Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition

Neuroradiomics

Digital Poster

Analysis Methods

Wednesday, 13 May 2026

Digital Posters Row F

14:35 - 15:30

Session Number: 565-04

No CME/CE Credit

Similar Sessions

This session will cover the application of radiomics approaches for brain, head, and neck applications areas.

Skill Level: Basic,Intermediate

		565-04-001. Cluster-Specific Radiomics Predicting Induction Chemotherapy Response in Nasopharyngeal Carcinoma Zhenhuan Huang, Zhaoxue Tu, Peng Wu, Lihang Cao, Jing Qiu, Qikui You, Dandan Lin, Tingyu Yu, Hui Ma, Yueming Li Longyan First Affiliated Hospital of Fujian Medical University, Longyan, China Impact: This study utilizes a cluster-based radiomics which can provide clinicians with a non-invasive tool to personalize treatment, potentially improving outcomes by identifying patients who will benefit from induction chemotherapy. It enables more targeted therapy decisions.
		565-04-002. Improving Radiomics-Based Differentiation of Supratentorial Brain Tumors with DWI: A Three-Class Machine Learning Algorithm Jing Yan, Zeyu Ma, Chaoli Zhang, Mengxiang Si, Yong Zhang The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China Impact: Our work constructs a three-class multiparametric radiomic model for classifying supratentorial brain tumors [HGG, BM, and PCNSL], and investigate whether DWI-based radiomic features offer incremental value and improve diagnostic performance.
		565-04-003. Multimodal MRI-Based Habitat Model for Predicting WHO Grading and Ki-67 Labeling Index in HGG: A Multicenter Study Wei Zhao, Yunling Wang, Yushan Chang, Hanjiaerbieke Kukun, Rui Xu, Pahati Tuxunjiang, Wei Sheng The First Affiliated Hospital of Xinjiang Medical University, Urumqi, China Impact: This work establishes a standardized habitat imaging framework for non-invasively assessing glioma heterogeneity. This approach provides valuable preoperative insights into tumor grade and proliferation, potentially informing surgical planning and personalized treatment strategies, and advancing precision neuro-oncology.
		565-04-004. Visual vs. Radiomics Assessment of Post-contrast T2 FLAIR - T1WI Enhancement Mismatch for IDH1 Prediction Hongzhong Liang, Ruixiang Wei, Huimin Wang, Kang Chen, Mengxia Zhang, Zhihuan Li, Miyuan Hu, Jiale Zhu, Rui Guo, Ying Qin, Jun Peng, Yunping Xiao Liuzhou People’s Hospital Affiliated to Guangxi Medical University, Liuzhou, China Impact: The CE-T2 FLAIR–T1WI enhancement mismatch enables practical, noninvasive prediction of glioma IDH1 status. A simple visual model outperforms complex radiomics, offering clinicians a more interpretable and accessible tool, potentially guiding surgical planning and stimulating new research in imaging biomarkers.
		565-04-005. A Multimodal MRI Model Integrating Radiomics and Habitat Features for Glioma Grading and Molecular Biomarker Prediction Yang Yifeng, Sun Lianxi, Cao Zehong, Feng Shi, Li Shihong, Guangwu Lin Huadong Hospital, Fudan University, Shanghai, China Impact: This AI-driven tool decodes glioma complexity from standard MRIs to non-invasively predict tumor malignancy, molecular state, and patient prognosis. It provides clinicians a powerful guide for personalizing treatment, aiming to improve patient outcomes and deepen our understanding of tumor biology.
		565-04-006. Radiomics-Distilled Self-Supervised Deep leaning framework for Label-Efficient Glioma Treatment Response Prediction Yuchi Tian, Xiaorui Su, Xiaoyun Liang, Qiang Yue Neusoft Medical Systems Co., Ltd, Shanghai, China Impact: RADIATE-Net enables clinically scalable, label-efficient prognosis prediction, integrating expert knowledge and deep learning. It facilitates precision oncology with limited data and opens avenues for cross-disease, multi-modal imaging biomarker discovery.
		565-04-007. Radiomics-based differentiation between GBM and PCNSL: a combination of structural MRI, DCE and DTI Huiquan Yang, Zhengyang Zhu, Xin Zhang, Bing Zhang Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China Impact: A radiomics model based on structural MRI, DCE and DTI was developed and demonstrated excellent performance in discriminating between GBM and PCNSL.
