Treatment-wise Glioblastoma Survival Inference with Multi-parametric Preoperative MRI
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866909101810778112 |
|---|---|
| author | Liu, Xiaofeng Shusharina, Nadya Shih, Helen A Kuo, C. -C. Jay Fakhri, Georges El Woo, Jonghye |
| author_facet | Liu, Xiaofeng Shusharina, Nadya Shih, Helen A Kuo, C. -C. Jay Fakhri, Georges El Woo, Jonghye |
| contents | In this work, we aim to predict the survival time (ST) of glioblastoma (GBM) patients undergoing different treatments based on preoperative magnetic resonance (MR) scans. The personalized and precise treatment planning can be achieved by comparing the ST of different treatments. It is well established that both the current status of the patient (as represented by the MR scans) and the choice of treatment are the cause of ST. While previous related MR-based glioblastoma ST studies have focused only on the direct mapping of MR scans to ST, they have not included the underlying causal relationship between treatments and ST. To address this limitation, we propose a treatment-conditioned regression model for glioblastoma ST that incorporates treatment information in addition to MR scans. Our approach allows us to effectively utilize the data from all of the treatments in a unified manner, rather than having to train separate models for each of the treatments. Furthermore, treatment can be effectively injected into each convolutional layer through the adaptive instance normalization we employ. We evaluate our framework on the BraTS20 ST prediction task. Three treatment options are considered: Gross Total Resection (GTR), Subtotal Resection (STR), and no resection. The evaluation results demonstrate the effectiveness of injecting the treatment for estimating GBM survival. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_06982 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Treatment-wise Glioblastoma Survival Inference with Multi-parametric Preoperative MRI Liu, Xiaofeng Shusharina, Nadya Shih, Helen A Kuo, C. -C. Jay Fakhri, Georges El Woo, Jonghye Computer Vision and Pattern Recognition Artificial Intelligence Medical Physics In this work, we aim to predict the survival time (ST) of glioblastoma (GBM) patients undergoing different treatments based on preoperative magnetic resonance (MR) scans. The personalized and precise treatment planning can be achieved by comparing the ST of different treatments. It is well established that both the current status of the patient (as represented by the MR scans) and the choice of treatment are the cause of ST. While previous related MR-based glioblastoma ST studies have focused only on the direct mapping of MR scans to ST, they have not included the underlying causal relationship between treatments and ST. To address this limitation, we propose a treatment-conditioned regression model for glioblastoma ST that incorporates treatment information in addition to MR scans. Our approach allows us to effectively utilize the data from all of the treatments in a unified manner, rather than having to train separate models for each of the treatments. Furthermore, treatment can be effectively injected into each convolutional layer through the adaptive instance normalization we employ. We evaluate our framework on the BraTS20 ST prediction task. Three treatment options are considered: Gross Total Resection (GTR), Subtotal Resection (STR), and no resection. The evaluation results demonstrate the effectiveness of injecting the treatment for estimating GBM survival. |
| title | Treatment-wise Glioblastoma Survival Inference with Multi-parametric Preoperative MRI |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Medical Physics |
| url | https://arxiv.org/abs/2402.06982 |