Keywords: Tumors (Pre-Treatment), Tumor, Glioma,Tumor habitat,prognosis

Motivation: High-grade glioma (HGG) is a highly invasive neoplasm characterized by significant intra-tumoral spatial heterogeneity. However, the clinical relevance of the observed spatial and physical imaging characteristics remains unknown. 

Goal(s): To identify tumor subregions and quantify their image-based habitat characteristics associated with survival time.

Approach: We retrospectively analyzed quantitative tumor habitat based on initial MRI scans in 2 groups (long-term and short-term survivals) of patients diagnosed with HGGs. Kmeans clustering, Univariate and multivariate logistic and survival analysis were used.

Results: The features of the high MK and low FLAIR habitat was most effective for predicting survival groups (AUC 0.91, Sensitivity 0.844, Specificity 0.867).

Impact: Tumor habitat is a novel method and It’s an earlier attempt to use habitats from diffusion and T1 based perfuison to predict the survival time of HGG. It has high prediction capabilities for prognosis. 

Keywords: Tumors (Pre-Treatment), Multimodal, Glioma, Survival, presurgical

Motivation: Gliomas of similar histologic grade show a lot of difference in the growth and development. And the survival of patients with similar histologic grades also vary.

Goal(s): The shape variations of gliomas impact survival or not.

Approach: We calculated the fractal dimension and lacunarity of the subcomponents of gliomas and analyzed them along with survival data to obtain differences in overall survival.

Results: Variations in fractal Dimension and Lacunarity also present variations in overall survival. Subjects with higher enhancing fractal dimension had shortened survival and it was opposite for nonenhancing fractal dimension and enhancing lacunarity. Survival did not depend on edema subcomponent.

Impact: The study's results could revolutionize glioma patient care. Clinicians may integrate fractal dimension and lacunarity as prognostic markers for tailored treatment decisions. Scientists may explore their use in combination with genetic factors for accurate survival predictions and improving patient outcomes.

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

Motivation: ATRX gene mutation is correlated with better prognosis in IDH1-mutant lower-grade gliomas (LrGGs).

Goal(s): We aimed to investigate the predictability of ATRX mutation status using multipool CEST MRI.

Approach: The CEST Z-spectrum was fitted with 5 Lorentzian functions. We compared the differences in 5 metrics between ATRX mutant and wild-type gliomas. And ROC analyses were performed to evaluate predictive performances of metrics.

Results: ATRX mutant IDH1-mutant LrGGs showed significantly decreased direct saturated water (DS), semi-solid magnetization transfer (MT) signals and increased amine signal compared to ATRX wild-type group. The combination of metrics yielded the highest AUC of 0.773.

Impact: Multipool CEST MRI demonstrated good ability to distinguish ATRX mutant gliomas from wild-type gliomas, it may be as a useful imaging biomarker for precise prediction of ATRX mutation status and facilitate the diagnosis and prognosis of glioma patients. 

Keywords: Tumors (Pre-Treatment), Brain, Ki67, IDH, Glioma

Motivation: WHO grade, Ki67 and IDH mutation are important for management and prognosis evaluation in glioma patients.

Goal(s): To investigate the value of Tri-exponential model (TEM) in preidcting WHO grade, Ki67 and IDH mutation of gliomas

Approach: 12 b-value DWI were obtained from glioma patients, TEM, SEM and IVIM model were analyzed for each patient. Univariate and Multivariate Logistic Regression were utilized to construct prediction model.

Results: TEM model achieved highest AUC on WHO Grade prediction task, while IVIM performed better on Ki67 and IDH mutation prediction task.

Impact: This study illustrated the potential of applying TEM model on predicting WHO grade Ki67 and IDH mutation in adult diffuse gliomas.

Keywords: Tumors (Pre-Treatment), Tumor

Motivation: Homozygous deletion (HD) of cyclin-dependent kinase inhibitor 2A/B (CDKN2A/B) holds important prognostic value in gliomas.

