Shao Dan, Su Fei, Zou Xueyu, Lu Jie, Wu Sitong, Tian Ruijun, Ran Dongmei, Guo Zhiyong, Jin Dayong
School of Electronic and Information, Yangtze University, Jingzhou434023, China.
UTS-SUSTech Joint Research Centre for Biomedical Materials and Devices, Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen518055, China.
Anal Chem. 2023 Feb 7;95(5):2664-2670. doi: 10.1021/acs.analchem.2c03020. Epub 2023 Jan 26.
Lung adenocarcinoma is the most common histologic type of lung cancer. The pixel-level labeling of histologic patterns of lung adenocarcinoma can assist pathologists in determining tumor grading with more details than normal classification. We manually annotated a dataset containing a total of 1000 patches (200 patches for each pattern) of 512 × 512 pixels and 420 patches (contains test sets) of 1024 × 1024 pixels according to the morphological features of the five histologic patterns of lung adenocarcinoma (lepidic, acinar, papillary, micropapillary, and solid). To generate an even large amount of data patches, we developed a data stitching strategy as a data augmentation for classification in model training. Stitched patches improve the Dice similarity coefficient (DSC) scores by 24.06% on the whole-slide image (WSI) with the solid pattern. We propose a WSI analysis framework for lung adenocarcinoma pathology, intelligently labeling lung adenocarcinoma histologic patterns at the pixel level. Our framework contains five branches of deep neural networks for segmenting each histologic pattern. We test our framework with 200 unclassified patches. The DSC scores of our results outpace comparing networks (U-Net, LinkNet, and FPN) by up to 10.78%. We also perform results on four WSIs with an overall accuracy of 99.6%, demonstrating that our network framework exhibits better accuracy and robustness in most cases.
肺腺癌是肺癌最常见的组织学类型。肺腺癌组织学模式的像素级标注可以帮助病理学家比常规分类更详细地确定肿瘤分级。我们根据肺腺癌的五种组织学模式（鳞屑状、腺泡状、乳头状、微乳头状和实性）的形态特征，手动标注了一个数据集，其中包含总共1000个512×512像素的切片块（每种模式200个切片块）以及420个1024×1024像素的切片块（包含测试集）。为了生成更多的数据切片块，我们开发了一种数据拼接策略作为模型训练中分类的数据增强方法。拼接后的切片块在带有实性模式的全切片图像（WSI）上使骰子相似系数（DSC）得分提高了24.06%。我们提出了一个用于肺腺癌病理学的WSI分析框架，在像素级别智能标注肺腺癌组织学模式。我们的框架包含五个深度神经网络分支，用于分割每种组织学模式。我们用200个未分类的切片块测试了我们的框架。我们结果的DSC得分比对比网络（U-Net、LinkNet和FPN）高出多达10.78%。我们还在四个WSI上进行了测试，总体准确率为99.6%，表明我们的网络框架在大多数情况下表现出更好的准确性和鲁棒性。
