Deep learning–based multimodal segmentation of oropharyngeal squamous cell carcinoma on CT and MRI using self-configuring nnU-Net

• Published in: European radiology 2024-08, Vol.34 (8), p.5389-5400
• Main Authors: Choi, Yangsean, Bang, Jooin, Kim, Sang-Yeon, Seo, Minkook, Jang, Jinhee
• Format: Article
• Language: English
• Subjects: Adult; Aged; Cancer; Carcinoma, Squamous Cell - diagnostic imaging; Cell culture; Computed tomography; Correlation coefficient; Correlation coefficients; Deep Learning; Diagnostic Radiology; Female; Head & neck cancer; Head and Neck; Humans; Image Interpretation, Computer-Assisted - methods; Image processing; Image segmentation; Imaging; Internal Medicine; Interventional Radiology; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Male; Medical imaging; Medicine; Medicine & Public Health; Metric space; Middle Aged; Multimodal Imaging - methods; Neuroradiology; Oropharyngeal Neoplasms - diagnostic imaging; Oropharyngolaryngeal carcinoma; Patients; Radiation therapy; Radiology; Radiomics; Reproducibility of Results; Retrospective Studies; Segmentation; Simultaneous discrimination learning; Squamous cell carcinoma; Throat cancer; Tomography, X-Ray Computed - methods; Ultrasound
• ISSN: 1432-1084; 0938-7994; 1432-1084
• DOI: 10.1007/s00330-024-10585-y

Purpose To evaluate deep learning–based segmentation models for oropharyngeal squamous cell carcinoma (OPSCC) using CT and MRI with nnU-Net. Methods This single-center retrospective study included 91 patients with OPSCC. The patients were grouped into the development ( n  = 56), test 1 ( n  = 13), and test 2 ( n  = 22) cohorts. In the development cohort, OPSCC was manually segmented on CT, MR, and co-registered CT-MR images, which served as the ground truth. The multimodal and multichannel input images were then trained using a self-configuring nnU-Net. For evaluation metrics, dice similarity coefficient (DSC) and mean Hausdorff distance (HD) were calculated for test cohorts. Pearson’s correlation and Bland–Altman analyses were performed between ground truth and prediction volumes. Intraclass correlation coefficients (ICCs) of radiomic features were calculated for reproducibility assessment. Results All models achieved robust segmentation performances with DSC of 0.64 ± 0.33 (CT), 0.67 ± 0.27 (MR), and 0.65 ± 0.29 (CT-MR) in test cohort 1 and 0.57 ± 0.31 (CT), 0.77 ± 0.08 (MR), and 0.73 ± 0.18 (CT-MR) in test cohort 2. No significant differences were found in DSC among the models. HD of CT-MR (1.57 ± 1.06 mm) and MR models (1.36 ± 0.61 mm) were significantly lower than that of the CT model (3.48 ± 5.0 mm) ( p  = 0.037 and p  = 0.014, respectively). The correlation coefficients between the ground truth and prediction volumes for CT, MR, and CT-MR models were 0.88, 0.93, and 0.9, respectively. MR models demonstrated excellent mean ICCs of radiomic features (0.91–0.93). Conclusion The self-configuring nnU-Net demonstrated reliable and accurate segmentation of OPSCC on CT and MRI. The multimodal CT-MR model showed promising results for the simultaneous segmentation on CT and MRI. Clinical relevance statement Deep learning–based automatic detection and segmentation of oropharyngeal squamous cell carcinoma on pre-treatment CT and MRI would facilitate radiologic response assessment and radiotherapy planning. Key Points • The nnU-Net framework produced a reliable and accurate segmentation of OPSCC on CT and MRI. • MR and CT-MR models showed higher DSC and lower Hausdorff distance than the CT model. • Correlation coefficients between the ground truth and predicted segmentation volumes were high in all the three models.