Artificial intelligence and circulating tumor DNA minimal residual disease in perioperative resectable non-small cell lung cancer

Abstract

Despite curative-intent treatment, recurrence risk in resectable non-small cell lung cancer (NSCLC) remains difficult to define, and postoperative management still depends on imperfect clinicopathologic assessment. Circulating tumor DNA (ctDNA)-based minimal residual disease (MRD) has emerged as a promising adjunct for postoperative risk stratification, early relapse detection, and perioperative response assessment. Its clinical translation, however, is still limited by low sensitivity in low-shedding diseases, imperfect specificity from biological background signals, interpretive uncertainty, and a lack of assay standardization. In this context, artificial intelligence (AI) is relevant not as a generic add-on, but as a potential means to improve weak-signal detection, refine variant-origin assignment, and translate serial ctDNA measurements into clinically interpretable risk estimates. This review considers the current clinical role of ctDNA-MRD in perioperative NSCLC, the barriers to routine implementation, and the extent to which AI may help make molecular monitoring more reliable and clinically useful.

Keywords

Circulating tumor DNA / minimal residual disease / non-small cell lung cancer / artificial intelligence / machine learning / tumor monitoring

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