Editorial for “Detecting adverse pathology of prostate cancer with a deep learning approach based on a 3D swin-transformer model and biparametric MRI: A multicenter retrospective study"

Adubeiro, Nuno; Nogueira, Luísa

http://hdl.handle.net/10400.22/26705

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Authors

Adubeiro, Nuno

Nogueira, Luísa

Abstract(s)

Prostate cancer (PCa) is the second most prevalent cancer among men worldwide. Timely and accurate diagnosis is important to avoid overtreatment of men with indolent, clinically insignificant PCa and to offer radical curative treatment with life-threatening, clinically significant PCa. Radical prostatectomy (RP) has become the standard care for eligible patients because of its cancer control and improved survival. Although most patients remained disease-free after RP, 20%–30% of patients develop recurrence of the disease at follow-up.3 Therefore, the assessment of reliable prognostic predictors of recurrence after RP is clinically important for guiding clinical decision-making and patient counseling. To date, several factors are considered adverse pathology (AP) features such as preoperative prostate-specific antigen (PSA) levels, Gleason score, tumor stage, surgical margin status, lymph node invasion, extracapsular extension (ECE), and seminal vesicle invasion (SVI). All of them have been identified as prognostic factors for recurrence after RP.

Keywords

Prostate cancer (PCa) Radical prostatectomy (RP)

URI

http://hdl.handle.net/10400.22/26705

Citation

Adubeiro, N., & Nogueira, M. L. (2024). Editorial for “Detecting adverse pathology of prostate cancer with a deep learning approach based on a 3D swin-transformer model and biparametric MRI: A multicenter retrospective study”. Journal of Magnetic Resonance Imaging, 59(6), 2113–2114. https://doi.org/10.1002/jmri.28956