Intraoperative microscopic autofluorescence detection and characterization in brain tumors using stimulated Raman histology and two-photon fluorescence.

Introduction: The intrinsic autofluorescence of biological tissues interferes with the detection of fluorophores administered for fluorescence guidance, an emerging auxiliary technique in oncological surgery. Yet, autofluorescence of the human brain and its neoplasia is sparsely examined. This study aims to assess autofluorescence of the brain and its neoplasia on a microscopic level by stimulated Raman histology (SRH) combined with two-photon fluorescence. Methods: With this experimentally established label-free microscopy technique unprocessed tissue can be imaged and analyzed within minutes and the process is easily incorporated in the surgical workflow. In a prospective observational study, we analyzed 397 SRH and corresponding autofluorescence images of 162 samples from 81 consecutive patients that underwent brain tumor surgery. Small tissue samples were squashed on a slide for imaging. SRH and fluorescence images were acquired with a dual wavelength laser (790 nm and 1020 nm) for excitation. In these images tumor and non-tumor regions were identified by a convolutional neural network that reliably differentiates between tumor, healthy brain tissue and low quality SRH images. The identified areas were used to define regions.of- interests (ROIs) and the mean fluorescence intensity was measured. Results: In healthy brain tissue, we found an increased mean autofluorescence signal in the gray (11.86, SD 2.61, n=29) compared to the white matter (5.99, SD 5.14, n=11, p<0.01) and in the cerebrum (11.83, SD 3.29, n=33) versus the cerebellum (2.82, SD 0.93, n=7, p<0.001), respectively. The signal of carcinoma metastases, meningiomas, gliomas and pituitary adenomas was significantly lower (each p<0.05) compared to the autofluorescence in the cerebrum and dura, and significantly higher (each p<0.05) compared to the cerebellum. Melanoma metastases were found to have a higher fluorescent signal (p<0.01) compared to cerebrum and cerebellum. Discussion: In conclusion we found that autofluorescence in the brain varies depending on the tissue type and localization and differs significantly among various brain tumors. This needs to be considered for interpreting photon signal during fluorescence-guided brain tumor surgery.

Novel rapid intraoperative qualitative tumor detection by a residual convolutional neural network using label-free stimulated Raman scattering microscopy.

Determining the presence of tumor in biopsies and the decision-making during resections is often dependent on intraoperative rapid frozen-section histopathology. Recently, stimulated Raman scattering microscopy has been introduced to rapidly generate digital hematoxylin-and-eosin-stained-like images (stimulated Raman histology) for intraoperative analysis. To enable intraoperative prediction of tumor presence, we aimed to develop a new deep residual convolutional neural network in an automated pipeline and tested its validity. In a monocentric prospective clinical study with 94 patients undergoing biopsy, brain or spinal tumor resection, Stimulated Raman histology images of intraoperative tissue samples were obtained using a fiber-laser-based stimulated Raman scattering microscope. A residual network was established and trained in ResNetV50 to predict three classes for each image: (1) tumor, (2) non-tumor, and (3) low-quality. The residual network was validated on images obtained in three small random areas within the tissue samples and were blindly independently reviewed by a neuropathologist as ground truth. 402 images derived from 132 tissue samples were analyzed representing the entire spectrum of neurooncological surgery. The automated workflow took in a mean of 240 s per case, and the residual network correctly classified tumor (305/326), non-tumorous tissue (49/67), and low-quality (6/9) images with an inter-rater agreement of 89.6% (κ = 0.671). An excellent internal consistency was found among the random areas with 90.2% (Cα = 0.942) accuracy. In conclusion, the novel stimulated Raman histology-based residual network can reliably detect the microscopic presence of tumor and differentiate from non-tumorous brain tissue in resection and biopsy samples within 4 min and may pave a promising way for an alternative rapid intraoperative histopathological decision-making tool.

CT artifacts after contrast media injection in chest imaging: evaluation of post-processing algorithms, virtual monoenergetic images and their combination for artifact reduction.

