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. 2023 Mar 1;152(5):986-997.
doi: 10.1002/ijc.34352. Epub 2022 Dec 3.

PREDICT-GTN 1: Can we improve the FIGO scoring system in gestational trophoblastic neoplasia?

Victoria L Parker  1 , Matthew C Winter  1   2 , John A Tidy  2 , Barry W Hancock  1 , Julia E Palmer  2 , Naveed Sarwar  3 , Baljeet Kaur  3 , Katie McDonald  2 , Xianne Aguiar  3 , Kamaljit Singh  2 , Nick Unsworth  3 , Imran Jabbar  2 , Allan A Pacey  1 , Robert F Harrison  4 , Michael J Seckl  3
Affiliations

PREDICT-GTN 1: Can we improve the FIGO scoring system in gestational trophoblastic neoplasia?

Victoria L Parker et al. Int J Cancer. .

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.

Keywords: FIGO; gestational trophoblastic neoplasia; scoring system.

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Conflict of interest statement

The authors declare no potential conflict of interests.

Figures

FIGURE 1
FIGURE 1
CONSORT diagram. ETT, epithelioid trophoblastic tumour; hCG, human chorionic gonadotrophin; FIGO, International Federation of Gynaecology and Obstetrics; GTN, gestational trophoblastic neoplasia; n, number; PSN, placental site nodule; PSTT, placental site trophoblastic tumour
FIGURE 2
FIGURE 2
Demographics of the STDC. (A, B, C) and CCTDC (D, E, F) datasets detailing the response to primary chemotherapy. (A) and (D) Scored data for the eight risk‐factors constituting the FIGO. (B) and (E) Raw data for the three FIGO risk‐factors that permit this investigation. (C) and (F) Breakdown of the FIGO stage and total FIGO score. The raw data for interval (B) and (E) is restricted to 24 months for data presentation purposes, with 125 patients (STDC n = 8, CCTDC n = 117) having intervals >24 months (median 42 months, interquartile range 43 [31‐74 months]). CCTDC, Charing Cross Trophoblastic Disease Centre; CR, complete response to primary chemotherapy; hCG, human chorionic gonadotrophin; FIGO, International Federation of Gynaecology and Obstetrics scoring system; STDC, Sheffield Trophoblastic Disease Centre; TR, resistance to primary chemotherapy
See this image and copyright information in PMC

References

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