Mirko Parasiliti-Caprino1, Fabio Bioletto1, Chiara Lopez1, Francesca Maletta2, Marina Caputo3, Valentina Gasco1, Antonio La Grotta4, Paolo Limone5, Giorgio Borretta6, Marco Volante7, Mauro Papotti2, Massimo Terzolo8, Mario Morino9, Barbara Pasini10, Franco Veglio11, Ezio Ghigo1, Emanuela Arvat12, Mauro Maccario1. 1. Endocrinology, Diabetes and Metabolism, Department of Medical Sciences. 2. Pathology Unit, Department of Oncology, University of Turin, Turin, Italy. 3. Endocrinology and Diabetes, University of Eastern Piedmont, Novara, Italy. 4. Endocrinology and Hypertension, Cardinal Massaia Hospital, Asti, Italy. 5. Endocrinology, Diabetes and Metabolism, A.O. Ordine Mauriziano, Turin, Italy. 6. Endocrinology and Metabolism, Santa Croce and Carle Hospital, Cuneo, Italy. 7. Pathology Unit, Department of Oncology, University of Turin, Orbassano, Italy. 8. Internal Medicine, Department of Biological and Clinical Sciences, University of Turin, Orbassano, Italy. 9. Surgery, Department of Surgical Sciences. 10. Medical Genetics, Department of Medical Sciences. 11. Internal Medicine and Hypertension Unit, Department of Medical Sciences. 12. Oncological Endocrinology, Department of Medical Sciences, University of Turin, Turin, Italy.
Abstract
Objective: Various features have been identified as predictors of relapse after complete resection of pheochromocytoma, but a comprehensive multivariable model for recurrence risk prediction is lacking. The aim of this study was to develop and internally validate an integrated predictive model for post-surgical recurrence of pheochromocytoma. Methods: The present research retrospectively enrolled 177 patients affected by pheochromocytoma and submitted to radical surgery from 1990 to 2016, in nine referral centers for adrenal diseases. Cox regression analysis was adopted for model development, and a bootstrapping procedure was used for internal validation. Results: Variables independently associated with recurrence were tumor size (hazard ratio (HR): 1.01, 95% CI: 1.00-1.02), positive genetic testing (HR: 5.14, 95% CI: 2.10-12.55), age (HR: 0.97, 95% CI: 0.94-0.99), and Pheochromocytoma of the Adrenal Gland Scaled Score (PASS) (HR: 1.16, 95% CI: 1.04-1.29). The predictive performance of the overall model, evaluated by Somers' D, was equal to 0.594, and was significantly higher than the ones of any single predictor alone (P = 0.002 compared to tumor size; P = 0.004 compared to genetic testing; P = 0.048 compared to age; P = 0.006 compared to PASS). Internal validation by bootstrapping techniques estimated an optimistic bias of 6.3%, which reassured about a small tendency towards overfit. Conclusions: We proposed a multivariable model for the prediction of post-surgical recurrence of pheochromocytoma, derived by the integration of genetic, histopathological, and clinical data. This predictive tool may be of value for a comprehensive tailoring of post-surgical follow-up in radically operated pheochromocytoma patients.
Objective: Various features have been identified as predictors of relapse after complete resection of pheochromocytoma, but a comprehensive multivariable model for recurrence risk prediction is lacking. The aim of this study was to develop and internally validate an integrated predictive model for post-surgical recurrence of pheochromocytoma. Methods: The present research retrospectively enrolled 177 patients affected by pheochromocytoma and submitted to radical surgery from 1990 to 2016, in nine referral centers for adrenal diseases. Cox regression analysis was adopted for model development, and a bootstrapping procedure was used for internal validation. Results: Variables independently associated with recurrence were tumor size (hazard ratio (HR): 1.01, 95% CI: 1.00-1.02), positive genetic testing (HR: 5.14, 95% CI: 2.10-12.55), age (HR: 0.97, 95% CI: 0.94-0.99), and Pheochromocytoma of the Adrenal Gland Scaled Score (PASS) (HR: 1.16, 95% CI: 1.04-1.29). The predictive performance of the overall model, evaluated by Somers' D, was equal to 0.594, and was significantly higher than the ones of any single predictor alone (P = 0.002 compared to tumor size; P = 0.004 compared to genetic testing; P = 0.048 compared to age; P = 0.006 compared to PASS). Internal validation by bootstrapping techniques estimated an optimistic bias of 6.3%, which reassured about a small tendency towards overfit. Conclusions: We proposed a multivariable model for the prediction of post-surgical recurrence of pheochromocytoma, derived by the integration of genetic, histopathological, and clinical data. This predictive tool may be of value for a comprehensive tailoring of post-surgical follow-up in radically operated pheochromocytoma patients.