论文标题
从CT扫描中的肺结节分割和EGFR突变状态预测的放射基因组学管道
A Radiogenomics Pipeline for Lung Nodules Segmentation and Prediction of EGFR Mutation Status from CT Scans
论文作者
论文摘要
肺癌是全球死亡的主要原因。肺癌的早期检测对于更有利的预后至关重要。放射基因组学是一门新兴学科，结合了医学成像和基因组学特征，以非侵入性地对患者进行建模。这项研究提出了一种放射基因组学管道，该管道具有：1）一种新型的混合结构（RA-SEG），可通过注意力和复发块分割肺癌； 2）深层特征分类器，以区分表皮生长因子受体（EGFR）突变状态。我们评估了多个公共数据集上提出的算法，以评估其概括性和鲁棒性。我们演示了所提出的分割和分类方法的表现如何优于现有基线和SOTA方法（73.54个骰子和93 F1分数）。
Lung cancer is a leading cause of death worldwide. Early-stage detection of lung cancer is essential for a more favorable prognosis. Radiogenomics is an emerging discipline that combines medical imaging and genomics features for modeling patient outcomes non-invasively. This study presents a radiogenomics pipeline that has: 1) a novel mixed architecture (RA-Seg) to segment lung cancer through attention and recurrent blocks; and 2) deep feature classifiers to distinguish Epidermal Growth Factor Receptor (EGFR) mutation status. We evaluate the proposed algorithm on multiple public datasets to assess its generalizability and robustness. We demonstrate how the proposed segmentation and classification methods outperform existing baseline and SOTA approaches (73.54 Dice and 93 F1 scores).
