Development of End-to-End AI–Based MRI Image Analysis System for Predicting IDH Mutation Status of Patients with Gliomas: Multicentric Validation.
Radiogenomics has shown potential to predict genomic phenotypes from medical images. The development of models using standard-of-care pre-operative MRI images, as opposed to advanced MRI images, enables a broader reach of such models. In this work, a radiogenomics model for IDH mutation status predi...
| Publicado en: | Journal of Digital Imaging Vol. 37; no. 1; pp. 31 - 45 |
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| Autores principales: | , , , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Springer Nature
Feb2024
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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=175966511&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 175966511 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Feb2024 vid: 37 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 175966511 175966511 175966511 10.1007/s10278-023-00918-6 175966511 ppf: 31 ppct: 14 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Development of End-to-End AI–Based MRI Image Analysis System for Predicting IDH Mutation Status of Patients with Gliomas: Multicentric Validation. aug: au: Santinha, João Katsaros, Vasileios Stranjalis, George Liouta, Evangelia Boskos, Christos Matos, Celso Viegas, Catarina Papanikolaou, Nickolas affil: https://ror.org/03g001n57 Computational Clinical Imaging Group, Champalimaud Research, Champalimaud Foundation, Av. Brasília, 1400-038, Lisbon, Portugal sug: subj: Genomics Mutation Evaluation Magnetic Resonance Imaging Glioma Cancer Patients Prediction Models Artificial Intelligence Human Phenotype Evaluation Multicenter Studies Logistic Regression Retrospective Design Hospitals ROC Curve Calibration Sensitivity and Specificity Descriptive Statistics Radiographic Image Enhancement ab: Radiogenomics has shown potential to predict genomic phenotypes from medical images. The development of models using standard-of-care pre-operative MRI images, as opposed to advanced MRI images, enables a broader reach of such models. In this work, a radiogenomics model for IDH mutation status prediction from standard-of-care MRIs in patients with glioma was developed and validated using multicentric data. A cohort of 142 (wild-type: 32.4%) patients with glioma retrieved from the TCIA/TCGA was used to train a logistic regression model to predict the IDH mutation status. The model was evaluated using retrospective data collected in two distinct hospitals, comprising 36 (wild-type: 63.9%) and 53 (wild-type: 75.5%) patients. Model development utilized ROC analysis. Model discrimination and calibration were used for validation. The model yielded an AUC of 0.741 vs. 0.716 vs. 0.938, a sensitivity of 0.784 vs. 0.739 vs. 0.875, and a specificity of 0.657 vs. 0.692 vs. 1.000 on the training, test cohort 1, and test cohort 2, respectively. The assessment of model fairness suggested an unbiased model for age and sex, and calibration tests showed a p < 0.05. These results indicate that the developed model allows the prediction of the IDH mutation status in gliomas using standard-of-care MRI images and does not appear to hold sex and age biases. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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