Shell功能：预测非小细胞肺癌和宫颈癌放疗后远处失败的新放射组学描述符
Shell feature: a new radiomics descriptor for predicting distant failure after radiotherapy in non-small cell lung cancer and cervix cancer
摘要：
目的建立一种新的预测非小细胞肺癌（NSCLC）和宫颈癌（CC）患者远处转移失败的肿瘤外壳特征。患者和方法使用48例接受立体定向体部放射治疗（SBRT）的NSCLC患者和52例接受体外放射治疗和同步化疗后接受高剂量率腔内近距离放射治疗的CC患者的治疗前正电子发射断层成像（PET）图像构建壳层预测模型。从一系列轴向PET切片中提取由肿瘤边界周围的外部体素组成的壳特征。这一特征背后的假设是，非侵袭性和侵袭性肿瘤在肿瘤周边可能具有不同的形态学模式，这反过来反映了PET图像中放射学表现的差异。利用支持向量机（SVM）分类器对贝壳进行分类，并与基于灰度共生矩阵（GLCM）的纹理、基于邻域灰度色调差矩阵（NGTDM）的纹理以及这四种特征的组合进行比较。根据准确性、灵敏度、特异性和受试者工作曲线下面积（AUC）对结果进行评估。结果对于NSCLC，外壳特征实现的AUC为0.82，而其他特征实现的最高AUC为0.76。同样，对于CC，外壳特征实现的AUC为0.83，而其他特征实现的最高AUC为0.76。此外，在所有情况下，外壳和其他特征之间的性能差异显著（P < 0.005）。结论我们提出了一种与肿瘤转移相关的基于边界的壳特征。对于NSCLC和CC的远端失效预测，外壳特征显示出比所有其他特征更好的预测性能。
英文摘要：
Purpose To develop and demonstrate a novel tumor shell feature for predicting distant failure in non-small cell lung cancer (NSCLC) and cervical cancer (CC) patients. Patients and Methods The shell predictive model was constructed using pretreatment positron emission tomography (PET) images from 48 NSCLC patients received stereotactic body radiation therapy (SBRT) and 52 CC patients underwent external beam radiation therapy and concurrent chemotherapy followed with high-dose-rate intracavitary brachytherapy. A shell feature, consisting of outer voxels around the tumor boundary, was extracted from a series of axial PET slices. The hypothesis behind this feature is that non-invasive and invasive tumors may have different morphologic patterns in the tumor periphery, in turn reflecting the differences in radiological presentations in the PET images. The utility of the shell was evaluated by the support vector machine (SVM) classifier in comparison with intensity, geometry, gray level co-occurrence matrix (GLCM) based texture, neighborhood gray tone difference matrix (NGTDM) based texture, and a combination of these four features. The results were assessed in terms of accuracy, sensitivity, specificity, and the area under the receiver operating curve (AUC). Results For NSCLC, the AUC achieved by the shell feature was 0.82 while the highest AUC achieved by the other features was 0.76. Similarly, for CC, the AUC achieved by the shell feature was 0.83 while the highest AUC achieved by the other features was 0.76. Also, the difference in performance between shell and the other features was significant (P < 0.005) in all cases. Conclusions We propose a boundary-based shell feature that correlates with tumor metastasis. The shell feature showed better predictive performance than all the other features for distant failure prediction in both NSCLC and CC.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
