Family History of Lung Cancer
Conditions
Keywords
CT Scan, Lung cancer, Screening
Brief summary
This research is being done to determine if an image-based deep learning model (Sybil) can accurately predict the likelihood of future lung cancer based on chest computed tomography (CT) imaging from individuals.
Detailed description
This non-therapeutic study will enroll individuals who have family history of lung cancer. Participants will undergo a low-dose non-contrast computed tomography of the chest (LDCT) and may also send images from any chest CT scan(s) obtained as part of routine clinical care, outside of the study. The images and data collected will be analyzed by an image-based deep learning model (Sybil). Sybil is a type of artificial intelligence model that has been shown to accurately predict individuals' future risk of lung cancer based solely on images from a CT Chest scan, but it remains unclear whether Sybil works well in people with a family history of lung cancer. The goals of this study are: 1) to obtain CT Chest images from individuals with a family history of lung cancer in order to test whether Sybil continues to work well, and 2) offer free screening CT scans to qualifying individuals. It is expected that 250 people will take part in this research study.
Interventions
DIAGNOSTIC_TESTCT scan
Computed tomography scan
OTHERSybil
Image-based deep learning model
Sponsors
Massachusetts General Hospital
Study design
Allocation
NA
Intervention model
SINGLE_GROUP
Primary purpose
SCREENING
Masking
NONE
Eligibility
Sex/Gender
ALL
Age
18 Years to 80 Years
Healthy volunteers
No
Inclusion criteria
* Age: Must meet both the upper and lower age limit criteria. * Upper age limit: ≤80 years of age * Lower age limit: * ≥40 years of age OR * ≥18 years of age AND ≤10 years of youngest relative's age at time of lung cancer diagnosis (e.g., if a relative was diagnosed at 35 years of age, participant can enroll at ≥25 years of age) * Positive family history of lung cancer (defined as): * Has ≥1 first-degree relative, OR * Has ≥2 second-degree relatives with a diagnosis of non-small cell lung cancer or small cell lung cancer (NB: a first-degree relative = parent, sibling, or child, a second-degree relative = grandparent, blood-related aunt or uncle, grandchild, blood-related niece or nephew, half-sibling)
Exclusion criteria
* Must not have a personal history of lung cancer at the time of enrollment. * Must not have a personal history of stage IV cancer of any type at the time of enrollment. * Must not have had surgical removal of any portion of the lung, excluding needle or core lung biopsy at the time of enrollment. * Must not have had a chest CT within 12 months prior to trial enrollment.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Sybil's performance in predicting future lung cancer diagnoses | Annually, from time of initial CT scan to up to 5 years after the scan. | All subjects will be followed for lung cancer diagnosis scan for up to 5 years following the baseline scan. Sybil's performance in predicting future lung cancer diagnoses across the study population will be calculated using the area under the receiver operating curve (AUROC), which is a measure of a risk prediction model's ability to discriminate between cases and controls. Sybil's output corresponds to the cumulative annual risk of lung cancer for up to 6 years following a given scan. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Compare the distribution of Sybil lung cancer risk scores in this trial to the distribution of Sybil risk scores from the NLST clinical trial | Initial provided CT scan will represent time 0. Additional provided CT scans will vary between individuals and will be measured in years relative to time 0 (e.g., time -3.5 years, time +2 years, etc). Sybil risk scores will be calculated for each scan. | Investigators will compare the distribution of Sybil scores (ranging from 0-1) from participants in this study with the distribution of Sybil scores from historical data from participants in the National Lung Screening Trial. |
| Incidence and prevalence of lung cancer in the study population | Annually, from time of initial CT scan to up to 5 years after the scan. | Investigators will estimate the incidence and prevalence of lung cancer in the LEGACY population. Incidence will be reported per person per year. Prevalence will be reported separately as a measure over the 5-year study follow up period. |
| Incidence of lung nodules in this population | Annually, from time of initial CT scan to up to 5 years after the scan. | Investigators will estimate the incidence of lung nodules in the LEGACY population. Incidence will be measured per person per year. |
| Prevalence of lung nodules in this population | Annually, from time of initial CT scan to up to 5 years after the scan. | Investigators will estimate the prevalence of lung nodules in the LEGACY population. This will be measured over the 5-year study follow up period. |
| Describe the characteristics of lung nodules in this population | At time of each provided CT scan to up to 5 years after the scan. | Investigators will describe the characteristics of lung nodules in the study population, including but not limited to size, location, and attenuation. |
Countries
United States
Contacts
CONTACTAllison Chang, MD
PRINCIPAL_INVESTIGATORAllison Chang, MD
Massachusetts General Hospital
Outcome results
None listed