Analysis of computer-aided diagnostics in the preoperative diagnosis of ovarian cancer: a systematic review

Insights into Imaging

Critical Review

Tim Boers¹  Fons van der Sommen¹  T. A. Gootzen²  Lara S. Jeelof²  Nienke M. A. van de Kruis²  Jurgen M. J. Piek²  Anna H. Koch²  Caroline L. P. Muntinga²  Joost Nederend³ 

[1] Department of Electrical Engineering, VCA Group, University of Technology Eindhoven, 5600 MB, Eindhoven, Noord-Brabant, The Netherlands;Department of Gynaecology and Obstetrics and Catharina Cancer Institute, Catharina Hospital, 5623 EJ, Eindhoven, Noord-Brabant, The Netherlands;Department of Radiology, Catharina Hospital, 5623 EJ, Eindhoven, Noord-Brabant, The Netherlands;

关键词: Diagnosis;    Computer-assisted;    Machine learning;    Ovarian neoplasms;   

DOI  :  10.1186/s13244-022-01345-x

 received in 2022-05-17, accepted in 2022-12-05,  发布年份 2022

来源: Springer

【 摘 要 】

ObjectivesDifferent noninvasive imaging methods to predict the chance of malignancy of ovarian tumors are available. However, their predictive value is limited due to subjectivity of the reviewer. Therefore, more objective prediction models are needed. Computer-aided diagnostics (CAD) could be such a model, since it lacks bias that comes with currently used models. In this study, we evaluated the available data on CAD in predicting the chance of malignancy of ovarian tumors.MethodsWe searched for all published studies investigating diagnostic accuracy of CAD based on ultrasound, CT and MRI in pre-surgical patients with an ovarian tumor compared to reference standards.ResultsIn thirty-one included studies, extracted features from three different imaging techniques were used in different mathematical models. All studies assessed CAD based on machine learning on ultrasound, CT scan and MRI scan images. Per imaging method, subsequently ultrasound, CT and MRI, sensitivities ranged from 40.3 to 100%; 84.6–100% and 66.7–100% and specificities ranged from 76.3–100%; 69–100% and 77.8–100%. Results could not be pooled, due to broad heterogeneity. Although the majority of studies report high performances, they are at considerable risk of overfitting due to the absence of an independent test set.ConclusionBased on this literature review, different CAD for ultrasound, CT scans and MRI scans seem promising to aid physicians in assessing ovarian tumors through their objective and potentially cost-effective character. However, performance should be evaluated per imaging technique. Prospective and larger datasets with external validation are desired to make their results generalizable.

【 授权许可】

CC BY   
© The Author(s) 2023

【 图 表 】

Fig. 4

Fig. 3