Evidence map›Paper›PMID 39917629›Full record

ArticleLaryngoscope investigative otolaryngology2025

Development of a Clinical Prediction Model to Predict Malignant Transformation of Sinonasal Inverted Papilloma Based on Hematological Indices and Clinical Features.

Kai Sun, Ce Wu, Zengxiao Zhang, Jiahong Chen, Xudong Yan, Shunke Li, Lin Wang, Longgang Yu, Yan Jiang

Abstract read
In one paragraph

Article in Laryngoscope investigative otolaryngology, 2025. 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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1 · What the graph read from it

What it found

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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

The trial behind it

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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

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

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

9 authors.

Kai SunDepartment of Otolaryngology, Head and Neck Surgery The Affiliated Hospital of Qingdao University Qingdao China.ORCID https://orcid.org/0009-0009-2006-2835
Ce WuDepartment of Otolaryngology, Head and Neck Surgery The Affiliated Hospital of Qingdao University Qingdao China.ORCID https://orcid.org/0000-0002-6447-0365
Zengxiao ZhangDepartment of Otolaryngology, Head and Neck Surgery The Affiliated Hospital of Qingdao University Qingdao China.ORCID https://orcid.org/0000-0002-4161-0994
Jiahong ChenDepartment of Otolaryngology Qingdao Women and Children's Hospital Qingdao China.
Xudong YanDepartment of Otolaryngology, Head and Neck Surgery The Affiliated Hospital of Qingdao University Qingdao China.ORCID https://orcid.org/0000-0001-5302-2850
Shunke LiDepartment of Otolaryngology, Head and Neck Surgery The Affiliated Hospital of Qingdao University Qingdao China.
Lin WangDepartment of Otolaryngology, Head and Neck Surgery The Affiliated Hospital of Qingdao University Qingdao China.
Longgang YuDepartment of Otolaryngology, Head and Neck Surgery The Affiliated Hospital of Qingdao University Qingdao China.ORCID https://orcid.org/0000-0001-8498-5182
Yan JiangDepartment of Otolaryngology, Head and Neck Surgery The Affiliated Hospital of Qingdao University Qingdao China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: Sinonasal inverted papilloma (SNIP) has a risk of malignant transformation into SNIP with squamous cell carcinoma (SNIP-SCC). Early detection of SNIP-SCC is crucial for patient outcomes. This study aimed to evaluate the utility of tumor-related hematological indices in the early diagnosis of SNIP-SCC and to develop a nomogram incorporating imaging and clinical features. Methods: This study included 159 patients with SNIP with ( Results: Elevated serum cytokeratin fragment antigen 21-1 (CYFRA 21-1), the loss of convoluted cerebriform pattern, bone destruction, headache/facial pain, and epistaxis/blood-tinged mucus were identified as independent risk factors for SNIP-SCC. The optimal cut-off value for serum CYFRA 21-1 was 3.51 ng/mL, with the nomogram area under the ROC curve of 0.966 based on the above indicators. Conclusion: The findings suggest that CYFRA 21-1 is a promising diagnostic marker for SNIP-SCC. The nomogram incorporating serum CYFRA 21-1, imaging features, and clinical factors demonstrates strong clinical utility and can be a reference tool for clinical decision-making.

Indexed as

cytokeratin fragment antigen 21‐1malignant transformationnomogramsinonasal inverted papilloma

Identifiers

PMID39917629
PMCPMC11800132

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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 OpenQuestion graph.