Comparison of PET and CT radiomics for prediction of local tumor control in head and neck squamous cell carcinoma
Comparison of PET and CT radiomics for prediction of local tumor control in head and neck squamous cell carcinoma
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DOI：
10.1080/0284186x.2017.1346382
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发表时间：
2017-01-01
期刊：
影响因子：
3.1
通讯作者：
Tanadini-Lang, Stephanie
中科院分区：
文献类型：
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作者：
Bogowicz, Marta;Riesterer, Oliver;Tanadini-Lang, Stephanie
目的：从CT中提取的放射组学特征与头颈部鳞状细胞癌（HNSCC）中的局部肿瘤控制之间的关联已被证明。本研究探讨了预处理功能成像（18F-FDG PET）放射组学建模的局部tumor control.Material和方法的价值：数据从HNSCC患者（n = 121）治疗与确定性放化疗被用于模型训练。总共从原发肿瘤区域的对比增强CT和18F-FDG PET图像中提取了569个放射组学特征。分别训练用于评估局部肿瘤控制的CT、PET和组合的PET/CT放射组学模型。实现了五种特征选择和三种分类方法。在训练队列中使用5倍交叉验证中的一致性指数（CI）量化模型的性能。在独立验证队列（n = 51）中比较并验证了每种图像模态的最佳模型。使用自举法研究CI的差异。此外，观察和放射学为基础的估计概率的局部肿瘤控制进行了比较，两个风险groups.Results之间：使用主成分分析的特征选择和分类的基础上的多变量考克斯回归与向后选择的变量导致最佳模型为所有的图像模式（CICT = 0.72，CIPET = 0.74，CIPET/CT = 0.77）。CT密度均匀性好（GLSZM（size_zone_entropy）降低）和FDG高摄取灶（GLSZM（SZLGE）升高）的肿瘤预后较好。验证队列中模型的性能没有观察到显着差异（CICT = 0.73，CIPET = 0.71，CIPET/CT = 0.73）。然而，CT放射组学为基础的模型高估了肿瘤控制的概率在预后不良组（预测= 68%，观察= 56%）。然而，基于CT的预测高估了不良预后验证队列的局部控制率，因此，我们建议基于18F-FDG PET的局部控制建模。
Purpose: An association between radiomic features extracted from CT and local tumor control in the head and neck squamous cell carcinoma (HNSCC) has been shown. This study investigated the value of pretreatment functional imaging (18F-FDG PET) radiomics for modeling of local tumor control.Material and Methods: Data from HNSCC patients (n = 121) treated with definitive radiochemotherapy were used for model training. In total, 569 radiomic features were extracted from both contrast-enhanced CT and 18F-FDG PET images in the primary tumor region. CT, PET and combined PET/CT radiomic models to assess local tumor control were trained separately. Five feature selection and three classification methods were implemented. The performance of the models was quantified using concordance index (CI) in 5-fold cross validation in the training cohort. The best models, per image modality, were compared and verified in the independent validation cohort (n = 51). The difference in CI was investigated using bootstrapping. Additionally, the observed and radiomics-based estimated probabilities of local tumor control were compared between two risk groups.Results: The feature selection using principal component analysis and the classification based on the multivariabale Cox regression with backward selection of the variables resulted in the best models for all image modalities (CICT = 0.72, CIPET = 0.74, CIPET/CT = 0.77). Tumors more homogenous in CT density (decreased GLSZM(size_zone_entropy)) and with a focused region of high FDG uptake (higher GLSZM(SZLGE)) indicated better prognosis. No significant difference in the performance of the models in the validation cohort was observed (CICT = 0.73, CIPET = 0.71, CIPET/CT = 0.73). However, the CT radiomics-based model overestimated the probability of tumor control in the poor prognostic group (predicted = 68%, observed = 56%).Conclusions: Both CT and PET radiomics showed equally good discriminative power for local tumor control modeling in HNSCC. However, CT-based predictions overestimated the local control rate in the poor prognostic validation cohort, and thus, we recommend to base the local control modeling on the 18F-FDG PET.