iCAN science seminar on Bridging the radiology-pathology gap for prostate cancer characterization with Assist. Prof. Mirabela Rusu

Welcome to the iCAN science seminar with Assistant Professor Mirabela Rusu (Stanford University) on Bridging the radiology-pathology gap for prostate cancer characterization.

Radiology imaging is an essential non-invasive tool for cancer care, facilitating diagnosis and treatment planning. Yet, despite technological advancements in imaging, radiologists still miss 12-36% of aggressive prostate cancers due to their subtle radiologic features. Some patients undergo surgery as cancer treatment and thus have rich data comprised of high-resolution pathology images showing the extent, heterogeneity, and aggressiveness of cancer. In this context, Prof. Rusu’s team develops advanced deep learning methods to assist radiologists in their image interpretation. The approaches are split between two categories. First, the team developed registration methods that align radiology and pathology images, e.g., in the prostate or breast, using both traditional and deep-learning based registration methods. Second, they developed radiology-pathology fusion strategies by constructing pathology-derived radiology (rad-pathomic) biomarkers. When included in deep learning models, these biomarkers improved by 7% the detection of aggressive vs indolent cancers on prostate MRI or kidney CT. While this cancer detection methods are trained when both radiology and pathology images are available, they only use radiology images at inference time in new unseen patients, rendering then useful to guide biopsy or local treatment procedures.

When: Tue 17.6.2025 at 11:45-12:45
Where: Bridge Hospital, seminar room Ullanlinna, and online
Host: Prof. Tuomas Mirtti and Doctoral Researcher Tolou Shabdah

Link for online participants: https://helsinki.zoom.us/j/69887400500

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Bio:  Dr. Rusu is an Assistant Professor, in the Department of Radiology, and, by courtesy, Department of Urology and Biomedical Data Science, at Stanford University, California, where she leads the Personalized Integrative Medicine Laboratory (PIMed). The PIMed Laboratory has a multi-disciplinary direction and focuses on developing analytic methods for biomedical data integration, with a particular interest in multimodal fusion, e.g., radiology-pathology fusion to facilitate radiology image labeling, or MRI-ultrasound for guiding procedure. These fusion approaches allow the downstream training of advanced multimodal machine learning for cancer detection and subtype identification at pixel-level. Our approaches have been applied in oncologic applications (prostate, breast, kidney).


Come early for a cup of coffee and pulla!