Prostate cancer risk assessment and avoidance of prostate biopsies using fully automatic deep learning in prostate MRI: comparison to PI-RADS and integration with clinical data in nomograms.
Adrian SchraderNils NetzerThomas HielscherMagdalena GörtzKevin Sun ZhangViktoria SchützAlbrecht StenzingerMarkus HohenfellnerHeinz-Peter SchlemmerDavid BonekampPublished in: European radiology (2024)
The current MRI-based nomograms result in many negative prostate biopsies. The addition of DL to nomograms with clinical data and PI-RADS improves patient stratification before biopsy. Fully automatic DL can be substituted for PI-RADS without sacrificing the quality of nomogram predictions. Prostate nomograms show cancer detection ability comparable to previous validation studies while being suitable for the addition of DL analysis.
Keyphrases
- prostate cancer
- deep learning
- radical prostatectomy
- benign prostatic hyperplasia
- risk assessment
- magnetic resonance imaging
- machine learning
- ultrasound guided
- contrast enhanced
- electronic health record
- big data
- artificial intelligence
- case report
- papillary thyroid
- diffusion weighted imaging
- magnetic resonance
- lymph node metastasis
- convolutional neural network
- computed tomography
- molecular docking
- human health
- label free