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.

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.

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

Purpose

In 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 design

Multiple 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.

Results

Visual 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.

Conclusion

Contactless 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.

Comparison of manual and automated digital image analysis systems for quantification of cellular protein expression

OBJECTIVE

Quantifying protein expression in immunohistochemically stained histological slides is an important tool for oncologic research. The use of computer-aided evaluation of IHC-stained slides significantly contributes to objectify measurements. Manual digital image analysis (mDIA) requires a user-dependent annotation of the region of interest (ROI). Others have built-in machine learning algorithms with automated digital image analysis (aDIA) and can detect the ROIs automatically. We aimed to investigate the agreement between the results obtained by aDIA and those derived from mDIA systems.

METHODS

We quantified chromogenic intensity (CI) and calculated the positive index (PI) in cohorts of tissue microarrays (TMA) using mDIA and aDIA. To consider the different distributions of staining within cellular sub-compartments and different tumor architecture our study encompassed nuclear and cytoplasmatic stainings in adenocarcinomas and squamous cell carcinomas.

RESULTS

Within all cohorts, we were able to show a high correlation between mDIA and aDIA for the CI (p<0.001) along with high agreement for the PI. Moreover, we were able to show that the cell detections of the programs were comparable as well and both proved to be reliable when compared to manual counting.

CONCLUSION

mDIA and aDIA show a high correlation in acquired IHC data. Both proved to be suitable to stratify patients for evaluation with clinical data. As both produce the same level of information, aDIA might be preferable as it is time-saving, can easily be reproduced, and enables regular and efficient output in large studies in a reasonable time period.

Performance of Different Diagnostic PD-L1 Clones in Head and Neck Squamous Cell Carcinoma

Background: The approval of immune checkpoint inhibitors in combination with specific diagnostic biomarkers presents new challenges to pathologists as tumor tissue needs to be tested for expression of programmed death-ligand 1 (PD-L1) for a variety of indications. As there is currently no requirement to use companion diagnostic assays for PD-L1 testing in Germany different clones are used in daily routine. While the correlation of staining results has been tested in various entities, there is no data for head and neck squamous cell carcinomas (HNSCC) so far.

Methods: We tested five different PD-L1 clones (SP263, SP142, E1L3N, 22-8, 22C3) on primary HNSCC tumor tissue of 75 patients in the form of tissue microarrays. Stainings of both immune and tumor cells were then assessed and quantified by pathologists to simulate real-world routine diagnostics. The results were analyzed descriptively and the resulting staining pattern across patients was further investigated by principal component analysis and non-negative matrix factorization clustering.

Results: Percentages of positive immune and tumor cells varied greatly. Both the resulting combined positive score as well as the eligibility for certain checkpoint inhibitor regimens was therefore strongly dependent on the choice of the antibody. No relevant co-clustering and low similarity of relative staining patterns across patients was found for the different antibodies.

Conclusions: Performance of different diagnostic anti PD-L1 antibody clones in HNSCC is less robust and interchangeable compared to reported data from other tumor entities. Determination of PD-L1 expression is critical for therapeutic decision making and may be aided by back-to-back testing of different PD-L1 clones.

Immunologic "Cold" Squamous Cell Carcinomas of the Head and Neck Are Associated With an Unfavorable Prognosis

Background: Head and neck squamous cell carcinoma (HNSCC) represents a common cancer worldwide. Past therapeutic advances have not significantly improved HNSCC prognosis. Therefore, it is necessary to further stratify HNSCC, especially with recent advances in tumor immunology.

Methods: Tissue microarrays were assembled from tumor tissue samples and were complemented with comprehensive clinicopathological data of n = 419 patients. H&E whole slides from resection specimen (n = 289) were categorized according to their immune cell infiltrate as “hot,” “cold,” or “excluded.”

Results: Investigating tumor immune cell patterns, we found significant differences in survival rates. Immunologic “hot” and “excluded” HNSCCs are associated with better overall survival than “cold” HNSCC patients (p < 0.05). Interestingly, the percentage of all three patterns is nearly identical in p16 positive and negative HNSCCs.

Conclusions: Using a plain histological H&E approach to categorize HNSCC as being immunologic “hot,” “cold,” or “excluded” can offer a forecast of patients' prognosis and may thus aid as a potential prognostic tool in routine pathology reports. This “hot-cold-excluded” scheme needs to be applied to more HNSCC cohorts and possibly to other cancer types to determine prognostic meaning, e.g., regarding OS or DFS. Furthermore, our cohort reflects epidemiological data in the national, European, and international context. It may, therefore, be of use for future HNSCC characterization.

