Prostate cancer is the most common cancer for men in the western world. For the first time, a dual-modality probe, combining Raman spectroscopy and tactile resonance technology, has been used for assessment of fresh human prostate tissue. The study investigates the potential of the dual-modality probe by testing its ability to differentiate prostate tissue types ex vivo. Measurements on four prostates show that the tactile resonance modality was able to discriminate soft epithelial tissue and stiff stroma (p < 0.05). The Raman spectra exhibited a strong fluorescent background at the current experimental settings. However, stroma could be discerned from epithelia by integrating the value of the spectral background. Combining both parameters by a stepwise analysis resulted in 100% sensitivity and 91% specificity. Although no cancer tissue was analysed, the results are promising for further development of the instrument and method for discriminating prostate tissues and cancer

Prostate cancer (PCa) is the most common cancer form for men in Europe. A sensor system combining Raman spectroscopy and stiffness sensing with a resonance sensor has recently been developed by us for prostate cancer detection. In this study the sensor system has been used for measurements on two slices of fresh human prostate tissue. The stiffness sensor could detect locations slices with significantly different stiffness contrasts (p < 0.05). Raman spectroscopic measurements could be performed with the dual-modality probe for tissue classification. The findings are important for the continued development of a combination probe for prostate cancer detection.

Prostate adenocarcinoma, or prostate cancer (PCa), is the most common type of cancer and the leading cause of cancer-related deaths for men in Sweden and Europe. Radical prostatectomy (RP) is the most common curative treatment for PCa. This is a surgical procedure where the entire prostate is removed. The surgeons strive to minimise postoperative side-effects, and will use nerve-sparing techniques if possible. The final decision on how much tissue to remove around the prostate is taken during surgery. After the prostate is resected it is histopathologically analysed. The risk of PCa recurrence is elevated when cancer cells are found in the surgical margin. Currently, there is no viable method to detect and localise PCa tumours near the resection margin during surgery. The general aim of this thesis was to develop a medical instrument with the ability to detect PCa tumours near the tissue surface.Datasets from the resonance sensor and from the Raman spectroscopy obtained on porcine model tissue were compared. Principal component analysis (PCA) was used to reduce the Raman spectroscopic dataset, and groups of tissue content were formed by a hierarchical cluster analysis (HCA) of the PCA results. The correlation of the two datasets was evaluated by a model using the PCA results for describing the stiffness of the groups.A support vector machine was evaluated as a method to combine the datasets of the two modalities. The method was used for classifying three types of porcine prostate tissues and to discern healthy vs. cancerous human prostate tissue. The cross-validation accuracy for the SVM classification was 87% for the porcine prostate and 77% for the human prostate tissue types for highly homogeneous tissue samples (>83%). Several important aspects regarding design and methods to be used for combining the two modalities into one probe were investigated. The effects of using different amounts of rubber latex for combining a TRM sensor an a fibre optic Raman probe substitute were determined. A description of the heat produced by the laser at the fibre optic tip was established, and the temperature dependence of Δf was investigated. Methods and conditions, e.g. instrument settings and light shields, for performing Raman spectroscopy with ambient light present were investigated.The dual-modality probe prototype was used to perform the first measurements on porcine model tissue. Methods for using the Raman spectroscopic modality of the combined probe with ambient light present were evaluated. The TRM modality could discern tissue with significantly different stiffness. Raman spectroscopy could be performed with ambient fluorescent light present. The first measurements on fresh human prostate tissue using the dual-modality probe were performed. Prostates with different average stiffnesses could be compared by calculating stiffness contrast. The TRM modality could discern tissue with significantly different stiffness contrast (p < 0.05). The background fluorescence in the Raman spectra of fresh human prostate tissue was higher than expected. The high-wavenumber region, 2700 cm−1 to 3200 cm−1, of the Raman spectra could be used to discern tissue characteristics using HCA of the PCA results. In this thesis, methods and design considerations for the development of a dualmodality probe, combining Raman spectroscopy and tactile resonance sensor technology, were identified and evaluated. In addition, methods for how to use the dual-modality probe for tissue classification and for combining of the datasets of the two modalities, were studied. The resonance sensor swiftly evaluates the tissue stiffness, like the palpation with a finger. The Raman spectroscopy would be applied when malignancy is suspected and adds detailed knowledge of the molecular content. This makes the dual-modality probe a promising instrument for use during RP.

