Breast cancer diagnosis using spatial light interference microscopy

Abstract

The standard practice in histopathology of breast cancers is to examine a hematoxylin and eosin (H&E) stained tissue biopsy under a microscope to diagnose whether a lesion is benign or malignant. This determination is made based on a manual, qualitative inspection, making it subject to investigator bias and resulting in low throughput. Hence, a quantitative, label-free, and high-throughput diagnosis method is highly desirable. We present here preliminary results showing the potential of quantitative phase imaging for breast cancer screening and help with differential diagnosis. We generated phase maps of unstained breast tissue biopsies using spatial light interference microscopy (SLIM). As a first step toward quantitative diagnosis based on SLIM, we carried out a qualitative evaluation of our label-free images. These images were shown to two pathologists who classified each case as either benign or malignant. This diagnosis was then compared against the diagnosis of the two pathologists on corresponding H&E stained tissue images and the number of agreements were counted. The agreement between SLIM and H&E based diagnosis was 88% for the first pathologist and 87% for the second. Our results demonstrate the potential and promise of SLIM for quantitative, label-free, and high-throughput diagnosis.

Quantitative Phase Imaging for Label-Free Analysis of Cancer Cells—Focus on Digital Holographic Microscopy

“…A typical approach for automated analysis consists of two major steps: The extraction of features from images, and the classification of cells into groups on the basis of the features. Studies have shown the ability of QPI to distinguish between cancer cell types in blood using relatively few basic features such as maximum and average intensity, and cell diameter [20,100]. However, the majority of studies use more features.…”

Ultra-high speed digital micro-mirror device based ptychographic iterative engine method

“…The maximum FoV reachable is limited by the size of the DMD sensor and the size of the CCD chip. Take our experiment as an example, the DMD used is the DLP Discovery D4100 of Texas Instruments with a sensor size of 14.01 by 10.51 mm 2 , the CCD used is AVT-PE 1100B with a chip size of 15.16 by 15.16 mm 2 , so the maximum sample size that can be imaged with the proposed method is 14.01 by 10.51mm 2 , which is about 45 times that of a 10 × objective (~3.21 mm 2 ) and is large enough for most of researches in biological and material sciences. The numerical aperture of 10 × objective is about 0.25, and the numerical aperture of the above mentioned experimental setup is about 0.7, however since AVT-PE 1100B is only an industry CCD, because of its low sensitivity, lots of weak high frequency components cannot be recorded, the spatial resolution reached is only about 5μm in experiments.…”

“…Compared to imaging techniques based on the intensity detection, phase imaging can easily obtain higher image contrast especially in imaging the semi-transparent biological samples, and since they can provide another perspective for the sample observation, phase imaging is becoming more and more important for medical examinations and disease diagnoses [1,2]. Qualitative phase imaging techniques including the phase contrast and differential interference contrast are mainly adopted to make the phase retardation of sample qualitatively visible to naked eyes.…”

Label-free, multi-scale imaging of ex-vivo mouse brain using spatial light interference microscopy

“…SLIM implements as a module attached to a Zernike phase contrast microscopy, extracts phase information in a quantitative way, with sub-nanometer sensitivity21. Thanks to these capabilities, SLIM has been used to study cell growth22, E. coli structure23, neural differentiation and network emergence24, cell tomography25, cancer diagnosis and prognosis2627. However, with regard to brain imaging, so far the focus has been limited to imaging hippocampal neurons and glial cells in culture21.…”