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MINI REVIEW article

Front. Med.

Sec. Precision Medicine

Volume 12 - 2025 | doi: 10.3389/fmed.2025.1612376

This article is part of the Research TopicProgressive Role of Artificial Intelligence in Treatment Decision - Making in the Field of Medical OncologyView all 9 articles

Integrating Artificial Intelligence with Circulating Tumor DNA for Non-Small Cell Lung Cancer: Opportunities, Challenges, and Future Directions

Provisionally accepted
Nishanth ThalambeduNishanth Thalambedu1*Mamtha BallaMamtha Balla2Barath Prashanth SivasubramanianBarath Prashanth Sivasubramanian3Prasanth SadaramPrasanth Sadaram1Krishna Prathiba MallaKrishna Prathiba Malla4Krishna P VasipalliKrishna P Vasipalli5Sunil KakadiaSunil Kakadia6
  • 1University of Arkansas for Medical Sciences, Little Rock, United States
  • 2University of Texas MD Anderson Cancer Center, Houston, Texas, United States
  • 3Northeast Georgia Medical Center, Ganiesville, United States
  • 4Dr. NTR University of Health Sciences, Vijayawada, Andhra Pradesh, India
  • 5Indira Gandhi Medical College, Shimla, Shimla, Himachal Pradesh, India
  • 6Genesis Cancer And Blood Institute, Littlerock, United States

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Non-small cell lung cancer (NSCLC) remains a leading cause of cancer mortality, with late-stage diagnosis contributing to poor survival. Circulating tumor DNA (ctDNA) has emerged as a non-invasive biomarker for screening, diagnosis, and monitoring, with limitations about sensitivity and specificity challenges. The integration of artificial intelligence (AI) offers a promising avenue to enhance ctDNA applications in NSCLC by improving mutation detection rates and sensitivities, refining minimal residual disease (MRD) predictions, enabling earlier detection of relapse, sometimes earlier than imaging, differentiating tumor vs non-tumor derived signals to improve specificities. AI achieves 0.002% mutant allelic fraction detection, 94% relapse detection sensitivity, and 5.2-month lead time over imaging . This narrative review explores the role of ctDNA in NSCLC management, highlighting how AI amplifies its utility across screening, diagnosis, treatment evaluation, MRD detection, and disease surveillance while outlining key opportunities, challenges, and future directions.

Keywords: lung cancer, screening, Minimal Residual Disease, Ctdna (circulating tumor DNA), artificial intelligence

Received: 15 Apr 2025; Accepted: 23 May 2025.

Copyright: © 2025 Thalambedu, Balla, Sivasubramanian, Sadaram, Malla, Vasipalli and Kakadia. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Nishanth Thalambedu, University of Arkansas for Medical Sciences, Little Rock, United States

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Supplementary Material