Generative AI in glioma: Ensuring diversity in training image phenotypes to improve diagnostic performance for IDH mutation prediction.
Neuro Oncol
; 26(6): 1124-1135, 2024 Jun 03.
Article
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| MEDLINE
| ID: mdl-38253989
ABSTRACT
BACKGROUND:
This study evaluated whether generative artificial intelligence (AI)-based augmentation (GAA) can provide diverse and realistic imaging phenotypes and improve deep learning-based classification of isocitrate dehydrogenase (IDH) type in glioma compared with neuroradiologists.METHODS:
For model development, 565 patients (346 IDH-wildtype, 219 IDH-mutant) with paired contrast-enhanced T1 and FLAIR MRI scans were collected from tertiary hospitals and The Cancer Imaging Archive. Performance was tested on internal (119, 78 IDH-wildtype, 41 IDH-mutant [IDH1 and 2]) and external test sets (108, 72 IDH-wildtype, 36 IDH-mutant). GAA was developed using a score-based diffusion model and ResNet50 classifier. The optimal GAA was selected in comparison with the null model. Two neuroradiologists (R1, R2) assessed realism, diversity of imaging phenotypes, and predicted IDH mutation. The performance of a classifier trained with optimal GAA was compared with that of neuroradiologists using the area under the receiver operating characteristics curve (AUC). The effect of tumor size and contrast enhancement on GAA performance was tested.RESULTS:
Generated images demonstrated realism (Turing's test 47.5-50.5%) and diversity indicating IDH type. Optimal GAA was achieved with augmentation with 110 000 generated slices (AUC 0.938). The classifier trained with optimal GAA demonstrated significantly higher AUC values than neuroradiologists in both the internal (R1, Pâ =â .003; R2, Pâ <â .001) and external test sets (R1, Pâ <â .01; R2, Pâ <â .001). GAA with large-sized tumors or predominant enhancement showed comparable performance to optimal GAA (internal test AUC 0.956 and 0.922; external test 0.810 and 0.749).CONCLUSIONS:
The application of generative AI with realistic and diverse images provided better diagnostic performance than neuroradiologists for predicting IDH type in glioma.Palabras clave
Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Neoplasias Encefálicas
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Inteligencia Artificial
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Imagen por Resonancia Magnética
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Glioma
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Isocitrato Deshidrogenasa
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Mutación
Tipo de estudio:
Diagnostic_studies
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Prognostic_studies
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Risk_factors_studies
Límite:
Adult
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Aged
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Female
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Humans
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Male
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Middle aged
Idioma:
En
Revista:
Neuro Oncol
Asunto de la revista:
NEOPLASIAS
/
NEUROLOGIA
Año:
2024
Tipo del documento:
Article