Development of Intelligent Model for Radioactive Brain Damage of Nasopharyngeal Carcinoma Based on Radio-metabolomics

Study Overview
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Brief Summary
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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.

Detailed Description
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Research Process The MRI based cohort data set of nasopharyngeal carcinoma was established, and the data of multiple follow-up time points before and after radiotherapy (including initial diagnosis, 6 months, 12 months and 24 months after radiotherapy) were standardized to obtain the longitudinal data set; Region of interest (ROI): it mainly delineates the bilateral temporal lobe, brain stem and other brain regions, and extracts the corresponding image features in ROI; Feature selection: using the strategy of radiomics combined with Artificial Neural Network to reduce the dimension of high-dimensional image features, the key features are selected and used for the subsequent construction of classification and prediction model; Extracting key features: using vertical axis data analysis method and logistic regression to establish dynamic prediction model.

Official Title
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Development of Intelligent Model for Radioactive Brain Damage of Nasopharyngeal Carcinoma Based on Radio-metabolomics

Conditions
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Nasopharyngeal Carcinoma

Intervention / Treatment
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* Radiation: intensity-modulated radiation therapy


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: Age 20-65; right handedness; Karnofsky physical condition score (KPS) ≥ 80; Histologically or cytologically confirmed non keratinizing squamous cell carcinoma of the nasopharynx with stage T3-4NxM0（AJCC 7th）; Plan to receive intensity-modulated radiation therapy, the primary dose is more than or equal to 66Gy, fractional dose is less than 2.3Gy; Concurrent chemotherapy with cisplatin during radiotherapy, the total dose of chemotherapy is more than or equal to 200mg / m^2; 7. Primary school education or above, be able and willing to participate in the clinical trial, be able and willing to sign the agreement and consent, and be able and willing to record the symptoms and treatment details as often as necessary; White blood cell count ≥ 3 × 10^9 / L, neutrophil count ≥ 1.5 × 10^9 / L, hemoglobin ≥ 90g / L and platelet count ≥ 100 × 10^9 / L; ALT / AST ≤ 1.5 times of upper limit of normal (ULN), alkaline phosphatase (ALP) < 2.5 × ULN, bilirubin < ULN; ALB ≥ 28g / L; Patients can receive magnetic resonance imaging (MRI). Exclusion Criteria: Unable or unwilling to give written and informed consent for MRI imaging, patients with claustrophobia, aneurysm clip, implantable nerve stimulator, implantable cardiac pacemaker or defibrillator, cochlear implant, eye foreign body or implant (such as metal chips, retinal clip) or pancreatic islet pump, and other contraindications for MRI scanning; He has a history of radiotherapy for the parts requiring radiotherapy in the past; There were no organic lesions in the brain, such as white matter lesions and brain atrophy, cerebrovascular diseases, brain tumors and brain trauma; Active, known or suspected autoimmune diseases, including but not limited to systemic lupus erythematosus, rheumatoid arthritis, Sjogren's syndrome, ulcerative colitis, Crohn's disease, myasthenia gravis, Hashimoto's thyroiditis, Graves' disease and asthma requiring bronchodilators. Subjects with type I diabetes, hypothyroidism requiring hormone replacement therapy only, and skin diseases (such as vitiligo, psoriasis, or alopecia) not requiring systemic therapy were included. Uncontrolled heart diseases, such as: (1) New York Heart Association classification grade 2 or above heart failure (2) unstable angina pectoris (3) myocardial infarction within one year (4) supraventricular or ventricular arrhythmias with clinical significance and requiring treatment or intervention. Pregnant or lactating women (for women with sexual life and fertility, pregnancy test should be considered); Previous or concurrent malignant tumors, except for non melanoma skin cancer, cervical carcinoma in situ and papillary thyroid cancer, which have recovered well after adequate treatment; Active infection requiring systemic treatment, positive for human immunodeficiency virus (HIV, HIV 1 / 2 antibody). A history of psychotropic drug abuse, alcoholism or drug abuse; Anti-vascular targeted drugs were used during induction chemotherapy before treatment; Other factors that may affect the safety of the subjects or the compliance of the test according to the judgment of the researcher. For example, serious diseases (including mental illness), serious laboratory abnormalities, or other family or social factors that need to be treated together. Currently or in the past, there are no major physical diseases, such as acute infection or untreated infection (viral, bacterial or fungal infection), heart disease, severe hypertension, diabetes, chronic kidney disease, genetic diseases, etc.

Ages Eligible for Study
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Minimum Age: 20 Years
Maximum Age: 65 Years

Sexes Eligible for Study
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All

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Radiation Encephalopathy Group<br>Patients with radioactive encephalopathy during follow-up | Radiation: intensity-modulated radiation therapy<br>* The patients got intensity-modulated radiation therapy during observation<br>|
| No Radiation Encephalopathy Group<br>Patients without radioactive encephalopathy during follow-up | Radiation: intensity-modulated radiation therapy<br>* The patients got intensity-modulated radiation therapy during observation<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | 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. | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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radioactive brain damage, radio-metabolomics, machine learning, Artificial Neural Network