Department of Stereotactic and Functional Neurosurgery, Center for Neurosurgery, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany.
Department of General Neurosurgery, Center for Neurosurgery, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany.
Clin Cancer Res. 2024 Sep 3;30(17):3824-3836. doi: 10.1158/1078-0432.CCR-23-3842.
Recent artificial intelligence 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.
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 using a portable fiber laser Raman scattering microscope. Three deep learning models were tested to (i) identify tumorous/nontumorous tissue as qualitative biopsy control; (ii) subclassify into high-grade glioma (central nervous system World Health Organization grade 4), diffuse low-grade glioma (central nervous system World Health Organization grades 2-3), metastases, lymphoma, or gliosis; and (iii) molecularly subtype IDH and 1p/19q statuses of adult-type diffuse gliomas. Model predictions were evaluated against frozen section analysis and final neuropathologic diagnoses.
The first model identified tumorous/nontumorous 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 images being smaller than hematoxylin and eosin images (4.1 ± 2.5 mm2 vs. 16.7 ± 8.2 mm2, P < 0.001). SRH images with more than 140 high-quality patches and a mean squeezed sample of 5.26 mm2 yielded 89.5% accuracy in subclassification and 93.9% in molecular subtyping of adult-type diffuse gliomas.
Artificial intelligence-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; however, refinement is needed for long-term application.
最近的人工智能算法通过在开颅术中的受激拉曼组织学（SRH）来辅助术中决策。本研究评估了深度学习算法在小立体定向引导脑活检中从 SRH 图像进行快速术中诊断的能力。它定义了一个最小组织样本量阈值，以确保诊断准确性。
一项前瞻性单中心研究检查了 84 名接受立体定向脑活检的颅内不明病变患者的 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签样本进行成像。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) 分为高级别胶质瘤（中枢神经系统世界卫生组织 4 级）、弥漫性低级别胶质瘤（中枢神经系统世界卫生组织 2-3 级）、转移瘤、淋巴瘤或神经胶质增生；以及 (iii) 对成人型弥漫性神经胶质瘤的 IDH 和 1p/19q 状态进行分子亚型分类。模型预测结果与冷冻切片分析和最终神经病理诊断进行了比较。
第一个模型对肿瘤/非肿瘤组织的识别准确率为 91.7%。载玻片上的样本大小影响了脑肿瘤分类的准确性（81.6%，κ=0.72 与冷冻切片；73.9%，κ=0.61 与第二个模型），SRH 图像比苏木精和伊红图像小（4.1±2.5mm²与 16.7±8.2mm²，P<0.001）。具有超过 140 个高质量斑块和平均挤压样本为 5.26mm²的 SRH 图像在分类和成人型弥漫性神经胶质瘤的分子亚型分类中分别达到了 89.5%和 93.9%的准确率。
在小立体定向引导活检中，基于人工智能的 SRH 图像分析在检测和分类脑肿瘤方面不逊于冷冻切片分析，一旦达到临界挤压样本量。除了冷冻切片分析外，它还能够对胶质母细胞瘤进行有效的分子亚型分类，从而为未来更快地做出治疗决策提供依据；然而，为了长期应用，还需要进一步的改进。
