Magnetic resonance imaging based on radiomics for differentiating T1-category nasopharyngeal carcinoma from nasopharyngeal lymphoid hyperplasia: a multicenter study

• Published in: Japanese journal of radiology Vol. 42; no. 7; pp. 709 - 719
• Main Authors: Cheng, Jingfeng, Su, Wenzhe, Wang, Yuzhe, Zhan, Yang, Wang, Yin, Yan, Shuyu, Yuan, Yuan, Chen, Lingxin, Wei, Zixun, Zhang, Shengjian, Gao, Xin, Tang, Zuohua
• Format: Journal Article
• Language: English
• Published: Singapore Springer Nature Singapore 01.07.2024; Springer Nature B.V
• Subjects: Datasets; Hyperplasia; Imaging; Magnetic resonance imaging; Medical imaging; Medicine; Medicine & Public Health; Nasopharyngeal carcinoma; Nuclear Medicine; Original Article; Radiology; Radiomics; Radiotherapy; Signatures; Throat cancer; Training; Variance analysis; Nasopharyngeal carcinoma; Lymphoid hyperplasia; Magnetic resonance imaging; Radiomics
• ISSN: 1867-1071; 1867-108X; 1867-108X
• DOI: 10.1007/s11604-024-01544-0

Purpose To investigate the role of magnetic resonance imaging (MRI) based on radiomics using T2-weighted imaging fat suppression (T2WI-FS) and contrast enhanced T1-weighted imaging (CE-T1WI) sequences in differentiating T1-category nasopharyngeal carcinoma (NPC) from nasopharyngeal lymphoid hyperplasia (NPH). Materials and methods This study enrolled 614 patients (training dataset: n = 390, internal validation dataset: n = 98, and external validation dataset: n = 126) of T1-category NPC and NPH. Three feature selection methods were used, including analysis of variance, recursive feature elimination, and relief. The logistic regression classifier was performed to construct the radiomics signatures of T2WI-FS, CE-T1WI, and T2WI-FS + CE-T1WI to differentiate T1-category NPC from NPH. The performance of the optimal radiomics signature (T2WI-FS + CE-T1WI) was compared with those of three radiologists in the internal and external validation datasets. Results Twelve, 15, and 15 radiomics features were selected from T2WI-FS, CE-T1WI, and T2WI-FS + CE-T1WI to develop the three radiomics signatures, respectively. The area under the curve (AUC) values for radiomics signatures of T2WI-FS + CE-T1WI and CE-T1WI were significantly higher than that of T2WI-FS (AUCs = 0.940, 0.935, and 0.905, respectively) for distinguishing T1-category NPC and NPH in the training dataset ( P s all < 0.05). In the internal and external validation datasets, the radiomics signatures based on T2WI-FS + CE-T1WI and CE-T1WI outperformed T2WI-FS with no significant difference (AUCs = 0.938, 0.925, and 0.874 for internal validation dataset and 0.932, 0.918, and 0.882 for external validation dataset; P s > 0.05). The radiomics signature of T2WI-FS + CE-T1WI significantly performed better than three radiologists in the internal and external validation datasets. Conclusion The MRI-based radiomics signature is meaningful in differentiating T1-category NPC from NPH and potentially helps clinicians select suitable therapy strategies.