Study of CT and MR in the Lung Cancer

NCT ID: NCT04034667

Last Updated: 2020-04-01

Study Results

Results pending

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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Recruitment Status

UNKNOWN

Total Enrollment

400 participants

Study Classification

OBSERVATIONAL

Study Start Date

2019-09-01

Study Completion Date

2023-12-01

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.

Conditions

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Lung Cancer Squamous Cell CT Genes Response MRI

Study Design

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Observational Model Type

CASE_CONTROL

Study Time Perspective

PROSPECTIVE

Interventions

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No intervention

No intervention

Intervention Type OTHER

Eligibility Criteria

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Inclusion Criteria

1. Consecutive patients with preoperative pathologically con-firmed lung cancer by endoscopy and preoperative imaging data were included.
2. No contraindications for MRI examination. No contraindications for iodinated contrast.
3. The patients participate in this study with informed consent.

Exclusion Criteria

1. The patients couldn't performed MSCT or MR scanning or artefacts affect the evaluation.
2. The patients are extremely anxious and uncooperative about surgery or neoadjuvant therapy .
3. PatientsThe patients refuse to participate in the project.
Minimum Eligible Age

18 Years

Maximum Eligible Age

80 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

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Henan Cancer Hospital

OTHER_GOV

Sponsor Role lead

Responsible Party

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Responsibility Role SPONSOR

Locations

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Henan Cancer Hospital

Zhengzhou, , China

Site Status RECRUITING

Countries

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China

Facility Contacts

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Baoxia He, MD

Role: primary

8637165588007

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.

Reference Type BACKGROUND
PMID: 31307024 (View on PubMed)

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.

Reference Type BACKGROUND
PMID: 27638103 (View on PubMed)

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.

Reference Type BACKGROUND
PMID: 31285150 (View on PubMed)

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.

Reference Type BACKGROUND
PMID: 30880291 (View on PubMed)

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.

Reference Type BACKGROUND
PMID: 29622078 (View on PubMed)

Other Identifiers

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FSK003

Identifier Type: -

Identifier Source: org_study_id

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