Vertebral HU value and the pectoral muscle index based on chest CT can be used to opportunistically screen for osteoporosis.

BACKGROUND: Existing studies have shown that computed tomography (CT) attenuation and skeletal muscle tissue are strongly associated with osteoporosis; however, few studies have examined whether vertebral HU values and the pectoral muscle index (PMI) measured at the level of the 4th thoracic vertebra (T4) are strongly associated with bone mineral density (BMD). In this study, we demonstrate that vertebral HU values and the PMI based on chest CT can be used to opportunistically screen for osteoporosis and reduce fracture risk through prompt treatment. METHODS: We retrospectively evaluated 1000 patients who underwent chest CT and DXA scans from August 2020-2022. The T4 HU value and PMI were obtained using manual chest CT measurements. The participants were classified into normal, osteopenia, and osteoporosis groups based on the results of dual-energy X-ray (DXA) absorptiometry. We compared the clinical baseline data, T4 HU value, and PMI between the three groups of patients and analyzed the correlation between the T4 HU value, PMI, and BMD to further evaluate the diagnostic efficacy of the T4 HU value and PMI for patients with low BMD and osteoporosis. RESULTS: The study ultimately enrolled 469 participants. The T4 HU value and PMI had a high screening capacity for both low BMD and osteoporosis. The combined diagnostic model-incorporating sex, age, BMI, T4 HU value, and PMI-demonstrated the best diagnostic efficacy, with areas under the receiver operating characteristic curve (AUC) of 0.887 and 0.892 for identifying low BMD and osteoporosis, respectively. CONCLUSIONS: The measurement of T4 HU value and PMI on chest CT can be used as an opportunistic screening tool for osteoporosis with excellent diagnostic efficacy. This approach allows the early prevention of osteoporotic fractures via the timely screening of individuals at high risk of osteoporosis without requiring additional radiation.

Measuring myofascial shear strain in chronic shoulder pain with ultrasound shear strain imaging: a case report.

BACKGROUND: Dysfunctional gliding of deep fascia and muscle layers forms the basis of myofascial pain and dysfunction, which can cause chronic shoulder pain. Ultrasound shear strain imaging may offer a non-invasive tool to quantitatively evaluate the extent of muscular dysfunctional gliding and its correlation with pain. This case study is the first to use ultrasound shear strain imaging to report the shear strain between the pectoralis major and minor muscles in shoulders with and without chronic pain. CASE PRESENTATION: The shear strain between the pectoralis major and minor muscles during shoulder rotation in a volunteer with chronic shoulder pain was measured with ultrasound shear strain imaging. The results show that the mean ± standard deviation shear strain was 0.40 ± 0.09 on the affected side, compared to 1.09 ± 0.18 on the unaffected side (p<0.05). The results suggest that myofascial dysfunction may cause the muscles to adhere together thereby reducing shear strain on the affected side. CONCLUSION: Our findings elucidate a potential pathophysiology of myofascial dysfunction in chronic shoulder pain and reveal the potential utility of ultrasound imaging to provide a useful biomarker for shear strain evaluation between the pectoralis major and minor muscles.

Neuromuscular electrical stimulation of humeral adductors in subjects with rotator cuff tear.

INTRODUCTION: In symptomatic patients with rotator cuff tear, MRI and radiographic studies have ascribed the pain symptom to insufficient humeral head depression during arm elevations. The arm adductors such as the teres major and pectoralis major may contribute to depression of the humerus head during arm elevations. Researchers have demonstrated that neuromuscular electrical stimulation (NMES) of the serratus anterior and lower trapezius can control scapular motions and improve acromiohumeral distance. It is unknown, however, if adductor neuromuscular training could help patients with rotator cuff tear. MATERIALS AND METHODS: A cross-sectional study of NMES of the teres major and pectoralis major was conducted on 30 symptomatic subjects with rotator cuff tear. We measured the acromiohumeral distance by ultrasonography and scapular kinematics during arm elevation with a three-dimensional motion tracking system. RESULTS: The acromiohumeral distance significantly increased during NMES of the teres major (0.73 mm, p < 0.001). However, the distance significantly decreased with NMES of the pectoralis major (0.78 mm, p < 0.001). Additionally, scapular upward rotation was greater during NMES of the teres major than during NMES of the pectoralis major (3.4°, p < 0.001). Scapular external rotation decreased significantly more during NMES of the pectoralis major than during NMES of the teres major (1.6°, p = 0.003). CONCLUSIONS: NMES of the teres major can increase acromiohumeral distance and scapular upward rotation during arm elevation. However, the decreased upward and external rotation of the scapula during arm elevation with NMES of the pectoralis major may be associated with subacromial impingement.

