that the accelerated muscle attenuation decline associated with smoking likely occurred at younger age, especially in females.

Conclusions

Among lung cancer screening participants, active cigarette smoking was associated with greater skeletal muscle fat infiltration in both males and females, and accelerated muscle adipose accumulation rate in males. These findings support the important role of smoking cessation in preserving muscle health.

Increased adipose tissue is associated with improved overall survival, independent of skeletal muscle mass in non-small cell lung cancer

BACKGROUND

The prognostic significance of non-cancer-related prognostic factors, such as body composition, has gained extensive attention in oncological research. Compared with sarcopenia, the prognostic significance of adipose tissue for overall survival in non-small cell lung cancer remains unclear. We investigated the prognostic value of skeletal muscle and adipose tissue in patients with non-small cell lung cancer.

METHODS

This retrospective study included 4434 patients diagnosed with non-small cell lung cancer between January 2014 and December 2016. Cross-sectional areas of skeletal muscle and subcutaneous fat were measured, and the pericardial fat volume was automatically calculated. The skeletal muscle index and subcutaneous fat index were calculated as skeletal muscle area and subcutaneous fat area divided by height squared, respectively, and the pericardial fat index was calculated as pericardial fat volume divided by body surface area. The association between body composition and outcomes was evaluated using Cox proportional hazards model.

RESULTS

A total of 750 patients (501 males [66.8%] and 249 females [33.2%]; mean age, 60.9 ± 9.8 years) were included. Sarcopenia (60.8% vs. 52.7%; P < 0.001), decreased subcutaneous fat index (51.4% vs. 25.2%; P < 0.001) and decreased pericardial fat index (55.4% vs. 16.5%; P < 0.001) were more commonly found in the deceased group than survived group. In multivariable Cox regression analysis, after adjusting for clinical variables, increased subcutaneous fat index (hazard ratio [HR] = 0.56, 95% confidence interval [CI]: 0.47-0.66, P < 0.001) and increased pericardial fat index (HR = 0.47, 95% CI: 0.40-0.56, P < 0.001) were associated with longer overall survival. For stage I-III patients, increased subcutaneous fat index (HR = 0.62, 95% CI: 0.48-0.76, P < 0.001) and increased pericardial fat index (HR = 0.43, 95% CI: 0.34-0.54, P < 0.001) were associated with better 5-year overall survival rate. Similar results were recorded in stage IV patients. For patients with surgery, the prognostic value of increased subcutaneous fat index (HR = 0.60, 95% CI: 0.44-0.80, P = 0.001) and increased pericardial fat index (HR = 0.51, 95% CI: 0.38-0.68, P < 0.001) remained and predicted favourable overall survival. Non-surgical patients showed similar results as surgical patients. No association was noted between sarcopenia and overall survival (P > 0.05).

CONCLUSIONS

Increased subcutaneous fat index and pericardial fat index were associated with a higher 5-year overall survival rate, independent of sarcopenia, in non-small cell lung cancer and may indicate a reduced risk of non-cancer-related death.

The impact of body compositions on contrast medium enhancement in chest CT: a randomised controlled trial

Objective

To compare a fixed-volume contrast medium (CM) protocol with a combined total body weight (TBW) and body composition-tailored protocol in chest CT.

Methods and materials

Patients referred for routine contrast enhanced chest CT were prospectively categorised as normal, muscular or overweight. Patients were accordingly randomised into two groups; Group 1 received a fixed CM protocol. Group 2 received CM volume according to a body composition-tailored protocol. Objective image quality comparisons between protocols and body compositions were performed. Differences between groups and correlation were analysed using t-test and Pearson's r.

