Adaptive therapy for perioperative non–small cell lung cancer: strategies guided by dynamic minimal residual disease adjustment

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Authors: Li-Jin Xie, FU Li-li, Shu-Cen Liu, Changsen Bai, B.X. Xu, Xiong-Wen He, Haifeng Lin, Yue-Can Zeng, Wenjun Tang
Cited by: 4
References: 77

Abstract

Lung cancer remains the leading cause of cancer incidence and mortality worldwide, with non-small cell lung cancer (NSCLC) accounting for about 85% of cases. The low rate of early diagnosis and the high rate of occult metastases limit the survival benefits of conventional treatments. The current TNM staging system fails to fully reflect tumor heterogeneity or the dynamic molecular evolution of the disease, thus affecting the prediction of recurrence and the prognostic stratification. Some recent advances in minimal residual disease (MRD) detection, such as ultra-sensitive liquid biopsy technologies, have largely overcome the limitations of traditional imaging and offered a transformative approach for continuous, precision-based management of lung cancer. This review systematically summarized the technological evolution of MRD detection and highlighted its clinical significance in guiding adaptive therapy for NSCLC, including treatment escalation, de-escalation, and the emerging concept of precision-guided drug holidays. Moreover, the authors comprehensively discussed the "Four-Dimensional TNMB Staging System," which incorporates continuous molecular monitoring to address the static limitations of conventional staging and enhance the accuracy of prognostic stratification. Although ongoing challenges, such as the lack of standardized interpretation criteria and limited detection sensitivity, the combinations with the third-generation liquid biopsy platforms, multi-omics analyses, and multi-center prospective validation studies are expected to advance the clinical implementation of MRD-guided strategies. The paradigm change will enable the transition of NSCLC management from conventional standardized models to a precision-guided, closed-loop system of "monitoring-intervention-remonitoring," establishing a solid theoretical and practical foundation for comprehensive, molecularly driven management strategies.

Fields of Study & Subjects

Lung Cancer Diagnosis and Treatment Radiomics and Machine Learning in Medical Imaging Lung Cancer Research Studies

Publication details

Journal / source
Translational Oncology
Publisher
Elsevier BV
Volume
64
Pages
102660–102660
Publication date
6 January 2026
Language
EN