Selective sampling using confocal Raman spectroscopy provides enhanced specificity for urinary bladder cancer diagnosis
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Non-muscle-invasive bladder cancer affects millions of people worldwide, resulting in significant discomfort to the patient and potential death. Today, cystoscopy is the gold standard for bladder cancer assessment, using white light endoscopy to detect tumor suspected lesion areas, followed by resection of these areas and subsequent histopathological evaluation. Not only does the pathological examination take days, but due to the invasive nature, the performed biopsy can result in significant harm to the patient. Nowadays, optical modalities, such as optical coherence tomography (OCT) and Raman spectroscopy (RS), have proven to detect cancer in real time and can provide more detailed clinical information of a lesion, e.g. its penetration depth (stage) and the differentiation of the cells (grade). In this paper, we present an ex vivo study performed with a combined piezoelectric tube-based OCT-probe and fiber optic RS-probe imaging system that allows large field-of-view imaging of bladder biopsies, using both modalities and co-registered visualization, detection and grading of cancerous bladder lesions. In the present study, 119 examined biopsies were characterized, showing that fiber-optic based OCT provides a sensitivity of 78% and a specificity of 69% for the detection of non-muscle-invasive bladder cancer, while RS, on the other hand, provides a sensitivity of 81% and a specificity of 61% for the grading of low- and high-grade tissues. Moreover, the study shows that a piezoelectric tube-based OCT probe can have significant endurance, suitable for future long-lasting in vivo applications. These results also indicate that combined OCT and RS fiber probe-based characterization offers an exciting possibility for label-free and morpho-chemical optical biopsies for bladder cancer diagnostics.
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Raman micro-spectroscopy is an optical technique that can provide information on the biochemical composition of biological cells. Raman cytology, whereby Raman spectra are recorded from the nucleus of human epithelial cells is an active area of research. This typically involves the application of multivariate statistical algorithms to classify cell type, or disease group, based on the Raman spectrum. Although this approach has been shown to improve the diagnostic sensitivity of clinical cervical, bladder, and oral cytology for the identification of cancer cells, there has been no clinical adoption to date. The main reasons for this are the slow recording time and lack of reproducibility. In this paper, we review a recently proposed automated Raman cytology system based on image processing that can record several thousands of cell nuclei/day. The automation process is implemented using an open-source microscopy control system called Micro-Manager, which can readily be adapted by those with existing Raman microscopes and is designed to target the unstained nucleus, identified by imaging a plane below the sample, which is the primary target for Raman cytology based cancer diagnostics. In this paper we investigate the application of automated Raman cytology for the classification of two sub-types of Triple Negative Breast Cancer Cell Lines prepared using the ThinPrep protocol and we discuss how this approach could potentially improve the diagnostic sensitivity of Fine Needle Aspiration Cytology for Breast Cancer Diagnosis.
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Selective-sampling Raman Imaging enables intraoperative assessment of excised surgical margins in cancer surgery, a review.
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Prostate cancer is one of the most common malignancies of the older males worldwide. Early diagnosis and treatment are important to improve the survival of patients. Recently, we developed a new method for prostate cancer screening: by measuring the serum surface-enhanced Raman spectroscopy of prostate cancer patients and normal subjects, combining with classification algorithms of support vector machines, the measured surface-enhanced Raman spectroscopy spectra are successfully classified with accuracy of 98.1%. Although the practical application faces several difficulties, we believe that this label-free serum surface-enhanced Raman spectroscopy analysis technique combined with support vector machine diagnostic algorithms will become a powerful tool for noninvasive prostate cancer screening in the future.
