Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition

Rectal Cancer

Digital Poster

Body

Tuesday, 12 May 2026

Digital Posters Row G

08:20 - 09:15

Session Number: 466-01

No CME/CE Credit

Similar Sessions

Quantitative MRI, such as QSM,DWI, DKI, and AI-based MRI technique for rectal cancer stage, prognosis and treatment response assessment in rectal cancer.

Skill Level: Intermediate

		466-01-001. MRI-Based Risk Stratification of Immunotherapy Response in Locally Advanced Rectal Cancer: A Pilot Study Jiali Zhang, Pu-Yeh Wu, Cong Sun Shandong Provincial Hospital Affiliated to Shandong First Medical University (Shandong Provincial Hospital), Jinan, China Impact: This study introduces a simple and clinically applicable MRI-based system that stratifies rectal cancer patients by immunotherapy benefit, enabling precision oncology through imaging biomarkers and promoting efficient use of immune checkpoint inhibitors in neoadjuvant treatment strategies.
		466-01-002. Application of synthetic MRI to evaluate rectal cancer grading and PIK3CA mutation status Gengyun Miao Zhongshan Hospital, Shanghai, China Impact: This study demonstrates that synthetic MRI enables noninvasive preoperative assessment of histological grade and PIK3CA mutation in rectal cancer. The findings may facilitate precise molecular stratification, guide personalized treatment, and inspire further research on quantitative imaging biomarkers for tumor heterogeneity.
		466-01-003. MRI-driven Deep Learning for Predicting Pathologic Complete Response After Neoadjuvant Chemoradiotherapy in Rectal Cancer Kaiting Han, Yunjun Yang, Zhifeng Xu The First People's Hospital of Foshan, Southern University of Science and Technology, China Impact: An interpretable deep learning radiomics model derived from pre-nCRT multiparametric MRI enables accurate, noninvasive prediction of pathological complete response in locally advanced rectal cancer, offering a robust imaging biomarker to support individualized, organ-preserving treatment strategies in clinical decision-making.
		466-01-004. QSM-Based Habitat Analysis Provides a Novel Approach for Identifying Hypoxia in Rectal Cancer:A Preliminary Prospective Study Chengbo Xiong, Yi Sun, Ximin Pan, Long Yang, Peiyi Xie, Xiao-chun Meng The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China Impact: QSM-MRI can be used to identify the hypoxic state of rectal cancer non-invasively. QSM_habitatis more effective than QSM_mean and DCE_Ktrans at identifying severe hypoxic RCs, which may facilitate more precise, personalized treatment plans.
		466-01-005. Prediction of Rectal Cancer Microsatellite Instability by Integrating MR Radiomics, Habitat Analysis, and Genomics lixin LI, yuping Bai, Duo Yang Department of Magnetic Resonance, Lanzhou, China Impact: This study provides a highly accurate, non-invasive predictive tool. Crucially, it offers, for the first time, a genomic-level explanation for the biological mechanisms underlying the imaging model, significantly enhancing its credibility and potential for clinical translation.
		466-01-006. Preoperative DCE-MRI Tumor Habitat Features Improve Postoperative Prognostic Stratification in Stage II–III Rectal Cancer Yayu Shang, Jialiang Ren, Li Yang The Fourth Hospital of Hebei Medical University, Shijiazhuang, China Impact: By combining preoperative DCE-MRI habitat features with routine clinical factors, we can flag stage II–III rectal cancer patients at high risk of postoperative recurrence, enabling intensified surveillance or escalation of adjuvant therapy beyond what TNM staging alone would suggest.
		466-01-007. Whole-Tumor IVIM-DWI Histogram Model for Preoperative Diagnosis of Tumor Deposits in Rectal Cancer Hongyu Zhao, Yantong Sun, Longxia Xu, Gesheng Song, Pu-Yeh Wu, Aiyin Li School of Medical Imaging, Shandong Second Medical University, Wei, China Impact: Whole-tumor IVIM-DWI histogram analysis captures intratumoral diffusion and perfusion heterogeneity, enabling accurate preoperative identification of tumor deposits. This technique offers a potential imaging biomarker for individualized treatment planning and risk stratification in rectal cancer.
		466-01-008. A Clinical-Radiomics Nomogram from Multiparametric MRI for Personalized Risk Stratification in Resectable Rectal Cancer Xiaoxian Zhang, MENGZHU WANG, xuejun chen, Chunmiao Xu The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, China Impact: This robust, preoperative clinical-radiomics model offers a superior, data-driven tool for predicting disease-free survival, potentially enabling more personalized and effective adjuvant therapy planning for patients with resectable rectal cancer.
		466-01-009. Deep learning radiomics-based prediction model for lymph node metastasis after neoadjuvant chemoradiotherapy in locally advan Qiurong Wei, Xian Liu, Weicui Chen, Jun Peng, Wei Yang The Second Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China Impact: Accurate evaluation of lymph node metastasis (LNM) after neoadjuvant chemoradiotherapy (nCRT) remains a major challenge in locally advanced rectal cancer (LARC).
		466-01-010. Histogram Analysis of DKI for T Restaging and Predicting pCR of LARC Following Following Neoadjuvant Immunotherapy Xin Li, Ziwei Jin, Na Hao, Peng Sun, Ning Zheng, Lan Zhang Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Jiefang Avenue #1277, Wuhan 430022, Hubei Province, China Impact: DKI-derived histogram parameters may indeed be a powerful non-invasive imaging technique for the selection of local excision candidates (ypT0-1) and predicting pathological complete response in locally advanced rectal cancer following neoadjuvant immunotherapy.
		466-01-011. MRI in Clinical Practice: Diagnosis of Regional Lymph Node Involvement in Rectal Cancer Using the Node-RADS Yuesheng Luo, Jiuquan Zhang, Lisha Nie, Leilei Liu Chongqing University Cancer Hospital & Chongqing Cancer Institute & Chongqing Cancer Hospital, ChongQing, China Impact: In terms of accuracy for assessing lymph node involvement in rectal cancer , MRI-based Node-RADS 1.0 performs better than the current ESGAR Criteria, while contrast-enhanced MRI outperforms contrast-enhanced CT due to its superior ability to characterize lymph node texture.
		466-01-012. Dynamic Evaluation of Tumor Microenvironment during Anti-angiogenic Therapy in Colorectal Cancer using Multi-parametric MRI Lingtao Zhang, Wenfeng Mai, wei cui, Dong Zhang, Liangping Luo, Changzheng Shi The First Affiliated Hospital of Jinan University, Guangzhou, China Impact: This study demonstrates that multi-parametric MRI can reliably evaluate tumor microenvironment alterations during anti-angiogenic therapy. It provides a noninvasive biomarker for monitoring treatment efficacy and optimizing therapeutic strategies in colorectal cancer.
		466-01-013. Segmenting Rectal Cancer from Multi-Sequence MR Images that Were Inconsistently Labelled Rongli Zhang, Wen Zhou, Mohamad Koohi-Moghadam, zhongbiao xu, Reza Safdari, Dariush Lotfi, Mahmoud Kazemi Haji Abadi, Ka Chun Lam, Zhihua Chen, Qi Yong Ai, Kyongtae Ty Bae The University of Hong Kong, Hong Kong, Hong Kong Impact: Our framework offers a strategy to mitigate annotation uncertainty in rectal cancer segmentation model development. By utilizing low-consensus cases as unlabeled data through semi-supervised learning, we may be able to reduce dependency on extensive high-quality annotations while preserving data scale.