Early diagnosis of lung cancer is often challenging, as it typically involves a series of comprehensive tests following an initial positive screening result. However, recent advancements in molecular diagnostics, coupled with machine learning techniques, are helping to improve this process. A noteworthy example of these efforts is the CyPath Lung test, which shows promise in streamlining the screening process and facilitating the early detection of lung cancer.
This blog discusses the details of the CyPath Lung test and its clinical applications in the early detection of lung cancer.
A dire need for early diagnosis of lung cancer
Lung cancer is a type of cancer that results from the uncontrolled multiplication of cells in the lungs. Globally, the number of new cases of lung cancer is rising and it is the leading cause of cancer-related deaths [1]. Lung cancer can be treated with current treatment options if it is diagnosed earlier at a stage when it has not spread to other parts of the body. Therefore, early diagnosis of lung cancer is crucial for successful treatment outcomes and for improving the chances of survival.
Current screening for lung cancer
Individuals at high risk of lung cancer, such as heavy smokers or those with a family history of lung cancer, are recommended to undergo an annual low-dose computed tomography (LDCT) screening test [2]. It is a type of X-ray examination that uses low levels of radiation to acquire detailed images of the lungs, allowing clinicians to examine them for small nodules or other abnormalities that are possible indicators of early-stage lung cancer.
LDCT is a convenient test that takes only a few minutes and has a high sensitivity; however, it has a low specificity [3]. Sensitivity is the test’s ability to correctly identify the individuals who have the disease (true positives). It is the proportion of people with the disease who test positive for it. On the other hand, specificity is the test’s ability to correctly identify the individuals who do not have the disease (true negatives). It is the proportion of people without the disease who test negative for it.
The low specificity of LDCT means that it is more likely to produce a false positive result and thus a positive result needs further investigations to confirm whether the individual has cancer or not. These additional tests include extensive imaging examinations and a biopsy which have low patient compliance and high operational cost making them unattractive in most settings. Therefore, better options are needed to confirm lung cancer in the context of a positive LDCT result.
What is cypath lung test?
Recently, a group of researchers has developed a sputum-based screening test, called CyPath Lung test, that can assist physicians in detecting cancer when small to intermediate-sized lung nodules are present [4]. This test combines flow cytometry and machine learning to identify lung cancer with high accuracy. The specimen required for the test is sputum, which presents a snapshot of the tumor itself. It consists of a variety of immune cells, cancer cells, and non-cancer cells.
The same group of scientists had previously reported a method to detect early lung cancer from sputum samples but that method required a closer examination of the slides by an expert pathologist under a microscope and thus was time-consuming and had a predilection to observer bias. But in CyPath Lung test, they have used a technique called a high-throughput approach using automated flow cytometry (FCM) to accelerate the analysis of sputum samples and used machine learning to evaluate the results.
How was cypath developed?
The researchers enrolled 216 participants and divided them into 2 groups; the non-cancer group and cancer group:
In the non-cancer group, the participants had a high risk of lung cancer due to their history of smoking but they had received a negative result from LDCT or another similar screening test.
Whereas, the participants of the cancer group had a confirmed diagnosis of lung cancer based on a positive LDCT, imaging tests, and finally a biopsy.
All the enrolled participants submitted their sputum samples for 3 consecutive days in a cup. Before this activity, they were trained on how to expel their sample by coughing into a specimen cup and storing it in a refrigerator.
After the collection of specimens, researchers studied the sputum samples using flow cytometry to look for specific cell types or indicators that might be associated with early lung cancer. To ensure that their findings were not influenced by a specific machine or research team, they used 2 distinct kinds of flow cytometers: Laser Scanning Cytometer - II and Navios EX.
The data obtained during the flow cytometry analysis was subsequently utilized by the researchers to construct a machine-learning model. The objective of the model was to identify patterns in the data that could be used to determine from a person's sputum sample's characteristics if they had lung cancer.
What is its clinical application?
CyPath Lung is a non-invasive, sputum-based test that can be used after a positive LDCT test for the early diagnosis of lung cancer. It has 82% sensitivity and 88% specificity for detecting lung cancer in its early stages (I and II) and in cases with nodules less than 20 mm in size. Moreover, it is robust to differences in sample handling which makes it suitable for clinical use.
Some important limitations of this study are the underrepresentation of females and minorities in the sample set as well as a lack of long-term follow-up of non-cancer participants to confirm they were indeed lung cancer-free. A prospective clinical trial with a larger sample size is required to address these limitations.
References
1. B. C. Bade and C. S. Dela Cruz, “Lung Cancer 2020: Epidemiology, Etiology, and Prevention,” Clin Chest Med, vol. 41, no. 1, pp. 1–24, Mar. 2020, doi: 10.1016/j.ccm.2019.10.001.
2. A. Bonney et al., “Impact of low-dose computed tomography (LDCT) screening on lung cancer-related mortality,” Cochrane Database Syst Rev, vol. 8, no. 8, p. CD013829, Aug. 2022, doi: 10.1002/14651858.CD013829.pub2.
3. A. Sadate et al., “Systematic review and meta-analysis on the impact of lung cancer screening by low-dose computed tomography,” Eur J Cancer, vol. 134, pp. 107–114, Jul. 2020, doi: 10.1016/j.ejca.2020.04.035.
4. M. E. Lemieux et al., “Detection of early-stage lung cancer in sputum using automated flow cytometry and machine learning,” Respir Res, vol. 24, no. 1, p. 23, Jan. 2023, doi: 10.1186/s12931-023-02327-3.
