Improved early outcome prediction by MRI-based 3D tumor volume assessment in patients with CNS lymphomas.

BACKGROUND: Central nervous system lymphomas (CNSL) display remarkable clinical heterogeneity, yet accurate prediction of outcomes remains challenging. The IPCG criteria are widely used in routine practice for the assessment of treatment response. However, the value of the IPCG criteria for ultimate outcome prediction is largely unclear, mainly due to the uncertainty in delineating complete from partial responses during and after treatment. METHODS: We explored various MRI features including semi-automated 3D tumor volume measurements at different disease milestones and their association with survival in 93 CNSL patients undergoing curative-intent treatment. RESULTS: At diagnosis, patients with more than 3 lymphoma lesions, periventricular involvement, and high 3D tumor volumes showed significantly unfavorable PFS and OS. At first interim MRI during treatment, the IPCG criteria failed to discriminate outcomes in responding patients. Therefore, we randomized these patients into training and validation cohorts to investigate whether 3D tumor volumetry could improve outcome prediction. We identified a 3D tumor volume reduction of ≥97% as the optimal threshold for risk stratification (=3D early response, 3D_ER). Applied to the validation cohort, patients achieving 3D_ER had significantly superior outcomes. In multivariate analyses, 3D_ER was independently prognostic of PFS and OS. Finally, we leveraged prognostic information from 3D MRI features and circulating biomarkers to build a composite metric that further improved outcome prediction in CNSL. CONCLUSIONS: We developed semi-automated 3D tumor volume measurements as strong and independent early predictors of clinical outcomes in CNSL patients. These radiologic features could help improve risk stratification and help guide future treatment approaches.

Direct comparison of digital subtraction myelography versus CT myelography in lateral decubitus position: evaluation of diagnostic yield for cerebrospinal fluid-venous fistulas.

BACKGROUND: Cerebrospinal fluid (CSF)-venous fistulas (CVFs) are increasingly identified as a cause of spontaneous intracranial hypotension (SIH). Lateral decubitus digital subtraction myelography (LD-DSM) and CT myelography (LD-CTM) are mainly used for detection, but the most sensitive method is yet unknown. OBJECTIVE: To compare LD-DSM with LD-CTM for diagnostic yield of CVFs. METHODS: Patients with SIH diagnosed with a CVF between January 2021 and December 2022 in which the area of CVF(s) was covered by both diagnostic modalities were included. LD-CTM immediately followed LD-DSM without repositioning the spinal needle, and the second half of the contrast agent was injected at the CT scanner. Patients were awake or mildly sedated. Retrospectively, two neuroradiologists evaluated data independently and blinded for the presence of CVF. RESULTS: Twenty patients underwent a total of 27 combined LD-DSM/LD-CTM examinations (4/20 with follow-up and 3/20 with bilateral examinations). Both raters identified significantly more CVFs with LD-CTM than with LD-DSM (rater 1: 39 vs 9, P<0.001; rater 2: 42 vs 12, P<0.001). Inter-rater agreement was substantial for LD-DSM (κ=0.732) and LD-CTM (κ=0.655). The results remained significant after considering the senior rating for cases of disagreement (39 vs 10; P<0.001), and no CVF detected on LD-DSM was missed on LD-CTM. CONCLUSION: In this study, LD-CTM has a higher diagnostic yield for the detection of CVFs than LD-DSM and should supplement LD-DSM, but further studies are needed. LD-CTM can be easily acquired in awake or mildly sedated patients with the second half of contrast injected just before CT scanning, or it may be considered as a stand-alone investigation.

Mesoscopic Assessment of Microstructure in Glioblastomas and Metastases by Merging Advanced Diffusion Imaging with Immunohistopathology.

