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Radiomics analysis on T2-MR image to predict lymphovascular space invasion in cervical cancer
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Paper Abstract

Lymphovascular space invasion (LVSI) is an important determinant for selecting treatment plan in cervical cancer (CC). For CC patients without LVSI, conization is recommended; otherwise, if LVSI is observed, hysterectomy and pelvic lymph node dissection are required. Despite the importance, current identification of LVSI can only be obtained by pathological examination through invasive biopsy or after surgery. In this study, we provided a non-invasive and preoperative method to identify LVSI by radiomics analysis on T2-magnetic resonance image (MRI), aiming at assisting personalized treatment planning. We enrolled 120 CC patients with T2 image and clinical information, and allocated them into a training set (n = 80) and a testing set (n= 40) according to the diagnostic time. Afterwards, 839 image features were extracted to reflect the intensity, shape, and high-dimensional texture information of CC. Among the 839 radiomic features, 3 features were identified to be discriminative by Least absolute shrinkage and selection operator (Lasso)-Logistic regression. Finally, we built a support vector machine (SVM) to predict LVSI status by the 3 radiomic features. In the independent testing set, the radiomics model achieved area under the receiver operating characteristic curve (AUC) of 0.7356, classification accuracy of 0.7287. The radiomics signature showed significant difference between non-LVSI and LVSI patients (p<0.05). Furthermore, we compared the radiomics model with clinical model that uses clinical information, and the radiomics model showed significant improvement than clinical factors (AUC=0.5967 in the validation cohort for clinical model).

Paper Details

Date Published: 13 March 2019
PDF: 6 pages
Proc. SPIE 10950, Medical Imaging 2019: Computer-Aided Diagnosis, 1095040 (13 March 2019); doi: 10.1117/12.2513129
Show Author Affiliations
Shou Wang, Institute of Automation (China)
Univ. of Chinese Academy of Sciences (China)
Xi Chen, Beijing Institute of Technology (China)
Zhenyu Liu, Institute of Automation (China)
Qingxia Wu, Henan Provincial People’s Hospital (China)
Yongbei Zhu, Institute of Automation (China)
Meiyun Wang, Henan Provincial People's Hospital (China)
Jie Tian, Institute of Automation (China)
Univ. of Chinese Academy of Sciences (China)


Published in SPIE Proceedings Vol. 10950:
Medical Imaging 2019: Computer-Aided Diagnosis
Kensaku Mori; Horst K. Hahn, Editor(s)

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