ISMRM25 - AI for Diagnosis/Prognosis: Body I

ISMRM & ISMRT Annual Meeting & Exhibition • 10-15 May 2025 • Honolulu, Hawai'i

ISMRM & ISMRT 2025 Annual Meeting & Exhibition

Digital Poster

AI for Diagnosis/Prognosis: Body I

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AI for Diagnosis/Prognosis: Body I

Digital Poster

AI & Machine Learning

Monday, 12 May 2025

Exhibition Hall

08:15 - 09:15

Session Number: D-41

No CME/CE Credit

		Computer Number: 17 1389. Enhancing Diagnostic Accuracy of American College of Radiology TI-RADS 4 Nodules: nomogram models based on MRI Morphological Features View Presentation Video B. Song, Q. Chen, H. Wang, L. Tang, X. Xie, Q. Fu, P-Y Wu, A. Mao, M. Zeng Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai Medical Imaging Institute, Shanghai, China Impact: MRI-based models demonstrated outstanding diagnostic performance for distinguishing benign from malignant ACR-TR4 thyroid nodules. Combined model, which utilizing restricted diffusion and reversed halo sign, holds promise for reducing the need for unnecessary FNA, while simultaneously minimizing risk of missed cancers.
		Computer Number: 18 1390. Multi-parametric MRI-based Radiomics to Predict Prognosis of Patients with Stages II-III Rectal Cancer View Presentation Video H. Chen, M. He, M. Sun, X. Liang, M. Ma Shengli Clinical College of Fujian Medical University & Department of Radiology, Fujian Provincial Hospital, Fuzhou University Affiliated Provincial Hospital, Fuzhou 350001, China Impact: The radiomics models can be used to predict prognosis RC patients. It has significant advantages, individualization radiomics model allows for more precise risk stratification and tailored treatment planning, which can improve patient outcomes.
		Computer Number: 19 1391. Improving prediction of pathological downstaging in rectal cancer using deep learning with preoperative MRI and clinicopathological data View Presentation Video S. H. Heo, C. M. Moon, K. H. Park, Y. Y. Lee, S. K. Kim, S. S. Shin Chonnam National University Medical School, Gwang-ju, Korea, Republic of Impact: This study’s deep learning models provide clinicians with an accurate tool for predicting pathological downstaging in locally advanced rectal cancer, improving preoperative assessment after chemoradiotherapy. It also encourages further research into integrating deep learning with multimodal data for cancer prognosis.
		Computer Number: 20 1392. Deep Learning Model based on multi-parametric MRI for Accurate Prediction of D-TACE Efficacy in Hepatocellular Carcinoma View Presentation Video Y. Tian, Z. Xi, D. Ren, X. Liang Institute of Research and Clinical Innovations,Neusoft Medical Systems Co., Ltd, Shanghai, China Impact: This model provides a non-invasive, reliable tool for predicting D-TACE outcomes, potentially transforming personalized treatment planning for HCC. Enhanced prediction accuracy can improve patient outcomes and optimize healthcare resources by tailoring treatment to individual needs
		Computer Number: 21 1393. Revolutionizing Prostate Segmentation: A Query Adaptive Pseudo 3D Attention Approach View Presentation Video C. Krishnan, E. Onuoha, A. Hung, K. Sung, H. Kim University of Alabama at Birmingham, Birmingham, United States Impact: This work supports clinicians in confidently interpreting prostate imaging, potentially reducing unnecessary biopsies and facilitatingtimely intervention. It also encourages advancements in relevance-based AI diagnostics, paving the way forenhanced accuracy and interpretability across medical imaging and broader diagnostic applications.
		Computer Number: 22 1394. Impact of image quality and clinical radiographic features on AI model for detecting clinically significant prostate cancer: a multicenter study Z. Sun, X. Wang Peking University First Hospital, Beijing, China Impact: It identifies the factors influencing the generalization performance of prostate AI, thereby enhancing clinicians' understanding and confidence in its application, ultimately enabling more effective use in practice.
		Computer Number: 23 1395. Robust 3D Landmark Detection Framework for One-Stop Automated Pelvic MRI Prescription View Presentation Video T. Koike, A. Kudo, T. Fuchigami, A. Tachibana, A. Ikegawa, W. Yokohama, K. Sakuragi, Y. Kitamura, M. Hori, N. Tomiyama Fujifilm Corporation, Tokyo, Japan Impact: The automated one-stop workflow enables single-button operation for pelvic MRI, including the challenging short-axis positioning of the uterine body and cervix. It reduces prescription variability among technicians and ensures reproducible imaging, even in anatomically complex cases due to diseases.