Pectoralis Muscle Area as a Predictor of Mortality in Patients Hospitalized with Bronchiectasis Exacerbation.

INTRODUCTION: Data on factors related to mortality in patients with bronchiectasis exacerbation are insufficient. Computed tomography (CT) can measure the pectoralis muscle area (PMA) and is a useful tool to diagnose sarcopenia. This study aimed to evaluate whether PMA can predict mortality in patients with bronchiectasis exacerbation. METHODS: Patients hospitalized due to bronchiectasis exacerbation at a single center were retrospectively divided into survivors and non-survivors based on 1-year mortality. Thereafter, a comparison of the clinical and radiologic characteristics was conducted between the two groups. RESULTS: A total of 66 (14%) patients died at 1 year. In the multivariate analysis, age, BMI <18.4 kg/m2, sex-specific PMA quartile, ≥3 exacerbations in the previous year, serum albumin <3.5 g/dL, cystic bronchiectasis, tuberculosis-destroyed lung, and diabetes mellitus were independent predictors for the 1-year mortality in patients hospitalized with bronchiectasis exacerbation. A lower PMA was associated with a lower overall survival rate in the survival analysis according to sex-specific quartiles of PMA. PMA had the highest area under the curve during assessment of prognostic performance in predicting the 1-year mortality. The lowest sex-specific PMA quartile group exhibited higher disease severity than the highest quartile group. CONCLUSIONS: CT-derived PMA was an independent predictor of 1-year mortality in patients hospitalized with bronchiectasis exacerbation. Patients with lower PMA exhibited higher disease severity. These findings suggest that PMA might be a useful marker for providing additional information regarding prognosis of patients with bronchiectasis exacerbation.

Common Wrist-Extensor Tendon and Pectoralis Muscle Stiffness in Healthy Recreational Tennis Players.

CONTEXT: Imbalances in upper-extremity soft tissue stiffness may play a role in the development of shoulder and elbow musculoskeletal injuries in tennis players. Ultrasound shear wave elastography provides quantifiable and specific data regarding muscle stiffness. The purpose of this study was to compare tendon and muscle stiffness in healthy tennis players to nontennis players. DESIGN: Cross-sectional study. METHODS: The shear wave modulus, measured in kilopascals, was obtained for the dominant pectoralis major, pectoralis minor, and common wrist-extensor tendon using 2-dimensional shear wave elastography ultrasound imaging (GE Logiq S8, L9 linear transducer). Independent t test was run to compare age, body mass index, and the activity index score between both groups. Within-day intrarater reliability was assessed using a within-examiner intraclass correlation coefficients (ICC [3, 1]) with 95% confidence intervals. A multivariate general linear model was run to compare the mean differences between the tennis and nontennis players for each of the soft tissues. RESULTS: Twenty-six individuals (13 tennis players and 13 nontennis players) were recruited. Within-day ICCs were very good (ICC > .78 for the pectoralis musculature) and excellent (ICC > .94 for the common wrist extensor). Common extensor tendon stiffness was significantly higher in tennis players compared to nontennis players (mean difference = 114.8 [61.8], confidence interval, -22.8 to 252.5 kPa for the dominant arm [P = .039]). Mean pectoralis major and minor stiffness differences were not significant (P > .214). CONCLUSIONS: Common wrist-extensor stiffness in healthy recreational tennis players is higher than those who do not play tennis. Therefore, clinicians may need to facilitate a greater soft tissue stiffness response with resistance training when rehabilitating recreational tennis players as compared to those not playing tennis. Additional normative data on a larger sample of recreational tennis players should be collected.

Acute Response of the Noninfarcted Myocardium and Surrounding Tissue Assessed by T2 Mapping After STEMI.

