NETosis Genes and Pathomic Signature: A Novel Prognostic Marker for Ovarian Serous Cystadenocarcinoma.
To evaluate the prognostic significance and molecular mechanism of NETosis markers in ovarian serous cystadenocarcinoma (OSC), we constructed a machine learning-based pathomic model utilizing hematoxylin and eosin (H&E) slides. We analyzed 333 patients with OSC from The Cancer Genome Atlas for progn...
| Publicado en: | Journal of Imaging Informatics in Medicine Vol. 38; no. 4; pp. 2412 - 2428 |
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| Autores principales: | , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Aug2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=187278989&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 187278989 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 29482925 NR3A jtl: Journal of Imaging Informatics in Medicine issn: 29482925 maglogo: N pubinfo: dt: Aug2025 vid: 38 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 187278989 187278989 187278989 10.1007/s10278-024-01366-6 187278989 ppf: 2412 ppct: 16 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: NETosis Genes and Pathomic Signature: A Novel Prognostic Marker for Ovarian Serous Cystadenocarcinoma. aug: au: Zhan, Feng Guo, Yina He, Lidan affil: https://ror.org/01cyb5v38 College of Engineering, Fujian Jiangxia University, Fuzhou, Fujian, China sug: subj: Ovarian Neoplasms Prognosis Neoplasms, Cystic, Mucinous, and Serous Prognosis Adenocarcinoma Prognosis Gene Expression Bioinformatics Prediction Models Machine Learning Algorithms Genetic Markers Human Female Middle Age Aged Aged, 80 and Over Funding Source Cancer Patients Support Vector Machine Logistic Regression Mutation Neoplasm Metastasis Neoplasm Invasiveness Overall Survival ROC Curve Kaplan-Meier Estimator Descriptive Statistics Disease Progression Genetic Screening Survival Analysis Cox Proportional Hazards Model Log-Rank Test Chi Square Test Spearman's Rank Correlation Coefficient Data Analysis Software Cluster Analysis Staining and Labeling Benzopyrans Fluorescent Dyes Middle Aged: 45-64 years Aged: 65+ years Aged, 80 & over Female ab: To evaluate the prognostic significance and molecular mechanism of NETosis markers in ovarian serous cystadenocarcinoma (OSC), we constructed a machine learning-based pathomic model utilizing hematoxylin and eosin (H&E) slides. We analyzed 333 patients with OSC from The Cancer Genome Atlas for prognostic-related neutrophil extracellular trap formation (NETosis) genes through bioinformatics analysis. Pathomic features were extracted from 54 cases with complete pathological images, genetic matrices, and clinical information. Two pathomic prognostic models were constructed using support vector machine (SVM) and logistic regression (LR) algorithms. Additionally, we established a predictive scoring system that integrated pathomic scores based on the NETcluster subtypes and clinical signature. We identified four NETosis genes significantly correlated with OSC prognosis, which were functionally associated with immune response, somatic mutations, tumor invasion, and metastasis. Five robust pathomic features were selected for overall survival prediction. The LR and SVM pathomic models demonstrated strong predictive performance for the NETcluster subtype classification through five-fold cross-validation. Time-dependent ROC analysis revealed excellent prognostic capability of the LR pathomic model's score for the overall survival (AUC values of 0.658, 0.761, and 0.735 at 36, 48, and 60 months, respectively), further validated by Kaplan–Meier analysis. The expression levels of NETosis genes greatly affected OSC patients' prognoses. The pathomic analysis of H&E slide pathological images provides an effective approach for predicting both NETcluster subtype and overall survival in OSC patients. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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