Dear editorial,
Cervical cancer is presently a global health challenge for all, particularly in low- and middle-income countries. A total of 661,000 new cases and 348,000 deaths were reported in the year of 2022 alone, highlighting an active need for effective prevention, screening, and vaccination strategies[
1]. Although there have been clear advancements in chemoradiotherapy, there remains an unsatisfactory survival outcome for patients with locally advanced cervical cancer (LACC). In this context, systemic inflammatory markers—such as the neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR), and systemic immune-inflammation index (SII)—have gained attention as inexpensive, non-invasive prognostic tools.
The study by Lee et al. through dynamic assessment of inflammatory markers is a step forward toward refining survival prediction[
2]. The authors present a new approach that focuses on post/pre-treatment changes rather than the base values by reflecting host-tumor immune dynamics and response to treatment. With increased post/pre-treatment ratios, this method achieved better risk classification correlating with poorer 2-year overall survival. Notably, hazard ratios remained significant—5.53 for NLR (
p = 0.006), 3.39 for MLR (
p = 0.007), 5.11 for systemic inflammation response index (SIRI) (
p = 0.001), and 6.57 for SII (
p = 0.005)—independent of stage and age. These biomarkers can potentially be integrated into oncology practice, especially in resource-limited regions, as they are feasible and easily accessible.
There are limitations to the study’s findings. It has a small sample size, with a retrospective design and a lack of standardized cut-off values, which limits external validation. Furthermore, systematic inflammatory markers alone cannot perceive the complex prognosis of cervical cancer, as also depends on tumor volume, nodal status, and imaging-derived characteristics. An improved correlation with progression-free survival in LACC patients evident by integration of MRI radiomics with SII[
3]. Likewise, stronger prognosis accuracy is shown by pan-immune-inflammation value (PIV) whereas individual indices are yet to be explored[
4]. Similarly, the PIV has been shown to provide stronger prognostic accuracy than individual indices yet remains underexplored[
4]. To further sharpen prognostic precision, there should be clinically meaningful threshold values—such as post-treatment NLR cutoffs of 0.830 for disease-free survival and 0.842 for overall survival.
Dynamic inflammatory markers have shown broader applicability in precision oncology, with demonstrated prognostic value in other malignancies, including lung, gastrointestinal, and breast cancers. To strengthen translational significance and help develop standardized frameworks for their use, cross-tumor validation could be conducted.
To validate the dynamic markers and explore their relationship with tumor immune microenvironment, future researchers should carry out large-scale, prospective multi-center studies. To enable adaptive treatment with improved post-treatment surveillance and personalized care for cervical cancer patients, researchers could integrate inflammatory indices with clinical, radiologic, and molecular data. In the era of immune checkpoint inhibitors, understanding and modulating systemic inflammation is critical to optimizing therapeutic outcomes. Furthermore, incorporating these parameters into artificial intelligence (AI)-assisted risk calculators may allow real-time, individualized monitoring and prediction, bridging the gap between laboratory findings and clinical oncology practice.
The Author(s) 2026. This article is published by Higher Education Press at journal.hep.com.cn.
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