Two-dimensional and three-dimensional T2 weighted imaging-based radiomic signatures for the preoperative discrimination of ovarian borderline tumors and malignant tumors

Autor: Xuefen Liu, Tianping Wang, Guofu Zhang, Keqin Hua, Hua Jiang, Shaofeng Duan, Jun Jin, He Zhang
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Zdroj: Journal of Ovarian Research, Vol 15, Iss 1, Pp 1-9 (2022)
Druh dokumentu: article
ISSN: 1757-2215
DOI: 10.1186/s13048-022-00943-z
Popis: Abstract Background Ovarian cancer is the most women malignancy in the whole world. It is difficult to differentiate ovarian cancers from ovarian borderline tumors because of some similar imaging findings.Radiomics study may help clinicians to make a proper diagnosis before invasive surgery. Purpose To evaluate the ability of T2-weighted imaging (T2WI)-based radiomics to discriminate ovarian borderline tumors (BOTs) from malignancies based on two-dimensional (2D) and three-dimensional (3D) lesion segmentation methods. Methods A total of 95 patients with pathologically proven ovarian BOTs and 101 patients with malignancies were retrospectively included in this study. We evaluated the diagnostic performance of the signatures derived from T2WI-based radiomics in their ability to differentiate between BOTs and malignancies and compared the performance differences in the 2D and 3D segmentation models. The least absolute shrinkage and selection operator method (Lasso) was used for radiomics feature selection and machine learning processing. Results The radiomics score between BOTs and malignancies in four types of selected T2WI-based radiomics models differed significantly at the statistical level (p
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