1. Streamlined Intraoperative Brain Tumor Classification and Molecular Subtyping in Stereotactic Biopsies Using Stimulated Raman Histology and Deep Learning.
- Author
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Reinecke D, Ruess D, Meissner AK, Fürtjes G, von Spreckelsen N, Ion-Margineanu A, Khalid F, Blau T, Stehle T, Al-Shugri A, Büttner R, Goldbrunner R, Ruge MI, and Neuschmelting V
- Subjects
- Humans, Male, Female, Middle Aged, Aged, Adult, Prospective Studies, Stereotaxic Techniques, Biopsy, Neoplasm Grading, Algorithms, Deep Learning, Brain Neoplasms pathology, Brain Neoplasms classification, Brain Neoplasms genetics, Brain Neoplasms diagnosis, Brain Neoplasms surgery, Spectrum Analysis, Raman methods, Glioma pathology, Glioma classification, Glioma genetics, Glioma surgery, Glioma diagnosis
- Abstract
Purpose: 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., 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 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., Results: 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., Conclusions: 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., (©2024 American Association for Cancer Research.)
- Published
- 2024
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