Artificial intelligence-aided CT segmentation for body composition analysis: a validation study

Female-specific pancreatic cancer survival from CT imaging of visceral fat implicates glutathione metabolism in solid tumors

RATIONALE AND OBJECTIVES

To identify if body composition, assessed with preoperative CT-based visceral fat ratio quantification as well as tumor metabolic gene expression, predicts sex-dependent overall survival (OS) in patients with pancreatic ductal adenocarcinoma (PDAC).

MATERIALS AND METHODS

This was a retrospective analysis of preoperative CT in 98 male and 107 female patients with PDAC. Relative visceral fat (rVFA; visceral fat normalized to total fat) was measured automatically using software and corrected manually. Median and optimized rVFA thresholds were determined according to published methods. Kaplan Meier and log-rank tests were used to estimate OS. Multivariate models were developed to identify interactions between sex, rVFA, and OS. Unsupervised gene expression analysis of PDAC tumors from The Cancer Genome Atlas (TCGA) was performed to identify metabolic pathways with similar survival patterns to rVFA.

RESULTS

Optimized preoperative rVFA threshold of 38.9% predicted significantly different OS in females with a median OS of 15 months (above threshold) vs 24 months (below threshold; p = 0.004). No significant threshold was identified in males. This female-specific significance was independent of age, stage, and presence of chronic pancreatitis (p = 0.02). Tumor gene expression analysis identified female-specific stratification from a five-gene signature of glutathione S-transferases. This was observed for PDAC as well as clear cell renal carcinoma and glioblastoma.

CONCLUSION

CT-based assessments of visceral fat can predict pancreatic cancer OS in females. Glutathione S-transferase expression in tumors predicts female-specific OS in a similar fashion.

Body composition analysis by radiological imaging - methods, applications, and prospects

BACKGROUND

 This review discusses the quantitative assessment of tissue composition in the human body (body composition, BC) using radiological methods. Such analyses are gaining importance, in particular, for oncological and metabolic problems. The aim is to present the different methods and definitions in this field to a radiological readership in order to facilitate application and dissemination of BC methods. The main focus is on radiological cross-sectional imaging.

METHODS

 The review is based on a recent literature search in the US National Library of Medicine catalog (pubmed.gov) using appropriate search terms (body composition, obesity, sarcopenia, osteopenia in conjunction with imaging and radiology, respectively), as well as our own work and experience, particularly with MRI- and CT-based analyses of abdominal fat compartments and muscle groups.

RESULTS AND CONCLUSION

 Key post-processing methods such as segmentation of tomographic datasets are now well established and used in numerous clinical disciplines, including bariatric surgery. Validated reference values are required for a reliable assessment of radiological measures, such as fatty liver or muscle. Artificial intelligence approaches (deep learning) already enable the automated segmentation of different tissues and compartments so that the extensive datasets can be processed in a time-efficient manner - in the case of so-called opportunistic screening, even retrospectively from diagnostic examinations. The availability of analysis tools and suitable datasets for AI training is considered a limitation.

KEY POINTS

  · Radiological imaging methods are increasingly used to determine body composition (BC).. · BC parameters are usually quantitative and well reproducible.. · CT image data from routine clinical examinations can be used retrospectively for BC analysis.. · Prospectively, MRI examinations can be used to determine organ-specific BC parameters.. · Automated and in-depth analysis methods (deep learning or radiomics) appear to become important in the future..

CITATION FORMAT

· Linder N, Denecke T, Busse H. Body composition analysis by radiological imaging - methods, applications, and prospects. Fortschr Röntgenstr 2024; DOI: 10.1055/a-2263-1501.

