In this study, we assessed the prognostic relevance of temporal muscle thickness (TMT), likely reflecting patient’s frailty, in patients with primary central nervous system lymphoma (PCNSL). In 128 newly diagnosed PCNSL patients TMT was analyzed on cranial magnetic resonance images. Predefined sex-specific TMT cutoff values were used to categorize the patient cohort. Survival analyses, using a log-rank test as well as Cox models adjusted for further prognostic parameters, were performed. The risk of death was significantly increased for PCNSL patients with reduced muscle thickness (hazard ratio of 3.189, 95% CI: 2–097–4.848, p < 0.001). Importantly, the results confirmed that TMT could be used as an independent prognostic marker upon multivariate Cox modeling (hazard ratio of 2.504, 95% CI: 1.608–3.911, p < 0.001) adjusting for sex, age at time of diagnosis, deep brain involvement of the PCNSL lesions, Eastern Cooperative Oncology Group (ECOG) performance status, and methotrexate-based chemotherapy. A TMT value below the sex-related cutoff value at the time of diagnosis is an independent adverse marker in patients with PCNSL. Thus, our results suggest the systematic inclusion of TMT in further translational and clinical studies designed to help validate its role as a prognostic biomarker.

Few data exist regarding the prognostic value of L-[S-methyl-¹¹C]methionine (MET) PET for treatment-naïve gliomas. A total of 160 glioma patients (89 men, 71 women; mean age: 45, range 18–84 y) underwent a MET PET prior to any therapy. The PET scans were evaluated visually and semiquantitatively by tumor-to-background (T/N) ratio thresholds chosen by analysis of receiver operating characteristics. Additionally, isocitrate dehydrogenase 1–R132H (IDH1-R132H) immunohistochemistry was performed. Survival analysis was done using Kaplan–Meier estimates and the Cox proportional hazards model. Significantly shorter mean survival times (7.2 vs 8.6 y; P = 0.024) were seen in patients with amino acid avid gliomas (n = 137) compared with visually negative tumors (n = 33) in MET PET. T/N ratio thresholds of 2.1 and 3.5 were significantly associated with survival (10.3 vs 7 vs 4.3 y; P < 0.001). Mean survival differed significantly using the median T/N ratio of 2.4 as cutoff, independent of histopathology (P < 0.01; mean survival: 10.2 ± 0.8 y vs 5.5 ± 0.6 y). In the subgroup of 142 glioma patients characterized by IDH1-R132H status, METT/N ratio demonstrated a significant prognostic impact in IDH1-R132H wildtype astrocytomas and glioblastoma (P = 0.001). Additionally, multivariate testing revealed semiquantitative MET PET as an independent prognostic parameter for treatment-naïve glioma patients without (P = 0.031) and with IDH1-R132H characterization of gliomas (P = 0.024; odds ratio 1.57). This retrospective analysis demonstrates the value of MET PET as a prognostic parameter on survival in treatment-naïve glioma patients.

Fluorescence-guided surgery is a state-of-the-art approach for intraoperative imaging during neurosurgical removal of tumor tissue. While the visualization of high-grade gliomas is reliable, lower grade glioma often lack visible fluorescence signals. Here, we present a hybrid prototype combining visible light optical coherence microscopy (OCM) and high-resolution fluorescence imaging for assessment of brain tumor samples acquired by 5-aminolevulinic acid (5-ALA) fluorescence-guided surgery. OCM provides high-resolution information of the inherent tissue scattering and absorption properties of tissue. We here explore quantitative attenuation coefficients derived from volumetric OCM intensity data and quantitative high-resolution 5-ALA fluorescence as potential biomarkers for tissue malignancy including otherwise difficult-to-assess low-grade glioma. We validate our findings against the gold standard histology and use attenuation and fluorescence intensity measures to differentiate between tumor core, infiltrative zone and adjacent brain tissue. Using large field-of-view scans acquired by a near-infrared swept-source optical coherence tomography setup, we provide initial assessments of tumor heterogeneity. Finally, we use cross-sectional OCM images to train a convolutional neural network that discriminates tumor from non-tumor tissue with an accuracy of 97%. Collectively, the present hybrid approach offers potential to translate into an in vivo imaging setup for substantially improved intraoperative guidance of brain tumor surgeries.

Optical coherence tomography (OCT) is an optical imaging technique, which was introduced in the early 1990’s. OCT has since become one of the most important imaging and diagnosis tools in ophthalmology.¹ The use of OCT or optical coherence microscopy (OCM) also expanded into a wide range of application fields such as neuro-, skin, and endoscopic imaging.^{2 – 4} In neuroimaging, OCT has been utilized to investigate diseases such as Alzheimer’s disease (AD),^{5 – 7} Parkinson’s disease,^8,9 and a variety of brain tumors.^{10 – 12} However, in comparison to histology, OCT often lacks tissue-specific contrast of anatomical structures since conventional OCT image contrast is mainly based on light scattering. Current research in the OCT community has therefore also focused on validating and/or combining OCT with other established imaging modalities. One promising possibility is to combine OCT and fluorescence imaging (FI).¹³

In brain tumor therapy, the key factor for improved patient outcome is complete resection.¹ Even though fluorescence-guided surgery using 5-aminolevulinic acid (5-ALA)-induced protoporphyrin IX (PpIX) allows the surgeon to locate malignant tissue, its use is mostly limited to high-grade tumors.² As a consequence, current research has focused on more sensitive detection schemes to visualize fluorescence that is not visible to the surgeon’s eye. However, most of the proposed in vivo methods rely on endoscopic platforms with limited field of view (FOV).³ Hence, visualization methods with increased FOV and working distance (WD) are needed to inspect larger areas. More sensitive detection of PpIX fluorescence is limited among others by tissue autofluorescence. Therefore, spectrally resolved fluorescence imaging³ has been proposed to detect nonvisible PpIX accumulations. However, it relies on the knowledge or parallel measurement of the optical tissue properties. This requires additional measurement channels making these devices more complex. On the other hand, fluorescence lifetime imaging (FLIM) relies on the time delay between the excitation and subsequent fluorescence emission and is thereby intrinsically independent of any intensity variations due to altered scattering or absorption in the tissue.⁴

Polarization‐sensitive optical coherence tomography (PS‐OCT) enables three‐dimensional imaging of biological tissues based on the inherent contrast provided by scattering and polarization properties. In fibrous tissue such as the white matter of the brain, PS‐OCT allows quantitative mapping of tissue birefringence. For the popular PS‐OCT layout using a single circular input state, birefringence measurements are based on a straight‐forward evaluation of phase retardation data. However, the accuracy of these measurements strongly depends on the signal‐to‐noise ratio (SNR) and is prone to mapping artifacts when the SNR is low. Here we present a simple yet effective approach for improving the accuracy of PS‐OCT phase retardation and birefringence measurements. By performing a noise bias correction of the detected OCT signal amplitudes, the impact of the noise floor on retardation measurements can be markedly reduced. We present simulation data to illustrate the influence of the noise bias correction on phase retardation measurements and support our analysis with real‐world PS‐OCT image data.