Machine Learning for Automatic Paraspinous Muscle Area and Attenuation Measures on Low-Dose Chest CT Scans

Prognostic utility of paraspinal muscle index in elderly patients with community-acquired pneumonia

OBJECTIVE

This study investigated the associations between paraspinal muscle measurements on chest computed tomography and clinical outcomes of elderly patients with community-acquired pneumonia (CAP).

METHODS

This single-center, retrospective, observational study analyzed elderly patients (≥65 years) with CAP hospitalized through an emergency department between March 2020 and December 2022. We collected their baseline characteristics and laboratory data at the time of admission. The paraspinal muscle index and attenuation were calculated at the level of the 12th thoracic vertebra using chest computed tomography taken within 48 hours before or after admission. Univariable and multivariable logistic regression analyses were conducted to evaluate the association between paraspinal muscle measurements and 28-day mortality. Receiver operating characteristic (ROC) curve and area under the curve (AUC) analyses were used to evaluate the prognostic predictive power.

RESULTS

Of the 338 enrolled patients, 60 (17.8%) died within 28 days after admission. A high paraspinal muscle index was associated with low 28-day mortality in elderly patients with CAP (adjusted odds ratio, 0.994; 95% confidence interval, 0.992-0.997). The area under the ROC curve for the muscle index was 0.75, which outperformed the pneumonia severity index and the CURB-65 (confusion, urea, respiratory rate, blood pressure, age ≥65 years) metric, both of which showed an AUC of 0.64 in predicting mortality.

CONCLUSION

A high paraspinal muscle index was associated with low 28-day mortality in patients aged 65 years or older with CAP.

Body composition assessment with ultrasound muscle measurement: optimization through the use of semi-automated tools in colorectal cancer

Colorectal cancer (CRC) is a disease with a high prevalence and major impact on global health. Body composition (BC) data are of great importance in the assessment of nutritional status. Ultrasound (US) is an emerging, accessible and non-invasive technique that could be an alternative when it is not feasible to perform computed tomography (CT). The aim of this study is to evaluate the correlation between CT, as a reference technique, and US of the rectus femoris (RF) as a "proof of concept," in a cohort of patients with CRC and assess the optimisation of results obtained by US when performed by our new semi-automated tool. A single-centre cross-sectional study including 174 patients diagnosed with CRC and undergoing surgery was carried out at the Vall d'Hebron Hospital. We found a strong correlation between CT and US of the RF area (r = 0.67; p < 0.005). The latter, is able to discriminate patients with worse prognosis in terms of length of hospital stay and discharge destination (AUC-ROC = 0.64, p 0.015). These results improve when they are carried out with the automatic tool (area AUC-ROC = 0.73, p 0.023), especially when normalised by height and eliminating patients who associate overflow. According to our results, the US could be considered as a valuable alternative for the quantitative assessment of muscle mass when CT is not feasible. These measurements are improved when measuring software is applied, such as "Bat" software.

Association of CT-Derived Skeletal Muscle and Adipose Tissue Metrics with Frailty in Older Adults

RATIONALE AND OBJECTIVES

Tools are needed for frailty screening of older adults. Opportunistic analysis of body composition could play a role. We aim to determine whether computed tomography (CT)-derived measurements of muscle and adipose tissue are associated with frailty.

MATERIALS AND METHODS

Outpatients aged ≥ 55 years consecutively imaged with contrast-enhanced abdominopelvic CT over a 3-month interval were included. Frailty was determined from the electronic health record using a previously validated electronic frailty index (eFI). CT images at the level of the L3 vertebra were automatically segmented to derive muscle metrics (skeletal muscle area [SMA], skeletal muscle density [SMD], intermuscular adipose tissue [IMAT]) and adipose tissue metrics (visceral adipose tissue [VAT], subcutaneous adipose tissue [SAT]). Distributions of demographic and CT-derived variables were compared between sexes. Sex-specific associations of muscle and adipose tissue metrics with eFI were characterized by linear regressions adjusted for age, race, ethnicity, duration between imaging and eFI measurements, and imaging parameters.

RESULTS

The cohort comprised 886 patients (449 women, 437 men, mean age 67.9 years), of whom 382 (43%) met the criteria for pre-frailty (ie, 0.10 < eFI ≤ 0.21) and 138 (16%) for frailty (eFI > 0.21). In men, 1 standard deviation changes in SMD (β = -0.01, 95% confidence interval [CI], -0.02 to -0.001, P = .02) and VAT area (β = 0.008, 95% CI, 0.0005-0.02, P = .04), but not SMA, IMAT, or SAT, were associated with higher frailty. In women, none of the CT-derived muscle or adipose tissue metrics were associated with frailty.

CONCLUSION

We observed a positive association between frailty and CT-derived biomarkers of myosteatosis and visceral adiposity in a sex-dependent manner.

Deep Learning-Based Fully Automated Segmentation of Regional Muscle Volume and Spatial Intermuscular Fat Using CT

RATIONALE AND OBJECTIVES

We aim to develop a CT-based deep learning (DL) system for fully automatic segmentation of regional muscle volume and measurement of the spatial intermuscular fat distribution of the gluteus maximus muscle.