Goal(s): This study aims to explore the predictive potential of conventional MRI imaging parameters combined with dynamic contrast-enhanced (DCE) MRI parameters in predicting CDKN2A/B HD status in gliomas.

Approach: Conventional MRI features and DCE-MRI qualitative parameter time-intensity curve  types, quantitative parameters Ktrans, Kep, Ve, Vp, and iAUC were obtained. Logistic regression models for prediction of CDKN2A/B HD status were constructed.

Results: Ktrans can serve as valuable predictive parameters for identifying CDKN2A/B HD status in all glioma patients as well as patients with IDH-mutant or IDH-wild gliomas.

Impact: Our findings provide a foundation for precise preoperative non-invasive diagnosis and personalized treatment approaches for glioma patients.

Keywords: Tumors (Pre-Treatment), Tumor, perfusion, glioma

Motivation: Deliniating non-angiogenic and early-angiogenic areas of tumor prevents detection of the full extent of glioblastoma invasion.

Goal(s): This study investigated the relationship between perfusion and radio-pathomic estimates of cell density in glioblastoma.

Approach: This study compared ASL- and DSC-based perfusion estimates to predicted cellularity maps in two large publicly available datasets.

Results: Positive cellularity-perfusion associations were observed within contrast enhancement but not in non-enhancing regions. Per-subject positive cellularity-perfusion associations within FLAIR hyperintensity were associated with worse prognosis in glioblastoma patients following gross total resection.

Impact: Areas of increased perfusion and hypercellularity can be used to direct surgical intervention to capture early-angiogenic areas of tumor missed by contrast enhancement, which may in turn improve survival outcomes. Non-angiogenic hypercellular tumor may persist outside even this margin.

Keywords: Tumors (Post-Treatment), Cancer, DTI, AI, Progression, GBM

Motivation: Utilizing the knowledge of glioma cells' infiltration along white matter pathways to better predict GBM progression.

Goal(s): To enhance GBM progression prediction by analyzing the map of adjacent white matter fibers and building models to incorporate that map with anatomical MR. 

Approach: Developed a novel algorithm, DW-WMPL, from Diffusion-Tensor Imaging data that adjusts white matter fiber lengths to reveal possible tumor advancement. Employed deep learning models to predict progression with anatomical MRI and DW-WMPL maps. 

Results: DW-WMPL-enhanced deep learning models achieved higher precision in tumor delineation and reduced normal brain inclusion versus the standard 2cm radiation margin. 

Impact: The introduction of density-weighted white-matter path-length maps provides valuable insights into tumor cell migration, significantly refining GBM progression prediction. This advancement indicates a pivotal step towards personalized, more effective radiation therapy planning. 

Keywords: Tumors (Pre-Treatment), Brain, glioma; glioblastoma; molecular glioblastoma

Motivation: Whether molecular GBMs are equivalent to early histological GBMs are controversial. 

Goal(s): To compare the clinicopathologic, imaging, surgical factors, and prognosis between molecular GBMs and histological GBMs. 

Approach: Retrospective chart and imaging review was performed in 974 IDH-wildtype GBM patients (43 molecular GBMs and 931 histological GBMs) from a single institution. 

Results: Molecular GBMs were significantly younger with higher rate of TERTp, higher incidence of gliomatosis cerebri and non-lobar location compared with histological GBMs, resulting in less aggressive resection. Survival was significantly longer in molecular GBM, which was attributed to the longer OS in histological grade 2 molecular GBMs with isolated TERTp mutation. 

Impact: Discordant clinicopathologic, imaging, and surgical features suggest molecular GBMs may not be equivalent to early histological GBMs. Histological grade 2 molecular GBMs with isolated TERTp mutation may not be sufficient to assume an aggressive clinical behavior similar to histological GBM.

