CorrSigNet: Learning CORRelated Prostate Cancer SIGnatures from Radiology and Pathology Images for Improved Computer Aided Diagnosis

• URL: http://arxiv.org/abs/2008.00119v1
• Date: Fri, 31 Jul 2020 23:44:25 GMT
• Title: CorrSigNet: Learning CORRelated Prostate Cancer SIGnatures from Radiology and Pathology Images for Improved Computer Aided Diagnosis
• Authors: Indrani Bhattacharya and Arun Seetharaman and Wei Shao and Rewa Sood and Christian A. Kunder and Richard E. Fan and Simon John Christoph Soerensen and Jeffrey B. Wang and Pejman Ghanouni and Nikola C. Teslovich and James D. Brooks and Geoffrey A. Sonn and Mirabela Rusu
• Abstract summary: We propose CorrSigNet, an automated two-step model that localizes prostate cancer on MRI. First, the model learns MRI signatures of cancer that are correlated with corresponding histopathology features. Second, the model uses the learned correlated MRI features to train a Convolutional Neural Network to localize prostate cancer.
• Score: 1.63324350193061
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Magnetic Resonance Imaging (MRI) is widely used for screening and staging prostate cancer. However, many prostate cancers have subtle features which are not easily identifiable on MRI, resulting in missed diagnoses and alarming variability in radiologist interpretation. Machine learning models have been developed in an effort to improve cancer identification, but current models localize cancer using MRI-derived features, while failing to consider the disease pathology characteristics observed on resected tissue. In this paper, we propose CorrSigNet, an automated two-step model that localizes prostate cancer on MRI by capturing the pathology features of cancer. First, the model learns MRI signatures of cancer that are correlated with corresponding histopathology features using Common Representation Learning. Second, the model uses the learned correlated MRI features to train a Convolutional Neural Network to localize prostate cancer. The histopathology images are used only in the first step to learn the correlated features. Once learned, these correlated features can be extracted from MRI of new patients (without histopathology or surgery) to localize cancer. We trained and validated our framework on a unique dataset of 75 patients with 806 slices who underwent MRI followed by prostatectomy surgery. We tested our method on an independent test set of 20 prostatectomy patients (139 slices, 24 cancerous lesions, 1.12M pixels) and achieved a per-pixel sensitivity of 0.81, specificity of 0.71, AUC of 0.86 and a per-lesion AUC of $0.96 \pm 0.07$, outperforming the current state-of-the-art accuracy in predicting prostate cancer using MRI.

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