Concomitant Prediction of the Ki67 and PIT-1 Expression in Pituitary Adenoma Using Different Radiomics Models.
Objectives: To preoperatively predict the high expression of Ki67 and positive pituitary transcription factor 1 (PIT-1) simultaneously in pituitary adenoma (PA) using three different radiomics models. Methods: A total of 247 patients with PA (training set: n = 198; test set: n = 49) were included in...
| Publicado en: | Journal of Imaging Informatics in Medicine Vol. 38; no. 1; pp. 394 - 410 |
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| Autores principales: | , , , , , , , , , |
| Formato: | diagnostic images pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Feb2025
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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=184471455&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 184471455 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: Feb2025 vid: 38 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 184471455 184471455 184471455 10.1007/s10278-024-01121-x 184471455 ppf: 394 ppct: 16 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Concomitant Prediction of the Ki67 and PIT-1 Expression in Pituitary Adenoma Using Different Radiomics Models. aug: au: Liu, Fangzheng Zang, Yuying Feng, Limei Shi, Xinyao Wu, Wentao Liu, Xin Song, Yifan Xu, Jintian Gui, Songbai Chen, Xuzhu affil: https://ror.org/013xs5b60 Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, No.119 South Fourth Ring West Road, Fengtai District, 100070, Beijing, China sug: subj: Predictive Value of Tests Evaluation Prediction Models Pituitary Neoplasms Radiography Pituitary Neoplasms Physiopathology Tumor Markers, Biological Blood Radiomics Utilization Transcription Factors Blood Pituitary Neoplasms Metabolism Human Male Female Adult Middle Age Retrospective Design Record Review Funding Source Deep Learning Machine Learning Magnetic Resonance Imaging Methods Spearman's Rank Correlation Coefficient Correlation Coefficient Support Vector Machine Algorithms ROC Curve Sensitivity and Specificity Validity Decision Making, Clinical Electronic Health Records Immunohistochemistry Staining and Labeling Cell Line, Tumor Linear Regression Probability Multivariate Analysis Image Processing, Computer Assisted Logistic Regression Confidence Intervals Chi Square Test T-Tests Descriptive Statistics Data Analysis Software Adult: 19-44 years Middle Aged: 45-64 years Male Female ab: Objectives: To preoperatively predict the high expression of Ki67 and positive pituitary transcription factor 1 (PIT-1) simultaneously in pituitary adenoma (PA) using three different radiomics models. Methods: A total of 247 patients with PA (training set: n = 198; test set: n = 49) were included in this retrospective study. The imaging features were extracted from preoperative contrast-enhanced T1WI (T1CE), T1-weighted imaging (T1WI), and T2-weighted imaging (T2WI). Feature selection was performed using Spearman's rank correlation coefficient and least absolute shrinkage and selection operator (LASSO). The classic machine learning (CML), deep learning (DL), and deep learning radiomics (DLR) models were constructed using logistic regression (LR), support vector machine (SVM), and multi-layer perceptron (MLP) algorithms. The area under the receiver operating characteristic (ROC) curve (AUC), sensitivity, specificity, accuracy, negative predictive value (NPV) and positive predictive value (PPV) were calculated for the training and test sets. In addition, combined with clinical characteristics, the best CML and the best DL models (SVM classifier), the DL radiomics nomogram (DLRN) was constructed to aid clinical decision-making. Results: Seven CML features, 96 DL features, and 107 DLR features were selected to construct CML, DL and DLR models. Compared to CML and DL model, the DLR model had the best performance. The AUC, sensitivity, specificity, accuracy, NPV and PPV were 0.827, 0.792, 0.800, 0.796, 0.800 and 0.792 in the test set, respectively. Conclusions: Compared with CML and DL models, the DLR model shows the best performance in predicting the Ki67 and PIT-1 expression in PAs simultaneously. pubtype: Academic Journal doctype: diagnostic images pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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