MATERIALS AND METHODS

A total of 472 subjects were enrolled and randomly assigned to one of three groups: a training set, test set 1, and test set 2. For each subject in the training set and test set 1, we selected six slices of the CT images as the region of interest for manual segmentation by a radiologist. For each subject in test set 2, we selected all slices of the gluteus maximus muscle on the CT images for manual segmentation. The DL system was constructed using Attention U-Net and the Otsu binary thresholding method to segment the muscle and measure the fat fraction of the gluteus maximus muscle. The segmentation results of the DL system were evaluated using the Dice similarity coefficient (DSC), Hausdorff distance (HD), and the average surface distance (ASD) as metrics. Intraclass correlation coefficients (ICCs) and Bland-Altman plots were used to assess agreement in the measurements of fat fraction between the radiologist and the DL system.

RESULTS

The DL system showed good segmentation performance on the two test sets, with DSCs of 0.930 and 0.873, respectively. The fat fraction of the gluteus maximus muscle measured by the DL system was in agreement with the radiologist (ICC=0.748).

CONCLUSION

The proposed DL system showed accurate, fully automated segmentation performance and good agreement with the radiologist at fat fraction evaluation, and can be further used for muscle evaluation.

The association between morphological characteristics of paraspinal muscle and spinal disorders

BACKGROUND

Spinal disorders affect millions of people worldwide, and can cause significant disability and pain. The paraspinal muscles, located on either side of the spinal column, play a crucial role in the movement, support, and stabilization of the spine. Many spinal disorders can affect paraspinal muscles, as evidenced by changes in their morphology, including hypertrophy, atrophy, and degeneration.

OBJECTIVES

The objectives of this review were to examine the current literature on the relationship between the paraspinal muscles and spinal disorders, summarize the methods used in previous studies, and identify areas for future research.

METHODS

We reviewed studies on the morphological characteristics of the paravertebral muscle and discussed their relationship with spinal disorders, as well as the current limitations and future research directions.

RESULTS

The paraspinal muscles play a critical role in spinal disorders and are important targets for the treatment and prevention of spinal disorders. Clinicians should consider the role of the paraspinal muscles in the development and progression of spinal disorders and incorporate assessments of the paraspinal muscle function in clinical practice.

CONCLUSION

The findings of this review highlight the need for further research to better understand the relationship between the paraspinal muscles and spinal disorders, and to develop effective interventions to improve spinal health and reduce the burden of spinal disorders.

Prognostic Impact of Quantifying Sarcopenia and Adipopenia by Chest CT in Severe Aplastic Anemia Patients Treated With Allogeneic Hematopoietic Stem Cell Transplantation

RATIONALE AND OBJECTIVES

To investigate the prognostic role of chest CT-defined sarcopenia and adipopenia in severe aplastic anemia (SAA) patients treated with hematopoietic stem cell transplantation (HSCT).

MATERIALS AND METHODS

This was a retrospective study of 123 consecutive SAA patients treated with HSCT. CT imaging was performed to quantify the pectoralis muscle (including major and minor) index (PMI) and the corresponding subcutaneous adipose tissue index (SAI). Sarcopenia and adipopenia were defined as PMI and SAI lower than the respective sex-specific medians. Correlations of the PMI and SAI with anthropometric indexes were calculated. Transplant-related outcomes were compared between the sarcopenia and adipopenia groups. Prognostic factors for overall survival (OS) and fail-free survival (FFS) were identified by Cox regression and were used to create a nomogram. The accuracy of the nomogram was evaluated by ROC curves.

RESULTS

PMI showed good correlation with BMI and fat-free mass index (p < 0.001). SAI correlated with BMI and fat mass index (p < 0.001). The sarcopenia group (47.2%) had a significantly worse 3-year OS (90.8% vs. 77.6%, p = 0.045) and 3-year FFS (89.2% vs. 74.1%, p = 0.035) than the nonsarcopenia group. Sarcopenia status and diagnostic category were used to construct the nomogram of OS, as these were independent prognostic factors in the multivariate analysis for OS and FFS (p < 0.05). The area under the curve of the nomogram at one year and three years was 0.801 and 0.721, respectively.

CONCLUSION

Sarcopenia indicates a poor prognosis in SAA patients undergoing HSCT. Intensive supportive care is suggested for SAA patients with sarcopenia before transplantation.

Segmentation of multi-regional skeletal muscle in abdominal CT image for cirrhotic sarcopenia diagnosis

Background

Sarcopenia is generally diagnosed by the total area of skeletal muscle in the CT axial slice located in the third lumbar (L3) vertebra. However, patients with severe liver cirrhosis cannot accurately obtain the corresponding total skeletal muscle because their abdominal muscles are squeezed, which affects the diagnosis of sarcopenia.

Purpose

This study proposes a novel lumbar skeletal muscle network to automatically segment multi-regional skeletal muscle from CT images, and explores the relationship between cirrhotic sarcopenia and each skeletal muscle region.

