We present a method for prostate cancer (PCa) detection using temporal enhanced ultrasound (TeUS) data obtained either from radiofrequency (RF) ultrasound signals or B-mode images.
For the first time, we demonstrate that by applying domain adaptation and transfer learning methods, a tissue classification model trained on TeUS RF data (source domain) can be deployed for classification using TeUS B-mode data alone (target domain), where both data are obtained on the same ultrasound scanner. This is a critical step for clinical translation of tissue classification techniques that primarily rely on accessing RF data, since this imaging modality is not readily available on all commercial scanners in clinics. Proof of concept is provided for in vivo characterization of PCa using TeUS B-mode data, where different nonlinear processing filters in the pipeline of the RF to B-mode conversion result in a distribution shift between the two domains.
Our in vivo study includes data obtained in MRI-guided targeted procedure for prostate biopsy. We achieve comparable area under the curve using TeUS RF and B-mode data for medium to large cancer tumor sizes in biopsy cores (>4 mm).
Our result suggests that the proposed adaptation technique is successful in reducing the divergence between TeUS RF and B-mode data.
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This work was supported in part by the Natural Sciences and Engineering Research Council of Canada (NSERC) and in part by the Canadian Institutes of Health Research (CIHR).
Conflict of interest
The authors declare that they have no conflict of interest.
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.
Informed consent was obtained from all individual participants included in the study.
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Azizi, S., Mousavi, P., Yan, P. et al. Transfer learning from RF to B-mode temporal enhanced ultrasound features for prostate cancer detection. Int J CARS 12, 1111–1121 (2017). https://doi.org/10.1007/s11548-017-1573-x
- Temporal enhanced ultrasound
- Radiofrequency signal
- Deep learning
- Deep belief network
- Transfer learning
- Cancer diagnosis
- Prostate cancer