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Summary Background Because most pancreatic intraductal papillary mucinous neoplasms (IPMNs) will never become malignant, currently advocated long‐term surveillance is low‐yield for most individuals. Aim To develop a score chart identifying IPMNs at lowest risk of developing worrisome features or high‐risk stigmata. Methods We combined prospectively maintained pancreatic cyst surveillance databases of three academic institutions. Patients were included if they had a presumed side‐branch IPMN, without worrisome features or high‐risk stigmata at baseline (as defined by the 2012 international Fukuoka guidelines), and were followed ≥ 12 months. The endpoint was development of one or more worrisome features or high‐risk stigmata during follow‐up. We created a multivariable prediction model using Cox‐proportional logistic regression analysis and performed an internal‐external validation. Results 875 patients were included. After a mean follow‐up of 50 months (range 12‐157), 116 (13%) patients developed worrisome features or high‐risk stigmata. The final model included cyst size (HR 1.12, 95% CI 1.09‐1.15), cyst multifocality (HR 1.49, 95% CI 1.01‐2.18), ever having smoked (HR 1.40, 95% CI 0.95‐2.04), history of acute pancreatitis (HR 2.07, 95% CI 1.21‐3.55), and history of extrapancreatic malignancy (HR 1.34, 95% CI 0.91‐1.97). After validation, the model had good discriminative ability (C‐statistic 0.72 in the Mayo cohort, 0.71 in the Columbia cohort, 0.64 in the Erasmus cohort). Conclusion In presumed side branch IPMNs without worrisome features or high‐risk stigmata at baseline, the Dutch‐American Risk stratification Tool (DART‐1) successfully identifies pancreatic lesions at low risk of developing worrisome features or high‐risk stigmata.

Objective: To provide an overview of prediction models for the risk of developing endometrial cancer in women of the general population or for the presence of endometrial cancer in symptomatic women. Methods: We systematically searched the Embase and Pubmed database until September 2017 for relevant publications. We included studies describing the development, the external validation, or the updating of a multivariable model for predicting endometrial cancer in the general population or symptomatic women. Results: Out of 2756 references screened, 14 studies were included. We found two prediction models for developing endometrial cancer in the general population (risk models) and one extension. Eight studies described the development of models for symptomatic women (diagnostic models), one comparison of the performance of two diagnostic models and two external validation. Sample size varied from 60 (10 with cancer) to 201,811 (855 with cancer) women. The age of the women was included as a predictor in almost all models. The risk models included epidemiological variables related to the reproductive history of women, hormone use, BMI, and smoking history. The diagnostic models also included clinical predictors, such as endometrial thickness and recurrent bleeding. The concordance statistic (c), assessing the discriminative ability, varied from 0.68 to 0.77 in the risk models and from 0.73 to 0.957 in the diagnostic models. Methodological information was often limited, especially on the handling of missing data, and the selection of predictors. One risk model and four diagnostic models were externally validated. Conclusions: Only a few models have been developed to predict endometrial cancer in asymptomatic or symptomatic women. The usefulness of most models is unclear considering methodological shortcomings and lack of external validation. Future research should focus on external validation and extension with new predictors or biomarkers, such as genetic and epigenetic markers.

Summary Background Patients with actinic keratosis (AK) are at increased risk for developing keratinocyte carcinoma (KC) but predictive factors and their risk rates are unknown. Objectives To develop and internally validate a prediction model to calculate the absolute risk of a first KC in patients with AK. Methods The risk‐prediction model was based on the prospective population‐based Rotterdam Study cohort. We hereto analysed the data of participants with at least one AK lesion at cohort baseline using a multivariable Cox proportional hazards model and included 13 a priori defined candidate predictor variables considering phenotypic, genetic and lifestyle risk factors. KCs were identified by linkage of the data with the Dutch Pathology Registry. Results Of the 1169 AK participants at baseline, 176 (15·1%) developed a KC after a median follow‐up of 1·8 years. The final model with significant predictors was obtained after backward stepwise selection and comprised the presence of four to nine AKs [hazard ratio (HR) 1·68, 95% confidence interval (CI) 1·17–2·42], 10 or more AKs (HR 2·44, 95% CI 1·65–3·61), AK localization on the upper extremities (HR 0·75, 95% CI 0·52–1·08) or elsewhere except the head (HR 1·40, 95% CI 0·98–2·01) and coffee consumption (HR 0·92, 95% CI 0·84–1·01). Evaluation of the discriminative ability of the model showed a bootstrap validated concordance index (c‐index) of 0·60. Conclusions We showed that the risk of KC in patients with AK can be calculated with the use of four easily assessable predictor variables. Given the c‐index, extension of the model with additional, currently unknown predictor variables is desirable. Linked Comment: Kim et al. Br J Dermatol 2020; 183:415–416.