Zhiying He1,2,3, Yitao Mao4, Shanhong Lu1,2,3, Lei Tan5, Juxiong Xiao4, Pingqing Tan6, Hailin Zhang6, Guo Li1,2,3, Helei Yan1,2,3, Jiaqi Tan1,2,3, Donghai Huang1,2,3, Yuanzheng Qiu1,2,3, Xin Zhang1,2,3,4, Xingwei Wang7,8,9, Yong Liu10,11,12,13. 1. Department of Otolaryngology Head and Neck Surgery, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan, 410008, People's Republic of China. 2. Otolaryngology Major Disease Research, Key Laboratory of Hunan Province, 87 Xiangya Road, Changsha, Hunan, 410008, People's Republic of China. 3. Clinical Research Center for Pharyngolaryngeal Diseases and Voice Disorders in Hunan Province, 87 Xiangya Road, Changsha, Hunan, 410008, People's Republic of China. 4. Department of Radiology, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, People's Republic of China. 5. College of Computer and Information Engineering, Hunan University of Technology and Business, Changsha, Hunan, People's Republic of China. 6. NHC Key Laboratory of Carcinogenesis, Hunan Cancer Hospital and The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, People's Republic of China. 7. Department of Otolaryngology Head and Neck Surgery, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan, 410008, People's Republic of China. wang-xingwei@126.com. 8. Otolaryngology Major Disease Research, Key Laboratory of Hunan Province, 87 Xiangya Road, Changsha, Hunan, 410008, People's Republic of China. wang-xingwei@126.com. 9. Clinical Research Center for Pharyngolaryngeal Diseases and Voice Disorders in Hunan Province, 87 Xiangya Road, Changsha, Hunan, 410008, People's Republic of China. wang-xingwei@126.com. 10. Department of Otolaryngology Head and Neck Surgery, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan, 410008, People's Republic of China. liuyongent@csu.edu.cn. 11. Otolaryngology Major Disease Research, Key Laboratory of Hunan Province, 87 Xiangya Road, Changsha, Hunan, 410008, People's Republic of China. liuyongent@csu.edu.cn. 12. Clinical Research Center for Pharyngolaryngeal Diseases and Voice Disorders in Hunan Province, 87 Xiangya Road, Changsha, Hunan, 410008, People's Republic of China. liuyongent@csu.edu.cn. 13. National Clinical Research Center for Geriatric Disorders (Xiangya Hospital), 87 Xiangya Road, Changsha, Hunan, 410008, People's Republic of China. liuyongent@csu.edu.cn.
Abstract
OBJECTIVES: To evaluate the effectiveness of machine learning models based on morphological magnetic resonance imaging (MRI) radiomics in the classification of parotid tumors. METHODS: In total, 298 patients with parotid tumors were randomly assigned to a training and test set at a ratio of 7:3. Radiomics features were extracted from the morphological MRI images and screened using the Select K Best and LASSO algorithm. Three-step machine learning models with XGBoost, SVM, and DT algorithms were developed to classify the parotid neoplasms into four subtypes. The ROC curve was used to measure the performance in each step. Diagnostic confusion matrices of these models were calculated for the test cohort and compared with those of the radiologists. RESULTS: Six, twelve, and eight optimal features were selected in each step of the three-step process, respectively. XGBoost produced the highest area under the curve (AUC) for all three steps in the training cohort (0.857, 0.882, and 0.908, respectively), and for the first step in the test cohort (0.826), but produced slightly lower AUCs than SVM in the latter two steps in the test cohort (0.817 vs. 0.833, and 0.789 vs. 0.821, respectively). The total accuracies of XGBoost and SVM in the confusion matrices (70.8% and 59.6%) outperformed those of DT and the radiologist (46.1% and 49.2%). CONCLUSION: This study demonstrated that machine learning models based on morphological MRI radiomics might be an assistive tool for parotid tumor classification, especially for preliminary screening in absence of more advanced scanning sequences, such as DWI. KEY POINTS: • Machine learning algorithms combined with morphological MRI radiomics could be useful in the preliminary classification of parotid tumors. • XGBoost algorithm performed better than SVM and DT in subtype differentiation of parotid tumors, while DT seemed to have a poor validation performance. • Using morphological MRI only, the XGBoost and SVM algorithms outperformed radiologists in the four-type classification task for parotid tumors, thus making these models a useful assistant diagnostic tool in clinical practice.
OBJECTIVES: To evaluate the effectiveness of machine learning models based on morphological magnetic resonance imaging (MRI) radiomics in the classification of parotid tumors. METHODS: In total, 298 patients with parotid tumors were randomly assigned to a training and test set at a ratio of 7:3. Radiomics features were extracted from the morphological MRI images and screened using the Select K Best and LASSO algorithm. Three-step machine learning models with XGBoost, SVM, and DT algorithms were developed to classify the parotid neoplasms into four subtypes. The ROC curve was used to measure the performance in each step. Diagnostic confusion matrices of these models were calculated for the test cohort and compared with those of the radiologists. RESULTS: Six, twelve, and eight optimal features were selected in each step of the three-step process, respectively. XGBoost produced the highest area under the curve (AUC) for all three steps in the training cohort (0.857, 0.882, and 0.908, respectively), and for the first step in the test cohort (0.826), but produced slightly lower AUCs than SVM in the latter two steps in the test cohort (0.817 vs. 0.833, and 0.789 vs. 0.821, respectively). The total accuracies of XGBoost and SVM in the confusion matrices (70.8% and 59.6%) outperformed those of DT and the radiologist (46.1% and 49.2%). CONCLUSION: This study demonstrated that machine learning models based on morphological MRI radiomics might be an assistive tool for parotid tumor classification, especially for preliminary screening in absence of more advanced scanning sequences, such as DWI. KEY POINTS: • Machine learning algorithms combined with morphological MRI radiomics could be useful in the preliminary classification of parotid tumors. • XGBoost algorithm performed better than SVM and DT in subtype differentiation of parotid tumors, while DT seemed to have a poor validation performance. • Using morphological MRI only, the XGBoost and SVM algorithms outperformed radiologists in the four-type classification task for parotid tumors, thus making these models a useful assistant diagnostic tool in clinical practice.
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