Ultrasound in medicine & biology · 2025
Diagnosis of Salivary Gland Tumors Using Ultrasound Radiomics.
СтатьяПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод пока не подготовлен
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.
Cheng Ping-Chia, Lo Wu-Chia, Liao Li-Jen, Chiang Huihua Kenny
Аннотация
OBJECTIVE: The current diagnosis of salivary gland tumors (SGTs) is dependent on subjective ultrasound features. Here we aimed to develop an objective method using ultrasound radiomics.
METHODS: We collected 248 benign and 46 malignant images and divided them into training (80%) and testing (20%) groups, with 105 radiomic features extracted from each image. Data re-sampling, feature selection and classification were conducted. The diagnostic accuracy of different combinations was evaluated.
RESULTS: After data re-sampling using the Synthetic Minority Over Sampling Technique (SMOTE) and feature selection with LASSO+ANOVA, 10 radiomic features were selected. Using the Random Forest classifier, the testing set achieved an area under the receiver operating characteristic curve of 0.85, accuracy of 90%, sensitivity of 78% and specificity of 92% for diagnosing SGTs. It maintained an accuracy of 85% in a separate internal validation set.
CONCLUSION: This study offers significant insights into the use of radiomics for the diagnosis of SGTs. When selected properly and paired with a suitable classification model, radiomics can be used to differentiate between benign and malignant SGTs.
METHODS: We collected 248 benign and 46 malignant images and divided them into training (80%) and testing (20%) groups, with 105 radiomic features extracted from each image. Data re-sampling, feature selection and classification were conducted. The diagnostic accuracy of different combinations was evaluated.
RESULTS: After data re-sampling using the Synthetic Minority Over Sampling Technique (SMOTE) and feature selection with LASSO+ANOVA, 10 radiomic features were selected. Using the Random Forest classifier, the testing set achieved an area under the receiver operating characteristic curve of 0.85, accuracy of 90%, sensitivity of 78% and specificity of 92% for diagnosing SGTs. It maintained an accuracy of 85% in a separate internal validation set.
CONCLUSION: This study offers significant insights into the use of radiomics for the diagnosis of SGTs. When selected properly and paired with a suitable classification model, radiomics can be used to differentiate between benign and malignant SGTs.