Ian Ganly1, Moran Amit2, Lei Kou3, Frank L Palmer4, Jocelyn Migliacci4, Nora Katabi4, Changhong Yu3, Michael W Kattan3, Yoav Binenbaum2, Kanika Sharma5, Ramer Naomi6, Agbetoba Abib7, Brett Miles7, Xinjie Yang8, Delin Lei8, Kristine Bjoerndal9, Christian Godballe9, Thomas Mücke10, Klaus-Dietrich Wolff10, Dan Fliss11, André M Eckardt12, Copelli Chiara13, Enrico Sesenna13, Safina Ali14, Lukas Czerwonka15, David P Goldstein15, Ziv Gil16, Snehal G Patel4. 1. Head and Neck Surgery Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA. Electronic address: ganlyi@mskcc.org. 2. The Laboratory for Applied Cancer Research, Rambam Medical Center, Rappaport Medical School, The Technion, Israel Institute of Technology, Haifa, Israel. 3. Department of Quantitative Health Sciences, Cleveland Clinic, 9500 Euclid Ave, Cleveland, OH 44195, USA. 4. Head and Neck Surgery Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA. 5. Department of Radiotherapy & Oncology, Maulana Azad Medical College and Lok Nayak Hospital, Bahadur Shah Zafar Marg, New Delhi, India. 6. Mount Sinai Medical Center, Department of Pathology, The Mount Sinai School of Medicine, New York, NY, USA. 7. Department of Otolaryngology, The Mount Sinai School of Medicine, New York, NY, USA. 8. Department of Oral and Maxillofacial Surgery, School of Stomatology, The Fourth Military Medical University, Xi'an 710032, PR China. 9. Department of Otolaryngology Head and Neck Surgery, Odense University Hospital, Denmark. 10. Department of Oral and Maxillofacial Surgery, Klinikum rechts der Isar, Technische Universität München, Germany. 11. Department of Otolaryngology Head and Neck Surgery, Tel Aviv Medical Center, Tel Aviv, Israel. 12. Department of Cranio-Maxillofacial Surgery, Hannover Medical School, Hannover, Germany. 13. Maxillo-Facial Surgery, University-Hospital of Parma, Italy. 14. Department of Otolaryngology Head and Neck Surgery, Edinburgh, UK. 15. Department of Otolaryngology Head and Neck Surgery, Princess Margaret Cancer Center, University of Toronto, Canada. 16. The Laboratory for Applied Cancer Research, Rambam Medical Center, Rappaport Medical School, The Technion, Israel Institute of Technology, Haifa, Israel; Department of Otolaryngology Head and Neck Surgery, Rambam Medical Center, Rappaport Medical School, The Technion, Israel Institute of Technology, Haifa, Israel.
Abstract
BACKGROUND: Due to the rarity of adenoid cystic carcinoma (ACC), information on outcome is based upon small retrospective case series. The aim of our study was to create a large multiinstitutional international dataset of patients with ACC in order to design predictive nomograms for outcome. METHODS: ACC patients managed at 10 international centers were identified. Patient, tumor, and treatment characteristics were recorded and an international collaborative dataset created. Multivariable competing risk models were then built to predict the 10 year recurrence free probability (RFP), distant recurrence free probability (DRFP), overall survival (OS) and cancer specific mortality (CSM). All predictors of interest were added in the starting full models before selection, including age, gender, tumor site, clinical T stage, perineural invasion, margin status, pathologic N-status, and M-status. Stepdown method was used in model selection to choose predictive variables. An external dataset of 99 patients from 2 other institutions was used to validate the nomograms. FINDINGS: Of 438 ACC patients, 27.2% (119/438) died from ACC and 38.8% (170/438) died of other causes. Median follow-up was 56 months (range 1-306). The nomogram for OS had 7 variables (age, gender, clinical T stage, tumor site, margin status, pathologic N-status and M-status) with a concordance index (CI) of 0.71. The nomogram for CSM had the same variables, except margin status, with a concordance index (CI) of 0.70. The nomogram for RFP had 7 variables (age, gender, clinical T stage, tumor site, margin status, pathologic N status and perineural invasion) (CI 0.66). The nomogram for DRFP had 6 variables (gender, clinical T stage, tumor site, pathologic N-status, perineural invasion and margin status) (CI 0.64). Concordance index for the external validation set were 0.76, 0.72, 0.67 and 0.70 respectively. INTERPRETATION: Using an international collaborative database we have created the first nomograms which estimate outcome in individual patients with ACC. These predictive nomograms will facilitate patient counseling in terms of prognosis and subsequent clinical follow-up. They will also identify high risk patients who may benefit from clinical trials on new targeted therapies for patients with ACC. FUNDING: None.
BACKGROUND: Due to the rarity of adenoid cystic carcinoma (ACC), information on outcome is based upon small retrospective case series. The aim of our study was to create a large multiinstitutional international dataset of patients with ACC in order to design predictive nomograms for outcome. METHODS: ACC patients managed at 10 international centers were identified. Patient, tumor, and treatment characteristics were recorded and an international collaborative dataset created. Multivariable competing risk models were then built to predict the 10 year recurrence free probability (RFP), distant recurrence free probability (DRFP), overall survival (OS) and cancer specific mortality (CSM). All predictors of interest were added in the starting full models before selection, including age, gender, tumor site, clinical T stage, perineural invasion, margin status, pathologic N-status, and M-status. Stepdown method was used in model selection to choose predictive variables. An external dataset of 99 patients from 2 other institutions was used to validate the nomograms. FINDINGS: Of 438 ACC patients, 27.2% (119/438) died from ACC and 38.8% (170/438) died of other causes. Median follow-up was 56 months (range 1-306). The nomogram for OS had 7 variables (age, gender, clinical T stage, tumor site, margin status, pathologic N-status and M-status) with a concordance index (CI) of 0.71. The nomogram for CSM had the same variables, except margin status, with a concordance index (CI) of 0.70. The nomogram for RFP had 7 variables (age, gender, clinical T stage, tumor site, margin status, pathologic N status and perineural invasion) (CI 0.66). The nomogram for DRFP had 6 variables (gender, clinical T stage, tumor site, pathologic N-status, perineural invasion and margin status) (CI 0.64). Concordance index for the external validation set were 0.76, 0.72, 0.67 and 0.70 respectively. INTERPRETATION: Using an international collaborative database we have created the first nomograms which estimate outcome in individual patients with ACC. These predictive nomograms will facilitate patient counseling in terms of prognosis and subsequent clinical follow-up. They will also identify high risk patients who may benefit from clinical trials on new targeted therapies for patients with ACC. FUNDING: None.
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