BACKGROUND: ST-segment elevation myocardial infarction (STEMI) is associated with a systemic and local inflammatory response with edema. However, their role at the tissue level is poorly characterized. OBJECTIVES: This study aims to characterize T2 values of the noninfarcted myocardium (NIM) and surrounding tissue and to investigate prognostic relevance of higher NIM T2 values after STEMI. METHODS: A total of 171 consecutive patients with STEMI without prior cardiovascular events who underwent cardiac magnetic resonance after primary percutaneous coronary intervention were analyzed in terms of standard infarct characteristics. Edema of the NIM, liver, spleen, and pectoralis muscle was assessed based on T2 mapping. Follow-up was available for 130 patients. The primary endpoint was major adverse cardiac events (MACE), defined as cardiovascular death, myocardial infarction, unplanned coronary revascularization or rehospitalization for heart failure. The median time from primary percutaneous coronary intervention to cardiac magnetic resonance was 3 days (IQR: 2-5 days). RESULTS: Higher (above the median value of 45 ms) T2 values in the NIM area were associated with larger infarct size, microvascular obstruction, and left ventricular dysfunction and did not correlate with C-reactive protein, white blood cells, or T2 values of the pectoralis muscle, liver, and spleen. At a median follow-up of 17 months, patients with higher (>45 ms) NIM T2 values had increased risk of MACE (P < 0.001) compared with subjects with NIM T2 values ≤45 ms, mainly caused by a higher rate of myocardial reinfarction (26.3% vs 1.4%; P < 0.001). At multivariable analysis, higher NIM T2 values independently predicted MACE (HR: 2.824 [95% CI: 1.254-6.361]; P = 0.012). CONCLUSIONS: Higher NIM T2 values after STEMI are independently associated with worse cardiovascular outcomes, mainly because of higher risk of myocardial infarction.

Pectoral muscle removal in mammogram images: A novel approach for improved accuracy and efficiency.

PURPOSE: Accurate pectoral muscle removal is critical in mammographic breast density estimation and many other computer-aided algorithms. We propose a novel approach to remove pectoral muscles form mediolateral oblique (MLO) view mammograms and compare accuracy and computational efficiency with existing method (Libra). METHODS: A pectoral muscle identification pipeline was developed. The image is first binarized to enhance contrast and then the Canny algorithm was applied for edge detection. Robust interpolation is used to smooth out the pectoral muscle region. Accuracy and computational speed of pectoral muscle identification was assessed using 951 women (1,902 MLO mammograms) from the Joanne Knight Breast Health Cohort at Washington University School of Medicine. RESULTS: Our proposed algorithm exhibits lower mean error of 12.22% in comparison to Libra's estimated error of 20.44%. This 40% gain in accuracy was statistically significant (p < 0.001). The computational time for the proposed algorithm is 5.4 times faster when compared to Libra (5.1 s for proposed vs. 27.7 s for Libra per mammogram). CONCLUSION: We present a novel approach for pectoral muscle removal in mammogram images that demonstrates significant improvement in accuracy and efficiency compared to existing method. Our findings have important implications for the development of computer-aided systems and other automated tools in this field.

Research on Pectoral Muscle Segmentation Algorithm of CT Image Based on Deep Learning.

Chronic obstructive pulmonary disease (COPD) is a common respiratory disease, which seriously endangers human health and is also one of the important causes of death. The death rate of COPD in China is the highest in the world, and the problem of under-diagnosis of the disease is very serious. The gold standard for the diagnosis of COPD is lung function examination, and clinical studies have shown that CT and other imaging methods can be included in the auxiliary diagnosis of COPD. CT images can be used to assess pectoral muscle area, which is associated with COPD severity. Patients with lower pectoral muscle area often have more severe expiratory airflow obstruction and other problems. Therefore, the key of the research is to accurately segment the pectoral muscle in CT images. The medical image segmentation method based on deep learning can dig out more abundant internal information of data, so it has gradually become the preferred method in the aspect of medical image segmentation. In this paper, a pectoral muscle segmentation algorithm based on U-Net and its variant U-Net++ is proposed, which is of great significance for evaluating the severity of disease in patients with COPD. The network is composed of symmetrical encoders and decoders, which can effectively learn from very little labelled data by using appropriate data enhancement methods, and is therefore very suitable for medical image segmentation. The experimental results on the data set provided by Jiangsu Province Hospital show that the average Dice coefficient of the proposed algorithm is more than 94% and the average accuracy rate is 91%. The algorithm can accurately segment the pectoral muscle in CT images and has good segmentation performance.

The in vivo passive stretch response of the pectoralis major is region-specific.

