Streamlined intraoperative brain tumor classification and molecular subtyping in stereotactic biopsies using stimulated Raman histology and deep learning.

Abstract

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. 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. 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. 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.