Combination of multi-modal MRI radiomics and liquid biopsy technique for preoperatively non-invasive diagnosis of glioma based on deep learning: protocol for a double-center, ambispective, diagnostical observational study

被引：1

作者：

Hu, Ping ^{[1
,2
]}

Xu, Ling ^{[3
]}

Qi, Yangzhi ^{[2
]}

Yan, Tengfeng ^{[1
]}

Ye, Liguo ^{[4
]}

Wen, Shen ^{[3
]}

Yuan, Dalong ^{[3
]}

Zhu, Xinyi ^{[2
]}

Deng, Shuhang ^{[2
]}

Liu, Xun ^{[3
]}

Xu, Panpan ^{[3
]}

You, Ran ^{[3
]}

Wang, Dongfang ^{[3
]}

Liang, Shanwen ^{[2
]}

Wu, Yu ^{[2
]}

Xu, Yang ^{[2
]}

Sun, Qian ^{[2
]}

Du, Senlin ^{[1
]}

Yuan, Ye ^{[1
]}

Deng, Gang ^{[2
]}

Cheng, Jing ^{[2
]}

Zhang, Dong ^{[3
]}

Chen, Qianxue ^{[2
]}

Zhu, Xingen ^{[1
]}

机构：

[1] Nanchang Univ, Affiliated Hosp 2, Dept Neurosurg, Nanchang, Jiangxi, Peoples R China

[2] Wuhan Univ, Renmin Hosp, Dept Neurosurg, Wuhan, Hubei, Peoples R China

[3] Wuhan Univ, Sch Phys & Technol, Wuhan, Hubei, Peoples R China

[4] Chinese Acad Med Sci, Peking Union Med Coll Hosp, Peking Union Med Coll, Dept Neurosurg, Beijing, Peoples R China

来源：

FRONTIERS IN MOLECULAR NEUROSCIENCE | 2023年 / 16卷

基金：

中国国家自然科学基金;

关键词：

glioma; radiomic; liquid biopsy; circulating tumor cell; histopathology; molecular pathology; diagnosis; CLASSIFICATION; TUMORS;

D O I：

10.3389/fnmol.2023.1183032

中图分类号：

Q189 [神经科学];

学科分类号：

071006 ;

摘要：

Background: 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. Methods: 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. Discussion: 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.

引用

页数：11