		Computer Number: 24 1396. Explainable Radiomics-Based ML for Predicting Clinically Significant Prostate Cancer in Biparametric MRI View Presentation Video M. Morafegh, G. Valizadeh, F. Moodi, M. Ghafoori, A. Mostaar, H. Saligheh Rad Tehran University of Medical Sciences, Tehran, Iran (Islamic Republic of) Impact: The development of explainable ML models using biparametric MRI radiomic features enhances csPCa classification, proposing a framework that connects prediction and interpretability. This approach can lead further research into transparent AI tools, benefiting clinical decision-making.
		Computer Number: 25 1397. Deep learning-based auto-segmentation and radiomics classification cascade model for endometrial cancer based on MRI: A Multi-center study View Presentation Video K. wang, X. Liu, X. Gou, J. Lian, J. Cheng, N. Hong Peking University People’s Hospital, Beijing, China Impact: We have established an automated segmentation-radiomics classification cascade model for identifying molecular subtypes of endometrial cancer. This model could be used for assisting radiologists in screening the molecular subtypes of endometrial cancer, demonstrating its promising clinical application prospects.
		Computer Number: 26 1398. Radiomics Combined with Dosiomics and Clinical Omics for Predicting Response to Neoadjuvant Therapy in Rectal Cancer View Presentation Video S. Li, Z. Li, Y. Zhang, F. Wang, C. Zhang, Y. Lu Peking University Health Science Center, Beijing, China Impact: Predicting patients who achieve pCR after nCRT can assist doctors in formulating personalized treatment strategies. This helps determine whether patients require surgery or if the "watch-and-wait" approach can be adopted.
		Computer Number: 27 1399. Early Detection of Bladder Cancer in MRI Using Deep Learning Segmentation and Rule-Based Classification: a Pilot study View Presentation Video K. C. Sim, M. J. Kim, D. J. Sung, B. J. Park, N. Y. Han, Y. E. Han, K. Shin, T. Kim Korea University Anam Hospital, Seoul, Korea, Republic of Impact: Improved early detection of bladder cancer will enable early treatment, which will have a positive impact on treatment outcomes.
		Computer Number: 28 1400. Thyroid nodule segmentation on dynamic contrast-enhanced magnetic resonance imaging based on Spatial -Temporal information fusion View Presentation Video B. Han, Q. Yang, M. Wu, K. Chen, W. Deng, W. Cui, D. Luo, D. Liang, H. Zheng, Q. Wan, Z. Liu, N. Zhang Southern University of Science and Technology (SUSTech), shenzhen, China Impact: The success of our model may inspire further research into advanced deep-learning architectures that harness individual intensity variations, morphological priors, and temporal pharmacokinetic information. This general approach could extend beyond DCE-MRI to encompass other medical imaging modalities containing temporal information.
		Computer Number: 29 1401. Automatic Pancreas Segmentation Based on Deep Learning-assisted Active Contour Model Framework using MR PDFF images View Presentation Video L. Yang, C. Cheng, Z. Hu, X. Liu, H. Zheng, C. Zou Shenzhen institutes of advanced technology, Chinese Academy of Sciences, Shenzhen, China Impact: This method effectively improves the accuracy of pancreas segmentation, enabling further analysis of fatty pancreas diseases. Furthermore, the method extends active contour model from 2D to 3D, addressing the difficulty of the active contour model when dealing with 3D images.
		Computer Number: 30 1402. MRI-Based Radiomics and Deep Learning of Thyroid Eye Disease: One Slice Speaks Volume H. Zhang, H. Zhang, J. Li, M. Jiang, X. Tao, H. C. Chan, J. Li, Y. Li, J. Sun, X. Song, X. Fan, H. Zhou Department of Ophthalmology, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China Impact: This study highlights the feasibility of single-slice MRI as an efficient, cost-effective alternative to multi-slice segmentation for predicting IVGC treatment response in TED patients. It opens avenues for more accessible clinical applications, reducing time and resource requirements while maintaining performance.
		Computer Number: 31 1403. MRI-based Habitat Radiomics for Evaluating Lymph Node Metastasis in Renal Cell Carcinoma View Presentation Video X. Bai, H. Wang, X. Fu, H. Xu, S. Zhou, S. Yi, L. Xie, H. Liu, X. Mu, M. Zhang, H. Ye, X. Ma Chinese PLA General Hospital, Beijing, China Impact: The MRI-based habitat radiomics combined model demonstrates a robust non-invasive capability for assessing regional lymph node metastasis in renal cell carcinoma, providing significant insights for clinical staging, surgical decision-making, and prognostic evaluation.
		Computer Number: 32 1404. Tumor ecology model based on quantitative parameters of DCE-MRI for predicting lymph node metastasis in rectal cancer Y. Sun, K. Ai, G. Huang Gansu University Of Chinese Medicine, Lanzhou, China Impact: Accurately identifying the LN status of RC patients prior to initial treatment is crucial for determining treatment strategies. The proposed quantitative DEC-MRI tumor ecology model is a promising tool for predicting LNM in RC.

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