Methods

This study utilizes the skeletal muscle characteristics of different spatial regions to improve the 2.5D U-Net enhanced by residual structure. Specifically, a 3D texture attention enhancement block is proposed to tackle the issue of blurred edges with similar intensities and poor segmentation between different skeletal muscle regions, which contains skeletal muscle shape and muscle fibre texture to spatially constrain the integrity of skeletal muscle region and alleviate the difficulty of identifying muscle boundaries in axial slices. Subsequentially, a 3D encoding branch is constructed in conjunction with a 2.5D U-Net, which segments the lumbar skeletal muscle in multiple L3-related axial CT slices into four regions. Furthermore, the diagnostic cut-off values of the L3 skeletal muscle index (L3SMI) are investigated for identifying cirrhotic sarcopenia in four muscle regions segmented from CT images of 98 patients with liver cirrhosis.

Results

Our method is evaluated on 317 CT images using the five-fold cross-validation method. For the four skeletal muscle regions segmented in the images from the independent test set, the avg. DSC is 0.937 and the avg. surface distance is 0.558 mm. For sarcopenia diagnosis in 98 patients with liver cirrhosis, the cut-off values of Rectus Abdominis, Right Psoas, Left Psoas, and Paravertebral are 16.67, 4.14, 3.76, and 13.20 cm²/m² in females, and 22.51, 5.84, 6.10, and 17.28 cm²/m² in males, respectively.

Conclusion

The proposed method can segment four skeletal muscle regions related to the L3 vertebra with high accuracy. Furthermore, the analysis shows that the Rectus Abdominis region can be used to assist in the diagnosis of sarcopenia when the total muscle is not available.

Machine and deep learning-based clinical characteristics and laboratory markers for the prediction of sarcopenia

BACKGROUND

Sarcopenia is an age-related progressive skeletal muscle disorder involving the loss of muscle mass or strength and physiological function. Efficient and precise AI algorithms may play a significant role in the diagnosis of sarcopenia. In this study, we aimed to develop a machine learning model for sarcopenia diagnosis using clinical characteristics and laboratory indicators of aging cohorts.

METHODS

We developed models of sarcopenia using the baseline data from the West China Health and Aging Trend (WCHAT) study. For external validation, we used the Xiamen Aging Trend (XMAT) cohort. We compared the support vector machine (SVM), random forest (RF), eXtreme Gradient Boosting (XGB), and Wide and Deep (W&D) models. The area under the receiver operating curve (AUC) and accuracy (ACC) were used to evaluate the diagnostic efficiency of the models.

RESULTS

The WCHAT cohort, which included a total of 4057 participants for the training and testing datasets, and the XMAT cohort, which consisted of 553 participants for the external validation dataset, were enrolled in this study. Among the four models, W&D had the best performance (AUC = 0.916 ± 0.006, ACC = 0.882 ± 0.006), followed by SVM (AUC =0.907 ± 0.004, ACC = 0.877 ± 0.006), XGB (AUC = 0.877 ± 0.005, ACC = 0.868 ± 0.005), and RF (AUC = 0.843 ± 0.031, ACC = 0.836 ± 0.024) in the training dataset. Meanwhile, in the testing dataset, the diagnostic efficiency of the models from large to small was W&D (AUC = 0.881, ACC = 0.862), XGB (AUC = 0.858, ACC = 0.861), RF (AUC = 0.843, ACC = 0.836), and SVM (AUC = 0.829, ACC = 0.857). In the external validation dataset, the performance of W&D (AUC = 0.970, ACC = 0.911) was the best among the four models, followed by RF (AUC = 0.830, ACC = 0.769), SVM (AUC = 0.766, ACC = 0.738), and XGB (AUC = 0.722, ACC = 0.749).

CONCLUSIONS

The W&D model not only had excellent diagnostic performance for sarcopenia but also showed good economic efficiency and timeliness. It could be widely used in primary health care institutions or developing areas with an aging population.

TRIAL REGISTRATION

Chictr.org, ChiCTR 1800018895.

Opportunistic screening for osteoporosis by abdominal CT in a British population

BACKGROUND

It has previously been shown that CT scans performed for other indications can be used to identify patients with osteoporosis. This has not yet been tested in a British population. We sought to evaluate the use of vertebral CT attenuation measures for predicting osteoporosis in a British cohort, using dual-energy X-ray absorptiometry (DEXA) as a reference standard.

METHODS

Patients who underwent abdominal CT in 2018 and concomitantly underwent DEXA within a six-month interval were retrospectively included. CT attenuation values in Hounsfield units (HU) were measured by placement of a region-of-interest at the central portion of the L1 vertebral body and then compared to their corresponding DEXA score. Receiver operating characteristic (ROC) curves were generated to evaluate the performance of a logistic regression model and to determine sensitivity and specificity thresholds.

RESULTS

536 patients (394 females, mean age 65.8) were included, of which 174 had DEXA-defined osteoporosis. L1 attenuation measures were significantly different (p < 0.01) between the three DEXA-defined groups of osteoporosis (118 HU), osteopenia (143 HU) and normal bone density (178 HU). The area under the ROC curve was 0.74 (95% CI 0.69-0.78). A threshold of 169 HU was 90% sensitive, and a threshold of 104 HU was 90% specific for diagnosing osteoporosis.

