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Clinical Trial Details — Status: Recruiting

Administrative data

NCT number NCT03394703
Other study ID # klucas2017
Secondary ID
Status Recruiting
Phase N/A
First received December 19, 2017
Last updated January 3, 2018
Start date April 10, 2017
Est. completion date December 31, 2018

Study information

Verified date January 2018
Source National Cancer Center, Korea
Contact Yeol Kim, MD, PhD
Phone 82-31-920-1753
Email drheat@ncc.re.kr
Is FDA regulated No
Health authority
Study type Observational

Clinical Trial Summary

Lung cancer is by far the leading cause of cancer death and has a lower relative survival rate than other types of cancer because most lung cancers are detected at an advanced stage when they are first diagnosed.

Recently, a randomized control trial suggests that low-dose computed tomography (LDCT) enables an early stage detection and it has been increasingly accepted as an efficient screening method for high-risk individuals to reduce lung cancer mortality.

In 2011, The National Lung Screening Trial (NLST) in the U.S. has produced results that screening high-risk smoking groups (who have at least 30 pack-year smoking history and currently smoke or have quit within the past 15 years) aged 55 to 74 years with LDCT reduced lung cancer mortality by 20%.

Based on the evidence, Korean National Cancer Center has developed and published the guideline of lung cancer screening using LDCT for high-risk populations in 2015. The guideline recommends annual LDCT screening for high-risk smoking groups aged 55 to 74 years, with at least 30 pack-year smoking history and current smokers or past smokers who quit smoking within 15 years.

The Korean Lung Cancer Screening project (K-LUCAS), a nationwide, multicenter, prospective study started to evaluate the effectiveness and feasibility of lung cancer screening with LDCT for considering implementation of a national lung cancer screening program in Korea.


Description:

◎ Objective

This study is to evaluate the effectiveness and feasibility of lung cancer screening with LDCT for considering implementation of a national Lung Cancer Screening Program in Korea.

◎ Recruiting procedure

K-LUCAS involves 14 general hospitals located nationwide. The participants in K-LUCAS are recruited from the visitors in these hospitals for receiving national cancer screenings or smoking cessation services. The candidates are evaluated based on the questionnaire that is completed in prior to the national cancer screenings or smoking cessation services. Invitations will be sent to those candidates who meet our selection criteria to take part in LDCT lung cancer screening. Advertising to public including the information both of screening benefit and harm will be held in hospitals, newspaper, local bus stations and subways.

In addition to the criteria-based participant selection, a lung cancer risk prediction model will be adopted to improve the effectiveness of participant selection. The lung cancer risk prediction model considers various lung cancer risk factors in addition to age, smoking history and smoking quit duration which are already examined in the inclusion criteria. The model evaluates drinking amount, physical activity, family history of cancer, past history of lung disease and so on, in participant selection.

◎ Screening procedure

If the candidate meets the selection criteria or is approved by risk prediction model, investigators carefully explain the benefits or harms of the LDCT screening and offer them to participate in a LDCT lung cancer screening. If the candidate agrees on screening participation, an informed consent form is obtained and LDCT screening date is scheduled and confirmed. K-LUCAS also provides a smoking cessation counselling to current smokers on revisiting for the result counselling. A follow-up call is made after 6 months from the LDCT screening in order to assess smoking cessation status following LDCT screening.

◎Reporting LDCT results

The LDCT screening results are evaluated by radiologists in accordance with Lung imaging reporting and data system (Lung-RADS).

Network-based computer-aided detection (CAD) system will be used in K-LUCAS to assist reducing diagnostic errors and increasing lung nodule detection sensitivity.


Recruitment information / eligibility

Status Recruiting
Enrollment 8000
Est. completion date December 31, 2018
Est. primary completion date December 31, 2017
Accepts healthy volunteers No
Gender All
Age group 55 Years to 74 Years
Eligibility We select participants based on the following criteria.

- Inclusion Criteria (1)

- Age : 55-74 years old

- Smoking history of at least 30 pack-years

1. current smokers

2. past smokers who quit smoking within 15 years

- Inclusion Criteria (2) The lung cancer risk prediction model considers various lung risk factors in addition to age, smoking history and smoking quit duration. This includes drinking amount, physical activity, family history of cancer, information on lung disease and so on, in participant selection. Following criteria is applied when the lung cancer risk prediction model is used for participant selection.

- Age : 50-74 years old

- Smoking history of at least 20 pack-years

1. current smokers

2. past smokers who quit smoking within 15 years

- Exclusion Criteria:

- Lung cancer diagnosed and treated

- Inability to move without help (ECOG status 2 or higher)

- Have been treating regularly for tuberculosis, pneumonia and interstitial lung disease

- Treated for any cancer within the last 5 years (*Exception: Thyroid cancer, Skin cancer)

- A chest CT examination less than 6 months

Study Design


Related Conditions & MeSH terms


Locations

Country Name City State
Korea, Republic of National Cancer Center Goyang Gyeonggi

Sponsors (6)

Lead Sponsor Collaborator
National Cancer Center, Korea Korean Association for Lung Cancer, The Korea Academy of Tuberculosis and Respiratory diseases, The Korean Academy of Family Medicine, The Korean Society for Preventive Medicine, The Korean Society of Radiology

Country where clinical trial is conducted

Korea, Republic of, 

References & Publications (11)

