Prostate cancer detection using combined auto-fluorescence and light reflectance spectroscopy: ex vivo study of human prostates

Dual excitation spectral autofluorescence lifetime and reflectance imaging for fast macroscopic characterization of tissues

Advancements in optical imaging techniques have revolutionized the field of biomedical research, allowing for the comprehensive characterization of tissues and their underlying biological processes. Yet, there is still a lack of tools to provide quantitative and objective characterization of tissues that can aid clinical assessment in vivo to enhance diagnostic and therapeutic interventions. Here, we present a clinically viable fiber-based imaging system combining time-resolved spectrofluorimetry and reflectance spectroscopy to achieve fast multiparametric macroscopic characterization of tissues. An essential feature of the setup is its ability to perform dual wavelength excitation in combination with recording time-resolved fluorescence data in several spectral intervals. Initial validation of this bimodal system was carried out in freshly resected human colorectal cancer specimens, where we demonstrated the ability of the system to differentiate normal from malignant tissues based on their autofluorescence and reflectance properties. To further highlight the complementarity of autofluorescence and reflectance measurements and demonstrate viability in a clinically relevant scenario, we also collected in vivo data from the skin of a volunteer. Altogether, integration of these modalities in a single platform can offer multidimensional characterization of tissues, thus facilitating a deeper understanding of biological processes and potentially advancing diagnostic and therapeutic approaches in various medical applications.

Distinguishing tumor from healthy tissue in human liver ex vivo using machine learning and multivariate analysis of diffuse reflectance spectra

The aim of this work was to evaluate the capability of diffuse reflectance spectroscopy to distinguish malignant liver tissues from surrounding tissues and to determine whether an extended wavelength range (450-1550 nm) offers any advantages over using the conventional wavelength range. Furthermore, multivariate analysis combined with a machine learning algorithm, either linear discriminant analysis or the more advanced support vector machine, was used to discriminate between and classify freshly excised human liver specimens from 18 patients. Tumors were distinguished from surrounding liver tissues with a sensitivity of 99%, specificity of 100%, classification rate of 100% and a Matthews correlation coefficient of 100% using the extended wavelength range and a combination of principal component analysis and support vector techniques. The results indicate that this technology may be useful in clinical applications for real-time tissue diagnostics of tumor margins where rapid classification is important.

Emerging Intraoperative Imaging Technologies in Urologic Oncology

As surgical cases become more complex, intraoperative imaging is increasingly being used. This article discusses emerging imaging technologies used in prostate, kidney, and bladder cancer surgery, including ultrasound, fluorescence-based, and enhanced endoscopy techniques including their strengths and limitations.

Development and Testing of an LED-Based Near-Infrared Sensor for Human Kidney Tumor Diagnostics

Optical spectroscopy is increasingly used for cancer diagnostics. Tumor detection feasibility in human kidney samples using mid- and near-infrared (NIR) spectroscopy, fluorescence spectroscopy, and Raman spectroscopy has been reported (Artyushenko et al., Spectral fiber sensors for cancer diagnostics in vitro. In Proceedings of the European Conference on Biomedical Optics, Munich, Germany, 21–25 June 2015). In the present work, a simplification of the NIR spectroscopic analysis for cancer diagnostics was studied. The conventional high-resolution NIR spectroscopic method of kidney tumor diagnostics was replaced by a compact optical sensing device constructively represented by a set of four light-emitting diodes (LEDs) at selected wavelengths and one detecting photodiode. Two sensor prototypes were tested using 14 in vitro clinical samples of 7 different patients. Statistical data evaluation using principal component analysis (PCA) and partial least-squares discriminant analysis (PLS-DA) confirmed the general applicability of the LED-based sensing approach to kidney tumor detection. An additional validation of the results was performed by means of sample permutation.