Results

A total of 179 patients were included: 87 in Group 1 (mean age, 51 ± 17 years); and 92 in Group 2 (mean age, 52 ± 17 years). Compared to Group 2, Group 1 showed lower vascular attenuation in muscular (mean 346 Hounsfield unit (HU) vs 396 HU; p = 0.004) and overweight categories (mean 342 HU vs 367 HU; p = 0.12), while normal category patients showed increased attenuation (385 vs 367; p = 0.61). In Group 1, strongest correlation was found between attenuation and TBW in muscular (r = -.49, p = 0.009) and waist circumference in overweight patients (r = -.50, p = 0.005). In Group 2, no significant correlations were found for the same body size parameters. In Group 1, 13% of the overweight patients was below 250 HU (p = 0.053).

Conclusion

A combined TBW and body composition-tailored CM protocol in chest CT resulted in more homogenous enhancement and fewer outliers compared to a fixed-volume protocol.

Advances in knowledge

This is, to our knowledge, the first study to investigate the impact of various body compositions on contrast medium enhancement in chest CT.

Body composition assessment with limited field-of-view computed tomography: A semantic image extension perspective

Field-of-view (FOV) tissue truncation beyond the lungs is common in routine lung screening computed tomography (CT). This poses limitations for opportunistic CT-based body composition (BC) assessment as key anatomical structures are missing. Traditionally, extending the FOV of CT is considered as a CT reconstruction problem using limited data. However, this approach relies on the projection domain data which might not be available in application. In this work, we formulate the problem from the semantic image extension perspective which only requires image data as inputs. The proposed two-stage method identifies a new FOV border based on the estimated extent of the complete body and imputes missing tissues in the truncated region. The training samples are simulated using CT slices with complete body in FOV, making the model development self-supervised. We evaluate the validity of the proposed method in automatic BC assessment using lung screening CT with limited FOV. The proposed method effectively restores the missing tissues and reduces BC assessment error introduced by FOV tissue truncation. In the BC assessment for large-scale lung screening CT datasets, this correction improves both the intra-subject consistency and the correlation with anthropometric approximations. The developed method is available at https://github.com/MASILab/S-EFOV.

Deep Learning for Medical Image-Based Cancer Diagnosis

(1) Background: The application of deep learning technology to realize cancer diagnosis based on medical images is one of the research hotspots in the field of artificial intelligence and computer vision. Due to the rapid development of deep learning methods, cancer diagnosis requires very high accuracy and timeliness as well as the inherent particularity and complexity of medical imaging. A comprehensive review of relevant studies is necessary to help readers better understand the current research status and ideas. (2) Methods: Five radiological images, including X-ray, ultrasound (US), computed tomography (CT), magnetic resonance imaging (MRI), positron emission computed tomography (PET), and histopathological images, are reviewed in this paper. The basic architecture of deep learning and classical pretrained models are comprehensively reviewed. In particular, advanced neural networks emerging in recent years, including transfer learning, ensemble learning (EL), graph neural network, and vision transformer (ViT), are introduced. Five overfitting prevention methods are summarized: batch normalization, dropout, weight initialization, and data augmentation. The application of deep learning technology in medical image-based cancer analysis is sorted out. (3) Results: Deep learning has achieved great success in medical image-based cancer diagnosis, showing good results in image classification, image reconstruction, image detection, image segmentation, image registration, and image synthesis. However, the lack of high-quality labeled datasets limits the role of deep learning and faces challenges in rare cancer diagnosis, multi-modal image fusion, model explainability, and generalization. (4) Conclusions: There is a need for more public standard databases for cancer. The pre-training model based on deep neural networks has the potential to be improved, and special attention should be paid to the research of multimodal data fusion and supervised paradigm. Technologies such as ViT, ensemble learning, and few-shot learning will bring surprises to cancer diagnosis based on medical images.

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.

Role of Machine Learning-Based CT Body Composition in Risk Prediction and Prognostication: Current State and Future Directions

CT body composition analysis has been shown to play an important role in predicting health and has the potential to improve patient outcomes if implemented clinically. Recent advances in artificial intelligence and machine learning have led to high speed and accuracy for extracting body composition metrics from CT scans. These may inform preoperative interventions and guide treatment planning. This review aims to discuss the clinical applications of CT body composition in clinical practice, as it moves towards widespread clinical implementation.

