Hu Ping, Xu Ling, Qi Yangzhi, Yan Tengfeng, Ye Liguo, Wen Shen, Yuan Dalong, Zhu Xinyi, Deng Shuhang, Liu Xun, Xu Panpan, You Ran, Wang Dongfang, Liang Shanwen, Wu Yu, Xu Yang, Sun Qian, Du Senlin, Yuan Ye, Deng Gang, Cheng Jing, Zhang Dong, Chen Qianxue, Zhu Xingen
Department of Neurosurgery, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China.
Department of Neurosurgery, Renmin Hospital of Wuhan University, Wuhan, Hubei, China.
Front Mol Neurosci. 2023 May 2;16:1183032. doi: 10.3389/fnmol.2023.1183032. eCollection 2023.
2021 World Health Organization (WHO) Central Nervous System (CNS) tumor classification increasingly emphasizes the important role of molecular markers in glioma diagnoses. Preoperatively non-invasive "integrated diagnosis" will bring great benefits to the treatment and prognosis of these patients with special tumor locations that cannot receive craniotomy or needle biopsy. Magnetic resonance imaging (MRI) radiomics and liquid biopsy (LB) have great potential for non-invasive diagnosis of molecular markers and grading since they are both easy to perform. This study aims to build a novel multi-task deep learning (DL) radiomic model to achieve preoperative non-invasive "integrated diagnosis" of glioma based on the 2021 WHO-CNS classification and explore whether the DL model with LB parameters can improve the performance of glioma diagnosis.
This is a double-center, ambispective, diagnostical observational study. One public database named the 2019 Brain Tumor Segmentation challenge dataset (BraTS) and two original datasets, including the Second Affiliated Hospital of Nanchang University, and Renmin Hospital of Wuhan University, will be used to develop the multi-task DL radiomic model. As one of the LB techniques, circulating tumor cell (CTC) parameters will be additionally applied in the DL radiomic model for assisting the "integrated diagnosis" of glioma. The segmentation model will be evaluated with the Dice index, and the performance of the DL model for WHO grading and all molecular subtype will be evaluated with the indicators of accuracy, precision, and recall.
Simply relying on radiomics features to find the correlation with the molecular subtypes of gliomas can no longer meet the need for "precisely integrated prediction." CTC features are a promising biomarker that may provide new directions in the exploration of "precision integrated prediction" based on the radiomics, and this is the first original study that combination of radiomics and LB technology for glioma diagnosis. We firmly believe that this innovative work will surely lay a good foundation for the "precisely integrated prediction" of glioma and point out further directions for future research.
This study was registered on ClinicalTrails.gov on 09/10/2022 with Identifier NCT05536024.
2021年世界卫生组织（WHO）中枢神经系统（CNS）肿瘤分类越来越强调分子标志物在胶质瘤诊断中的重要作用。对于那些因肿瘤位置特殊而无法接受开颅手术或穿刺活检的患者，术前非侵入性的“综合诊断”将给其治疗和预后带来极大益处。磁共振成像（MRI）放射组学和液体活检（LB）因其操作简便，在分子标志物的非侵入性诊断及分级方面具有巨大潜力。本研究旨在构建一种新型多任务深度学习（DL）放射组学模型，以基于2021年WHO-CNS分类实现胶质瘤的术前非侵入性“综合诊断”，并探索纳入LB参数的DL模型是否能提高胶质瘤诊断性能。
这是一项双中心、双盲、诊断性观察研究。将使用一个名为2019年脑肿瘤分割挑战数据集（BraTS）的公共数据库以及两个原始数据集（包括南昌大学第二附属医院和武汉大学人民医院的数据集）来开发多任务DL放射组学模型。作为LB技术之一，循环肿瘤细胞（CTC）参数将被额外应用于DL放射组学模型，以辅助胶质瘤的“综合诊断”。分割模型将用Dice指数进行评估，DL模型对WHO分级和所有分子亚型的性能将用准确性、精确性和召回率指标进行评估。
单纯依靠放射组学特征来寻找与胶质瘤分子亚型的相关性已无法满足“精确综合预测”的需求。CTC特征是一种有前景的生物标志物，可能为基于放射组学的“精确综合预测”探索提供新方向，这是第一项将放射组学和LB技术结合用于胶质瘤诊断的原创性研究。我们坚信，这项创新性工作必将为胶质瘤的“精确综合预测”奠定良好基础，并为未来研究指明进一步方向。
本研究于2022年10月9日在ClinicalTrails.gov上注册，标识符为NCT0553602
