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PREDICT-GTN 2: Two-factor streamlined models match FIGO performance in gestational trophoblastic neoplasia.
Parker, Victoria L; Winter, Matthew C; Tidy, John A; Palmer, Julia E; Sarwar, Naveed; Singh, Kamaljit; Aguiar, Xianne; Hancock, Barry W; Pacey, Allan A; Seckl, Michael J; Harrison, Robert F.
Afiliação
  • Parker VL; 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.
  • Winter MC; 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
  • Tidy JA; Sheffield Centre for Trophoblastic Disease, Weston Park Cancer Centre, Sheffield Teaching Hospitals NHS Foundation Trust, Whitham Road, Sheffield S10 2SJ, UK.
  • Palmer JE; Sheffield Centre for Trophoblastic Disease, Weston Park Cancer Centre, Sheffield Teaching Hospitals NHS Foundation Trust, Whitham Road, Sheffield S10 2SJ, UK.
  • Sarwar N; Gestational Trophoblastic Disease Centre, Department of Medical Oncology, Charing Cross Hospital, Fulham Palace Road, London W6 8RF, UK.
  • Singh K; Sheffield Centre for Trophoblastic Disease, Weston Park Cancer Centre, Sheffield Teaching Hospitals NHS Foundation Trust, Whitham Road, Sheffield S10 2SJ, UK.
  • Aguiar X; Gestational Trophoblastic Disease Centre, Department of Medical Oncology, Charing Cross Hospital, Fulham Palace Road, London W6 8RF, UK.
  • Hancock BW; 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.
  • Pacey AA; Faculty of Biology, Medicine and Health, Core Technology Facility, 46 Grafton Street, University of Manchester, Manchester, M13 9NT, UK.
  • Seckl MJ; Gestational Trophoblastic Disease Centre, Department of Medical Oncology, Charing Cross Hospital, Fulham Palace Road, London W6 8RF, UK.
  • Harrison RF; Department of Automatic Control and Systems Engineering, The University of Sheffield, Mappin Street, Sheffield S1 3JD, UK.
Gynecol Oncol ; 180: 152-159, 2024 Jan.
Article em En | MEDLINE | ID: mdl-38091775
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.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Limite: Female / Humans / Pregnancy Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Limite: Female / Humans / Pregnancy Idioma: En Ano de publicação: 2024 Tipo de documento: Article