The pectoralis major (PM) is a broad muscle commonly divided into three regions, which contribute uniquely to shoulder stability and movement. The PM muscle regions likely respond differently to stretch, but this has never been shown in vivo. We used shear wave elastography to assess the stretch response of different PM muscle regions during shoulder abduction and external rotation in 20 healthy male participants. Participants' shoulder was passively rotated through their range of motion in 5.7° increments and shear wave velocities (SWV) were obtained for each muscle region. A piece-wise model was fitted to the SWV-joint angle data, from which slack angle, slack stiffness and elasticity coefficient were determined. For shoulder abduction, we found that the sternocostal region had a significantly smaller slack angle (p = 0.049) and greater slack stiffness (p = 0.005) than the abdominal region, but there was no difference for elasticity coefficient (p = 0.074). For external rotation, only slack stiffness was greater for the sternocostal than the abdominal region (p < 0.001) with no differences found for slack angle (p = 0.18) and elasticity coefficient (p = 0.74). However, our data indicates that neither region was slack in this condition. These findings indicate that the sternocostal and abdominal regions respond differently to passive stretch, highlighting the PM's functional differentiation. This differentiation should be considered during treatment interventions such as PM muscle harvesting or treatments for breast cancer.

Pectoralis major and triceps brachii cross-sectional area measured on different planes: the effect on the muscle size-strength relationship.

BACKGROUND: Magnetic resonance imaging (MRI) is a well-used technique to assess muscle size and can be acquired on different planes. Pectoralis major (PM) and triceps brachii (TB) muscles are often acquired and analyzed on the axial plane, however it is unknown if anatomical cross-sectional area (CSA) calculated from different planes will affect the muscle size-strength relationship. Thus, the first aim of the present study was to identify if the CSA of the PM and TB measured on different planes presents a similar muscle size-strength relationship. A secondary aim was to investigate if the quantification of CSA of the PM and TB muscles are similar between sagittal and axial plane. METHODS: Fifteen males underwent an MRI examination, and after that, one-repetition maximum (1RM) test was performed. RESULTS: There was a significant relationship between 1RM and PM CSA measured on the axial and sagittal plane (r≤0.81), while the relationship with TB CSA was only good on the axial plane (r=0.65) and not significant on the sagittal plane (r=0.27). ICC between planes was excellent for PM CSA (0.96) with Bland-Altman procedure showing agreement between planes (d=0.376; P=0.612). Contrarily, TB CSA ICC was week (0.07), with Bland-Altman procedure showing no agreement between planes (d=-24.49; P=0.022). CONCLUSIONS: CSA measured at axial plane from PM and TB muscles showed a significant relationship with 1RM, while only PM CSA on the sagittal plane showed a significant relationship with 1RM. Finally, it was demonstrated that PM images showed a great reliability between planes, which was not true for TB muscle.

Pectoralis muscle index might be a factor associated with frailty in older women with breast cancer.

BACKGROUND: To investigate the possible relationship between pectoralis muscle (PM) measurement and frailty in older women with breast cancer (BC) (preoperatively defined as stage 1, 2, and 3 diseases). METHODS: This retrospective, observational study was conducted at Konya Training and Research Hospital between June and December 2020. A total of 102 patients [median age 62.5 years, median follow-up period two years] were included in the study. PM measurements were obtained from thorax computerized tomography (CT). Pectoralis muscle index (PMI) was calculated by dividing the PM area by the height square of the patients (cm2/m2). Pectoralis muscle density (PMD) was evaluated using CT findings, including their Hounsfield Units (HU). Frailty status and sarcopenia-risk assessments were done by a telephone interview in September 2020 using the FRAIL index (categorized as robust or nonrobust) and SARC-F questionnaire (classified as no sarcopenia-risk or risk of sarcopenia), respectively. PM measurements were compared between robust and nonrobust patients and between patients with a risk of sarcopenia and no sarcopenia risk. RESULTS: The nonrobust patients had lower pectoralis major muscle index (PMaMI) (p = 0.041) and pectoralis major muscle density(PMaD) (p = 0.020) levels than robust patients in the whole study sample. PMI (p = 0.017) and PMaMI (p = 0.010) levels were significantly lower in the nonrobust patients than in robust patients with early-stage BC. Frailty status was positively correlated with age (rho: 0.621; p < 0.001), BC stage (rho: 0.220; p = 0.026), and SARC-F score (rho: 0.747; p < 0.001), and negatively correlated with PMaMI (rho: -0.197; p = 0.047) and PMaD (rho: -0.237; p = 0.016). There were significant associations between PMaMI (OR: 0.467, 95% Confidence Interval (CI): 0.226-0.962 p = 0.039) and PMI (OR: 0.543, 95% CI: 0.299-0.986 p = 0.045) levels with frailty status (being nonrobust) in regression models. DISCUSSION: In the study, it has been shown that pectoralis muscle assessment might be a related parameter to frailty in older women with breast cancer.

