Abstract
ObjectivesThe aim of this study was to develop and validate a prognostic model incorporating [18F]FDG PET/CT radiomics for patients of minor salivary gland carcinoma (MSGC).MethodsWe retrospectively reviewed the pretreatment [18F]FDG PET/CT images of 75 MSGC patients treated with curative intent. Using a 1.5:1 ratio, the patients were randomly divided into a training and validation group. The main outcome measurements were overall survival (OS) and relapse-free survival (RFS). All of the patients were followed up for at least 30 months or until death. Following segmentation of tumors and lymph nodes on PET images, radiomic features were extracted. The prognostic significance of PET radiomics and clinical parameters in the training group was examined using receiver operating characteristic curve analysis. Variables showing a significant impact on OS and RFS were entered into multivariable Cox regression models. Recursive partitioning analysis was subsequently implemented to devise a prognostic index, whose performance was examined in the validation group. Finally, the performance of the index was compared with clinical variables in the entire cohort and nomograms for surgically treated cases.ResultsThe training and validation groups consisted of 45 and 30 patients, respectively. The median follow-up time in the entire cohort was 59.5 months. Eighteen relapse, 19 dead, and thirteen relapse, eight dead events were found in the training and validation cohorts, respectively. In the training group, two factors were identified as independently associated with poor OS, i.e., (1) tumors with both high maximum standardized uptake value (SUVmax) and discretized intensity entropy and (2) poor performance status or N2c-N3 stage. A prognostic model based on the above factors was devised and showed significant higher concordance index (C-index) for OS than those of AJCC stage and high-risk histology (C-index: 0.83 vs. 0.65, P = 0.005; 0.83 vs. 0.54, P < 0.001, respectively). This index also demonstrated superior performance than nomogram for OS (C-index: 0.88 vs. 0.70, P = 0.017) and that for RFS (C-index: 0.87 vs. 0.72, P = 0.004).ConclusionsWe devised a novel prognostic model that incorporates [18F]FDG PET/CT radiomics and may help refine outcome prediction in patients with MSGC.
Highlights
Minor salivary gland carcinoma (MSGC)—with an annual incidence of 0.16–0.4 new cases per 100,000 population [1]—is a rare malignancy that accounts for only 0.3–1.8% of all head and neck tumors [2, 3]
Two factors were identified as independently associated with poor overall survival (OS), i.e., (1) tumors with both high maximum standardized uptake value (SUVmax) and discretized intensity entropy and (2) poor performance status or N2c-N3 stage
A prognostic model based on the above factors was devised and showed significant higher concordance index (C-index) for OS than those of American Joint Committee on Cancer (AJCC) stage and high-risk histology (C-index: 0.83 vs. 0.65, P = 0.005; 0.83 vs. 0.54, P < 0.001, respectively)
Summary
Minor salivary gland carcinoma (MSGC)—with an annual incidence of 0.16–0.4 new cases per 100,000 population [1]—is a rare malignancy that accounts for only 0.3–1.8% of all head and neck tumors [2, 3]. The American Joint Committee on Cancer (AJCC) tumor stage and the histology risk group according to the 2005 World Health Organization (WHO) classification system [12] are currently considered as the main prognostic factors for both patient survival and disease relapse [11]. Ali S et al had proposed postoperative nomograms for prediction of major salivary gland malignancy. Five variables were used for predictive for OS: age, clinical T4 stage, histological grade, perineural invasion, and tumor dimension [13]. Lu CH et al demonstrated a nomogram including clinicopathologic variables of smoking, tumor grade, perineural invasion, lymphatic invasion, and pathologic T- and Nstage to predict the recurrent probability of major gland cancer [14]. Surgical and pathological results were required for those nomograms and their utility in MSGC remains unclear
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