A Data-Driven Approach to Refine Predictions of Differentiated Thyroid Cancer Outcomes: A Prospective Multicenter Study-Reference-Cited by-同舟云学术

A Data-Driven Approach to Refine Predictions of Differentiated Thyroid Cancer Outcomes: A Prospective Multicenter Study

Published:2023-02-16 Issue:8 Volume:108 Page:1921-1928
ISSN:0021-972X
Container-title:The Journal of Clinical Endocrinology & Metabolism
language:en
Short-container-title:

Author:

Grani Giorgio¹^ORCID,Gentili Michele²,Siciliano Federico²,Albano Domenico³,Zilioli Valentina³,Morelli Silvia⁴,Puxeddu Efisio⁴,Zatelli Maria Chiara⁵,Gagliardi Irene⁵,Piovesan Alessandro⁶,Nervo Alice⁶,Crocetti Umberto⁷,Massa Michela⁷,Samà Maria Teresa⁸,Mele Chiara⁸,Deandrea Maurilio⁹,Fugazzola Laura¹⁰¹¹,Puligheddu Barbara¹²,Antonelli Alessandro¹³,Rossetto Ruth¹⁴,D’Amore Annamaria¹⁵,Ceresini Graziano¹⁶,Castello Roberto¹⁷,Solaroli Erica¹⁸,Centanni Marco¹⁹,Monti Salvatore²⁰,Magri Flavia²¹,Bruno Rocco²²,Sparano Clotilde²³,Pezzullo Luciano²⁴,Crescenzi Anna²⁵,Mian Caterina²⁶,Tumino Dario²⁷,Repaci Andrea²⁸,Castagna Maria Grazia²⁹,Triggiani Vincenzo³⁰,Porcelli Tommaso³¹,Meringolo Domenico³²,Locati Laura³³³⁴,Spiazzi Giovanna³⁵,Di Dalmazi Giulia³⁶,Anagnostopoulos Aris²,Leonardi Stefano²,Filetti Sebastiano¹,Durante Cosimo¹^ORCID

Affiliation:

1. Department of Translational and Precision Medicine, Sapienza University of Rome , 00161 Rome , Italy

2. Department of Computer, Control, and Management Engineering “Antonio Ruberti”, Sapienza University of Rome , 00185 Rome , Italy

3. Department of Nuclear Medicine, Università e ASST-Spedali Civili- Brescia , 25123 Brescia , Italy

4. Department of Medicine and Surgery, University of Perugia , 06123 Perugia , Italy

5. Section of Endocrinology, Geriatrics and Internal Medicine, Department of Medical Sciences, University of Ferrara , 44121 Ferrara , Italy

6. Oncological Endocrinology Unit, Città della Salute e della Scienza Hospital , 10126 Turin , Italy

7. Department of Medical Sciences, Fondazione IRCCS Casa Sollievo della Sofferenza , 71013 San Giovanni Rotondo , Italy

8. Division of Endocrinology, Department of Translational Medicine, University of Piemonte Orientale, Maggiore della Carità University Hospital , 28100 Novara , Italy

9. UO Endocrinologia, Diabetologia e Malattie del metabolismo, AO Ordine Mauriziano Torino , 10128 Torino , Italy

10. Department of Endocrinology and Metabolic Diseases, IRCCS Istituto Auxologico Italiano , 20145 Milan , Italy

11. Department of Pathophysiology and Transplantation, University of Milan , 20122 Milan , Italy

12. Department of Endocrinology and Andrology, Humanitas Gradenigo, University of Turin , 10153 Turin , Italy

13. Department of Surgical, Medical and Molecular Pathology and Critical Area, University of Pisa , 56126 Pisa , Italy

14. Department of Endocrinology and Metabolic Diseases, AO Città della Salute e della Scienza Turin, University of Turin , 10126 Turin , Italy

15. Division of Endocrine Surgery, Department of Gastroenterologic, Endocrine-Metabolic and Nephro-Urologic sciences, Fondazione Policlinico Universitario A. Gemelli IRCCS , 00168 Rome , Italy

16. Department of Medicine and Surgery, University Hospital of Parma , 43121 Parma , Italy

17. Department of Medicine, Hospital and University of Verona , 37129 Verona , Italy

18. Unit of Endocrinology, Department of Medicine, AUSL , 40124 Bologna , Italy

19. Department of Medico-surgical Sciences and Biotechnologies, Sapienza University of Rome, and UOC Endocrinologia, AUSL Latina , 04100 Latina , Italy

