Takaharu Kudoh1, Akihiro Haga2, Keiko Kudoh3, Akira Takahashi3, Motoharu Sasaki4, Yasusei Kudo5, Hitoshi Ikushima4, Youji Miyamoto3. 1. Department of Oral Surgery, Tokushima University Graduate School of Biomedical Sciences, Kuramoto-cho, Tokushima, Japan. kudoh@tokushima-u.ac.jp. 2. Department of Medical Image Informatics, Tokushima University Graduate School of Biomedical Sciences, Kuramoto-cho, Tokushima, Japan. 3. Department of Oral Surgery, Tokushima University Graduate School of Biomedical Sciences, Kuramoto-cho, Tokushima, Japan. 4. Department of Therapeutic Radiology, Tokushima University Graduate School of Biomedical Sciences, Kuramoto-cho, Tokushima, Japan. 5. Department of Oral Bioscience, Tokushima University Graduate School of Biomedical Sciences, Kuramoto-cho, Tokushima, Japan.
Abstract
OBJECTIVES: This study aimed to create a predictive model for cervical lymph node metastasis (CLNM) in patients with tongue squamous cell carcinoma (SCC) based on radiomics features detected by [18F]-fluoro-2-deoxyglucose (18F-FDG) positron emission tomography (PET). METHODS: A total of 40 patients with tongue SCC who underwent 18F-FDG PET imaging during their first medical examination were enrolled. During the follow-up period (mean 28 months), 20 patients had CLNM, including six with late CLNM, whereas the remaining 20 patients did not have CLNM. Radiomics features were extracted from 18F-FDG PET images of all patients irrespective of metal artifact, and clinicopathological factors were obtained from the medical records. Late CLNM was defined as the CLNM that occurred after major treatment. The least absolute shrinkage and selection operator (LASSO) model was used for radiomics feature selection and sequential data fitting. The receiver operating characteristic curve analysis was used to assess the predictive performance of the 18F-FDG PET-based model and clinicopathological factors model (CFM) for CLNM. RESULTS: Six radiomics features were selected from LASSO analysis. The average values of the area under the curve (AUC), accuracy, sensitivity, and specificity of radiomics analysis for predicting CLNM from 18F-FDG PET images were 0.79, 0.68, 0.65, and 0.70, respectively. In contrast, those of the CFM were 0.54, 0.60, 0.60, and 0.60, respectively. The 18F-FDG PET-based model showed significantly higher AUC than that of the CFM. CONCLUSIONS: The 18F-FDG PET-based model has better potential for diagnosing CLNM and predicting late CLNM in patients with tongue SCC than the CFM.
OBJECTIVES: This study aimed to create a predictive model for cervical lymph node metastasis (CLNM) in patients with tongue squamous cell carcinoma (SCC) based on radiomics features detected by [18F]-fluoro-2-deoxyglucose (18F-FDG) positron emission tomography (PET). METHODS: A total of 40 patients with tongue SCC who underwent 18F-FDG PET imaging during their first medical examination were enrolled. During the follow-up period (mean 28 months), 20 patients had CLNM, including six with late CLNM, whereas the remaining 20 patients did not have CLNM. Radiomics features were extracted from 18F-FDG PET images of all patients irrespective of metal artifact, and clinicopathological factors were obtained from the medical records. Late CLNM was defined as the CLNM that occurred after major treatment. The least absolute shrinkage and selection operator (LASSO) model was used for radiomics feature selection and sequential data fitting. The receiver operating characteristic curve analysis was used to assess the predictive performance of the 18F-FDG PET-based model and clinicopathological factors model (CFM) for CLNM. RESULTS: Six radiomics features were selected from LASSO analysis. The average values of the area under the curve (AUC), accuracy, sensitivity, and specificity of radiomics analysis for predicting CLNM from 18F-FDG PET images were 0.79, 0.68, 0.65, and 0.70, respectively. In contrast, those of the CFM were 0.54, 0.60, 0.60, and 0.60, respectively. The 18F-FDG PET-based model showed significantly higher AUC than that of the CFM. CONCLUSIONS: The 18F-FDG PET-based model has better potential for diagnosing CLNM and predicting late CLNM in patients with tongue SCC than the CFM.
Authors: Heiko Schöder; Diane L Carlson; Dennis H Kraus; Hilda E Stambuk; Mithat Gönen; Yusuf E Erdi; Henry W D Yeung; Andrew G Huvos; Jatin P Shah; Steven M Larson; Richard J Wong Journal: J Nucl Med Date: 2006-05 Impact factor: 10.057