Deep Learning Prediction of Ovarian Malignancy at US Compared with O-RADS and Expert Assessment

Autor:	Hui Chen, Bo-Wen Yang, Le Qian, Yi-Shuang Meng, Xiang-Hui Bai, Xiao-Wei Hong, Xin He, Mei-Jiao Jiang, Fei Yuan, Qin-Wen Du, Wei-Wei Feng
Rok vydání:	2022
Předmět:	Ovarian Neoplasms Deep Learning ROC Curve Humans Female Radiology Nuclear Medicine and imaging Middle Aged Algorithms Retrospective Studies
Zdroj:	Radiology. 304:106-113
ISSN:	1527-1315 0033-8419
Popis:	Background Deep learning (DL) algorithms could improve the classification of ovarian tumors assessed with multimodal US. Purpose To develop DL algorithms for the automated classification of benign versus malignant ovarian tumors assessed with US and to compare algorithm performance to Ovarian-Adnexal Reporting and Data System (O-RADS) and subjective expert assessment for malignancy. Materials and Methods This retrospective study included consecutive women with ovarian tumors undergoing gray scale and color Doppler US from January 2019 to November 2019. Histopathologic analysis was the reference standard. The data set was divided into training (70%), validation (10%), and test (20%) sets. Algorithms modified from residual network (ResNet) with two fusion strategies (feature fusion [hereafter, DL
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c193d175f6bb796501ffa30c45b76b4b https://doi.org/10.1148/radiol.211367 Zobrazit plný text záznamu