Nomograms for predicting survival and recurrence in patients with adenoid cystic carcinoma. An international collaborative study

Ian Ganly, Moran Amit, Lei Kou, Frank L. Palmer, Jocelyn Migliacci, Nora Katabi, Changhong Yu, Michael W. Kattan, Yoav Binenbaum, Kanika Sharma, Ramer Naomi, Agbetoba Abib, Brett Miles, Xinjie Yang, Delin Lei, Kristine Bjoerndal, Christian Godballe, Thomas Mücke, Klaus Dietrich Wolff, Dan Fliss & 8 others André M. Eckardt, Copelli Chiara, Enrico Sesenna, Safina Ali, Lukas Czerwonka, David P. Goldstein, Ziv Gil, Snehal G. Patel

Research output: Contribution to journalArticle

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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.

Original languageEnglish
Pages (from-to)2768-2776
Number of pages9
JournalEuropean Journal of Cancer
Volume51
Issue number18
DOIs
Publication statusPublished - Dec 1 2015

Fingerprint

Adenoid Cystic Carcinoma
Nomograms
Recurrence
Survival
Neoplasms
Mortality
Counseling
Clinical Trials
Databases

Keywords

  • Adenoid cystic cancer
  • Nomogram

ASJC Scopus subject areas

  • Cancer Research
  • Oncology
  • Medicine(all)

Cite this

Nomograms for predicting survival and recurrence in patients with adenoid cystic carcinoma. An international collaborative study. / Ganly, Ian; Amit, Moran; Kou, Lei; Palmer, Frank L.; Migliacci, Jocelyn; Katabi, Nora; Yu, Changhong; Kattan, Michael W.; Binenbaum, Yoav; Sharma, Kanika; Naomi, Ramer; Abib, Agbetoba; Miles, Brett; Yang, Xinjie; Lei, Delin; Bjoerndal, Kristine; Godballe, Christian; Mücke, Thomas; Wolff, Klaus Dietrich; Fliss, Dan; Eckardt, André M.; Chiara, Copelli; Sesenna, Enrico; Ali, Safina; Czerwonka, Lukas; Goldstein, David P.; Gil, Ziv; Patel, Snehal G.

In: European Journal of Cancer, Vol. 51, No. 18, 01.12.2015, p. 2768-2776.

Research output: Contribution to journalArticle

Ganly, I, Amit, M, Kou, L, Palmer, FL, Migliacci, J, Katabi, N, Yu, C, Kattan, MW, Binenbaum, Y, Sharma, K, Naomi, R, Abib, A, Miles, B, Yang, X, Lei, D, Bjoerndal, K, Godballe, C, Mücke, T, Wolff, KD, Fliss, D, Eckardt, AM, Chiara, C, Sesenna, E, Ali, S, Czerwonka, L, Goldstein, DP, Gil, Z & Patel, SG 2015, 'Nomograms for predicting survival and recurrence in patients with adenoid cystic carcinoma. An international collaborative study', European Journal of Cancer, vol. 51, no. 18, pp. 2768-2776. https://doi.org/10.1016/j.ejca.2015.09.004
Ganly, Ian ; Amit, Moran ; Kou, Lei ; Palmer, Frank L. ; Migliacci, Jocelyn ; Katabi, Nora ; Yu, Changhong ; Kattan, Michael W. ; Binenbaum, Yoav ; Sharma, Kanika ; Naomi, Ramer ; Abib, Agbetoba ; Miles, Brett ; Yang, Xinjie ; Lei, Delin ; Bjoerndal, Kristine ; Godballe, Christian ; Mücke, Thomas ; Wolff, Klaus Dietrich ; Fliss, Dan ; Eckardt, André M. ; Chiara, Copelli ; Sesenna, Enrico ; Ali, Safina ; Czerwonka, Lukas ; Goldstein, David P. ; Gil, Ziv ; Patel, Snehal G. / Nomograms for predicting survival and recurrence in patients with adenoid cystic carcinoma. An international collaborative study. In: European Journal of Cancer. 2015 ; Vol. 51, No. 18. pp. 2768-2776.
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T1 - Nomograms for predicting survival and recurrence in patients with adenoid cystic carcinoma. An international collaborative study

AU - Ganly, Ian

AU - Amit, Moran

AU - Kou, Lei

AU - Palmer, Frank L.

AU - Migliacci, Jocelyn

AU - Katabi, Nora

AU - Yu, Changhong

AU - Kattan, Michael W.

AU - Binenbaum, Yoav

AU - Sharma, Kanika

AU - Naomi, Ramer

AU - Abib, Agbetoba

AU - Miles, Brett

AU - Yang, Xinjie

AU - Lei, Delin

AU - Bjoerndal, Kristine

AU - Godballe, Christian

AU - Mücke, Thomas

AU - Wolff, Klaus Dietrich

AU - Fliss, Dan

AU - Eckardt, André M.

AU - Chiara, Copelli

AU - Sesenna, Enrico

AU - Ali, Safina

AU - Czerwonka, Lukas

AU - Goldstein, David P.

AU - Gil, Ziv

AU - Patel, Snehal G.

PY - 2015/12/1

Y1 - 2015/12/1

N2 - 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.

AB - 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.

KW - Adenoid cystic cancer

KW - Nomogram

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