Open-sourced Raman spectroscopy data processing package implementing a baseline removal algorithm validated from multiple datasets acquired in human tissue and biofluids.

Significance: Standardized data processing approaches are required in the field of bio-Raman spectroscopy to ensure information associated with spectral data acquired by different research groups, and with different systems, can be compared on an equal footing. Aim: An open-sourced data processing software package was developed, implementing algorithms associated with all steps required to isolate the inelastic scattering component from signals acquired using Raman spectroscopy devices. The package includes a novel morphological baseline removal technique (BubbleFill) that provides increased adaptability to complex baseline shapes compared to current gold standard techniques. Also incorporated in the package is a versatile tool simulating spectroscopic data with varying levels of Raman signal-to-background ratios, baselines with different morphologies, and varying levels of stochastic noise. Results: Application of the BubbleFill technique to simulated data demonstrated superior baseline removal performance compared to standard algorithms, including iModPoly and MorphBR. The data processing workflow of the open-sourced package was validated in four independent in-human datasets, demonstrating it leads to inter-systems data compatibility. Conclusions: A new open-sourced spectroscopic data pre-processing package was validated on simulated and real-world in-human data and is now available to researchers and clinicians for the development of new clinical applications using Raman spectroscopy.

Image-guided Raman spectroscopy navigation system to improve transperineal prostate cancer detection. Part 1: Raman spectroscopy fiber-optics system and in situ tissue characterization.

SIGNIFICANCE: The diagnosis of prostate cancer (PCa) and focal treatment by brachytherapy are limited by the lack of precise intraoperative information to target tumors during biopsy collection and radiation seed placement. Image-guidance techniques could improve the safety and diagnostic yield of biopsy collection as well as increase the efficacy of radiotherapy. AIM: To estimate the accuracy of PCa detection using in situ Raman spectroscopy (RS) in a pilot in-human clinical study and assess biochemical differences between in vivo and ex vivo measurements. APPROACH: A new miniature RS fiber-optics system equipped with an electromagnetic (EM) tracker was guided by trans-rectal ultrasound-guided imaging, fused with preoperative magnetic resonance imaging to acquire 49 spectra in situ (in vivo) from 18 PCa patients. In addition, 179 spectra were acquired ex vivo in fresh prostate samples from 14 patients who underwent radical prostatectomy. Two machine-learning models were trained to discriminate cancer from normal prostate tissue from both in situ and ex vivo datasets. RESULTS: A support vector machine (SVM) model was trained on the in situ dataset and its performance was evaluated using leave-one-patient-out cross validation from 28 normal prostate measurements and 21 in-tumor measurements. The model performed at 86% sensitivity and 72% specificity. Similarly, an SVM model was trained with the ex vivo dataset from 152 normal prostate measurements and 27 tumor measurements showing reduced cancer detection performance mostly attributable to spatial registration inaccuracies between probe measurements and histology assessment. A qualitative comparison between in situ and ex vivo measurements demonstrated a one-to-one correspondence and similar ratios between the main Raman bands (e.g., amide I-II bands, phenylalanine). CONCLUSIONS: PCa detection can be achieved using RS and machine learning models for image-guidance applications using in situ measurements during prostate biopsy procedures.

Image-guided Raman spectroscopy navigation system to improve transperineal prostate cancer detection. Part 2: in-vivo tumor-targeting using a classification model combining spectral and MRI-radiomics features.

SIGNIFICANCE: The diagnosis and treatment of prostate cancer (PCa) are limited by a lack of intraoperative information to accurately target tumors with needles for biopsy and brachytherapy. An innovative image-guidance technique using optical devices could improve the diagnostic yield of biopsy and efficacy of radiotherapy. AIM: To evaluate the performance of multimodal PCa detection using biomolecular features from in-situ Raman spectroscopy (RS) combined with image-based (radiomics) features from multiparametric magnetic resonance images (mpMRI). APPROACH: In a prospective pilot clinical study, 18 patients were recruited and underwent high-dose-rate brachytherapy. Multimodality image fusion (preoperative mpMRI with intraoperative transrectal ultrasound) combined with electromagnetic tracking was used to navigate an RS needle in the prostate prior to brachytherapy. This resulting dataset consisted of Raman spectra and co-located radiomics features from mpMRI. Feature selection was performed with the constraint that no more than 10 features were retained overall from a combination of inelastic scattering spectra and radiomics. These features were used to train support vector machine classifiers for PCa detection based on leave-one-patient-out cross-validation. RESULTS: RS along with biopsy samples were acquired from 47 sites along the insertion trajectory of the fiber-optics needle: 26 were confirmed as benign or grade group = 1, and 21 as grade group >1, according to histopathological reports. The combination of the fingerprint region of the RS and radiomics showed an accuracy of 83% (sensitivity = 81 % and a specificity = 85 % ), outperforming by more than 9% models trained with either spectroscopic or mpMRI data alone. An optimal number of features was identified between 6 and 8 features, which have good potential for discriminating grade group ≥1 / grade group <1 (accuracy = 87 % ) or grade group >1 / grade group ≤1 (accuracy = 91 % ). CONCLUSIONS: In-situ Raman spectroscopy combined with mpMRI radiomics features can lead to highly accurate PCa detection for improved in-vivo targeting of biopsy sample collection and radiotherapy seed placement.

Pre-clinical evaluation of an image-guided in-situ Raman spectroscopy navigation system for targeted prostate cancer interventions.