CONCLUSIONS

Routine abdominal CT can be used to opportunistically screen for osteoporosis without additional cost or radiation exposure. The thresholds identified in this study are comparable with previous studies in other populations. We recommend radiologists engage with primary care and rheumatology providers to determine appropriate cut-off values for further investigation.

Ultrasound and magnetic resonance imaging as diagnostic tools for sarcopenia in immune-mediated rheumatic diseases (IMRDs)

Sarcopenia is characterized by loss of muscle mass, altered muscle composition, fat and fibrous tissue infiltration, and abnormal innervation, especially in older individuals with immune-mediated rheumatic diseases (IMRDs). Several techniques for measuring muscle mass, strength, and performance have emerged in recent decades. The portable dynamometer and gait speed represent the most frequently used tools for the evaluation of muscle strength and physical efficiency, respectively. Aside from dual-energy, X-ray, absorptiometry, and bioelectrical impedance analysis, ultrasound (US) and magnetic resonance imaging (MRI) techniques appear to have a potential role in evaluating muscle mass and composition. US and MRI have been shown to accurately identify sarcopenic biomarkers such as inflammation (edema), fatty infiltration (myosteatosis), alterations in muscle fibers, and muscular atrophy in patients with IMRDs. US is a low-cost, easy-to-use, and safe imaging method for assessing muscle mass, quality, architecture, and biomechanical function. This review summarizes the evidence for using US and MRI to assess sarcopenia.

Imaging of Sarcopenia

Sarcopenia is currently underdiagnosed and undertreated, but this is expected to change because sarcopenia is now recognized with a specific diagnosis code that can be used for billing in some countries, as well as an expanding body of research on prevention, diagnosis, and management. This article focuses on practical issues of increasing interest by highlighting 3 hot topics fundamental to understanding sarcopenia in older adults: definitions and terminology, current diagnostic imaging techniques, and the emerging role of opportunistic computed tomography.

Determination of Low Muscle Mass by Muscle Surface Index of the First Lumbar Vertebra Using Low-Dose Computed Tomography

The muscle index of the first vertebra (L1MI) derived from computed tomography (CT) is an indicator of total skeletal muscle mass. Nevertheless, the cutoff value and utility of L1MI derived from low-dose chest CT (LDCT) remain unclear. Adults who received LDCT for health check-ups in 2017 were enrolled. The cutoff values of L1MI were established in subjects aged 20-60 years. The cutoff values were used in chronic obstructive pulmonary disease (COPD) patients to determine muscle quantity. A total of 1780 healthy subjects were enrolled. Subjects (n = 1393) aged 20-60 years were defined as the reference group. The sex-specific cutoff values of L1MI were 26.2 cm²/m² for males and 20.9 cm²/m² for females. Six subjects in the COPD group (6/44, 13.6%) had low L1MI. COPD subjects with low L1MI had lower forced expiratory volume in one second (0.81 ± 0.17 vs. 1.30 ± 0.55 L/s, p = 0.046) and higher COPD assessment test scores (19.5 ± 2.6 vs. 15.0 ± 4.9, p = 0.015) than those with normal L1MI. In conclusion, LDCT in health assessments may provide additional information on sarcopenia.

Sarcopenia in Children with Solid Organ Tumors: An Instrumental Era

Sarcopenia has recently been studied in both adults and children and was found to be a prognostic marker for adverse outcome in a variety of patient groups. Our research showed that sarcopenia is a relevant marker in predicting outcome in children with solid organ tumors, such as hepatoblastoma and neuroblastoma. This was especially true in very ill, high-risk groups. Children with cancer have a higher likelihood of ongoing loss of skeletal muscle mass due to a mismatch in energy intake and expenditure. Additionally, the effects of cancer therapy, hormonal alterations, chronic inflammation, multi-organ dysfunction, and a hypermetabolic state all contribute to a loss of skeletal muscle mass. Sarcopenia seems to be able to pinpoint this waste to a high degree in a new and objective way, making it an additional tool in predicting and improving outcome in children. This article focuses on the current state of sarcopenia in children with solid organ tumors. It details the pathophysiological mechanisms behind sarcopenia, highlighting the technical features of the available methods for measuring muscle mass, strength, and function, including artificial intelligence (AI)-based techniques. It also reviews the latest research on sarcopenia in children, focusing on children with solid organ tumors.

The Value of Artificial Intelligence-Assisted Imaging in Identifying Diagnostic Markers of Sarcopenia in Patients with Cancer

Sarcopenia is defined as the loss of skeletal muscle mass and muscle function. It is common in patients with malignancies and often associated with adverse clinical outcomes. The presence of sarcopenia in patients with cancer is determined by body composition, and recently, radiologic technology for the accurate estimation of body composition is under development. Artificial intelligence- (AI-) assisted image measurement facilitates the detection of sarcopenia in clinical practice. Sarcopenia is a prognostic factor for patients with cancer, and confirming its presence helps to recognize those patients at the greatest risk, which provides a guide for designing individualized cancer treatments. In this review, we examine the recent literature (2017-2021) on AI-assisted image assessment of body composition and sarcopenia, seeking to synthesize current information on the mechanism and the importance of sarcopenia, its diagnostic image markers, and the interventions for sarcopenia in the medical care of patients with cancer. We concluded that AI-assisted image analysis is a reliable automatic technique for segmentation of abdominal adipose tissue. It has the potential to improve diagnosis of sarcopenia and facilitates identification of oncology patients at the greatest risk, supporting individualized prevention planning and treatment evaluation. The capability of AI approaches in analyzing series of big data and extracting features beyond manual skills would no doubt progressively provide impactful information and greatly refine the standard for assessing sarcopenia risk in patients with cancer.

