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Agenda of October 3, 2022 meeting
Bridging the gap between prostate radiology and pathology through machine learning Mirabela Rusu, PhD, Stanford University The subtle difference in MRI appearance of prostate cancer and benign prostate tissue renders the interpretation of prostate MRI challenging, causing many false positives, false negatives, and wide variations in interpretation. My laboratory focuses on improving the interpretation of prostate MRI by developing deep learning models that automatically localize indolent and aggressive prostate cancers on MRI scans. The novelty of our methods comes from using whole-mount pathology images to label MRI images and to create pathomic MRI biomarkers of aggressive and indolent cancers. Our approach achieved an area under the receiver operator characteristics curve of 0.93 evaluated on a per-lesion basis and outperformed existing deep learning models. In patients outside our training cohorts, such predictive models will outline the extent of cancer on radiology images in the absence of pathology images, thus helping guide the prostate biopsy and local treatment. The talk will focus on discussing recent contributions from my lab on registering whole-mount pathology images with MRI, training deep learning models to extract pathomic MRI biomarkers and using them in training deep learning models to detect and distinguish indolent and aggressive prostate cancers on MRI, and showing the benefits of using labels from pathology in training deep learning models to distinguish indolent from aggressive prostate cancer on MRI.TBD |
TCIA Update Justin Kirby (FNLCR) |
IDC Update Ulli Wagner (FNLCR) |
Announcements |
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| July 4, 2022 | Independence Day - no webinar |
| August 1, 2022 | Summer break - no webinar |
| September 5, 2022 | Labor Day - no webinar |
| October 3, 2022 | |
| November 7, 2022 | |
| December 5, 2022 |
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