Evidence map›Paper›PMID 40192950›Full record

ArticleDiscover oncology2025

Development and validation of a prognostic nomogram for ovarian clear cell carcinoma: a study based on the SEER database and a Chinese cohort.

Yao Shen, Fei Xi, Pingge Zhao, Yuhang Zhang, Guanlin Guo, Xueyuan Jia, Jie Wu, Ye Kuang

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PubMed Full text at PMC Publisher
In one paragraph

Article in Discover oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
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.

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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

Who cites it

1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Yao Shen *Laboratory of Medical Genetics, Harbin Medical University, Harbin, China.
Fei Xi *Laboratory of Medical Genetics, Harbin Medical University, Harbin, China.
Pingge Zhao *Department of Gynecology and Obstetrics, The 2nd Affiliated Hospital of Harbin Medical University, Harbin, China.
Yuhang ZhangDepartment of Gynecology and Obstetrics, The 2nd Affiliated Hospital of Harbin Medical University, Harbin, China.
Guanlin GuoLaboratory of Medical Genetics, Harbin Medical University, Harbin, China.
Xueyuan JiaLaboratory of Medical Genetics, Harbin Medical University, Harbin, China.
Jie WuLaboratory of Medical Genetics, Harbin Medical University, Harbin, China. wujie@hrbmu.edu.cn.
Ye KuangDepartment of Gynecology and Obstetrics, The 2nd Affiliated Hospital of Harbin Medical University, Harbin, China. 600964@hrbmu.edu.cn.

Funding

Natural Science Foundation of Heilongjiang Province LH2021H034
6 · The paper itself

Abstract

backgroundThe clinical prognostic factors for ovarian clear cell carcinoma (OCCC) are limited, and we aim to construct a model to predict the survival of OCCC patients.

methodsData were extracted from the SEER database for patients diagnosed with OCCC. Cox regression analyses were used to identify independent risk factors for OCCC. Two nomograms were developed, and the results were evaluated comprehensively by C-index, ROC curve, calibration curve, and DCA curve. Patients diagnosed with OCCC were used as the validation set to verify the model.

resultsA total of 1855 OCCC patients from the SEER database were used as the training set, and 101 patients from our hospital were used as the validation set. Cox regression analysis of the independent risk factors affecting the prognosis of OCCC was used to construct nomograms. The C-index of the training set OS was 0.76, and the validation set OS was 0.75. The AUC of the training set OS is 0.803, 0.794, and 0.802 for 1, 3, and 5 years, and 0.774, 0.800, and 0.923 for the validation set. The calibration curve and DCA curve also showed that OS and CSS have good predictive power.

conclusionsA nomogram based on 8 prognostic factors analyzed by Cox regression can predict the prognosis of OCCC patients effectively.

Indexed as

Cancer-specific survivalNomogramOvarian clear cell carcinomaOverall survivalSEER database

Identifiers

PMID40192950
PMCPMC11977087

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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.