The current practice of surgical pathology relies on external contrast agents to reveal tissue architecture, which is then qualitatively examined by a trained pathologist. The diagnosis is based on the comparison with standardized empirical, qualitative assessments of limited objectivity. We propose an approach to pathology based on interferometric imaging of “unstained” biopsies, which provides unique capabilities for quantitative diagnosis and automation. We developed a label-free tissue scanner based on “quantitative phase imaging,” which maps out optical path length at each point in the field of view and, thus, yields images that are sensitive to the “nanoscale” tissue architecture. Unlike analysis of stained tissue, which is qualitative in nature and affected by color balance, staining strength and imaging conditions, optical path length measurements are intrinsically quantitative, i.e., images can be compared across different instruments and clinical sites. These critical features allow us to automate the diagnosis process. We paired our interferometric optical system with highly parallelized, dedicated software algorithms for data acquisition, allowing us to image at a throughput comparable to that of commercial tissue scanners while maintaining the nanoscale sensitivity to morphology. Based on the measured phase information, we implemented software tools for autofocusing during imaging, as well as image archiving and data access. To illustrate the potential of our technology for large volume pathology screening, we established an “intrinsic marker” for colorectal disease that detects tissue with dysplasia or colorectal cancer and flags specific areas for further examination, potentially improving the efficiency of existing pathology workflows.
More than 1 million men in the United States undergo a prostate biopsy procedure annually and approximately 200,000 men receive a diagnosis of prostate cancer. 5-10% of these men have to undergo a repeat biopsy due to insufficient tissue sampling. We are studying the utility of a multi-spectral time resolved fluorescence spectroscopy (MS-TRFS) technique for real-time prostate cancer diagnosis. The MS-TRFS imaging setup, which includes a fiberoptic set-up with a 355nm excitation light source coupled with a blue (450nm) aiming beam, was used to image ex-vivo prostatectomy specimen. The prostate tissue from 11 patients was sectioned at 2mm thickness and the fluorescence lifetime information was overlaid spatially for histology and thus, diagnostic co-registration. Initial results show that fluorescence lifetime in the 390±40nm channel, which measures collagen and elastin signatures, is longer for glandular regions than in the stromal regions. Additionally, lifetime in the 452±45nm channel, corresponding to NAD redox state, is longer in the cancerous glandular region in comparison with the normal glandular regions. Current work is focused on developing real-time quantitative algorithms to combine the fluorescence signatures from the two channels for performing prostate cancer diagnosis on biopsies.
Differentiating radiation-induced necrosis from recurrent tumor in the brain remains a significant challenge to the neurosurgeon. Clinical imaging modalities are not able to reliably discriminate the two tissue types, making biopsy location selection and surgical management difficult. Label-free fluorescence lifetime techniques have previously been shown to be able to delineate human brain tumor from healthy tissues. Thus, fluorescence lifetime techniques represent a potential means to discriminate the two tissues in real-time during surgery. This study aims to characterize the endogenous fluorescence lifetime signatures from radiation induced brain necrosis in a tumor-free rat model. Fischer rats received a single fraction of 60 Gy of radiation to the right hemisphere using a linear accelerator. Animals underwent a terminal live surgery after gross necrosis had developed, as verified with MRI. During surgery, healthy and necrotic brain tissue was measured with a fiber optic needle connected to a multispectral fluorescence lifetime system. Measurements of the necrotic tissue showed a 48% decrease in intensity and 20% increase in lifetimes relative to healthy tissue. Using a support vector machine classifier and leave-one-out validation technique, the necrotic tissue was correctly classified with 94% sensitivity and 97% specificity. Spectral contribution analysis also confirmed that the primary source of fluorescence contrast lies within the redox and bound-unbound population shifts of nicotinamide adenine dinucleotide. A clinical trial is presently underway to measure these tissue types in humans. These results show for the first time that radiation-induced necrotic tissue in the brain contains significantly different metabolic signatures that are detectable with label-free fluorescence lifetime techniques.