Keywords: Tumors (Pre-Treatment), Cancer, IDH-Mutation, 1p/19q Codeletion

Motivation: There is a critical need for improved diagnostic precision in distinguishing between IDH-mutant, 1p/19q non-codeleted astrocytomas and 1p/19q co-deleted oligodendrogliomas. While the T2/FLAIR-mismatch sign is specific for IDH-mutant astrocytomas, its limited sensitivity calls for enhanced diagnostic methods in glioma cohorts.

Goal(s): To evaluate the role of T2/FLAIR-mismatch in combination with qualitative VASARI features to classify IDH-mutant glioma.

Approach: We analyzed VASARI features and the presence of T2/FLAIR-mismatch in 179 IDH-mutant gliomas and determined the predictive accuracy of these features.

Results: Findings indicate the limitations of the T2/FLAIR-mismatch sign for accurate preoperative diagnosis, emphasizing the need for refined noninvasive methods to enhance diagnostic accuracy.

Impact: This research underscores the critical need for improved diagnostic tools in distinguishing glioma subtypes, as the T2/FLAIR-mismatch sign exhibits limitations, risking misclassification. Addressing these challenges is essential for accurate patient management and treatment planning in the context of IDH-mutant gliomas. 

Keywords: Tumors (Pre-Treatment), Brain, glioma

Motivation: Accurate preoperative noninvasive diagnosis and classification of diffuse gliomas have a significant impact on clinical management and are of significant prognostic importance. 

Goal(s): To investigate the diagnostic efficacy and clinical value of preoperative DCE-MRI in predicting genotype and prognosis of glioma. 

Approach: Adult diffuse gliomas were enrolled. Univariate and multivariate logistic or cox regression analysis are performed for diagnosing glioma gene and prognosis.

Results: Preoperative DCE-MRI exhibits favorable diagnostic capabilities in identification of IDH mutation status with AUC value of 0.77, 1p19q codeletion with AUC value of 0.71, and CDKN2A/B homozygous deletion with AUC value of 0.93, and assessment of glioma prognosis.
 

Impact: This study highlighted potential clinical application of DCE-MRI by demonstrating the robust diagnostic performance of DCE-derived parameters. These parameters exhibited not only excellent diagnostic accuracy in identifying functional biomarkers such as IDH, 1p19q and CDKN2A/B, but also in predicting prognosis. 

Keywords: Tumors (Pre-Treatment), Tumor, DCE-MRI, IDH, Glioma, Machine learning

Motivation: IDH mutation status of glioma have important influence on its occurrence and prognosis.

Goal(s): To build radiomics models in DCE-MRI for predicting IDH mutation in adult diffuse gliomas.

Approach: Several groups of features were extracted through multiparametric image: 1) Automatically calculated DCE-MRI metrics; 2) Structural MRI radiomics features, and 3) DCE-MRI radiomics features. Z-score normalization was used for feature normalization. Mann–Whitney U test was used for tumor selection. Stochastic gradient descent was used for machine learning classifier.

Results: We achived an AUC of 0.874 for model combing structural-MRI, DCE-MRI radiomics and automatically-calculated DCE-MRI metrics.

Impact: DCE-MRI radiomics models demonstrated great potential to predict IDH mutation status in Gliomas.

Keywords: Tumors (Post-Treatment), Tumor, High-Field MRI, Multi-contrast

Motivation: Multi-contrast MRI at 7T can potentially lead to enhanced detection and characterization of brain tumor with unpresented sensitivity and specificity.

Goal(s): To evaluate the improvement in tumor boundary, vasculature and hemorrhage detection using a set of complementary MRI sequences at 3T and 7T.

Approach: Five brain tumor patients were scanned with multi-contrast MRI protocols at 7T and 3T. All 7T protocols were implemented with submillimeter resolution in similar scan time as the 3T methods.

Results: At 7T, high image quality was observed in all the protocols because of high contrast and resolution. This led to improved detection of tumor boundary, vasculature, and hemorrhage.

Impact: Multi-contrast ultrahigh field MRI has the potential to non-invasively detect brain tumor in the early stage, provide precise tumor delineation, and visualize tumor-specific processes not seen on conventional MRI.

