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The aim of this retrospective study was to determine the prevalence of ovarian masses and calculate the diagnostic performance of the pattern recognition approach in ovarian pathology. A total of 1001 patients diagnosed with ovarian mass were included, of which 92.6% were diagnosed with ovarian pathology and the presence of a pathological result, while 7.4% of cases were diagnosed with functional ovarian cyst. The prevalence of ovarian malignancy was 15%. A specific ultrasound diagnosis was suggested in 62.9% of all cases, while sonographers did not explicitly provide a diagnosis in remaining cases. A subjective assessment showed 80.3% sensitivity (95% confidence interval (CI) 68.7–89.1) and 97.6% specificity (95% CI 96–98.6) in differentiating between benign and malignant ovarian masses. The sensitivity and specificity for the diagnosis of endometriotic cyst were 77.03% and 90.63% and 63.19% and 94.3% for mature cystic teratoma, respectively. In conclusion, assessment showed good performance in differentiating between benign and malignant ovarian mass and it was possible to diagnose several specific ovarian tumours.Impact StatementWhat is already known on this subject?  Pattern recognition is an acceptable method for classifying ovarian mass, which exhibits specific morphological features on grey-scale ultrasonography, and can be used to predict nature and histological type.Whatdothe results of this study add?  Even in the hands of an expert examiner, there were a number of cases in which the diagnoses could not be specifically stated. Pattern recognition correctly classified 90.3% of ovarian masses as either benign or malignant and correctly provided specific histologic diagnoses after exclusion of unspecified diagnosis in 80.6% of all cases. The diagnostic performance of this approach was high in differentiating between benign and malignant ovarian mass and in diagnosing some specific ovarian pathologies.Whatarethe implicationsof these findings for clinical practice and/or further research?  A subjective assessment is simple and easy to use in clinical practice and has shown promising results in classifying benign and malignant ovarian mass. Moreover, it can also be used to make some specific diagnoses. However, specialised and experienced gynaecological ultrasound examiners are required to provide the most accurate diagnosis. Therefore, criteria to describe ultrasound features and convincing operators to make a definite diagnosis as often as possible should be encouraged. A prospective study to verify diagnostic performance of pattern recognition or comparing with other ultrasonographic diagnostic tools should be considered. What is already known on this subject?  Pattern recognition is an acceptable method for classifying ovarian mass, which exhibits specific morphological features on grey-scale ultrasonography, and can be used to predict nature and histological type. Whatdothe results of this study add?  Even in the hands of an expert examiner, there were a number of cases in which the diagnoses could not be specifically stated. Pattern recognition correctly classified 90.3% of ovarian masses as either benign or malignant and correctly provided specific histologic diagnoses after exclusion of unspecified diagnosis in 80.6% of all cases. The diagnostic performance of this approach was high in differentiating between benign and malignant ovarian mass and in diagnosing some specific ovarian pathologies. Whatarethe implicationsof these findings for clinical practice and/or further research?  A subjective assessment is simple and easy to use in clinical practice and has shown promising results in classifying benign and malignant ovarian mass. Moreover, it can also be used to make some specific diagnoses. However, specialised and experienced gynaecological ultrasound examiners are required to provide the most accurate diagnosis. Therefore, criteria to describe ultrasound features and convincing operators to make a definite diagnosis as often as possible should be encouraged. A prospective study to verify diagnostic performance of pattern recognition or comparing with other ultrasonographic diagnostic tools should be considered.

journal of obstetrics and gynaecology

Prevalence of ovarian mass and diagnostic performance of ultrasonography pattern recognition among women at the Gynaecologic Ultrasonography Unit at University Hospital in Thailand