Sarcopenic obesity and therapeutic outcomes in gastrointestinal surgical oncology: A meta-analysis

Background

Sarcopenic obesity (SO) has been indicated as a scientific and clinical priority in oncology. This meta-analysis aimed to investigate the impacts of preoperative SO on therapeutic outcomes in gastrointestinal surgical oncology.

Methods

We searched the PubMed, EMBASE, and Cochrane Library databases through March 4th 2022 to identify cohort studies. Endpoints included postoperative complications and survival outcomes. Newcastle Ottawa Scale was used for quality assessment. Heterogeneity and publication bias were assessed. Subgroup analyses and sensitivity analyses were performed.

Results

Twenty-six studies (8,729 participants) with moderate to good quality were included. The pooled average age was 65.6 [95% confidence interval (CI) 63.7-67.6] years. The significant heterogeneity in SO definition and diagnosis among studies was observed. Patients with SO showed increased incidences of total complications (odds ratio 1.30, 95% CI: 1.03-1.64, P = 0.030) and major complications (Clavien-Dindo grade ≥ IIIa, odds ratio 2.15, 95% CI: 1.39-3.32, P = 0.001). SO was particularly associated with the incidence of cardiac complications, leak complications, and organ/space infection. SO was also predictive of poor overall survival (hazard ratio 1.73, 95% CI: 1.46-2.06, P < 0.001) and disease-free survival (hazard ratio 1.41, 95% CI: 1.20-1.66, P < 0.001). SO defined as sarcopenia in combination with obesity showed greater association with adverse outcomes than that defined as an increased ratio of fat mass to muscle mass. A low prevalence rate of SO (< 10%) was associated with increased significance for adverse outcomes compared to the high prevalence rate of SO (> 20%).

Conclusion

The SO was associated with increased complications and poor survival in gastrointestinal surgical oncology. Interventions aiming at SO have potentials to promote surgery benefits for patients with gastrointestinal cancers. The heterogeneity in SO definition and diagnosis among studies should be considered when interpreting these findings.

Systematic Review Registration

[https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=255286], identifier [CRD42021255286].

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 pretreatment diagnostic and radiotherapy planning chest CT examinations. Objective: 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. 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 55.8-68.9), 148 (52.5%) patients 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] versus 0.61 [95% CI 0.53-0.70], p < .01). The model's most important clinical or imaging feature based on mean standardized regression coefficients was the PA-to-aorta ratio. Conclusions: 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.

CT-Derived Body Composition Values and Complications After Pneumonectomy in Lung Cancer Patients: Time for a Sex-Related Analysis?

Purpose

This study aimed to assess if CT-derived body composition values and clinical characteristics are associated with the risk of postsurgical complications in men and women who underwent pneumonectomy for lung cancer.

Materials and Methods

Patients who underwent pneumonectomy between 2004 and 2008 were selected. The ethics committee approved this retrospective study with waiver of informed content. Main clinical data collected were sex, age, weight and height to calculate body mass index (BMI), albumin, C-reactive protein, smoking status, side, sarcopenia, presurgical treatments, reoperation, and complications within 30 days after pneumonectomy, classified as: lung complications, cardiac complications, other complications, and any complication. From an axial CT image at the level of L3, automatic segmentations were performed to calculate skeletal muscle area (SMA), skeletal muscle density, subcutaneous adipose tissue, and visceral adipose tissue. Skeletal muscle index was calculated as SMA/square height. Univariate and multivariate logistic regression analyses were performed to estimate the risk of any complication, both on the total population and in a by sex subgroup analysis. All tests were two tailed and considered significant at 5% level.

Results

A total of 107 patients (84 men and 23 women) were included. Despite no significant differences in BMI, there were significant differences of body composition values in muscle and adipose tissue parameters between men and women, with women being significantly more sarcopenic than men (p = 0.002). Separate analyses for men and women showed that age and SMA were significantly associated with postoperative complications in men (p = 0.03 and 0.02, respectively).

