Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
660-01-009 ISMRM Abstract

Deep Learning-Based Detection Models For Pancreatic Cystic Lesions In Whole-Body MRI For Cancer Screening

Primary: Analysis Methods - Segmentation and Detection
Secondary: Body - Whole Body Imaging
660-01-009 · A Tour of Body Oncology · Thursday, 14 May, 8:30 AM–9:25 AM · Digital Posters Row A
Keywords: Clinical application Whole-Body MRI Lesion Detection Pancreatic cystic neoplasms
Accepted
Eleonora Fioretti1,2,3, Cornelius Jacob 3,4,5,6, Francesca Camagni1, Chiara Paganelli1, Federico Colombo7, Francesco Cicchetti7, Cristiano M Girlando2, Marco Stella2,3, Paul Summers2,8, Giuseppe Petralia2,3
1Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milano, Italy
2IEO European Institute of Oncology IRCCS, Milan, Italy
3ASC Italia S.r.l., Sarnico, Italy
4Research & Clinical Translation, Magnetic Resonance, Siemens Healthineers AG, Erlangen, Germany
5Division of Radiology, IEO European Institute of Oncology IRCCS, Milan, Italy
6Cornelius Jacob - Ingenieurdienstleistungen, Erlangen, Germany
7Postgraduate School in Radiodiagnostics, Università degli Studi di Milano, Milano, Italy
8QMRI Tech S.r.l., Pescara, Italy
Presenting Author: Cornelius Jacob

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References

1. Petralia G, Koh DM, Attariwala R, et al. Oncologically Relevant Findings Reporting and Data System (ONCO-RADS): Guidelines for the acquisition, interpretation, and reporting of Whole-Body MRI for Cancer Screening. Radiology. 2021;299(3):494-507. doi:10.1148/radiol.2021201740 [doi]
2. Abel L, Wasserthal J, Weikert T, et al. Automated detection of pancreatic cystic lesions on CT using deep learning. Diagnostics. 2021;11(5):901. doi:10.3390/diagnostics11050901 [doi]
3. Dai J, He C, Jin L, Chen C, Wu J, Bian Y. A deep learning detection method for pancreatic cystic neoplasm based on Mamba architecture. Journal of X-Ray Science and Technology. February 2025. doi:10.1177/08953996251313719 [doi]
4. Duh MM, Torra-Ferrer N, Riera-Marín M, et al. Deep learning to detect pancreatic cystic lesions on abdominal computed tomography scans: Development and Validation study. JMIR AI. 2023;2:e40702. doi:10.2196/40702 [doi]
5. Park HJ, Shin K, You MW, et al. Deep learning–based detection of solid and cystic pancreatic neoplasms at contrast-enhanced CT. Radiology. 2022;306(1):140-149. doi:10.1148/radiol.220171 [doi]
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7. Isensee F, Jaeger PF, Kohl S a. A, Petersen J, Maier-Hein KH. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nature Methods. 2020;18(2):203-211. doi:10.1038/s41592-020-01008-z [doi]
8. Ji Y, Bai H, Yang J, et al. AMOS: a Large-Scale Abdominal Multi-Organ benchmark for versatile medical image segmentation. arXiv (Cornell University). January 2022. doi:10.48550/arxiv.2206.08023 [doi]
9. Baumgartner M, Jäger PF, Isensee F, Maier-Hein KH. NNDetection: a self-configuring method for medical object detection. In: Lecture Notes in Computer Science.; 2021:530-539. doi:10.1007/978-3-030-87240-3_51 [doi]
10. Jaeger PF, Kohl S a. A, Bickelhaupt S, et al. Retina U-Net: Embarrassingly simple exploitation of segmentation supervision for medical object detection. arXiv (Cornell University). January 2018. doi:10.48550/arxiv.1811.08661 [doi]
11. Lin TY, Goyal P, Girshick R, He K, Dollár P. Focal loss for dense object detection. arXiv (Cornell University). January 2017. doi:10.48550/arxiv.1708.02002 [doi]
12. Chakraborty DP. Recent advances in observer performance methodology: jackknife free-response ROC (JAFROC). Radiation Protection Dosimetry. 2005;114(1-3):26-31. doi:10.1093/rpd/nch512 [doi]

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http://echo.ismrm.org/p/ISMRM2026/660-01-009