Academic Journal
A data-driven ultrasound approach discriminates pathological high grade prostate cancer
| العنوان: | A data-driven ultrasound approach discriminates pathological high grade prostate cancer |
|---|---|
| المؤلفون: | Akatsuka, Jun, Numata, Yasushi, Morikawa, Hiromu, Sekine, Tetsuro, Kayama, Shigenori, Mikami, Hikaru, Yanagi, Masato, Endo, Yuki, Takeda, Hayato, Toyama, Yuka, Yamaguchi, Ruri, Kimura, Go, Kondo, Yukihiro, Yamamoto, Yoichiro |
| المساهمون: | MEXT KAKENHI, the Moonshot Research and Development Program |
| المصدر: | Scientific Reports ; volume 12, issue 1 ; ISSN 2045-2322 |
| بيانات النشر: | Springer Science and Business Media LLC |
| سنة النشر: | 2022 |
| الوصف: | Accurate prostate cancer screening is imperative for reducing the risk of cancer death. Ultrasound imaging, although easy, tends to have low resolution and high inter-observer variability. Here, we show that our integrated machine learning approach enabled the detection of pathological high-grade cancer by the ultrasound procedure. Our study included 772 consecutive patients and 2899 prostate ultrasound images obtained at the Nippon Medical School Hospital. We applied machine learning analyses using ultrasound imaging data and clinical data to detect high-grade prostate cancer. The area under the curve (AUC) using clinical data was 0.691. On the other hand, the AUC when using clinical data and ultrasound imaging data was 0.835 ( p = 0.007). Our data-driven ultrasound approach offers an efficient tool to triage patients with high-grade prostate cancers and expands the possibility of ultrasound imaging for the prostate cancer detection pathway. |
| نوع الوثيقة: | article in journal/newspaper |
| اللغة: | English |
| DOI: | 10.1038/s41598-022-04951-3 |
| الاتاحة: | http://dx.doi.org/10.1038/s41598-022-04951-3 https://www.nature.com/articles/s41598-022-04951-3.pdf https://www.nature.com/articles/s41598-022-04951-3 |
| Rights: | https://creativecommons.org/licenses/by/4.0 ; https://creativecommons.org/licenses/by/4.0 |
| رقم الانضمام: | edsbas.687C2761 |
| قاعدة البيانات: | BASE |
| DOI: | 10.1038/s41598-022-04951-3 |
|---|