Share Email Print
cover

Proceedings Paper

Deep learning radiomics for non-invasive diagnosis of benign and malignant thyroid nodules using ultrasound images
Author(s): Hui Zhou; Kun Wang; Jie Tian
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

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.

Paper Details

Date Published: 16 March 2020
PDF: 8 pages
Proc. SPIE 11319, Medical Imaging 2020: Ultrasonic Imaging and Tomography, 1131908 (16 March 2020); doi: 10.1117/12.2549433
Show Author Affiliations
Hui Zhou, Institute of Automation (China)
Univ. of Chinese Academy of Sciences (China)
Kun Wang, Institute of Automation (China)
Univ. of Chinese Academy of Sciences (China)
Jie Tian, Institute of Automation (China)
Univ. of Chinese Academy of Sciences (China)
Beihang Univ. (China)


Published in SPIE Proceedings Vol. 11319:
Medical Imaging 2020: Ultrasonic Imaging and Tomography
Brett C. Byram; Nicole V. Ruiter, Editor(s)

© SPIE. Terms of Use
Back to Top
PREMIUM CONTENT
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?
close_icon_gray