PURPOSE: Transrectal ultrasound (TRUS) image guidance is the standard of care for diagnostic and therapeutic interventions in prostate cancer (PCa) patients, but can lead to high false-negative rates, compromising downstream effectiveness of therapeutic choices. A promising approach to improve in-situ detection of PCa lies in using the optical properties of the tissue to discern cancer from healthy tissue. In this work, we present the first in-situ image-guided navigation system for a spatially tracked Raman spectroscopy probe integrated in a PCa workflow, capturing the optical tissue fingerprint. The probe is guided with fused TRUS/MR imaging and tested with both tissue-simulating phantoms and ex-vivo prostates. The workflow was designed to be integrated the clinical workflow for trans-perineal prostate biopsies, as well as for high-dose rate (HDR) brachytherapy. METHODS: The proposed system developed in 3D Slicer includes an electromagnetically tracked Raman spectroscopy probe, along with tracked TRUS imaging automatically registered to diagnostic MRI. The proposed system is tested on both custom gelatin tissue-simulating optical phantoms and biological tissue phantoms. A random-forest classifier was then trained on optical spectrums from ex-vivo prostates following prostatectomy using our optical probe. Preliminary in-human results are presented with the Raman spectroscopy instrument to detect malignant tissue in-situ with histopathology confirmation. RESULTS: In 5 synthetic gelatin and biological tissue phantoms, we demonstrate the ability of the image-guided Raman system by detecting over 95% of lesions, based on biopsy samples. The included lesion volumes ranged from 0.1 to 0.61 cc. We showed the compatibility of our workflow with the current HDR brachytherapy setup. In ex-vivo prostates of PCa patients, the system showed a 81% detection accuracy in high grade lesions. CONCLUSION: Pre-clinical experiments demonstrated promising results for in-situ confirmation of lesion locations in prostates using Raman spectroscopy, both in phantoms and human ex-vivo prostate tissue, which is required for integration in HDR brachytherapy procedures.

Quantitative spectral quality assessment technique validated using intraoperative in vivo Raman spectroscopy measurements.

SIGNIFICANCE: Ensuring spectral quality is prerequisite to Raman spectroscopy applied to surgery. This is because the inclusion of poor-quality spectra in the training phase of Raman-based pathology detection models can compromise prediction robustness and generalizability to new data. Currently, there exists no quantitative spectral quality assessment technique that can be used to either reject low-quality data points in existing Raman datasets based on spectral morphology or, perhaps more importantly, to optimize the in vivo data acquisition process to ensure minimal spectral quality standards are met. AIM: To develop a quantitative method evaluating Raman signal quality based on the variance associated with stochastic noise in important tissue bands, including C─C stretch, CH2 / CH3 deformation, and the amide bands. APPROACH: A single-point hand-held Raman spectroscopy probe system was used to acquire 315 spectra from 44 brain cancer patients. All measurements were classified as either high or low quality based on visual assessment (qualitative) and using a quantitative quality factor (QF) metric. Receiver-operator-characteristic (ROC) analyses were performed to evaluate the performance of the quantitative metric to assess spectral quality and improve cancer detection accuracy. RESULTS: The method can separate high- and low-quality spectra with a sensitivity of 89% and a specificity of 90% which is shown to increase cancer detection sensitivity and specificity by up to 20% and 12%, respectively. CONCLUSIONS: The QF threshold is effective in stratifying spectra in terms of spectral quality and the observed false negatives and false positives can be linked to limitations of qualitative spectral quality assessment.

Interstitial imaging with multiple diffusive reflectance spectroscopy projections for in vivo blood vessels detection during brain needle biopsy procedures.

Blood vessel injury during image-guided brain biopsy poses a risk of hemorrhage. Approaches that reduce this risk may minimize related patient morbidity. We present here an intraoperative imaging device that has the potential to detect the brain vasculature in situ. The device uses multiple diffuse reflectance spectra acquired in an outward-viewing geometry to detect intravascular hemoglobin, enabling the construction of an optical image in the vicinity of the biopsy needle revealing the proximity to blood vessels. This optical detection system seamlessly integrates into a commercial biopsy system without disrupting the neurosurgical clinical workflow. Using diffusive brain tissue phantoms, we show that this device can detect 0.5-mm diameter absorptive carbon rods up to ∼ 2 mm from the biopsy window. We also demonstrate feasibility and practicality of the technique in a clinical environment to detect brain vasculature in an in vivo model system. In situ brain vascular detection may add a layer of safety to image-guided biopsies and minimize patient morbidity.

A new method using Raman spectroscopy for in vivo targeted brain cancer tissue biopsy.

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.

Sensitivity analysis aimed at blood vessels detection using interstitial optical tomography during brain needle biopsy procedures.

A brain needle biopsy procedure is performed for suspected brain lesions in order to sample tissue that is subsequently analysed using standard histopathology techniques. A common complication resulting from this procedure is brain hemorrhaging from blood vessels clipped off during tissue extraction. Interstitial optical tomography (iOT) has recently been introduced by our group as a mean to assess the presence of blood vessels in the vicinity of the needle. The clinical need to improve safety requires the detection of blood vessels within 2 mm from the outer surface of the needle, since this distance is representative of the volume of tissue that is aspirated durirng tissue extraction. Here, a sensitivity analysis is presented to establish the intrinsic detection limits of iOT based on simulations and experiments using brain tissue phantoms. It is demonstrated that absorbers can be detected with diameters >300 µm located up to >2 mm from the biopsy needle core for bulk optical properties consistent with brain tissue.