Overview of Noninterpretive Artificial Intelligence Models for Safety, Quality, Workflow, and Education Applications in Radiology Practice

Artificial intelligence has become a ubiquitous term in radiology over the past several years, and much attention has been given to applications that aid radiologists in the detection of abnormalities and diagnosis of diseases. However, there are many potential applications related to radiologic image quality, safety, and workflow improvements that present equal, if not greater, value propositions to radiology practices, insurance companies, and hospital systems. This review focuses on six major categories for artificial intelligence applications: study selection and protocoling, image acquisition, worklist prioritization, study reporting, business applications, and resident education. All of these categories can substantially affect different aspects of radiology practices and workflows. Each of these categories has different value propositions in terms of whether they could be used to increase efficiency, improve patient safety, increase revenue, or save costs. Each application is covered in depth in the context of both current and future areas of work. Keywords: Use of AI in Education, Application Domain, Supervised Learning, Safety © RSNA, 2022.

Sarcopenia in rheumatic disorders: what the radiologist and rheumatologist should know

Sarcopenia is defined as the loss of muscle mass, strength, and function. Increasing evidence shows that sarcopenia is common in patients with rheumatic disorders. Although sarcopenia can be diagnosed using bioelectrical impedance analysis or DXA, increasingly it is diagnosed using CT, MRI, and ultrasound. In rheumatic patients, CT and MRI allow "opportunistic" measurement of body composition, including surrogate markers of sarcopenia, from studies obtained during routine patient care. Recognition of sarcopenia is important in rheumatic patients because sarcopenia can be associated with disease progression and poor outcomes. This article reviews how opportunistic evaluation of sarcopenia in rheumatic patients can be accomplished and potentially contribute to improved patient care.

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.

Sarcopenia: imaging assessment and clinical application

Sarcopenia is a progressive, generalized skeletal muscle disorder characterized by reduction of muscle mass and strength. It is associated with increased adverse outcomes including falls, fractures, physical disability, and mortality, particularly, in elderly patients. Nowadays, sarcopenia has become a specific imaging biomarker able to predict clinical outcomes of patients. Muscle fibre reduction has shown to be an unfavourable pre-operative predictive factor in patients with cancer, and is associated with worse clinical outcomes in terms of postoperative complications, morbidity, mortality, and lower tolerance of chemoradiation therapy. Several imaging modalities, including dual-energy X-ray absorptiometry, CT, MRI, and US can be used to estimate muscle mass and quality to reach the diagnosis of sarcopenia. This article reviews the clinical implications of sarcopenia, how this condition can be assessed through different imaging modalities, and future perspectives of imaging of sarcopenia.

Whole-body Composition Profiling Using a Deep Learning Algorithm: Influence of Different Acquisition Parameters on Algorithm Performance and Robustness

OBJECTIVES

To develop, test, and validate a body composition profiling algorithm for automated segmentation of body compartments in whole-body magnetic resonance imaging (wbMRI) and to investigate the influence of different acquisition parameters on performance and robustness.

MATERIALS AND METHODS

A segmentation algorithm for subcutaneous and visceral adipose tissue (SCAT and VAT) and total muscle mass (TMM) was designed using a deep learning U-net architecture convolutional neuronal network. Twenty clinical wbMRI scans were manually segmented and used as training, validation, and test datasets. Segmentation performance was then tested on different data, including different magnetic resonance imaging protocols and scanners with and without use of contrast media. Test-retest reliability on 2 consecutive scans of 16 healthy volunteers each as well as impact of parameters slice thickness, matrix resolution, and different coil settings were investigated. Sorensen-Dice coefficient (DSC) was used to measure the algorithms' performance with manual segmentations as reference standards. Test-retest reliability and parameter effects were investigated comparing respective compartment volumes. Abdominal volumes were compared with published normative values.

RESULTS

Algorithm performance measured by DSC was 0.93 (SCAT) to 0.77 (VAT) using the test dataset. Dependent from the respective compartment, similar or slightly reduced performance was seen for other scanners and scan protocols (DSC ranging from 0.69-0.72 for VAT to 0.83-0.91 for SCAT). No significant differences in body composition profiling was seen on repetitive volunteer scans (P = 0.88-1) or after variation of protocol parameters (P = 0.07-1).

CONCLUSIONS

Body composition profiling from wbMRI by using a deep learning-based convolutional neuronal network algorithm for automated segmentation of body compartments is generally possible. First results indicate that robust and reproducible segmentations equally accurate to a manual expert may be expected also for a range of different acquisition parameters.