BACKGROUND AND PURPOSE: Glioblastomas and metastases are the most common malignant intra-axial brain tumors in adults and can be difficult to distinguish on conventional MR imaging due to similar imaging features. We used advanced diffusion techniques and structural histopathology to distinguish these tumor entities on the basis of microstructural axonal and fibrillar signatures in the contrast-enhancing tumor component. MATERIALS AND METHODS: Contrast-enhancing tumor components were analyzed in 22 glioblastomas and 21 brain metastases on 3T MR imaging using DTI-fractional anisotropy, neurite orientation dispersion and density imaging-orientation dispersion, and diffusion microstructural imaging-micro-fractional anisotropy. Available histopathologic specimens (10 glioblastomas and 9 metastases) were assessed for the presence of axonal structures and scored using 4-level scales for Bielschowsky staining (0: no axonal structures, 1: minimal axonal fragments preserved, 2: decreased axonal density, 3: no axonal loss) and glial fibrillary acid protein expression (0: no glial fibrillary acid protein positivity, 1: limited expression, 2: equivalent to surrounding parenchyma, 3: increased expression). RESULTS: When we compared glioblastomas and metastases, fractional anisotropy was significantly increased and orientation dispersion was decreased in glioblastomas (each P < .001), with a significant shift toward increased glial fibrillary acid protein and Bielschowsky scores. Positive associations of fractional anisotropy and negative associations of orientation dispersion with glial fibrillary acid protein and Bielschowsky scores were revealed, whereas no association between micro-fractional anisotropy with glial fibrillary acid protein and Bielschowsky scores was detected. Receiver operating characteristic curves revealed high predictive values of both fractional anisotropy (area under the curve = 0.8463) and orientation dispersion (area under the curve = 0.8398) regarding the presence of a glioblastoma. CONCLUSIONS: Diffusion imaging fractional anisotropy and orientation dispersion metrics correlated with histopathologic markers of directionality and may serve as imaging biomarkers in contrast-enhancing tumor components.

Diffusion Microstructure Imaging to Analyze Perilesional T2 Signal Changes in Brain Metastases and Glioblastomas.

Purpose: Glioblastomas (GBM) and brain metastases are often difficult to differentiate in conventional MRI. Diffusion microstructure imaging (DMI) is a novel MR technique that allows the approximation of the distribution of the intra-axonal compartment, the extra-axonal cellular, and the compartment of interstitial/free water within the white matter. We hypothesize that alterations in the T2 hyperintense areas surrounding contrast-enhancing tumor components may be used to differentiate GBM from metastases. Methods: DMI was performed in 19 patients with glioblastomas and 17 with metastatic lesions. DMI metrics were obtained from the T2 hyperintense areas surrounding contrast-enhancing tumor components. Resected brain tissue was assessed in six patients in each group for features of an edema pattern and tumor infiltration in the perilesional interstitium. Results: Within the perimetastatic T2 hyperintensities, we observed a significant increase in free water (p < 0.001) and a decrease in both the intra-axonal (p = 0.006) and extra-axonal compartments (p = 0.024) compared to GBM. Perilesional free water fraction was discriminative regarding the presence of GBM vs. metastasis with a ROC AUC of 0.824. Histologically, features of perilesional edema were present in all assessed metastases and absent or marginal in GBM. Conclusion: Perilesional T2 hyperintensities in brain metastases and GBM differ significantly in DMI-values. The increased free water fraction in brain metastases suits the histopathologically based hypothesis of perimetastatic vasogenic edema, whereas in glioblastomas there is additional tumor infiltration.

Differentiation of Perilesional Edema in Glioblastomas and Brain Metastases: Comparison of Diffusion Tensor Imaging, Neurite Orientation Dispersion and Density Imaging and Diffusion Microstructure Imaging.

