Brozgol Eugene, Kumar Pramod, Necula Daniela, Bronshtein-Berger Irena, Lindner Moshe, Medalion Shlomi, Twito Lee, Shapira Yotam, Gondra Helena, Barshack Iris, Garini Yuval
Physics Department and Nanotechnology Institute, Bar Ilan University, Ramat Gan, Israel.
Contributed equally.
Biomed Opt Express. 2022 Mar 28;13(4):2503-2515. doi: 10.1364/BOE.445782. eCollection 2022 Apr 1.
The escalating demand for diagnosing pathological biopsies requires the procedures to be expedited and automated. The existing imaging systems for measuring biopsies only measure color, and even though a lot of effort is invested in deep learning analysis, there are still serious challenges regarding the performance and validity of the data for the intended medical setting. We developed a system that rapidly acquires spectral images from biopsies, followed by spectral classification algorithms. The spectral information is remarkably more informative than the color information, and leads to very high accuracy in identifying cancer cells, as tested on tens of cancer cases. This was improved even more by using artificial intelligence algorithms that required a rather small training set, indicating the high level of information that exists in the spectral images. The most important spectral differences are observed in the nucleus and they are related to aneuploidy in tumor cells. Rapid spectral imaging measurement therefore can bridge the gap in the machine-aided diagnostics of whole biopsies, thus improving patient care, and expediting the treatment procedure.
对病理活检诊断的需求不断增加，这就要求加快并自动化相关程序。现有的用于测量活检样本的成像系统仅测量颜色，尽管在深度学习分析方面投入了大量精力，但对于预期的医疗环境而言，数据的性能和有效性仍面临严峻挑战。我们开发了一种系统，该系统能快速从活检样本中获取光谱图像，随后采用光谱分类算法。光谱信息比颜色信息更具信息量，在对数十个癌症病例进行测试时，能在识别癌细胞方面实现非常高的准确率。通过使用所需训练集相当小的人工智能算法，这一准确率得到了进一步提高，表明光谱图像中存在高水平的信息。最重要的光谱差异在细胞核中观察到，且它们与肿瘤细胞中的非整倍体有关。因此，快速光谱成像测量可以弥合全活检样本机器辅助诊断方面的差距，从而改善患者护理，并加快治疗程序。
