MRI-based interpretable clinicoradiological and radiomics machine learning model for preoperative prediction of pituitary macroadenomas consistency: a dual-center study.
Purpose: To establish an interpretable and non-invasive machine learning (ML) model using clinicoradiological predictors and magnetic resonance imaging (MRI) radiomics features to predict the consistency of pituitary macroadenomas (PMAs) preoperatively. Methods: Total 350 patients with PMA (272 from...
| Publicado en: | Neuroradiology Vol. 67; no. 10; pp. 2763 - 2777 |
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| Autores principales: | , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Springer Nature
Oct2025
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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=189357969&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 189357969 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00283940 NYZ jtl: Neuroradiology issn: 00283940 maglogo: N pubinfo: dt: Oct2025 vid: 67 iid: 10 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 189357969 186487602 189357969 189357969 10.1007/s00234-025-03698-8 189357969 ppf: 2763 ppct: 14 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: MRI-based interpretable clinicoradiological and radiomics machine learning model for preoperative prediction of pituitary macroadenomas consistency: a dual-center study. aug: au: Liang, Meiheng Wang, Fei Yang, Yan Wen, Li Wang, Shunan Zhang, Dong affil: https://ror.org/02d217z27 Department of Radiology, Xinqiao Hospital, Army Medical University, Chongqing, China sug: subj: Magnetic Resonance Imaging Radiomics Machine Learning Algorithms Prediction Models Evaluation Preoperative Care Adenoma, Pituitary Radiography Sensitivity and Specificity Adenoma, Pituitary Surgery Human China Academic Medical Centers Random Assignment Univariate Statistics Multivariate Statistics Logistic Regression Random Forest ROC Curve Multicenter Studies Male Female Adult Middle Age Random Sample Retrospective Design Record Review Stratified Random Sample Data Analysis Software Funding Source Adult: 19-44 years Middle Aged: 45-64 years Male Female ab: Purpose: To establish an interpretable and non-invasive machine learning (ML) model using clinicoradiological predictors and magnetic resonance imaging (MRI) radiomics features to predict the consistency of pituitary macroadenomas (PMAs) preoperatively. Methods: Total 350 patients with PMA (272 from Xinqiao Hospital of Army Medical University and 78 from Daping Hospital of Army Medical University) were stratified and randomly divided into training and test cohorts in a 7:3 ratio. The tumor consistency was classified as soft or firm. Clinicoradiological predictors were examined utilizing univariate and multivariate regression analyses. Radiomics features were selected employing the minimum redundancy maximum relevance (mRMR) and least absolute shrinkage and selection operator (LASSO) algorithms. Logistic regression (LR) and random forest (RF) classifiers were applied to construct the models. Receiver operating characteristic (ROC) curves and decision curve analyses (DCA) were performed to compare and validate the predictive capacities of the models. A comparative study of the area under the curve (AUC), accuracy (ACC), sensitivity (SEN), and specificity (SPE) was performed. The Shapley additive explanation (SHAP) was applied to investigate the optimal model's interpretability. Results: The combined model predicted the PMAs' consistency more effectively than the clinicoradiological and radiomics models. Specifically, the LR-combined model displayed optimal prediction performance (test cohort: AUC = 0.913; ACC = 0.840). The SHAP-based explanation of the LR-combined model suggests that the wavelet-transformed and Laplacian of Gaussian (LoG) filter features extracted from T2WI and CE-T1WI occupy a dominant position. Meanwhile, the skewness of the original first-order features extracted from T2WI (T2WI_original_first-order_Skewness) demonstrated the most substantial contribution. Conclusion: An interpretable machine learning model incorporating clinicoradiological predictors and multiparametric MRI (mpMRI)-based radiomics features may predict PMAs consistency, enabling tailored and precise therapies for patients with PMA. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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