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- W2983199194 abstract "Due to the recent developments of both hardware and software technologies, multimodality medical imaging techniques have been increasingly applied in clinical practice and research studies. Previously, the application of multimodality imaging in oncology has been mainly related to combining anatomical and functional imaging to improve diagnostic specificity and/or target definition, such as positron emission tomography/computed tomography (PET/CT) and single-photon emission CT (SPECT)/CT. More recently, the fusion of various images, such as multiparametric magnetic resonance imaging (MRI) sequences, different PET tracer images, PET/MRI, has become more prevalent, which has enabled more comprehensive characterization of the tumor phenotype. In order to take advantage of these valuable multimodal data for clinical decision making using radiomics, we present two ways to implement the multimodal image analysis, namely radiomic (handcrafted feature) based and deep learning (machine learned feature) based methods. Applying advanced machine (deep) learning algorithms across multimodality images have shown better results compared with single modality modeling for prognostic and/or prediction of clinical outcomes. This holds great potentials for providing more personalized treatment for patients and achieve better outcomes." @default.
- W2983199194 created "2019-11-22" @default.
- W2983199194 creator A5016472783 @default.
- W2983199194 creator A5037635983 @default.
- W2983199194 creator A5044303463 @default.
- W2983199194 creator A5071233739 @default.
- W2983199194 date "2019-12-01" @default.
- W2983199194 modified "2023-10-16" @default.
- W2983199194 title "Machine learning for radiomics-based multimodality and multiparametric modeling" @default.
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