Study of CT and MR in the Lung Cancer

Study Overview
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Brief Summary
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Lung cancer is one of the leading causes of cancer-related deaths in China. Despite advances in systemic therapy and improvement nonsurvival rates for patients with advanced lung cancer, morbidity and mortality remain high. Recently, many studies reported that patients with positive driving genes such as EGFR(epidermal growth factor receptor,EGFR), ALK(anaplastic lymphoma kinase,ALK), ROS1(c-ros oncogene 1 receptor,ROS1), BRAF (V-raf murine sarcoma viral oncogene homolog B1, BRAF)and so on have clearly targeted drugs, which bring survival benefits to patients. However, about half of patients still lack a clear driving gene target, which may have improved survival due to higher response rates to radiation therapy and other chemotherapy medications. Development of noninvasive imaging biomarkers such as CT (computed tomography,CT)and MRI (magnetic resonance imaging,MRI)may not only evaluate the response to therapy ,but also could predict the efficacy of drug therapy and whether the driving gene is positive or not, through analysing the relationship between clinical related data and imaging features to find the imaging characteristics for making clinical decisions, and, consequently, contribute to an improved prognosis.

Detailed Description
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To explore the value of CT and MR using multiple sequences, including T2-TSE-BLADE, T2 maps StarVIBE, and iShim-DWI in evaluating the driving genes and prediction of response to therapy and OS in patients with lung cancer. Patients with biopsy-proven lung cancer were prospectively enrolled for imaging on CT and a 3T MRI scanner . The MRI protocol included T2-TSE-BLADE, T2 maps,iShim-DWI and StarVIBE sequences, and so on. Patients received treatment according to NCCN( National Comprehensive Cancer Network) guideline. CT and MRI features were analyzed to find the correlation between pretreatment imaging features and driving genes and therapy response. The study will include 400 patients. Inter-reader agreements of TN staging were analyzed excellent for CT and MRI. Diagnostic accuracy of CT and MRI will be calculated separately.

Official Title
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Clinical Study of CT and MR in Prediction of Driving Genes and Response in Patients With Lung Cancer

Conditions
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Lung Cancer Squamous Cell, CT, Genes, Response, MRI

Intervention / Treatment
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* Other: No intervention


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: Consecutive patients with preoperative pathologically con-firmed lung cancer by endoscopy and preoperative imaging data were included. No contraindications for MRI examination. No contraindications for iodinated contrast. The patients participate in this study with informed consent. Exclusion Criteria: The patients couldn't performed MSCT or MR scanning or artefacts affect the evaluation. The patients are extremely anxious and uncooperative about surgery or neoadjuvant therapy . PatientsThe patients refuse to participate in the project. Other situations considered by investigators not meet the inclusion criteria.

Ages Eligible for Study
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Minimum Age: 18 Years
Maximum Age: 80 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

| Intervention/Treatment |
| --- |
|Other: No intervention|No intervention|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Study of relationship between clinical related data(driving genes and response) and imaging features(MSCT and MRI) in lung Cancer | Retrospectively reviewed data for patients diagnosed with lung cancer . All patients had received a histopathologic diagnosis of lung cancer based on bronchoscopic, percutaneous needle-guided, or surgical biopsies and had undergone gene mutation studies. Analysed the relationship between clinical related data(driving genes and response) and imaging features. | up to 2 year | 
| MSCT and MRI prediction of prognosis in lung cancer | To construct a model,a depth convolution neural network based on MSCT and multi-modal MR quantitative images which can automatically mine key images characterization, combined with imaging features,driving genes and prognosis,could further help to improve the prediction of response and OS of lung cancer treated with systematic therapy . | up to 2 year | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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Lung Cancer, Imaging features, Driving genes, Prediction, Therapy response