PRISTINE: Pre-cancer histology identification of Endobronchial biopsies using deep learning

项目总结 肺癌是癌症死亡的主要原因。为了提高存活率，迫切需要治疗方法。 阻断癌症的发展过程，降低晚期癌症患者的发病率 疾病。一个潜在的有希望的截获点是开发逆转或延缓发展的治疗方法。 肺部癌前病变(PML)。大约20%的肺癌发生在支气管上皮层 在呼吸道和这些疾病发生之前，PML是肺癌的重要临床指标 在呼吸道或偏远实质部位的风险。作为NCI-Moonshot的一部分，我们的团队致力于创建 多组肺癌前图谱(PCA)。该项目在创建临床相关生物标志物方面的成功 而治疗依赖于对PML的组织学和免疫渗透的准确评估。目前， 然而，病理评估的形态异常增加的阶段从增生， 化生、不典型增生(轻、中、重度)，到浸润性癌是具有挑战性的，也不是常规的。这个 拟议研究的目标是开发和传播计算效率高的深度学习 用于注释来自肺Pca的PML的各种组织学特征并将这些特征关联起来的框架 临床和基因组数据。我们的中心假设是深度学习可以应用于数字化的高等教育 支气管PML的完整幻灯片图像(WSIS)，以确定一组全面的组织学特征和指标 总结它们的空间组织，可能会增强PML进展为癌症的生物标志物。我们将测试 这一假设通过追求两个具体目标来实现。首先，我们将注释PML并开发一个语义 应用深度学习的分割框架预测PML的组织学特征。第二，我们将传播 我们的深度学习框架，并显示其在增强PML相关生物标记物方面的效用。建议进行的研究 具有重要意义，因为我们开发的框架可以应用于从PML预测WSIS中的其他要素 并被修改为也包括肺的其他PML(例如与肺腺癌相关的那些PML 就像在其他类型的上皮组织中发现的其他癌前病变一样，例如乳腺、结肠、前列腺等。目前， 深度学习方法尚未被应用于PMLS，该方案在独特的临床应用中具有创新性 它利用与相应的基因组和临床数据相结合的样本，并在开发补丁时- 基于卷积神经网络的PML组织学特征预测。我们的长期目标是开发一种 深度学习框架预测肺PML WSIS的各种特征并将其与基因组学相结合 这些样本的数据，以发现PML进展的强大生物标记物和预防的治疗方法 浸润性癌症的发展。

PROJECT SUMMARY Lung cancer is the leading cause of cancer death. In order to increase survival, therapies are urgently needed to intercept the cancer development process and decrease the rate of patients presenting with advanced disease. A potential promising point of interception is to develop therapies to reverse or delay the development of lung premalignant lesions (PMLs). About 20% of lung cancers arise in the epithelial layer of the bronchial airways and these are preceded by the development of PMLs that are important clinical indicators of lung cancer risk in the airways or at remote parenchymal sites. As part of the NCI-Moonshot our group is engaged in creating a multi-omic lung Pre-Cancer Atlas (PCA). The success of this project in creating clinically relevant biomarkers and therapeutics depends on accurate assessments of histology and immune infiltrates in PMLs. Currently, however, pathologic assessment of the morphological stages of increasing abnormality from hyperplasia, metaplasia, dysplasia (mild, moderate, and severe), to invasive carcinoma is challenging and not routine. The objective of the proposed study is to develop and disseminate a computationally efficient deep learning framework to annotate a variety of histologic features in PMLs from the Lung PCA and associate these features with clinical and genomic data. Our central hypothesis is that deep learning can be applied to digitized H&E whole slide images (WSIs) of bronchial PMLs to identify a comprehensive set of histologic features and metrics summarizing their spatial organization that may enhance biomarkers of PML progression to cancer. We will test this hypothesis by pursuing two specific aims. First, we will annotate PMLs and develop a semantic segmentation framework using deep learning to predict histologic features of PMLs. Second, we will disseminate our deep learning framework and show its utility in enhancing PML-associated biomarkers. The proposed study is significant because the framework we develop can be applied to predict other features in the WSIs from PMLs and be modified to encompass other PMLs of the lung (e.g. those associated with lung adenocarcinoma) as well as other premalignant lesions found in other epithelial tissue types such as breast, colon, prostate, etc. Currently, deep learning approaches have not been applied to PMLs, and this proposal is innovative in the unique clinical specimens that it leverages with corresponding genomic and clinical data and in its development of a patch- based convolutional neural network to predict histologic features of PMLs. Our long-term goal is to develop a deep learning framework to predict a variety of features from lung PML WSIs and integrate these with genomic data on these same samples to discover robust biomarkers of PML progression and therapeutics to prevent invasive cancer development.