Mossinelli Chiara, Tagliabue Marta, Ruju Francesca, Cammarata Giulio, Volpe Stefania, Raimondi Sara, Zaffaroni Mattia, Isaksson Johannes Lars, Garibaldi Cristina, Cremonesi Marta, Corso Federica, Gaeta Aurora, Emili Ilaria, Zorzi Stefano, Alterio Daniela, Marvaso Giulia, Pepa Matteo, De Fiori Elvio, Maffini Fausto, Preda Lorenzo, Benazzo Marco, Jereczek-Fossa Barbara Alicja, Ansarin Mohssen
Department of Otorhinolaryngology and Head and Neck Surgery, European Institute of Oncology, IRCCS, Milan, Italy.
Department of Biomedical Sciences, University of Sassari, Sassari, Italy.
Head Neck. 2023 Apr;45(4):849-861. doi: 10.1002/hed.27299. Epub 2023 Feb 13.
Radiomics represents an emerging field of precision-medicine. Its application in head and neck is still at the beginning.
Retrospective study about magnetic resonance imaging (MRI) based radiomics in oral tongue squamous cell carcinoma (OTSCC) surgically treated (2010-2019; 79 patients). All preoperative MRIs include different sequences (T1, T2, DWI, ADC). Tumor volume was manually segmented and exported to radiomic-software, to perform feature extraction. Statistically significant variables were included in multivariable analysis and related to survival endpoints. Predictive models were elaborated (clinical, radiomic, clinical-radiomic models) and compared using C-index.
In almost all clinical-radiomic models radiomic-score maintained statistical significance. In all cases C-index was higher in clinical-radiomic models than in clinical ones. ADC provided the best fit to the models (C-index 0.98, 0.86, 0.84 in loco-regional recurrence, cause-specific mortality, overall survival, respectively).
MRI-based radiomics in OTSCC represents a promising noninvasive method of precision medicine, improving prognosis prediction before surgery.
放射组学是精准医学领域中一个新兴的研究方向。其在头颈部的应用尚处于起步阶段。
对2010年至2019年期间接受手术治疗的79例口腔舌鳞状细胞癌（OTSCC）患者进行基于磁共振成像（MRI）的放射组学回顾性研究。所有术前MRI均包括不同序列（T1、T2、DWI、ADC）。手动分割肿瘤体积并将其导出至放射组学软件以进行特征提取。具有统计学意义的变量纳入多变量分析，并与生存终点相关联。构建预测模型（临床模型、放射组学模型、临床 - 放射组学模型），并使用C指数进行比较。
在几乎所有临床 - 放射组学模型中，放射组学评分均保持统计学意义。在所有情况下，临床 - 放射组学模型的C指数均高于临床模型。ADC对模型的拟合效果最佳（在局部区域复发、特定病因死亡率、总生存方面的C指数分别为0.98、0.86、0.84）。
OTSCC中基于MRI的放射组学是一种有前景的非侵入性精准医学方法，可改善术前预后预测。
