Study of CT and MR in the Lung Cancer

NCT04034667 | Unknown

• 1 other identifier: FSK003
• Study Type: observational
• Target: 400
• Locations: 1 country
• Sites: 1

Brief Summary

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.

Conditions

Lung Cancer Squamous Cell; CT; Genes; Response; MRI

Keywords

Lung Cancer; Imaging features; Driving genes; Prediction; Therapy response

Outcome Measures

Primary Outcomes (2)

• 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

Interventions

No intervention OTHER

No intervention

Eligibility Criteria

• Age: 18 Years - 80 Years
• Sex: all
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Probability Sample

Study Population

Subjects with biopsy-proven lung cancer will receive treatment

You may qualify if:

• 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.

You may not qualify if:

• 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.

Sponsors & Collaborators

• Henan Cancer Hospital: lead

Study Sites (1)

Henan Cancer Hospital

Zhengzhou, China

RECRUITING

Related Publications (5)

• 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.

  PMID: 31307024 BACKGROUND

• 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.

  PMID: 27638103 BACKGROUND

• 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.

  PMID: 31285150 BACKGROUND

• 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.

  PMID: 30880291 BACKGROUND

• 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.

  PMID: 29622078 BACKGROUND

MeSH Terms

Conditions

Lung Neoplasms

Condition Hierarchy (Ancestors)

Respiratory Tract Neoplasms; Thoracic Neoplasms; Neoplasms by Site; Neoplasms; Lung Diseases; Respiratory Tract Diseases

Study Design

• Study Type: observational
• Observational Model: CASE CONTROL
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER GOV
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: July 17, 2019
• First Posted: July 26, 2019
• Study Start: September 1, 2019
• Primary Completion: December 1, 2023
• Study Completion: December 1, 2023
• Last Updated: April 1, 2020

Record last verified: 2019-07

Locations

CN (1)