Although the free water content within the perilesional T2 hyperintense region should differ between glioblastomas (GBM) and brain metastases based on histological differences, the application of classical MR diffusion models has led to inconsistent results regarding the differentiation between these two entities. Whereas diffusion tensor imaging (DTI) considers the voxel as a single compartment, multicompartment approaches such as neurite orientation dispersion and density imaging (NODDI) or the recently introduced diffusion microstructure imaging (DMI) allow for the calculation of the relative proportions of intra- and extra-axonal and also free water compartments in brain tissue. We investigate the potential of water-sensitive DTI, NODDI and DMI metrics to detect differences in free water content of the perilesional T2 hyperintense area between histopathologically confirmed GBM and brain metastases. Respective diffusion metrics most susceptible to alterations in the free water content (MD, V-ISO, V-CSF) were extracted from T2 hyperintense perilesional areas, normalized and compared in 24 patients with GBM and 25 with brain metastases. DTI MD was significantly increased in metastases (p = 0.006) compared to GBM, which was corroborated by an increased DMI V-CSF (p = 0.001), while the NODDI-derived ISO-VF showed only trend level increase in metastases not reaching significance (p = 0.060). In conclusion, diffusion MRI metrics are able to detect subtle differences in the free water content of perilesional T2 hyperintense areas in GBM and metastases, whereas DMI seems to be superior to DTI and NODDI.

SPectroscOpic prediction of bRain Tumours (SPORT): study protocol of a prospective imaging trial.

BACKGROUND: The revised 2016 WHO-Classification of CNS-tumours now integrates molecular information of glial brain tumours for accurate diagnosis as well as for the development of targeted therapies. In this prospective study, our aim is to investigate the predictive value of MR-spectroscopy in order to establish a solid preoperative molecular stratification algorithm of these tumours. We will process a 1H MR-spectroscopy sequence within a radiomics analytics pipeline. METHODS: Patients treated at our institution with WHO-Grade II, III and IV gliomas will receive preoperative anatomical (T2- and T1-weighted imaging with and without contrast enhancement) and proton MR spectroscopy (MRS) by using chemical shift imaging (MRS) (5 × 5 × 15 mm3 voxel size). Tumour regions will be segmented and co-registered to corresponding spectroscopic voxels. Raw signals will be processed by a deep-learning approach for identifying patterns in metabolic data that provides information with respect to the histological diagnosis as well patient characteristics obtained and genomic data such as target sequencing and transcriptional data. DISCUSSION: By imaging the metabolic profile of a glioma using a customized chemical shift 1H MR spectroscopy sequence and by processing the metabolic profiles with a machine learning tool we intend to non-invasively uncover the genetic signature of gliomas. This work-up will support surgical and oncological decisions to improve personalized tumour treatment. TRIAL REGISTRATION: This study was initially registered under another name and was later retrospectively registered under the current name at the German Clinical Trials Register (DRKS) under DRKS00019855.

MR-spectroscopic imaging of glial tumors in the spotlight of the 2016 WHO classification.

BACKGROUND: The purpose of this study is to map spatial metabolite differences across three molecular subgroups of glial tumors, defined by the IDH1/2 mutation and 1p19q-co-deletion, using magnetic resonance spectroscopy. This work reports a new MR spectroscopy based classification algorithm by applying a radiomics analytics pipeline. MATERIALS: 65 patients received anatomical and chemical shift imaging (5 × 5 × 20 mm voxel size). Tumor regions were segmented and registered to corresponding spectroscopic voxels. Spectroscopic features were computed (n = 860) in a radiomic approach and selected by a classification algorithm. Finally, a random forest machine-learning model was trained to predict the molecular subtypes. RESULTS: A cluster analysis identified three robust spectroscopic clusters based on the mean silhouette widths. Molecular subgroups were significantly associated with the computed spectroscopic clusters (Fisher's Exact test p < 0.01). A machine-learning model was trained and validated by public available MRS data (n = 19). The analysis showed an accuracy rate in the Random Forest model by 93.8%. CONCLUSIONS: MR spectroscopy is a robust tool for predicting the molecular subtype in gliomas and adds important diagnostic information to the preoperative diagnostic work-up of glial tumor patients. MR-spectroscopy could improve radiological diagnostics in the future and potentially influence clinical and surgical decisions to improve individual tumor treatment.