Making the invisible visible: imaging techniques for assessing muscle mass and muscle quality in chronic kidney disease

Muscle wasting and low muscle mass are prominent features of protein energy wasting (PEW), sarcopenia and sarcopenic obesity in patients with chronic kidney disease (CKD). In addition, muscle wasting is associated with low muscle strength, impaired muscle function and adverse clinical outcomes such as low quality of life, hospitalizations and increased mortality. While assessment of muscle mass is well justified, the assessment of skeletal muscle should go beyond quantity. Imaging techniques provide the means for non-invasive, comprehensive, in-depth assessment of the quality of the muscle such as the infiltration of ectopic fat. These techniques include computed tomography (CT), magnetic resonance imaging (MRI) and ultrasound. Dual energy X-ray absorptiometry is also an imaging technique, but one that only provides quantitative and not qualitative data on muscle. The main advantage of imaging techniques compared with other methods such as bioelectrical impedance analysis and anthropometry is that they offer higher precision and accuracy. On the other hand, the higher cost for acquiring and maintaining the imaging equipment, especially CT and MRI, makes these less-used options and available mostly for research purposes. In the field of CKD and end-stage kidney disease (ESKD), imaging techniques are gaining attention for evaluating muscle quantity and more recently muscle fat infiltration. This review describes the potential of these techniques in CKD and ESKD settings for muscle assessment beyond that of muscle quantity.

Artificial intelligence-based analysis of body composition predicts outcome in patients receiving long-term mechanical circulatory support

BACKGROUND

Obesity is a known cardiovascular risk factor and associated with higher postoperative complication rates in patients undergoing cardiac surgery. In heart failure (HF), conflicting evidence in terms of survival has been reported, whereas sarcopenia is associated with poor prognosis. An increasing number of HF patients require left ventricular assist device (LVAD) implantations. The postoperative mortality has improved in recent years but is still relatively high. The impact of body composition on outcome in this population remains unclear. The aim of this investigation was to examine the preoperative computed tomography (CT) body composition as a predictor of the postoperative outcome in advanced HF patients, who receive LVAD implantations.

METHODS

Preoperative CT scans of 137 patients who received LVADs between 2015 and 2020 were retrospectively analysed using an artificial intelligence (AI)-powered automated software tool based on a convolutional neural network, U-net, developed for image segmentation (Visage Version 7.1, Visage Imaging GmbH, Berlin, Germany). Assessment of body composition included visceral and subcutaneous adipose tissue areas (VAT and SAT), psoas and total abdominal muscle areas and sarcopenia (defined by lumbar skeletal muscle indexes). The body composition parameters were correlated with postoperative major complication rates, survival and postoperative 6-min walk distance (6MWD) and quality of life (QoL).

RESULTS

The mean age of patients was 58.21 ± 11.9 years; 122 (89.1%) were male. Most patients had severe HF requiring inotropes (Interagency Registry for Mechanically Assisted Circulatory Support [INTERMACS] profile I-III, 71.9%) secondary to coronary artery diseases or dilated cardiomyopathy (96.4%). Forty-four (32.1%) patients were obese (body mass index ≥ 30 kg/m2 ), 96 (70.1%) were sarcopene and 19 (13.9%) were sarcopene obese. Adipose tissue was associated with a significantly higher risk of postoperative infections (VAT 172.23 cm2 [54.96, 288.32 cm2 ] vs. 124.04 cm2 [56.57, 186.25 cm2 ], P = 0.022) and in-hospital mortality (VAT 168.11 cm2 [134.19, 285.27 cm2 ] vs. 135.42 cm2 [49.44, 227.91 cm2 ], P = 0.033; SAT 227.28 cm2 [139.38, 304.35 cm2 ] vs. 173.81 cm2 [97.65, 254.16 cm2 ], P = 0.009). Obese patients showed no improvement of 6MWD and QoL within 6 months postoperatively (obese: +0.94 ± 161.44 months, P = 0.982; non-obese: +166.90 ± 139.00 months, P < 0.000; obese: +0.088 ± 0.421, P = 0.376; non-obese: +0.199 ± 0.324, P = 0.002, respectively). Sarcopenia did not influence the postoperative outcome and survival within 1 year after LVAD implantation.

CONCLUSIONS

Preoperative AI-based CT body composition identifies patients with poor outcome after LVAD implantation. Greater adipose tissue areas are associated with an increased risk for postoperative infections, in-hospital mortality and impaired 6MWD and QoL within 6 months postoperatively.

