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Deep transfer learning-based prostate cancer classification using 3 Tesla multi-parametric MRI

  • Special Section: Radiogenomics
  • Published:
Abdominal Radiology Aims and scope Submit manuscript

Abstract

Purpose

The purpose of the study was to propose a deep transfer learning (DTL)-based model to distinguish indolent from clinically significant prostate cancer (PCa) lesions and to compare the DTL-based model with a deep learning (DL) model without transfer learning and PIRADS v2 score on 3 Tesla multi-parametric MRI (3T mp-MRI) with whole-mount histopathology (WMHP) validation.

Methods

With IRB approval, 140 patients with 3T mp-MRI and WMHP comprised the study cohort. The DTL-based model was trained on 169 lesions in 110 arbitrarily selected patients and tested on the remaining 47 lesions in 30 patients. We compared the DTL-based model with the same DL model architecture trained from scratch and the classification based on PIRADS v2 score with a threshold of 4 using accuracy, sensitivity, specificity, and area under curve (AUC). Bootstrapping with 2000 resamples was performed to estimate the 95% confidence interval (CI) for AUC.

Results

After training on 169 lesions in 110 patients, the AUC of discriminating indolent from clinically significant PCa lesions of the DTL-based model, DL model without transfer learning and PIRADS v2 score ≥ 4 were 0.726 (CI [0.575, 0.876]), 0.687 (CI [0.532, 0.843]), and 0.711 (CI [0.575, 0.847]), respectively, in the testing set. The DTL-based model achieved higher AUC compared to the DL model without transfer learning and PIRADS v2 score ≥ 4 in discriminating clinically significant lesions in the testing set.

Conclusion

The DeLong test indicated that the DTL-based model achieved comparable AUC compared to the classification based on PIRADS v2 score (p = 0.89).

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Acknowledgements

The study was supported by Siemens Medical Solutions and by funding from the Integrated Diagnostics program of the Departments of Radiological Sciences & Pathology & Laboratory Medicine, David Geffen School of Medicine at UCLA.

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Authors and Affiliations

  1. Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA

    Xinran Zhong, Ruiming Cao, Sepideh Shakeri, Yeejin Lee, Dieter R. Enzmann, Holden H. Wu, Steven S. Raman & Kyunghyun Sung

  2. Physics and Biology in Medicine IDP, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA

    Xinran Zhong, Holden H. Wu & Kyunghyun Sung

  3. Department of Computer Science, School of Engineering, University of California, Los Angeles, Los Angeles, CA, USA

    Ruiming Cao

  4. Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA

    Fabien Scalzo

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  1. Xinran Zhong
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Correspondence to Xinran Zhong.

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Conflict of interest

All the authors declare no conflict of interest.

Ethics approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Since the study involved purely retrospective analysis of previously acquired data, the Institutional Review Board waived the need for additional informed consent.

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Zhong, X., Cao, R., Shakeri, S. et al. Deep transfer learning-based prostate cancer classification using 3 Tesla multi-parametric MRI. Abdom Radiol 44, 2030–2039 (2019). https://doi.org/10.1007/s00261-018-1824-5

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  • DOI: https://doi.org/10.1007/s00261-018-1824-5

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