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No AccessJournal of Urology1 Feb 1988Cancer Detection by Quantitative Fluorescence Image Analysis William L. Parry and George P. Hemstreet William L. ParryWilliam L. Parry and George P. HemstreetGeorge P. Hemstreet View All Author Informationhttps://doi.org/10.1016/S0022-5347(17)42384-6AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Quantitative fluorescence image analysis is a rapidly evolving biophysical cytochemical technology with the potential for multiple clinical and basic research applications. We report the application of this technique for bladder cancer detection and discuss its potential usefulness as an adjunct to methods used currently by urologists for the diagnosis and management of bladder cancer. Quantitative fluorescence image analysis is a cytological method that incorporates 2 diagnostic techniques, quantitation of nuclear deoxyribonucleic acid and morphometric analysis, in a single semiautomated system to facilitate the identification of rare events, that is individual cancer cells. When compared to routine cytopathology for detection of bladder cancer in symptomatic patients, quantitative fluorescence image analysis demonstrated greater sensitivity (76 versus 33per cent) for the detection of low grade transitional cell carcinoma. The specificity of quantitative fluorescence image analysis in a small control group was 94per cent and with the manual method for quantitation of absolute nuclear fluorescence intensity in the screening of high risk asymptomatic subjects the specificity was 96.7per cent. The more familiar flow cytometry is another fluorescence technique for measurement of nuclear deoxyribonucleic acid. However, rather than identifying individual cancer cells, flow cytometry identifies cellular pattern distributions, that is the ratio of normal to abnormal cells. Numerous studies by others have shown that flow cytometry is a sensitive method to monitor patients with diagnosed urological disease. Based upon results in separate quantitative fluorescence image analysis and flow cytometry studies, it appears that these 2 fluorescence techniques may be complementary tools for urological screening, diagnosis and management, and that they also may be useful separately or in combination to elucidate the oncogenic process, determine the biological potential of tumors and monitor the results of chemopreventive, immunological and chemotherapeutic regimens. To our knowledge there has been no study in which quantitative fluorescence image analysis and flow cytometry were compared directly to assess the relative strengths and weaknesses for urinary tract cytology. Such a study could provide important information for urologists. © 1988 by The American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetailsCited bySIJMONS R, KIEMENEY L, WITJES J and VASEN H (2018) URINARY TRACT CANCER AND HEREDITARY NONPOLYPOSIS COLORECTAL CANCER: RISKS AND SCREENING OPTIONSJournal of Urology, VOL. 160, NO. 2, (466-470), Online publication date: 1-Aug-1998.Schmetter B, Habicht K, Lamm D, Morales A, Bander N, Grossman H, Hanna M, Silberman S and Butman B (2018) A Multicenter Trial Evaluation of the Fibrin/Fibrinogen Degradation Products Test for Detection and Monitoring of Bladder CancerJournal of Urology, VOL. 158, NO. 3, (801-805), Online publication date: 1-Sep-1997.Johnston B, Morales A, Emerson L and Lundie M (2018) RAPID PROTECTION OF BLADDER CANCER: A COMPARATIVE STUDY OF POINT OF CARE TESTSJournal of Urology, VOL. 158, NO. 6, (2098-2101), Online publication date: 1-Dec-1997.Slaton J, Dinney C, Veltri R, Miller M, Liebert M, O'Dowd G and Grossman H (2018) Deoxyribonucleic Acid Ploidy Enhances the Cytological Prediction of Recurrent Transitional Cell Carcinoma of the BladderJournal of Urology, VOL. 158, NO. 3, (806-811), Online publication date: 1-Sep-1997.Mora L, Nicosia S, Pow-Sang J, Ku N, Diaz J, Lockhart J and Einstein A (2018) Ancillary Techniques in the Followup of Transitional Cell Carcinoma: A Comparison of Cytology, Histology and Deoxyribonucleic Acid Image Analysis Cytometry in 91 PatientsJournal of Urology, VOL. 156, NO. 1, (49-55), Online publication date: 1-Jul-1996.Prout G, Barton B, Griffin P and Friedell G (2018) Treated History of Noninvasive Grade 1 Transitional Cell CarcinomaJournal of Urology, VOL. 148, NO. 5 Part 1, (1413-1419), Online publication date: 1-Nov-1992.Hemstreet G, Rollins S, Jones P, Rao J, Hurst R, Bonner R, Hewett T and Smith B (2018) Identification of a High Risk Subgroup of Grade 1 Transitional Cell Carcinoma Using Image Analysis Based Deoxyribonucleic Acid Ploidy Analysis of Tumor TissueJournal of Urology, VOL. 146, NO. 6, (1525-1529), Online publication date: 1-Dec-1991.Moon T, Harmon E, Hurst R, Bass R, Colcolough M and Hemstreet G (2018) Quantitative Fluorescence Image Analysis of Deoxyribonucleic Acid Ploidy in Urine From Normal ChildrenJournal of Urology, VOL. 145, NO. 6, (1236-1237), Online publication date: 1-Jun-1991. Volume 139Issue 2February 1988Page: 270-274 Advertisement Copyright & Permissions© 1988 by The American Urological Association Education and Research, Inc.MetricsAuthor Information William L. Parry More articles by this author George P. Hemstreet More articles by this author Expand All Advertisement PDF downloadLoading ...
