Hyukjin Yoon1, Seunggyun Ha2, Soo Jin Kwon1, Sonya Youngju Park1, Jihyun Kim3, Joo Hyun O1, Ie Ryung Yoo1. 1. Division of Nuclear Medicine, Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, South Korea. 2. Division of Nuclear Medicine, Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, South Korea. seunggyun.ha@gmail.com. 3. Division of Nuclear Medicine, Department of Radiology, Daejeon St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Daejeon, South Korea.
Abstract
OBJECTIVE: Tumor metabolic phenotype can be assessed with integrated image pattern analysis of 18F-fluoro-deoxy-glucose (FDG) Positron Emission Tomography/Computed Tomography (PET/CT), called radiomics. This study was performed to assess the prognostic value of radiomics PET parameters in head and neck squamous cell carcinoma (HNSCC) patients. METHODS: 18F-fluoro-deoxy-glucose (FDG) PET/CT data of 215 patients from HNSCC collection free database in The Cancer Imaging Archive (TCIA), and 122 patients in Seoul St. Mary's Hospital with baseline FDG PET/CT for locally advanced HNSCC were reviewed. Data from TCIA database were used as a training cohort, and data from Seoul St. Mary's Hospital as a validation cohort. With the training cohort, primary tumors were segmented by Nestles' adaptive thresholding method. Segmental tumors in PET images were preprocessed using relative resampling of 64 bins. Forty-two PET parameters, including conventional parameters and texture parameters, were measured. Binary groups of homogeneous imaging phenotypes, clustered by K-means method, were compared for overall survival (OS) and disease-free survival (DFS) by log-rank test. Selected individual radiomics parameters were tested along with clinical factors, including age and sex, by Cox-regression test for OS and DFS, and the significant parameters were tested with multivariate analysis. Significant parameters on multivariate analysis were again tested with multivariate analysis in the validation cohort. RESULTS: A total of 119 patients, 70 from training, and 49 from validation cohort, were included in the study. The median follow-up period was 62 and 52 months for the training and the validation cohort, respectively. In the training cohort. binary groups with different metabolic radiomics phenotypes showed significant difference in OS (p = 0.036), and borderline difference in DFS (p = 0.086). Gray-Level Non-Uniformity for zone (GLNUGLZLM) was the most significant prognostic factor for both OS (hazard ratio [HR] 3.1, 95% confidence interval [CI] 1.4-7.3, p = 0.008) and DFS (HR 4.5, CI 1.3-16, p = 0.020). Multivariate analysis revealed GLNUGLZLM as an independent prognostic factor for OS (HR 3.7, 95% CI 1.1-7.5, p = 0.032). GLNUGLZLM remained as an independent prognostic factor in the validation cohort (HR 14.8. 95% CI 3.3-66, p < 0.001). CONCLUSIONS: Baseline FDG PET radiomics contain risk information for survival prognosis in HNSCC patients. The metabolic heterogeneity parameter, GLNUGLZLM, may assist clinicians in patient risk assessment as a feasible prognostic factor.
OBJECTIVE:Tumor metabolic phenotype can be assessed with integrated image pattern analysis of 18F-fluoro-deoxy-glucose (FDG) Positron Emission Tomography/Computed Tomography (PET/CT), called radiomics. This study was performed to assess the prognostic value of radiomics PET parameters in head and neck squamous cell carcinoma (HNSCC) patients. METHODS: 18F-fluoro-deoxy-glucose (FDG) PET/CT data of 215 patients from HNSCC collection free database in The Cancer Imaging Archive (TCIA), and 122 patients in Seoul St. Mary's Hospital with baseline FDG PET/CT for locally advanced HNSCC were reviewed. Data from TCIA database were used as a training cohort, and data from Seoul St. Mary's Hospital as a validation cohort. With the training cohort, primary tumors were segmented by Nestles' adaptive thresholding method. Segmental tumors in PET images were preprocessed using relative resampling of 64 bins. Forty-two PET parameters, including conventional parameters and texture parameters, were measured. Binary groups of homogeneous imaging phenotypes, clustered by K-means method, were compared for overall survival (OS) and disease-free survival (DFS) by log-rank test. Selected individual radiomics parameters were tested along with clinical factors, including age and sex, by Cox-regression test for OS and DFS, and the significant parameters were tested with multivariate analysis. Significant parameters on multivariate analysis were again tested with multivariate analysis in the validation cohort. RESULTS: A total of 119 patients, 70 from training, and 49 from validation cohort, were included in the study. The median follow-up period was 62 and 52 months for the training and the validation cohort, respectively. In the training cohort. binary groups with different metabolic radiomics phenotypes showed significant difference in OS (p = 0.036), and borderline difference in DFS (p = 0.086). Gray-Level Non-Uniformity for zone (GLNUGLZLM) was the most significant prognostic factor for both OS (hazard ratio [HR] 3.1, 95% confidence interval [CI] 1.4-7.3, p = 0.008) and DFS (HR 4.5, CI 1.3-16, p = 0.020). Multivariate analysis revealed GLNUGLZLM as an independent prognostic factor for OS (HR 3.7, 95% CI 1.1-7.5, p = 0.032). GLNUGLZLM remained as an independent prognostic factor in the validation cohort (HR 14.8. 95% CI 3.3-66, p < 0.001). CONCLUSIONS: Baseline FDG PET radiomics contain risk information for survival prognosis in HNSCC patients. The metabolic heterogeneity parameter, GLNUGLZLM, may assist clinicians in patient risk assessment as a feasible prognostic factor.