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Background: The fatty infiltration and atrophy in the muscle after a rotator cuff (RC) tear are important in surgical decision-making and are linked to poor clinical outcomes after rotator cuff repair. An accurate and reliable quantitative method should be developed to assess the entire RC muscles. Purpose: To develop a fully automated approach based on a deep neural network to segment RC muscles from clinical magnetic resonance imaging (MRI) scans. Material and Methods: In total, 94 shoulder MRI scans (mean age = 62.3 years) were utilized for the training and internal validation datasets, while an additional 20 MRI scans (mean age = 62.6 years) were collected from another institution for external validation. An orthopedic surgeon and a radiologist manually segmented muscles and bones as reference masks. Segmentation performance was evaluated using the Dice score, sensitivities, precision, and percent difference in muscle volume (%). In addition, the segmentation performance was assessed based on sex, age, and the presence of a RC tendon tear. Results: The average Dice score, sensitivities, precision, and percentage difference in muscle volume of the developed algorithm were 0.920, 0.933, 0.912, and 4.58%, respectively, in external validation. There was no difference in the prediction of shoulder muscles, with the exception of teres minor, where significant prediction errors were observed (0.831, 0.854, 0.835, and 10.88%, respectively). The segmentation performance of the algorithm was generally unaffected by age, sex, and the presence of RC tears. Conclusion: We developed a fully automated deep neural network for RC muscle and bone segmentation with excellent performance from clinical MRI scans. [ABSTRACT FROM AUTHOR]

Purpose: To conduct a volumetric and movement analysis of lung parenchyma in prone positioning using deep neural networks (DNNs). Method: We included patients with suspected interstitial lung abnormalities or disease who underwent full-inspiratory supine and prone chest CT at a single institution between June 2021 and March 2022. A thoracic radiologist visually assessed the fibrosis extent in the total lung (using units of 10%) on supine CT. After preprocessing the images into 192×192×192 resolution, a DNN automatically segmented the whole lung and pulmonary lobes in prone and supine CT images. Affine registration matched the patient's center and location, and the DNN deformably registered prone and supine CT images to calculate the x-, y-, z-axis, and 3D pixel movements. Results: In total, 108 CT pairs had successful registration. Prone positioning significantly increased the left lower (90.2±69.5 mL, P = 0.000) and right lower lobar volumes (52.5±74.2 mL, P = 0.000). During deformable registration, the average maximum whole-lung pixel movements between the two positions were 1.5, 1.9, 1.6, and 2.8 cm in each axis and 3D plane. Compared to patients with <30% fibrosis, those with ≥30% fibrosis had smaller volume changes (P<0.001) and smaller pixel movements in all axes between the positions (P = 0.000–0.007). Forced vital capacity (FVC) correlated with the left lower lobar volume increase (Spearman correlation coefficient, 0.238) and the maximum whole-lung pixel movements in all axes (coefficients, 0.311 to 0.357). Conclusions: Prone positioning led to the preferential expansion of the lower lobes, correlated with FVC, and lung fibrosis limited lung expansion during prone positioning. [ABSTRACT FROM AUTHOR]

Purpose: To prospectively evaluate whether Lung-RADS classification and volumetric nodule assessment were feasible with ultralow-dose (ULD) chest CT scans with deep learning image reconstruction (DLIR). Methods: The institutional review board approved this prospective study. This study included 40 patients (mean age, 66±12 years; 21 women). Participants sequentially underwent LDCT and ULDCT (CTDIvol, 0.96±0.15 mGy and 0.12±0.01 mGy) scans reconstructed with the adaptive statistical iterative reconstruction-V 50% (ASIR-V50) and DLIR. CT image quality was compared subjectively and objectively. The pulmonary nodules were assessed visually by two readers using the Lung-RADS 1.1 and automatically using a computerized assisted tool. Results: DLIR provided a significantly higher signal-to-noise ratio for LDCT and ULDCT images than ASIR-V50 (all P <.001). In general, DLIR showed superior subjective image quality for ULDCT images (P <.001) and comparable quality for LDCT images compared to ASIR-V50 (P =.01–1). The per-nodule sensitivities of observers for Lung-RADS category 3–4 nodules were 70.6–88.2% and 64.7–82.4% for DLIR-LDCT and DLIR-ULDCT images (P = 1) and categories were mostly concordant within observers. The per-nodule sensitivities of the computer-assisted detection for nodules ≥4 mm were 72.1% and 67.4% on DLIR-LDCT and ULDCT images (P =.50). The 95% limits of agreement for nodule volume differences between DLIR-LDCT and ULDCT images (-85.6 to 78.7 mm3) was similar to the within-scan nodule volume differences between DLIR- and ASIR-V50-LDCT images (-63.9 to 78.5 mm3), with volume differences smaller than 25% in 88.5% and 92.3% of nodules, respectively (P =.65). Conclusion: DLIR enabled comparable Lung-RADS and volumetric nodule assessments on ULDCT images to LDCT images. [ABSTRACT FROM AUTHOR]