Association of central sensitization, visceral fat, and surgical outcomes in lumbar spinal stenosis

BACKGROUND

Controversy remains regarding predictors of surgical outcomes for patients with lumbar spinal stenosis (LSS). Pain sensitization may be an underlying mechanism contributing to LSS surgical outcomes. Further, obesity is associated with dissatisfaction and poorer outcomes after surgery for LSS. Therefore, this study aimed to examine the relationship between central sensitization (CS), visceral fat, and surgical outcomes in LSS.

METHODS

Patients with LSS were categorized based on their central sensitization inventory (CSI) scores into low- (CSI < 40) and high- (CSI ≥ 40) CSI subgroups. The participants completed clinical outcome assessments preoperatively and 12 months postoperatively.

RESULTS

Overall, 60 patients were enrolled in the study (28 men, 32 women; mean age: 62.1 ± 2.8 years). The high-CSI group had significantly higher mean low back pain (LBP), leg pain, and leg numbness visual analogue scale (VAS) scores than the low-CSI group (p < 0.01). The high-CSI group had a significantly higher mean visceral fat area than the low-CSI group (p < 0.01). Postoperatively, LBP VAS score was significantly worse in the high-CSI group. Relative to preoperatively, postoperative leg pain and leg numbness improved significantly in both groups.

CONCLUSIONS

We believe that neuro decompression can be effective for LSS surgical outcomes in patients with CS; nonetheless, it should be approached with caution owing to the potential for worsening LBP. Additionally, visceral fat is an important indicator suggesting the involvement of CS.

Association Between Insulin Resistance and Myosteatosis Measured by Abdominal Computed Tomography

CONTEXT

Ectopic fat deposition in skeletal muscle, termed myosteatosis, is a key factor in developing insulin resistance.

OBJECTIVE

This work aimed to evaluate the association between insulin resistance and myosteatosis in a large Asian population.

METHODS

A total of 18 251 participants who had abdominal computed tomography were included in this cross-sectional study. Patients were categorized into 4 groups according to quartiles of Homeostatic Model Assessment for Insulin Resistance (HOMA-IR). The total abdominal muscle area (TAMA) at the L3 vertebral level was segmented into normal-attenuation muscle area (NAMA), low-attenuation muscle area (LAMA), and intermuscular adipose tissue (IMAT). The absolute values of TAMA, NAMA, LAMA, and IMAT and the ratios of NAMA/BMI, LAMA/BMI, and NAMA/TAMA were used as myosteatosis indices.

RESULTS

The absolute values of TAMA, NAMA, LAMA, and IMAT appeared to increase with higher HOMA-IR levels, and LAMA/BMI showed a similar upward trend. Meanwhile, the NAMA/BMI and NAMA/TAMA index showed downward trends. As HOMA-IR levels increased, the odds ratios (ORs) of the highest quartile of NAMA/BMI and NAMA/TAMA index decreased and that of LAMA/BMI increased. Compared with the lowest HOMA-IR group, the adjusted ORs (95% CI) in the highest HOMA-IR group for the lowest NAMA/TAMA quartile were 0.414 (0.364-0.471) in men and 0.464 (0.384-0.562) in women. HOMA-IR showed a negative correlation with NAMA/BMI (r = -0.233 for men and r = -0.265 for women), and NAMA/TAMA index (r = -0.211 for men and r = -0.214 for women), and a positive correlation with LAMA/BMI (r = 0.160 for men and r = 0.119 for women); P was less than .001 for all.

CONCLUSION

In this study, a higher HOMA-IR level was significantly associated with a high risk of myosteatosis.

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.

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.

Standardization of Body Composition Status in Patients with Advanced Urothelial Tumors: The Role of a CT-Based AI-Powered Software for the Assessment of Sarcopenia and Patient Outcome Correlation

BACKGROUND

Sarcopenia is a well know prognostic factor in oncology, influencing patients' quality of life and survival. We aimed to investigate the role of sarcopenia, assessed by a Computed Tomography (CT)-based artificial intelligence (AI)-powered-software, as a predictor of objective clinical benefit in advanced urothelial tumors and its correlations with oncological outcomes.