Analysis of the paraspinal muscle morphology of the lumbar spine using a convolutional neural network (CNN)

PURPOSE

This single-center study aimed to develop a convolutional neural network to segment multiple consecutive axial magnetic resonance imaging (MRI) slices of the lumbar spinal muscles of patients with lower back pain and automatically classify fatty muscle degeneration.

METHODS

We developed a fully connected deep convolutional neural network (CNN) with a pre-trained U-Net model trained on a dataset of 3,650 axial T2-weighted MRI images from 100 patients with lower back pain. We included all qualities of MRI; the exclusion criteria were fractures, tumors, infection, or spine implants. The training was performed using k-fold cross-validation (k = 10), and performance was evaluated using the dice similarity coefficient (DSC) and cross-sectional area error (CSA error). For clinical correlation, we used a simplified Goutallier classification (SGC) system with three classes.

RESULTS

The mean DSC was high for overall muscle (0.91) and muscle tissue segmentation (0.83) but showed deficiencies in fatty tissue segmentation (0.51). The CSA error was small for the overall muscle area of 8.42%, and fatty tissue segmentation showed a high mean CSA error of 40.74%. The SGC classification was correctly predicted in 75% of the patients.

CONCLUSION

Our fully connected CNN segmented overall muscle and muscle tissue with high precision and recall, as well as good DSC values. The mean predicted SGC values of all available patient axial slices showed promising results. With an overall Error of 25%, further development is needed for clinical implementation. Larger datasets and training of other model architectures are required to segment fatty tissue more accurately.

Automatic segmentation of paravertebral muscles in abdominal CT scan by U-Net: The application of data augmentation technique to increase the Jaccard ratio of deep learning

ABSTRACT

Sarcopenia, characterized by a decline of skeletal muscle mass, has emerged as an important prognostic factor for cancer patients. Trunk computed tomography (CT) is a commonly used modality for assessment of cancer disease extent and treatment outcome. CT images can also be used to analyze the skeletal muscle mass filtered by the appropriate range of Hounsfield scale. However, a manual depiction of skeletal muscle in CT scan images for assessing skeletal muscle mass is labor-intensive and unrealistic in clinical practice. In this paper, we propose a novel U-Net based segmentation system for CT scan of paravertebral muscles in the third and fourth lumbar spines. Since the number of training samples is limited (i.e., 1024 CT images only), it is well-known that the performance of the deep learning approach is restricted due to overfitting. A data augmentation strategy to enlarge the diversity of the training set to boost the performance further is employed. On the other hand, we also discuss how the number of features in our U-Net affects the performance of the semantic segmentation. The efficacies of the proposed methodology based on w/ and w/o data augmentation and different feature maps are compared in the experiments. We show that the Jaccard score is approximately 95.0% based on the proposed data augmentation method with only 16 feature maps used in U-Net. The stability and efficiency of the proposed U-Net are verified in the experiments in a cross-validation manner.

Sarcopenia measured with paraspinous muscle using computed tomography for predicting prognosis in elderly pneumonia patients

Background:

In the elderly, diagnostic findings of pneumonia are often atypical. Computed tomography was recommended for the diagnosis of pneumonia in elderly patients. Recently, the usage of computed tomography as a screening tool for pneumonia in emergency departments has increased. Sarcopenia is defined as the loss of skeletal muscle mass and strength with aging. In this study, the association between sarcopenia and prognosis measured through computed tomography was evaluated compared to CURB-65.

Methods:

This study was conducted on patients diagnosed with pneumonia through computed tomography from 1 March 2018 to 31 March 2020. The paraspinous muscle size and attenuation were measured at a level located at the T12 pedicle level on axial computed tomography images. Paraspinous muscle size was presented as paraspinous muscle index. Differences in the prognostic performance among the paraspinous muscle size and attenuation, and CURB-65 were evaluated by the area under the receiver operating characteristic curve.

Results:

A total of 509 patients were included and 132 patients (25.9%) were admitted to the ICU, and 58 patients (11.4%) died in hospital. Paraspinous muscle index was the significant factor for predicting in-hospital mortality and ICU admission. The area under the receiver operating characteristic value of paraspinous muscle index for prediction of mortality was 0.738 and CURB-65 was 0.707. The area under the receiver operating characteristic of paraspinous muscle index and CURB-65 for predicting ICU admission were 0.766 and 0.704, respectively.

Conclusion:

As a method of measuring sarcopenia, paraspinous muscle index was superior to CURB-65 in elderly pneumonia patients. The use of computed tomography in predicting prognosis for elderly pneumonia patients will ease the economic burden.

Use of artificial intelligence in the imaging of sarcopenia: A narrative review of current status and perspectives

Sarcopenia is a muscle disease which previously was associated only with aging, but in recent days it has been gaining more attention for its predictive value in a vast range of conditions and its potential link with overall health. Up to this point, evaluating sarcopenia with imaging methods has been time-consuming and dependent on the skills of the physician. The solution for this problem may be found in artificial intelligence, which may assist radiologists in repetitive tasks such as muscle segmentation and body-composition analysis. The major aim of this review was to find and present the current status and future perspectives of artificial intelligence in the imaging of sarcopenia. We searched the PubMed database to find articles concerning the use of artificial intelligence in diagnostic imaging and especially in body-composition analysis in the context of sarcopenia. We found that artificial-intelligence systems could potentially help with evaluating sarcopenia and better predicting outcomes in a vast range of clinical situations, which could get us closer to the true era of precision medicine.

