研究动态
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IDH基因突变胶质瘤的高效诊断:1p/19qNET采用弱监督学习评估1p/19q拷贝数缺失状态。

Efficient diagnosis of IDH-mutant gliomas: 1p/19qNET assesses 1p/19q codeletion status using weakly-supervised learning.

发表日期:2023 Sep 16
作者: Gi Jeong Kim, Tonghyun Lee, Sangjeong Ahn, Youngjung Uh, Se Hoon Kim
来源: MOLECULAR & CELLULAR PROTEOMICS

摘要:

准确鉴定胶质瘤的分子变化对其诊断和治疗至关重要。然而,荧光原位杂交(FISH)虽然可以观察到多样且异质的变化,但由于分子方法的限制,它本身具有耗时和挑战性。在这里,我们报告了一种名为1p/19qNET的先进深度学习网络的开发,该网络旨在预测1p和19q染色体的倍变值,并从整张切片图像中对异柠檬酸脱氢酶(IDH)突变的胶质瘤进行分类。我们在发现组(DS)的288例患者的下一代测序数据上对1p/19qNET进行了训练,并采用了弱监督方法和切片级别标签,以减少偏差和工作量。然后,我们在由综合的癌症基因组资源The Cancer Genome Atlas提供的385个样本的独立验证组(IVS)上进行了验证。1p/19qNET的性能优于传统的FISH,1p和19q的R2值分别达到了0.589和0.547。作为IDH突变的胶质瘤分类器,在DS和IVS中,1p/19qNET的AUC分别达到了0.930和0.837。1p/19qNET的弱监督特性提供了结果的可解释性热图。这项研究证明了深度学习在精确确定1p/19q缺失状态和将IDH突变的胶质瘤分类为星形细胞瘤或少突胶质瘤方面的成功应用。1p/19qNET与FISH的结果相当,并提供了有关空间信息的相关信息。这种方法在肿瘤分类方面具有更广泛的应用。© 2023. Nature Publishing Group UK.
Accurate identification of molecular alterations in gliomas is crucial for their diagnosis and treatment. Although, fluorescence in situ hybridization (FISH) allows for the observation of diverse and heterogeneous alterations, it is inherently time-consuming and challenging due to the limitations of the molecular method. Here, we report the development of 1p/19qNET, an advanced deep-learning network designed to predict fold change values of 1p and 19q chromosomes and classify isocitrate dehydrogenase (IDH)-mutant gliomas from whole-slide images. We trained 1p/19qNET on next-generation sequencing data from a discovery set (DS) of 288 patients and utilized a weakly-supervised approach with slide-level labels to reduce bias and workload. We then performed validation on an independent validation set (IVS) comprising 385 samples from The Cancer Genome Atlas, a comprehensive cancer genomics resource. 1p/19qNET outperformed traditional FISH, achieving R2 values of 0.589 and 0.547 for the 1p and 19q arms, respectively. As an IDH-mutant glioma classifier, 1p/19qNET attained AUCs of 0.930 and 0.837 in the DS and IVS, respectively. The weakly-supervised nature of 1p/19qNET provides explainable heatmaps for the results. This study demonstrates the successful use of deep learning for precise determination of 1p/19q codeletion status and classification of IDH-mutant gliomas as astrocytoma or oligodendroglioma. 1p/19qNET offers comparable results to FISH and provides informative spatial information. This approach has broader applications in tumor classification.© 2023. Nature Publishing Group UK.