Prognostic utility of inflammatory indices and circulating nucleic acids for neoadjuvant chemotherapy outcomes in breast cancer: a systematic review
Bibliographic record
Abstract
Background: Neoadjuvant chemotherapy (NAC) plays a central role in the management of early breast cancer (BC), offering prognostic information and improving surgical outcomes. Blood-based biomarkers, including inflammatory markers-such as neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR)-and circulating nucleic acids-such as circulating tumor DNA (ctDNA), cell-free DNA (cfDNA), and cfDNA integrity index (cfDI)-have been investigated for their potential to predict treatment response. Objectives: To systematically evaluate the prognostic value of inflammatory indices and circulating nucleic acids measured during NAC for predicting pathological complete response (pCR) in early BC. Design: Systematic review with qualitative synthesis. Data sources and methods: Twenty-four studies were included, examining changes in NLR, PLR, ctDNA, cfDNA, and cfDI during NAC. Study quality was assessed using the Newcastle-Ottawa Scale, and the strength of evidence was evaluated with a qualitative GRADE approach. Due to heterogeneity in biomarker definitions, sampling timepoints, and statistical methods, data were synthesized narratively. Results: A reduction in PLR from baseline to mid-NAC (ΔPLR <0) was consistently associated with higher pCR rates. ctDNA clearance, particularly at mid- and post-NAC, was frequently linked to increased pCR and better overall survival. In contrast, findings on NLR dynamics were inconsistent. Evidence for cfDNA and cfDI was limited and mixed, with some studies suggesting lower cfDNA and higher cfDI may be associated with improved outcomes. Variability in biomarker thresholds and timepoints was a common limitation. Conclusion: Mid-NAC PLR decrease and ctDNA clearance show moderate evidence as predictors of pCR and may help guide treatment decisions. The prognostic value of NLR, cfDNA, and cfDI remains uncertain and requires further prospective, standardized investigation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".