Near real-time intraoperative brain tumor diagnosis using stimulated Raman histology and deep neural networks

By Todd C. Hollon, B. Pandian, S. Khalsa, R. D’Amico, M. Sisti, J. Bruce, K. Muraszko, B. Thompson, C. Freudiger, S. Hervey-Jumper and 5 more. Nature Medicine, 2020

642 citations recorded by the source corpus.

Intraoperative diagnosis is essential for providing safe and effective care during cancer surgery1. The existing workflow for intraoperative diagnosis based on hematoxylin and eosin staining of processed tissue is time, resource and labor intensive2,3. Moreover, interpretation of intraoperative histologic images is dependent on a contracting, unevenly distributed, pathology workforce4. In the present study, we report a parallel workflow that combines stimulated Raman histology (SRH)5–7, a label-free optical imaging method and deep convolutional neural networks (CNNs) to predict diagnosis at the bedside in near real-time in an automated fashion. Specifically, our CNNs, trained on over 2.5 million SRH images, predict brain tumor diagnosis in the operating room in under 150 s, an order of magnitude faster than conventional techniques (for example, 20–30 min)2. In a multicenter, prospective clinical trial (n = 278), we demonstrated that CNN-based diagnosis of SRH images was noninferior to pathologist-based interpretation of conventional histologic images (overall accuracy, 94.6% versus 93.9%). Our CNNs learned a hierarchy of recognizable histologic feature representations to classify the major histopathologic classes of brain tumors. In addition, we implemented a semantic segmentation method to identify tumor-infiltrated diagnostic regions within SRH images. These results demonstrate how intraoperative cancer diagnosis can be streamlined, creating a complementary pathway for tissue diagnosis that is independent of a traditional pathology laboratory. A prospective, multicenter, case–control clinical trial evaluates the potential of artificial intelligence for providing accurate bedside diagnosis of patients with brain tumors.

Subfields: Artificial Intelligence, Biophysics, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/3ea82a8c74fdf3bdbbee4c96354746ccbf8e22ee

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Citations in the corpus

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Annotation-efficient deep learning for automatic medical image segmentation (2020, background): “Medical imaging contributes significantly to progress in scientific discoveries and medicine 1 .”
Accurate brain tumor detection using deep convolutional neural network (2022, background): “Just in 2019, about 86,970 cases of benign and malignant brain tumors are diagnosed [5].”
Artificial intelligence for digital and computational pathology (2023, neutral): “sensitivity from WSIs, allowing the identification of patients who do not require further testing 133 .”