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

Victoria L Parker; Matthew C Winter; John A Tidy; Julia E Palmer; Naveed Sarwar; Kamaljit Singh; Xianne Aguiar; Barry W Hancock; Allan A Pacey; Michael J Seckl; Robert F Harrison

doi:10.1016/j.ygyno.2023.11.017

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

Gynecol Oncol. 2024 Jan:180:152-159. doi: 10.1016/j.ygyno.2023.11.017. Epub 2023 Dec 12.

Authors

Affiliations

¹ Division of Clinical Medicine, School of Medicine and Population Health, The University of Sheffield, Level 4 The Jessop Wing, Tree Root Walk, Sheffield S10 2SF, UK. Electronic address: v.parker@sheffield.ac.uk.
² Division of Clinical Medicine, School of Medicine and Population Health, The University of Sheffield, Level 4 The Jessop Wing, Tree Root Walk, Sheffield S10 2SF, UK; Sheffield Centre for Trophoblastic Disease, Weston Park Cancer Centre, Sheffield Teaching Hospitals NHS Foundation Trust, Whitham Road, Sheffield S10 2SJ, UK.
³ Sheffield Centre for Trophoblastic Disease, Weston Park Cancer Centre, Sheffield Teaching Hospitals NHS Foundation Trust, Whitham Road, Sheffield S10 2SJ, UK.
⁴ Gestational Trophoblastic Disease Centre, Department of Medical Oncology, Charing Cross Hospital, Fulham Palace Road, London W6 8RF, UK.
⁵ Division of Clinical Medicine, School of Medicine and Population Health, The University of Sheffield, Level 4 The Jessop Wing, Tree Root Walk, Sheffield S10 2SF, UK.
⁶ Faculty of Biology, Medicine and Health, Core Technology Facility, 46 Grafton Street, University of Manchester, Manchester, M13 9NT, UK.
⁷ Department of Automatic Control and Systems Engineering, The University of Sheffield, Mappin Street, Sheffield S1 3JD, UK.

PMID: 38091775
DOI: 10.1016/j.ygyno.2023.11.017

Abstract

Objective: The 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.

Methods: Using 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.

Results: Three 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.

Conclusions: Our 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.

Keywords: FIGO; Gestational trophoblastic disease; Gestational trophoblastic neoplasia; Refine; Streamline; Two-factor model.

MeSH terms

Female
Gestational Trophoblastic Disease* / drug therapy
Humans
Nomograms
Pregnancy
Retrospective Studies
Risk Factors