PREDICT-GTN 2: Two-factor streamlined models match FIGO performance in gestational trophoblastic neoplasia

Abstract

ObjectiveThe International Federation of Gynecology and Obstetrics (FIGO) scoring system uses the sum of eight risk-factors to predict single-agent chemotherapy resistance in Gestational Trophoblastic Neoplasia (GTN). To improve ease of use, this study aimed to generate: (i) streamlined models that match FIGO performance and; (ii) visual-decision aids (nomograms) for guiding management. MethodsUsing training (n = 4191) and validation datasets (n = 144) of GTN patients from two UK specialist centres, logistic regression analysis generated two-factor models for cross-validation and exploration. Performance was assessed using true and false positive rate, positive and negative predictive values, Bland-Altman calibration plots, receiver operating characteristic (ROC) curves, decision-curve analysis (DCA) and contingency tables. Nomograms were developed from estimated model parameters and performance cross-checked upon the training and validation dataset. ResultsThree streamlined, two-factor models were selected for analysis: (i) M1, pre-treatment hCG + history of failed chemotherapy; (ii) M2, pre-treatment hCG + site of metastases and; (iii) M3, pre-treatment hCG + number of metastases. Using both training and validation datasets, these models showed no evidence of significant discordance from FIGO (McNemar's test p > 0.78) or across a range of performance parameters. This behaviour was maintained when applying algorithms simulating the logic of the nomograms. ConclusionsOur streamlined models could be used to assess GTN patients and replace FIGO, statistically matching performance. Given the importance of imaging parameters in guiding treatment, M2 and M3 are favoured for ongoing validation. In resource-poor countries, where access to specialist centres is problematic, M1 could be pragmatically implemented. Further prospective validation on a larger cohort is recommended.