Pectoralis muscle predicts distant metastases in breast cancer by deep learning radiomics.

BACKGROUND: Sarcopenia is associated with a poor prognosis in patients with breast cancer (BC). Currently, there are few quantitative assessments carried out between muscle biomarkers and distant metastasis using existing methods. PURPOSE: To assess the predictive value of the pectoralis muscle for BC distant metastasis, we developed a deep learning radiomics nomogram model (DLR-N) in this study. MATERIAL AND METHODS: A total of 493 patients with pathologically confirmed BC were registered. Image features were extracted from computed tomography (CT) images for each patient. Univariate and multivariate Cox regression analyses were performed to determine the independent prognostic factors for distant metastasis. The DLR-N was built based on independent prognostic factors and CT images to predict distant metastases. The model was assessed in terms of overall performance, discrimination, calibration, and clinical value. Finally, the predictive performance of the model was validated using the testing cohort. RESULTS: The developed DLR-N combined multiple radiomic features and clinicopathological factors and demonstrated excellent predictive performance. The C-index of the training cohort was 0.983 (95% confidence interval [CI] = 0.969-0.998) and the C-index of the testing cohort was 0.948 (95% CI = 0.917-0.979). Decision curve analysis (DCA) showed that patients could benefit more from incorporating multimodal radiomic features into clinicopathological models. CONCLUSIONS: DLR-N verified that there were biomarkers at the level of the fourth thoracic vertebra (T4) that affected distant metastasis. Multimodal prediction models based on deep learning could be a potential method to aid in the prediction of distant metastases in patients with BC.

Optimal needle electromyography approach to the serratus anterior muscle.

INTRODUCTION/AIMS: There are two conventional needle electromyography (EMG) approaches to the serratus anterior (SA), both of which can result in erroneous insertion into adjacent structures such as the latissimus dorsi (LD), teres major, or external oblique abdominis muscles and pose a risk of long thoracic nerve (LTN) injury. Therefore, we identified a novel needle insertion point for the SA in cadavers that avoids other muscles and LTN injury. METHODS: This study included 17 cadavers: 12 to devise the new method and 5 to verify its accuracy. Novel landmarks were the inferior angle of the scapula (I), sternal notch (S), and xiphoid process (X). The relationships of the LD, pectoralis major (PM), SA, and LTN were determined relative to these landmarks. RESULTS: When inserting a needle into the proximal one third along the line connecting points I and X, there were adequate safety margins around the LD, PM, and LTN, and the new method had excellent accuracy. DISCUSSION: Compared to the conventional midaxillary method, our novel method improved the accuracy of needle EMG of the SA. Follow-up studies using clinical imaging techniques are needed to verify whether above findings are equally applicable in living subjects.

Several models combined with ultrasound techniques to predict breast muscle weight in broilers.

The weight of breast muscle (WBM) is a highly monitored indicator in broiler breeding that can be obtained after slaughtering. Currently, due to the lack of accurate in vivo phenotypes for both genomic and phenotypic selection, genetic gains in WBM fall short of initial expectations. In this study, 1,006 market-age (42 d) broilers from 3 generations over 2 yr were randomly selected, and the breast width (BW), fossil bone length (FBL), breast muscle thickness (BMT), and live weight (LW) were measured exactly in vivo. Eight models, including multiple linear regression (MLR), ridge regression (RR), least absolute shrinkage and selection operator (LASSO), and elastic net (EN), were fitted to explore the best regression relationships between breast muscle weight and these indicators. Support vector machine (SVM) methods with both linear kernels and radial kernels were used to fit the models, while 2 decision tree-based machine learning algorithms, random forest (RF), and extreme gradient boosting (XGBoost), were used to establish the prediction model. The predictive effects of different combinations of independent variables were compared, leading to the conclusion that the EN model achieves the best predictive power when all 4 live features are used as inputs and is slightly better than the other models (R2 = 0.7696). This method could be applied in practical production and breeding work, leading to substantial cost savings and enhancements in the breeding process.

