ISSN# 1545-4428 | Published date: 19 April, 2024
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At-A-Glance Session Detail
   
Tumors in the Brain, Head & Neck
Oral
Neuro
Tuesday, 07 May 2024
Nicoll 2
08:15 -  10:15
Moderators: Gilbert Hangel & Manabu Kinoshita
Session Number: O-46
CME Credit

08:150396.
Pretreatment arterial spin labelling combined with depth of invasion predict disease progression in nonmetastatic NPC after IMRT
Fan Yang1, Haoran Wei1, Xiaoduo Yu1, Meng Lin1, and Hongmei Zhang1
1Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China, Beijing, China

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.

08:270397.
Multiparametric quantitative MRI for assessment of clinical response to M032 oncolytic virotherapy in patients with high-grade glioma
Carlos A Gallegos1, Ameer Mansur1, Dagoberto Estevez-Ordonez2, James M Markert2, and Anna G Sorace1,3,4
1Department of Biomedical Engineering, University of Alabama at Birmingham, Birmingham, AL, United States, 2Department of Neurosurgery, University of Alabama at Birmingham, Birmingham, AL, United States, 3Department of Radiology, University of Alabama at Birmingham, Birmingham, AL, United States, 4O'Neal Comprehensive Cancer Center, University of Alabama at Birmingham, Birmingham, AL, United States

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.

08:390398.
Deep Learning Algorithm for Prediction of Molecular Subtypes and Grades in Adult-type Diffuse Gliomas: According to the 2021 WHO Updates
Yunsu Byeon1, Yae Won Park2, Soohyun Lee1, HyungSeob Shin1, Doohyun Park1, Sung Soo Ahn2, Seung-Koo Lee2, and Dosik Hwang1,2,3,4
1School of Electrical and Electronic Engineering, Yonsei University, Seoul, Korea, Republic of, 2Radiology, Yonsei University College of Medicine, Seoul, Korea, Republic of, 3Center for Healthcare Robotics, Korea Institute of Science and Technology, Seoul, Korea, Republic of, 4Department of Oral and Maxillofacial Radiology, Yonsei University College of Dentistry, Seoul, Korea, Republic of

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.

08:510399.
Integration of Whole Brain Spectroscopic Imaging in Planning Workflow for Personalized Delivery of TTFields in Glioblastomas
Laiz Laura de Godoy1, Arthich Rajan1, Brian Berger2, Sulaiman Sheriff3, Atom Sarkar4, Rinku Shah4, Dawit Aregawi5, Tara Morrison6, Srilatha Hosur7, Sunjay Shah8, Gaurav Shukla8, Rachelle Lanciano9, Varsha Jain10, Scott Herbert10, Nduka Amankulor11, Stephanie Weiss12, Anshu Giri13, Aruna Padmanabhan12, Stephen Bagley1, Kheng Choon Lim14, Demetrius Ribeiro de Paula15, Demetrius Lee1, Kristina Vineis1, Lisa Desiderio1, MacLean Nasrallah1, Suyash Mohan1, and Sanjeev Chawla1
1Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, United States, 2Novocure, Portsmouth, NH, United States, 3University of Miami, Miami, FL, United States, 4St Mary Medical Center, Langhorne, PA, United States, 5Hershey Medical Center, Hershey, PA, United States, 6Lehigh Valley Hospital-Cedar Crest, Allentown, PA, United States, 7Lancaster General Hospital, Lancaster, PA, United States, 8Christiana Care Heath System, Wilmington, DE, United States, 9Crozer-Chester Medical Center, Upland, PA, United States, 10Jefferson Abington Hospital, Abington, PA, United States, 11Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Philadelphia, PA, United States, 12Temple University Lewis Katz School of Medicine, Philadelphia, PA, United States, 13Fox Chase Cancer Center, Philadelphia, PA, United States, 14Singapore General Hospital, Singapore, Singapore, 15Radboud University Medical Centre, Nijmegen, Netherlands

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.

09:030400.
A targeted Fe-based MR contrast agent for glioma imaging
Yue Zhu1,2, Lei Zhang1, Shizhen Chen1, and Xin Zhou1
1Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, WuHan, China, 2Huazhong University of Science and Technology, WuHan, China

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.

09:150401.
Intensity normalization of ASL measured perfusion improves reproducibility and treatment evaluation in glioblastoma patients
Limin Zhou1, Yiming Wang2, Durga Udayakumar1,3, Marco C. Pinho1,3, Michael Youssef4,5, Joseph A. Maldjian1,3, and Ananth J. Madhuranthakam1,3
1Department of Radiology, UT Southwestern Medical Center, Dallas, TX, United States, 2Philips Healthcare, Shanghai, China, 3Advanced Imaging Research Center, UT Southwestern Medical Center, Dallas, TX, United States, 4Department of Neurology, UT Southwestern Medical Center, Dallas, TX, United States, 5Department of Hematology and Oncology, UT Southwestern Medical Center, Dallas, TX, United States

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.

09:270402.
Predicting IDH Mutation and MGMT Methylation Status in Glioma Patients at the Voxel Level using CEST-Based Deep Learning
Siyu Wang1, Jue Lu2, Xinli Zhang2, Jing Wang2, and Lin Chen1
1Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, School of Electronic Science and Engineering, National Model Microelectronics College, Xiamen University, Xiamen, China, 2Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China

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.

09:390403.
Characterizing asymmetric deep medullary veins by 7.0T susceptibility-weighted MRI to predict glioma genotype and Ki-67 index
Chenxi Li1, Jinhao Lyu1, Xiaoxiao Ma1, Caohui Duan1, Jianxun Qu2, Qi Duan1, and Xin Lou1
1Chinese PLA General Hospital, Beijing, China, 2MR Research Collaboration Team, Siemens Healthineers, Beijing, China

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.

09:510404.
Monitoring intranasal treatment to brain tumor using multiple CEST contrasts
Lok Hin Law1, Haoyun Hin Su1,2, Yang Hin Liu1,2, and Kannie WY Hin Chan1,2,3,4,5
1Biomedical Engineering, City University of Hong Kong, Hong Kong, China, 2Hong Kong Centre for Cerebro-Cardiovascular Health Engineering (COCHE), Hong Kong, China, 3Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, United States, 4City University of Hong Kong Shenzhen Research Institute, Shenzhen, China, 5Tung Biomedical Sciences Centre, City University of Hong Kong, Hong Kong, China

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.

10:030405.
Application of accelerated quantitative magnetic resonance imaging in predicting drug resistance in pituitary prolactinomas
Rong Lu1, Lijin Ji2, Weijun Tang1, Qing Li3, Caixia Fu4, Ying-Hua Chu3, Zheyuan Wu5, Tobias Kober6,7,8, Tom Hibert6,7,8, Shangxuan Shi9, and Tingfang Hwang1
1Radiology, Huashan Hospital, Fudan University, Shanghai, China, 2Endocrinology, Huashan Hospital, Fudan University, Shanghai, China, 3MR Research Collaboration, Siemens Healthineers Ltd., Shanghai, China, 4Application Developments, Siemens Shenzhen Magnetic Resonance Ltd., 518057 Shenzhen, China, Shanghai, China, 5Shanghai Municipal Center for Disease Control and Prevention, Shanghai, China, 6LTS5, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland, 7Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland, 8Advanced Clinical Imaging Technology, Siemens Healthineers International AG, Lausanne, Switzerland, 9ShanghaiTech University, Shanghai, China

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.