We present an approach for automatic diagnosis of tissue biopsies. Our methodology consists of a quantitative phase imaging tissue scanner and machine learning algorithms to process these data. We illustrate the performance by automatic Gleason grading of prostate specimens. The imaging system operates on the principle of interferometry and, as a result, reports on the nanoscale architecture of the unlabeled specimen. We use these data to train a random forest classifier to learn textural behaviors of prostate samples and classify each pixel in the image into different classes. Automatic diagnosis results were computed from the segmented regions. By combining morphological features with quantitative information from the glands and stroma, logistic regression was used to discriminate regions with Gleason grade 3 versus grade 4 cancer in prostatectomy tissue. The overall accuracy of this classification derived from a receiver operating curve was 82%, which is in the range of human error when interobserver variability is considered. We anticipate that our approach will provide a clinically objective and quantitative metric for Gleason grading, allowing us to corroborate results across instruments and laboratories and feed the computer algorithms for improved accuracy.
Quantitative phase imaging has been used in the past to study the dry mass of cells and study cell growth under various treatment conditions. However, the relationship between cellular redox and growth rates has not yet been studied in this context. This study employed the recombinant Glrx-roGFP2 redox biosensor targeted to the mitochondrial matrix or cytosolic compartments of A549 lung epithelial carcinoma cells. The Glrx-roGFP2s biosensor consists of a modified GFP protein containing internal cysteine residues sensitive to the local redox environment. The formation/dissolution of sulfide bridges contorts the internal chromophore, dictating corresponding changes in florescence emission that provide direct measures of the local redox potential. Combining 2-channel florescent imaging of the redox sensor with quantitative phase imaging allowed observation of redox homeostasis alongside measurements of cellular mass during full cycles of cellular division. The results indicate that mitochondrial redox showed a stronger inverse correlation with cell growth than cytoplasmic redox states; although redox changes are restricted to a 5% range. We are now studying the relationship between mitochondrial redox and cell growth in an isogenic series of breast cell lines built upon the MCF-10A genetic background that vary both in malignancy and metastatic potential.
Traction force microscopy is the most widely used technique for studying the forces exerted by cells on deformable substrates. However, the method is computationally intense and cells have to be detached from the substrate prior to measuring the displacement map. We have developed a new method, referred to as Hilbert phase dynamometry (HPD), which yields real-time force fields and, simultaneously, cell dry mass and growth information. HPD operates by imaging cells on a deformable substrate that is patterned with a grid of fluorescent proteins. A Hilbert transform is used to extract the phase map associated with the grid deformation, which provides the displacement field. By combining this information with substrate stiffness, an elasticity model was developed to measure forces exerted by cells with high spatial resolution. In our study, we prepared 10kPa gels and them with a 2-D grid of FITC-conjugated fibrinogen/fibronectin mixture, an extracellular matrix protein to which cells adhere. We cultured undifferentiated mesenchymal stem cells (MSC), and MSCs that were in the process of undergoing adipogenesis and osteogenesis. The cells were measured over the course of 24 hours using Spatial Light Interference Microscopy (SLIM) and wide-field epi-fluorescence microscopy allowing us to simultaneously measure cell growth and the forces exerted by the cells on the substrate.
We present a new method, referred to as phase correlation imaging (PCI), to study cell dynamics and function through temporal phase correlation analysis. PCI offers label-free, high-performance, simple-design, as well as suitability for operation in a conventional microscopy setting. PCI works without the need for controlled or synchronized photoactivation and sophisticated acquisition schemes, and only involves taking a sequence of phase images. The PCI image incorporates information on the phase fluctuations induced by both Brownian motion and deterministic motion of intracellular transport across large scales. We employed spatial light interference microscopy (SLIM) recently developed in our laboratory to experimentally measure quantitative phase information which renders the thickness and refractive index of cellular components without adding contrast agents. The acquisition process is repeated to obtain time-lapse phase images. We calculate the correlation time at each pixel for acquired time-lapse phase images and obtain the correlation time map in space. By temporal correlation analysis, PCI reveals cell dynamics information, which is complementary to quantitative phase imaging itself.
