流体衰减反转恢复
接收机工作特性
异柠檬酸脱氢酶
胶质瘤
相关性
IDH1
野生型
磁共振成像
突变体
人工智能
核医学
计算机科学
生物
医学
遗传学
机器学习
放射科
物理
核磁共振
数学
基因
几何学
酶
作者
Hye Hyeon Moon,Jiheon Jeong,Ji Eun Park,Namkug Kim,Chang‐Yong Choi,Young‐Hoon Kim,Sang Woo Song,Chang‐Ki Hong,Jeong Hoon Kim,Ho Sung Kim
出处
期刊:Neuro-oncology
[Oxford University Press]
日期:2024-01-22
被引量:1
标识
DOI:10.1093/neuonc/noae012
摘要
This study evaluated whether generative artificial intelligence-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.For model development, 565 patients (346 IDH-wildtype, 219 IDH-mutant) with paired contrast-enhanced T1 and FLAIR MRI scans were collected from tertiary hospital 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 score-based diffusion model and ResNet50 classifier. The optimal GAA was selected in comparison with 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 area under the receiver operating characteristics curve (AUC). The effect of tumor size and contrast enhancement on GAA performance was tested.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, P=.003; R2, P<.001) and external test sets (R1, P<.01; R2, P<.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).Application of generative AI with realistic and diverse images provided better diagnostic performance than neuroradiologists for predicting IDH type in glioma.
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