Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition

Radiomics: Applications

Digital Poster

Analysis Methods

Tuesday, 12 May 2026

Digital Posters Row J

16:55 - 17:50

Session Number: 469-06

No CME/CE Credit

Similar Sessions

This is a digital poster section about applications of radiomics in general.

Skill Level: Intermediate

		469-06-001. Perfusion MRI-Based Quantification of Intratumoral Heterogeneity for Risk Stratification in Glioblastoma Jing Yan, Chaoli Zhang, Mengxiang Si The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China Impact: Our study proposes a noninvasive, MRI-derived ITH index that serves as a biologically interpretable predictor of survival outcomes in IDH-wildtype GBM, offering valuable insights for prognosis and personalized treatment.
		469-06-002. MRI-based Radiomics Model for the Evaluation of CAS in Thyroid Eye Disease Patients Yuanyuan Cui, Yunmeng Wang, Xin Zhang, Wenhao Jia, Jiankun Dai, Li Fan, Shiyuan Liu, Yi Xiao The Second Affiliated Hospital of Naval Medical University, SHANGHAI, China Impact: This study develops an automated whole-orbit structure segmentation method that simplifies the complex segmentation process and enables comprehensive orbital analysis. This approach provides complete structural coverage and quantitative assessment, overcoming limitations of conventional clinical evaluations for Thyroid Eye Disease.
		469-06-003. MRI Radiomics-Based Multimodel Fusion for Diagnosis of Refractory Epilepsy Hanjiaerbieke Kukun, Yuhui Xiong, Yuchen Liu, Yunling Wang The First Affiliated Hospital of Xinjiang Medical University, Urumqi, China Impact: This study developed a machine learning model using MRI radiomics to detect refractory epilepsy. By combining hippocampal and amygdalar features, it achieved high diagnostic accuracy, offering a noninvasive, objective tool for earlier diagnosis and personalized treatment in epilepsy.
		469-06-004. Chemo-Informed Radiomics: Integrating Temozolomide Status for Improved Glioblastoma Response Prediction Taha Belbadaoui, Andrew Forester, Philippe Dionne, Gérémy Michaud, Louis Gagnon Université Laval, Québec, Canada Impact: Encoding whether patients recently received temozolomide measurably improves post-treatment GBM classification on routine MRI. Chemotherapy status alone increases AUC beyond radiomics; combined with cellular-tumor volumetry, performance reaches 0.864, aligning model behavior with expected treatment-modulated imaging phenotypes.
		469-06-005. A Radiomics-Based Model for Early Differentiation of Pseudoprogression in Post-Treatment Glioblastoma Catarina Passarinho, Gulnur Ungan, Carles Majós, Albert Pons-Escoda, Ana Matoso, Marta Loureiro, Patricia Figueiredo, Rita Nunes, Margarida Julià-Sapé Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal Impact: This work demonstrates that robust machine learning applied to contrast-enhanced T1 MRI radiomics can distinguish progression from pseudoprogression in glioblastoma. This supports simplified early follow-up imaging protocols and provides a baseline for future multi-modal integration to improve neuro-oncologic assessment.
		469-06-006. Radiomics Analysis of Liver Using Routine Cardiac Magnetic Resonance Cine Images to Identify Abnormalities in Fontan Patients Ming-Ruei Ou, Yu-Chieh Wang, Ming-Ting Wu, Ken-Pen Weng, Hsu-Hsia Peng National Tsing Hua University, Hsinchu, Taiwan Impact: Liver radiomics analysis from CMR cine images can effectively differentiate Fontan patients from normal controls. The correlations between radiomic features and liver T1 suggest that texture features may reflect hepatic tissue heterogeneity and serve as potential indicators of fibrosis risk.
		469-06-007. Multimodal Radiomics and Machine Learning for Preoperative Classification of Meningioma Grade: Multi algorithm Comparison and Yuxia Liang, Jin wang, Qinqin Xie, Haining Li, Yu Shang, Ming Zhang, chen niu The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China Impact: The proposed models enable reliable preoperative grading of meningioma. The nomogram is readily deployable for risk estimation, potentially reducing invasive procedures and optimizing individualized surgical and adjuvant-treatment decisions.
		469-06-008. Can VASARI Serve as a Lightweight MRI Tool for Comprehensive Prediction of Adult-Type Diffuse Glioma Under WHO CNS5? Jiaxin Lin, Fangrong Liang, Xiaoyi Huang, Yujie He, Yongzhou Xu, Ruimeng Yang the Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou, China Impact: We confirmed VASARI’s capacity to serve as a preoperative toolkit for ADG prediction, readily mastered by grassroots clinicians, independent of specialized image-processing pipelines. Future work will further validate its robustness across expanded populations and establish a task-specific VASARI checklist.
		469-06-009. Functional MRI-Derived Intra- and Peritumoral Signatures Predict Prognosis in Glioblastoma Xiaorui Su, Shuang Tang, Shuang Li, Qianqian Zheng, Yujiao Deng, Yuchi Tian, Qiang Yue West China Hospital of Sichuan University, Chendu, China Impact: Functional‑MR derived intra‑ and peritumoral signatures in IDH‑wildtype glioblastoma may enable more accurate pre‑treatment risk stratification, guide individualized surgical/radiotherapy planning, and open new avenues into the biology of peritumoral infiltration.
		469-06-010. Multiregional radiomics based on MRI for predicting microvascular invasion and vessels that encapsulate tumor clusters in HCC Lizhe Chen, Zixin Liu, Tao Zhang, Xiance Zhao, Sicong Huang Nantong University, Nantong, China Impact: Providing risk stratification for recurrence-free survival (RFS) in patients post-hepatocellular carcinoma (HCC) resection supports the delivery of personalized therapy or modification of follow-up frequency.
		469-06-011. Decision Fusion Radiomics for Predicting Microvascular Invasion in Hepatocellular Carcinoma Zhenhuan Huang, Hui Ma, Peng Wu, Jiayi Li, Di Wu, Yuchen Xie, Wanrong Huang, Shuping Weng, Yueming Li Longyan First Affiliated Hospital of Fujian Medical University, Longyan, China Impact: This study provides clinicians with a noninvasive decision fusion radiomics tool for preoperative MVI prediction, enabling better surgical planning. It also enables scientists to further validate this biomarker and explore its biological links to cancer aggression.
		469-06-012. MRI Radiomics for Survival Risk Stratification of Molecular Glioblastoma Defined by the 2021 WHO Classification Yangyang Li, Yan Tan First Hospital of Shanxi Medical University, Taiyuan, China Impact: This combined model aids clinicians in personalized mGBM survival stratification, guides treatment planning, and provides a direction for researchers to conduct prospective multicenter validations.