Nai-Ming Cheng1,2, Cheng-En Hsieh3, Yu-Hua Dean Fang4, Chun-Ta Liao5, Shu-Hang Ng6, Hung-Ming Wang7, Wen-Chi Chou7, Chien-Yu Lin8, Tzu-Chen Yen9,10. 1. Department of Nuclear Medicine and Molecular Imaging Center, Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Taoyuan, Taiwan. 2. Department of Nuclear Medicine, Chang Gung Memorial Hospital, Keelung, Taiwan. 3. Department of Radiation Oncology, Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Taoyuan, Taiwan. 4. Department of Radiology, University of Alabama at Birmingham, Birmingham, Alabama, USA. 5. Department of Otolaryngology - Head & Neck Surgery, Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Taoyuan, Taiwan. 6. Department of Diagnostic Radiology, Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Taoyuan, Taiwan. 7. Division of Hematology/Oncology, Department of Internal Medicine, Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Taoyuan, Taiwan. 8. Department of Radiation Oncology, Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Taoyuan, Taiwan. qqvirus1022@gmail.com. 9. Department of Nuclear Medicine and Molecular Imaging Center, Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Taoyuan, Taiwan. yen1110@adm.cgmh.org.tw. 10. Department of Nuclear Medicine, Xiamen Chang Gung Hospital, Xiamen, China. yen1110@adm.cgmh.org.tw.
Abstract
OBJECTIVES: The 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). METHODS: We 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. RESULTS: The 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). CONCLUSIONS: We devised a novel prognostic model that incorporates [18F]FDG PET/CT radiomics and may help refine outcome prediction in patients with MSGC.
OBJECTIVES: The 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). METHODS: We 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. RESULTS: The 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). CONCLUSIONS: We devised a novel prognostic model that incorporates [18F]FDG PET/CT radiomics and may help refine outcome prediction in patients with MSGC.