Machine Learning–Based Radiomics for Differentiating Pancreatic Lesions: A Potential Tool to Enhance Clinical Decision‐Making and Nursing Management.
Background: The noninvasive diagnosis of pancreatic lesions is a critical clinical challenge. This study aims to create machine learning (ML) radiomic models for differentiating pancreatic lesions and an integrated model for pancreatic ductal adenocarcinoma (PDAC) detection. Methods: 640 patients wi...
| Publicado en: | Journal of Nursing Management Vol. 2025; pp. 1 - 16 |
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| Autores principales: | , , , , , , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
12/16/2025
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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=190280081&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 190280081 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09660429 81U jtl: Journal of Nursing Management issn: 09660429 maglogo: Y pubinfo: dt: 12/16/2025 vid: 2025 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 190280081 190280081 190280081 10.1155/jonm/8038903 190280081 ppf: 1 ppct: 15 formats: tig: atl: Machine Learning–Based Radiomics for Differentiating Pancreatic Lesions: A Potential Tool to Enhance Clinical Decision‐Making and Nursing Management. aug: au: Li, Xiaoxuan Liu, Jiani Luan, Xinchi Cui, Jinfeng Xing, Xiaomin Wang, Shibo Guo, Jing Yan, Pengfei affil: Department of Oncology,, The Affiliated Hospital of Qingdao University,, Qingdao, Shandong, China, qdu.edu.cn sug: subj: Machine Learning Radiomics Cell Differentiation Pancreatic Neoplasms Decision Making, Clinical Nursing Management Quality Improvement Human Funding Source Male Female Retrospective Design Record Review Prospective Studies Random Assignment Descriptive Statistics Data Analysis Software Quantitative Studies Analysis of Variance Chi Square Test Pearson's Correlation Coefficient Confidence Intervals ROC Curve Algorithms Sensitivity and Specificity Male Female ab: Background: The noninvasive diagnosis of pancreatic lesions is a critical clinical challenge. This study aims to create machine learning (ML) radiomic models for differentiating pancreatic lesions and an integrated model for pancreatic ductal adenocarcinoma (PDAC) detection. Methods: 640 patients with pathologically confirmed malignant (n = 450), borderline (n = 108), or benign (n = 82) lesions were enrolled and divided into training (70%) and validation (30%) cohorts. Radiomic features were extracted from regions of interest on arterial and venous phase CT scans. LASSO logistic regression was used to select 36 features for building ML models, including random forest, logistic regression, support vector machine, and artificial neural networks. An integrated nomogram combining radiomic features and CA19‐9 levels was developed to distinguish PDAC from borderline tumors. Model performance was evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis. Results: All ML models effectively differentiated the three tumor types. The random forest algorithm showed the best performance, achieving an area under the curve (AUC) of 0.99 and 0.95 in the training and validation sets, respectively. CA19‐9 was identified as an independent diagnostic factor for PDAC. The nomogram integrating radiomics and CA19‐9 achieved an AUC of 0.89 and accuracy of 0.85 in the training set, with corresponding values of 0.85 and 0.82 in the validation set. Conclusions: Radiomics‐based ML models effectively differentiated benign, borderline, and malignant pancreatic tumors. The nomogram combining radiomic features with CA19‐9 demonstrated robust performance, showing considerable potential to streamline the diagnostic process and facilitate timely care planning for patients with suspected pancreatic cancer. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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