METHODS

We retrospectively searched patients with advanced urothelial tumors, treated with systemic platinum-based chemotherapy and an available total body CT, performed before and after therapy. An AI-powered software was applied to CT to obtain the Skeletal Muscle Index (SMI-L3), derived from the area of the psoas, long spine, and abdominal muscles, at the level of L3 on CT axial images. Logistic and Cox-regression modeling was implemented to explore the association of sarcopenic status and anthropometric features to the clinical benefit rate and survival endpoints.

RESULTS

97 patients were included, 66 with bladder cancer and 31 with upper-tract urothelial carcinoma. Clinical benefit outcomes showed a linear positive association with all the observed body composition variables variations. The chances of not experiencing disease progression were positively associated with ∆_SMI-L3, ∆_psoas, and ∆_long spine muscle when they ranged from ~10-20% up to ~45-55%. Greater survival chances were matched by patients achieving a wider ∆_SMI-L3, ∆_abdominal and ∆_long spine muscle.

CONCLUSIONS

A CT-based AI-powered software body composition and sarcopenia analysis provide prognostic assessments for objective clinical benefits and oncological outcomes.

Artificial intelligence and body composition

AIMS

Although obesity is associated with chronic disease, a large section of the population with high BMI does not have an increased risk of metabolic disease. Increased visceral adiposity and sarcopenia are also risk factors for metabolic disease in people with normal BMI. Artificial Intelligence (AI) techniques can help assess and analyze body composition parameters for predicting cardiometabolic health. The purpose of the study was to systematically explore literature involving AI techniques for body composition assessment and observe general trends.

METHODS

We searched the following databases: Embase, Web of Science, and PubMed. There was a total of 354 search results. After removing duplicates, irrelevant studies, and reviews(a total of 303), 51 studies were included in the systematic review.

RESULTS

AI techniques have been studied for body composition analysis in the context of diabetes mellitus, hypertension, cancer and many specialized diseases. Imaging techniques employed for AI methods include CT (Computerized Tomography), MRI (Magnetic Resonance Imaging), ultrasonography, plethysmography, and EKG(Electrocardiogram). Automatic segmentation of body composition by deep learning with convolutional networks has helped determine and quantify muscle mass. Limitations include heterogeneity of study populations, inherent bias in sampling, and lack of generalizability. Different bias mitigation strategies should be evaluated to address these problems and improve the applicability of AI to body composition analysis.

CONCLUSIONS

AI assisted measurement of body composition might assist in improved cardiovascular risk stratification when applied in the appropriate clinical context.

Risk factors for umbilical incisional hernia after laparoscopic colorectal surgery

BACKGROUND

Laparoscopic colorectal surgery (LCRS) requires a small laparotomy at the umbilicus. The wound is small and inconspicuous, but if the patient develops an umbilical incisional hernia (UIH), the wound is visible and the patient suffers from symptoms of discomfort. However, the incidence of UIH after LCRS and its risk factors are not well understood. The purpose of this study was to investigate the risk factors for UIH after LCRS for colorectal cancer.

METHODS

This was a single-centre retrospective study of 135 patients with colorectal cancer, conducted at our hospital from April 2013 to March 2019. The diagnosis of UIH was based on computed tomography and physical examination findings. Preoperative patient data such as enlargement of the umbilical orifice (EUO), subcutaneous fat thickness (SFT) and intraperitoneal thickness (IPT) were collected and analysed using univariate and multivariate analyses for the presence of risk factors for UIH.

RESULTS

A total of 135 patients who underwent LCRS were analysed. The incidence of UIH was 20.7%. Univariate analysis revealed significantly high body mass index (BMI) ≥ 25 (P = 0.032), EUO (P < 0.001), SFT ≥18 mm (P = 0.011), and IPT ≥61 mm (P < 0.01) in the UIH group. Multivariate analysis revealed significant differences in EUO (P < 0.001), SFT ≥18 mm (P = 0.046) and IPT ≥61 mm (P = 0.022).

CONCLUSION

EUO was the most important risk factor for UIH, followed by IPT and SFT. These findings are predictive indicators of the development of UIH after LCRS and can be assessed objectively and easily with preoperative computed tomography.

