Label-free colorectal cancer screening using deep learning and spatial light interference microscopy (SLIM).

Yuchen R. He,Jingfang K. Zhang,Nahil Sobh,Gabriel Popescu

doi:10.1063/5.0004723

Yuchen R. He, Jingfang K. Zhang + Show 2 more

Open Access

https://doi.org/10.1063/5.0004723

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Journal: APL photonics	Publication Date: Apr 1, 2020
Citations: 43	License type: cc-by

Affiliation: University of Illinois Urbana-Champaign

Abstract

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 1,660 SLIM images of colon glands and validating on 144 glands, we obtained a benign vs. cancer classification accuracy of 99%. 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.

Highlights

Quantitative phase imaging (QPI)1 has emerged as a powerful label-free method for biomedical applications.2 More recently, due to its high sensitivity to tissue nanoarchitecture and quantitative output, QPI has been proven valuable in pathology.3,4 Combining spatial light interference microscopy (SLIM)5,6 and dedicated software for whole slide imaging (WSI) allowed us to demonstrate the value of the tissue refractive index as an intrinsic marker for diagnosis and prognosis.7–14 So far, we have used various metrics derived from the QPI map to obtain clinically relevant information
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
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% and 99%, resulting in benign vs cancer classification accuracy of 97%, defined as area under the receiver operating characteristic curve

Summary

Introduction

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. Quantitative phase imaging (QPI)1 has emerged as a powerful label-free method for biomedical applications.2 More recently, due to its high sensitivity to tissue nanoarchitecture and quantitative output, QPI has been proven valuable in pathology.3,4 Combining spatial light interference microscopy (SLIM)5,6 and dedicated software for whole slide imaging (WSI) allowed us to demonstrate the value of the tissue refractive index as an intrinsic marker for diagnosis and prognosis.7–14 So far, we have used various metrics derived from the QPI map to obtain clinically relevant information.

Results

Conclusion