		565-04-008. Radiomics-Based Assessment of Overall Survival in Olfactory Neuroblastoma yongchao wu, Xiwen Wang, Shanbin Sun, Tao Liu, Dandan Zheng, Zhaohui Liu Beijing Tongren Hospital, Capital Medical University, Beijing, China Impact: Preoperative MRI radiomics can provide noninvasive prognostic biomarkers for ONB, enabling risk stratification and early identification of patients with poor survival. This approach may guide individualized treatment and follow-up strategies in this rare malignancy.
		565-04-009. A Radiomics-Based Nomogram for Predicting Complete Remission to Chemoradiotherapy in Intracranial Germ Cell Tumors Yaxi Chen, Liangping Luo, wei cui The First Affiliated Hospital of Jinan University, Guangzhou, China Impact: This radiomics-based nomogram provides a non-invasive tool for predicting chemoradiotherapy response in IGCT patients. The risk stratification strategy enables personalized treatment planning and may help reduce unnecessary treatment-related toxicity in pediatric populations.
		565-04-010. Multimodal MRI Radiomics and Clinical Indicators for Predicting Short-Term Targeted Therapy Response in Locally Advanced NPC Zijing Lin, Zhiqiang Chen, Guangxu Han The First Affiliated Hospital of Hainan Medical College‌, Hainan, China Impact: The combined clinical-radiomics model may aid personalized treatment decisions for locally advanced nasopharyngeal carcinoma, potentially reducing ineffective therapies and improving resource use, with promising value for clinical decision support.
		565-04-011. Development of an Early Differential Diagnostic Model Using MRI Radiomics for Postoperative Granuloma versus Recurrent Tumor Chen-Tien Hsieh, Hsiu-Mei Wu, Chia-Feng Lu National Yang Ming Chiao Tung University, Taipei, Taiwan Impact: This study developed an early differential diagnosis model of postoperative granulomas and recurrent tumors using MRI radiomics features and identified reliable clinical imaging characteristics to improve the diagnostic accuracy and support postoperative clinical decision-making.
		565-04-012. A Multimodal MRI Radiomics-Clinical Nomogram for Predicting IVGC Response in Graves’ Ophthalmopathy: A Multicenter Study Yanhu Zhou, Jing Zhang, Kuanyu Che, fei jia, Xuelian Zhao, Xiaojin Ma The Second Hospital & Clinical Medical School, Lanzhou University, Lanzhou, China Impact: The proposed nomogram, which integrates multimodal MRI-based RDL features and key clinical predictors, provides a non-invasive and individualized tool for predicting IVGC treatment response in GO patients, potentially facilitating personalized treatment planning and clinical decision-making.
		565-04-013. Optimizing MRI Sequence Selection for Glioma Classification - Do we really need all sequences? Kimberly Amador, Helge Kniep, Nils Forkert, Jens Fiehler, Thomas Lindner University of Calgary, Calgary, Canada Impact: The study highlights the potential of reducing MRI sequences in glioma grading based on the premise that multiple sequences provide an overlap of information and thus a reduction to the most important sequences could speed up examinations.
		565-04-014. Comparative and Integrative Analysis of DCE-MRI, DWI-MRI, and Radiomic Features for Pre-Operative Grading of Meningiomas Sanskriti Srivastava, Rakesh Kumar Gupta, Anup Singh Indian Institute of Technology, Delhi, India Impact: The proposed methodology is a non-invasive method that can differentiate between high-grade Meningiomas (HGMs) and low-grade Meningiomas (LGMs) and help in preoperative grading.
		565-04-015. Development and validation of a multiparametric MRI–based radiomic model to distinguish benign from malignant sinonasal tumor Chaofan Sui, Guodan Wei, Hangzhi Liu, Xiaoxia Qu, Xinyan Wang, Junfang Xian Beijing Tongren Hospital, Beijing, China Impact: Multiparametric MRI radiomics, especially combined-sequence models, accurately differentiates benign from malignant sinonasal tumors with robust external validation, supporting improved diagnostic decision-making.
		565-04-016. Differentiation between Glioblastoma and Solitary Brain Metastases: A Subregional Diagnostic Comparison Yini Chen, Li Ding, Bo Sun, Linyou Wang Taizhou Municipal Hospital, Taizhou, China Impact: The various subregions reveal distinct heterogeneities from individual viewpoints. Combining insights from both whole tumor mass and specific local areas, they jointly depict the heterogeneous characteristics inherent to GBM and SBM. Such pathological disparities aid in distinguishing these two malignancies.