论文标题
空间及文化感知（空间）“虚拟活检”放射基因组图，以靶向肿瘤突变状态在结构MRI上
Spatial-And-Context aware (SpACe) "virtual biopsy" radiogenomic maps to target tumor mutational status on structural MRI
论文作者
论文摘要
随着对个性化癌症治疗的强调，放射基因组学在识别常规成像（即MRI）扫描中识别靶肿瘤突变状态的希望。这些方法分为两类：（1）使用来自整个肿瘤的图像特征来识别基因突变状态，或（2）基于人群统计数据的基因突变状态的可能性。尽管许多基因（即EGFR，MGMT）在空间上是变异的，但在成像上可靠评估基因突变状态的一个重大挑战是缺乏用于训练模型的可用共定位的地面真相。我们介绍了空间和封闭式Aware（空间）“虚拟活检”地图，其中包含来自共定位活检站点的上下文功能以及人口地图的空间 - 培训，在最小绝对的收缩和选择操作员（LASSO）回归模型中，以获得每伏的人均突变状态（m+ vs m-s）的情况。然后，我们使用概率的成对马尔可夫模型来提高体素预测概率。我们评估了通过相应活检获得的共定位地面真理对太空图在MRI扫描中的疗效，以预测胶质母细胞瘤中2个驱动基因的突变状态：（1）EGFR（N = 91）和（2）MGMT（n = 81）。当与深度学习（DL）和放射线模型进行比较时，在识别EGFR扩增状态的训练和测试精度为90％（n = 71）和90.48％（n = 21）的训练和测试准确性时，与80％和71.4％通过radiomics，以及74.28％和74.28％和65.5％的DL。对于MGMT状态，使用空间的训练和测试精度为88.3％（n = 61）和71.5％（n = 20），而使用DL则为52.4％和66.7％，使用DL为79.3％和68.4％。经过验证，空间图可以提供手术导航，以改善采样位点的定位，以靶向癌症中特定驱动基因。
With growing emphasis on personalized cancer-therapies,radiogenomics has shown promise in identifying target tumor mutational status on routine imaging (i.e. MRI) scans. These approaches fall into 2 categories: (1) deep-learning/radiomics (context-based), using image features from the entire tumor to identify the gene mutation status, or (2) atlas (spatial)-based to obtain likelihood of gene mutation status based on population statistics. While many genes (i.e. EGFR, MGMT) are spatially variant, a significant challenge in reliable assessment of gene mutation status on imaging has been the lack of available co-localized ground truth for training the models. We present Spatial-And-Context aware (SpACe) "virtual biopsy" maps that incorporate context-features from co-localized biopsy site along with spatial-priors from population atlases, within a Least Absolute Shrinkage and Selection Operator (LASSO) regression model, to obtain a per-voxel probability of the presence of a mutation status (M+ vs M-). We then use probabilistic pair-wise Markov model to improve the voxel-wise prediction probability. We evaluate the efficacy of SpACe maps on MRI scans with co-localized ground truth obtained from corresponding biopsy, to predict the mutation status of 2 driver genes in Glioblastoma: (1) EGFR (n=91), and (2) MGMT (n=81). When compared against deep-learning (DL) and radiomic models, SpACe maps obtained training and testing accuracies of 90% (n=71) and 90.48% (n=21) in identifying EGFR amplification status,compared to 80% and 71.4% via radiomics, and 74.28% and 65.5% via DL. For MGMT status, training and testing accuracies using SpACe were 88.3% (n=61) and 71.5% (n=20), compared to 52.4% and 66.7% using radiomics,and 79.3% and 68.4% using DL. Following validation,SpACe maps could provide surgical navigation to improve localization of sampling sites for targeting of specific driver genes in cancer.
