Development of a radiomic model to predict CEACAM1 expression and prognosis in ovarian cancer.

Xiaoxue Zhang, Liping Han, Fangfang Nie, Huimin Zhang, Liming Li, Ruopeng Liang

Abstract read

In one paragraph

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

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1citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

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3 · Its place in the literature

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1 citing paper in PubMed.

Non-invasive prediction of Ki-67 and p53 biomarkers in spinal ependymoma via deep learning: using multimodal magnetic resonance imaging and clinical data.Biomarker research · 2025
Article

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5 · Who and what money

Authors and funding

6 authors.

Xiaoxue Zhang *Department of Physical Examination, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, P. R. China.

Liping Han *Department of Obstetrics and Gynecology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, P. R. China.

Fangfang NieDepartment of Obstetrics and Gynecology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, P. R. China.

Huimin ZhangDepartment of Obstetrics and Gynecology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, P. R. China.

Liming LiDepartment of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, P. R. China. perfect1989a@163.com.

Ruopeng LiangDepartment of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, P. R. China. fccliangrp@zzu.edu.cn.

Funding

Co-operation Research Plan of Medical Science and Technology of Henan Province LHGJ20220409Henan Provincial Science and Technology Association Youth Talent Promotion Project 2023HYTP039Key science and technology project s of Henan Province 232102311048

6 · The paper itself

Abstract

We aimed to investigate the prognostic role of CEACAM1 and to construct a radiomic model to predict CEACAM1 expression and prognosis in ovary cancer (OC). Sequencing data and CT scans in OC were sourced from TCGA and TCIA databases. CEACAM1 expression was assessed by Cox regression analyses, Kaplan-Meier curves and GSVA enrichment analysis. Furthermore, radiomic features were extracted from CT scans and selected by LASSO and ROC. The selected radiomic features were used to construct a radiomic model to predict CEACAM1 expression. In addition, the radiomic score (RS) and its relationship with OC survival were investigated by Kaplan-Meier and ROC curves. At last, RS and clinical features were included into LASSO, using nomogram to predict OC prognosis. Cox regression analyses showed that CEACAM1 expression was an independent prognostic factor and associated with immune cell infiltration in OC. By LASSO and ROC, six radiomic features were selected and used to construct a radiomic model. The PR, calibration, DCA and ROC curves revealed the good performance and clinical utility of the radiomic model to predict CEACAM1 expression. In addition, RS based on radiomic features was found to be associated with OC survival. At last, a nomogram based on RS, age, chemotherapy and tumor residual disease was constructed and was found to have high accuracy in predicting OC prognosis. For the first time, our study constructed a radiomic model to predict CEACAM1 expression and prognosis of OC patients. Those findings may guide novel diagnosis and treatment for OC patients.

Indexed as

Antigens, CDCell Adhesion MoleculesOvarian NeoplasmsBiomarkers, TumorFemaleGene Expression Regulation, NeoplasticHumansKaplan-Meier EstimateMiddle AgedNomogramsPrognosisRadiomicsROC CurveTomography, X-Ray ComputedAntigens, CDBiomarkers, TumorCD66 antigensCell Adhesion MoleculesCEACAM1Ovarian cancerPrognosisRadiomics

Identifiers

PMID40307488

DOI10.1038/s41598-025-99625-1

PMCPMC12044014

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LicenceCC BY-NC-ND

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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.