Automated Muscle Measurement on Chest CT Predicts All-Cause Mortality in Older Adults From the National Lung Screening Trial

BACKGROUND

Muscle metrics derived from computed tomography (CT) are associated with adverse health events in older persons, but obtaining these metrics using current methods is not practical for large datasets. We developed a fully automated method for muscle measurement on CT images. This study aimed to determine the relationship between muscle measurements on CT with survival in a large multicenter trial of older adults.

METHOD

The relationship between baseline paraspinous skeletal muscle area (SMA) and skeletal muscle density (SMD) and survival over 6 years was determined in 6,803 men and 4,558 women (baseline age: 60-69 years) in the National Lung Screening Trial (NLST). The automated machine learning pipeline selected appropriate CT series, chose a single image at T12, and segmented left paraspinous muscle, recording cross-sectional area and density. Associations between SMA and SMD with all-cause mortality were determined using sex-stratified Cox proportional hazards models, adjusted for age, race, height, weight, pack-years of smoking, and presence of diabetes, chronic lung disease, cardiovascular disease, and cancer at enrollment.

RESULTS

After a mean 6.44 ± 1.06 years of follow-up, 635 (9.33%) men and 265 (5.81%) women died. In men, higher SMA and SMD were associated with a lower risk of all-cause mortality, in fully adjusted models. A one-unit standard deviation increase was associated with a hazard ratio (HR) = 0.85 (95% confidence interval [CI] = 0.79, 0.91; p < .001) for SMA and HR = 0.91 (95% CI = 0.84, 0.98; p = .012) for SMD. In women, the associations did not reach significance.

CONCLUSION

Higher paraspinous SMA and SMD, automatically derived from CT exams, were associated with better survival in a large multicenter cohort of community-dwelling older men.

Sarcopenia in cancer: Risking more than muscle loss

Sarcopenia is characterised by progressive and extensive skeletal muscle degeneration and is associated with functional decline. Sarcopenia has primary and secondary aetiology, arising as a result of the ageing process or through chronic cytokine-mediated inflammation (associated with health conditions including cancer), respectively. Diagnosis of sarcopenia is dependent upon detection of reduced skeletal muscle strength, mass and performance. A combination of non-radiological and radiological methods can be used to assess each of these in turn to accurately diagnose sarcopenia. Sarcopenia is known to adversely affect outcomes of patients with various forms of cancer. Early identification of sarcopenia is imperative in improving patient care and overall prognosis. Various interventions, such as resistance exercise, nutritional support, and amino acid and vitamin supplementation have shown promise in the management of sarcopenia. However, further insight into novel interventions and indeed, assessment of the benefits of management of sarcopenia in terms of survival, are required to better support cancer patients.

Opportunistic muscle measurements on staging chest CT for extremity and truncal soft tissue sarcoma are associated with survival

BACKGROUND AND OBJECTIVES

Computed tomography (CT) measurements of sarcopenia have been proposed as biomarkers associated with outcomes in various cancers and have typically been evaluated at the L3 vertebral level. However, staging imaging for patients with extremity and truncal soft tissue sarcoma (STS) often only includes chest CT imaging which precludes evaluation at L3. Therefore, we sought to evaluate muscle metrics at T12 on standard staging chest CT scans and evaluate for correlation with overall and event-free survival in patients with STS.

METHODS

CT chest imaging for 89 patients with intermediate and high-grade STS (53 male, 36 female; 58.5 ± 19.0 years old, follow-up 37.4 ± 27.1 months) was reviewed on PACS at T12 for skeletal muscle density (SMD) and skeletal muscle index (SMI).

RESULTS

Overall survival increased with increased SMD on univariate (hazard ratio [HR] = 0.61 [0.43, 0.86]) and age-adjusted analysis (HR = 0.65 [0.42, 0.89]. Event-free survival also increased with increased SMD in univariate analyses (HR = 0.68 [0.49, 0.95]) but did not maintain significance after adjusting for age (HR = 0.68 [0.43, 1.07]). SMI was not a predictor of overall or event-free survival.

CONCLUSIONS

Higher SMD measured on routinely obtained staging chest CTs in STS patients is associated with improved survival.

Evaluating body composition by combining quantitative spectral detector computed tomography and deep learning-based image segmentation

PURPOSE

Aim of this study was to develop and evaluate a software toolkit, which allows for a fully automated body composition analysis in contrast enhanced abdominal computed tomography leveraging the strengths of both, quantitative information from dual energy computed tomography and simple detection and segmentation tasks performed by deep convolutional neuronal networks (DCNN).