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With brain tumour incidence increasing, there is an urgent need for better diagnostic tools. Intraoperatively, brain tumours are diagnosed using a smear preparation reported by a neuropathologist. These have many limitations, including the time taken for the specimen to reach the pathology department and for results to be communicated to the surgeon. There is also a need to assist with resection rates and identifying infiltrative tumour edges intraoperatively to improve clearance. We present a novel study using a handheld Raman probe in conjunction with gold nanoparticles, to detect primary and metastatic brain tumours from fresh brain tissue sent for intraoperative smear diagnosis. Fresh brain tissue samples sent for intraoperative smear diagnosis were tested using the handheld Raman probe after application of gold nanoparticles. Derived Raman spectra were inputted into forward feature extraction algorithms to build a predictive model for sensitivity and specificity of outcome. These results demonstrate an ability to detect primary from metastatic tumours (especially for normal and low grade lesions), in which accuracy, sensitivity and specificity were respectively equal to 98.6%, 94.4% and 99.5% for normal brain tissue; 96.1%, 92.2% and 97.0% for low grade glial tumours; 90.3%, 89.7% and 90.6% for high grade glial tumours; 94.8%, 63.9% and 97.1% for meningiomas; 95.4%, 79.2% and 98.8% for metastases; and 99.6%, 88.9% and 100% for lymphoma, based on smear samples (κ = 0.87). Similar results were observed when compared to the final formalin-fixed paraffin embedded tissue diagnosis (κ = 0.85). Overall, our results have demonstrated the ability of Raman spectroscopy to match results provided by intraoperative smear diagnosis and raise the possibility of use intraoperatively to aid surgeons by providing faster diagnosis. Moving this technology into theatre will allow it to develop further and thus reach its potential in the clinical arena.
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Modern cancer diagnosis requires histological, molecular, and genomic tumor analyses. Tumor sampling is often achieved using a targeted needle biopsy approach. Targeting errors and cancer heterogeneity causing inaccurate sampling are important limitations of this blind technique leading to non-diagnostic or poor quality samples, and the need for repeated biopsies pose elevated patient risk. An optical technology that can analyze the molecular nature of the tissue prior to harvesting could improve cancer targeting and mitigate patient risk. Here we report on the design, development, and validation of an in situ intraoperative, label-free, cancer detection system based on high wavenumber Raman spectroscopy. This optical detection device was engineered into a commercially available biopsy system allowing tumor analysis prior to tissue harvesting without disrupting workflow. Using a dual validation approach we show that high wavenumber Raman spectroscopy can detect human dense cancer with >60% cancer cells in situ during surgery with a sensitivity and specificity of 80% and 90%, respectively. We also demonstrate for the first time the use of this system in a swine brain biopsy model. These studies set the stage for the clinical translation of this optical molecular imaging method for high yield and safe targeted biopsy.
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There is a real need for improvements in cancer detection. Significant problems are encountered when utilising the gold standard of excisional biopsy combined with histopathology. This can include missed lesions, perforation and high levels of inter- and intra-observer discrepancies. The clinical requirements for an objective, non-invasive real time probe for accurate and repeatable measurement of tissue pathological state are overwhelming. This study has evaluated the potential for Raman spectroscopy to achieve this goal. The technique measures the molecular specific inelastic scattering of laser light within tissue, thus enabling the analysis of biochemical changes that precede and accompany disease processes. Initial work has been carried out to optimise a commercially available Raman microspectrometer for tissue measurements; to target potential malignancies with a clinical need for diagnostic improvements (oesophagus. colon, breast, andd prostate) and to build and test spectral libraries and prediction algorithms for tissue types and pathologies. This study has followed rigorous sample collection protocols and histopathological analysis using a board of expert pathologists. Only the data from samples with full agreement of a homogeneous pathology have been used to construct a training data set of Raman spectra. Measurements of tissue specimens from the full spectrum of different pathological groups found in each tissue have been made. Diagnostic predictive models have been constructed and optimised using multivariate analysis techniques. They have been tested using cross-validation or leave-one-out and demonstrated high levels of discrimination between pathology groups (greater than 90% sensitivity and specificity for all tissues). However larger sample numbers are required for further evaluation. The discussions outline the likely work required for successful implementation of in vivo Raman detection of early malignancies.
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