We have integrated tissue quantitative phase imaging with Raman spectroscopy, and used it for analyzing benign and malignant cancer tissue samples without staining. We used the acquired stain-free OPD-based texture features of the tissues and deep learning to segment the urothelium layers, where cancer starts from, with the stained tissue as a ground-truth for training. Then, location-guided Raman spectroscopy measurements were acquired from the urothelium according to the segmentation results. We then classified the tissue type based on the location-guided Raman spectroscopy measurement with higher accuracy compared to classifying them without urothelium segmentation.
Hundreds of thousands of people are diagnosed with bladder cancer yearly, with recurrence rates of 60% in the first year. We propose a new label-free imaging technique for noninvasive and automated individual cell processing with high discriminating power in detecting cancer cells in urine samples. We analyzed urine samples from bladder cancer patients by acquiring holograms of cells during flow. We then extracted highly discriminative features and classified the cells to their types. This noninvasive label-free technique allows us to monitor and diagnose cancer progression from a simple urine sample and has the potential to substitute the invasive cystoscopy procedure.
We propose a multimodal quantitative, label-free and nondestructive diagnostic metrology technique by integrating off-axis interferometric phase microscopy (IPM) and Raman spectroscopy (RS), for analyzing normal and malignant bladder tissue samples. We built a Mach–Zehnder interferometer connected to a commercial confocal microscope for imaging a large area of tissue slices, up to a few millimeters, by semi-automatic scanning of the tissue. Bright-field image of hematoxylin and eosin stained tissue slice of the same area was also acquired. Measurements of Raman spectra were acquired using our RS system with excitation wavelength of 561 nm. Using the quantitative phase information, we obtained various spatial and morphological parameters of the tissues such as the anisotropy factor, which demonstrated their direct correlation with tumor presence. This method is expected to be useful for stain-free cancer diagnosis, while obtaining both quantitative information about tissue morphological modifications and changes in tissue Raman scattering properties induced by cancer.
We developed stain-free optical biomarkers for high-grade T1 bladder cancer tissues based on interferometric phase microscopy (IPM). IPM records a topographic map that represents, for each spatial point, the integral of the refractive-index values along the tissue sample thickness, thus accounting for both the tissue internal morphology and contents. Following the extraction of various parameters from this IPM-based topographic maps of healthy and bladder cancer tissues, we employed a machine learning multivariant analysis for stain-free grading of the bladder cancer invasiveness. Our tools are expected to be used for rapid and inexpensive diagnosis of bladder cancer tissues.
Kidney cancer affects 65,000 new patients every. As computerized tomography became ubiquitous, the number of small, incidentally detected renal masses increased. About 6,000 benign cases are misclassified radiographically as malignant and removed surgically. Raman spectroscopy (RS) has been widely demonstrated for disease discrimination, however intense near-infrared auto-fluorescence of certain tissues (e.g kidney) can present serious challenges to bulk tissue diagnosis. A 1064nm excitation dispersive detection RS system demonstrated the ability to collect spectra with superior quality in tissues with strong auto-fluorescence. Our objective is to develop a 1064 nm dispersive detection RS system capable of differentiating normal and malignant renal tissue. We will report on the design and development of a clinical system for use in nephron sparing surgeries. We will present pilot data that has been collected from normal and malignant ex vivo kidney specimens using a benchtop RS system. A total of 93 measurements were collected from 12 specimens (6 Renal Cell Carcinoma, 6 Normal ). Spectral classification was performed using sparse multinomial logistic regression (SMLR). Correct classification by SMLR was obtained in 78% of the trials with sensitivity and specificity of 82% and 75% respectively. We will present the association of spectral features with biological indicators of healthy and diseased kidney tissue. Our findings indicate that 1064nm RS is a promising technique for differentiation of normal and malignant renal tissue. This indicates the potential for accurately separating healthy and cancerous tissues and suggests implications for utilizing RS for optical biopsy and surgical guidance in nephron sparing surgery.
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