Diagnostic value of high-frequency ultrasound for Poland syndrome.

BACKGROUND: The imaging diagnosis of Poland syndrome is mostly computed tomography (CT) or magnetic resonance imaging (MRI), whereas high-frequency ultrasound for the diagnosis of Poland syndrome is relatively rare. PURPOSE: To investigate the diagnostic value of high-frequency ultrasound for Poland syndrome. MATERIAL AND METHODS: A retrospective analysis of 15 patients diagnosed with Poland syndrome was performed, and the characteristics of ultrasound images were summarized. RESULTS: High-frequency ultrasound clearly depict the anatomical structures of each layer of the chest wall in patients with Poland syndrome. Ultrasonography mainly showed partial or total absence of the pectoralis major muscle on the affected side, and some of which were combined with the absence of the pectoralis minor muscle. The difference was statistically significant in the thickness of the affected chest wall compared with the healthy side (P < 0.01). Out of 15 cases with Poland syndrome, 11 were associated with ipsilateral brachydactyly or syndactyly, and high-frequency ultrasonography showed that the bifurcation position of the common palmar digital artery on the affected finger was lower than that on the healthy side. CONCLUSION: High-frequency ultrasound is an effective imaging method for the diagnosis of Poland syndrome.

CT-defined pectoralis muscle mass and muscle density are associated with mortality in acute pulmonary embolism. A multicenter analysis.

BACKGROUND & AIMS: Computed tomography (CT) defined muscle mass can be used as a surrogate parameter for sarcopenia. The present study used thoracic CT to assess pectoralis muscle area and density as an imaging biomarker for prognosis of 30-day mortality in patients with acute pulmonary embolism (PE) METHODS: The clinical database was retrospectively screened for patients with thoracic CT in 3 centers. Pectoralis musculature was measured on axial slices of the thoracic CT at the level of T4 of contrast enhanced pulmonary angiography CT. Skeletal muscle area (SMA), skeletal muscle index (SMI), muscle density and gauge were calculated. RESULTS: Overall, 981 patients (440 female, 44.9%) with a mean age of 63.5 ± 15.9 years were included into the study and 144 patients (14.6%) died within the 30-days period. Every pectoral muscle value was higher in survivors compared to non-survivors (exemplarily for SMI 9.9 ± 3.5 cm2/m2 versus 7.8 ± 2.6 cm2/m2, p < 0.001). Moreover, 91 patients were defined as hemodynamically instable (9.3%). Comparable, every pectoral muscle parameter was higher in patients with hemodynamically stable course compared to instable course. Different muscle variables are related to 30-day mortality: SMA, OR = 0.94 (95%CI= (0.92; 0.96), p < 0.001); SMI, OR = 0.78 (95%CI= (0.72; 0.84), p < 0.001); muscle density, OR = 0.96 (95%CI = (0.94; 0.97), p < 0.001); muscle gauge OR = 0.96 (95%CI = (0.94; 0.99), p < 0.001). SMI and muscle density were independently associated with 30-days mortality: SMI, OR = 0.81 (95%CI = (0.75; 0.88), p < 0.001); muscle density: OR = 0.96 (95%CI= (0.95; 0.98), p < 0.001). CONCLUSION: Parameters of the pectoralis musculature are associated with 30-day mortality in patients with acute PE. These findings should lead to an independent validation study and ultimately to the inclusion into clinical routine as a prognostic factor.

Uncertainty estimation for deep learning-based pectoral muscle segmentation via Monte Carlo dropout.

Objective. Deep Learning models are often susceptible to failures after deployment. Knowing when your model is producing inadequate predictions is crucial. In this work, we investigate the utility of Monte Carlo (MC) dropout and the efficacy of the proposed uncertainty metric (UM) for flagging of unacceptable pectoral muscle segmentations in mammograms.Approach. Segmentation of pectoral muscle was performed with modified ResNet18 convolutional neural network. MC dropout layers were kept unlocked at inference time. For each mammogram, 50 pectoral muscle segmentations were generated. The mean was used to produce the final segmentation and the standard deviation was applied for the estimation of uncertainty. From each pectoral muscle uncertainty map, the overall UM was calculated. To validate the UM, a correlation between the dice similarity coefficient (DSC) and UM was used. The UM was first validated in a training set (200 mammograms) and finally tested in an independent dataset (300 mammograms). ROC-AUC analysis was performed to test the discriminatory power of the proposed UM for flagging unacceptable segmentations.Main results. The introduction of dropout layers in the model improved segmentation performance (DSC = 0.95 ± 0.07 versus DSC = 0.93 ± 0.10). Strong anti-correlation (r= -0.76,p< 0.001) between the proposed UM and DSC was observed. A high AUC of 0.98 (97% specificity at 100% sensitivity) was obtained for the discrimination of unacceptable segmentations. Qualitative inspection by the radiologist revealed that images with high UM are difficult to segment.Significance. The use of MC dropout at inference time in combination with the proposed UM enables flagging of unacceptable pectoral muscle segmentations from mammograms with excellent discriminatory power.