Objective: The purpose of this study was to present the results of our investigation of the prognostic value of adipopenia and sarcopenia in patients with amyotrophic lateral sclerosis (ALS). Methods: Consecutive patients with ALS with abdominal computed tomography (CT) were retrospectively identified at a single tertiary hospital between January 2010 and July 2021. Deep learning‐based volumetric CT body composition analysis software was used to obtain abdominal waist fat volume, fat attenuation, and skeletal muscle area at the L3 level, then normalized to the fat volume index (FVI) and skeletal muscle index (SMI). Adipopenia and sarcopenia were defined as the sex‐specific lowest quartile and SMI reference values, respectively. The associations of CT‐derived body composition parameters with clinical variables, such as body mass index (BMI) and creatinine, were evaluated by Pearson correlation analyses, and associations with survival were assessed using the multivariable Cox regression analysis. Results: Eighty subjects (40 men, 65.5 ± 9.4 years of age) were investigated (median interval between disease onset and CT examination = 25 months). The mean BMI at the CT examination was 20.3 ± 4.3 kg/m2. The BMI showed a positive correlation with both FVI (R = 0.70, p < 0.001) and SMI (R = 0.63, p < 0.001), and the serum creatinine level was associated with SMI (R = 0.68, p < 0.001). After adjusting for sex, age, King's stage, BMI, creatinine, progression rate, and sarcopenia, adipopenia was associated with shorter survival (hazard ratio [HR] = 5.94, 95% confidence interval [CI] = 1.01, 35.0, p = 0.049). In a subgroup analysis for subjects with nutritional failure (stage 4a), the HR of adipopenia was 15.1 (95% CI = 2.45, 93.4, p = 0.003). Interpretation: Deep learning‐based CT‐derived adipopenia in patients with ALS is an independent poor prognostic factor for survival. ANN NEUROL 2023;94:1116–1125 [ABSTRACT FROM AUTHOR]

Objectives To investigate computer-aided quantitative scores from high‐resolution CT (HRCT) images and determine their longitudinal changes and clinical significance in patients with idiopathic inflammatory myopathies (IIMs)-related interstitial lung disease (IIMs-ILD). Methods The clinical data and HRCT images of 80 patients with IIMs who underwent serial HRCT scans at least twice were retrospectively analysed. Quantitative ILD (QILD) scores (%) were calculated as the sum of the extent of lung fibrosis, ground-glass opacity, and honeycombing. The individual time-estimated ΔQILD between two consecutive scans was derived using a linear approximation of yearly changes. Results The baseline median QILD (interquartile range) scores in the whole lung were 28.1% (19.1–43.8). The QILD was significantly correlated with forced vital capacity (r   =  −0.349, P   =  0.002) and diffusing capacity for carbon monoxide (r   =  −0.381, P  = 0.001). For ΔQILD between the first two scans, according to the visual ILD subtype, QILD aggravation was more frequent in patients with usual interstitial pneumonia (UIP) than non-UIP (80.0% vs 44.4%, P  = 0.013). Multivariable logistic regression analyses identified UIP was significantly related to radiographic ILD progression (ΔQILD >2%, P  = 0.015). Patients with higher baseline QILD scores (>28.1%) had a higher risk of lung transplantation or death (P  = 0.015). In the analysis of three serial HRCT scans (n  = 41), dynamic ΔQILD with four distinct patterns (improving, worsening, convex and concave) was observed. Conclusion QILD changes in IIMs-ILD were dynamic, and baseline UIP patterns seemed to be related to a longitudinal progression in QILD. These may be potential imaging biomarkers for lung function, changes in ILD severity and prognosis in IIMs-ILD. [ABSTRACT FROM AUTHOR]