High spatiotemporal resolution dynamic contrast-enhanced MRI improves the image-based discrimination of histopathology risk groups of peripheral zone prostate cancer: a supervised machine learning approach
| Autor: | Tobias Heye, Hanns-Christian Breit, David J. Winkel, Tobias K. Block, Daniel T. Boll |
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| Rok vydání: | 2020 |
| Předmět: |
Image-Guided Biopsy
Male medicine.medical_specialty Contrast Media Machine learning computer.software_genre 030218 nuclear medicine & medical imaging 03 medical and health sciences Prostate cancer 0302 clinical medicine Biopsy medicine Humans Effective diffusion coefficient Radiology Nuclear Medicine and imaging Aged Neoplasm Staging Retrospective Studies Neuroradiology medicine.diagnostic_test Receiver operating characteristic business.industry Prostatic Neoplasms Magnetic resonance imaging General Medicine medicine.disease Diffusion Magnetic Resonance Imaging ROC Curve 030220 oncology & carcinogenesis Dynamic contrast-enhanced MRI Supervised Machine Learning Artificial intelligence Radiology business computer |
| Zdroj: | European Radiology. 30:4828-4837 |
| ISSN: | 1432-1084 0938-7994 |
| DOI: | 10.1007/s00330-020-06849-y |
| Popis: | To assess if adding perfusion information from dynamic contrast-enhanced (DCE MRI) acquisition schemes with high spatiotemporal resolution to T2w/DWI sequences as input features for a gradient boosting machine (GBM) machine learning (ML) classifier could better classify prostate cancer (PCa) risk groups than T2w/DWI sequences alone. One hundred ninety patients (68 ± 9 years) were retrospectively evaluated at 3T MRI for clinical suspicion of PCa. Included were 201 peripheral zone (PZ) PCa lesions. Histopathological confirmation on fusion biopsy was matched with normal prostate parenchyma contralaterally. Biopsy results were grouped into benign tissue and low-, intermediate-, and high-risk groups (Gleason sum score 6, 7, and > 7, respectively). DCE MRI was performed using golden-angle radial sparse MRI. Perfusion maps (Ktrans, Kep, Ve), apparent diffusion coefficient (ADC), and absolute T2w signal intensity were determined and used as input features for building two ML models: GBM with/without perfusion maps. Areas under the receiver operating characteristic curve (AUC) values for correlated models were compared. For the classification of benign vs. malignant and intermediate- vs. high-grade PCa, perfusion information added relevant information (AUC values 1 vs. 0.953 and 0.909 vs. 0.700, p |
| Databáze: | OpenAIRE |
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