Associations between visceral fat chronic low back pain and central sensitization in patients with lumbar spinal stenosis

BACKGROUND

Pain sensitization may be one of the mechanisms contributing to chronic low back pain (CLBP).

OBJECTIVE

To evaluate the association between visceral fat, CLBP, and central sensitization (CS); describe the relationship between low back pain (LBP) intensity and CS; and identify possible correlation between visceral fat and LBP intensity.

METHODS

Patients with CLBP were divided using their CS inventory (CSI) scores into low- (CSI < 40) and high-CSI (CSI ⩾ 40) subgroups. We compared computed tomography (CT) measurements and scores for association with pain according to the visual analogue scale (VAS) between the two groups.

RESULTS

The low-CSI and the high-CSI groups had 47 patients (67.1%; 21 men, 26 women) and 23 patients (32.9%; 11 men and 12 women), respectively. The high-CSI group had a significantly higher mean VAS score (p< 0.01) and estimated mean visceral fat area (p< 0.05) than the low-CSI group. There was a moderate positive correlation between VAS score and visceral fat (standardised partial regression coefficient: 0.659, p< 0.01) in the high-CSI group according to multiple linear regression analysis adjusted for age and sex.

CONCLUSIONS

Visceral fat is associated with CLBP, regardless of sex or age, and may be a potential therapeutic target for CLBP with CS.

Concordance of Computed Tomography Regional Body Composition Analysis Using a Fully Automated Open-Source Neural Network versus a Reference Semi-Automated Program with Manual Correction

Quick, efficient, fully automated open-source programs to segment muscle and adipose tissues from computed tomography (CT) images would be a great contribution to body composition research. This study examined the concordance of cross-sectional areas (CSA) and densities for muscle, visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), and intramuscular adipose tissue (IMAT) from CT images at the third lumbar (L3) between an automated neural network (test method) and a semi-automatic human-based program (reference method). Concordance was further evaluated by disease status, sex, race/ethnicity, BMI categories. Agreement statistics applied included Lin’s Concordance (CCC), Spearman correlation coefficient (SCC), Sorensen dice-similarity coefficient (DSC), and Bland−Altman plots with limits of agreement (LOA) within 1.96 standard deviation. A total of 420 images from a diverse cohort of patients (60.35 ± 10.92 years; body mass index (BMI) of 28.77 ± 7.04 kg/m2; 55% female; 53% Black) were included in this study. About 30% of patients were healthy (i.e., received a CT scan for acute illness or pre-surgical donor work-up), while another 30% had a diagnosis of colorectal cancer. The CCC, SCC, and DSC estimates for muscle, VAT, SAT were all greater than 0.80 (>0.80 indicates good performance). Agreement analysis by diagnosis showed good performance for the test method except for critical illness (DSC 0.65−0.87). Bland−Altman plots revealed narrow LOA suggestive of good agreement despite minimal proportional bias around the zero-bias line for muscle, SAT, and IMAT CSA. The test method shows good performance and almost perfect concordance for L3 muscle, VAT, SAT, and IMAT per DSC estimates, and Bland−Altman plots even after stratification by sex, race/ethnicity, and BMI categories. Care must be taken to assess the density of the CT images from critically ill patients before applying the automated neural network (test method).

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.

Evaluation of AI-Based Segmentation Tools for COVID-19 Lung Lesions on Conventional and Ultra-low Dose CT Scans

A reliable diagnosis and accurate monitoring are pivotal steps for treatment and prevention of COVID-19. Chest computed tomography (CT) has been considered a crucial diagnostic imaging technique for the injury assessment of the viral pneumonia. Furthermore, the automatization of the segmentation methods for lung alterations helps to speed up the diagnosis and lighten radiologists' workload. Considering the assiduous pathology monitoring, ultra-low dose (ULD) chest CT protocols have been implemented to drastically reduce the radiation burden. Unfortunately, the available AI technologies have not been trained on ULD-CT data and validated and their applicability deserves careful evaluation. Therefore, this work aims to compare the results of available AI tools (BCUnet, CORADS AI, NVIDIA CLARA Train SDK and CT Pneumonia Analysis) on a dataset of 73 CT examinations acquired both with conventional dose (CD) and ULD protocols. COVID-19 volume percentage, resulting from each tool, was statistically compared. This study demonstrated high comparability of the results on CD-CT and ULD-CT data among the four AI tools, with high correlation between the results obtained on both protocols (R > .68, P < .001, for all AI tools).

