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Lower-extremity peripheral arterial disease (PAD) results in limb ischemia and is strongly associated with sarcopenia. This study aimed to retrospectively evaluate the association between the quantity of muscle mass in the lower extremities and the severity of vascular stenosis in PAD patients.Between January 2018 and August 2021, 128 patients with PAD and 53 individuals without PAD, diagnosed by computed tomography, were enrolled. The severity of stenosis of lower-extremity arteries was measured using a grading system. The muscle and fat mass areas were calculated in the abdomen at the L3 or L4 level, mid-thigh, and lower leg. Multivariable logistic regression was conducted to clarify the risk associated with low muscle mass. The difference in muscle mass between PAD and non-PAD patients was evaluated by using propensity score matching.A strong positive correlation between the abdomen muscle area and leg muscle area was observed. The muscle area and muscle index of the leg were lower in PAD patients. These changes occurred earlier than in the abdomen muscle area. The group with more severe artery stenosis had more muscle wasting in the lower extremities. Greater age, female, lower BMI, and PAD were associated with low muscle mass. After propensity score matching, the leg muscle area was still lower in PAD patients.There is a direct association between PAD and regional muscle wasting. This occurs earlier regionally in the lower extremities than in central muscles. Early diagnosis of PAD might prevent progressive muscle loss, improving disease outcome and quality of life.• Peripheral arterial disease is strongly associated with sarcopenia. • Muscle wasting in the lower extremities is earlier and more prominent than that in the abdomen. • More severe arterial stenoses are associated with higher muscle wasting in the lower extremities.

Background To develop an artificial intelligence (AI) model with radiomics and deep learning (DL) features extracted from CT images to distinguish benign from malignant ovarian tumors. Methods We enrolled 149 patients with pathologically confirmed ovarian tumors. A total of 185 tumors were included and divided into training and testing sets in a 7:3 ratio. All tumors were manually segmented from preoperative contrast-enhanced CT images. CT image features were extracted using radiomics and DL. Five models with different combinations of feature sets were built. Benign and malignant tumors were classified using machine learning (ML) classifiers. The model performance was compared with five radiologists on the testing set. Results Among the five models, the best performing model is the ensemble model with a combination of radiomics, DL, and clinical feature sets. The model achieved an accuracy of 82%, specificity of 89% and sensitivity of 68%. Compared with junior radiologists averaged results, the model had a higher accuracy (82% vs 66%) and specificity (89% vs 65%) with comparable sensitivity (68% vs 67%). With the assistance of the model, the junior radiologists achieved a higher average accuracy (81% vs 66%), specificity (80% vs 65%), and sensitivity (82% vs 67%), approaching to the performance of senior radiologists. Conclusions We developed a CT-based AI model that can differentiate benign and malignant ovarian tumors with high accuracy and specificity. This model significantly improved the performance of less-experienced radiologists in ovarian tumor assessment, and may potentially guide gynecologists to provide better therapeutic strategies for these patients.