Prediction of therapy response of breast cancer patients with machine learning based on clinical data and imaging data derived from breast [ 18 F]FDG-PET/MRI.
Kai JannuschFrederic DietzelNils Martin BruckmannJanna MorawitzMatthias BoschheidgenPeter MinkoAnn-Kathrin BittnerSvjetlana MohrmannHarald H QuickKen HerrmannLale UmutluGerald AntochChristian RubbertJulian KirchnerJulian CaspersPublished in: European journal of nuclear medicine and molecular imaging (2023)
The evaluated multi-source XGBoost model shows promising results for reliably predicting pathological complete response in breast cancer patients prior to NAST. However, yielded performance is yet insufficient to be implemented in the clinical decision-making process.
Keyphrases
- machine learning
- decision making
- big data
- electronic health record
- pet ct
- magnetic resonance imaging
- positron emission tomography
- computed tomography
- high resolution
- pet imaging
- contrast enhanced
- stem cells
- magnetic resonance
- mesenchymal stem cells
- mass spectrometry
- young adults
- photodynamic therapy
- diffusion weighted imaging