Rheumatology (Oxford, England) · 2026
Detecting Sjögren's Disease from Parotid Gland Ultrasound Radiomics.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Akkuzu Gamze, Durugol Omer Faruk, Tolu Sena, Dasdelen Muhammed Furkan, Karagulle Mehmet, Suleyman Kanullah, Karaalioğlu Bilgin, Deniz Rabia, Özgür Duygu Sevinç, Yıldırım Fatih, Bes Cemal
Аннотация
OBJECTIVES: Salivary gland ultrasonography is a promising non-invasive modality for the evaluation of Sjögren's disease (SjD), but its diagnostic utility is limited by operator dependency. This study aimed to evaluate the classification performance of radiomics-based machine learning using parotid gland ultrasonography and to compare it with conventional visual assessment.
METHODS: A total of 866 parotid gland ultrasound images from 202 participants were included: 123 patients fulfilling the 2016 ACR/EULAR criteria for SjD, 33 healthy controls, 24 non-Sjögren sicca patients, and 22 incomplete SjD cases. A total of 104 radiomic features describing intensity, texture, and micro-texture patterns were extracted. A 5-fold soft-voting SVM ensemble was trained on confirmed SjD and healthy participants; non-Sjögren sicca and incomplete SjD cases were reserved for the held-out test set. SHAP analysis was used for model interpretability.
RESULTS: The SVM ensemble achieved an area under the receiver operating characteristic curve (AUC) of 0.99 for binary classification between SjD and healthy controls, with 0.94 accuracy, 0.86 sensitivity, and 0.96 specificity, outperforming radiologist assessments (accuracy: 0.62 and 0.72). SHAP analysis identified intensity dispersion metrics, GLCM-based texture features, and LBP micro-texture patterns as the strongest predictors. PCA and feature-level analyses demonstrated substantial overlap in radiomic features between non-Sjögren sicca and confirmed SjD patients.
CONCLUSION: Radiomics-based machine learning demonstrated high classification performance for distinguishing SjD from healthy controls using parotid gland ultrasonography. Quantitative ultrasound analysis may serve as an objective adjunctive tool in SjD assessment, although validation in larger multicenter cohorts is required.
METHODS: A total of 866 parotid gland ultrasound images from 202 participants were included: 123 patients fulfilling the 2016 ACR/EULAR criteria for SjD, 33 healthy controls, 24 non-Sjögren sicca patients, and 22 incomplete SjD cases. A total of 104 radiomic features describing intensity, texture, and micro-texture patterns were extracted. A 5-fold soft-voting SVM ensemble was trained on confirmed SjD and healthy participants; non-Sjögren sicca and incomplete SjD cases were reserved for the held-out test set. SHAP analysis was used for model interpretability.
RESULTS: The SVM ensemble achieved an area under the receiver operating characteristic curve (AUC) of 0.99 for binary classification between SjD and healthy controls, with 0.94 accuracy, 0.86 sensitivity, and 0.96 specificity, outperforming radiologist assessments (accuracy: 0.62 and 0.72). SHAP analysis identified intensity dispersion metrics, GLCM-based texture features, and LBP micro-texture patterns as the strongest predictors. PCA and feature-level analyses demonstrated substantial overlap in radiomic features between non-Sjögren sicca and confirmed SjD patients.
CONCLUSION: Radiomics-based machine learning demonstrated high classification performance for distinguishing SjD from healthy controls using parotid gland ultrasonography. Quantitative ultrasound analysis may serve as an objective adjunctive tool in SjD assessment, although validation in larger multicenter cohorts is required.