Rapid intraoperative multi-molecular diagnosis of glioma with ultrasound radio frequency signals and deep learning.

Dec 04, 2023

Contributors: Xuan Xie,Chao Shen,Xiandi Zhang,Guoqing Wu,Bojie Yang,Zengxin Qi,Qisheng Tang,Yuanyuan Wang,Hong Ding,Zhifeng Shi,Jinhua Yu

Molecular diagnosis is crucial for biomarker-assisted glioma resection and management. However, some limitations of current molecular diagnostic techniques prevent their widespread use intraoperatively. With the unique advantages of ultrasound, this study developed a rapid intraoperative molecular diagnostic method based on ultrasound radio-frequency signals.
We built a brain tumor ultrasound bank with 169 cases enrolled since July 2020, of which 43483 RF signal patches from 67 cases with a pathological diagnosis of glioma were a retrospective cohort for model training and validation. IDH1 and TERT promoter (TERTp) mutations and 1p/19q co-deletion were detected by next-generation sequencing. We designed a spatial-temporal integration model (STIM) to diagnose the three molecular biomarkers, thus establishing a rapid intraoperative molecular diagnostic system for glioma, and further analysed its consistency with the fifth edition of the WHO Classification of Tumors of the Central Nervous System (WHO CNS5). We tested STIM in 16-case prospective cohorts, which contained a total of 10384 RF signal patches. Two other RF-based classical models were used for comparison. Further, we included 20 cases additional prospective data for robustness test (ClinicalTrials.govNCT05656053).
In the retrospective cohort, STIM achieved a mean accuracy and AUC of 0.9190 and 0.9650 (95% CI, 0.94-0.99) respectively for the three molecular biomarkers, with a total time of 3 s and a 96% match to WHO CNS5. In the prospective cohort, the diagnostic accuracy of STIM is 0.85 ± 0.04 (mean ± SD) for IDH1, 0.84 ± 0.05 for TERTp, and 0.88 ± 0.04 for 1p/19q. The AUC is 0.89 ± 0.02 (95% CI, 0.84-0.94) for IDH1, 0.80 ± 0.04 (95% CI, 0.71-0.89) for TERTp, and 0.85 ± 0.06 (95% CI, 0.73-0.98) for 1p/19q. Compared to the second best available method based on RF signal, the diagnostic accuracy of STIM is improved by 16.70% and the AUC is improved by 19.23% on average.
STIM is a rapid, cost-effective, and easy-to-manipulate AI method to perform real-time intraoperative molecular diagnosis. In the future, it may help neurosurgeons designate personalized surgical plans and predict survival outcomes.
A full list of funding bodies that contributed to this study can be found in the Acknowledgements section.

Copyright © 2023 The Author(s). Published by Elsevier B.V. All rights reserved.

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ABOUT THE CONTRIBUTORS

Xuan Xie,Chao Shen,Xiandi Zhang,Guoqing Wu,Bojie Yang,Zengxin Qi,Qisheng Tang,Yuanyuan Wang,Hong Ding,Zhifeng Shi,Jinhua Yu
Xuan Xie

School of Information Science and Technology, Fudan University, Shanghai, China; Neurosurgical Institute of Fudan University, Shanghai, China.

Chao Shen

Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China; National Center for Neurological Disorders, Shanghai, China; Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Shanghai, China; Neurosurgical Institute of Fudan University, Shanghai, China.

Xiandi Zhang

Department of Ultrasound, Huashan Hospital, Fudan University, Shanghai, China.

Guoqing Wu

School of Information Science and Technology, Fudan University, Shanghai, China; Neurosurgical Institute of Fudan University, Shanghai, China.

Bojie Yang

Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China; National Center for Neurological Disorders, Shanghai, China; Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Shanghai, China; Neurosurgical Institute of Fudan University, Shanghai, China.

Zengxin Qi

Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China; National Center for Neurological Disorders, Shanghai, China; Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Shanghai, China; Neurosurgical Institute of Fudan University, Shanghai, China.

Qisheng Tang

Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China; National Center for Neurological Disorders, Shanghai, China; Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Shanghai, China; Neurosurgical Institute of Fudan University, Shanghai, China.

Yuanyuan Wang

School of Information Science and Technology, Fudan University, Shanghai, China.

Hong Ding

Department of Ultrasound, Huashan Hospital, Fudan University, Shanghai, China. Electronic address: ding_hong@fudan.edu.cn.

Zhifeng Shi

Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China; National Center for Neurological Disorders, Shanghai, China; Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Shanghai, China; Neurosurgical Institute of Fudan University, Shanghai, China. Electronic address: shizhifeng@fudan.edu.cn.

Jinhua Yu

School of Information Science and Technology, Fudan University, Shanghai, China; Neurosurgical Institute of Fudan University, Shanghai, China. Electronic address: jhyu@fudan.edu.cn.