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基于成像的成人胶质瘤分层可预测生存率,并与2021年世界卫生组织分类相关。

Imaging-based stratification of adult gliomas prognosticates survival and correlates with the 2021 WHO classification.

作者信息

Kamble Akshaykumar N, Agrawal Nidhi K, Koundal Surabhi, Bhargava Salil, Kamble Abhaykumar N, Joyner David A, Kalelioglu Tuba, Patel Sohil H, Jain Rajan

机构信息

University Hospitals Coventry & Warwickshire, Coventry, UK.

Deep Learning Institute of Radiological Sciences (DeLoRIS), Mumbai, India.

出版信息

Neuroradiology. 2023 Jan;65(1):41-54. doi: 10.1007/s00234-022-03015-7. Epub 2022 Jul 25.

Abstract

BACKGROUND

Because of the lack of global accessibility, delay, and cost-effectiveness of genetic testing, there is a clinical need for an imaging-based stratification of gliomas that can prognosticate survival and correlate with the 2021-WHO classification.

METHODS

In this retrospective study, adult primary glioma patients with pre-surgery/pre-treatment MRI brain images having T2, FLAIR, T1, T1 post-contrast, DWI sequences, and survival information were included in TCIA training-dataset (n = 275) and independent validation-dataset (n = 200). A flowchart for imaging-based stratification of adult gliomas(IBGS) was created in consensus by three authors to encompass all adult glioma types. Diagnostic features used were T2-FLAIR mismatch sign, central necrosis with peripheral enhancement, diffusion restriction, and continuous cortex sign. Roman numerals (I, II, and III) denote IBGS types. Two independent teams of three and two radiologists, blinded to genetic, histology, and survival information, manually read MRI into three types based on the flowchart. Overall survival-analysis was done using age-adjusted Cox-regression analysis, which provided both hazard-ratio (HR) and area-under-curve (AUC) for each stratification system(IBGS and 2021-WHO). The sensitivity and specificity of each IBSG type were analyzed with cross-table to identify the corresponding 2021-WHO genotype.

RESULTS

Imaging-based stratification was statistically significant in predicting survival in both datasets with good inter-observer agreement (age-adjusted Cox-regression, AUC > 0.5, k > 0.6, p < 0.001). IBGS type-I, type-II, and type-III gliomas had good specificity in identifying IDHmut 1p19q-codel oligodendroglioma (training - 97%, validation - 85%); IDHmut 1p19q non-codel astrocytoma (training - 80%, validation - 85.9%); and IDHwt glioblastoma (training - 76.5%, validation- 87.3%) respectively (p-value < 0.01).

CONCLUSIONS

Imaging-based stratification of adult diffuse gliomas predicted patient survival and correlated well with 2021-WHO glioma classification.

摘要

背景

由于基因检测缺乏全球可及性、存在延迟且成本效益不高,临床上需要一种基于影像学的胶质瘤分层方法,能够预测生存情况并与2021年世界卫生组织(WHO)分类相关联。

方法

在这项回顾性研究中,成年原发性胶质瘤患者术前/治疗前的脑部MRI图像包含T2、液体衰减反转恢复序列(FLAIR)、T1、T1增强后、扩散加权成像(DWI)序列以及生存信息,被纳入癌症成像存档(TCIA)训练数据集(n = 275)和独立验证数据集(n = 200)。三位作者共同制定了一个基于影像学的成人胶质瘤分层(IBGS)流程图,涵盖所有成人胶质瘤类型。所使用的诊断特征包括T2-FLAIR不匹配征、中央坏死伴周边强化、扩散受限和连续皮质征。罗马数字(I、II和III)表示IBGS类型。两个独立的团队,分别由三名和两名放射科医生组成,在对基因、组织学和生存信息不知情的情况下,根据流程图将MRI手动分为三种类型。使用年龄调整的Cox回归分析进行总生存分析,该分析为每个分层系统(IBGS和2021-WHO)提供风险比(HR)和曲线下面积(AUC)。通过交叉表分析每种IBSG类型的敏感性和特异性,以确定相应的2021-WHO基因型。

结果

基于影像学的分层在两个数据集中对生存的预测具有统计学意义,观察者间一致性良好(年龄调整的Cox回归,AUC > 0.5,κ > 0.6,p < 0.001)。IBGS I型、II型和III型胶质瘤在识别异柠檬酸脱氢酶(IDH)突变1p19q共缺失少突胶质细胞瘤(训练集 - 97%,验证集 - 85%);IDH突变1p19q非共缺失星形细胞瘤(训练集 - 80%,验证集 - 85.9%);以及IDH野生型胶质母细胞瘤(训练集 - 76.5%,验证集 - 87.3%)方面分别具有良好的特异性(p值 < 0.01)。

结论

基于影像学的成人弥漫性胶质瘤分层可预测患者生存情况,并与2021-WHO胶质瘤分类具有良好的相关性。

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