Preoperatively Predicting PIT1 Expression in Pituitary Adenomas Using Habitat, Intra-tumoral and Peri-tumoral Radiomics Based on MRI.
The study aimed to predict expression of pituitary transcription factor 1 (PIT1) in pituitary adenomas using habitat, intra-tumoral and peri-tumoral radiomics models. A total of 129 patients with pituitary adenoma (training set, n = 103; test set, n = 26) were retrospectively enrolled. A total of 12...
| Publicado en: | Journal of Imaging Informatics in Medicine Vol. 38; no. 6; pp. 3972 - 3984 |
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| Autores principales: | , , , , |
| Formato: | diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Dec2025
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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=190236341&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 190236341 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: Dec2025 vid: 38 iid: 6 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 190236341 189894349 190236341 190236341 10.1007/s10278-024-01376-4 190236341 ppf: 3972 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Preoperatively Predicting PIT1 Expression in Pituitary Adenomas Using Habitat, Intra-tumoral and Peri-tumoral Radiomics Based on MRI. aug: au: Zang, Yuying Zheng, Fei Feng, Limei Shi, Xinyao Chen, Xuzhu affil: https://ror.org/00zw6et16 Department of Radiology, The Affiliated Children's Hospital, Capital Institute of Pediatrics, Beijing, China sug: subj: Adenoma, Pituitary Radiography Magnetic Resonance Imaging Methods Adenoma, Pituitary Pathology Radiomics Utilization Preoperative Period Pituitary Gland Pathology Gene Expression Profiling Predictive Value of Tests Evaluation Prediction Models Transcription Factors Blood Human Funding Source Male Female Retrospective Design Record Review Machine Learning Algorithms Logistic Regression Support Vector Machine Multilayer Perceptrons Chi Square Test Fisher's Exact Test T-Tests Mann-Whitney U Test Data Analysis Software Descriptive Statistics Male Female ab: The study aimed to predict expression of pituitary transcription factor 1 (PIT1) in pituitary adenomas using habitat, intra-tumoral and peri-tumoral radiomics models. A total of 129 patients with pituitary adenoma (training set, n = 103; test set, n = 26) were retrospectively enrolled. A total of 12, 18, 14, 13, and 14 radiomics features were selected from the ROIintra, ROIintra+peri (ROIintra+2mm, ROIintra+4mm, ROIintra+6mm), and ROIhabitat, respectively. Then, three machine learning algorithms were employed to develop radiomic models, including logistic regression (LR), support vector machines (SVM), and multilayer perceptron (MLP). The performances of the intra-tumoral, combined intra-tumoral and peri-tumoral, and habitat models were evaluated. The peritumoral region (ROI2mm, ROI4mm, ROI6mm) of the combined model with the highest performance was individually selected for further peritumoral analysis. Moreover, a deep learning radiomics nomogram (DLRN) was constructed incorporating clinical characteristics and the peri-tumoral and habitat models for individual prediction. The combined modelintra+2mm based on ROIintra+2mm achieved a better performance (AUC, 0.800) than that of the intra-tumoral model alone (AUC, 0.731). And the habitat model showed a higher performance (AUC, 0.806) than that of the intra-tumoral model. In addition, the performance of the peri-tumoral model based on ROI2mm was 0.694 in the testing set. Furthermore, the DLRN achieved the highest performance of 0.900 in the test set. The DLRN showed the best performance for PIT1 expression in pituitary adenomas, followed by the habitat, combined modelintra+2mm, intra-tumoral model, and peri-tumoral model based on ROI2mm, respectively. These different models are helpful for the model choice in clinical work. pubtype: Academic Journal doctype: diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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