Purpose: Recent artificial intelligence (AI) algorithms aided intraoperative decision-making via stimulated Raman histology (SRH) during craniotomy. This study assesses deep-learning algorithms for rapid intraoperative diagnosis from SRH images in small stereotactic-guided brain biopsies. It defines a minimum tissue sample size threshold to ensure diagnostic accuracy. Experimental Design: A prospective single-center study examined 121 SRH images from 84 patients with unclear intracranial lesions undergoing stereotactic brain biopsy. Unprocessed, label-free samples were imaged with a portable fiber-laser Raman scattering microscope. Three deep-learning models were tested to (I) identify tumorous/non-tumorous tissue as qualitative biopsy control, (II) subclassify into high-grade glioma (CNS WHO grade 4), diffuse low-grade glioma (CNS WHO grade 2-3), metastases, lymphoma, or gliosis, and (III) molecularly subtype IDH- and 1p/19q-status of adult-type diffuse gliomas. Model predictions were evaluated against frozen section analysis and final neuropathological diagnoses. Results: The first model identified tumorous/non-tumorous tissue with 91.7% accuracy. Sample size on slides impacted accuracy in brain tumor subclassification (81.6%, κ=0.72 frozen section; 73.9%, κ=0.61 second model), with SRH being smaller than H&E (4.1±2.5mm² vs 16.7±8.2mm², p<0.001). SRH images with over 140 high-quality patches and a mean squeezed sample of 5.26mm² yielded 89.5% accuracy in subclassification and 93.9% in molecular subtyping of adult-type diffuse gliomas. Conclusions: AI-based SRH image analysis is non-inferior to frozen section analysis in detecting and subclassifying brain tumors during small stereotactic-guided biopsies once a critical squeezed sample size is reached. Beyond frozen section analysis, it enables valid molecular glioma subtyping, allowing faster treatment decisions in the future. Refinement is needed for long-term application.

中文翻译：

---

使用刺激拉曼组织学和深度学习简化立体定向活检中的术中脑肿瘤分类和分子亚型


目的：最新的人工智能 (AI) 算法在开颅手术期间通过受激拉曼组织学 (SRH) 辅助术中决策。本研究评估了深度学习算法，用于在小型立体定向脑活检中根据 SRH 图像进行快速术中诊断。它定义了最小组织样本量阈值以确保诊断准确性。实验设计：一项前瞻性单中心研究检查了 84 名颅内病变不明且接受立体定向脑活检的患者的 121 张 SRH 图像。使用便携式光纤激光拉曼散射显微镜对未经处理的无标记样品进行成像。测试了三种深度学习模型，以 (I) 识别肿瘤/非肿瘤组织作为定性活检对照，(II) 细分为高级别胶质瘤（CNS WHO 4 级）、弥漫性低级别胶质瘤（CNS WHO 2 级） 3)、转移、淋巴瘤或神经胶质瘤，以及(III)成人型弥漫性神经胶质瘤的分子亚型IDH-和1p/19q-状态。根据冰冻切片分析和最终神经病理学诊断评估模型预测。结果：第一个模型识别肿瘤/非肿瘤组织的准确度为 91.7%。载玻片上的样本大小影响脑肿瘤亚分类的准确性（81.6%，κ=0.72 冰冻切片；73.9%，κ=0.61 第二个模型），SRH 小于 H&E（4.1±2.5mm² vs 16.7±8.2mm²，p<0.001 ）。具有 140 多个高质量斑块和 5.26mm² 平均压缩样本的 SRH 图像在成人型弥漫性胶质瘤的子分类中产生了 89.5% 的准确率，在分子亚型分型中产生了 93.9% 的准确率。结论：一旦达到临界压缩样本量，在小型立体定向活检期间，基于人工智能的 SRH 图像分析在检测和细分脑肿瘤方面并不逊色于冰冻切片分析。 除了冰冻切片分析之外，它还可以实现有效的分子神经胶质瘤亚型分析，从而在未来更快地做出治疗决策。长期应用需要完善。