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- Title
Prognostic Evaluation Method of Ovarian Granulosa Cell Tumor Based on Semi-supervised Collaborative Intelligence Model.
- Authors
Xin Liao; Xin Zheng; Juan Zou; Min Feng; Liang Sun; Yan Li; Kaixuan Yang
- Abstract
Prognostic evaluation of ovarian granulosa cell tumors (GCT) is difficult because of limited case samples, differences in recurrence periods, and challenges in follow-up. Therefore, an evaluation method using artificial intelligence was proposed to provide stable prognosis on ovarian GCTs. First, data of GCT samples were preprocessed, and prognostic evaluation was conducted using a semi-supervised collaborative intelligence model. Experiments were conducted on 102 samples from real GCT cases to confirm the validity of the method. Results show that the method has superior prognostic evaluation effect on different pathological sample sets of ovarian GCTs and has practical value to evaluate the prognosis. After deleting the outliers from the pathological sample sets of ovarian GCTs, the true positive rate (TPR) and false positive rate (FPR) are improved in the intelligent model-based prognostic evaluation, and the area under its performance curve is increased from 0.741 to 0.958. Prognostic evaluation of ovarian GCTs by using semi-supervised collaborative intelligence model can be used to evaluate the prognosis and provides a solution to the difficulties in the prognostic evaluation of ovarian GCTs. This method can help clinicians precisely evaluate the recurrence risk of patients, select an optimal treatment scheme, and increase the long-term survival ratio of patients.
- Subjects
OVARIAN cancer diagnosis; GRANULOSA cell tumors; OVARIAN tumors; ARTIFICIAL intelligence; PROGNOSIS
- Publication
Journal of Engineering Science & Technology Review, 2017, Vol 10, Issue 6, p96
- ISSN
1791-2377
- Publication type
Article
- DOI
10.25103/jestr.106.13