In this paper, we present an updated automatic diagnostic procedure for prostate cancer using quantitative phase imaging (QPI). In a recent report [1], we demonstrated the use of Random Forest for image segmentation on prostate cores imaged using QPI. Based on these label maps, we developed an algorithm to discriminate between regions with Gleason grade 3 and 4 prostate cancer in prostatectomy tissue. The Area-Under-Curve (AUC) of 0.79 for the Receiver Operating Curve (ROC) can be obtained for Gleason grade 4 detection in a binary classification between Grade 3 and Grade 4. Our dataset includes 280 benign cases and 141 malignant cases. We show that textural features in phase maps have strong diagnostic values since they can be used in combination with the label map to detect presence or absence of basal cells, which is a strong indicator for prostate carcinoma. A support vector machine (SVM) classifier trained on this new feature vector can classify cancer/non-cancer with an error rate of 0.23 and an AUC value of 0.83.
Classification of arthropods is performed by characterization of fine features such as setae and cuticles. An unstained whole arthropod specimen mounted on a slide can be preserved for many decades, but is difficult to study since current methods require sample manipulation or tedious image processing. Spatial light interference microscopy (SLIM) is a quantitative phase imaging (QPI) technique that is an add-on module to a commercial phase contrast microscope. We use SLIM to image a whole organism springtail Ceratophysella denticulata mounted on a slide. This is the first time, to our knowledge, that an entire organism has been imaged using QPI. We also demonstrate the ability of SLIM to image fine structures in addition to providing quantitative data that cannot be obtained by traditional bright field microscopy.
Spatiotemporal patterns of intracellular transport are very difficult to quantify and, consequently, continue to be insufficiently understood. While it is well documented that mass trafficking inside living cells consists of both random and deterministic motions, quantitative data over broad spatiotemporal scales are lacking. We studied the intracellular transport in live cells using spatial light interference microscopy, a high spatiotemporal resolution quantitative phase imaging tool. The results indicate that in the cytoplasm, the intracellular transport is mainly active (directed, deterministic), while inside the nucleus it is both active and passive (diffusive, random). Furthermore, we studied the behavior of the two-dimensional mass density over 30 h in HeLa cells and focused on the active component. We determined the standard deviation of the velocity distribution at the point of cell division for each cell and compared the standard deviation velocity inside the cytoplasm and the nucleus. We found that the velocity distribution in the cytoplasm is consistently broader than in the nucleus, suggesting mechanisms for faster transport in the cytosol versus the nucleus. Future studies will focus on improving phase measurements by applying a fluorescent tag to understand how particular proteins are transported inside the cell.
We report, for the first time, the use of Quantitative Phase Imaging (QPI) images to perform automatic prostate cancer diagnosis. A machine learning algorithm is implemented to learn textural behaviors of prostate samples imaged under QPI and produce labeled maps of different regions for testing biopsies (e.g. gland, stroma, lumen etc.). From these maps, morphological and textural features are calculated to predict outcomes of the testing samples. Current performance is reported on a dataset of more than 300 cores of various diagnosis results.
1 in 7 men receive a diagnosis of prostate cancer in their lifetime. The aggressiveness of the treatment plan adopted by the patient is strongly influenced by Gleason grade. Gleason grade is determined by the pathologist based on the level of glandular formation and complexity seen in the patient’s biopsy. However, studies have shown that the disagreement rate between pathologists on Gleason grades 3 and 4 is high and this affects treatment options. We used quantitative phase imaging to develop an objective method for Gleason grading. Using the glandular solidity, which is the ratio of the area of the gland to a convex hull fit around it, and anisotropy of light scattered from the stroma immediately adjoining the gland, we were able to quantitatively separate Gleason grades 3 and 4 with 81% accuracy in 43 cases marked as difficult by pathologists.
We used a new quantitative high spatiotemporal resolution phase imaging tool to explore the nuclear structure and dynamics of individual cells. We used a novel analysis tool to quantify the diffusion outside and inside the nucleus of live cells. We also obtained information about the nuclear spatio temporal mass density in metastatic cells. The results indicate that in the cytoplasm, the intracellular transport is mainly active (direct, deterministic), while inside the nucleus it is both active and passive (diffusive, random). We calculated the standard deviation of velocities in active transport and the diffusion coefficient for passive transport.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.