Evidence map›Paper›PMID 41555965›Full record

ArticleClinical Medicine Insights. Oncology2026

Development of Nomogram for Predicting Postoperative Progression Risk in High-Risk Thymoma and Thymic Carcinoma Utilizing Clinical and Preoperative CT Features.

Qian Meng, Nan Jiang, Jun Chen, Chunjiao Weng, Huanmin Miao, Xiaoxia Ping, Chunhong Hu

Abstract read
In one paragraph

Article in Clinical Medicine Insights. 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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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Qian MengDepartment of Radiology, The First Affiliated Hospital of Soochow University, Soochow University, Suzhou, China.ORCID https://orcid.org/0000-0002-3252-0794
Nan JiangDepartment of Radiology, The First Affiliated Hospital of Soochow University, Soochow University, Suzhou, China.ORCID https://orcid.org/0000-0002-8849-7976
Jun ChenDepartment of Thoracic Surgery, The First Affiliated Hospital of Soochow University, Suzhou, China.
Chunjiao WengDepartment of Radiology, The First Affiliated Hospital of Soochow University, Soochow University, Suzhou, China.
Huanmin MiaoDepartment of Radiology, The First Affiliated Hospital of Soochow University, Soochow University, Suzhou, China.
Xiaoxia PingDepartment of Radiology, The First Affiliated Hospital of Soochow University, Soochow University, Suzhou, China.
Chunhong HuDepartment of Radiology, The First Affiliated Hospital of Soochow University, Soochow University, Suzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Computed tomography (CT) features and clinical characteristics have been shown in recent studies to be effective predictive indicators for risk stratification of thymic epithelial tumors. High-risk thymoma and thymic carcinoma (HRT-TC) are highly aggressive and are associated with poor prognoses. The aim of this study is to evaluate the predictive value of CT features and clinical characteristics to assess postoperative progression in patients with HRT-TC. Methods: Clinical and enhanced CT data were retrospectively collected from patients who underwent HRT-TC surgery between June 1, 2012, and June 1, 2022. A univariate Cox regression analysis was conducted to identify the risk factors associated with postoperative progression. A multivariate Cox regression analysis was then used to determine the independent risk factors. Three-year and 5-year single-factor models as well as multifactorial combined models were then constructed based on the results of these analyses to assess their efficacy, accuracy, and net benefit. The best-performing model was selected to create a nomogram for a consistency assessment. Results: A total of 215 patients were included in the study. The multivariate Cox regression analysis revealed that independent prognostic factors that influenced postoperative progression were the tumor length (hazard ratio [HR] = 1.027; 95% confidence interval [CI] = 1.004-1.049, Conclusions: A nomogram based on clinical and CT features provided effective predictions of progression following HRT-TC. This prognostic tool holds significant value for clinicians to guide therapeutic decisions and personalize survival assessments.

Indexed as

Computed tomographyhigh-risk thymomanomogramprogressionthymic carcinoma

Identifiers

PMID41555965
PMCPMC12812200

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