First measurements on a combined instrument with a thin fibre optic Raman probe mounted inside a hollow tactile resonance sensor have been performed in ambient light on porcine tissue. The ambient fluorescent light was removed successfully from the spectra. The stiffness and the biomolecular composition of the tissue were analysed.

Histopathology is the golden standard for cancer diagnosis and involves the characterization of tissue components. It is labour intensive and time consuming. We have earlier proposed a combined fibre-optic near-infrared Raman spectroscopy (NIR-RS) and tactile resonance method (TRM) probe for detecting positive surgical margins as a complement to interoperative histopathology. The aims of this study were to investigate the effects of attaching an RS probe inside a cylindrical TRM sensor and to investigate how laser-induced heating of the fibre-optic NIR-RS affected the temperature of the RS probe tip and an encasing TRM sensor. In addition, the possibility to perform fibre-optic NIR-RS in a well-lit environment was investigated. A small amount of rubber latex was preferable for attaching the thin RS probe inside the TRM sensor. The temperature rise of the TRM sensor due to a fibre-optic NIR-RS at 270 mW during 20 s was less than 2 °C. Fibre-optic NIR-RS was feasible in a dimmed bright environment using a small light shield and automatic subtraction of a pre-recorded contaminant spectrum. The results are promising for a combined probe for tissue characterization.

The tactile resonance method (TRM) and Raman spectroscopy (RS) are promising for tissue characterisation in vivo. Our goal is to combine these techniques into one instrument, to use TRM for swift scanning, and RS for increasing the diagnostic power. The aim of this study was to determine the classification accuracy, using support vector machines, for measurements on porcine tissue and also produce preliminary data on human prostate tissue. This was done by developing a new experimental setup combining micro-scale TRM — scanning haptic microscopy (SHM) — for assessing stiffness on a micro-scale, with fibre optic RS measurements for assessing biochemical content. We compared the accuracy for using SHM alone versus SHM combined with RS, for different degrees of tissue homogeneity. The cross-validation classification accuracy for healthy porcine tissue types using SHM alone was 65–81%, and when RS was added it was increased to 81–87%. The accuracy for healthy and cancerous human tissue was 67–70% when only SHM was used, and increased to 72–77% for the combined measurements. This shows that the potential for swift and accurate classification of healthy and cancerous prostate tissue is high. This is promising for developing a tool for probing the surgical margins during prostate cancer surgery.

Prostate cancer (PCa) is the most common cancer type among men in Sweden. The most prevalent curative treatment for PCa is radical prostatectomy (RP), i.e., complete surgical removal of the prostate. Unfortunately, cancer cells are found near the resection surface in 35 % of the RP patients. This indicates an increased risk of PCa reccurence.Our main objective is to develop a novel medical instrument for detecting PCa. The idea is to combine the techniques of Raman spectroscopy (RS) and the tactile resonance method (TRM) into one integrated instrument. The TRM would provide a swift and gentle survey of the region of interest, while the RS adds detailed information of the molecular content where malignancy is suspected. The dual mode instrument could be well suited for detecting and locating tumour cells in the surgical margin during RP. The studies included in this thesis are important steps towards this objective.Paper A investigated how the two data sets from each of the technologies could be compared and combined for tissue characterisation. The data set of RS was a spectrum with peaks characteristic to the sample's molecular content. The TRM output variable was a scalar value related to the sample stiness. The data sets could be compared and combined by applying principal component analysis (PCA) to the RS spectra followed by an hierarchical cluster analysis (HCA). A linear regression analysis showed that the PCs explained 67% of the stiffness variations. HCA was used to classify each RS measurement into groups consisting of similar measurements. The TRM's sensitivity and specificity of classifying these groups were evaluated by ROC curves and the area under the curve (AUC). The harder group could successfully be discriminated from the softer groups (AUC = 0.99).Paper B used support vector machines (SVM) as a method to classify and differentiate porcine and human prostate tissue types using the combined data sets. Prostate tissue is highly inhomogenous, with streaks of small anatomical structures. The analysis was evaluated within areas of three levels of homogenity, to avoid mismatching the measured tissue. The tissue homogenity was evaluated within the RS measurement area and the tissue type was set to the main histological content. Areas in which no single tissue type surpassed the threshold level were excluded from the analysis. The cross-validation accuracy for determining the tissues types within homogenous (main tissue type > 83%) porcine samples was 82% using TRM data alone. It increased to 87% while using the combined data sets of TRM and RS. For discerning healthy and cancerous human prostate tissue, the cross-validation accuracy was 67% and 77% for TRM alone versus TRM and RS combined.Paper C covered a number of design considerations which have to be addressed during the combination of TRM and RS. The effects of attaching an RS probe into a tubular TRM element were investigated. We investigated the temperature increase caused by the laser illumination from the RS and its eect on the TRM measurement parameter Δf. We also investigated if and how RS could be performed under ambient light. A thin RS probe and a small amount of rubber latex was preferable for attaching the RS probe inside the TRM sensor. The temperature rise of the TRM sensor due to a fibreoptic NIR-RS at 270 mWduring 20 s was less than 2ºC. The variation of Δf during a 5ºC temperature change was approximately 20 Hz. This is small compared to previous in vitro TRM studies. Fibre-optic NIR-RS was feasible in a dimmed bright environment using a small light shield and automatic subtraction of a pre-recorded contaminant spectrum. The results of these studies indicate how the hardware and and software could be combined into one integrated probe for prostate cancer detection.

