An attempt to identify brain tumour tissue in neurosurgery by mechanical indentation measurements.

BACKGROUND: The intraoperative differentiation between tumour tissue, healthy brain tissue, and any sensitive structure of the central nervous system is carried out in modern neurosurgery using various multimodal technologies such as neuronavigation, fluorescent dyes, intraoperative ultrasound or the use of intraoperative MRI, but also the haptic experience of the neurosurgeon. Supporting the surgeon by developing instruments with integrated haptics could provide a further objective dimension in the intraoperative recognition of healthy and diseased tissue. METHODS: In this study, we describe intraoperative mechanical indentation measurements of human brain tissue samples of different tumours taken during neurosurgical operation and measured directly in the operating theatre, in a time frame of maximum five minutes. We present an overview of the Young's modulus for the different brain tumour entities and potentially differentiation between them. RESULTS: We examined 238 samples of 75 tumour removals. Neither a clear distinction of tumour tissue against healthy brain tissue, nor differentiation of different tumour entities was possible on solely the Young's modulus. Correlation between the stiffness grading of the surgeon and our measurements could be found. CONCLUSION: The mechanical behaviour of brain tumours given by the measured Young's modulus corresponds well to the stiffness assessment of the neurosurgeon and can be a great tool for further information on mechanical characteristics of brain tumour tissue. Nevertheless, our findings imply that the information gained through indentation is limited.

Mechanical characteristics of glioblastoma and peritumoral tumor-free human brain tissue.

BACKGROUND: The diagnosis of brain tumor is a serious event for the affected patient. Surgical resection is a crucial part in the treatment of brain tumors. However, the distinction between tumor and brain tissue can be difficult, even for experienced neurosurgeons. This is especially true in the case of gliomas. In this project we examined whether the biomechanical parameters elasticity and stress relaxation behavior are suitable as additional differentiation criteria between tumorous (glioblastoma multiforme; glioblastoma, IDH-wildtype; GBM) and non-tumorous, peritumoral tissue. METHODS: Indentation measurements were used to examine non-tumorous human brain tissue and GBM samples for the biomechanical properties of elasticity and stress-relaxation behavior. The results of these measurements were then used in a classification algorithm (Logistic Regression) to distinguish between tumor and non-tumor. RESULTS: Differences could be found in elasticity spread and relaxation behavior between tumorous and non-tumorous tissue. Classification was successful with a sensitivity/recall of 83% (sd = 12%) and a precision of 85% (sd = 9%) for detecting tumorous tissue. CONCLUSION: The findings imply that the data on mechanical characteristics, with particular attention to stress relaxation behavior, can serve as an extra element in differentiating tumorous brain tissue from non-tumorous brain tissue.

Phase unwrapping for MHz optical coherence elastography and application to brain tumor tissue.

During neuro-oncologic surgery, phase-sensitive optical coherence elastography (OCE) can be valuable for distinguishing between healthy and diseased tissue. However, the phase unwrapping process required to retrieve the original phase signal is a challenging and critical task. To address this issue, we demonstrate a one-dimensional unwrapping algorithm that recovers the phase signal from a 3.2 MHz OCE system. With a processing time of approximately 0.11 s per frame on the GPU, multiple 2π wraps are detected and corrected. By utilizing this approach, exact and reproducible information on tissue deformation can be obtained with pixel accuracy over the entire acquisition time. Measurements of brain tumor-mimicking phantoms and human ex vivo brain tumor samples verified the algorithm's reliability. The tissue samples were subjected to a 200 ms short air pulse. A correlation with histological findings confirmed the algorithm's dependability.

Microscope-integrated optical coherence tomography for in vivo human brain tumor detection with artificial intelligence.

OBJECTIVE: It has been shown that optical coherence tomography (OCT) can identify brain tumor tissue and potentially be used for intraoperative margin diagnostics. However, there is limited evidence on its use in human in vivo settings, particularly in terms of its applicability and accuracy of residual brain tumor detection (RTD). For this reason, a microscope-integrated OCT system was examined to determine in vivo feasibility of RTD after resection with automated scan analysis. METHODS: Healthy and diseased brain was 3D scanned at the resection edge in 18 brain tumor patients and investigated for its informative value in regard to intraoperative tissue classification. Biopsies were taken at these locations and labeled by a neuropathologist for further analysis as ground truth. Optical OCT properties were obtained, compared, and used for separation with machine learning. In addition, two artificial intelligence-assisted methods were utilized for scan classification, and all approaches were examined for RTD accuracy and compared to standard techniques. RESULTS: In vivo OCT tissue scanning was feasible and easily integrable into the surgical workflow. Measured backscattered light signal intensity, signal attenuation, and signal homogeneity were significantly distinctive in the comparison of scanned white matter to increasing levels of scanned tumor infiltration (p < 0.001) and achieved high values of accuracy (85%) for the detection of diseased brain in the tumor margin with support vector machine separation. A neuronal network approach achieved 82% accuracy and an autoencoder approach 85% accuracy in the detection of diseased brain in the tumor margin. Differentiating cortical gray matter from tumor tissue was not technically feasible in vivo. CONCLUSIONS: In vivo OCT scanning of the human brain has been shown to contain significant value for intraoperative RTD, supporting what has previously been discussed for ex vivo OCT brain tumor scanning, with the perspective of complementing current intraoperative methods for this purpose, especially when deciding to withdraw from further resection toward the end of the surgery.