KEYWORDS: Raman spectroscopy, Tissues, Signal to noise ratio, In vivo imaging, Cancer, Brain, Data acquisition, Luminescence, Tissue optics, Visualization
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.
Raman spectroscopy is an optical technique that can assess a sample’s molecular content by probing its vibrational modes and has been used over the last decades to diagnose multiple types of cancer. The standard method used to build the classification models, based on machine learning algorithms, is the source of two majors limitations: the small size of the collected training datasets and the issue of portability of statistical models across imaging systems and medical centers. Model portability can be adressed by using a spectrum processing method that totally removes the hardware influence from the processed Raman measurements. We focus here on the results of two experiments conducted to evaluate the reproductibility of Raman measurements made with nine different point-probe systems. For the first experiment, we used a nylon phantom to assess inter-systems differences and applied the data processing method which lowered the inter-systems deviation for the processed nylon peaks under 3%. Furthermore, system #1 was used in vivo in a human brain surgery to acquire 15 Raman measurements from normal and tumor tissue. We evaluated the deviation between classes and found that it was superior to the 3% inter-systems reproductibility for 10 Raman peaks associated with proteins, lipids and nucleic acids. The second experiment was done with the system #1 as a master system and systems #2 to #9 as slave systems. The master system was used to build a Support Vector Machine classification model to discriminate white matter from grey matter on fixed ex vivo monkey brain slices. The model was exported from master to slaves performing a diagnosis accuracy consistently over 95%. The reported results indicate the possibility to succesfully export statistical model from one system to another and to greatly increase the size of dataset using multiple imaging systems.
Ovarian cancer is the fifth most deadly cancer among women in North America. Because this type of cancer is often diagnosed late, cytoreductive surgery is often the first therapeutic step. Currently, visual inspection of the surgical cavity is the only technique used to detect residual tumors. Therefore, there is a need for the development of new imaging techniques that can detect cancer tissue with high specificity and sensitivity during cytoreductive procedures.
To address this unmet clinical need, we developed an intraoperative wide-field Raman spectroscopy (RS) imaging system to be used alongside tissue classification models trained to recognize cancer tissue using artificial intelligence techniques. The system can sequentially acquire up to 5 Raman bands in imaging mode over a macroscopic tissue area of more than 1-centimeter diameter. Preliminary analyses are presented demonstrating the ability of the system to recover the main Raman tissue bands in synthetic and biologic tissue. Two types of tissues in a biological sample can also be differentiated by the system. Moreover, cancer detection models are produced using a single-point RS probe based on ex vivo human measurements collected from 20 ovarian cancer patients. Using supervised machine learning techniques, it is demonstrated the model can detect tissue containing epithelial cancer cells with an accuracy higher than 90%. Based on this dataset, multivariate statistical analyzes were performed demonstrating the 5 features contributing the most to the classification. These studies pave the way to the development of a new generation wide-field Raman spectroscopy techniques for macroscopic tissue characterization during surgery.
Surgical excision of the whole prostate through a radical prostatectomy procedure is part of the standard of care for prostate cancer. Positive surgical margins (cancer cells having spread into surrounding nonresected tissue) occur in as many as 1 in 5 cases and strongly correlate with disease recurrence and the requirement of adjuvant treatment. Margin assessment is currently only performed by pathologists hours to days following surgery and the integration of a real-time surgical readout would benefit current prostatectomy procedures. Raman spectroscopy is a promising technology to assess surgical margins: its in vivo use during radical prostatectomy could help insure the extent of resected prostate and cancerous tissue is maximized. We thus present the design and development of a dual excitation Raman spectroscopy system (680- and 785-nm excitations) integrated to the robotic da Vinci surgical platform for in vivo use. Following validation in phantoms, spectroscopic data from 20 whole human prostates immediately following radical prostatectomy are obtained using the system. With this dataset, we are able to distinguish prostate from extra prostatic tissue with an accuracy, sensitivity, and specificity of 91%, 90.5%, and 96%, respectively. Finally, the integrated Raman spectroscopy system is used to collect preliminary spectroscopic data at the surgical margin in vivo in four patients.
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