***TASK*** the task is to summarize an input biomedical literature in six sentences 
    ***INPUT*** the input is a biomedical literature 
    ***OUTPUT*** the output is the summary of an input biomedical literature in six sentences 
    ***DOCUMENTATION*** 
    ***EXAMPLES*** 
    Input: The aim of this work was to test and compare the accuracy of five different morphological scoring systems to identify  malignant ovarian masses  in a prospect i ve multicenter study . Four of the systems had previously been reported by Granberg , Sassone , De Priest and Lerner and the fifth is newly developed . A total of 330 ovarian neoplasms were collected in three different centers , which adopted the same diagnostic procedures . Of these , 261 masses were benign ( mean diameter 50 + /- 26 mm ) and 69 were malignant ( mean diameter 69 + /- 33 mm ) ( prevalence 21 % ) . The area under the receiver operating characteristic ( ROC ) curve for the multicenter score was 0.84 . This was significantly better than the areas of the other four scores which ranged from 0.72 to 0.75 . The cut-off levels derived from the five ROC curves achieved a sensitivity that ranged from 74 % ( Sassone score ) to 88 % ( De Priest score > or = 5 ) , and a specificity from 40 % ( De Priest ) to 67 % ( multicenter ) ; the highest positive predictive value was 41 % ( multicenter ) . With a cut-off level of 9 , the accuracy of the multicenter score was significantly better than the scores of Granberg and De Priest ( McNemar 's test p < 0.0001 ) . Similar results were obtained in 207 ovarian masses of < or = 5 cm in mean diameter , and when 19 borderline and 11 stage 1 cancers only were considered . For the clinical purpose s of a screening test we also checked a possible cut-off level of > or = 8 , which increased the sensitivity to 93 % with a drop of specificity to 56 % . With the use of the same criteria for the scores of the different authors , the following values were obtained for sensitivity : 96 % , 81 % , 93 % and 90 % ; and for specificity : 23 % , 56 % , 28 % and 49 % . The multicenter score performed well at distinguishing malignant from benign lesions , and was better than the other four traditional scores , for both large and small masses . This was mainly due to the introduction of two criteria that allowed correction for typical dermoids and endohemorrhagic corpora lutea . A completely reliable differentiation of benign from malignant masses can not be obtained by sonographic imaging alone Kobal B , Rakar S , Ribic-Pucelj M , Tomazevie T , Zaletel-Kragelj L. Pretreatment evaluation of adnexal tumors predicting ovarian cancer . The objective of this study was to determine the ability of tumor marker assessment , gray-scale transvaginal with color Doppler ultrasonography to predict ovarian malignancy .  One hundred thirty-four subjects with ovarian masses who entered the study prospect ively underwent pelvic examination , tumor marker assessment and gray-scale transvaginal with color flow Doppler ultrasonography preoperatively . Malignancy predictors were statistically evaluated with stepwise multiple logistic regression , and the scores from the model were transformed to probability for having a malignant disease . The presence of neovascularization , intracystic papillary projections , elevated serum CA 125 , and age over 45 years were significant predictors for malignancy . Positive predictive value ( PPV ) for the regression model was 89.0 % , and negative predictive value ( NPV ) was 96.8 % . Probability for malignancy ranged from 0.004 to 0.991 depending on which covariates were included . Logistic regression analysis of pretreatment diagnostic gray-scale and color Doppler ultrasonographic characteristics , together with CA 125 enabled a creation of probability assessment scale for individual estimation of ovarian mass , which may contribute to final clinical decision In this work , we develop and evaluate several least squares support vector machine ( LS-SVM ) classifiers within the Bayesian evidence framework , in order to preoperatively predict malignancy of ovarian tumors . The analysis includes exploratory data analysis , optimal input variable selection , parameter estimation , and performance evaluation via receiver operating characteristic ( ROC ) curve analysis . LS-SVM models with linear and radial basis function ( RBF ) kernels , and logistic regression models have been built on 265 training data , and tested on 160 newly collected patient data . The LS-SVM model with nonlinear RBF kernel achieves the best performance , on the test set with the area under the ROC curve ( AUC ) , sensitivity and specificity equal to 0.92 , 81.5 % and 84.0 % , respectively . The best averaged performance over 30 