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A Data-Driven Approach to Refine Predictions of Differentiated Thyroid Cancer Outcomes: A Prospective Multicenter Study (01a Articolo in rivista)

Grani Giorgio, Gentili Michele, Siciliano Federico, Albano Domenico, Zilioli Valentina, Morelli Silvia, Puxeddu Efisio, Zatelli MARIA CHIARA, Gagliardi Irene, Piovesan Alessandro, Nervo Alice, Crocetti Umberto, Massa Michela, Teresa Samà Maria, Mele Chiara, Deandrea Maurilio, Fugazzola Laura, Puligheddu Barbara, Antonelli Alessandro, Rossetto Ruth, D’Amore Annamaria, Ceresini Graziano, Castello Roberto, Solaroli Erica, Centanni Marco, Monti Salvatore, Magri Flavia, Bruno Rocco, Sparano Clotilde, Pezzullo Luciano, Crescenzi Anna, Mian Caterina, Tumino Dario, Repaci Andrea, Grazia Castagna Maria, Triggiani Vincenzo, Porcelli Tommaso, Meringolo Domenico, Locati Laura, Spiazzi Giovanna, Di Dalmazi Giulia, Anagnostopoulos Aristidis, Leonardi Stefano, Filetti Sebastiano, Durante Cosimo

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.
Gruppo di ricerca: Algorithms and Data Science
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