Ultrasound radiomics-based artificial intelligence model to assist in the differential diagnosis of ovarian endometrioma and ovarian dermoid cyst

Lu Liu; Wenjun Cai; Chenyang Zhou; Hongyan Tian; Beibei Wu; Jing Zhang; Guanghui Yue; Yi Hao

doi:10.3389/fmed.2024.1362588

Ultrasound radiomics-based artificial intelligence model to assist in the differential diagnosis of ovarian endometrioma and ovarian dermoid cyst

Front Med (Lausanne). 2024 Mar 8:11:1362588. doi: 10.3389/fmed.2024.1362588. eCollection 2024.

Authors

Lu Liu^#¹, Wenjun Cai^#², Chenyang Zhou³, Hongyan Tian¹, Beibei Wu¹, Jing Zhang¹, Guanghui Yue⁴, Yi Hao¹

Affiliations

¹ Department of Ultrasound Medicine, South China Hospital, Medical School, Shenzhen University, Shenzhen, P. R. China.
² Department of Ultrasound, Shenzhen University General Hospital, Medical School, Shenzhen University, Shenzhen, P. R. China.
³ Department of Information, South China Hospital, Medical School, Shenzhen University, Shenzhen, P. R. China.
⁴ National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, P. R. China.

^# Contributed equally.

Abstract

Background: Accurately differentiating between ovarian endometrioma and ovarian dermoid cyst is of clinical significance. However, the ultrasound appearance of these two diseases is variable, occasionally causing confusion and overlap with each other. This study aimed to develop a diagnostic classification model based on ultrasound radiomics to intelligently distinguish and diagnose the two diseases.

Methods: We collected ovarian ultrasound images from participants diagnosed as patients with ovarian endometrioma or ovarian dermoid cyst. Feature extraction and selection were performed using the Mann-Whitney U-test, Spearman correlation analysis, and the least absolute shrinkage and selection operator (LASSO) regression. We then input the final features into the machine learning classifiers for model construction. A nomogram was established by combining the radiomic signature and clinical signature.

Results: A total of 407 participants with 407 lesions were included and categorized into the ovarian endometriomas group (n = 200) and the dermoid cyst group (n = 207). In the test cohort, Logistic Regression (LR) achieved the highest area under curve (AUC) value (0.981, 95% CI: 0.963-1.000), the highest accuracy (94.8%), and the highest sensitivity (95.5%), while LightGBM achieved the highest specificity (97.1%). A nomogram incorporating both clinical features and radiomic features achieved the highest level of performance (AUC: 0.987, 95% CI: 0.967-1.000, accuracy: 95.1%, sensitivity: 88.0%, specificity: 100.0%, PPV: 100.0%, NPV: 88.0%, precision: 93.6%). No statistical difference in diagnostic performance was observed between the radiomic model and the nomogram (P > 0.05). The diagnostic indexes of radiomic model were comparable to that of senior radiologists and superior to that of junior radiologist. The diagnostic performance of junior radiologists significantly improved with the assistance of the model.

Conclusion: This ultrasound radiomics-based model demonstrated superior diagnostic performance compared to those of junior radiologists and comparable diagnostic performance to those of senior radiologists, and it has the potential to enhance the diagnostic performance of junior radiologists.

Keywords: artificial intelligence; machine learning; ovarian dermoid cyst; ovarian endometrioma; ultrasound radiomics.

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This research was funded by National Natural Science Foundation of China (No. 62001302), Guangdong Basic and Applied Basic Research Foundation (No. 2021A1515011348), Shenzhen Engineering Research Center of Multi-modal Fusion medical intelligent Diagnosis Technology (XMHT20220104016), and General Project of the Science and Technology Innovation Committee of Shenzhen (JCYJ20190814110207603 and JCYJ20220530154600001).