MRI-based deep learning radiomics in predicting histological differentiation of oropharyngeal cancer: a multicenter cohort study.
Authors
Affiliations (11)
Affiliations (11)
- Department of Otolaryngology Head and Neck Surgery, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, 410008, China.
- Otolaryngology Major Disease Research Key Laboratory of Hunan Province, Changsha, China.
- Clinical Research Center for Pharyngolaryngeal Diseases and Voice Disorders in Hunan Province, Changsha, China.
- Department of Otolaryngology Head and Neck Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
- Henan Medical School of Zhengzhou University, Zhengzhou, China.
- Department of Head and Neck Surgery, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China.
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Changsha, China.
- Department of Otolaryngology Head and Neck Surgery, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, 410008, China. [email protected].
- Otolaryngology Major Disease Research Key Laboratory of Hunan Province, Changsha, China. [email protected].
- Clinical Research Center for Pharyngolaryngeal Diseases and Voice Disorders in Hunan Province, Changsha, China. [email protected].
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Changsha, China. [email protected].
Abstract
The primary aim of this research was to create and rigorously assess a deep learning radiomics (DLR) framework utilizing magnetic resonance imaging (MRI) to forecast the histological differentiation grades of oropharyngeal cancer. This retrospective analysis encompassed 122 patients diagnosed with oropharyngeal cancer across three medical institutions in China. The participants were divided at random into two groups: a training cohort comprising 85 individuals and a test cohort of 37. Radiomics features derived from MRI scans, along with deep learning (DL) features, were meticulously extracted and carefully refined. These two sets of features were then integrated to build the DLR model, designed to assess the histological differentiation of oropharyngeal cancer. The model's predictive efficacy was gaged through the area under the receiver operating characteristic curve (AUC) and decision curve analysis (DCA). The DLR model demonstrated impressive performance, achieving strong AUC scores of 0.871 on the training cohort and 0.803 on the test cohort, outperforming both the standalone radiomics and DL models. Additionally, the DCA curve highlighted the significance of the DLR model in forecasting the histological differentiation of oropharyngeal cancer. The MRI-based DLR model demonstrated high predictive ability for histological differentiation of oropharyngeal cancer, which might be important for accurate preoperative diagnosis and clinical decision-making.