SNCG (synuclein, gamma (breast cancer-specific protein 1))

Review on SNCG (synuclein, gamma (breast cancer-specific protein 1)), with data on DNA, on the protein encoded, and where the gene is implicated

Predicting the risk of malignancy in adnexal masses based on the Simple Rules from the International Ovarian Tumor Analysis group

BACKGROUND: Accurate methods to preoperatively characterize adnexal tumors are pivotal for optimal patient management. A recent metaanalysis concluded that the International Ovarian Tumor Analysis algorithms such as the Simple Rules are the best approaches to preoperatively classify adnexal masses as benign or malignant. OBJECTIVE: We sought to develop and validate a model to predict the risk of malignancy in adnexal masses using the ultrasound features in the Simple Rules. STUDY DESIGN: This was an international cross-sectional cohort study involving 22 oncology centers, referral centers for ultrasonography, and general hospitals. We included consecutive patients with an adnexal tumor who underwent a standardized transvaginal ultrasound examination and were selected for surgery. Data on 5020 patients were recorded in 3 phases from 2002 through 2012. The 5 Simple Rules features indicative of a benign tumor (B-features) and the 5 features indicative of malignancy (M-features) are based on the presence of ascites, tumor morphology, and degree of vascularity at ultrasonography. Gold standard was the histopathologic diagnosis of the adnexal mass (pathologist blinded to ultrasound findings). Logistic regression analysis was used to estimate the risk of malignancy based on the 10 ultrasound features and type of center. The diagnostic performance was evaluated by area under the receiver operating characteristic curve, sensitivity, specificity, positive likelihood ratio (LR+), negative likelihood ratio (LR-), positive predictive value (PPV), negative predictive value (NPV), and calibration curves. RESULTS: Data on 4848 patients were analyzed. The malignancy rate was 43% (1402/3263) in oncology centers and 17% (263/1585) in other centers. The area under the receiver operating characteristic curve on validation data was very similar in oncology centers (0.917; 95% confidence interval, 0.901-0.931) and other centers (0.916; 95% confidence interval, 0.873-0.945). Risk estimates showed good calibration. In all, 23% of patients in the validation data set had a very low estimated risk (<1%) and 48% had a high estimated risk (≥30%). For the 1% risk cutoff, sensitivity was 99.7%, specificity 33.7%, LR+ 1.5, LR- 0.010, PPV 44.8%, and NPV 98.9%. For the 30% risk cutoff, sensitivity was 89.0%, specificity 84.7%, LR+ 5.8, LR- 0.13, PPV 75.4%, and NPV 93.9%. CONCLUSION: Quantification of the risk of malignancy based on the Simple Rules has good diagnostic performance both in oncology centers and other centers. A simple classification based on these risk estimates may form the basis of a clinical management system. Patients with a high risk may benefit from surgery by a gynecological oncologist, while patients with a lower risk may be managed locally

Evaluating the risk of ovarian cancer before surgery using the ADNEX model to differentiate between benign, borderline, early and advanced stage invasive, and secondary metastatic tumours: prospective multicentre diagnostic study

Objectives To develop a risk prediction model to preoperatively discriminate between benign, borderline, stage I invasive, stage II-IV invasive, and secondary metastatic ovarian tumours. Design Observational diagnostic study using prospectively collected clinical and ultrasound data. Setting 24 ultrasound centres in 10 countries. Participants Women with an ovarian (including para-ovarian and tubal) mass and who underwent a standardised ultrasound examination before surgery. The model was developed on 3506 patients recruited between 1999 and 2007, temporally validated on 2403 patients recruited between 2009 and 2012, and then updated on all 5909 patients. Main outcome measures Histological classification and surgical staging of the mass. Results The Assessment of Different NEoplasias in the adneXa (ADNEX) model contains three clinical and six ultrasound predictors: age, serum CA-125 level, type of centre (oncology centres v other hospitals), maximum diameter of lesion, proportion of solid tissue, more than 10 cyst locules, number of papillary projections, acoustic shadows, and ascites. The area under the receiver operating characteristic curve (AUC) for the classic discrimination between benign and malignant tumours was 0.94 (0.93 to 0.95) on temporal validation. The AUC was 0.85 for benign versus borderline, 0.92 for benign versus stage I cancer, 0.99 for benign versus stage II-IV cancer, and 0.95 for benign versus secondary metastatic. AUCs between malignant subtypes varied between 0.71 and 0.95, with an AUC of 0.75 for borderline versus stage I cancer and 0.82 for stage II-IV versus secondary metastatic. Calibration curves showed that the estimated risks were accurate. Conclusions The ADNEX model discriminates well between benign and malignant tumours and offers fair to excellent discrimination between four types of ovarian malignancy. The use of ADNEX has the potential to improve triage and management decisions and so reduce morbidity and mortality associated with adnexal pathology

Strategies to diagnose ovarian cancer: new evidence from phase 3 of the multicentre international IOTA study

