Evidence map›Paper›PMID 42064863›Full record

ArticleJournal of bone oncology2026

Development and validation of a biopsy-based model using tumor-associated neutrophils to predict neoadjuvant chemotherapy response in osteosarcoma: a large single-center retrospective cohort study.

Wanjiang Feng, Zibo Xu, Ziming Yan, Hongyu Wu, Haoyu Guo, Yuejun Luo, Weifeng Liu

Abstract read
In one paragraph

Article in Journal of bone oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

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4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Wanjiang FengDepartment of Orthopedic Oncology Surgery, Beijing Jishuitan Hospital, Capital Medical University, Beijing, China.
Zibo XuDepartment of Orthopedic Oncology Surgery, Beijing Jishuitan Hospital, Capital Medical University, Beijing, China.
Ziming YanDepartment of Orthopedic Oncology Surgery, Beijing Jishuitan Hospital, Capital Medical University, Beijing, China.
Hongyu WuDepartment of Orthopedic Oncology Surgery, Beijing Jishuitan Hospital, Capital Medical University, Beijing, China.
Haoyu GuoDepartment of Orthopedic Oncology Surgery, Beijing Jishuitan Hospital, Capital Medical University, Beijing, China.
Yuejun LuoDepartment of Orthopedic Oncology Surgery, Beijing Jishuitan Hospital, Capital Medical University, Beijing, China.
Weifeng LiuDepartment of Orthopedic Oncology Surgery, Beijing Jishuitan Hospital, Capital Medical University, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Accurately predicting response to neoadjuvant chemotherapy (NACT) in osteosarcoma remains challenging, as the gold standard-tumor necrosis rate can only be assessed postoperatively. The tumor immune microenvironment, accessible via pretreatment biopsy, holds promise for early prediction, but the specific role of tumor-associated neutrophils (TANs) in this setting is unexplored. Methods: TANs abundance in pretreatment biopsies was quantified as CD66b Results: A total of 168 patients were analysed. Higher TANs density was independently associated with poor NACT response (adjusted odds ratios for Q2-Q4: 4.41-8.12; all P < 0.05). A predictive model incorporating TANs density demonstrated good discrimination (training AUC: 0.75; testing AUC: 0.74), calibration (Hosmer-Lemeshow P = 0.42 in training, P = 0.306 in testing), and clinical utility across risk thresholds of 30-80%. Survival analysis indicated that elevated TANs density correlated significantly with worse overall survival (P = 0.038) and recurrence-free survival (P = 0.026), but not with metastasis-free or disease-free survival. Conclusion: This study is the first to confirm that TANs density in osteosarcoma biopsy specimens is negatively correlated with NACT response. The predictive model constructed based on this finding enables pretreatment risk stratification and early intervention in clinical practice.

Indexed as

BiopsyNeoadjuvant chemotherapyOsteosarcomaPredictive modelTumor-associated neutrophils

Identifiers

PMID42064863
PMCPMC13127271

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.