Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
361-02-014 ISMRM Abstract

AI-Powered Hybrid Multidimensional MRI Improves Prostate Cancer Diagnosis

Primary: Analysis Methods - Classification and Prediction
Secondary: Body - Prostate
361-02-014 · Prostate: Clinical Applications, AI, and Post-Processing · Monday, 11 May, 9:15 AM–10:10 AM · Digital Posters Row B
Keywords: Prostate cancer Hybrid Multidimensional MRI CONVOLUTIONAL NEURAL NETWORK Artificial Intelligence (AI)
Accepted
Enze Zheng1, Sarit R Bose2, Emadeldeen Hamdan3, Roger Englemann2, Abel L Campos1,2, Gregory S Karczmar1,2,4, Aytekin Oto1,2,4, Aritrick Chatterjee 1,4
1Department of Radiology, University of Chicago, Chicago IL, Chicago, United States of America
2University of Chicago, Chicago IL, United States of America
3Electrical and Computer Engineering Department, University of Illinois Chicago, Chicago, United States of America
4Sanford J. Grossman Center of Excellence in Prostate Imaging and Image Guided Therapy, University of Chicago, Chicago, United States of America
Presenting Author: Aritrick Chatterjee

Synopsis

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References

1. Westphalen, A. C., McCulloch, C. E., Anaokar, J. M., Arora, S., Barashi, N. S., Barentsz, J. O., Bathala, T. K., Bittencourt, L. K., Booker, M. T., Braxton, V. G., Carroll, P. R., Casalino, D. D., Chang, S. D., Coakley, F. V., Dhatt, R., Eberhardt, S. C., Foster, B. R., Froemming, A. T., Fütterer, J. J., Ganeshan, D. M., … Rosenkrantz, A. B. (2020). Variability of the Positive Predictive Value of PI-RADS for Prostate MRI across 26 Centers: Experience of the Society of Abdominal Radiology Prostate Cancer Disease-focused Panel. Radiology, 296(1), 76–84. https://doi.org/10.1148/radiol.2020190646 [doi]
2. Chatterjee, A., Bourne, R. M., Wang, S., Devaraj, A., Gallan, A. J., Antic, T., Karczmar, G. S., & Oto, A. (2018). Diagnosis of Prostate Cancer with Noninvasive Estimation of Prostate Tissue Composition by Using Hybrid Multidimensional MR Imaging: A Feasibility Study. Radiology, 287(3), 864–873. https://doi.org/10.1148/radiol.2018171130 [doi]
3. Gundogdu, B., Chatterjee, A., Medved, M., Bagci, U., Karczmar, G. S., & Oto, A. (2025). Physics-Informed Autoencoder for Prostate Tissue Microstructure Profiling with Hybrid Multidimensional MRI. Radiology. Artificial intelligence, 7(2), e240167. https://doi.org/10.1148/ryai.240167 [doi]
4. V. G. Biradar, P. K. Pareek, V. K. S and N. P, "Lung Cancer Detection and Classification using 2D Convolutional Neural Network," 2022 IEEE 2nd Mysore Sub Section International Conference (MysuruCon), Mysuru, India, 2022, pp. 1-5, doi: 10.1109/MysuruCon55714.2022.9972595. keywords: {Support vector machines;Computed tomography;Neural networks;Lung cancer;Lung;Classification algorithms;Convolutional neural networks;lung nodule detection;Convolutional Neural Network;Deep Learning}, [doi]
5. Chatterjee, A., Yousuf, A. N., Engelmann, R., Harmath, C., Lee, G., Medved, M., Jamison, E. B., Lorente Campos, A., Gundogdu, B., Gerber, G., Reynolds, L. F., Modi, P. K., Antic, T., Giurcanu, M., Eggener, S., Karczmar, G. S., & Oto, A. (2025). Prospective Validation of an Automated Hybrid Multidimensional MRI Tool for Prostate Cancer Detection Using Targeted Biopsy: Comparison with PI-RADS-based Assessment. Radiology. Imaging cancer, 7(1), e240156. https://doi.org/10.1148/rycan.240156 [doi]
6. Campos, AL., Chatterjee, A., Engelmann, R., Karczmar, G. S., & Oto, A. (2025). Multi-voxel, spatially aware Radiomics analysis for prostate cancer diagnosis using PIA-Hybrid MRI biomarkers (unpublished manuscript)

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/361-02-014