In a recent study published in The Lancet Digital Health, researchers discuss the event and validation of a combined model comprising imaging, clinical, and cell-free deoxyribonucleic acid (DNA) methylation biomarkers for improved classification of pulmonary nodules and the sooner diagnosis of lung cancer.

Study: Accurate classification of pulmonary nodules by a combined model of clinical, imaging, and cell-free DNA methylation biomarkers: a model development and external validation study. Image Credit: create jobs 51 / Shutterstock.com

Background

Lung cancer accounts for a considerable portion of cancer-associated mortality worldwide. Despite significant progress within the treatment of lung cancer, including chemotherapy, immunotherapy, surgical resection, targeted therapy, and radiotherapy, the prognosis for lung cancer patients stays poor.

The first cause for the poor prognosis of lung cancer patients is late diagnosis. In actual fact, lung cancer is commonly diagnosed when the disease has progressed to stage III or IV, with five-year survival rates for late-stage cancers below 10%.

The early detection of lung cancer, when the disease is within the curable stages of 0–II, can significantly reduce mortality rates. Nevertheless, the shortage of sensitive technologies that may detect lung cancer at early stages, comined with the absence of clinical symptoms within the early stages of lung cancer, are major challenges.

DNA methylation biomarkers are a promising approach for the early detection of lung cancer, as evidence from various studies indicates that DNA methylation in promoter CpG islands and other specific regions indicate events related to the initiation of tumors. Moreover, the detection of methylation patterns in circulating tumor DNA using next-generation sequencing methods might be used to non-invasively screen for lung cancer.

Low-dose computerized tomography (LDCT) has been effective within the early detection of lung cancer in high-risk populations. Nevertheless, determining the malignancy risk of pulmonary nodules using LDCT stays difficult.

Concerning the study

In the current study, researchers develop a combined model of clinical and imaging biomarkers (CIBM) that uses machine learning algorithms, in addition to imaging and clinical features, to categorise malignant and benign pulmonary nodules. When combined with a model called PulmoSeek, which is a cell-free DNA methylation model previously designed by the identical team of scientists, the CIBM model can detect small-sized pulmonary nodules to ultimately classify lung cancer within the early stages.

Study participants were recruited through a masked, retrospective evaluation study for prospective sample collection from hospitals across 20 Chinese cities. Individuals included within the study were 18 years or older, with 5-30 millimeter (mm) pulmonary nodules that were solitary and non-calcified, in addition to solid, part-solid, or pure ground-glass nodules.

A cohort of over 800 samples was used to coach the machine-learning algorithm of the CIBM model to categorise benign and malignant tumors. The CIBM model was then integrated with PulmoSeek to create a combined model called PulmoSeek Plus.

A call curve evaluation was applied to judge the clinical use of the model. High and low cut-offs for prime sensitivity and high specificity, respectively, were used to categorise pulmonary nodules into low-, medium-, and high-risk groups. The examined primary end result was the performance and diagnostic ability of the three models PulmoSeek, CIBM, and PulmoSeek Plus.

Study findings

The PulmoSeek Plus model has the potential to successfully diagnose pulmonary nodules as benign or malignant within the early stages. When combined with LDCT, PulmoSeek Plus might be a sturdy tool for the early clinical assessment and management of lung cancer. Furthermore, the one requirements for the integrated model were non-invasively collected blood samples and CT images.

Combining CIBM with the PulmoSeek model increased the sensitivity of the classification of pulmonary nodules by 6% and negative predictive value by 24%. Moreover, the performance of the model was robust across pulmonary nodules of differing types, sizes, and stages.

The sensitivities of characterization for early-stage nodules, in addition to those smaller than one centimeter in size were 0.98 and 0.99, respectively. For sub-solid nodules, that are difficult to characterize using LDCT results alone, the characterization sensitivity was 100%.

Conclusions

The integrated PulmoSeek Plus model incorporates imaging, clinical, and cell-free DNA methylation biomarkers, in addition to a machine-learning algorithm, for the early detection and classification of pulmonary nodules.

The validation of this model using independent cohorts confirms the high sensitivity and robust performance of PulmoSeek Plus across a spread of samples. When combined with LDCT, PulmoSeek Plus could facilitate the early detection of lung cancers, thus improving the prognosis for a lot of lung cancer patients.