Centrilobular Emphysema Is Associated with Pectoralis Muscle Reduction in Current Smokers without Airflow Limitation.

BACKGROUND: Physiological and prognostic associations of centrilobular emphysema (CLE) and paraseptal emphysema (PSE) in smokers with and without chronic obstructive pulmonary disease (COPD) have been increasingly recognized, but the associations with extrapulmonary abnormalities, such as muscle wasting, osteoporosis, and cardiovascular diseases, remain unestablished. OBJECTIVES: The aim of the study was to investigate whether CLE was associated with extrapulmonary abnormalities independent of concomitant PSE in smokers without airflow limitation. METHODS: This retrospective study consecutively enrolled current smokers without airflow limitation who underwent lung cancer screening with computed tomography and spirometry. CLE and PSE were visually identified based on the Fleischner Society classification system. Cross-sectional areas of pectoralis muscles (PM) and adjacent subcutaneous adipose tissue (SAT), bone mineral density (BMD), and coronary artery calcification (CAC) were evaluated. RESULTS: Of 310 current smokers without airflow limitation, 83 (26.8%) had CLE. The PSE prevalence was higher (67.5% vs. 23.3%), and PM area, SAT area, and BMD were lower in smokers with CLE than in those without (PM area (mean), 34.5 versus 38.6 cm2; SAT area (mean), 29.3 versus 36.8 cm2; BMD (mean), 158.3 versus 178.4 Hounsfield unit), while CAC presence did not differ. In multivariable models, CLE was associated with lower PM area but not with SAT area or BMD, after adjusting for PSE presence, demographics, and forced expiratory volume in 1 s. CONCLUSIONS: The observed association between CLE and lower PM area suggests that susceptibility to skeletal muscle loss could be high in smokers with CLE even without COPD.

Prognostic Role of the Pectoralis Musculature in Patients with COVID-19. A Multicenter Study.

RATIONALE AND OBJECTIVES: To evaluate the impact of low skeletal muscle mass in patients with COVID-19 on relevant outcomes like 30-day mortality, need for intubation and need for intensive care unit admission. MATERIALS AND METHODS: For this study, data from six centers were acquired. The acquired sample comprises 1138 patients. There were 547 women (48.1%) and 591 men (51.9%) with a mean age of 54.5 ± 18.8 years; median age, 55 years; range, 18-84 years). In every case, thoracic CT without intravenous application of contrast medium was performed. The following parameters of the pectoralis muscles were estimated: muscle area as a sum of the bilateral areas of the pectoralis major and minor muscles, muscle density, muscle index (PMI) (pectoralis muscle area divided by the patient's body height square) as a ratio pectoralis major and minor muscles divided by the patient's body height2, and muscle gauge as PMI x muscle density. RESULTS: Overall, 220 patients (19.33%) were admitted to the intensive care unit. In 171 patients (15.03%), mechanical lung ventilation was performed. Finally, 154 patients (13.53%) died within the observation time of 30-day. All investigated parameters of pectoralis muscle were lower in the patients with unfavorable courses of Covid-19. All pectoralis muscle parameters were associated with 30-day mortality in multivariate analyses adjusted for age and sex: pectoralis muscle area, HR = 0.93 CI 95% (0.91-0.95) p < 0.001; pectoralis muscle density, HR = 0.94 CI 95% (0.93-0.96) p < 0.001; pectoralis muscle index, HR = 0.79 CI 95% (0.75-0.85) p < 0.001, pectoralis muscle gauge, HR = 0.995 CI 95% (0.99-0.996) p < 0.001. CONCLUSION: in COVID-19, survivors have larger areas and higher index, gauge and density of the pectoralis muscles in comparison to nonsurvivors. However, the analyzed muscle parameters cannot be used for prediction of disease courses.