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Genetic variants of prospectively demonstrated phenocopies in .

Hereditary cancer in clinical practice

Dominguez-Valentin M, Evans DGR, Nakken S, Tubeuf H, Vodak D, Ekstrøm PO, Nissen AM, Morak M, Holinski-Feder E, Martins A, Møller P, Hovig E.
PMID: 29371908
Hered Cancer Clin Pract. 2018 Jan 15;16:4. doi: 10.1186/s13053-018-0086-0. eCollection 2018.

BACKGROUND: In kindreds carrying METHODS: Women with BC or gynecological cancer who had tested negative for RESULTS: We identified 48 women who were tested negative for their family's CONCLUSION: All women in breast or breast/ovarian cancer kindreds would benefit...

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Dominguez-Valentin M, Evans DGR, Nakken S, et al. Genetic variants of prospectively demonstrated phenocopies in . Hered Cancer Clin Pract. 2018;16:4doi: 10.1186/s13053-018-0086-0.
Dominguez-Valentin, M., Evans, D. G. R., Nakken, S., Tubeuf, H., Vodak, D., Ekstrøm, P. O., Nissen, A. M., Morak, M., Holinski-Feder, E., Martins, A., Møller, P., & Hovig, E. (2018). Genetic variants of prospectively demonstrated phenocopies in . Hereditary cancer in clinical practice, 164. https://doi.org/10.1186/s13053-018-0086-0
Dominguez-Valentin, Mev, et al. "Genetic variants of prospectively demonstrated phenocopies in ." Hereditary cancer in clinical practice vol. 16 (2018): 4. doi: https://doi.org/10.1186/s13053-018-0086-0
Dominguez-Valentin M, Evans DGR, Nakken S, Tubeuf H, Vodak D, Ekstrøm PO, Nissen AM, Morak M, Holinski-Feder E, Martins A, Møller P, Hovig E. Genetic variants of prospectively demonstrated phenocopies in . Hered Cancer Clin Pract. 2018 Jan 15;16:4. doi: 10.1186/s13053-018-0086-0. eCollection 2018. PMID: 29371908; PMCID: PMC5769521.
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Prediction of reader estimates of mammographic density using convolutional neural networks.

Journal of medical imaging (Bellingham, Wash.)

Ionescu GV, Fergie M, Berks M, Harkness EF, Hulleman J, Brentnall AR, Cuzick J, Evans DG, Astley SM.
PMID: 30746393
J Med Imaging (Bellingham). 2019 Jul;6(3):031405. doi: 10.1117/1.JMI.6.3.031405. Epub 2019 Jan 31.

Mammographic density is an important risk factor for breast cancer. In recent research, percentage density assessed visually using visual analogue scales (VAS) showed stronger risk prediction than existing automated density measures, suggesting readers may recognize relevant image features not...

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Ionescu GV, Fergie M, Berks M, et al. Prediction of reader estimates of mammographic density using convolutional neural networks. J Med Imaging (Bellingham). 2019;6(3):031405doi: 10.1117/1.JMI.6.3.031405.
Ionescu, G. V., Fergie, M., Berks, M., Harkness, E. F., Hulleman, J., Brentnall, A. R., Cuzick, J., Evans, D. G., & Astley, S. M. (2019). Prediction of reader estimates of mammographic density using convolutional neural networks. Journal of medical imaging (Bellingham, Wash.), 6(3), 031405. https://doi.org/10.1117/1.JMI.6.3.031405
Ionescu, Georgia V, et al. "Prediction of reader estimates of mammographic density using convolutional neural networks." Journal of medical imaging (Bellingham, Wash.) vol. 6,3 (2019): 031405. doi: https://doi.org/10.1117/1.JMI.6.3.031405
Ionescu GV, Fergie M, Berks M, Harkness EF, Hulleman J, Brentnall AR, Cuzick J, Evans DG, Astley SM. Prediction of reader estimates of mammographic density using convolutional neural networks. J Med Imaging (Bellingham). 2019 Jul;6(3):031405. doi: 10.1117/1.JMI.6.3.031405. Epub 2019 Jan 31. PMID: 30746393; PMCID: PMC6357906.
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Showing 1 to 2 of 2 entries