***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: Background : Recently , a Risk of Ovarian Malignancy Algorithm ( ROMA ) utilising human epididymis secretory protein 4 ( HE4 ) and CA125 successfully classified patients as presenting a high or low risk for epithelial ovarian cancer ( EOC ) . We vali date d this algorithm in an independent prospect i ve study . Methods : Women with a pelvic mass , who were scheduled to have surgery , were enrolled in a prospect i ve study .  Preoperative serum levels of HE4 and CA125  were measured in 389 patients . The performance of each of the markers , as well as that of ROMA , was analysed . Results : When all malignant tumours were included , ROMA ( receiver operator characteristic (ROC)-area under curve (AUC)=0.898 ) and HE4 (ROC-AUC)=0.857 ) did not perform significantly better than CA125 alone ( ROC – AUC=0.877 ) . Using a cutoff for ROMA of 12.5 % for pre-menopausal patients , the test had a sensitivity of 67.5 % and a specificity of 87.9 % . With a cutoff of 14.4 % for post-menopausal patients , the test had a sensitivity of 90.8 % and a specificity of 66.3 % . For EOC vs benign disease , the ROC – AUC of ROMA increased to 0.913 and for invasive EOC vs benign disease to 0.957 . Conclusion : This independent validation study demonstrated similar performance indices to those recently published . However , in this study , HE4 and ROMA did not increase the detection of malignant disease compared with CA125 alone . Although the initial reports were promising , measurement of HE4 serum levels does not contribute to the diagnosis of ovarian cancer Objective Women presenting with a large or complex ovarian cyst are referred to extensive surgical staging to ensure the correct diagnosis and treatment of a possible epithelial ovarian cancer . We hypothesized that measurement of the biomarkers HE4 and CA-125 preoperatively would improve the assignment of these patients to the correct level of care . Methods Patients diagnosed with a cystic ovarian mass and scheduled for an operation at our center of excellence for ovarian cancer surgery from 2001 to 2010 were prospect ively included ( n=394 ) and plasma was collected consecutively . Cut-off for HE4 was calculated at 75 % specificity ( 85 pM and 71.8 pM for post and premenopausal women ) . For CA-125 , 35 U/mL cut-off was used . The study population included women with malignant ( n=114 ) , borderline ( n=45 ) , and benign ( n=215 ) ovarian tumors . Results Receiver operator characteristic ( ROC ) area under the curve ( AUC ) in the benign versus malignant cohorts was 86.8 % for CA-125 and 84.4 % for HE4 . Negative predictive value was 91.7 % when at least one of the biomarkers was positive , with only early stage epithelial ovarian cancer showing false negative results . Sensitivity at set specificity ( 75 % ) was 87 % for risk of ovarian malignancy algorithm ( ROMA ) in the postmenopausal cohort ( cut-off point , 26.0 % ) and 81 % in the premenopausal cohort ( cut-off point , 17.3 % ) . ROC AUC in the benign versus stage I epithelial ovarian cancer was only 72 % for HE4 and 76 % for CA-125 . Conclusion In our study , population HE4 did not outperform CA-125 . Based on our data a prospect i ve trial with patients already diagnosed with an ovarian cyst may be conducted OBJECTIVE To derive simple and clinical ly useful ultrasound-based rules for discriminating between benign and malignant adnexal masses . METHODS In a multicenter study involving nine centers consecutive patients with persistent adnexal tumors underwent transvaginal gray-scale and Doppler ultrasound examination using a st and ardized examination technique and st and ardized terms and definitions . Information on 42 gray-scale ultrasound variables and six Doppler variables was collected and entered into a research protocol . When developing simple ultrasound-based rules to predict malignancy ( M-rules ) we chose the ultrasound variable or the combination of ultrasound variables that had the highest positive predictive value ( PPV ) with regard to malignancy ; when developing simple rules to predict a benign tumor ( B-rules ) we chose the ultrasound variable or the combination of ultrasound variables that had the lowest PPV with regard to malignancy . We selected ten rules that were in agreement with our clinical experience and were applicable to at least 30 tumors and then tested them prospect ively on 507 tumors examined in three of the nine centers . RESULTS 1066 patients with 1233 adnexal tumors were included . There were 903 benign tumors ( 73 % ) and 330 malignant tumors ( 27 % ) . In 167 patients the tumors were bilateral . We selected five simple rules to predict malignancy ( M-rules ) : ( 1 ) irregular solid tumor ; ( 2 ) ascites ; ( 3 ) at least four papillary structures ; ( 4 ) irregular multilocular-solid tumor with a largest diameter of at least 100 mm ; and ( 5 ) very high color content on color Doppler examination . We chose five simple rules to suggest a benign tumor ( B-rules ) : ( 1 ) unilocular cyst ; ( 2 ) presence of solid components where the largest solid component is < 7 mm in largest diameter ; ( 3 ) acoustic shadows ; ( 4 ) smooth multilocular tumor less than 100 mm in largest diameter ; and ( 5 ) no detectable blood flow on Doppler examination . These ten rules were applicable to 76 % of all tumors , where they result ed in a sensitivity of 93 % , specificity of 90 % , positive likelihood ratio ( LR+ ) of 9.45 and negative likelihood ratio ( LR- ) of 0.08 . When prospect ively tested the rules were applicable in 76 % ( 386/507 ) of the tumors , where they had a sensitivity of 95 % ( 106/112 ) , a specificity of 91 % ( 249/274 ) , LR+ of 10.37 , and LR- of 0.06 . CONCLUSION Most adnexal tumors in an ordinary tumor population can be correctly classified as benign or malignant using simple ultrasound-based rules . For tumors that can not be classified using simple rules , ultrasound examination by an expert examiner might be useful OBJECTIVE : It is often difficult to distinguish a benign pelvic mass from a malignancy and tools to help referring physician are needed . The purpose of this study was to vali date the Risk of Ovarian Malignancy Algorithm in women presenting with a pelvic mass . METHODS : This was a prospect i ve , multicenter , blinded clinical trial that included women who presented to a gynecologist , a family practitioner , an internist , or a general surgeon with an adnexal mass .  Serum HE4 and CA 125 were determined preoperatively . A Risk of Ovarian Malignancy Algorithm score was calculated and classified patients into high-risk and low-risk groups for having a malignancy . The sensitivity , specificity , negative predictive value , and positive predictive value of the Risk of Ovarian Malignancy Algorithm were estimated . RESULTS : A total of 472 patients were evaluated with 383 women diagnosed with benign disease and 89 women with a malignancy . The incidence of all cancers was 15 % and 10 % for ovarian cancer . In the postmenopausal group , a sensitivity of 92.3 % and a specificity of 76.0 % and for the premenopausal group the Risk of Ovarian Malignancy Algorithm had a sensitivity of 100 % and specificity of 74.2 % for detecting ovarian cancer . When considering all women together , the Risk of Ovarian Malignancy Algorithm had a sensitivity of 93.8 % , a specificity of 74.9 % , and a negative predictive value of 99.0 % . CONCLUSION : The use of the serum biomarkers HE4 and CA 125 with the Risk of Ovarian Malignancy Algorithm has a high sensitivity for the prediction of ovarian cancer in women with a pelvic mass . These findings support the use of the Risk of Ovarian Malignancy Algorithm as a tool for the triage of women with an adnexal mass to gynecologic oncologists . LEVEL OF EVIDENCE : 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 OBJECTIVE To assess the value of ovarian Histo-Scanning ( ™ ) , a novel computerized technique for interpreting ultrasound data , in combination with the risk of malignancy index ( RMI ) in improving triage for women with adnexal masses . METHODS RMI indices were assessed in 199 women enrolled in a prospect i ve study to investigate the use of HistoScanning .  Ultrasound scores were obtained by blinded analysis of archived images . The following sequential test was developed : HistoScanning was modeled as a second-line test for RMI between a lower cut-off and an upper cut-off . The optimal combination of these cut-offs that together maximized the Youden index ( Sensitivity + Specificity - 1 ) was determined . RESULTS Using RMI at the st and ard cut-off value of 250 result ed in a sensitivity of 74 % and a specificity of 86 % . When RMI was combined with HistoScanning , the highest accuracy was achieved by using HistoScanning as a sequential second-line test for patients with RMI values between 105 and 2100 . At these cut-off values , sequential use of RMI and HistoScanning result ed in mean sensitivity and specificity estimates of 88 % and 95 % , respectively . CONCLUSIONS Our data suggest that HistoScanning may have the potential to improve the diagnostic accuracy of RMI , which could result in better triage for women with adnexal masses . Further prospect i ve validation is warranted Background : Correct characterisation of ovarian tumours is critical to optimise patient care . The purpose of this study is to evaluate the diagnostic performance of the International Ovarian Tumour Analysis ( IOTA ) logistic regression model ( LR2 ) , ultrasound Simple Rules ( SR ) , the Risk of Malignancy Index ( RMI ) and subjective assessment ( SA ) for preoperative characterisation of adnexal masses , when ultrasonography is performed by examiners with different background training and experience . Methods : A 2-year prospect i ve multicentre cross-sectional study . Thirty-five level II ultrasound examiners contributed in three UK hospitals .  Transvaginal ultrasonography was performed using a st and ardised approach . The final outcome was the surgical findings and histological diagnosis . To characterise the adnexal masses , the six-variable prediction model ( LR2 ) with a cutoff of 0.1 , the RMI with cutoff of 200 , ten SR ( five rules for malignancy and five rules for benignity ) and SA were applied . The area under the curves ( AUCs ) for performance of LR2 and RMI were calculated . Diagnostic performance measures for all models assessed were sensitivity , specificity , positive and negative likelihood ratios ( LR+ and LR− ) , and the diagnostic odds ratio ( DOR ) . Results : Nine-hundred and si 
    Output:
    
The Risk of Malignancy Index ( RMI ) was the most frequently vali date d model .