PDF(Pubmed)
Abstract：
OBJECTIVE: Radiomics is an emerging field that utilizes quantitative features extracted from medical images to predict clinically meaningful outcomes. Validating findings is crucial to assess radiomics applicability. We aimed to validate previously published magnetic resonance imaging (MRI) radiomics models to predict oncological outcomes in oral tongue squamous cell carcinoma (OTSCC).
METHODS: Retrospective multicentric study on OTSCC surgically treated from 2010 to 2019. All patients performed preoperative MRI, including contrast-enhanced T1-weighted (CE-T1), diffusion-weighted sequences and apparent diffusion coefficient map. We evaluated overall survival (OS), locoregional recurrence-free survival (LRRFS), cause-specific mortality (CSM). We elaborated different models based on clinical and radiomic data. C-indexes assessed the prediction accuracy of the models.
RESULTS: We collected 112 consecutive independent patients from three Italian Institutions to validate the previously published MRI radiomic models based on 79 different patients. The C-indexes for the hybrid clinical-radiomic models in the validation cohort were lower than those in the training cohort but remained > 0.5 in most cases. CE-T1 sequence provided the best fit to the models: the C-indexes obtained were 0.61, 0.59, 0.64 (pretreatment model) and 0.65, 0.69, 0.70 (posttreatment model) for OS, LRRFS and CSM, respectively.
CONCLUSIONS: Our clinical-radiomic models retain a potential to predict OS, LRRFS and CSM in heterogeneous cohorts across different centers. These findings encourage further research, aimed at overcoming current limitations, due to the variability of imaging acquisition, processing and tumor volume delineation.
METHODS: Retrospective multicentric study on OTSCC surgically treated from 2010 to 2019. All patients performed preoperative MRI, including contrast-enhanced T1-weighted (CE-T1), diffusion-weighted sequences and apparent diffusion coefficient map. We evaluated overall survival (OS), locoregional recurrence-free survival (LRRFS), cause-specific mortality (CSM). We elaborated different models based on clinical and radiomic data. C-indexes assessed the prediction accuracy of the models.
RESULTS: We collected 112 consecutive independent patients from three Italian Institutions to validate the previously published MRI radiomic models based on 79 different patients. The C-indexes for the hybrid clinical-radiomic models in the validation cohort were lower than those in the training cohort but remained > 0.5 in most cases. CE-T1 sequence provided the best fit to the models: the C-indexes obtained were 0.61, 0.59, 0.64 (pretreatment model) and 0.65, 0.69, 0.70 (posttreatment model) for OS, LRRFS and CSM, respectively.
CONCLUSIONS: Our clinical-radiomic models retain a potential to predict OS, LRRFS and CSM in heterogeneous cohorts across different centers. These findings encourage further research, aimed at overcoming current limitations, due to the variability of imaging acquisition, processing and tumor volume delineation.
摘要：
目的：影像组学是一个新兴领域，它利用从医学图像中提取的定量特征来预测有临床意义的结果。验证结果对于评估影像组学适用性至关重要。我们旨在验证先前发表的磁共振成像（MRI）影像组学模型，以预测口腔舌鳞状细胞癌（OTSCC）的肿瘤学结果。
方法：2010年至2019年手术治疗的OTSCC的回顾性多中心研究。所有患者术前均行MRI检查,包括对比增强T1加权（CE-T1），扩散加权序列和表观扩散系数图。我们评估了总生存期(OS)，局部无复发生存率（LRRFS），原因特异性死亡率（CSM）。我们根据临床和影像数据阐述了不同的模型。C指数评估了模型的预测精度。
结果：我们从三个意大利机构收集了112名连续的独立患者，以验证先前发表的基于79名不同患者的MRI影像组学模型。验证队列中混合临床-放射组学模型的C指数低于训练队列中的C指数，但在大多数情况下仍>0.5。CE-T1序列提供了与模型的最佳拟合：获得的C指数分别为OS的0.61、0.59、0.64（预处理模型）和0.65、0.69、0.70（后处理模型）。LRRFS和CSM,分别。
结论：我们的临床影像组学模型保留了预测OS的潜力，不同中心异质队列中的LRRFS和CSM。这些发现鼓励进一步的研究，旨在克服目前的局限性，由于成像采集的可变性，处理和肿瘤体积描绘。
方法：2010年至2019年手术治疗的OTSCC的回顾性多中心研究。所有患者术前均行MRI检查,包括对比增强T1加权（CE-T1），扩散加权序列和表观扩散系数图。我们评估了总生存期(OS)，局部无复发生存率（LRRFS），原因特异性死亡率（CSM）。我们根据临床和影像数据阐述了不同的模型。C指数评估了模型的预测精度。
结果：我们从三个意大利机构收集了112名连续的独立患者，以验证先前发表的基于79名不同患者的MRI影像组学模型。验证队列中混合临床-放射组学模型的C指数低于训练队列中的C指数，但在大多数情况下仍>0.5。CE-T1序列提供了与模型的最佳拟合：获得的C指数分别为OS的0.61、0.59、0.64（预处理模型）和0.65、0.69、0.70（后处理模型）。LRRFS和CSM,分别。
结论：我们的临床影像组学模型保留了预测OS的潜力，不同中心异质队列中的LRRFS和CSM。这些发现鼓励进一步的研究，旨在克服目前的局限性，由于成像采集的可变性，处理和肿瘤体积描绘。
