Share Email Print
cover

Proceedings Paper

Computer-aided staging of gastric cancer using radiomics signature on computed tomography imaging
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

Gastric cancer is one of the most common malignant tumors with high mortality rate worldwide. In order to optimally treating gastric cacner patients and reduce cancer mortality, it requires to accurately predict Tumor, Node, Metastasis (TNM) staging of the tumor, which will determine whether the patients need neoadjuvant chemotherapy before surgery. However, subjectively reading CT images is difficult to predict TNM with large intra- and inter-reader variability. To address this challenge, we developed and tested a new CAD approach that uses radiomics features computed from the segmented tumor regions depicting on CT images to build a machine learning classifier to predict TNM and divide patients into two groups of whether need neoadjuvant chemotherapy. A CT image dataset involving 219 gastric cancer patients was retrospectively assembled and used in this study. In addition, 3 clinicopathological markers were also acquired and used. From an initial pool of 367 radiomics features, an optimal set of 11 features was selected. Then, 3 machine learning classifiers using (1) 11 CT image features, (2) 3 clinicopathological markers, and (3) fusion of both 11 CT image features and 3 clinicopathological markers, were trained and tested using a leave-one-case-out validation methods. Areas under ROC curves of three classifies are 0.74, 0.71, and 0.79, respectively. The results indicated that (1) radiomics image features computed from CT images carry higher discriminatory power to predict TNM than using clinicopathological markers acquired from surgically resected specimen and (2) fusion of CT image features and clinicopathological markers can further increase performance to predict TNM of gastric cancer patients.

Paper Details

Date Published: 16 March 2020
PDF: 6 pages
Proc. SPIE 11314, Medical Imaging 2020: Computer-Aided Diagnosis, 1131439 (16 March 2020); doi: 10.1117/12.2549667
Show Author Affiliations
Lili Wang, Fujian Medical Univ. (China)
The Univ. of Oklahoma (United States)
Jie Wu, East China Normal Univ. (China)
Guang Yang, East China Normal Univ. (China)
Bin Zheng, The Univ. of Oklahoma (United States)


Published in SPIE Proceedings Vol. 11314:
Medical Imaging 2020: Computer-Aided Diagnosis
Horst K. Hahn; Maciej A. Mazurowski, 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