Emerging Trends in Lung Cancer Detection: Multiomics and Deep Learning Synergy

International Journal of Electronics and Communication Engineering

© 2025 by SSRG - IJECE Journal

Volume 12 Issue 3

Year of Publication : 2025

Authors : Nrupal Sankpal, Sandeep Musale, Supriya Mangale

10.14445/23488549/IJECE-V12I3P116

How to Cite?

Nrupal Sankpal, Sandeep Musale, Supriya Mangale, "Emerging Trends in Lung Cancer Detection: Multiomics and Deep Learning Synergy," SSRG International Journal of Electronics and Communication Engineering, vol. 12,  no. 3, pp. 161-170, 2025. Crossref, https://doi.org/10.14445/23488549/IJECE-V12I3P116

Abstract:

Lung cancer remains the leading cause of cancer-related deaths worldwide, with Lung Adenocarcinoma (LUAD) accounting for a significant portion of non-small cell lung cancer cases. Early detection and accurate prognosis prediction are critical for improving treatment outcomes. Recent advancements in multi-omics data integration, including genomics, transcriptomics, and histopathology, have shown promise in enhancing the prediction accuracy for LUAD. Lung cancer detection benefits significantly from machine learning models trained on multi-omics datasets, including gene expression, methylation, and mutations. Techniques such as Random Forest, SVM, and GLM have been employed to achieve robust prediction outcomes. The contribution of features was further analyzed using SHAP values. In particular, models such as LungDWM, which uses Generative Adversarial Networks (GANs) and attention-based feature encoders, have shown superior performance in diagnosing LUAD subtypes and predicting patient outcomes. This review explores the application of multiomics approaches, such as identifying key prognostic genes and developing machine learning models, to improve survival prediction and cancer staging while highlighting the current state of research, challenges, and future directions in using multi-omics for lung cancer detection and prognosis.

Keywords:

Lung cancer, Machine Learning, Multiomics, Oncology.

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