Recurrent HNSCC Harbor an Immunosuppressive Tumor Immune Microenvironment Suggesting Successful Tumor Immune Evasion

PURPOSE

Recurrent tumors (RT) of head and neck squamous cell carcinoma (HNSCC) occur in up to 60%, with poor therapeutic response and detrimental prognosis. We hypothesized that HNSCC RTs successfully evade antitumor immune response and aimed to reveal tumor immune microenvironment (TIME) changes of primary tumors (PT) and corresponding RTs.

EXPERIMENTAL DESIGN

Tumor-infiltrating leukocytes (TIL) of 300 PTs and 108 RTs from two large independent and clinically well-characterized HNSCC cohorts [discovery cohort (DC), validation cohort (VD)] were compared by IHC. mRNA expression analysis of 730 immune-related genes was performed for 18 PTs and RTs after adjuvant chemoradiotherapy (CRT). The effect of chemotherapy and radiation resistance was assessed with an in vitro spheroid/immunocyte coculture model.

RESULTS

TIME analysis revealed overall decrease of TILs with significant loss of CD8⁺ T cells (DC P = 0.045/VC P < 0.0001) and B lymphocytes (DC P = 0.036/VC P < 0.0001) in RTs compared with PTs in both cohorts. Decrease predominantly occurred in RTs after CRT. Gene expression analysis confirmed loss of TILs (P = 0.0004) and B lymphocytes (P < 0.0001) and showed relative increase of neutrophils (P = 0.018), macrophages (P < 0.0001), dendritic cells (P = 0.0002), and mast cells (P = 0.0057) as well as lower overall expression of immune-related genes (P = 0.018) in RTs after CRT. Genes involved in B-lymphocyte functions and number of tertiary lymphoid structures showed the strongest decrease. SPP1 and MAPK1 were upregulated in vivo and in vitro, indicating their potential suitability as therapeutic targets in CRT resistance.

CONCLUSIONS

HNSCC RTs have an immunosuppressive TIME, which is particularly apparent after adjuvant CRT and might substantially contribute to poor therapeutic response and prognosis.

NR2F6 as a Prognostic Biomarker in HNSCC

Head and neck squamous cell carcinoma (HNSCC)is the 6th most common cancer in humans worldwide and is associated with a poor prognosis for patients. NR2F6 has been identified as an immune checkpoint molecule in tumor-infiltrating T lymphocytes and is associated with a poor prognostic outcome in various cancers. The prognostic value of NR2F6 in HNSCC has not been described yet. We used a large, representative and clinically well-characterized cohort of 383 HNSCC patients, of which 22.4% developed a local recurrence. The NR2F6 expression was analyzed by using immunohistochemistry and was afterward correlated with clinical characteristics and clinicopathological features of HNSCC patients. Primary tumors from patients who develop a local recurrence have a higher NR2F6 expression than primary tumors which do not develop a local recurrence. Furthermore, a high NR2F6 expression is associated with poorer recurrence-free survival, although there is no correlation with overall survival. NR2F6 expression is independent of the T stage and UICC stage. NR2F6 might be a new prognostic biomarker for the early detection of local recurrences in HNSCC patients. Therefore, it may help to improve the recognition of patients who would benefit from more frequent follow-up examinations.

EVI1 as a Marker for Lymph Node Metastasis in HNSCC

BACKGROUND

HNSCC is the sixth most common cancer in humans and has still a very poor prognosis. The treatment methods so far are very often associated with mutilation and impairment in the quality of life. Except for p16 expression, there are no reliable prognostic markers in HNSCC so far. Ecotropic Viral Integration Site 1 (EVI1) is a well-described prognostic marker in leukemia and different types of solid cancers. In these, a high EVI1 expression is associated with a poor prognosis. In HNSCC, it is not known so far if EVI1 has any prognostic relevance.

MATERIALS AND METHODS

We used our representative tissue cohort of 389 primary HNSCCs, of which 57.2% had one or more lymph node metastases. Here EVI1 expression was analyzed via immunohistochemistry and correlated with the clinical characteristics of these patients.

RESULTS

Although in HNSCC EVI1 expression does not predict poor survival, a high EVI1 expression in the primary tumor correlates with a lymph node metastatic disease.

CONCLUSION

Consequently, EVI1 may serve as a biomarker to predict an occult lymph node metastasis in a clinical nodal negative (cN0) HNSCC.