Keywords: Head & Neck/ENT, Arterial spin labelling, Nasopharyngeal carcinoma; prognosis; depth of invasion

Motivation: Arterial spin labeling (ASL) showed the promising value in diagnosis and early treatment outcome prediction in head and neck. Whether ASL combined with tumor invasion depth could help predicting disease progression needs further investigate.

Goal(s): To explore the value of CBF derived from ASL and depth of invasion in predicting 3-year disease progression in NPC.

Approach: Prospective inclusion of consecutive patients with regular follow-up. Selection of appropriate statistical methods to construct and compare models.

Results: CBF and tumor invasion depth are significantly correlated with progression-free survival, and both of them could help predicting 3-year disease progression.

Impact: ASL and tumor infiltration depth shown for the first time to predict disease progression in NPC, which could help with clinical treatment decisions.

Keywords: Tumors (Post-Treatment), Quantitative Imaging, Multiparametric

Motivation: Standard-of-care MRI in high-grade glioma (HGG) immunotherapy offers limited value for early response assessment and monitoring given its inability to distinguish tumor progression from treatment-induced inflammatory responses.

Goal(s): This study aims to evaluate multiparametric MRI and voxel-wise habitat mapping of vascular and cellular properties to assess response to M032 virotherapy in HGG.

Approach: Multiparametric quantitative assessment of cellularity and vascularity, through DWI-MRI and DSC-MRI, was explored for the early evaluation of intratumoral changes post-immunotherapy and associations with overall survival.  

Results: Anatomical and quantitative MRI metrics revealed changes early over the course of therapy and showed significant associations with overall survival in this cohort. 

Impact: Characterization of multiparametric quantitative MRI metrics associated with early immunotherapy positive response can aid in the assessment and monitoring of therapeutic efficacy and allow for optimization of clinical care in patients with high-grade glioma.

Keywords: Tumors (Pre-Treatment), Cancer

Motivation: Noninvasive prediction of molecular subtype and grade in adult-type diffuse gliomas based on 2021 WHO classification can aid in clinical practice.

Goal(s): To establish a robust and interpretable deep learning model for molecular subtyping and grading in adult-type diffuse gliomas.

Approach: Institutional multiparametric MRI data (n=1,053) were used to train deep learning models, including 2D CNN and Vision Transformer. Our models were externally validated on the TCGA dataset (n=200). Explainable AI methods were used to interpret the predictions of our models.

Results: ViT outperformed CNN with AUCs of 0.87, 0.73, and 0.81 for prediction of IDH mutation, 1p/19q codeletion, and grading, respectively.

Impact: Our study demonstrates that Vision Transformer provides reliable and interpretable prediction of molecular subtype and grades in adult-type diffuse gliomas based on the 2021 WHO classification using multiparametric MRI data.

Keywords: Tumors (Post-Treatment), Spectroscopy, glioblastoma; TTFields; WBSI; MRI

Motivation: Optimal treatment for GBM requires precise targeting of all viable tumor cells, many of which are not visible on conventional neuroimaging.

Goal(s): We aimed to utilize WBSI to identify infiltrating tumor cells in GBM patients for selecting a precise target volume for personalized mapping of transducer arrays for enhanced delivery of TTFields.

Approach: A mean value of choline/NAA was computed from normal mask, and all voxels that exceeded two-fold threshold value were included in a 3D-composite mask from the tumor region.

Results: WBSI provided higher yield of voxels with good spectral quality, resulting in improved brain tumor coverage compared to anatomical MRI sequences.

Impact: Alternative array configuration created from WBSI will allow precise delineation of tumor margins for enhanced delivery of TTFields dose to all proliferating regions of a GBM, decreasing the rate of local recurrence and ultimately improving overall survival.

Keywords: Contrast Agents, Brain, glioma

Motivation: Neurological adverse effects caused by the deposition of gadolinium-based contrast agents in the brain remain uncertain, and it is necessary to develop a biocompatible alternative molecule for  evaluation of glioma malignancy and site occupancy.

Goal(s): To demonstrate that Fe-based contrast agents have superior safety, stability, and longevity in glioma imaging compared to gadolinium-based contrast agents. 

Approach: The  T7-Fe-PyC3A was synthesized, and its stability was validated through  in vitro kinetic thermodynamic experiments. Furthermore, A in situ glioma model mice was choosen for investigating the imaging ability.

Results: A targeted Fe-based MR contrast agent provides clear glioma contours with a 15-60 minutes post-dose imaging window.

Impact: Although gadolinium-based contrast agents are widely used in the clinic, iron-based contrast agents targeting gliomas show the enhanced safety and stability profile. The extended imaging window allows them as preferable alternative for gliomas imaging.

