AI diagnostic performance based on multiple imaging modalities for ovarian tumor: A systematic review and meta-analysis

Ma, Lin; Huang, Luyi; Chen, Yan; Zhang, Lei; Nie, Dunli; He, Weiqun; Qi, Xiaoxue

doi:10.3389/fonc.2023.1133491

Cited by 3 publications

(2 citation statements)

References 34 publications

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“…Certainly, there are other recent meta-analyses addressing the performance of artificial intelligence approaches to the differential diagnosis of adnexal masses [24,70,71]. However, our results cannot be compared with these meta-analyses.…”

Section: Strengths and Limitationsmentioning

confidence: 80%

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Ultrasound-Based Deep Learning Models Performance versus Expert Subjective Assessment for Discriminating Adnexal Masses: A Head-to-Head Systematic Review and Meta-Analysis

Lourenço,

Arrufat,

Satorres

et al. 2024

Applied Sciences

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(1) Background: Accurate preoperative diagnosis of ovarian masses is crucial for optimal treatment and postoperative outcomes. Transvaginal ultrasound is the gold standard, but its accuracy depends on operator skill and technology. In the absence of expert imaging, pattern-based approaches have been proposed. The integration of artificial intelligence, specifically deep learning (DL), shows promise in improving diagnostic precision for adnexal masses. Our meta-analysis aims to evaluate DL’s performance compared to expert evaluation in diagnosing adnexal masses using ultrasound images. (2) Methods: Studies published between 2000 and 2023 were searched in PubMed, Scopus, Cochrane and Web of Science. The study quality was assessed using QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies-2). Pooled sensitivity and specificity for both methods were estimated and compared. (3) Results: From 1659 citations, we selected four studies to include in this meta-analysis. The mean prevalence of ovarian cancer was 30.6%. The quality of the studies was good with low risk of bias for index and reference tests, but with high risk of bias for patient selection domain. Pooled sensitivity and specificity were 86.0% and 90.0% for DL and 86.0% and 89.0% for expert accuracy (p = 0.9883). (4) Conclusion: We found no significant differences between DL systems and expert evaluations in detecting and differentially diagnosing adnexal masses using ultrasound images.

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Section: Strengths and Limitationsmentioning

confidence: 80%

“…Finally, Ma et al reported a meta-analysis including 11 studies, three of them using ultrasound-based approaches [71]. Two of them used DL, but did not compare this to expert examination and it was not included in our meta-analysis [36].…”

Section: Strengths and Limitationsmentioning

confidence: 99%

Ultrasound-Based Deep Learning Models Performance versus Expert Subjective Assessment for Discriminating Adnexal Masses: A Head-to-Head Systematic Review and Meta-Analysis

Lourenço,

Arrufat,

Satorres

et al. 2024

Applied Sciences

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Accuracy of machine learning in the preoperative identification of ovarian borderline tumors: a meta-analysis

Qi,

Li,

Yang

et al. 2024

Clinical Radiology

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

Liu,

Cai,

Zhou

et al. 2024

Front. Med.

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BackgroundAccurately 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.MethodsWe 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.ResultsA 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.ConclusionThis 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.

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AI diagnostic performance based on multiple imaging modalities for ovarian tumor: A systematic review and meta-analysis

Cited by 3 publications

References 34 publications

Ultrasound-Based Deep Learning Models Performance versus Expert Subjective Assessment for Discriminating Adnexal Masses: A Head-to-Head Systematic Review and Meta-Analysis

Ultrasound-Based Deep Learning Models Performance versus Expert Subjective Assessment for Discriminating Adnexal Masses: A Head-to-Head Systematic Review and Meta-Analysis

Accuracy of machine learning in the preoperative identification of ovarian borderline tumors: a meta-analysis

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

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