Tissue characterization is fundamental for identification of pathological conditions. Raman spectroscopy (RS) and tactile resonance measurement (TRM) are two promising techniques that measure biochemical content and stiffness, respectively. They have potential to complement the golden standard-–histological analysis. By combining RS and TRM, complementary information about tissue content can be obtained and specific drawbacks can be avoided. The aim of this study was to develop a multivariate approach to compare RS and TRM information. The approach was evaluated on measurements at the same points on porcine abdominal tissue. The measurement points were divided into five groups by multivariate analysis of the RS data. A regression analysis was performed and receiver operating characteristic (ROC) curves were used to compare the RS and TRM data. TRM identified one group efficiently (area under ROC curve 0.99). The RS data showed that the proportion of saturated fat was high in this group. The regression analysis showed that stiffness was mainly determined by the amount of fat and its composition. We concluded that RS provided additional, important information for tissue identification that was not provided by TRM alone. The results are promising for development of a method combining RS and TRM for intraoperative tissue characterization.

Prostate cancer is a major health problem among men in Europe and the USA. Tactile resonance technology and Raman spectroscopy have both shown promising results in vitro, detecting and diagnosing cancer tumors respectively. A new approach, combining the strength of resonance technology and Raman spectroscopy is investigated. This study deals with the effects of molding a Raman fiber optic probe into a cylindrical resonance sensor element (RSE) to achieve a combined probe. Heat induced by the Raman spectroscopy laser might affect temperature dependent properties of the RSE. Also, molding a Raman probe into a RSE will affect its properties. The RSE temperature dependency was investigated using the resonance sensor system Venustron®. The Raman fiber optic probe was simulated by a thin steel pipe which was molded into a single cylindrical RSE. The effects on the frequency characteristics when modifying the RSE were investigated with a network analyzer. Although the resonance frequency of a RSE is temperature dependent, the frequency shift, as used for calculating stiffness, is not noticeably affected by moderate temperature variations. The RSE properties change less by using a small amount of filler material and a small diameter of the Raman probe.

Prostate cancer is the most common form of cancer and the third leading cause of cancer-related death in European men. There is a need for new methods that can accurately localize and diagnose prostate cancer. In this study a new approach is presented: a combination of resonance sensor technology and Raman spectroscopy. Both methods have shown promising results for prostate cancer detection in vitro. The aim of this study was to evaluate the combined information from measurements with a Raman fiberoptic probe and a resonance sensor system. Pork belly tissue was used as a model system. A three-dimensional translation table was equipped with an in-house developed software, allowing measurements to be performed at the same point using two separate instruments. The Raman data was analyzed using principal component analysis and hierarchical clustering analysis. The spectra were divided into 5 distinct groups. The mean stiffness of each group was calculated from the resonance sensor measurements. One of the groups differed significantly (p < 0.05) from the others. A regression analysis, with the stiffness parameter as response variable and the principal component scores of the Raman data as the predictor variables, explained 67% of the total variability. The use of a smaller resonance sensor tip would probably increase the degree of correlation. In conclusion, Raman spectroscopy provides additional discriminatory power to the resonance sensor