Multi-center application of a convolutional neural network for preoperative detection of cavernous sinus invasion in pituitary adenomas.
Objective: Cavernous sinus invasion (CSI) plays a pivotal role in determining management in pituitary adenomas. The study aimed to develop a Convolutional Neural Network (CNN) model to diagnose CSI in multiple centers. Methods: A total of 729 cases were retrospectively obtained in five medical cente...
| Publicado en: | Neuroradiology Vol. 66; no. 3; pp. 353 - 361 |
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| Autores principales: | , , , , , , , , , , , , , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Springer Nature
Mar2024
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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=175359645&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 175359645 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00283940 NYZ jtl: Neuroradiology issn: 00283940 maglogo: N pubinfo: dt: Mar2024 vid: 66 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 175359645 174859242 175359645 175359645 10.1007/s00234-024-03287-1 175359645 ppf: 353 ppct: 8 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Multi-center application of a convolutional neural network for preoperative detection of cavernous sinus invasion in pituitary adenomas. aug: au: Fang, Yi Wang, He Cao, Demao Cai, Shengyu Qian, Chengxing Feng, Ming Zhang, Wentai Cao, Lei Chen, Hongjie Wei, Liangfeng Mu, Shuwen Pei, Zhijie Li, Jun Wang, Renzhi Wang, Shousen affil: Department of Neurosurgery, the Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 1 Shuai Fu Yuan, Dongcheng District, 100730, Beijing, China sug: subj: Preoperative Care Cavernous Sinuses Pathology Neoplasm Invasiveness Diagnosis Adenoma, Pituitary Diagnosis Neural Networks (Computer) Magnetic Resonance Imaging Methods Prediction Models Sensitivity and Specificity Human Male Female Adult Middle Age Deep Learning Methods Descriptive Statistics Funding Source Retrospective Design ROC Curve Confidence Intervals Adult: 19-44 years Middle Aged: 45-64 years Male Female ab: Objective: Cavernous sinus invasion (CSI) plays a pivotal role in determining management in pituitary adenomas. The study aimed to develop a Convolutional Neural Network (CNN) model to diagnose CSI in multiple centers. Methods: A total of 729 cases were retrospectively obtained in five medical centers with (n = 543) or without CSI (n = 186) from January 2011 to December 2021. The CNN model was trained using T1-enhanced MRI from two pituitary centers of excellence (n = 647). The other three municipal centers (n = 82) as the external testing set were imported to evaluate the model performance. The area-under-the-receiver-operating-characteristic-curve values (AUC-ROC) analyses were employed to evaluate predicted performance. Gradient-weighted class activation mapping (Grad-CAM) was used to determine models' regions of interest. Results: The CNN model achieved high diagnostic accuracy (0.89) in identifying CSI in the external testing set, with an AUC-ROC value of 0.92 (95% CI, 0.88–0.97), better than CSI clinical predictor of diameter (AUC-ROC: 0.75), length (AUC-ROC: 0.80), and the three kinds of dichotomizations of the Knosp grading system (AUC-ROC: 0.70–0.82). In cases with Knosp grade 3A (n = 24, CSI rate, 0.35), the accuracy the model accounted for 0.78, with sensitivity and specificity values of 0.72 and 0.78, respectively. According to the Grad-CAM results, the views of the model were confirmed around the sellar region with CSI. Conclusions: The deep learning model is capable of accurately identifying CSI and satisfactorily able to localize CSI in multicenters. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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