Role of Sonographic Gray-Scale Pattern Recognition in the Diagnosis of Adnexal Masses
Journal Title: Journal of Clinical and Diagnostic Research - Year 2017, Vol 11, Issue 9
Abstract
Introduction: Characterization of adnexal masses as benign or malignant is of utmost importance for optimal management and prognostication. Ultrasound examination plays an important role in the differentiation of adnexal masses. Various sonographic characteristics have been recognised to differentiate benign and malignant adnexal masses. Subjective evaluation of gray-scale ultrasound images by an experienced ultrasound examiner to discriminate adnexal masses is known as “pattern recognition”. Aim: To access the efficacy of pattern recognition at predicting an accurate histological diagnosis of adnexal masses. Materials and Methods: All adnexal masses diagnosed clinically or during screening sonography were included in the study (n=136). Sonographic pattern recognition was performed and documented with specific diagnosis whenever feasible. Risk of Malignancy Index 3 (RMI3) score was also calculated. Results were compared with the gold standard histology. Chi-square test was used to assess the significance of the results and a p-value <0.05 was considered statistically significant. Results: In the final cohort of 136 women, on pattern recognition, 91 were suspected to have benign adnexal masses and 45 were reported as malignant adnexal masses. However, on final histopathology, 94 patients had benign tumours and 42 patients had malignant disease. The benign group pattern recognition could render a specific diagnosis in 85.7% as compared to RMI3 pattern recognition conferred a sensitivity of 95.2% (RMI3 78.6%), with a slight compromise in the specificity (94.7% versus 96.8%). Conclusion: Pattern recognition is a sensitive and specific sonographic tool in discriminating benign and malignant adnexal masses. Moreover, it is also useful in differentiating various benign adnexal masses.
Authors and Affiliations
Jyothi Shetty, Grishma Reddy, Deeksha Pandey
Keywords
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- EP ID EP363435
- DOI 10.7860/JCDR/2017/28533.10614
- Views 71
- Downloads 0
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