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A scoring system to differentiate malignant from benign masses in specific ultrasound-based subgroups of adnexal
L Ameye1, L Valentin, A C Testa
1Department of Electrical Engineering (ESAT-SCD), University Hospital, Katholieke Universiteit Leuven, Leuven, Belgium.
Objective:
To investigate if the prediction of malignant adnexal masses can be improved by considering different ultrasound-based subgroups of tumors and constructing a scoring system for each subgroup instead of using a risk estimation model applicable to all tumors.
Methods:
We used a multicenter database of 1573 patients with at least one persistent adnexal mass. The masses were categorized into four subgroups based on their ultrasound appearance: (1) unilocular cyst; (2) multilocular cyst; (3) presence of a solid component but no papillation; and (4) presence of papillation. For each of the four subgroups a scoring system to predict malignancy was developed in a development set consisting of 754 patients in total (respective numbers of patients: (1) 228; (2) 143; (3) 183; and (4) 200). The subgroup scoring system was then tested in 312 patients and prospectively validated in 507 patients. The sensitivity and specificity, with regard to the prediction of malignancy, of the scoring system were compared with that of the subjective evaluation of ultrasound images by an experienced examiner (pattern recognition) and with that of a published logistic regression (LR) model for the calculation of risk of malignancy in adnexal masses. The gold standard was the pathological classification of the mass as benign or malignant (borderline, primary invasive, or metastatic).
Results:
In the prospective validation set, the sensitivity of pattern recognition, the LR model and the subgroup scoring system was 90% (129/143), 95% (136/143) and 88% (126/143), respectively, and the specificity was 93% (338/364), 74% (270/364) and 90% (329/364), respectively.
Conclusions:
In the hands of experienced ultrasound examiners, the subgroup scoring system for diagnosing malignancy has a performance that is similar to that of pattern recognition, the latter method being the best diagnostic method currently available. The scoring system is less sensitive but more specific than the LR model.
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