A Class XII student from Hyderabad has developed an artificial intelligence (AI)-assisted model for the early detection of lung cancer, combining DNA methylation biomarkers with machine learning to improve the accuracy of non invasive cancer screening. The research highlights the growing potential of AI-driven diagnostics in identifying cancer at earlier, more treatable stages.

The study, titled “DNA Methylation Biomarkers for Lung Cancer Detection: An AI-Driven Approach Using EGFR, PD-L1, SHOX2, RASSF1A and PTGER4,” was developed by Ansh Kumar and analysed data from more than 7,000 patient samples. The model integrates multi-gene biomarker profiling with artificial intelligence-based prediction algorithms to identify molecular changes associated with lung cancer, offering a data-driven approach to early diagnosis.

The research focuses on DNA methylation, an epigenetic process in which chemical modifications alter gene activity without changing the DNA sequence. Abnormal methylation patterns in genes such as EGFR, PD-L1, SHOX2, RASSF1A and PTGER4 have previously been associated with the development and progression of lung cancer. By combining these biomarkers with machine learning, the model aims to improve the sensitivity and specificity of early cancer detection.

Lung cancer remains one of the world’s deadliest cancers. According to the World Health Organization (WHO), it is the leading cause of cancer related deaths globally, accounting for nearly 1.8 million deaths each year. In India, the Indian Council of Medical Research (ICMR) estimates that lung cancer is among the most common cancers in men, with thousands of new cases diagnosed annually. Early diagnosis significantly improves survival rates, yet many patients continue to be diagnosed only after the disease has reached an advanced stage.

Researchers and healthcare experts increasingly view artificial intelligence as a transformative tool in oncology. AI algorithms can rapidly analyse complex genomic datasets, detect subtle molecular patterns and assist clinicians in identifying high risk patients who may benefit from further diagnostic evaluation. Integrating AI with biomarker based screening also has the potential to reduce reliance on invasive procedures and support more personalized treatment strategies.

The study contributes to the growing field of precision medicine, where genetic and molecular information is used to tailor disease detection and clinical decision making. While further clinical validation and large scale studies will be necessary before the model can be adopted in routine practice, experts believe such innovations could play an important role in the future of cancer diagnostics.

The development also highlights the increasing contribution of young researchers to healthcare innovation. By combining genomics, artificial intelligence and data analytics, the project demonstrates how emerging technologies can support earlier disease detection and improve long term outcomes for patients with lung cancer.