Lung Cancer
Conditions
Brief summary
Overview Sierra Medical is at the forefront of early detection of lung cancer by creating AIR-DS, a non-invasive early detection test. This innovative approach aims to significantly enhance the early identification of lung cancer, potentially catching it at its most treatable stages through a simple cheek swab. How it works The effectiveness of AIR-DS stems from its ability to identify small biochemical changes in cells from the inner cheek. These biochemical changes can serve as early indicators of lung cancer. The procedure involves taking a cheek swab, which is then analysed using non-damaging infrared light technology. The RADICAL REACT study To introduce this technology into a healthcare setting the sponsor needs to validate its effectiveness through rigorous testing. The RADICAL REACT trial plans to involve around 450 participants highly suspected to have lung cancer. Each participant will provide a cheek swab and basic medical history information during a single clinic visit. The data collected will be analysed with AIR-DS to identify whether individuals with lung cancer can be identified accurately. Why it matters AIR-DS could significantly advance lung cancer detection, focusing on early, accurate diagnosis through a non-invasive cheek swab. Beyond improving patient outcomes by enabling timely intervention, it also introduces a cost-effective approach to early lung cancer detection. AIR-DS aims to alleviate the financial burden on healthcare systems and patients by reducing the need for more expensive and/or invasive diagnostic procedures.
Interventions
DIAGNOSTIC_TESTAIR-DS
AIR-DS is a supportive, non-diagnostic, risk-indicating software that assists healthcare providers in the early detection and risk assessment of lung cancer in high-risk populations, aiming to improve the cost-effectiveness and accessibility of lung cancer screening. It is imperative to note that the efficacy of the AIR-DS may be affected by the completeness and accuracy of the input data. Hence, any missing data fields should be identified, as they could impact the risk assessment's accuracy.
Sponsors
Queen Alexandra Hospital
Sierra Medical Ltd.
Study design
Observational model
OTHER
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
Yes
Inclusion criteria
To be eligible to participate in this study, study participants must meet all the following criteria: * Can understand and provide signed consent. * Are aged 18 years or older. * Can provide a cheek swab sample. * Are symptomatic of, and/or are being investigated for, lung cancer. * Have not commenced treatment for lung cancer.
Exclusion criteria
Study participants who meet any of the following criteria will be excluded from participation in this study: * Are pregnant. * Have been diagnosed with another known malignancy within five years (excluding localised squamous cell carcinoma of the skin, cervical intraepithelial neoplasia, grade III and low-grade prostate cancer (Gleason score \<5 with no metastases)). * Have a concurrent disease, medical condition, or extenuating circumstances that, in the opinion of the investigator, might compromise study completion or data collection, including: 1. Localised oral disease, including but not limited to periodontitis, bleeding gums, dysplastic mouth disease. 2. Taking anti-coagulants. 3. Alpha 1 anti-trypsin deficiency. 4. Active infections such as tuberculosis. 5. Conditions that in the opinion of the investigator are not controlled e.g. hypertension, asthma, chronic kidney disease, bowel disease, etc.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Sensitivity | Day 1 | Evaluate the sensitivity of AIR-DS pre-specified algorithm in identifying lung cancer when compared to a clinical diagnosis in a high-incidence lung cancer population. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Specificity, PPV, NPV | Day 1 | Evaluate the specificity, positive and negative predictive values of AIR-DS pre-specified algorithm in identifying lung cancer compared to a clinical diagnosis of lung cancer, in a high-incidence lung cancer population. |
| Predictive performance of refined predictive algorithm | 12 months after the last participant is recruited | Predictive performance (e.g. accuracy, sensitivity, specificity, NPV, PPV, AUC) classifying lung cancer as compared with a clinical diagnosis, in a high-risk lung cancer population using the updated algorithm following refinement of the predictive algorithm and cutting points of AIR-DS |
| Performance of predictive algorithm across lung cancer type and stage | Day 1 | Predictive performance (e.g. accuracy, sensitivity, specificity, NPV, PPV) of AIR-DS pre-specified algorithm compared to a clinical diagnosis of lung cancer in participants with: no lung cancer vs. NSCLC; no lung cancer vs. SCLC; no lung cancer vs. stage 1 lung cancer; and no lung cancer vs. stage 2 lung cancer. |
Countries
United Kingdom
Contacts
Primary ContactKim Ibsen
Outcome results
None listed