Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition - Integrated MRI-Based Approaches for Preoperative Diagnosis, Grading, and Prognostic Prediction of Brain Tumors

Integrated MRI-Based Approaches for Preoperative Diagnosis, Grading, and Prognostic Prediction of Brain Tumors

Traditional Poster

Neuro B

Tuesday, 12 May 2026

Traditional Posters | Exhibition Hall

16:55 - 17:50

Session Number: 470-10

No CME/CE Credit

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This session explores the critical role of multimodal MRI imaging in preoperative evaluation and prognostic assessment of brain tumors, with particular emphasis on glioblastoma and other high-grade neoplasms. Participants will learn how advanced imaging biomarkers—including the Dark Band Sign on T2WI, diffusion-weighted imaging metrics, radiomics, and deep learning-based analysis—can predict tumor grade, molecular subtypes, and patient survival outcomes prior to surgical intervention. The session integrates traditional imaging interpretation with cutting-edge artificial intelligence frameworks to enhance differential diagnosis accuracy and inform treatment planning strategies in clinical practice.

Skill Level: Intermediate

		470-10-148. Intra vs postoperative MRI in Patients after resection of a Glioma grade 4 Barbara Hristoska, Karl Rössler, Gregor Kasprian, Juliane Hennenberg, Vitalij Zeiser, Pascal Baltzer, Gilbert Hangel Medical University of Vienna, Vienna, Austria Impact: This study highlights the value of integrating intraoperative and postoperative MRI with subtraction imaging to improve evaluation of resection completeness, enhance surgical precision and ultimately optimize clinical decision-making and patient outcomes in glioblastoma surgery.
		470-10-149. Dark Band Sign on Preoperative T2-Weighted Imaging Predicts Survival After Surgery in Patients with Glioblastoma Ying Zhang, Siheng Liu, Dongcun Huang, Zhenqiang He, Wanming Hu, Sheng Zhong, Yanchun Lv, Yonggao Mou Sun yat-sen university cancer center, Guangzhou, China Impact: The dark band sign on preoperative T2WI provides a reliable biomarker that predicts gross total resection and prolonged progression free survival in glioblastoma. Histopathologically, this sign corresponds to relatively normal brain tissue, offering valuable guidance for patient counseling before treatment.
		470-10-150. Diffusion weighted imaging-based tumor growth rate for predicting long-term survival, a multi-center research Fan Yang, Haoran Wei, zhenyu Huo, Meng Lin, Hongmei Zhang Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China Impact: Our study fills the knowledge gap on how NPC grows in patients and identified a substituted indicator, ADC_down, which reflects tumor growth rate. Understanding growth process of NPC provides a basis for clinical research and helps to reduce patient stress.
		470-10-151. MRI in Clinical Practice: Extracranial Metastases of Diffuse Midline Glioma—Imaging Clues, Whole-Body Assessment, and Managem juan wang, Sicong Wang, Yi Xing, Xueying Zhao, Lixia Zhou The Second Hospital of Hebei Medical University, Shijiazhuang, China Impact: MRI—extended beyond the brain—revealed characteristic extracranial DMG metastases, enabling earlier systemic staging and informed palliative planning. Whole-body MRI complements PET/CT and strengthens surveillance when contrast is limited or contraindicated.
		470-10-152. A Giant Cystic Suprasellar Mass in an Adult: Diagnosing Adamantinomatous Craniopharyngioma on MRI. ABDUL RASHID KARIM, Maxwell Adu, Chikumbutso Khomba, Joseph Wilson Spectra Health Imaging and Interventional Radiology Center., Kumasi, Ghana Impact: MRI accurately characterized a large suprasellar craniopharyngioma, defining its cystic and solid parts and relation to vital structures. This improved diagnostic confidence, guided surgery, and highlighted MRI’s essential role in managing complex sellar and parasellar brain lesions.
		470-10-153. An Integrated Deep Learning Framework for the Differential Diagnosis and Prognostic Prediction of Meningeal Tumors Xiaohong Liang, Jinglin Zhou, Jinlin Zhou, Xuzhu Chen, Jie Lu Beijing Xuan Hospital, Beijing, China Impact: We developed a deep learning–based automated MRI pipeline for tumor segmentation, differential diagnosis, and prognosis prediction, enabling accurate, noninvasive, and reproducible differentiation and risk stratification of meningiomas and ISFTs to enhance precision neurosurgical planning and personalized treatment.
		470-10-154. Improving Glioblastoma Classification Using Quantitative Transport Mapping with a Synthetic Tumor Trained Deep Neural Network Dominick Romano, Alexandra Roberts, Benjamin Weppner, Qihao Zhang, Maneesh John, Renjiu Hu, Gloria Chiang, Pascal Spincemaille , Yi Wang Cornell University, Ithaca, United States of America Impact: To ensure best performance in disease, morphological and physiological features are necessary to include during training data synthesis. The tumor addition is a modification to our simulation pipeline. Domain matching is a top priority in neural network training data synthesis.
		470-10-155. Improving Brain Tumor Diagnosis Through Hyperparameter-Optimized CNN Models Dr. Abdullah Asiri Najran University, Najran, Saudi Arabia Impact: This study enhances MRI-based brain tumor diagnosis through hyperparameter-optimized CNNs, improving accuracy and generalization. It empowers clinicians with reliable automated tools for early detection, inspires further research on parameter optimization, and advances AI-driven diagnostic precision across medical imaging fields.
		470-10-156. MRI-based Habitat Analysis for MYD88 L265P Mutations Prediction in Primary Central Nervous System Yujiao Deng, Yuqi Jin, Ming Jiang, Qiang Yue West China Hospital of Sichuan University, Chendu, China Impact: This approach empowers clinicians to identify candidates for targeted therapy (e.g., BTK inhibitors) noninvasively, especially when biopsy is high-risk. This could significantly shorten the time to precision treatment initiation.