Current pathology workflow involves staining of thin tissue slices, which otherwise would be transparent, followed by manual investigation under the microscope by a trained pathologist. While the hematoxylin and eosin (H&E) stain is well-established and a cost-effective method for visualizing histology slides, its color variability across preparations and subjectivity across clinicians remain unaddressed challenges. To mitigate these challenges, recently, we have demonstrated that spatial light interference microscopy (SLIM) can provide a path to intrinsic objective markers that are independent of preparation and human bias. Additionally, the sensitivity of SLIM to collagen fibers yields information relevant to patient outcome, which is not available in H&E. Here, we show that deep learning and SLIM can form a powerful combination for screening applications: training on 1660 SLIM images of colon glands and validating on 144 glands, we obtained an accuracy of 98% (validation dataset) and 99% (test dataset), resulting in benign vs cancer classification accuracy of 97%, defined as area under the receiver operating characteristic curve. We envision that the SLIM whole slide scanner presented here paired with artificial intelligence algorithms may prove valuable as a pre-screening method, economizing the clinician’s time and effort.

中文翻译：

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使用深度学习和空间光干涉显微镜 (SLIM) 进行无标记结直肠癌筛查

当前的病理工作流程涉及对薄组织切片进行染色，否则这些切片将是透明的，然后由训练有素的病理学家在显微镜下进行手动调查。虽然苏木精和伊红 (H&E) 染色是一种成熟且具有成本效益的可视化组织学载玻片的方法，但其跨制剂的颜色变异性和跨临床医生的主观性仍然存在未解决的挑战。为了缓解这些挑战，最近，我们已经证明空间光干涉显微镜 (SLIM) 可以提供一条获得独立于制备和人类偏见的内在客观标记的途径。此外，SLIM 对胶原纤维的敏感性产生了与患者结果相关的信息，这在 H&E 中是不可用的。这里，我们表明深度学习和 SLIM 可以形成一个强大的筛选应用组合：对 1660 张结肠腺体 SLIM 图像进行训练并在 144 个腺体上进行验证，我们获得了 98%（验证数据集）和 99%（测试数据集）的准确率，结果良性与癌症分类准确率为 97%，定义为受试者工作特征曲线下的面积。我们设想，此处介绍的 SLIM 全玻片扫描仪与人工智能算法相结合，作为一种预筛选方法可能会被证明是有价值的，从而节省临床医生的时间和精力。定义为受试者工作特征曲线下的面积。我们设想，此处介绍的 SLIM 全玻片扫描仪与人工智能算法相结合，作为一种预筛选方法可能会被证明是有价值的，从而节省临床医生的时间和精力。定义为受试者工作特征曲线下的面积。我们设想，此处介绍的 SLIM 全玻片扫描仪与人工智能算法相结合，作为一种预筛选方法可能会被证明是有价值的，从而节省临床医生的时间和精力。