Parker, V.L. orcid.org/0000-0002-8748-4583, Winter, M.C. orcid.org/0000-0001-6192-9874, Tidy, J.A. et al. (12 more authors) (2023) PREDICT-GTN 1: Can we improve the FIGO scoring system in gestational trophoblastic neoplasia? International Journal of Cancer, 152 (5). pp. 986-997. ISSN 0020-7136
Abstract
Gestational trophoblastic neoplasia (GTN) patients are treated according to the eight-variable International Federation of Gynaecology and Obstetrics (FIGO) scoring system, that aims to predict first-line single-agent chemotherapy resistance. FIGO is imperfect with one-third of low-risk patients developing disease resistance to first-line single-agent chemotherapy. We aimed to generate simplified models that improve upon FIGO. Logistic regression (LR) and multilayer perceptron (MLP) modelling (n = 4191) generated six models (M1-6). M1, all eight FIGO variables (scored data); M2, all eight FIGO variables (scored and raw data); M3, nonimaging variables (scored data); M4, nonimaging variables (scored and raw data); M5, imaging variables (scored data); and M6, pretreatment hCG (raw data) + imaging variables (scored data). Performance was compared to FIGO using true and false positive rates, positive and negative predictive values, diagnostic odds ratio, receiver operating characteristic (ROC) curves, Bland-Altman calibration plots, decision curve analysis and contingency tables. M1-6 were calibrated and outperformed FIGO on true positive rate and positive predictive value. Using LR and MLP, M1, M2 and M4 generated small improvements to the ROC curve and decision curve analysis. M3, M5 and M6 matched FIGO or performed less well. Compared to FIGO, most (excluding LR M4 and MLP M5) had significant discordance in patient classification (McNemar's test P < .05); 55-112 undertreated, 46-206 overtreated. Statistical modelling yielded only small gains over FIGO performance, arising through recategorisation of treatment-resistant patients, with a significant proportion of under/overtreatment as the available data have been used a priori to allocate primary chemotherapy. Streamlining FIGO should now be the focus.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2022 The Authors. International Journal of Cancer published by John Wiley & Sons Ltd on behalf of UICC. This is an open access article under the terms of the Creative Commons Attribution License (CC BY), which permits use, distribution and reproduction in any medium, provided the original work is properly cited (https://creativecommons.org/licenses/by/4.0/). |
Keywords: | FIGO; gestational trophoblastic neoplasia; scoring system |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Medicine, Dentistry and Health (Sheffield) > Department of Human Metabolism (Sheffield) The University of Sheffield > Faculty of Medicine, Dentistry and Health (Sheffield) > Department of Oncology (Sheffield) The University of Sheffield > Sheffield Teaching Hospitals The University of Sheffield > Faculty of Medicine, Dentistry and Health (Sheffield) > The Medical School (Sheffield) > Academic Unit of Medical Education (Sheffield) |
Funding Information: | Funder Grant number WESTON PARK HOSPITAL CANCER CHARITY CA154 WESTON PARK HOSPITAL CANCER CHARITY CA154 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 05 Dec 2022 17:13 |
Last Modified: | 26 Sep 2024 09:12 |
Status: | Published |
Publisher: | Wiley |
Refereed: | Yes |
Identification Number: | 10.1002/ijc.34352 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:194068 |