Stain-free identification of tissue pathology using a generative adversarial network to infer nanomechanical signatures.
Stain-free identification of tissue pathology using a generative adversarial network to infer nanomechanical signatures.
复制标题
使用生成对抗网络来推断纳米力学特征的组织病理学无染色鉴定。
DOI：
10.1039/d1na00527h
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发表时间：
2021-11-09
影响因子：
4.7
通讯作者：
Stoyanov D
中科院分区：
文献类型：
--
作者：
Neary-Zajiczek L;Essmann C;Rau A;Bano S;Clancy N;Jansen M;Heptinstall L;Miranda E;Gander A;Pawar V;Fernandez-Reyes D;Shaw M;Davidson B;Stoyanov D
术中冰冻切片分析可以通过对切除组织的快速处理和病理评估来提高肿瘤切除手术中肿瘤边缘估计的准确性。它的适用性在某些情况下是有限的，因为与长时间手术相关的额外风险，主要是由于耗时的染色程序。我们的工作使用大块组织的一种可测量的属性来绕过染色过程：当肿瘤细胞增殖时，它们会影响周围的细胞外基质，由此产生的弹性模数的变化提供了潜在病理的特征。在这项工作中，我们准确地定位了人体肝脏组织样本的原子力显微镜测量，并训练了一个生成性对抗网络来从未染色组织切片的低分辨率图像中推断弹性系数。病理是通过对表征推断值分布的参数的无监督聚类来预测的，基于名义评估的所有样本的准确率达到89%(n=28)，对于已经由两个独立的病理学家通过后点染色验证的样本的准确率达到95%(n=20)。我们的结果表明，这项技术可以增加术中冰冻切片分析用于切除手术的可行性，并改善患者的预后。从冰冻肝组织切片的未染色图像中推断出样本范围的弹性模数。分布参数可以预测组织病理，作为术中诊断工具。
Intraoperative frozen section analysis can be used to improve the accuracy of tumour margin estimation during cancer resection surgery through rapid processing and pathological assessment of excised tissue. Its applicability is limited in some cases due to the additional risks associated with prolonged surgery, largely from the time-consuming staining procedure. Our work uses a measurable property of bulk tissue to bypass the staining process: as tumour cells proliferate, they influence the surrounding extra-cellular matrix, and the resulting change in elastic modulus provides a signature of the underlying pathology. In this work we accurately localise atomic force microscopy measurements of human liver tissue samples and train a generative adversarial network to infer elastic modulus from low-resolution images of unstained tissue sections. Pathology is predicted through unsupervised clustering of parameters characterizing the distributions of inferred values, achieving 89% accuracy for all samples based on the nominal assessment (n = 28), and 95% for samples that have been validated by two independent pathologists through post hoc staining (n = 20). Our results demonstrate that this technique could increase the feasibility of intraoperative frozen section analysis for use during resection surgery and improve patient outcomes. Sample-wide elastic modulus is inferred from unstained images of frozen liver tissue sections. Distribution parameters can predict tissue pathology for use as an intraoperative diagnostic tool.
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