runs of r and omized cross-validation is also obtained by an LS-SVM RBF model , with AUC , sensitivity and specificity equal to 0.94 , 90.0 % and 80.6 % , respectively . These results show that the LS-SVM models have the potential to obtain a reliable preoperative distinction between benign and malignant ovarian tumors , and to assist the clinicians for making a correct diagnosis Objective : To assess the accuracy of pelvic sonography in distinguishing benign from malignant lesions in postmenopausal women , using morphologic criteria and Doppler flow characteristics . Methods : All postmenopausal patients scanned from March 1992 to April 1993 with sonographically identified and pathologically confirmed adnexal masses formed the study group . The adnexal lesions were morphologically categorized prospect ively as benign or malignant , and pulsed Doppler flow studies were measured using the lowest resistance index obtained from each mass . The sensitivity and specificity were determined for morphologic and Doppler flow assessment s , as well as for a combination of these methods , for predicting the presence of malignancy . Results : Thirty-three postmenopausal patients formed the study group ; 12 lesions were malignant and 21 were benign pathologically . Using morphologic criteria alone , the sensitivity in detecting malignancy was 91 % and specificity was 52 % . Using pulsed Doppler alone with a resistance index limit of 0.6 , the sensitivity in predicting malignancy was 66 % , with a specificity of 81 % . If a resistance index limit of 0.8 were used , the sensitivity and specificity would be the same as those for morphology alone . Combining morphology and resistance index , a single malignancy would still have been missed ( sensitivity 91 % ) . Conclusion : In our experience , Doppler flow studies did not add substantially to the prediction of malignancy using morphologic assessment alone Objective To compare color and pulsed Doppler sonography with gray-scale ultrasound imaging and serum CA 125 levels in establishing accurate preoperative diagnoses of adnexal masses . Methods Medical records of 109 patients referred with preexisting adnexal lesions were review ed retrospectively by comparing preoperative ultrasonic data ( gray-scale imaging and color and pulsed Doppler findings ) with serum CA 125 levels . Results Eighty-three masses were removed surgically , confirming seven malignancies and 76 benign tumors , and 26 masses were followed ; 15 regressed and 11 persisted . Color and pulsed Doppler sonography showed the highest sensitivity , followed by gray-scale imaging , whereas serum CA 125 levels revealed the highest specificity in distinguishing malignant from benign adnexal tumors . All three methods had high negative predictive values ( 96–100 % ) , whereas only serum CA 125 had a positive predictive value greater than 50 % . Conclusion Color and pulsed Doppler sonography , which demonstrate a tumor angiogenic activity , are as accurate as gray-scale imaging in the assessment of adnexal lesions . Together with serum CA 125 marker levels , they produce high negative predictive values , providing reassurance that an adnexal mass is benign OBJECTIVE The aim of this study was to generate and evaluate artificial neural network ( ANN ) models from simple clinical and ultrasound-derived criteria to predict whether or not an adnexal mass will have histological evidence of malignancy . DESIGN The data were collected prospect ively from 173 consecutive patients who were scheduled to undergo surgical investigations at the University Hospitals , Leuven , between August 1994 and August 1996 . The outcome measure was the histological classification of excised tissues as malignant ( including borderline ) or benign . METHODS Age , menopausal status and serum CA 125 levels and sonographic features of the adnexal mass were encoded as variables . The ANNs were trained on a r and omly selected set of 116 patient records and tested on the remainder ( n = 57 ) . The performance of each model was evaluated using receiver operating characteristic ( ROC ) curves and compared with corresponding data from an established risk of malignancy index ( RMI ) and a logistic regression model . RESULTS There were 124 benign masses , five of borderline malignancy and 44 invasive cancers ( of which 29 % were metastatic ) ; 37 % of patients with a malignant or borderline tumor had stage I disease . The best ANN gave an area under the ROC curve of 0.979 for the whole data set , a sensitivity of 95.9 % and specificity of 93.5 % . The corresponding values for the RMI were 0.882 , 67.3 % and 91.1 % , and for the logistic regression model 0.956 , 95.9 % and 85.5 % , respectively . CONCLUSION An ANN can be trained to