A Data-Driven Approach to Refine Predictions of Differentiated Thyroid Cancer Outcomes: A Prospective Multicenter Study
| العنوان: | A Data-Driven Approach to Refine Predictions of Differentiated Thyroid Cancer Outcomes: A Prospective Multicenter Study |
|---|---|
| المؤلفون: | Giorgio Grani, Michele Gentili, Federico Siciliano, Domenico Albano, Valentina Zilioli, Silvia Morelli, Efisio Puxeddu, Maria Chiara Zatelli, Irene Gagliardi, Alessandro Piovesan, Alice Nervo, Umberto Crocetti, Michela Massa, Maria Teresa Samà, Chiara Mele, Maurilio Deandrea, Laura Fugazzola, Barbara Puligheddu, Alessandro Antonelli, Ruth Rossetto, Annamaria D’Amore, Graziano Ceresini, Roberto Castello, Erica Solaroli, Marco Centanni, Salvatore Monti, Flavia Magri, Rocco Bruno, Clotilde Sparano, Luciano Pezzullo, Anna Crescenzi, Caterina Mian, Dario Tumino, Andrea Repaci, Maria Grazia Castagna, Vincenzo Triggiani, Tommaso Porcelli, Domenico Meringolo, Laura Locati, Giovanna Spiazzi, Giulia Di Dalmazi, Aris Anagnostopoulos, Stefano Leonardi, Sebastiano Filetti, Cosimo Durante |
| المساهمون: | Grani, Giorgio, Gentili, Michele, Siciliano, Federico, Albano, Domenico, Zilioli, Valentina, Morelli, Silvia, Puxeddu, Efisio, Chiara Zatelli, Maria, 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, Ari, Leonardi, Stefano, Filetti, Sebastiano, Durante, Cosimo |
| المصدر: | The Journal of Clinical Endocrinology & Metabolism. |
| بيانات النشر: | The Endocrine Society, 2023. |
| سنة النشر: | 2023 |
| مصطلحات موضوعية: | Endocrinology, Endocrinology, Diabetes and Metabolism, Biochemistry (medical), Clinical Biochemistry, differentiated thyroid cancer, risk stratification, evidence-based guidelines, Biochemistry, clinical practice |
| الوصف: | ContextThe 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.ObjectiveTo develop a comprehensive data-driven model to predict persistent/recurrent disease that can capture all available features and determine the weight of predictors.MethodsIn 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.ResultsBy 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.ConclusionCurrent 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. |
| تدمد: | 1945-7197 0021-972X |
| الوصول الحر: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::74498f7aca4cd8f96543c512aad2a978Test https://doi.org/10.1210/clinem/dgad075Test |
| حقوق: | CLOSED |
| رقم الانضمام: | edsair.doi.dedup.....74498f7aca4cd8f96543c512aad2a978 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 19457197 0021972X |
|---|