Keywords: Tumors (Post-Treatment), Tumor, Glioblastoma, Tumor recurrent, Treatment-related response, Distributed parameter model, Dynamic contrast-enhanced

Motivation: It is challenging to differentiate recurrent tumor from treatment response in glioblastoma.

Goal(s): This study explored the issue using an advanced tracer kinetic model.

Approach: Glioblastoma patients were examined using dynamic contrast-enhanced MRI and stratified into recurrent and treatment-related group based on histopathological results. Imaging data were analyzed using distributed parameter model.

Results: Blood flow in lesion was significantly higher and permeability in peritumoral edema was significantly smaller in recurrent than in treatment-related group (p=0.002, 0.023). Combining two parameters together, diagnostic performance was attained at AUC (area under ROC curve) 0.93. AUC of extended-Tofts model was 0.89.

Impact: With separate account of blood flow and vessel-wall permeability, advanced tracer kinetic model allowed more precise modeling the feature of tissue microenvironment, which could shed light on the difference between recurrent tumor and treatment-related response in glioblastoma.

Keywords: Tumors (Pre-Treatment), Neuro, diffuse midline glioma; H3K27M altered; whole-brain tumor burden; cortical myelin content; cortical thickness;

Motivation: Diffuse midline glioma (DMG) represents a systemic disease due to its ability to disseminate tumor cells throughout the whole brain. Current imaging techniques, however, provide information only about the main tumor and its immediate surroundings. 

Goal(s): We employed comprehensive cortical mapping to gain insights into the individual tumor burden across whole-brain using structural MRI.

Approach: Cortical thickness and myelin content was calculated from participants using Human Connectome Project pipeline.

Results: DMG has the capacity to induce cortical thickness compensation while concurrently leading to cortical demyelination in numerous non-lesional regions. Notably, DMG harboring H3K27M altered exhibited specific cortical myelin and thickness reorganization patterns.

Impact: These findings may open up the possibility of tailoring treatment strategies to the individual disease severity and distribution within the patient's brain, potentially enhancing the effectiveness of both current and future treatment approaches.

Keywords: Tumors (Pre-Treatment), CEST & MT

Motivation: In the setting of lower grade glioma follow heterogeneity of prognosis, it is currently necessitating risk stratification.

Goal(s): To determine the predictive ability of amide proton transfer-weighted (APTw) imaging phenotypes in lower grade glioma.

Approach: The ability of APTw phenotypes to PFS was evaluated using biomarker threshold model, The predictive model was trained on 67%, and tested on the remainder.

Results: APTw imaging phenotypes can predict the progression free survival of lower grade gliomas.

Impact: The independent and additional prognostic value of imaging phenotypes in APTw suggests that APTw imaging phenotypes can provide a noninvasive characterization of tumor cellular, proliferation and invasiveness to augment personalized prognosis and treatment in patients with lower grade glioma.

Keywords: Tumors (Pre-Treatment), Tumor, DCE-MRI, IDH, Glioma, P53, CDKN2A/B

Motivation: IDH, P53 and CDKN2A/2B are key molecular markers for adult diffuse gliomas.

Goal(s): This study aimed to compare values of conventional and advanced tracer kinetic models based on dynamic contrast enhanced (DCE)-MRI in predicting IDH, P53 and CDKN2A/B status in glioma patinets.

Approach: Patients diagnosed as adult diffuse gliomas were examined using dynamic contrast-enhanced MRI. Imaging data were analyzed using tracer kinetic models.

Results: Extofts model attained best performance in predicting IDH mutation.ATH model attained best performance in predicting P53 mutation. DP model attained best performance in predicting CDKN2A/B homozygous deletion.  

Impact: Different Tracer Kinetic Models have illustrated excellent performance in predicting different molecular markers in glioma patients.

Keywords: Tumors (Pre-Treatment), Software Tools, AI/ML Software; Brain; Machine Learning/Artificial Intelligence;Neuro; Spectroscopy; Tumors

Motivation: Acquiring single-voxel Magnetic Resonance Spectroscopy (MRS) data in clinic currently involves manual voxel placement by technicians without the time capacity to review tumor biology in detail, leading to poor-quality spectra.

Goal(s): To achieve consistent and accurate single-voxel placement to minimize variability in metabolite quantification.

Approach: We developed an auto-placement algorithm that identifies an optimized MRS single-voxel position and rotation based on tumor biology (tumor core, necrosis, and edema) and outputs this voxel as a mask on MR Imaging.

Results: Performance of the automated MRS single-voxel placement rivals clinical placement and integrates with an existing clinically implemented automated brain tumor segmentation workflow.

Impact: Our new algorithm will assist radiology technicians in reliably placing MR Spectroscopy single-voxels with accuracy that rivals clinical placement. This is a primary need for non-invasive diagnosis and management of diffuse gliomas.   

Keywords: Tumors (Pre-Treatment), Tumor, fMRI, Cerebrovascular reactivity, Multi-echo, Glioma

Motivation: Cerebrovascular reactivity (CVR) with BOLD fMRI during breath-holding offers a feasible technique for examining neurovascular alterations in tumor-affected regions. However, this examination may have reduced accuracy due to breath-hold-induced artifacts.

Goal(s): This study explores the use of multi-echo fMRI techniques to improve the accuracy and reliability of CVR mapping and vascular lag estimation in glioma patients.

Approach: We employed optimized ME-fMRI procedures in 21 patients with diverse glioma characteristics, including lagged regression analysis, nuisance modeling with ME-ICA.

Results: Our protocol robustly mapped reductions in CVR in all patients, and showed the vascular lag provides differential clinically valuable insights into tumor and peritumoral areas.

Impact: This work present a robust and feasible multi-echo fMRI protocol with breath-holds that enhances cerebrovascular reactivity (CVR) mapping by obtaining complementary vascular lag maps, which offer critical insights into vascular delay and vasodilatory dynamics in glioma patients