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Purpose: In glioma surgery, it is critical to maximize tumor resection without compromising adjacent noncancerous brain tissue. Optical coherence tomography (OCT) is a noninvasive, label-free, real-time, high-resolution imaging modality that has been explored for glioma infiltration detection. Here, we report a novel artificial intelligence (AI)-assisted method for automated, real-time, in situ detection of glioma infiltration at high spatial resolution. Experimental Design: Volumetric OCT datasets were intraoperatively obtained from resected brain tissue specimens of 21 patients with glioma tumors of different stages and labeled as either noncancerous or glioma-infiltrated on the basis of histopathology evaluation of the tissue specimens (gold standard). Labeled OCT images from 12 patients were used as the training dataset to develop the AI-assisted OCT-based method for automated detection of glioma-infiltrated brain tissue. Unlabeled OCT images from the other 9 patients were used as the validation dataset to quantify the method detection performance. Results: Our method achieved excellent levels of sensitivity (∼100%) and specificity (∼85%) for detecting glioma-infiltrated tissue with high spatial resolution (16 μm laterally) and processing speed (∼100,020 OCT A-lines/second). Conclusions: Previous methods for OCT-based detection of glioma-infiltrated brain tissue rely on estimating the tissue optical attenuation coefficient from the OCT signal, which requires sacrificing spatial resolution to increase signal quality, and performing systematic calibration procedures using tissue phantoms. By overcoming these major challenges, our AI-assisted method will enable implementing practical OCT-guided surgical tools for continuous, real-time, and accurate intraoperative detection of glioma-infiltrated brain tissue, facilitating maximal glioma resection and superior surgical outcomes for patients with glioma.

In brain cancer surgery, maximal tumor resection improves overall survival and quality of life survival in low-grade and high-grade glioma. Different technologies such as intraoperative magnetic resonance imaging and computed tomography have made major contributions; however, these technologies do not provide quantitative, real-time and three-dimensional continuous guidance. Optical Coherence Tomography (OCT) is a non-invasive, label-free, real-time, high-resolution imaging modality that has been explored for glioma infiltration detection. Here we report a novel Artificial Neural Network (ANN)-based computer-aided diagnosis (CAD) method for automated, real-time, in situ detection of glioma-infiltrated tumor margins. Near 500 volumetric OCT samples were intraoperatively obtained from resected brain tissue specimens of 21 patients with glioma tumors of different stages and labeled as either non-cancerous or glioma-infiltrated based on histopathology evaluation (gold standard). Labeled OCT images from 12 patients were used as training dataset to develop the artificial neural network. Unlabeled OCT images from the other 9 patients were used as a validation dataset to quantify the method detection performance. The CAD system achieved excellent levels of both sensitivity and specificity (~90%) for detecting glioma-infiltrated tissue with high spatial resolution (~16 μm laterally). Previous methods for OCT-based detection of glioma-infiltrated brain tissue rely on underlying optical properties such as attenuation coefficient from the OCT signal requiring sacrificing spatial resolution and cumbersome calibration procedures. By overcoming these major challenges, our novel ANN-assisted CAD system will enable implementing practical OCT-guided surgical tools for continuous, real-time and accurate intra-operative detection of glioma-infiltrated brain tissue, facilitating maximal glioma resection and superior surgical outcomes for glioma patients.

Gliomas are CNS neoplasms that infiltrate the surrounding brain parenchyma, complicating their treatment. Tools that increase extent of resection while preventing neurological deficit are essential to improve prognosis of patients diagnosed with gliomas. Tools such as intraoperative MRI, ultrasound and fluorescence-guided microsurgery have been used in the surgical resection of CNS gliomas with the goal of maximizing extent of resection to improve patient outcomes. In addition, emerging experimental techniques, for example, optical coherence tomography and Raman spectroscopy are promising techniques which could 1 day add to the increasing armamentarium used in the surgical resection of CNS gliomas. Here, we present the potential advantages and limitations of these imaging techniques for the purposes of identifying gliomas in the operating room.

In brain cancer surgery, it is critical to achieve extensive resection without compromising adjacent healthy, non-cancerous regions. Various technological advances have made major contributions in imaging, including intraoperative magnetic imaging (MRI) and computed tomography (CT). However, these technologies have pros and cons in providing quantitative, real-time and three-dimensional (3D) continuous guidance in brain cancer detection. Optical Coherence Tomography (OCT) is a non-invasive, label-free, cost-effective technique capable of imaging tissue in three dimensions and real time. The purpose of this study is to reliably and efficiently discriminate between non-cancer and cancer-infiltrated brain regions using OCT images. To this end, a mathematical model for quantitative evaluation known as the Blind End- Member and Abundances Extraction method (BEAE). This BEAE method is a constrained optimization technique which extracts spatial information from volumetric OCT images. Using this novel method, we are able to discriminate between cancerous and non-cancerous tissues and using logistic regression as a classifier for automatic brain tumor margin detection. Using this technique, we are able to achieve excellent performance using an extensive cross-validation of the training dataset (sensitivity 92.91% and specificity 98.15%) and again using an independent, blinded validation dataset (sensitivity 92.91% and specificity 86.36%). In summary, BEAE is well-suited to differentiate brain tissue which could support the guiding surgery process for tissue resection.

