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
Head and Neck Surgery Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
The Laboratory for Applied Cancer Research, Rambam Medical Center, Rappaport Medical School, The Technion, Israel Institute of Technology, Haifa, Israel.
Eur J Cancer. 2015 Dec;51(18):2768-76. doi: 10.1016/j.ejca.2015.09.004. Epub 2015 Nov 19.
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.
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.
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.
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.
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由于腺样囊性癌（ACC）较为罕见，关于其预后的信息基于小型回顾性病例系列。我们研究的目的是创建一个大型的多机构国际ACC患者数据集，以便设计预后预测列线图。
确定了在10个国际中心接受治疗的ACC患者。记录患者、肿瘤和治疗特征，并创建一个国际协作数据集。然后建立多变量竞争风险模型，以预测10年无复发生存概率（RFP）、无远处复发生存概率（DRFP）、总生存期（OS）和癌症特异性死亡率（CSM）。在选择之前，将所有感兴趣的预测因素添加到初始完整模型中，包括年龄、性别、肿瘤部位、临床T分期、神经周围侵犯、切缘状态、病理N分期和M分期。模型选择采用逐步回归法选择预测变量。使用来自另外2个机构的99例患者的外部数据集对列线图进行验证。
在438例ACC患者中，27.2%（119/438）死于ACC，38.8%（170/438）死于其他原因。中位随访时间为56个月（范围1 - 306个月）。OS列线图有7个变量（年龄、性别、临床T分期、肿瘤部位、切缘状态、病理N分期和M分期），一致性指数（CI）为0.71。CSM列线图有相同的变量，但不包括切缘状态，一致性指数（CI）为0.70。RFP列线图有7个变量（年龄、性别、临床T分期、肿瘤部位、切缘状态、病理N分期和神经周围侵犯）（CI 0.66）。DRFP列线图有6个变量（性别、临床T分期、肿瘤部位、病理N分期、神经周围侵犯和切缘状态）（CI 0.64）。外部验证集的一致性指数分别为0.76、0.72、0.67和0.70。
通过国际协作数据库，我们创建了首个估计ACC个体患者预后的列线图。这些预测列线图将有助于患者的预后咨询和后续临床随访。它们还将识别可能从ACC患者新靶向治疗临床试验中获益的高危患者。
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