Background The traditional Tumor-Node-Metastasis (TNM) staging system for solid tumors relies on anatomical assessment but possesses inherent limitations in capturing systemic micrometastases and molecular-level disease burden. Recently, the Tumor-Node-Metastasis-Blood (TNMB) staging system has garnered significant attention. It aims to provide a systematic synthesis of the origin of the TNMB staging system, its current applications in tumors, and its future prospects, while evaluating its potential value in enhancing early diagnosis, prognostic assessment, and the precision of therapeutic decision-making. Methods A systematic literature search was conducted in PubMed and Web of Science databases from 2018 to 2026 using keywords related to neoplasms and TNMB staging system. Articles were screened for inclusion, and 21 studies focusing on cutaneous T-cell lymphoma and lung cancer were included for analysis. Results The TNMB staging system enhances risk stratification and prognostic accuracy in both cutaneous T-cell lymphoma and non-small cell lung cancer. In cutaneous lymphoma, TNMB staging system correlates with disease progression, treatment response, and molecular biomarkers. In lung cancer, integrating circulating tumor DNA (ctDNA) into TNMB staging system improves recurrence prediction and guides adjuvant therapy decisions, outperforming traditional TNM staging in prognostic discrimination. Beyond CTCL and non-small cell lung cancer (NSCLC), the utility of key biomarkers such as ctDNA for refining staging precision suggests a broader potential for the TNMB framework, even in cancers where its formal application is still forthcoming. Conclusion The TNMB staging system represents a transformative approach in oncology by incorporating blood-based molecular data into traditional anatomical staging. It enables earlier detection of micrometastases, improves risk stratification, and supports personalized treatment strategies. Despite challenges in standardization and clinical integration, TNMB staging system holds significant promise for advancing precision oncology across multiple cancer types.
Li et al. (2026) studied this question.