Keywords: Tumors (Post-Treatment), Translational Studies, Treatment response, Cancer, Glioblastoma (GBM), Reproducibility, Perfusion, Quantitative Imaging

Motivation: Chemoradiation in patients with glioblastoma (GBM) causes a 10-13% perfusion decrease in normal appearing tissue, confounding reproducibility of ASL measurements and longitudinal treatment evaluations. This confounds intra-patient and inter-patient comparisons, irrespective of perfusion variations from tumor progression/response. 

Goal(s): To improve ASL measurement reproducibility and longitudinal treatment assessment in GBM patients using intensity normalization methods.

Approach: Different normalization methods were applied to ASL measured perfusion in a prospective study for reproducibility analyses and response assessment.

Results: Intensity normalization of ASL measured perfusion in GBM patients improved reproducibility enabling longitudinal treatment evaluation for intra- and inter-patient comparisons. 

Impact: Intensity normalization of ASL reduces variability, improves reproducibility, and enables accurate quantitative intra- and inter-patient comparison. This can play an important role in evaluating treatment response assessment and building predictive models with ASL across different studies and sites.

Keywords: CEST / APT / NOE, Tumor

Motivation: Predicting glioma subtypes based on molecular profiles is crucial for treatment decisions and predicting survival rates.

Goal(s): We proposed a CEST-based deep learning method to predict IDH mutation and MGMT methylation status in glioma patients at the voxel level.

Approach: 86 patients were recruited for CEST experiments on 3T MRI scanner. A CEST-based deep learning method, composed of a 1D convolutional neural network, was proposed for different types of status prediction at the voxel level. The confusion matrix and ROC were conducted to evaluate the performance of the proposed method.

Results: Our method achieves higher accuracy compared to existing CEST-based prediction methods.

Impact: The proposed method may facilitate the application of CEST MRI in the diagnosis of glioma.

Keywords: Tumors (Pre-Treatment), High-Field MRI, susceptibility-weighted MRI

Motivation: Knowing the genotype of gliomas is critical for prognostic assessment and treatment selection. 7.0T susceptibility-weighted imaging (SWI) allows visualizing the deep medullary veins and provides additional metabolic information.

Goal(s): We used the asymmetric deep medullary vein (ADMV) sign on 7.0T SWI to predict glioma isocitrate dehydrogenase mutation status and Ki-67 expression level.

Approach: We assessed the ADMV sign and conventional morphological and screening features (P<0.1) via multivariate logistic regression to evaluate the predicted performance.

Results: The ADMV sign on 7.0T images was independently associated with isocitrate dehydrogenase mutation status and Ki-67 index and improved the images’ diagnostic efficacy.

Impact: Discovery of the ADMV sign as an imaging biomarker and the advantages of 7.0T MRI may help markedly improve the diagnosis and management of gliomas and may have broader applications in medical imaging and biomarker development.

Keywords: Tumors (Post-Treatment), CEST & MT, Glioblastoma, Intranasal, Theranostic

Motivation: Theranostic application of intranasal drug delivery to glioblastoma using multiple CEST contrast.

Goal(s): Our goal is to monitor the drug delivery to brain tumor and evaluate the treatment effect simultaneously.

Approach: We investigated the imaging of liposome-based drug delivery to the brain tumor via intranasal administration,in which the amount of liposome and the tumor response can be detected by CEST MRI at 3T.

Results: CEST contrast at 3.5ppm of tumor region and the tumor size comparison between treatment and control group could indicate the therapeutic effect.CEST contrast at 4.3and-3.5ppm from pre-injection to post-injection in-vivo,could indicate the liposome drug delivery efficacy and drug distribution.

Impact: CEST MRI guided intranasal drug delivery could provide valuable information for assessing efficacy of drug delivery and treatment outcome. This can potentially translate to clinics as a non-invasive theranostic approach for glioblastoma treatment.

Keywords: Tumors (Post-Treatment), Endocrine, Pituitary prolactinoma

Motivation: Predicting and managing dopamine agonists (DA) resistance of prolactinomas remain a challenge. There is no reliable quantitative imaging marker.

Goal(s): The goal is to use accelerated quantitative T2 mapping(GRAPPATNI) for early diagnosis of drug resistance in pituitary prolactinomas to guide treatment.

Approach: This is a cross-sectional study. It will analyze the differences in T2 values between drug-resistant and sensitive groups and explore their diagnostic value in predicting drug sensitivity.

Results: Quantitative T2 values have better sensitivity than T2 signal intensity (SI) in predicting drug resistance in pituitary prolactinoma.

Impact: This is the first study to apply GRAPPATINI in prolactinoma. We found that T2 values of tumors were lower in drug-resistant prolactinoma than sensitive patients. T2 values might be a promising predictive imaging tool.