A deep learning and radiomics fusion model based on contrast-enhanced computer tomography improves preoperative identification of cervical lymph node metastasis of oral squamous cell carcinoma

• Published in: Clinical oral investigations 2023-12, Vol.28 (1), p.39-39, Article 39
• Main Authors: Chen, Zhen, Yu, Yao, Liu, Shuo, Du, Wen, Hu, Leihao, Wang, Congwei, Li, Jiaqi, Liu, Jianbo, Zhang, Wenbo, Peng, Xin
• Format: Article
• Language: English
• Subjects: Computed tomography; Deep learning; Dentistry; Lymph nodes; Lymphatic system; Medical diagnosis; Medicine; Metastases; Metastasis; Oral cancer; Oral carcinoma; Oral squamous cell carcinoma; Patients; Radiomics; Squamous cell carcinoma
• ISSN: 1436-3771; 1432-6981; 1436-3771
• DOI: 10.1007/s00784-023-05423-2

Objectives In this study, we constructed and validated models based on deep learning and radiomics to facilitate preoperative diagnosis of cervical lymph node metastasis (LNM) using contrast-enhanced computed tomography (CECT). Materials and methods CECT scans of 100 patients with OSCC (217 metastatic and 1973 non-metastatic cervical lymph nodes: development set, 76 patients; internally independent test set, 24 patients) who received treatment at the Peking University School and Hospital of Stomatology between 2012 and 2016 were retrospectively collected. Clinical diagnoses and pathological findings were used to establish the gold standard for metastatic cervical LNs. A reader study with two clinicians was also performed to evaluate the lymph node status in the test set. The performance of the proposed models and the clinicians was evaluated and compared by measuring using the area under the receiver operating characteristic curve (AUC), accuracy (ACC), sensitivity (SEN), and specificity (SPE). Results A fusion model combining deep learning with radiomics showed the best performance (ACC, 89.2%; SEN, 92.0%; SPE, 88.9%; and AUC, 0.950 [95% confidence interval: 0.908–0.993, P