We present recent updates on quantitative OCT for guiding brain cancer surgery in real time. Results from pilot intra-operative imaging of more than 20 human subject will be discussed.

Summary Background During laparoscopic or robotic assisted laparoscopic prostatectomy, the surgeon lacks tactile feedback which can help him tailor the size of the excision. Ultrasound elastography (USE) is an emerging imaging technology which maps the stiffness of tissue. In the paper we are evaluating USE as a palpation equivalent tool for intraoperative image guided robotic assisted laparoscopic prostatectomy. Material/Methods Two studies were performed: 1) A laparoscopic ultrasound probe was used in a comparative study of manual palpation versus USE in detecting tumor surrogates in synthetic and ex-vivo tissue phantoms; N=25 participants (students) were asked to provide the presence, size and depth of these simulated lesions, and 2) A standard ultrasound probe was used for the evaluation of USE on ex-vivo human prostate specimens (N=10 lesions in N=6 specimens) to differentiate hard versus soft lesions with pathology correlation. Results were validated by pathology findings, and also by in-vivo and ex-vivo MR imaging correlation. Results In the comparative study, USE displayed higher accuracy and specificity in tumor detection (sensitivity=84%, specificity=74%). Tumor diameters and depths were better estimated using USE versus with manual palpation. USE also proved consistent in identification of lesions in ex-vivo prostate specimens; hard and soft, malignant and benign, central and peripheral. Conclusions USE is a strong candidate for assisting surgeons by providing palpation equivalent evaluation of the tumor location, boundaries and extra-capsular extension. The results encourage us to pursue further testing in the robotic laparoscopic environment.

More complete brain cancer resection can prolong survival and delay recurrence. However, it is challenging to distinguish cancer from noncancer tissues intraoperatively, especially at the transitional, infiltrative zones. This is especially critical in eloquent regions (for example, speech and motor areas). This study tested the feasibility of label-free, quantitative optical coherence tomography (OCT) for differentiating cancer from noncancer in human brain tissues. Fresh ex vivo human brain tissues were obtained from 32 patients with grade II to IV brain cancer and 5 patients with noncancer brain pathologies. On the basis of volumetric OCT imaging data, pathologically confirmed brain cancer tissues (both high- and low-grade) had significantly lower optical attenuation values at both cancer core and infiltrated zones when compared with noncancer white matter, and OCT achieved high sensitivity and specificity at an attenuation threshold of 5.5 mm(-1) for brain cancer patients. We also used this attenuation threshold to confirm the intraoperative feasibility of performing in vivo OCT-guided surgery using a murine model harboring human brain cancer. Our OCT system was capable of processing and displaying a color-coded optical property map in real time at a rate of 110 to 215 frames per second, or 1.2 to 2.4 s for an 8- to 16-mm(3) tissue volume, thus providing direct visual cues for cancer versus noncancer areas. Our study demonstrates the translational and practical potential of OCT in differentiating cancer from noncancer tissue. Its intraoperative use may facilitate safe and extensive resection of infiltrative brain cancers and consequently lead to improved outcomes when compared with current clinical standards.

This study uses OCT and optical property mapping to detect brain tumors in vivo in mice bearing human glioblastoma and in fresh ex vivo brain tissues from 32 patients with high sensitivity and specificity.

Ex vivo human brain tissues from 8 glioma patients and 5 controls are imaged using SS-OCT, with higher glioma attenuation values and easily detectable and specific tumor features (microcyst, necrosis and pallisading) for intraoperative resections.

Purpose: To compare the accuracy of detecting tumor location and size in the prostate using both manual palpation and ultrasound elastography (UE). Methods: Tumors in the prostate were simulated using both synthetic and ex vivo tissue phantoms. 25 participants were asked to provide the presence, size and depth of these simulated lesions using manual palpation and UE. Ultrasound images were captured using a laparoscopic ultrasound probe, fitted with a Gore-Tetrad transducer with frequency of 7.5 MHz and a RF capture depth of 4-5 cm. A MATLAB GUI application was employed to process the RF data for ex vivo phantoms, and to generate UE images using a cross-correlation algorithm. Ultrasonix software was used to provide real time elastography during laparoscopic palpation of the synthetic phantoms. Statistical analyses were performed based on a two-tailed, student t-test with α = 0.05. Results: UE displays both a higher accuracy and specificity in tumor detection (sensitivity = 84%, specificity = 74%). Tumor diameters and depths are better estimated using ultrasound elastography when compared with manual palpation. Conclusions: Our results indicate that UE has strong potential in assisting surgeons to intra-operatively evaluate the tumor depth and size. We have also demonstrated that ultrasound elastography can be implemented in a laparoscopic environment, in which manual palpation would not be feasible. With further work, this application can provide accurate and clinically relevant information for surgeons during prostate resection.