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

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Title: A Data-Driven Approach to Refine Predictions of Differentiated Thyroid Cancer Outcomes: A Prospective Multicenter Study.
Authors: 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, Samà, Maria Teresa, Mele, Chiara, Deandrea, Maurilio, Fugazzola, Laura, Puligheddu, Barbara, Antonelli, Alessandro, Rossetto, Ruth
Source: Journal of Clinical Endocrinology & Metabolism; Aug2023, Vol. 108 Issue 8, p1921-1928, 8p
Subject Terms: THYROID cancer, CLINICAL trials
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. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Clinical Endocrinology & Metabolism is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: A Data-Driven Approach to Refine Predictions of Differentiated Thyroid Cancer Outcomes: A Prospective Multicenter Study.
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  Data: <searchLink fieldCode="AR" term="%22Grani%2C+Giorgio%22">Grani, Giorgio</searchLink><br /><searchLink fieldCode="AR" term="%22Gentili%2C+Michele%22">Gentili, Michele</searchLink><br /><searchLink fieldCode="AR" term="%22Siciliano%2C+Federico%22">Siciliano, Federico</searchLink><br /><searchLink fieldCode="AR" term="%22Albano%2C+Domenico%22">Albano, Domenico</searchLink><br /><searchLink fieldCode="AR" term="%22Zilioli%2C+Valentina%22">Zilioli, Valentina</searchLink><br /><searchLink fieldCode="AR" term="%22Morelli%2C+Silvia%22">Morelli, Silvia</searchLink><br /><searchLink fieldCode="AR" term="%22Puxeddu%2C+Efisio%22">Puxeddu, Efisio</searchLink><br /><searchLink fieldCode="AR" term="%22Zatelli%2C+Maria+Chiara%22">Zatelli, Maria Chiara</searchLink><br /><searchLink fieldCode="AR" term="%22Gagliardi%2C+Irene%22">Gagliardi, Irene</searchLink><br /><searchLink fieldCode="AR" term="%22Piovesan%2C+Alessandro%22">Piovesan, Alessandro</searchLink><br /><searchLink fieldCode="AR" term="%22Nervo%2C+Alice%22">Nervo, Alice</searchLink><br /><searchLink fieldCode="AR" term="%22Crocetti%2C+Umberto%22">Crocetti, Umberto</searchLink><br /><searchLink fieldCode="AR" term="%22Massa%2C+Michela%22">Massa, Michela</searchLink><br /><searchLink fieldCode="AR" term="%22Samà%2C+Maria+Teresa%22">Samà, Maria Teresa</searchLink><br /><searchLink fieldCode="AR" term="%22Mele%2C+Chiara%22">Mele, Chiara</searchLink><br /><searchLink fieldCode="AR" term="%22Deandrea%2C+Maurilio%22">Deandrea, Maurilio</searchLink><br /><searchLink fieldCode="AR" term="%22Fugazzola%2C+Laura%22">Fugazzola, Laura</searchLink><br /><searchLink fieldCode="AR" term="%22Puligheddu%2C+Barbara%22">Puligheddu, Barbara</searchLink><br /><searchLink fieldCode="AR" term="%22Antonelli%2C+Alessandro%22">Antonelli, Alessandro</searchLink><br /><searchLink fieldCode="AR" term="%22Rossetto%2C+Ruth%22">Rossetto, Ruth</searchLink>
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  Data: Journal of Clinical Endocrinology & Metabolism; Aug2023, Vol. 108 Issue 8, p1921-1928, 8p
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  Data: <searchLink fieldCode="DE" term="%22THYROID+cancer%22">THYROID cancer</searchLink><br /><searchLink fieldCode="DE" term="%22CLINICAL+trials%22">CLINICAL trials</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: 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. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of Journal of Clinical Endocrinology & Metabolism is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1210/clinem/dgad075
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        Text: English
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        StartPage: 1921
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        Type: general
      – SubjectFull: CLINICAL trials
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              Text: Aug2023
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