Prostate Cancer Detection Using Composite Impedance Metric

Prostate cancer (PCa) recurrences are often predicted by assessing the status of surgical margins (SM)- positive surgical margins (PSM) increase the chances of biochemical recurrence by 2-4 times which may lead to PCa recurrence. To this end, an electrical impedance acquisition system with a microendoscopic probe was employed in an ex-vivo study of human prostates. This system measures the tissue bioimpedance over a range of frequencies (1 kHz to 1MHz), and computes a number of Composite Impedance Metrics (CIM). A classifier trained using CIM data can be used to classify tissue as benign or cancerous. The system was used to collect the impedance spectra from 14 excised prostates, which were obtained from men undergoing radical prostatectomy, for a total of 23 cancerous and 53 benign measurements. The data revealed statistically significant (p < 0.05) differences in the impedance properties of the benign and tumorous tissues, and among the measurements taken on the apical, base, and lateral surface of the prostate. Further, in the leave-one-patient-out cross validation, a maximum predictive accuracy of 90.79% was achieved by combining high frequency CIM phase data to train a support vector machine classifier with a radial basis function kernel. The observations are consistent with the physiology and morphology of benign and malignant prostate tissue. CIMs were found to be an effective tool in distinguishing benign from cancerous tissues.

Real-Time Visualization of Tissue Surface Biochemical Features Derived From Fluorescence Lifetime Measurements

Fiber based fluorescence lifetime imaging has shown great potential for intraoperative diagnosis and guidance of surgical procedures. Here we describe a novel method addressing a significant challenge for the practical implementation of this technique, i.e., the real-time display of the quantified biochemical or functional tissue properties superimposed on the interrogated area. Specifically, an aiming beam (450 nm) generated by a continuous-wave laser beam was merged with the pulsed fluorescence excitation light in a single delivery/collection fiber and then imaged and segmented using a color-based algorithm. We demonstrate that this approach enables continuous delineation of the interrogated location and dynamic augmentation of the acquired frames with the corresponding fluorescence decay parameters. The method was evaluated on a fluorescence phantom and fresh tissue samples. Current results demonstrate that 34 frames per second can be achieved for augmenting videos of 640 × 512 pixels resolution. Also we show that the spatial resolution of the fluorescence lifetime map depends on the tissue optical properties, the scanning speed, and the frame rate. The dice similarity coefficient between the fluorescence phantom and the reconstructed maps was estimated to be as high as 93%. The reported method could become a valuable tool for augmenting the surgeon's field of view with diagnostic information derived from the analysis of fluorescence lifetime data in real-time using handheld, automated, or endoscopic scanning systems. Current method provides also a means for maintaining the tissue light exposure within safety limits. This study provides a framework for using an aiming beam with other point spectroscopy applications.

Diffuse reflectance spectroscopy can differentiate high grade and low grade prostatic carcinoma

Prostate tumors are graded by the revised Gleason Score (GS) which is the sum of the two predominant Gleason grades present ranging from 6-10. GS 6 cancer exclusively with Gleason grade 3 is designated as low grade (LG) and correlates with better clinical prognosis for patients. GS >7 cancer with at least one of the Gleason grades 4 and 5 is designated as HG indicate worse prognosis for patients. Current transrectal ultrasound guided prostate biopsies often fail to correctly diagnose HG prostate cancer due to sampling errors. Diffuse reflectance spectra (DRS) of biological tissue depend on tissue morphology and architecture. Thus, DRS could potentially differentiate between HG and LG prostatic carcinoma. A 15-gauge optical biopsy needle was prototyped to take prostate biopsies after measuring DRS with a laboratory fluorometer. This needle has an optical sensor that utilizes 8×100 μm optical fibers for tissue excitation and a single 200 μm central optical fiber to measure DRS. Tissue biopsy cores were obtained from 20 surgically excised prostates using this needle after measuring DRS at 5 nm intervals between 500-700 nm wavelengths. Tissue within a measurement window was histopathologically classified as either benign, LG, or HG and correlated with DRS. Partial least square analysis of DRS identified principal components (PC) as potential classifiers. Statistically significant PCs (p<;0.05) were tested for their ability to classify biopsy tissue using support vector machine and leave-one-out cross validation method. There were 29 HG and 49 LG cancers among 187 biopsy cores included in the study. Study results show 76% sensitivity, 80% specificity, 93% negative predictive value, and 50% positive predictive value for HG versus benign, and 76%, 73%, 84%, and 63%, for HG versus LG prostate tissue classification. DRS failed to diagnose 7/29 (24%) HG cancers. This study demonstrated that an optical biopsy needle guided by DRS has sufficient accuracy to differentiate HG from LG carcinoma and benign tissue. It may allow precise targeting of HG prostate cancer providing more accurate assessment of the disease and improvement in patient care.

