방사선의학 웹진

바로가기  >

그림1. 분할 및 타원 피팅 결과 (a) 양성 노듈 (b) 노듈의 센터와 경계를 각각 수동 입력 (c) 영상 분할 결과 (white boundary), (d) 타원 피팅 결과 (an 타원 및 장단축 : 빨강), (e) 악성 노듈, (f) 영상 분할 결과 (white boundary) (f) 타원 피팅 결과 (an 타원 및 장단축 : 빨강)

​그림2. Thyroid CAD와 영상의학과 의사의 ROC 비교

​

Abstract

PURPOSE:

To develop a semiautomated computer-aided diagnosis (cad) system for thyroid cancer using two-dimensional ultrasound images that can be used to yield a second opinion in the clinic to differentiate malignant and benign lesions.

METHODS:

A total of 118 ultrasound images that included axial and longitudinal images from patients with biopsy-confirmed malignant (n = 30) and benign (n = 29) nodules were collected. Thyroid cad software was developed to extract quantitative features from these images based on thyroid nodule segmentation in which adaptive diffusion flow for active contours was used. Various features, including histogram, intensity differences, elliptical fit, gray-level co-occurrence matrixes, and gray-level run-length matrixes, were evaluated for each region imaged. Based on these imaging features, a support vector machine (SVM) classifier was used to differentiate benign and malignant nodules. Leave-one-out cross-validation with sequential forward feature selection was performed to evaluate the overall accuracy of this method. Additionally, analyses with contingency tables and receiver operating characteristic (ROC) curves were performed to compare the performance of cad with visual inspection by expert radiologists based on established gold standards.

RESULTS:

Most univariate features for this proposed cad system attained accuracies that ranged from 78.0% to 83.1%. When optimal SVM parameters that were established using a grid search method with features that radiologists use for visual inspection were employed, the authors could attain rates of accuracy that ranged from 72.9% to 84.7%. Using leave-one-out cross-validation results in a multivariate analysis of various features, the highest accuracy achieved using the proposed cad system was 98.3%, whereas visual inspection by radiologists reached 94.9% accuracy. To obtain the highest accuracies, "axial ratio" and "max probability" in axial images were most frequently included in the optimal feature sets for the authors' proposed cad system, while "shape" and "calcification" in longitudinal images were most frequently included in the optimal feature sets for visual inspection by radiologists. The computed areas under curves in the ROC analysis were 0.986 and 0.979 for the proposed cad system and visual inspection by radiologists, respectively; no significant difference was detected between these groups.

CONCLUSIONS:

The use of thyroid cad to differentiate malignant from benign lesions shows accuracy similar to that obtained via visual inspection by radiologists. Thyroid cad might be considered a viable way to generate a second opinion for radiologists in clinical practice. 

Author information

Chang Y1, Paul AK2, Kim N3, Baek JH3, Choi YJ3, Ha EJ4, Lee KD5, Lee HS5, Shin D6, Kim N6. 

1School of Electrical Engineering, Korea Advanced Institute of Science and Technology, 291, Daehak-ro, Yuseong-gu, Daejeon 34141, South Korea.

2Funzin, Inc., 148 Ankuk-dong, Jongro-gu, Seoul 03060, South Korea.

3Department of Radiology, University of Ulsan College of Medicine, 388-1 Pungnap2-dong, Songpa-gu, Seoul 05505, South Korea.

4Department of Radiology, Ajou University School of Medicine, Wonchon-Dong, Yeongtong-Gu, Suwon 16499, South Korea.

5Department of Otolaryngology Head and Neck Surgery, Kosin University College of Medicine, 34 Amnamdong, Seu-Gu, Busan 49267, South Korea.

6MIDAS Information Technology, Pangyo-ro 228, Bundang-gu, Seongnam-si, Gyeonggi 13487, South Korea.