Mathematical models to discriminate between benign and malignant XXXX : potential diagnostic improvement using ovarian HistoScanning.

@entity1 @entity10224 @entity1274 @entity4134
PURPOSE: Accurate preoperative clinical assessment of @entity4134 can optimize outcomes by ensuring appropriate and timely surgery. This article addresses whether a new technology, ovarian HistoScanning, has an additional diagnostic value in mathematical models developed for the differential diagnosis of @entity4134 . @entity1 AND METHODS: Transvaginal sonography-based morphological variables were obtained through blinded analysis of archived images in 199 @entity1 enrolled in a prospective study to assess the performance of ovarian HistoScanning. Logistic regression (LR) and @entity1274 ( @entity1274 ) models including these variables and clinical and @entity1 data along with the HistoScanning score (HSS) (range, 0-125; based on mathematical algorithms) were developed in a learning set (60% @entity1 ). The remaining 40% @entity1 (evaluation set) were used to assess model performance. RESULTS: Of all morphological and clinical variables tested, serum @entity10224 , presence of a solid component, and HSS were most significant and used to develop the LR model. The @entity1274 model included all variables. The novel variable, HSS, offered significant improvement in the LR and @entity1274 models' performance. The LR and @entity1274 models in an independent evaluation set were found to have area under the receiver operating characteristic curve = 0.97 (95% confidence interval [CI], 94-99) and 0.93 (95% CI, 88-98), sensitivities = 83% (95% CI, 71%-91%) and 80% (95% CI, 67%-89%), and specificities = 98% (95% CI, 89%-99%) and 86% (95% CI, 72%-95%), respectively. In addition, these models showed an improved performance when compared with 3 other existing models (all P < 0.05). CONCLUSIONS: This initial report shows a clear benefit of including ovarian HistoScanning into mathematical models used for discriminating benign from malignant ovarian masses. These models may be specifically helpful to the less experienced examiner. Future research should assess performance of these models in prospective clinical trials in different populations.