Al Mohammad B, Brennan PC, Mello-Thoms C. A review of lung cancer screening and the role of computer-aided detection. Clin Radiol. 2017 Jun;72(6):433-442. doi: 10.1016/j.crad.2017.01.002. Epub 2017 Feb 6. Review. — View Citation

Brain K, Carter B, Lifford KJ, Burke O, Devaraj A, Baldwin DR, Duffy S, Field JK. Impact of low-dose CT screening on smoking cessation among high-risk participants in the UK Lung Cancer Screening Trial. Thorax. 2017 Oct;72(10):912-918. doi: 10.1136/thoraxjnl-2016-209690. Epub 2017 Jul 14. — View Citation

Fintelmann FJ, Bernheim A, Digumarthy SR, Lennes IT, Kalra MK, Gilman MD, Sharma A, Flores EJ, Muse VV, Shepard JA. The 10 Pillars of Lung Cancer Screening: Rationale and Logistics of a Lung Cancer Screening Program. Radiographics. 2015 Nov-Dec;35(7):1893-908. doi: 10.1148/rg.2015150079. Epub 2015 Oct 23. Review. — View Citation

Han SS, Ten Haaf K, Hazelton WD, Munshi VN, Jeon J, Erdogan SA, Johanson C, McMahon PM, Meza R, Kong CY, Feuer EJ, de Koning HJ, Plevritis SK. The impact of overdiagnosis on the selection of efficient lung cancer screening strategies. Int J Cancer. 2017 Jun 1;140(11):2436-2443. doi: 10.1002/ijc.30602. — View Citation

McMahon PM, Kong CY, Bouzan C, Weinstein MC, Cipriano LE, Tramontano AC, Johnson BE, Weeks JC, Gazelle GS. Cost-effectiveness of computed tomography screening for lung cancer in the United States. J Thorac Oncol. 2011 Nov;6(11):1841-8. doi: 10.1097/JTO.0b013e31822e59b3. — View Citation

National Lung Screening Trial Research Team, Aberle DR, Adams AM, Berg CD, Black WC, Clapp JD, Fagerstrom RM, Gareen IF, Gatsonis C, Marcus PM, Sicks JD. Reduced lung-cancer mortality with low-dose computed tomographic screening. N Engl J Med. 2011 Aug 4;365(5):395-409. doi: 10.1056/NEJMoa1102873. Epub 2011 Jun 29. — View Citation

Paci E, Puliti D, Lopes Pegna A, Carrozzi L, Picozzi G, Falaschi F, Pistelli F, Aquilini F, Ocello C, Zappa M, Carozzi FM, Mascalchi M; and the ITALUNG Working Group. Mortality, survival and incidence rates in the ITALUNG randomised lung cancer screening trial. Thorax. 2017 Sep;72(9):825-831. doi: 10.1136/thoraxjnl-2016-209825. Epub 2017 Apr 4. — View Citation

Pedersen JH, Sørensen JB, Saghir Z, Fløtten Ø, Brustugun OT, Ashraf H, Strand TE, Friesland S, Koyi H, Ek L, Nyrén S, Bergman P, Jekunen A, Nieminen EM, Gudbjartsson T. Implementation of lung cancer CT screening in the Nordic countries. Acta Oncol. 2017 Oct;56(10):1249-1257. doi: 10.1080/0284186X.2017.1329592. Epub 2017 Jun 1. — View Citation

Ten Haaf K, Tammemägi MC, Bondy SJ, van der Aalst CM, Gu S, McGregor SE, Nicholas G, de Koning HJ, Paszat LF. Performance and Cost-Effectiveness of Computed Tomography Lung Cancer Screening Scenarios in a Population-Based Setting: A Microsimulation Modeling Analysis in Ontario, Canada. PLoS Med. 2017 Feb 7;14(2):e1002225. doi: 10.1371/journal.pmed.1002225. eCollection 2017 Feb. — View Citation

Yang SC, Lai WW, Lin CC, Su WC, Ku LJ, Hwang JS, Wang JD. Cost-effectiveness of implementing computed tomography screening for lung cancer in Taiwan. Lung Cancer. 2017 Jun;108:183-191. doi: 10.1016/j.lungcan.2017.04.001. Epub 2017 Apr 4. — View Citation

Yousaf-Khan U, van der Aalst C, de Jong PA, Heuvelmans M, Scholten E, Walter J, Nackaerts K, Groen H, Vliegenthart R, Ten Haaf K, Oudkerk M, de Koning H. Risk stratification based on screening history: the NELSON lung cancer screening study. Thorax. 2017 Sep;72(9):819-824. doi: 10.1136/thoraxjnl-2016-209892. Epub 2017 Mar 30. — View Citation

* Note: There are 11 references in allClick here to view all references

Outcome

Type Measure Description Time frame Safety issue
Primary Early stage lung cancer detection rate 2 years
Primary False positive rate 2 years
Primary Cost-effectiveness of screening Lung cancer screening cost per QALY 2 years
Primary Complications of diagnostic procedure 2 years
Secondary Participation rate among eligible criteria 2 years
Secondary Positve rate of LDCT diagnosis reporting system in Korean population 2 years
Secondary Effectiveness of quality control of screening units by network-based, computer-aided detection (CAD) system Comparing the nodule positive rate and false positive rate between convetional reading process and CAD system 2 years
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