背景:分子诊断对于生物标志物辅助的神经胶质瘤切除和治疗至关重要。然而,当前分子诊断技术的一些局限性阻碍了它们在术中的广泛使用.凭借超声波的独特优势,这项研究开发了一种基于超声射频信号的快速术中分子诊断方法。
方法:自2020年7月以来,我们建立了169例脑肿瘤超声库,其中67例病理诊断为胶质瘤的患者的43483个射频信号片是回顾性队列,用于模型训练和验证。通过下一代测序检测IDH1和TERT启动子(TERTp)突变和1p/19q共缺失。我们设计了一个时空整合模型(STIM)来诊断三种分子生物标志物,从而建立了脑胶质瘤的快速术中分子诊断系统,并进一步分析了其与世界卫生组织中枢神经系统肿瘤分类第五版(WHOCNS5)的一致性。我们在16例前瞻性队列中测试了STIM,总共包含10384个射频信号补丁。使用另外两个基于RF的经典模型进行比较。Further,我们纳入了20例其他前瞻性数据进行稳健性检验(ClinicalTrials.govNCT05656053).
结果:在回顾性队列中,STIM的平均准确度和AUC分别为0.9190和0.9650(95%CI,0.94-0.99),总时间为3s,与WHOCNS5的匹配率为96%。在前瞻性队列中,STIM对IDH1的诊断准确率为0.85±0.04(平均值±SD),对TERTp的诊断准确率为0.84±0.05,1p/19q为0.88±0.04。IDH1的AUC为0.89±0.02(95%CI,0.84-0.94),TERTp的AUC为0.80±0.04(95%CI,0.71-0.89),1便士/19q为0.85±0.06(95%CI,0.73-0.98)。与基于射频信号的第二好方法相比,STIM的诊断准确率提高了16.70%,AUC平均提高了19.23%.
结论:STIM是一种快速,成本效益高,和易于操作的AI方法来执行实时术中分子诊断。在未来,它可以帮助神经外科医生指定个性化的手术计划并预测生存结局.
背景:为这项研究做出贡献的资助机构的完整列表可以在确认部分找到。
BACKGROUND: 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.
METHODS: 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).
RESULTS: 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.
CONCLUSIONS: 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.
BACKGROUND: A full list of funding bodies that contributed to this study can be found in the Acknowledgements section.