Background: The differential diagnosis of benign and malignant thyroid nodules from ultrasound (US) images remained challengeable in clinical practice. We aimed to develop and validate a highly automatic and objective diagnostic model named deep learning Radiomics of thyroid (DLRT) for the differential diagnosis of benign and malignant thyroid nodules from US images. Methods: We retrospectively enrolled US images and corresponding fine-needle aspiration biopsies from 1645 thyroid nodules. A basic convolutional neural network (CNN) model, a transfer learning model, and a newly designed model named deep learning Radiomics of thyroid (DLRT) were used for the investigation. Their diagnostic accuracy was further compared with human observers (one senior and one junior US radiologist). Results: AUCs of DLRT were 0.96 (95% confidence interval [CI]: 0.94-0.98) and 0.95 (95% confidence interval [CI]: 0.93-0.97) in the training and validation cohort, respectively, for the differential diagnosis of benign and malignant thyroid nodules, which were significantly better than other deep learning models (P < 0.05) and human observers (P < 0.05). Conclusions: DLRT shows the best overall performance comparing with other deep learning models and human observers. It holds great promise for improving the differential diagnosis of benign and malignant thyroid nodules.
We developed the deep learning Radiomics of elastography (DLRE) which adopted Convolutional Neural Network (CNN) based on transfer learning as a noninvasive method to assess liver fibrosis stages, which is essential for prognosis, surveillance of chronic hepatitis B (CHB) patients. Methods: 297 patients were prospectively enrolled from 4 hospitals, and finally 1485 images were included into analysis randomly. DLRE adopted the Convolutional Neural Network (CNN) based on transfer learning, one of the deep learning radiomic techniques, for the automatic analysis of 2D-SWE images. This study was conducted to assess the accuracy of DLRE in comparison with 2D-SWE, transient elastography (TE), transaminase-to-platelet ratio index (APRI), and fibrosis index based on the four factors (FIB-4), by using liver biopsy as the gold standard. Results: AUCs of DLRE were both 0.98 for cirrhosis (95% confidence interval [CI]: 0.95-0.99) and advanced fibrosis (95% CI: 0.94-0.99), which were significantly better than other methods, as well as 0.76 (95% CI: 0.72-0.81) for significance fibrosis (significantly better than APRI and FIB-4). Conclusions: DLRE shows the best overall performance in predicting liver fibrosis stages comparing with 2D-SWE, TE, and serological examinations.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.