Applications of Artificial Intelligence in 18F-Sodium Fluoride Positron Emission Tomography/Computed Tomography:: Current State and Future Directions

This review discusses the current state of artificial intelligence (AI) in ¹⁸F-NaF-PET/CT imaging and the potential applications to come in diagnosis, prognostication, and improvement of care in patients with bone diseases, with emphasis on the role of AI algorithms in CT bone segmentation, relying on their prevalence in medical imaging and utility in the extraction of spatial information in combined PET/CT studies.

Automated artificial intelligence-based analysis of skeletal muscle volume predicts overall survival after cystectomy for urinary bladder cancer

BACKGROUND

Radical cystectomy for urinary bladder cancer is a procedure associated with a high risk of complications, and poor overall survival (OS) due to both patient and tumour factors. Sarcopenia is one such patient factor. We have developed a fully automated artificial intelligence (AI)-based image analysis tool for segmenting skeletal muscle of the torso and calculating the muscle volume.

METHODS

All patients who have undergone radical cystectomy for urinary bladder cancer 2011-2019 at Sahlgrenska University Hospital, and who had a pre-operative computed tomography of the abdomen within 90 days of surgery were included in the study. All patients CT studies were analysed with the automated AI-based image analysis tool. Clinical data for the patients were retrieved from the Swedish National Register for Urinary Bladder Cancer. Muscle volumes dichotomised by the median for each sex were analysed with Cox regression for OS and logistic regression for 90-day high-grade complications. The study was approved by the Swedish Ethical Review Authority (2020-03985).

RESULTS

Out of 445 patients who underwent surgery, 299 (67%) had CT studies available for analysis. The automated AI-based tool failed to segment the muscle volume in seven (2%) patients. Cox regression analysis showed an independent significant association with OS (HR 1.62; 95% CI 1.07-2.44; p = 0.022). Logistic regression did not show any association with high-grade complications.

CONCLUSION

The fully automated AI-based CT image analysis provides a low-cost and meaningful clinical measure that is an independent biomarker for OS following radical cystectomy.

Computed tomography-based multiple body composition parameters predict outcomes in Crohn's disease

Background

The efficacy of computed tomography-based multiple body composition parameters in assessing disease behavior and prognosis has not been comprehensively evaluated in Crohn’s disease. This study aimed to assess the association of body composition parameters with disease behavior and outcomes in Crohn’s disease and to compare the efficacies of indexes derived from body and lumbar spinal heights in body composition analysis.

Results

One hundred twenty-two patients with confirmed Crohn’s disease diagnoses and abdominal computed tomography scans were retrospectively included in this study. Skeletal muscle, visceral, and subcutaneous fat indexes were calculated by dividing each type of tissue area by height² and lumbar spinal height². Parameters reflecting the distribution of adiposity were also assessed. Principal component analysis was used to deal with parameters with multicollinearity. Patients were grouped according to their disease behavior (inflammatory vs. structuring/penetrating) and outcomes. Adverse outcome included need for intestinal surgery or anti-TNF therapy. Predictors of disease course from multiple parameters were evaluated using multivariate analysis. Indexes derived from body and lumbar spinal heights were strongly correlated (r, 0.934–0.995; p < 0.001). Low skeletal muscle-related parameters were significantly associated with complicated disease behavior in multivariate analysis (p = 0.048). Complicated disease behavior (p < 0.001) and adipose tissue parameters-related first principal component (p = 0.029) were independent biomarkers for predicting adverse outcomes.

Conclusions

Skeletal muscle and adipose tissue principle component were associated with complicated Crohn’s disease behavior and adverse outcome, respectively. Indexes derived from body and lumbar spinal heights have similar efficacies in body composition analysis.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13244-021-01083-6.