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Sedghi A, Mehrtash A, Jamzad A, Amalou A, Wells WM, Kapur T, Kwak JT, Turkbey B, Choyke P, Pinto P, Wood B, Xu S, Abolmaesumi P, Mousavi P. Improving detection of prostate cancer foci via information fusion of MRI and temporal enhanced ultrasound. Int J Comput Assist Radiol Surg 2020; 15:1215-1223. [PMID: 32372384 DOI: 10.1007/s11548-020-02172-5] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2019] [Accepted: 04/16/2020] [Indexed: 11/28/2022]
Abstract
PURPOSE The detection of clinically significant prostate cancer (PCa) is shown to greatly benefit from MRI-ultrasound fusion biopsy, which involves overlaying pre-biopsy MRI volumes (or targets) with real-time ultrasound images. In previous literature, machine learning models trained on either MRI or ultrasound data have been proposed to improve biopsy guidance and PCa detection. However, quantitative fusion of information from MRI and ultrasound has not been explored in depth in a large study. This paper investigates information fusion approaches between MRI and ultrasound to improve targeting of PCa foci in biopsies. METHODS We build models of fully convolutional networks (FCN) using data from a newly proposed ultrasound modality, temporal enhanced ultrasound (TeUS), and apparent diffusion coefficient (ADC) from 107 patients with 145 biopsy cores. The architecture of our models is based on U-Net and U-Net with attention gates. Models are built using joint training through intermediate and late fusion of the data. We also build models with data from each modality, separately, to use as baseline. The performance is evaluated based on the area under the curve (AUC) for predicting clinically significant PCa. RESULTS Using our proposed deep learning framework and intermediate fusion, integration of TeUS and ADC outperforms the individual modalities for cancer detection. We achieve an AUC of 0.76 for detection of all PCa foci, and 0.89 for PCa with larger foci. Results indicate a shared representation between multiple modalities outperforms the average unimodal predictions. CONCLUSION We demonstrate the significant potential of multimodality integration of information from MRI and TeUS to improve PCa detection, which is essential for accurate targeting of cancer foci during biopsy. By using FCNs as the architecture of choice, we are able to predict the presence of clinically significant PCa in entire imaging planes immediately, without the need for region-based analysis. This reduces the overall computational time and enables future intra-operative deployment of this technology.
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Affiliation(s)
| | - Alireza Mehrtash
- The University of British Columbia, Vancouver, BC, Canada.,Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | | | - Amel Amalou
- The National Institutes of Health Research Center, Baltimore, MD, USA
| | - William M Wells
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Tina Kapur
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | | | - Baris Turkbey
- The National Institutes of Health Research Center, Baltimore, MD, USA
| | - Peter Choyke
- The National Institutes of Health Research Center, Baltimore, MD, USA
| | - Peter Pinto
- The National Institutes of Health Research Center, Baltimore, MD, USA
| | - Bradford Wood
- The National Institutes of Health Research Center, Baltimore, MD, USA
| | - Sheng Xu
- The National Institutes of Health Research Center, Baltimore, MD, USA
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