Machine learning for radiomics-based multimodality and multiparametric modeling.
Q J Nucl Med Mol Imaging
; 63(4): 323-338, 2019 Dec.
Article
en En
| MEDLINE
| ID: mdl-31527580
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.
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Procesamiento de Imagen Asistido por Computador
/
Aprendizaje Automático
Tipo de estudio:
Diagnostic_studies
/
Prognostic_studies
/
Risk_factors_studies
Límite:
Humans
Idioma:
En
Revista:
Q J Nucl Med Mol Imaging
Asunto de la revista:
MEDICINA NUCLEAR
Año:
2019
Tipo del documento:
Article
País de afiliación:
Estados Unidos