Supervised machine learning enables non-invasive lesion characterization in primary prostate cancer with [68Ga]Ga-PSMA-11 PET/MRI.
L PappC P SpielvogelB GrubmüllerM GrahovacD KrajncB EcsediR A M SareshgiD MohamadM HamboeckI RauschM MitterhauserW WadsakA R HaugL KennerP MazalM SusaniS HartenbachP BaltzerT H HelbichG KramerS F ShariatT BeyerM HartenbachMarcus HackerPublished in: European journal of nuclear medicine and molecular imaging (2020)
Our results demonstrate the potential to enhance risk classification in primary prostate cancer patients built on PET/MRI radiomics and machine learning without biopsy sampling.
Keyphrases
- pet ct
- machine learning
- prostate cancer
- contrast enhanced
- artificial intelligence
- magnetic resonance imaging
- radical prostatectomy
- end stage renal disease
- positron emission tomography
- big data
- deep learning
- computed tomography
- ejection fraction
- chronic kidney disease
- newly diagnosed
- pet imaging
- prognostic factors
- diffusion weighted imaging
- magnetic resonance
- patient reported outcomes
- climate change
- squamous cell carcinoma