Keywords: Tumors (Pre-Treatment), fMRI (resting state), connectivity dynamics, temporal autocorrelation, Intra-operative

Motivation: Map temporal fluctuations of functional connectivity (FC) in anesthetized brain tumor patients.

Goal(s): Map static FC (sFC), dynamic inter-region FC (dFC), and test-retest reliability between awake and anesthetized states in patients undergoing resection of brain tumors.

Approach: A sliding-window xDF method was developed to estimate variance of the correlation in spatial-temporal resolution resting-state fMRI, considering nonstationary autocorrelation and cross-correlation.

Results: The largest decrease in sFC during anesthesia was observed across, rather than within, networks. The sliding-window xDF increased sensitivity compared to the static model. Test-retest reliability between cortical areas was higher during anesthesia versus awake state, in contrast to subcortical and cortical-subcortical dFC.

Impact: These results demonstrate the feasibility of performing resting-state functional connectivity studies in intraoperative settings with high spatial-temporal resolution. The higher test-retest reliability within cortical areas during anesthesia versus awake state informs the design of minimum duration intra-operative resting-state fMRI protocols.

Keywords: Tumors (Pre-Treatment), Quantitative Imaging, Multi-parametric Quantitative MRI

Motivation: Multi-centric multi-parametric quantitative MRI (mp-qMRI) studies require short and vendor-neutral protocols.

Goal(s): The goal was to develop and validate an 8-minute, vendor-neutral protocol for multi-center mp-qMRI studies on brain tumor patients at 3T. 

Approach: 5 Volunteers were measured at two scanners. Application of the proposed method was demonstrated for one brain metastasis patient, where artefact free qMRI maps were obtained.

Results: qMRI maps (T1, T2*, PD and QSM) obtained from 5 volunteers on two scanners showed a very good reproducibility. Among the parameters, PD yielded the lowest COV. Artefact free qMRI maps are demonstrated in a brain metastasis patient. 

Impact: We propose an 8-minute, vendor-neutral mp-qMRI protocol for 3T studies in brain tumor patients. T1, T2*, PD and QSM maps are demonstrated in 5 healthy volunteers and one brain metastsis patient. Multi-centric qMRI studies could use this fast mp-qMRI protocol.

Keywords: Tumors (Post-Treatment), Cancer

Motivation: Understanding how MR imaging markers change in normal appearing brain tissue over the course of RT for different dose distributions could help shed light on which parts of the brain are more susceptible to RT.

Goal(s): To examine early changes in perfusion, diffusion, and MR spectroscopic imaging metrics in normal and tumor regions during and following RT for different dose regions.

Approach: Twelve patients were studied at before, in the middle of, and after the completion of RT. 

Results: Significant imaging differences in diffusion, perfusion, and NAA/creatine was found in the normal appearing white matter in different dosage maps after receiving RT. 

Impact: Our study has shown initial alterations in the normal-appearing brain after radiation therapy. We have detected these changes in the early stages.

Keywords: Tumors (Post-Treatment), DSC & DCE Perfusion, Low dose

Motivation: Gadolinium retention in the human body following dynamic contrast-enhanced (DCE) and dynamic susceptibility contrast (DSC) MRI remains a health concern to many patients, especially those who need long-term imaging follow-up.

Goal(s): This work aims to investigate the feasibility of using a recently developed technique, MR multitasking-based dynamic imaging for cerebrovascular evaluation (DICE), to quantify permeability and perfusion with a 0.03 mmol/kg dose.

Approach: Numerical simulations were conducted to determine the optimal dose level. Assessments for the agreement of low-dose DICE (LD-DICE) with full-dose DICE (FD-DICE) were performed.

Results: Good correlation was achieved. Brain tissue perfusion and permeability can be quantified simultaneously with LD-DICE. 

Impact: Low-dose DICE will allow for comprehensive tumor vascularity evaluation with considerably less contrast agent than clinical standard protocols, which will benefit brain tumor patients who need frequent imaging follow-ups.