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Progressie bij kanker wordt vaak afgemeten aan het percentage van de patiënten dat vijf jaar na de diagnose nog leeft. Dit percentage steeg de afgelopen twintig jaar in ons land van 51 procent naar 67 procent. Dat ging niet vanzelf. Innovaties als immunotherapie en CAR-T-behandelingen geven patiënten de kans om langer en met een betere […]
Het vleesrijke, vezelarme westerse dieet wordt sterk in verband gebracht met het risico op darmkanker. Mycoproteïne wordt al tientallen jaren verkoch...
Colorectale kanker is wereldwijd de op een na meest voorkomende vorm van kanker, met jaarlijks 1,7 miljoen diagnoses.1 De standaardbehandeling is chir...
An international team of researchers has identified 50 new areas across the genome that are associated with the risk of developing kidney cancer. The analysis identified genetic variants associated with the risk of developing papillary renal cell carcinoma and clear cell renal cell carcinoma.
Background: To construct a predictive model to direct the dissection of the central lymph nodes in papillary thyroid cancer (PTC) with BRAF V600E mutation by identifying the risk variables for central lymph node metastases (CLNM).Methods: Data from 466 PTC patients with BRAF V600E mutations underwent thyroid surgery was collected and analyzed retrospectively. For these patients, we conducted univariate and multivariate logistic regression analysis to find risk variables for CLNM. To construct a nomogram, the independent predictors were chosen. The calibration, discrimination, and clinical utility of the predictive model were assessed by training and validation data.Results: CLNM was present in 323/466 PTC patients with BRAF V600E mutations. By using univariate and multivariate logistic regression, we discovered that gender, age, tumor size, multifocality, and pathological subtype were all independent predictors of CLNM in PTC patients with BRAF V600E mutations. A predictive nomogram was created by combining these variables. In both training and validation groups, the nomogram demonstrated great calibration capacities. The training and validation groups’ areas under the curve (AUC) were 0.772 (specificity 0.694, sensitivity 0.728, 95% CI: 0.7195-0.8247) and 0.731 (specificity 0.778, sensitivity 0.653, 95% CI: 0.6386-0.8232) respectively. According to the nomogram’s decision curve analysis (DCA), the nomogram might be beneficial. As well, an online dynamic calculator was developed to make the application of this nomogram easier in the clinic.Conclusion: An online nomogram model based on the 5 predictors included gender, age, pathological subtype, multifocality, and tumor size was confirmed to predict CLNM and guide the central lymph nodes dissection in PTC patients with BRAF V600E mutations.