Deep-learning models for image-based gynecological cancer diagnosis: a systematic review and meta- analysis

Abstract

IntroductionGynecological cancers pose a significant threat to women worldwide, especially those in resource-limited settings. Human analysis of images remains the primary method of diagnosis, but it can be inconsistent and inaccurate. Deep learning (DL) can potentially enhance image-based diagnosis by providing objective and accurate results. This systematic review and meta-analysis aimed to summarize the recent advances of deep learning (DL) techniques for gynecological cancer diagnosis using various images and explore their future implications.MethodsThe study followed the PRISMA-2 guidelines, and the protocol was registered in PROSPERO. Five databases were searched for articles published from January 2018 to December 2022. Articles that focused on five types of gynecological cancer and used DL for diagnosis were selected. Two reviewers assessed the articles for eligibility and quality using the QUADAS-2 tool. Data was extracted from each study, and the performance of DL techniques for gynecological cancer classification was estimated by pooling and transforming sensitivity and specificity values using a random-effects model.ResultsThe review included 48 studies, and the meta-analysis included 24 studies. The studies used different images and models to diagnose different gynecological cancers. The most popular models were ResNet, VGGNet, and UNet. DL algorithms showed more sensitivity but less specificity compared to machine learning (ML) methods. The AUC of the summary receiver operating characteristic plot was higher for DL algorithms than for ML methods. Of the 48 studies included, 41 were at low risk of bias.ConclusionThis review highlights the potential of DL in improving the screening and diagnosis of gynecological cancer, particularly in resource-limited settings. However, the high heterogeneity and quality of the studies could affect the validity of the results. Further research is necessary to validate the findings of this study and to explore the potential of DL in improving gynecological cancer diagnosis.

MRI-Based Radiomics as a Promising Noninvasive Diagnostic Technique for Adenomyosis

“…Initial studies have explored the potential of artificial intelligence methods such as machine and deep learning in gynecological imaging overall, as well as in endometriosis and adenomyosis, and the findings appear promising [36][37][38][39][40]. For instance, in a sonographic preliminary study, a deep learning model showed a lower accuracy but a higher specificity in diagnosing adenomyosis on ultrasonographic images compared to intermediate-skilled trainees [36].…”

Developing a deep learning model for predicting ovarian cancer in Ovarian-Adnexal Reporting and Data System Ultrasound (O-RADS US) Category 4 lesions: A multicenter study

“…Arti cial intelligence (AI) has emerged as a signi cant tool with diverse medical applications (Wang et al, 2024). Deep learning (DL) can quantitatively analyze of medical images and has been applied in the eld of oncology (Taddese et al, 2024). Several studies demonstrated that DL can improve the diagnosis, predict treatment responses, and progression-free survival of patients with ovarian tumors (Arezzo et…”

Developing a deep learning model for predicting ovarian cancer in Ovarian-Adnexal Reporting and Data System Ultrasound (O-RADS US) Category 4 lesions: A multicenter study

“…Artificial intelligence (AI) has emerged as a significant tool with diverse medical applications (Wang et al 2024 ). Deep learning (DL) can quantitatively analyze of medical images and has been applied in the field of oncology (Taddese et al 2024 ). Several studies demonstrated that DL can improve the diagnosis, predict treatment responses, and progression-free survival of patients with ovarian tumors (Arezzo et al 2022 ; Boehm et al 2022 ; Na et al 2024 ; Sadeghi et al 2024 ; Yao et al 2021 ).…”