provide clinical ly accurate information , on whether or not an adnexal mass is malignant , from the patient 's menopausal status , serum CA 125 levels , and some simple ultrasonographic criteria OBJECTIVE To apply logistic regression analysis for several clinical and sonographic data for the construction of a predictive model that could be helpful in the preoperative differentiation of adnexal masses . MATERIAL S AND METHODS Two hundred and eight women with tumors thought to be of adnexal origin were examined preoperatively . Initial analysis included age and menopausal status , ultrasound derived morphological features of adnexal masses ( unilateral/bilateral tumors , papillae , septae , tumor size and volume ) as well as color Doppler criteria such as PI , RI , Peak Systolic Velocity , PSV assessment . In all examinations we used B&K 2002 ADI ( Denmark ) and Kretz Voluson V730 ( Austria ) scanners with transvaginal probes 5 - 9 MHz . Stepwise logistic regression analysis was used to construct a predictive model that would allow probability of malignancy calculation for individual patient . RESULTS There were 159 benign and 49 malignant masses . Seven cancers were in FIGO stage one . Statistical analysis revealed that only 5 of initially tested 14 variables had significant influence on the regression equation . These were : age , bilateral mass , presence of septa > 3 mm , papillary projections > 3 mm in the tumor wall and subjective color scale assessment according to Timmerman et al. ( 1999 ) . Sensitivity and specificity at the 50 % probability level of malignancy in the studied tumor were 77.5 % and 96.8 % , respectively . When 25 % cut-off probability level was used , sensitivity increased to 87.7 % and specificity dropped to 89.9 % . Prospect i ve testing in a new group of 30 patients ( 5 ovarian cancers ) gave sensitivity of 80 % and specificity of 100 % . CONCLUSIONS The use of logistic regression analysis can help in modeling clinical and sonographic data . Our model had better predictive value than individual tests and allowed to calculate true probability figure of ovarian malignancy for any given patient with adnexal mass OBJECTIVE To prospect ively evaluate the accuracy of a multiparameter , ultrasound-based triage and its impact on surgical management of adnexal masses . METHODS Masses evaluated as normal according to Ferrazzi 's sonographic morphological score were considered as being at low risk of malignancy and eligible for laparoscopic treatment without further evaluation . Masses evaluated as abnormal , but without additional risk factors such as ascites , diameter > or = 10 cm , bilaterality , immobility , resistance index < or = 0.6 and serum CA 125 > 35 IU/mL were considered at moderate risk and eligible for laparoscopic evaluation and treatment . Masses with abnormal morphological score and any of these additional risk factors were considered at high risk and treated by laparotomy . The results of pathological examination were obtained for each mass . RESULTS Two hundred and four ( 87 % ) masses were benign and 30 ( 13 % ) were malignant . Among 182 low-risk , 19 moderate-risk and 33 high-risk masses , the odds of malignancy were 1 : 90 , 1 : 18 and 4.5 : 1 , respectively . To calculate the diagnostic accuracy of this algorithm , low- and moderate-risk groups were considered together : the sensitivity was 90 % , specificity 97 % , positive predictive value 82 % and negative predictive value 99 % . The new algorithm was significantly more accurate than was morphological score alone ( P = 0.0002 ) . Ninety-six percent of benign masses were treated by laparoscopy . All three patients with malignant masses that were incorrectly assigned to laparoscopy underwent laparoscopic adnexectomy and frozen section . CONCLUSIONS The accuracy of this new algorithm was higher than that of the sonographic morphological scoring system alone . In the present series , it allowed the treatment by laparoscopy of 96 % of benign adnexal masses without mismanagement of any cases of ovarian cancer BACKGROUND Subjective evaluation of gray-scale and Doppler ultrasound findings ( i.e. , pattern recognition ) by an experienced examiner and preoperative serum levels of CA-125 can both discriminate 
    Output:
    
The model developed by Sassone was the most evaluated prediction model .
All models has acceptable sensitivity and specificity .
However , the Risk of Malignancy Index I and the Risk of Malignancy Index II , which use the product of the serum CA 125 level , an ultrasound scan result , and the menopausal state , were the best predictors .
: Based on our review , the Risk of Malignancy Index should be the prediction model of choice in the preoperative assessment of the adnexal mass