Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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UNKNOWN
400 participants
OBSERVATIONAL
2019-09-01
2023-12-01
Brief Summary
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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.
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Detailed Description
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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.
Conditions
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Study Design
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CASE_CONTROL
PROSPECTIVE
Interventions
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No intervention
No intervention
Eligibility Criteria
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Inclusion Criteria
2. No contraindications for MRI examination. No contraindications for iodinated contrast.
3. The patients participate in this study with informed consent.
Exclusion Criteria
2. The patients are extremely anxious and uncooperative about surgery or neoadjuvant therapy .
3. PatientsThe patients refuse to participate in the project.
18 Years
80 Years
ALL
No
Sponsors
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Henan Cancer Hospital
OTHER_GOV
Responsible Party
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Locations
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Henan Cancer Hospital
Zhengzhou, , China
Countries
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Facility Contacts
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References
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Shi L, Rong Y, Daly M, Dyer B, Benedict S, Qiu J, Yamamoto T. Cone-beam computed tomography-based delta-radiomics for early response assessment in radiotherapy for locally advanced lung cancer. Phys Med Biol. 2020 Jan 10;65(1):015009. doi: 10.1088/1361-6560/ab3247.
Lee G, Lee HY, Park H, Schiebler ML, van Beek EJR, Ohno Y, Seo JB, Leung A. Radiomics and its emerging role in lung cancer research, imaging biomarkers and clinical management: State of the art. Eur J Radiol. 2017 Jan;86:297-307. doi: 10.1016/j.ejrad.2016.09.005. Epub 2016 Sep 10.
Akinci D'Antonoli T, Farchione A, Lenkowicz J, Chiappetta M, Cicchetti G, Martino A, Ottavianelli A, Manfredi R, Margaritora S, Bonomo L, Valentini V, Larici AR. CT Radiomics Signature of Tumor and Peritumoral Lung Parenchyma to Predict Nonsmall Cell Lung Cancer Postsurgical Recurrence Risk. Acad Radiol. 2020 Apr;27(4):497-507. doi: 10.1016/j.acra.2019.05.019. Epub 2019 Jul 6.
Seki S, Fujisawa Y, Yui M, Kishida Y, Koyama H, Ohyu S, Sugihara N, Yoshikawa T, Ohno Y. Dynamic Contrast-enhanced Area-detector CT vs Dynamic Contrast-enhanced Perfusion MRI vs FDG-PET/CT: Comparison of Utility for Quantitative Therapeutic Outcome Prediction for NSCLC Patients Undergoing Chemoradiotherapy. Magn Reson Med Sci. 2020 Feb 10;19(1):29-39. doi: 10.2463/mrms.mp.2018-0158. Epub 2019 Mar 18.
Ciliberto M, Kishida Y, Seki S, Yoshikawa T, Ohno Y. Update of MR Imaging for Evaluation of Lung Cancer. Radiol Clin North Am. 2018 May;56(3):437-469. doi: 10.1016/j.rcl.2018.01.005.
Other Identifiers
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FSK003
Identifier Type: -
Identifier Source: org_study_id
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