论文标题
纹理建模以预测脑肿瘤患者的免疫细胞状态和存活
Modeling of Textures to Predict Immune Cell Status and Survival of Brain Tumour Patients
论文作者
论文摘要
放射素学显示了不同类型的癌症（例如神经胶质瘤）预测临床结果的能力。它可以具有在治疗前评估免疫疗法反应的非侵入性手段。但是，使用深卷积神经网络（CNN）的放射线学需要大量的训练图像集。为了避免此问题，我们研究了一种新的成像功能，该功能以学习的3D CNN特征的高斯混合模型（GMM）模型模型。使用这些深层放射线特征（DRF），我们旨在预测神经胶质瘤患者的免疫标记状态（低对与高）和总生存期。我们通过在标记的MRI扫描标记肿瘤区域内汇总预先训练的3D-CNN的激活图来提取DRF，这对应于151例患者的免疫标记。进行我们的实验以评估所提出的DRF，三个免疫细胞标记物（巨噬细胞M1，中性粒细胞和T细胞卵泡辅助器）之间的关系，并测量它们与整体生存的关联。使用随机森林（RF）模型，DRF能够预测巨噬细胞M1，中性粒细胞和T细胞卵泡辅助器的ROC曲线（AUC）下面的区域（AUC）下方的免疫标记状态。与DRF和临床变量，Kaplan-Meier估计量和对数秩检验结合的免疫标记，在预测的患者组（短期与长期生存与长期生存）之间达到了最重要的差异，P \，= \，4.31 $ \ times $ 10 $^{-7} $ f \，= \，0.03 $ f \，= \，0.03，= \，0.03变量和p \，= \，1.45 $ \ times $ 10 $^{ - 5} $用于DRFS。我们的发现表明，RF模型中使用的拟议特征（DRF）可能会通过定期获得的成像数据在手术前预测脑瘤患者。
Radiomics has shown a capability for different types of cancers such as glioma to predict the clinical outcome. It can have a non-invasive means of evaluating the immunotherapy response prior to treatment. However, the use of deep convolutional neural networks (CNNs)-based radiomics requires large training image sets. To avoid this problem, we investigate a new imaging features that model distribution with a Gaussian mixture model (GMM) of learned 3D CNN features. Using these deep radiomic features (DRFs), we aim to predict the immune marker status (low versus high) and overall survival for glioma patients. We extract the DRFs by aggregating the activation maps of a pre-trained 3D-CNN within labeled tumor regions of MRI scans that corresponded immune markers of 151 patients. Our experiments are performed to assess the relationship between the proposed DRFs, three immune cell markers (Macrophage M1, Neutrophils and T Cells Follicular Helper), and measure their association with overall survival. Using the random forest (RF) model, DRFs was able to predict the immune marker status with area under the ROC curve (AUC) of 78.67, 83.93 and 75.67\% for Macrophage M1, Neutrophils and T Cells Follicular Helper, respectively. Combined the immune markers with DRFs and clinical variables, Kaplan-Meier estimator and Log-rank test achieved the most significant difference between predicted groups of patients (short-term versus long-term survival) with p\,=\,4.31$\times$10$^{-7}$ compared to p\,=\,0.03 for Immune cell markers, p\,=\,0.07 for clinical variables , and p\,=\,1.45$\times$10$^{-5}$ for DRFs. Our findings indicate that the proposed features (DRFs) used in RF models may significantly consider prognosticating patients with brain tumour prior to surgery through regularly acquired imaging data.
