# Can posttreatment blood inflammatory markers predict poor survival in gynecologic cancer?: a systematic review and meta-analysis

## TL;DR

This study finds that blood inflammatory markers after cancer treatment can predict survival outcomes in gynecologic cancers, with early sampling being most effective.

## Contribution

The study introduces the prognostic value of posttreatment inflammatory markers and optimal sampling timing in gynecologic cancers.

## Key findings

- Posttreatment PBIMs (NLR, PLR, MLR, SII, SIRI) significantly associate with survival outcomes like OS, PFS, and DFS.
- Sampling within one month after treatment shows stronger prognostic significance compared to later sampling.
- Dynamic changes in PBIMs using threshold-defined methods provide more consistent results than directional changes.

## Abstract

Peripheral blood inflammatory markers (PBIMs) are widely used for prognostication of several malignancies, including gynecologic cancers. However, most studies do not report when PBIMs have been sampled, and the ones that do usually use pretreatment levels. Considering their potential to reflect the host immune status, posttreatment PBIMs and their dynamic changes from pretreatment levels may also carry prognostic information. A systematic review and meta-analysis were conducted to identify the prognostic value of posttreatment PBIMs and their dynamic changes from baseline in gynecologic cancers. Furthermore, among the inconsistent blood draw timing and analytical methods, we aimed to suggest the most suitable strategies in the clinical setting.

Fourteen eligible studies comprising 2,373 patients with cervical, ovarian, or endometrial cancer were included. The associations between survival outcomes, including overall survival (OS), progression-free survival (PFS), and disease-free survival (DFS), and the PBIMs were extracted or estimated. The PBIMs included the neutrophil-to-lymphocyte ratio (NLR), the platelet-to-lymphocyte ratio (PLR), the monocyte-to-lymphocyte ratio (MLR), the systemic immune-inflammation index (SII), and the systemic inflammation response index (SIRI). Subgroup analyses examined early versus late posttreatment sampling, as well as dynamic assessments based on threshold-defined change (increase or decrease) versus simple directional change (high or low).

All PBIMs (NLR, PLR, MLR, SII, and SIRI) demonstrated significant association with relevant survival endpoints (OS, PFS, and DFS). Early sampling of within one month after treatment completion (≤ median 15 days) showed prognostic significance (pooled hazard ratios 3.43–3.55; p < 0.0001), whereas late sampling demonstrated no significant associations. Dynamic classification using specific thresholds yielded more consistent and less heterogeneous estimates than directionality-based approaches.

This meta-analysis demonstrates the prognostic potential of posttreatment PBIMs and their dynamic change from baseline in gynecologic cancers. Sampling within one month after therapy was significantly associated with prognosis, which may reflect the importance of sampling time in relation to the different recovery times by immune cell compartments. However, considering the heterogeneity of confounders between studies, the results should be interpreted with caution. These findings warrant the need for further studies to standardize PBIM assessment in clinical practice.