Conclusions

Body composition measurements extracted from routine CT may help in predicting complications after pneumonectomy, with men and women being different in quantity and distribution of muscle and fat, and men significantly more prone to postpneumonectomy complications with the increase of age and the decrease of skeletal muscle area.

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.

Extending the value of routine lung screening CT with quantitative body composition assessment

Certain body composition phenotypes, like sarcopenia, are well established as predictive markers for post-surgery complications and overall survival of lung cancer patients. However, their association with incidental lung cancer risk in the screening population is still unclear. We study the feasibility of body composition analysis using chest low dose computed tomography (LDCT). A two-stage fully automatic pipeline is developed to assess the cross-sectional area of body composition components including subcutaneous adipose tissue (SAT), muscle, visceral adipose tissue (VAT), and bone on T5, T8 and T10 vertebral levels. The pipeline is developed using 61 cases of the VerSe'20 dataset, 40 annotated cases of NLST, and 851 inhouse screening cases. On a test cohort consisting of 30 cases from the inhouse screening cohort (age 55 - 73, 50% female) and 42 cases of NLST (age 55 - 75, 59.5% female), the pipeline achieves a root mean square error (RMSE) of 7.25 mm (95% CI: [6.61, 7.85]) for the vertebral level identification and mean Dice similarity score (DSC) 0.99 ± 0.02, 0.96 ± 0.03, and 0.95 ± 0.04 for SAT, muscle, and VAT, respectively for body composition segmentation. The pipeline is generalized to the CT arm of the NLST dataset (25,205 subjects, 40.8% female, 1,056 lung cancer incidences). Time-to-event analysis for lung cancer incidence indicates inverse association between measured muscle cross-sectional area and incidental lung cancer risks (p < 0.001 female, p < 0.001 male). In conclusion, automatic body composition analysis using routine lung screening LDCT is feasible.

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.

An explainable machine learning framework for lung cancer hospital length of stay prediction

This work introduces a predictive Length of Stay (LOS) framework for lung cancer patients using machine learning (ML) models. The framework proposed to deal with imbalanced datasets for classification-based approaches using electronic healthcare records (EHR). We have utilized supervised ML methods to predict lung cancer inpatients LOS during ICU hospitalization using the MIMIC-III dataset. Random Forest (RF) Model outperformed other models and achieved predicted results during the three framework phases. With clinical significance features selection, over-sampling methods (SMOTE and ADASYN) achieved the highest AUC results (98% with CI 95%: 95.3-100%, and 100% respectively). The combination of Over-sampling and under-sampling achieved the second-highest AUC results (98%, with CI 95%: 95.3-100%, and 97%, CI 95%: 93.7-100% SMOTE-Tomek, and SMOTE-ENN respectively). Under-sampling methods reported the least important AUC results (50%, with CI 95%: 40.2-59.8%) for both (ENN and Tomek- Links). Using ML explainable technique called SHAP, we explained the outcome of the predictive model (RF) with SMOTE class balancing technique to understand the most significant clinical features that contributed to predicting lung cancer LOS with the RF model. Our promising framework allows us to employ ML techniques in-hospital clinical information systems to predict lung cancer admissions into ICU.

How can we minimize the risks by optimizing patient's condition shortly before thoracic surgery?

The “moderate-to-high-risk” surgical patient is typically older, frail, malnourished, suffering from multiple comorbidities and presenting with unhealthy life style such as smoking, hazardous drinking and sedentarity. Poor aerobic fitness, sarcopenia and “toxic” behaviors are modifiable risk factors for major postoperative complications. The physiological challenge of lung cancer surgery has been likened to running a marathon. Therefore, preoperative patient optimization or “ prehabilitation “ should become a key component of improved recovery pathways to enhance general health and physiological reserve prior to surgery. During the short preoperative period, the patients are more receptive and motivated to adhere to behavioral interventions (e.g., smoking cessation, weaning from alcohol, balanced food intake and active mobilization) and to follow a structured exercise training program. Sufficient protein intake should be ensured (1.5–2 g/kg/day) and nutritional defects should be corrected to restore muscle mass and strength. Currently, there is strong evidence supporting the effectiveness of various modalities of physical training (endurance training and/or respiratory muscle training) to enhance aerobic fitness and to mitigate the risk of pulmonary complications while reducing the hospital length of stay. Multimodal interventions should be individualized to the patient's condition. These bundle of care are more effective than single or sequential intervention owing to synergistic benefits of education, nutritional support and physical training. An effective prehabilitation program is necessarily patient-centred and coordinated among health care professionals (nurses, primary care physician, physiotherapists, nutritionists) to help the patient regain some control over the disease process and improve the physiological reserve to sustain surgical stress.

