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This project focuses on the early prediction and diagnosis of radiation-induced brain injury in nasopharyngeal carcinoma patients. Based on the big data of imaging and serum metabonomics samples, combined with the machine learning analysis method, dynamic evolution mode of radio-metabolomics characteristics was analyzed . The potential internal relationship between brain structure and serum metabolic changes was explored, and the individualized prediction model was constructed to screen out the high-risk patients with brain injury after tumor radiotherapy, so as to provide reference for the diagnosis of radiation-induced brain injury caused by tumor. radiotherapy Intelligent diagnosis provides a new theoretical and practical basis.
Research Process
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Radiation Encephalopathy Group | Patients with radioactive encephalopathy during follow-up |
| |
| No Radiation Encephalopathy Group | Patients without radioactive encephalopathy during follow-up |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| intensity-modulated radiation therapy | Radiation | The patients got intensity-modulated radiation therapy during observation |
|
| Measure | Description | Time Frame |
|---|---|---|
| The variations in imaging features from initial diagnosis to 24 months after radiotherapy | Brain MRI image data of included patients with MRI sequence (T1 Wi, T2 Wi, T1 + C etc.) before and after radiotherapy (including initial diagnosis, 6 months, 12 months and 24 months after radiotherapy) were obtained for Artificial Neural Network analysis. The key features were found out by machine learning.The variations in imaging features from initial diagnosis to 24 months after radiotherapy were abtained to conduct an efficient prediction model for the probability of radiation encephalopathy. | Before and after radiotherapy (including initial diagnosis, 6 months, 12 months and 24 months after radiotherapy). All the data got from each time point were used to conduct an efficient prediction model. |
| Changes in metabolic feature from initial diagnosis to 24 months after radiotherapy | Since the changes in serum nucleotide metabolism, amino acid metabolism, fat metabolism were observed in the radiation encephalopathy patients. All Gas chromatography-mass spectrometer(GC-MS) data including retention features, peak intensity and integral mass spectrometry for each serum sample are used for analysis, to predict whether the separation between the radiation encephalopathy patients group and the control group is significant. The serum metabolism changes of patients during two years after radiotherapy are followed to obtain metabolic footprint. The serum sample got from different time points were applied in agglomerate hierarchical clustering for the screening and identification of various metabolites in the serum samples to get biomarkers, which can evaluate the changes of the metabolites in radiation encephalopathy.The PLS-DA model is used to represent changes in metabolic feature during metabolism. | Before and after radiotherapy (including initial diagnosis, 6 months, 12 months and 24 months after radiotherapy). The PLS-DA model is used to represent changes in metabolic feature during metabolism. |
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Inclusion Criteria:
Exclusion Criteria:
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Nasopharyngeal cancer patients in South University Xiangya Hospital, the First Affiliated Hospital of Nanhua University and the Affiliated Cancer Hospital of Central South University.
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Weihua Liao, PHD | Contact | 86-13973126486 | ouwenliao@163.com | |
| Youming Zhang, MD | Contact | 86-15974266761 | fsknpcxm@163.com |
| Name | Affiliation | Role |
|---|---|---|
| fsknpcxm@163.com Liao, PHD | Xiangya Hospital of Central South University | Study Director |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Xiangya Hospital of Central South University | Recruiting | Changsha | Hunan | 410008 | China |
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| ID | Term |
|---|---|
| D000077274 | Nasopharyngeal Carcinoma |
| ID | Term |
|---|---|
| D002277 | Carcinoma |
| D009375 | Neoplasms, Glandular and Epithelial |
| D009370 | Neoplasms by Histologic Type |
| D009369 | Neoplasms |
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| ID | Term |
|---|---|
| D050397 | Radiotherapy, Intensity-Modulated |
| ID | Term |
|---|---|
| D020266 | Radiotherapy, Conformal |
| D011881 | Radiotherapy, Computer-Assisted |
| D011878 | Radiotherapy |
| D013812 | Therapeutics |
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serum
| D009303 |
| Nasopharyngeal Neoplasms |
| D010610 | Pharyngeal Neoplasms |
| D010039 | Otorhinolaryngologic Neoplasms |
| D006258 | Head and Neck Neoplasms |
| D009371 | Neoplasms by Site |
| D009302 | Nasopharyngeal Diseases |
| D010608 | Pharyngeal Diseases |
| D009057 | Stomatognathic Diseases |
| D010038 | Otorhinolaryngologic Diseases |