Automated detection of aggressive prostate cancer on Magnetic Resonance Imaging (MRI) can help guide targeted biopsies and reduce unnecessary invasive biopsies. However, automated methods of prostate cancer detection often have a sensitivity-specificity trade-off (high sensitivity with low specificity or vice-versa), making them unsuitable for clinical use. Here, we study the utility of integrating prior information about the zonal distribution of prostate cancers with a radiology-pathology fusion model in reliably identifying aggressive and indolent prostate cancers on MRI. Our approach has two steps: 1) training a radiology-pathology fusion model that learns pathomic MRI biomarkers (MRI features correlated with pathology features) and uses them to selectively identify aggressive and indolent cancers, and 2) post-processing the predictions using zonal priors in a novel optimized Bayes’ decision framework. We compare this approach with other approaches that incorporate zonal priors during training. We use a cohort of 74 radical prostatectomy patients as our training set, and two cohorts of 30 radical prostatectomy patients and 53 biopsy patients as our test sets. Our rad-path-zonal fusion-approach achieves cancer lesion-level sensitivities of 0.77±0.29 and 0.79±0.38, and specificities of 0.79±0.23 and 0.62±0.27 on the two test sets respectively, compared to baseline sensitivities of 0.91±0.27 and 0.94±0.21 and specificities of 0.39±0.33 and 0.14±0.19, verifying its utility in achieving balance between sensitivity and specificity of lesion detection.
Prostate magnetic resonance imaging (MRI) allows the detection and treatment planning of clinically significant cancers. However, indolent cancers, e.g., those with Gleason scores 3+3, are not readily distinguishable on MRI. Thus an image-guided biopsy is still required before proceeding with a radical treatment for aggressive tumors or considering active surveillance for indolent disease. The excision of the prostate as part of radical prostatectomy treatments provides a unique opportunity to correlate whole-mount histology slices with MRI. Through a careful spatial alignment of histology slices and MRI, the extent of aggressive and indolent disease can be mapped on MRI which allows one to investigate MRI-derived features that might be able to distinguish aggressive from indolent cancers. Here, we introduce a framework for the 3D spatial integration of radiology and pathology images in the prostate. Our approach, first, uses groupwise-registration methods to reconstruct the histology specimen prior to sectioning, and incorporates the MRI as a spatial constraint, and, then, performs a multi-modal 3D affine and deformable alignment between the reconstructed histology specimen and the MRI. We tested our approach on 15 studies and found a Dice similarity coefficient of 0.94±0.02 and a urethra deviation of 1.11±0.34 mm between the histology reconstruction and the MRI. Our robust framework successfully mapped the extent of disease from histology slices on MRI and created ground truth labels for characterizing aggressive and indolent disease on MRI.
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