Quantifying Gleason scores with photoacoustic spectral analysis: feasibility study with human tissues

Gleason score is a highly prognostic factor for prostate cancer describing the microscopic architecture of the tumor tissue. The standard procedure for evaluating Gleason scores, namely biopsy, is to remove prostate tissue for observation under microscope. Currently, biopsies are guided by transrectal ultrasound (TRUS). Due to the low sensitivity of TRUS to prostate cancer (PCa), non-guided and saturated biopsies are frequently employed, unavoidably causing pain, damage to the normal prostate tissues and other complications. More importantly, due to the limited number of biopsy cores, current procedure could either miss early stage small tumors or undersample aggressive cancers. Photoacoustic (PA) measurement has the unique capability of evaluating tissue microscopic architecture information at ultrasonic resolution. By frequency domain analysis of the broadband PA signal, namely PA spectral analysis (PASA), the microscopic architecture within the assessed tissue can be quantified. This study investigates the feasibility of evaluating Gleason scores by PASA. Simulations with the classic Gleason patterns and experiment measurements from human PCa tissues have demonstrated strong correlation between the PASA parameters and the Gleason scores.

Light Reflectance Spectroscopy to Detect Positive Surgical Margins on Prostate Cancer Specimens

PURPOSE

Intraoperative frozen section analysis is not routinely performed to determine positive surgical margins at radical prostatectomy due to time requirements and unproven clinical usefulness. Light reflectance spectroscopy, which measures light intensity reflected or backscattered from tissues, can be applied to differentiate malignant from benign tissue. We used a novel light reflectance spectroscopy probe to evaluate positive surgical margins on ex vivo radical prostatectomy specimens and correlate its findings with pathological examination.

MATERIALS AND METHODS

Patients with intermediate to high risk disease undergoing radical prostatectomy were enrolled. Light reflectance spectroscopy was performed on suspected malignant and benign prostate capsule immediately following organ extraction. Each light reflectance spectroscopy at 530 to 830 nm was analyzed and correlated with pathological results. A regression model and forward sequential selection algorithm were developed for optimal feature selection. Eighty percent of light reflectance spectroscopy data were selected to train a logistic regression model, which was evaluated by the remaining 20% data. This was repeated 5 times to calculate averaged sensitivity, specificity and accuracy.

RESULTS

Light reflectance spectroscopy analysis was performed on 17 ex vivo prostate specimens, on which a total of 11 histologically positive and 22 negative surgical margins were measured. Two select features from 700 to 830 nm were identified as unique to malignant tissue. Cross-validation when performing the predictive model showed that the optical probe predicted positive surgical margins with 85% sensitivity, 86% specificity, 86% accuracy and an AUC of 0.95.

CONCLUSIONS

Light reflectance spectroscopy can identify positive surgical margins accurately in fresh ex vivo radical prostatectomy specimens. Further study is required to determine whether such analysis may be used in real time to improve surgical decision making and decrease positive surgical margin rates.

Multimodal nonlinear endo-microscopy probe design for high resolution, label-free intraoperative imaging

We present a portable, multimodal, nonlinear endo-microscopy probe designed for intraoperative oncological imaging. Application of a four-wave mixing noise suppression scheme using dual wavelength wave plates (DWW) and a polarization-maintaining fiber improves tissue signal collection efficiency, allowing for miniaturization. The probe, with a small 14 mm transversal diameter, includes a customized miniaturized two-axis MEMS (micro-electromechanical system) raster scanning mirror and micro-optics with an illumination laser delivered by a polarization-maintaining fiber. The probe can potentially be integrated into the arms of a surgical robot, such as da Vinci robotic surgery system, due to its minimal cross sectional area. It has the ability to incorporate multiple imaging modalities including CARS (coherent anti-Stokes Raman scattering), SHG (second harmonic generation), and TPEF (two-photon excited fluorescence) in order to allow the surgeon to locate tumor cells within the context of normal stromal tissue. The resolution of the endo-microscope is experimentally determined to be 0.78 µm, a high level of accuracy for such a compact probe setup. The expected resolution of the as-built multimodal, nonlinear, endo-microscopy probe is 1 µm based on the calculation tolerance allocation using Monte-Carlo simulation. The reported probe is intended for use in laparoscopic or radical prostatectomy, including detection of tumor margins and avoidance of nerve impairment during surgery.