Abdominal radiology (New York)
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This study investigates the multiparametric MRI (mpMRI) appearance of different types of benign prostatic hyperplasia (BPH) and whether quantitative mpMRI is effective in differentiating between prostate cancer (PCa) and BPH. ⋯ Multiparametric MRI and specifically quantitative ADC values can be used for differentiating PCa and BPH, improving PCa diagnosis in the transition zone. However, DCE-MRI metrics are not effective in distinguishing PCa and BPH. Glandular BPH are not hyperintense on ADC and T2 as previously thought and have similar quantitative mpMRI measurements to stromal BPH. Glandular and cystic BPH appear differently on mpMRI and are histologically different.
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Currently, all solid enhancing renal masses without microscopic fat are considered malignant until proven otherwise and there is substantial overlap in the imaging findings of benign and malignant renal masses, particularly between clear cell RCC (ccRCC) and benign oncocytoma (ONC). Radiomics has attracted increased attention for its utility in pre-operative work-up on routine clinical images. Radiomics based approaches have converted medical images into mineable data and identified prognostic imaging signatures that machine learning algorithms can use to construct predictive models by learning the decision boundaries of the underlying data distribution. The TensorFlow™ framework from Google is a state-of-the-art open-source software library that can be used for training deep learning neural networks for performing machine learning tasks. The purpose of this study was to investigate the diagnostic value and feasibility of a deep learning-based renal lesion classifier using open-source Google TensorFlow™ Inception in differentiating ccRCC from ONC on routine four-phase MDCT in patients with pathologically confirmed renal masses. ⋯ The best classification result was obtained in the EX phase among the thirteen classification methods tested. Our proof of concept study is the first step towards understanding the utility of machine learning in the differentiation of ccRCC from ONC on routine CT images. We hope this could lead to future investigation into the development of a multivariate machine learning model which may augment our ability to accurately predict renal lesion histology on imaging.