METHODS AND MATERIALS

Both, public and private datasets were used to train and validate DCNN. A combination of two DCNN and quantitative thresholding was used to classify axial CT slices to the abdominal region, classify voxels as fat and muscle and to differentiate between subcutaneous and visceral fat. For validation, patients undergoing repetitive examination (±21 days) and patients who underwent concurrent bioelectrical impedance analysis (BIA) were analyzed. Concordance correlation coefficient (CCC), linear regression and Bland-Altman-Analysis were used as statistical tests.

RESULTS

Results provided from the algorithm toolkit were visually validated. The automated classifier was able to extract slices of interest from the full body scans with an accuracy of 98.7 %. DCNN-based segmentation for subcutaneous fat reached a mean dice similarity coefficient of 0.95. CCCs were 0.99 for both muscle and subcutaneous fat and 0.98 for visceral fat in patients undergoing repetitive examinations (n = 48). Further linear regression and Bland-Altman-Analyses suggested good agreement (r²:0.67-0.88) between the software toolkit and patients who underwent concurrent BIA (n = 39).

CONCLUSION

We describe a software toolkit allowing for an accurate analysis of body composition utilizing a combination of DCNN- and threshold-based segmentations from spectral detector computed tomography.

Evaluation of Prevalence of the Sarcopenia Level Using Machine Learning Techniques: Case Study in Tijuana Baja California, Mexico

The article presents a study based on timeline data analysis of the level of sarcopenia in older patients in Baja California, Mexico. Information was examined at the beginning of the study (first event), three months later (second event), and six months later (third event). Sarcopenia is defined as the loss of muscle mass quality and strength. The study was conducted with 166 patients. A total of 65% were women and 35% were men. The mean age of the enrolled patients was 77.24 years. The research included 99 variables that consider medical history, pharmacology, psychological tests, comorbidity (Charlson), functional capacity (Barthel and Lawton), undernourishment (mini nutritional assessment (MNA) validated test), as well as biochemical and socio-demographic data. Our aim was to evaluate the prevalence of the level of sarcopenia in a population of chronically ill patients assessed at the Tijuana General Hospital. We used machine learning techniques to assess and identify the determining variables to focus on the patients’ evolution. The following classifiers were used: Support Vector Machines, Linear Support Vector Machines, Radial Basis Function, Gaussian process, Decision Tree, Random Forest, multilayer perceptron, AdaBoost, Gaussian Naive Bayes, and Quadratic Discriminant Analysis. In order of importance, we found that the following variables determine the level of sarcopenia: Age, Systolic arterial hypertension, mini nutritional assessment (MNA), Number of chronic diseases, and Sodium. They are therefore considered relevant in the decision-making process of choosing treatment or prevention. Analysis of the relationship between the presence of the variables and the classifiers used to measure sarcopenia revealed that the Decision Tree classifier, with the Age, Systolic arterial hypertension, MNA, Number of chronic diseases, and Sodium variables, showed a precision of 0.864, accuracy of 0.831, and an F1 score of 0.900 in the first and second events. Precision of 0.867, accuracy of 0.825, and an F1 score of 0.867 were obtained in event three with the same variables. We can therefore conclude that the Decision Tree classifier yields the best results for the assessment of the determining variables and suggests that the study population’s sarcopenia did not change from moderate to severe.

Abdominal muscle segmentation from CT using a convolutional neural network

CT is widely used for diagnosis and treatment of a variety of diseases, including characterization of muscle loss. In many cases, changes in muscle mass, particularly abdominal muscle, indicate how well a patient is responding to treatment. Therefore, physicians use CT to monitor changes in muscle mass throughout the patient's course of treatment. In order to measure the muscle, radiologists must segment and review each CT slice manually, which is a time-consuming task. In this work, we present a fully convolutional neural network (CNN) for the segmentation of abdominal muscle on CT. We achieved a mean Dice similarity coefficient of 0.92, a mean precision of 0.93, and a mean recall of 0.91 in an independent test set. The CNN-based segmentation method can provide an automatic tool for the segmentation of abdominal muscle. As a result, the time required to obtain information about changes in abdominal muscle using the CNN takes a fraction of the time associated with manual segmentation methods and thus can provide a useful tool in the clinical application.

Automated Segmentation of Tissues Using CT and MRI: A Systematic Review

RATIONALE AND OBJECTIVES

The automated segmentation of organs and tissues throughout the body using computed tomography and magnetic resonance imaging has been rapidly increasing. Research into many medical conditions has benefited greatly from these approaches by allowing the development of more rapid and reproducible quantitative imaging markers. These markers have been used to help diagnose disease, determine prognosis, select patients for therapy, and follow responses to therapy. Because some of these tools are now transitioning from research environments to clinical practice, it is important for radiologists to become familiar with various methods used for automated segmentation.

MATERIALS AND METHODS

The Radiology Research Alliance of the Association of University Radiologists convened an Automated Segmentation Task Force to conduct a systematic review of the peer-reviewed literature on this topic.

RESULTS

The systematic review presented here includes 408 studies and discusses various approaches to automated segmentation using computed tomography and magnetic resonance imaging for neurologic, thoracic, abdominal, musculoskeletal, and breast imaging applications.

CONCLUSION

These insights should help prepare radiologists to better evaluate automated segmentation tools and apply them not only to research, but eventually to clinical practice.