Recurrence prediction with local binary pattern-based dosiomics in patients with head and neck squamous cell carcinoma
Recurrence prediction with local binary pattern-based dosiomics in patients with head and neck squamous cell carcinoma
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DOI：
10.1007/s13246-022-01201-8
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发表时间：
2022-12
影响因子：
4.4
通讯作者：
H. Kamezawa;H. Arimura
中科院分区：
文献类型：
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作者：
H. Kamezawa;H. Arimura
我们研究了一种使用局部二元模式（LBP）为基础的剂量组学预测头颈部鳞状细胞癌（HNSCC）患者放疗后复发的方法。从131例调强放疗后的患者中收集复发/非复发数据。这些案例被分为训练（80%）和测试（20%）数据集。从原始剂量分布（ODD）和LBP中提取了总计327个剂量学特征，包括冷点体积、一阶特征和纹理特征。在训练数据集中采用CoxNet算法进行特征选择和剂量组学签名构建。基于剂量组学评分（DS）的考克斯比例风险模型（Cox proportional hazard model），利用ODD和LBP剂量组学特征构建了两个复发预测模型（DSODD和DSLBP）。这些模型用于使用一致性指数（CI）评价复发预测的总体充分性，并基于准确度和受试者工作特征曲线下面积（AUC）评估预测性能。DSODD和DSLBP测试数据集的CI分别为0.71和0.76。测试数据集的准确度和AUC对于DSODD模型分别为0.71和0.76，对于DSLBP模型分别为0.79和0.81。基于LBP的剂量组学模型可能更准确地预测HNSCC患者放疗后的复发。
We investigated an approach for predicting recurrence after radiation therapy using local binary pattern (LBP)-based dosiomics in patients with head and neck squamous cell carcinoma (HNSCC). Recurrence/non-recurrence data were collected from 131 patients after intensity-modulated radiation therapy. The cases were divided into training (80%) and test (20%) datasets. A total of 327 dosiomics features, including cold spot volume, first-order features, and texture features, were extracted from the original dose distribution (ODD) and LBP on gross tumor volume, clinical target volume, and planning target volume. The CoxNet algorithm was employed in the training dataset for feature selection and dosiomics signature construction. Based on a dosiomics score (DS)-based Cox proportional hazard model, two recurrence prediction models (DSODDand DSLBP) were constructed using the ODD and LBP dosiomics features. These models were used to evaluate the overall adequacy of the recurrence prediction using the concordance index (CI), and the prediction performance was assessed based on the accuracy and area under the receiver operating characteristic curve (AUC). The CIs for the test dataset were 0.71 and 0.76 for DSODDand DSLBP, respectively. The accuracy and AUC for the test dataset were 0.71 and 0.76 for the DSODDmodel and 0.79 and 0.81 for the DSLBPmodel, respectively. LBP-based dosiomics models may be more accurate in predicting recurrence after radiation therapy in patients with HNSCC.