Abstract

Background

Detection of glioma recurrence remains a challenge in modern neuro-oncology. Noninvasive radiographic imaging is unable to definitively differentiate true recurrence versus pseudoprogression. Even in biopsied tissue, it can be challenging to differentiate recurrent tumor and treatment effect. We hypothesized that intraoperative stimulated Raman histology (SRH) and deep neural networks can be used to improve the intraoperative detection of glioma recurrence.

Methods

We used fiber laser–based SRH, a label-free, nonconsumptive, high-resolution microscopy method (<60 sec per 1 × 1 mm²) to image a cohort of patients (n = 35) with suspected recurrent gliomas who underwent biopsy or resection. The SRH images were then used to train a convolutional neural network (CNN) and develop an inference algorithm to detect viable recurrent glioma. Following network training, the performance of the CNN was tested for diagnostic accuracy in a retrospective cohort (n = 48).

Results

Using patch-level CNN predictions, the inference algorithm returns a single Bernoulli distribution for the probability of tumor recurrence for each surgical specimen or patient. The external SRH validation dataset consisted of 48 patients (recurrent, 30; pseudoprogression, 18), and we achieved a diagnostic accuracy of 95.8%.

Conclusion

SRH with CNN-based diagnosis can be used to improve the intraoperative detection of glioma recurrence in near-real time. Our results provide insight into how optical imaging and computer vision can be combined to augment conventional diagnostic methods and improve the quality of specimen sampling at glioma recurrence.

中文翻译：

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使用术中刺激拉曼组织学和深度神经网络快速、无标记检测弥漫性胶质瘤复发

摘要

背景

神经胶质瘤复发的检测仍然是现代神经肿瘤学的一个挑战。无创放射成像无法明确区分真正的复发与假性进展。即使在活检组织中，区分复发性肿瘤和治疗效果也可能具有挑战性。我们假设术中刺激拉曼组织学 (SRH) 和深度神经网络可用于改善胶质瘤复发的术中检测。

方法

我们使用基于光纤激光的 SRH，一种无标记、非消耗性、高分辨率显微镜方法（<60 秒/1 × 1 mm ² ）对一组 疑似复发性胶质瘤患者（n = 35）进行了活检或切除。然后使用 SRH 图像训练卷积神经网络 (CNN) 并开发推理算法来检测可行的复发性神经胶质瘤。在网络训练之后，CNN 的性能在回顾性队列（n =  48）中进行了诊断准确性测试。

结果

使用补丁级 CNN 预测，推理算法返回单个伯努利分布，用于每个手术标本或患者的肿瘤复发概率。外部 SRH 验证数据集包含 48 名患者（复发，30；假性进展，18），我们的诊断准确率为 95.8%。

结论

具有基于 CNN 诊断的 SRH 可用于近实时改善胶质瘤复发的术中检测。我们的结果提供了有关如何将光学成像和计算机视觉相结合以增强传统诊断方法并提高胶质瘤复发时标本取样质量的见解。