论文标题
使用时空CBCT图像对肺癌患者的早期反应评估
Early Response Assessment in Lung Cancer Patients using Spatio-temporal CBCT Images
论文作者
论文摘要
我们报告了一个模型，以预测患者对非小细胞肺癌（NSCLC）的治疗放射治疗（RT）的放射学反应。 在六周的RT课程中，每周获取的锥束计算机断层扫描图像由高级辐射肿瘤学家使用53例患者（每位患者7张图像）与总肿瘤体积（GTV）进行了轮廓。 图像的可变形注册为每个患者的连续图像产生了六个变形场。 田野的雅各布提供了局部扩张/收缩的度量，并在我们的模型中使用。 比较了划定后注册后，以计算GTV内的不变（$ u $），新生长（$ g $）和减少（$ r $）区域。 这些区域的平均雅各比$μ_U$，$μ_g$和$μ_r$在统计上进行了比较，并提出了响应评估模型。 如果$μ_r<1.0 $，$μ_r<μ_U$和$μ_g<μ_U$ $，则假设良好的响应是一个良好的响应。 为了早期预测治疗后反应，使用了第一，三周的图像。 我们的模型预测临床反应，精度为$ 74 \％$。 使用降低CT数（CTN）和降低GTV百分比作为逻辑回归的特征，在P = 0.005的情况下，面积为0.65。 将逻辑回归模型与提出的假设相结合的比值比为20.0（p = 0.0）。
We report a model to predict patient's radiological response to curative radiation therapy (RT) for non-small-cell lung cancer (NSCLC). Cone-Beam Computed Tomography images acquired weekly during the six-week course of RT were contoured with the Gross Tumor Volume (GTV) by senior radiation oncologists for 53 patients (7 images per patient). Deformable registration of the images yielded six deformation fields for each pair of consecutive images per patient. Jacobian of a field provides a measure of local expansion/contraction and is used in our model. Delineations were compared post-registration to compute unchanged ($U$), newly grown ($G$), and reduced ($R$) regions within GTV. The mean Jacobian of these regions $μ_U$, $μ_G$ and $μ_R$ are statistically compared and a response assessment model is proposed. A good response is hypothesized if $μ_R < 1.0$, $μ_R < μ_U$, and $μ_G < μ_U$. For early prediction of post-treatment response, first, three weeks' images are used. Our model predicted clinical response with a precision of $74\%$. Using reduction in CT numbers (CTN) and percentage GTV reduction as features in logistic regression, yielded an area-under-curve of 0.65 with p=0.005. Combining logistic regression model with the proposed hypothesis yielded an odds ratio of 20.0 (p=0.0).
