Ex vivo and in vivo imaging of human brain tissue with different OCT systems

Strenge, Paul; Lange, Birgit; Grill, Christin; Draxinger, Wolfgang; Danicke, Veit; Theisen-Kunde, Dirk; Bonsanto, Matteo M.; Huber, Robert; Brinkmann, Ralf

doi:10.1117/12.2526932

Cited by 3 publications

(5 citation statements)

References 13 publications

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“…Research on neurosurgical in vivo application, however, is still mostly focused on feasibility. 1,5,[24][25][26] In this microscope-integrated approach, it was demonstrated for the first time that high RTD values could be achieved in vivo with automated image feature recognition, supporting what has well been described for ex vivo OCT brain tumor scanning. However, on-sight scan analysis was not yet feasible in this study.…”

Section: Discussionsupporting

confidence: 80%

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

Kuppler,

Strenge,

Lange

et al. 2024

Journal of Neurosurgery

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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.

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Section: Discussionsupporting

confidence: 80%

“…The field of vision was confined to a frame measuring 5.7 × 15.7 mm, as previously described. 5 The signal was measured to tissue depths as far as 1 mm. After tumor removal, 5 predetermined sites in the resection cavity were scanned to ensure comprehensive coverage of the entire resection zone.…”

Section: Microscope-integrated Oct Scanningmentioning

confidence: 99%

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

Kuppler,

Strenge,

Lange

et al. 2024

Journal of Neurosurgery

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“…In all scans, the working distance was set to 300 mm at zoom level 9 to achieve high resolution with sufficient relative back scattered signal intensity. The field of view was limited to a frame of 5.7x15.7, as described before ( 15 ). On average, 5 distinct locations at protocol-defined sites for representative coverage of the entire resection area were imaged after tumor tissue extraction by an experienced neurosurgeon.…”

Section: Methodsmentioning

confidence: 99%

“…Thus, image interpretation requires high levels of viewer expertise and its accuracy has only been reported for ex vivo imaging on small patient cohorts, varying in sensitivity and specificity in the range of 90-100% and 76-96%, respectively (9)(10)(11)(12). Research on neurosurgical in vivo application, in contrast, is still mostly focused on feasibility (8,9,(13)(14)(15). A variety of different OCT systems, e.g.…”

Section: Introductionmentioning

confidence: 99%

The neurosurgical benefit of contactless in vivo optical coherence tomography regarding residual tumor detection: A clinical study

et al. 2023

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PurposeIn brain tumor surgery, it is crucial to achieve complete tumor resection while conserving adjacent noncancerous brain tissue. Several groups have demonstrated that optical coherence tomography (OCT) has the potential of identifying tumorous brain tissue. However, there is little evidence on human in vivo application of this technology, especially regarding applicability and accuracy of residual tumor detection (RTD). In this study, we execute a systematic analysis of a microscope integrated OCT-system for this purpose.Experimental designMultiple 3-dimensional in vivo OCT-scans were taken at protocol-defined sites at the resection edge in 21 brain tumor patients. The system was evaluated for its intraoperative applicability. Tissue biopsies were obtained at these locations, labeled by a neuropathologist and used as ground truth for further analysis. OCT-scans were visually assessed with a qualitative classifier, optical OCT-properties were obtained and two artificial intelligence (AI)-assisted methods were used for automated scan classification. All approaches were investigated for accuracy of RTD and compared to common techniques.ResultsVisual OCT-scan classification correlated well with histopathological findings. Classification with measured OCT image-properties achieved a balanced accuracy of 85%. A neuronal network approach for scan feature recognition achieved 82% and an auto-encoder approach 85% balanced accuracy. Overall applicability showed need for improvement.ConclusionContactless in vivo OCT scanning has shown to achieve high values of accuracy for RTD, supporting what has well been described for ex vivo OCT brain tumor scanning, complementing current intraoperative techniques and even exceeding them in accuracy, while not yet in applicability.

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“…Alternative applications in neurosurgery include the use of intraoperative 3D-ultrasound and intraoperative MR-/CTimaging, both logistically complex to operate with [8][9][10][11][12]. Further, new methods to visualize brain tumor tissue are, e.g., optical coherence tomography (OCT) or Raman histology, which are usually associated with a restructuring of the operating workflow [13][14][15][16][17]. Other studies indicate that healthy brain and tumor tissue differ from each other based on their mechanical properties, such as elasticity [18][19][20][21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Toward intraoperative tissue classification: exploiting signal feedback from an ultrasonic aspirator for brain tissue differentiation

Bockelmann

Schetelig²,

Kesslau³

et al. 2022

Int J CARS

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Purpose During brain tumor surgery, care must be taken to accurately differentiate between tumorous and healthy tissue, as inadvertent resection of functional brain areas can cause severe consequences. Since visual assessment can be difficult during tissue resection, neurosurgeons have to rely on the mechanical perception of tissue, which in itself is inherently challenging. A commonly used instrument for tumor resection is the ultrasonic aspirator, whose system behavior is already dependent on tissue properties. Using data recorded during tissue fragmentation, machine learning-based tissue differentiation is investigated for the first time utilizing ultrasonic aspirators. Methods Artificial tissue model with two different mechanical properties is synthesized to represent healthy and tumorous tissue. 40,000 temporal measurement points of electrical data are recorded in a laboratory environment using a CNC machine. Three different machine learning approaches are applied: a random forest (RF), a fully connected neural network (NN) and a 1D convolutional neural network (CNN). Additionally, different preprocessing steps are investigated. Results Fivefold cross-validation is conducted over the data and evaluated with the metrics F1, accuracy, positive predictive value, true positive rate and area under the receiver operating characteristic. Results show a generally good performance with a mean F1 of up to 0.900 ± 0.096 using a NN approach. Temporal information indicates low impact on classification performance, while a low-pass filter preprocessing step leads to superior results. Conclusion This work demonstrates the first steps to successfully differentiate healthy brain and tumor tissue using an ultrasonic aspirator during tissue fragmentation. Evaluation shows that both neural network-based classifiers outperform the RF. In addition, the effects of temporal dependencies are found to be reduced when adequate data preprocessing is performed. To ensure subsequent implementation in the clinic, handheld ultrasonic aspirator use needs to be investigated in the future as well as the addition of data to reflect tissue diversity during neurosurgical operations.

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Ex vivo and in vivo imaging of human brain tissue with different OCT systems

Cited by 3 publications

References 13 publications

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

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

The neurosurgical benefit of contactless in vivo optical coherence tomography regarding residual tumor detection: A clinical study

Toward intraoperative tissue classification: exploiting signal feedback from an ultrasonic aspirator for brain tissue differentiation

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