Jinyu Liang1, Xiaowen Huang1, Hangtong Hu1, Yihao Liu2, Qian Zhou2, Qinghua Cao3, Wei Wang1, Baoxian Liu1, Yanling Zheng1, Xin Li4, Xiaoyan Xie1, Mingde Lu1, Sui Peng2, Longzhong Liu5, Haipeng Xiao6. 1. 1 Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-sen University , Guangzhou, People's Republic of China . 2. 2 Clinical Trial Unit, The First Affiliated Hospital of Sun Yat-sen University , Guangzhou, People's Republic of China . 3. 3 Department of Pathology, The First Affiliated Hospital of Sun Yat-sen University , Guangzhou, People's Republic of China . 4. 4 Research Center of GE Healthcare , Shanghai, People's Republic of China . 5. 5 Department of Ultrasound, Sun Yat-sen University Cancer Center , State Key Laboratory of Oncology in South China, Guangzhou, People's Republic of China . 6. 6 Department of Endocrinology, The First Affiliated Hospital of Sun Yat-sen University , Guangzhou, People's Republic of China .
Abstract
BACKGROUND: Visual interpretation of ultrasound (US) images alone may not be sensitive enough to detect important features of potentially malignant thyroid nodules. The aim of this study was to develop a radiomics score using US imaging to predict the probability for malignancy of thyroid nodules as compared with the Thyroid Imaging, Reporting, and Data System (TI-RADS) scoring criteria proposed by the American College of Radiology (ACR). METHODS: One hundred thirty-seven pathologically proven thyroid nodules from hospital 1 were enrolled as a training cohort, while 95 nodules from hospital 2 served as the validation cohort. A radiomics score using US images was developed from the training cohort. Two junior and two senior radiologists reviewed all images and scored each nodule according to the 2017 updated ACR TI-RADS scoring criteria. Univariate logistic regression analysis was used to develop the prediction models based on the radiomics score and ACR scores. The performance of the models was evaluated and compared with respect to discrimination, calibration, and clinical application in the validation cohort. RESULTS: Univariate regression indicated that the radiomics score and ACR scores were predictors for thyroid nodule malignancy (all p < 0.001). Five prediction models were built based on the above scores. The radiomics score showed good discrimination with an AUC of 0.921 in the training cohort and 0.931 in the validation cohort, which was significantly better than the ACR scores of junior radiologists in both cohorts. Although five models showed good calibration (all p > 0.05), the model based on the radiomics score presented the lowest errors (E max = 0.073 or E aver = 0.028) in predicting and calibrating probabilities. Decision curve analysis demonstrated that the model using the radiomics score added more benefit than using the ACR scores of junior radiologists. CONCLUSION: Compared with ACR TI-RADS evaluation by junior radiologists, the radiomics score showed good performance in predicting malignancy of thyroid nodules in our set of histologically verified thyroid nodules from two tertiary hospitals.
BACKGROUND: Visual interpretation of ultrasound (US) images alone may not be sensitive enough to detect important features of potentially malignant thyroid nodules. The aim of this study was to develop a radiomics score using US imaging to predict the probability for malignancy of thyroid nodules as compared with the Thyroid Imaging, Reporting, and Data System (TI-RADS) scoring criteria proposed by the American College of Radiology (ACR). METHODS: One hundred thirty-seven pathologically proven thyroid nodules from hospital 1 were enrolled as a training cohort, while 95 nodules from hospital 2 served as the validation cohort. A radiomics score using US images was developed from the training cohort. Two junior and two senior radiologists reviewed all images and scored each nodule according to the 2017 updated ACR TI-RADS scoring criteria. Univariate logistic regression analysis was used to develop the prediction models based on the radiomics score and ACR scores. The performance of the models was evaluated and compared with respect to discrimination, calibration, and clinical application in the validation cohort. RESULTS: Univariate regression indicated that the radiomics score and ACR scores were predictors for thyroid nodule malignancy (all p < 0.001). Five prediction models were built based on the above scores. The radiomics score showed good discrimination with an AUC of 0.921 in the training cohort and 0.931 in the validation cohort, which was significantly better than the ACR scores of junior radiologists in both cohorts. Although five models showed good calibration (all p > 0.05), the model based on the radiomics score presented the lowest errors (E max = 0.073 or E aver = 0.028) in predicting and calibrating probabilities. Decision curve analysis demonstrated that the model using the radiomics score added more benefit than using the ACR scores of junior radiologists. CONCLUSION: Compared with ACR TI-RADS evaluation by junior radiologists, the radiomics score showed good performance in predicting malignancy of thyroid nodules in our set of histologically verified thyroid nodules from two tertiary hospitals.
Authors: Xavier M Keutgen; Hui Li; Kelvin Memeh; Julian Conn Busch; Jelani Williams; Li Lan; David Sarne; Brendan Finnerty; Peter Angelos; Thomas J Fahey; Maryellen L Giger Journal: J Med Imaging (Bellingham) Date: 2022-05-26
Authors: Maryam Gul; Kimberley-Jane C Bonjoc; David Gorlin; Chi Wah Wong; Amirah Salem; Vincent La; Aleksandr Filippov; Abbas Chaudhry; Muhammad H Imam; Ammar A Chaudhry Journal: Front Oncol Date: 2021-07-07 Impact factor: 6.244
Authors: Ran Wei; Hao Wang; Lanyun Wang; Wenjuan Hu; Xilin Sun; Zedong Dai; Jie Zhu; Hong Li; Yaqiong Ge; Bin Song Journal: BMC Med Imaging Date: 2021-02-09 Impact factor: 1.930