BACKGROUND: After injection into a brachial vein, high contrast media concentration in axillary and subclavian veins can cause artifacts that impair diagnostic utility. This study assessed artifact reduction by artifact-reduction-algorithms (ARA) and virtual-monoenergetic-images (VMI), as well as their combination (VMIARA) compared to conventional CT-images (CI). METHODS: Forty-six spectral-detector-CT (SDCT) examinations of patients that received ARA-reconstructions due to perivenous-artifacts were included in this retrospective study. CI, ARA, VMI, and VMIARA (range: 70-200 keV) were reconstructed. Objective analysis was performed with ROI-based assessment of mean and standard deviation of attenuation (HU) in hypo- and hyperdense artifacts and impaired muscle and arteries as well as artifact-free reference-tissue. Extent of artifact reduction, assessment of surrounding soft tissue and vessels, and appearance of new artifacts were rated visually by two radiologists. RESULTS: Hypo- and hyperdense artifacts showed significant improvement as evidenced by decreasing attenuation differences between artifact impaired and artifact-free reference tissue in ARA, VMI ≥80 keV, and VMIARA between 70-200 keV (e.g., CI/ARA/VMI100keV/VMIARA100keV: hypodense artifacts, (-)264.8±150.9/(-)87.1±78.9/(-)48.6±64.6/9.9±63.9 HU; P<0.001); hyperdense artifacts, 164.2±51.1/82.1±73.2/7.9±34.7/(-)17.3±50.7 HU; P<0.001). Artifacts impairing surrounding muscle and arteries were also reduced by all three approaches. In visual assessment, ARA, VMI ≥100 keV, and VMIARA between 70-200 keV also showed significant artifact reduction and improved assessment; however, for assessment of arteries improvement was not significant using ARA alone. New artifacts were reported, particularly at higher keV-values. CONCLUSIONS: In presence of perivenous-artifacts, ARA, VMI and their combination allow for significant artifact reduction; however, their combination and VMI as a standalone approach yielded best results and should therefore be used, if available.

hTERT promoter methylation in meningiomas and central nervous hemangiopericytomas.

In meningiomas, prognostic impact of mutations in the human telomerase reverse transcriptase (hTERT) promoter region was recently shown, while studies of promoter methylation and analyses of hemangiopericytomas are lacking. hTERT promoter methylation was analyzed in 78 meningioma and 38 meningeal hemangiopericytoma samples by methylation-specific polymerase chain reaction (MS-PCR) and compared with histopathological and clinical variables and with immunohistochemical hTERT expression. Promoter methylation was found in 62 samples (53 %) and tended to be higher in meningiomas (N = 19/41, 46 %) than in hemangiopericytomas (N = 8/33, 24 %, p = .057). In meningiomas, methylation was 16, 60 and 77 % in grade I, II and III tumors (p < .001) and higher in recurrent (N = 33/37, 89 %) than in primary diagnosed (N = 19/41, 46 %) tumors (OR 5.14, 95 % CI 1.34-19.71, p = .017). Univariate analyses showed shorter mean progression free and overall survival in methylated than in unmethylated individuals (26 vs. 100 months; p = .045 and 110 vs. 113 months; p = .025, respectively). Moreover, hTERT expression was found in 70 % (N = 53) and was more frequent in methylated than in unmethylated samples (78 vs. 52 %, OR 3.36, 95 % CI 1.20-9.40, p = .021). In hemangiopericytomas, methylation was similar in grade II (24 %) and III (25 %, p > .05) and in primary (24 %) and recurrent tumors (40 %, p > .05). hTERT expression was similar as compared to meningiomas (74 %, N = 28, p > .05) but was independent of promoter methylation (OR 4.26, 95 % CI 0.47-39.0, p = .199). In meningeal tumors, hTERT promoter methylation is more common than mutations and in meningiomas but not in hemangiopericytomas positively correlated with WHO grade and hTERT expression.

hTERT promoter methylation in pituitary adenomas.

Telomerase reverse transcriptase (TERT) expression is a hallmark in tumorigenesis and upregulated due to mutations and methylation of the human (h)TERT promoter. As mutations are rare but methylation is common in pituitary adenomas (PA), we determined promoter methylation and its clinical impact in 85 primary and 15 recurrent PA by methylation-specific PCR. 40 females (47%) and 45 males (53%) with a median age of 53 years harboring micro-, macro-, and giant adenomas in 12, 82, and 6% were included (prolactinomas, corticotroph, somatotroph, gonadotroph, thyreotroph, plurihormonal, and null cell adenomas in 11, 18, 10, 29, 1, 10, and 21%, respectively). In primary diagnosed tumors, methylation rate was 27% and higher in males than in females (40 vs. 13%, p = 0.001) after uni- and multivariate analyses. Methylation differed among PA subtypes (0-42%, p = n.s.) and was not significantly correlated with tumor size, cavernous sinus invasion, or serum hormone levels. Ki67 labeling index and recurrence (N = 16, 19%) were independent of methylation. In recurrent tumors, methylation was similar to primary PA (N = 5/15, 33%) and remained unchanged along follow-up. Thus, while being commonly observed in PA, hTERT promoter methylation is stable along follow-up and independent of most clinical variables, PA subtype, proliferation, and without prognostic value.