Publicação
Combined MRI with non-image clinical data for brain tumor classification: a CNN/DL approach
| Resumo: | Prognosis and patient stratification for brain tumors is an important and clinically relevant task and a precise treatment outcome prediction would allow to choose an adequate therapy strategy and schedule the most appropriate follow-up examinations. Magnetic Resonance Imaging (MRI) is an already know imaging technique to assess these tumors. Next to medical imaging, other clinical information is important for patient management, e.g. genetic markers like O6-Methyl-Guanine-Methyl-Transferase (MGMT) methylation is a well-known prognostic marker in Glioblastoma (GBM) tumors. Therefore, the main goal of this thesis was to study Deep Learning (DL) approaches to combine MRI with non-image clinical data in two different classification scenarios: brain tumor segmentation and patient outcome prediction. There are studies that combine these two types of data, however, in two steps: extracting MRI features and then combining them with relevant non-image data. Here, end-to-end DL architectures with two input layers are presented, as well as an infrastructure that allows the easy development of future Machine Learning (ML) /DL models that consumes these two types of data in a clinical context. In this way, the classification in both scenarios is done in a single step, where Convolution Layers perform the feature extraction in MRI input. In brain tumor segmentation, the model with combined data achieved a slightly better Dice Similarity Coefficient (DSC) (0.894 ± 0.025) over image only model (0.882 ± 0.025). As for patient outcome prediction, when trying to predict the Progression-Free Survival (PFS) class (“bad”,” medium” and “good” outcomes), the combined model didn't improve when compared with the model where only MRI was used. Both models, however, outperformed models where only non-image data was used. The segmentation results point to a positive influence when adding the clinical information to MRI. Nevertheless, there is a lot more to investigate in this field, not only in the model architecture, but also in selecting relevant clinical information. In same way, more tests should be run for patient outcome prediction, especially using Overall Survival (OS) information. |
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| Autores principais: | Espanha, Raphael Alves |
| Assunto: | Engenharia e Tecnologia::Engenharia Médica |
| Ano: | 2017 |
| País: | Portugal |
| Tipo de documento: | dissertação de mestrado |
| Tipo de acesso: | acesso restrito |
| Instituição associada: | Universidade do Minho |
| Idioma: | inglês |
| Origem: | RepositóriUM - Universidade do Minho |
| Resumo: | Prognosis and patient stratification for brain tumors is an important and clinically relevant task and a precise treatment outcome prediction would allow to choose an adequate therapy strategy and schedule the most appropriate follow-up examinations. Magnetic Resonance Imaging (MRI) is an already know imaging technique to assess these tumors. Next to medical imaging, other clinical information is important for patient management, e.g. genetic markers like O6-Methyl-Guanine-Methyl-Transferase (MGMT) methylation is a well-known prognostic marker in Glioblastoma (GBM) tumors. Therefore, the main goal of this thesis was to study Deep Learning (DL) approaches to combine MRI with non-image clinical data in two different classification scenarios: brain tumor segmentation and patient outcome prediction. There are studies that combine these two types of data, however, in two steps: extracting MRI features and then combining them with relevant non-image data. Here, end-to-end DL architectures with two input layers are presented, as well as an infrastructure that allows the easy development of future Machine Learning (ML) /DL models that consumes these two types of data in a clinical context. In this way, the classification in both scenarios is done in a single step, where Convolution Layers perform the feature extraction in MRI input. In brain tumor segmentation, the model with combined data achieved a slightly better Dice Similarity Coefficient (DSC) (0.894 ± 0.025) over image only model (0.882 ± 0.025). As for patient outcome prediction, when trying to predict the Progression-Free Survival (PFS) class (“bad”,” medium” and “good” outcomes), the combined model didn't improve when compared with the model where only MRI was used. Both models, however, outperformed models where only non-image data was used. The segmentation results point to a positive influence when adding the clinical information to MRI. Nevertheless, there is a lot more to investigate in this field, not only in the model architecture, but also in selecting relevant clinical information. In same way, more tests should be run for patient outcome prediction, especially using Overall Survival (OS) information. |
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