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.

Pulmonary artery size on computed tomography is associated with major morbidity after pulmonary lobectomy

OBJECTIVE

To investigate the relationship of pulmonary artery diameter (PAD) measured by computed tomography (CT) with outcomes following lobectomy.

METHODS

Records of patients undergoing pulmonary lobectomy for lung cancer between 2011 and 2018 were reviewed. Baseline characteristics and postoperative outcome data were derived from the institutional Society of Thoracic Surgeons database. Luminal diameter of the central pulmonary arteries and ascending aorta were measured on preoperative CTs. Logistic regression analyses were performed to test the association of PAD with complications.

RESULTS

A total of 736 lobectomy patients were included, who had a preoperative CT scan (25% with contrast, 75% noncontrast) available for review. A total of 141 (19.2%) patients had an enlarged main PAD ≥30 mm, and 58 (7.9%) patients had a main PAD that was larger than the ascending aorta (PA/ascending aorta ratio > 1). The right or left PAD on the surgical side was associated with major complication (odds ratio per mm, 1.12; 95% confidence interval, 1.05-1.18; P < .001), unexpected intensive care unit admission (odds ratio per millimeter, 1.11; 95% confidence interval, 1.04-1.19; P = .002), and 30-day mortality (odds ratio per millimeter, 1.25; 95% confidence interval, 1.06-1.46; P = .007). On multivariable analysis, adjusted for cardiovascular comorbidities, pulmonary function, and the operative approach, surgical side PAD remained an independent factor associated with major complication.

CONCLUSIONS

CT-based measurements of the PAD on the operative side may inform of the about the risk of major complications after lobectomy. Review of PA size on preoperative CT scans may help identify patients who would benefit from formal evaluation of PA pressures to improve the operative risk assessment.

Deadlock of proctologic practice in Italy during COVID-19 pandemic: a national report from ProctoLock2020

Proctology is one of the surgical specialties that suffered the most during COVID-19 pandemic. Using data from a cross-sectional worldwide web survey, we aimed to snapshot the current status of proctologic practice in Italy with differences between three macro areas (North, Centre, South). Specialists affiliated to renowned scientific societies with an interest in coloproctology were invited to join a 27-item survey. Predictive power of respondents' and hospitals' demographics on the change of status of surgical activities was calculated. The study was registered at ClinicalTrials.gov (NCT04392245). Of 299 respondents from Italy, 94 (40%) practiced in the North, 60 (25%) in the Centrer and 82 (35%) in the South and Islands. The majority were men (79%), at consultant level (70%), with a mean age of 46.5 years, practicing in academic hospitals (39%), where a dedicated proctologist was readily available (68%). Southern respondents were more at risk of infection compared to those from the Center (OR, 3.30; 95%CI 1.46; 7.47, P = 0.004), as were males (OR, 2.64; 95%CI 1.09; 6.37, P = 0.031) and those who routinely tested patients prior to surgery (OR, 3.02; 95%CI 1.39; 6.53, P = 0.005). The likelihood of ongoing surgical practice was higher in the South (OR 1.36, 95%CI 0.75; 2.46, P = 0.304) and in centers that were not fully dedicated to COVID-19 care (OR 4.00, 95%CI 1.88; 8.50, P < 0.001). The results of this survey highlight important factors contributing to the deadlock of proctologic practice in Italy and may inform the development of future management strategies.