PREDICT‐GTN 1: Can we improve the FIGO scoring system in gestational trophoblastic neoplasia?. Issue 5 (3rd December 2022)

• Record Type: Journal Article
• Authors: Parker, Victoria L.; Winter, Matthew C.; Tidy, John A.; Hancock, Barry W.; Palmer, Julia E.; Sarwar, Naveed; Kaur, Baljeet; McDonald, Katie; Aguiar, Xianne; Singh, Kamaljit; Unsworth, Nick; Jabbar, Imran; Pacey, Allan A.; Harrison, Robert F.; Seckl, Michael J.
• Is Part Of: International journal of cancer. Volume 152:Issue 5(2023)
• Page Start: 986
• Page End: 997
• Publication Date: 2022-12-03
• Subjects: FIGO -- gestational trophoblastic neoplasia -- scoring system; Cancer -- Periodicals; Cancer -- Prevention -- Periodicals; 616.994
• DOI: 10.1002/ijc.34352
• Languages: English
• ISSNs: 0020-7136

Abstract

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. Abstract : What's new? The International Federation of Gynaecology and Obstetrics (FIGO) scoring system is used to predict resistance to first‐line single‐agent chemotherapy in gestational trophoblastic neoplasia (GTN) patients. HoweThis system is imperfect, however—one‐third of low‐risk patients develop resistance, and resistance rates increase with FIGO score. Here, six models containing raw and scored data combinations of FIGO variables were analysed in an attempt to improve FIGO scoring for GTN. Analyses show, however, that FIGO cannot be improved by modelling. Any small gains in performance were due to recategorization of treatment‐resistant patients. Future research should focus on optimising existing FIGO scoring strategies for GTN.