INTRODUCTION Cushing's syndrome is defined as chronic excess free cortisol in circulation. According to recent studies, midnight salivary cortisol is an accurate and non-stress method for screening and diagnosing Cushing's syndrome. However, there is limited data on midnight salivary cortisol for diagnosing Cushing's syndrome in the Chinese population. METHODS Among 61 suspected Chinese patients, 48 patients were confirmed to have Cushing's syndrome. We evaluated the midnight salivary cortisol, midnight serum cortisol and 24-hour urine free cortisol excretion for diagnosis. Midnight salivary cortisol was collected from 21 healthy volunteers for control purposes. RESULTS In the patient group, mean urine free cortisol excretion and midnight salivary cortisol levels were 296.50 ± 47.99 µg/day and 10.18 ± 1.29 ng/mL, respectively. Among the control group and normal participants, mean midnight salivary cortisol level was 0.53 ± 0.13 ng/mL and 0.50 ± 0.12 ng/mL, respectively. The cutoff value for midnight salivary cortisol was 1.7 ng/mL for diagnosing Cushing's syndrome, with a sensitivity of 98% and specificity of 100%. The diagnostic performance of midnight salivary cortisol (area under the curve [AUC] = 0.99) was superior to that of urine free cortisol (AUC = 0.89). CONCLUSION Our study confirmed the good diagnostic performance of midnight salivary cortisol for diagnosing Cushing's syndrome in a Chinese population. Correlation between midnight salivary cortisol and either urine free cortisol or midnight serum cortisol was good. Midnight salivary cortisol is a convenient and precise tool for diagnosing Cushing's syndrome and can be the screening test of choice for Chinese populations.
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.
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