Background: To compare different ultrasound-based international ovarian tumour analysis (IOTA) strategies and risk of malignancy index (RMI) for ovarian cancer diagnosis using a meta-analysis approach of centre-specific data from IOTA3. Methods: This prospective multicentre diagnostic accuracy study included 2403 patients with 1423 benign and 980 malignant adnexal masses from 2009 until 2012. All patients underwent standardised transvaginal ultrasonography. Test performance of RMI, subjective assessment (SA) of ultrasound findings, two IOTA risk models (LR1 and LR2), and strategies involving combinations of IOTA simple rules (SRs), simple descriptors (SDs) and LR2 with and without SA was estimated using a meta-analysis approach. Reference standard was histology after surgery. Results: The areas under the receiver operator characteristic curves of LR1, LR2, SA and RMI were 0.930 (0.917–0.942), 0.918 (0.905–0.930), 0.914 (0.886–0.936) and 0.875 (0.853–0.894). Diagnostic one-step and two-step strategies using LR1, LR2, SR and SD achieved summary estimates for sensitivity 90–96%, specificity 74–79% and diagnostic odds ratio (DOR) 32.8–50.5. Adding SA when IOTA methods yielded equivocal results improved performance (DOR 57.6–75.7). Risk of Malignancy Index had sensitivity 67%, specificity 91% and DOR 17.5. Conclusions: This study shows all IOTA strategies had excellent diagnostic performance in comparison with RMI. The IOTA strategy chosen may be determined by clinical preference

Применение модели искусственныx нейронных сетей в проспективной оценке злокачественности опухоли яичника

опухоли яичников, яичников новообразования, дифференциальная диагностика, искусственные нейронные сети, антиген СА-125, ультразвуковая диагностик

Unilocular adnexal cysts with papillary projections but no other solid components: is there a diagnostic method that can reliably classify them as benign or malignant before surgery?

Aim To develop a logistic regression model for discrimination between benign and malignant unilocular solid cysts with papillary projections but no other solid components, and to compare its diagnostic performance with that of subjective evaluation of ultrasound findings (subjective assessment), CA 125 and the risk of malignancy index (RMI). Methods Among the 3511 adnexal masses in the International Ovarian Tumor Analysis (IOTA) database there were 252 (7%) unilocular solid cysts with papillary projections but no other solid components ('unilocular cysts with papillations'). All had been examined with transvaginal ultrasound using the IOTA standardized research protocol. The ultrasound examiner also classified each mass as certainly or probably benign, unclassifiable, or certainly or probably malignant. A logistic regression model to discriminate between benignity and malignancy was developed for all unilocular cysts with papillations (175 tumors in training set, 77 in test set) and for unilocular cysts with papillations where the ultrasound examiner was not certain about benignity/malignancy (113 tumors in training set, 53 in test set). The gold standard was the histological diagnosis of the surgically removed adnexal mass. Results A model containing six variables was developed for all unilocular cysts with papillations. The model had an area under the receiver operating characteristic curve (AUC) on the test set of 0.83 (95% CI, 0.74-0.93). The optimal risk cutoff as defined on the training set (0.35) resulted in sensitivity 69% (20/29), specificity 83% (40/48), LR+ 4.14 and LR- 0.37 on the test set. The corresponding values for subjective assessment when using the ultrasound examiner's dichotomous classification of the mass as benign or malignant were 97% (28/29), 79% (38/48), 4.63 and 0.04. A model containing four variables was developed for unilocular cysts with papillations where the ultrasound examiner was not certain about benignity/malignancy. The model had an AUC of 0.74 (95% CI, 0.60-0.88) on the test set. The optimal risk cutoff of the model as defined on the training set (0.30) resulted in sensitivity 62% (13/21), specificity 72% (23/32), LR+ 2.20 and LR- 0.53 on the test set. The corresponding values for subjective assessment were 95% (20/21), 78% (25/32), 4.35 and 0.06. CA125 and RMI had virtually no diagnostic ability. Conclusion Even though logistic regression models to predict malignancy in unilocular cysts with papillations can be developed they have at most moderate performance and are not superior to subjective assessment for discrimination between benignity and malignancy

External Validation of Diagnostic Models to Estimate the Risk of Malignancy in Adnexal Masses

Abstract PURPOSE: To externally validate and compare the performance of previously published diagnostic models developed to predict malignancy in adnexal masses. EXPERIMENTAL DESIGN: We externally validated the diagnostic performance of 11 models developed by the International Ovarian Tumor Analysis (IOTA) group and 12 other (non-IOTA) models on 997 prospectively collected patients. The non-IOTA models included the original risk of malignancy index (RMI), three modified versions of the RMI, six logistic regression models, and two artificial neural networks. The ability of the models to discriminate between benign and malignant adnexal masses was expressed as the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and likelihood ratios (LR(+), LR(-)). RESULTS: Seven hundred and forty-two (74%) benign and 255 (26%) malignant masses were included. The IOTA models did better than the non-IOTA models (AUCs between 0.941 and 0.956 vs. 0.839 and 0.928). The difference in AUC between the best IOTA and the best non-IOTA model was 0.028 [95% confidence interval (CI), 0.011-0.044]. The AUC of the RMI was 0.911 (difference with the best IOTA model, 0.044; 95% CI, 0.024-0.064). The superior performance of the IOTA models was most pronounced in premenopausal patients but was also observed in postmenopausal patients. IOTA models were better able to detect stage I ovarian cancer. CONCLUSION: External validation shows that the IOTA models outperform other models, including the current reference test RMI, for discriminating between benign and malignant adnexal masses