Summary of Treatment-wise Glioblastoma Survival Inference with Multi-parametric Preoperative Mri, by Xiaofeng Liu et al.
Treatment-wise Glioblastoma Survival Inference with Multi-parametric Preoperative MRI
by Xiaofeng Liu, Nadya Shusharina, Helen A Shih, C.-C. Jay Kuo, Georges El Fakhri, Jonghye Woo
First submitted to arxiv on: 10 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Medical Physics (physics.med-ph)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a machine learning model that predicts the survival time of glioblastoma patients undergoing different treatments based on preoperative magnetic resonance scans. The authors propose a treatment-conditioned regression model that incorporates treatment information, allowing for personalized and precise treatment planning. The model is trained on the BraTS20 dataset and evaluated on three treatment options: Gross Total Resection, Subtotal Resection, and no resection. Results show the effectiveness of injecting treatment information into the model for estimating glioblastoma survival. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps doctors predict how long people with brain cancer will live after surgery or other treatments. They use special pictures called MRI scans to help make this prediction. The authors created a new way to look at these MRI scans and take into account what kind of treatment the patient is getting, like removing part of the tumor or not doing anything. This helps doctors give better care to patients with brain cancer. |
Keywords
» Artificial intelligence » Machine learning » Regression
