Sen, S.; Valindria, V.; Slator, P.J.; Pye, H.; Grey, A.; Freeman, A.; Moore, C.; Whitaker, H.; Punwani, S.; Singh, S.; Panagiotaki, E. Differentiating False Positive Lesions from Clinically Significant Cancer and Normal Prostate Tissue Using VERDICT MRI and Other Diffusion Models. Diagnostics2022, 12, 1631.
Sen, S.; Valindria, V.; Slator, P.J.; Pye, H.; Grey, A.; Freeman, A.; Moore, C.; Whitaker, H.; Punwani, S.; Singh, S.; Panagiotaki, E. Differentiating False Positive Lesions from Clinically Significant Cancer and Normal Prostate Tissue Using VERDICT MRI and Other Diffusion Models. Diagnostics 2022, 12, 1631.
Sen, S.; Valindria, V.; Slator, P.J.; Pye, H.; Grey, A.; Freeman, A.; Moore, C.; Whitaker, H.; Punwani, S.; Singh, S.; Panagiotaki, E. Differentiating False Positive Lesions from Clinically Significant Cancer and Normal Prostate Tissue Using VERDICT MRI and Other Diffusion Models. Diagnostics2022, 12, 1631.
Sen, S.; Valindria, V.; Slator, P.J.; Pye, H.; Grey, A.; Freeman, A.; Moore, C.; Whitaker, H.; Punwani, S.; Singh, S.; Panagiotaki, E. Differentiating False Positive Lesions from Clinically Significant Cancer and Normal Prostate Tissue Using VERDICT MRI and Other Diffusion Models. Diagnostics 2022, 12, 1631.
Abstract
False positives on multiparametric (mp)-MRI result in a large number of unnecessary biopsies in men with clinically insignificant diseases. This study investigates whether quantitative diffusion MRI can improve differentiation between false positives, true positives and normal tis-sue. Twenty-three patients underwent mp-MRI and Vascular, Extracellular and Restricted Diffu-sion for Cytometry in Tumours (VERDICT)-MRI, followed by transperineal biopsy. The patients were categorised into two groups following biopsy: (1) significant cancer - true positive (2) atro-phy/inflammation/high-grade prostatic intraepithelial neoplasia (PIN) - false positive. The clinical apparent diffusion coefficient (ADC) values of the lesions were obtained, and the intravoxel inco-herent motion (IVIM), diffusion kurtosis imaging (DKI) and VERDICT models were fitted using a deep learning approach. Significant differences (p < 0.05) between true positive and false positive lesions were found in ADC, IVIM perfusion fraction (f) and diffusivity (D), DKI diffusivity (DK) and kurtosis (K) and VERDICT intracellular volume fraction (fIC), extracellular-extravascular vol-ume fraction (fEES) and diffusivity (dEES) values. Significant differences between false positives and normal tissue were only found for the VERDICT fIC. These results demonstrate that model-based diffusion MRI could reduce the number of unnecessary biopsies due to false positive prostate lesions and shows promising sensitivity to benign diseases that mimic cancer.
Keywords
prostate cancer; diffusion MRI; false positives; biophysical modelling; deep learning
Subject
Medicine and Pharmacology, Other
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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