20. Endocrinology and Diabetes Unit, Azienda Ospedaliero-Universitaria Sant’Andrea, “Sapienza” University of Rome , 00189 Rome , Italy

21. Department of Internal Medicine and Therapeutics and Istituti Clinici Scientifici Maugeri IRCCS, Unit of Internal Medicine and Endocrinology, University of Pavia , 27100 Pavia , Italy

22. Thyroid Unit, Tinchi Hospital-ASM Matera , 75100 Matera , Italy

23. Endocrinology Unit, Department of Experimental and Clinical Biomedical Sciences “Mario Serio”, University of Florence , 50139 Florence , Italy

24. Struttura Complessa Chirurgia Oncologica della Tiroide, Istituto Nazionale Tumori-IRCCS-Fondazione G. Pascale , 80131 Naples , Italy

25. Unit of Endocrine Organs and Neuromuscular Pathology, Fondazione Policlinico Universitario Campus Bio-Medico , 00128 Rome , Italy

26. Unit of Endocrinology, Department of Medicine-DIMED University of Padua , 35122 Padua , Italy

27. Department of Clinical and Experimental Medicine, University of Catania , 95124 Catania , Italy

28. Division of Endocrinology and Diabetes Prevention and Care, IRCCS Azienda Ospedaliero-Universitaria di Bologna , 40138 Bologna , Italy

29. Department of Medical, Surgical and Neurological Sciences, University of Siena , 53100 Siena , Italy

30. Interdisciplinary Department of Medicine, Section of Internal Medicine, Geriatrics, Endocrinology and Rare Diseases, University of Bari “Aldo Moro” School of Medicine , 70121 Bari , Italy

31. Department of Public Health, University of Naples “Federico II” , 80138 Naples , Italy

32. Thyroid Unit, Istituto Oncologico Ramazzini , 40138 Bologna , Italy

33. Translational Oncology Unit, IRCCS ICS Maugeri , 27100 Pavia , Italy

34. Department of Internal Medicine and Therapeutics, University of Pavia , 27100 Pavia , Italy

35. Endocrinology and Diabetology Unit, Department of Medicine, Azienda Ospedaliera-Universitaria di Verona , 37129 Verona , Italy

36. Department of Medicine and Aging Sciences, University “G. d'Annunzio” of Chieti-Pescara , 66100 Chieti , Italy

Abstract

Abstract Context The risk stratification of patients with differentiated thyroid cancer (DTC) is crucial in clinical decision making. The most widely accepted method to assess risk of recurrent/persistent disease is described in the 2015 American Thyroid Association (ATA) guidelines. However, recent research has focused on the inclusion of novel features or questioned the relevance of currently included features. Objective To develop a comprehensive data-driven model to predict persistent/recurrent disease that can capture all available features and determine the weight of predictors. Methods In a prospective cohort study, using the Italian Thyroid Cancer Observatory (ITCO) database (NCT04031339), we selected consecutive cases with DTC and at least early follow-up data (n = 4773; median follow-up 26 months; interquartile range, 12-46 months) at 40 Italian clinical centers. A decision tree was built to assign a risk index to each patient. The model allowed us to investigate the impact of different variables in risk prediction. Results By ATA risk estimation, 2492 patients (52.2%) were classified as low, 1873 (39.2%) as intermediate, and 408 as high risk. The decision tree model outperformed the ATA risk stratification system: the sensitivity of high-risk classification for structural disease increased from 37% to 49%, and the negative predictive value for low-risk patients increased by 3%. Feature importance was estimated. Several variables not included in the ATA system significantly impacted the prediction of disease persistence/recurrence: age, body mass index, tumor size, sex, family history of thyroid cancer, surgical approach, presurgical cytology, and circumstances of the diagnosis. Conclusion Current risk stratification systems may be complemented by the inclusion of other variables in order to improve the prediction of treatment response. A complete dataset allows for more precise patient clustering.

Funder

Sapienza University of Rome to G.G. and C.D

Publisher

The Endocrine Society

Subject

Biochemistry (medical),Clinical Biochemistry,Endocrinology,Biochemistry,Endocrinology, Diabetes and Metabolism

Link

https://academic.oup.com/jcem/advance-article-pdf/doi/10.1210/clinem/dgad075/49446704/dgad075.pdf

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