Search

Abstract BACKGROUND Differentiating neoplastic tumor tissues from normal brain at the infiltrative tumor margin remains challenging. Stimulated Raman scattering microscopy (SRM) is a rapid, label-free technique that provides microscopic imaging of unprocessed tissues. However, it has never been studied as a tool to enhance extent of resection. METHODS In this single center study, patients with WHO II-IV gliomas undergoing surgical resection were included. Margin samples were taken with corresponding stereotactic coordinates. Each tumor margin sample was analyzed using (1) H&E, (2) SRM, and (3) immunohistochemical (IHC) stains for IDH1-R132H or p53. Specimens were imaged fresh with SRM, inked for orientation and placed in formalin for routine processing. Stained slides were scored by 3-neuro-pathologists using a semiquantitative 2-tiered scoring system for the presence or absence of residual tumor. SRM microscopy images were segmented into cellularity. The intraobserver measure of agreement between modalities for each pathologist was calculated using Cohen’s kappa. RESULTS A total of 31 patients and 179 margin specimens were acquired. Postoperative MRI confirmed that 169 (94%) of glioma margin sample coordinates were indeed at the tumor margins (10 of 179 were not true margins). All 10 samples which were not true margins were identified as having residual tumor by SRM. IHC semiquantitative scoring of margin specimen confirmed 72 of 128 samples (56%) had residual tumor. Similarly, H&E-scoring confirmed 82 of 169 samples (49%) and 82 of 167 samples (49%) by SRM-scoring had residual tumor. Intraobserver agreements between all 3 modalities confirmed agreement: IHC-SRM was near perfect (K-0.84, CI-0.750-0.94); IHC-H&E substantial, (K-0.67 CI-0.54-0.80); SRM-H&E agreement substantial (K-0.72 CI-0.62-0.83). Margin samples with residual tumor demonstrated higher tissue cellularity when compared with samples without tumor (p< 0.0001). CONCLUSIONS SRM allows for identification of residual microscopic disease at the infiltrative tumor margin and therefore may be a promising tool to enhance extent of resection.

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.

Rice blast caused by fungal pathogen Pyricularia oryzae has a major impact on reducing yield potential of rice. In this study, homozygous plants were selected using microsatellite markers from the BC3F2 population pyramided with four major genes in elite rice variety ADT 43. Background and selected lines with various blast resistance gene combinations were screened under natural conditions to study the effects of various gene combinations. Upon inspection of lines with different gene combinations, the three-gene pyramided line Pi54+Pi33+Pi1 was found to be highly resistant with the score of 3.3 followed by other three-gene pyramided lines Pi54+Pi2+Pi1 and Pi33+Pi2+Pi1, with the scores of 3.9 and 3.8, respectively. Two-gene pyramided lines Pi54+Pi1, Pi33+Pi1 and Pi2+Pi1 were found to be moderately resistant with a mean score of 4.0 each. In the case of monogenic lines, positive plants for Pi54 performed almost equal to three-gene pyramided lines with a mean score of 3.6. Lines with Pi2 and Pi1 were found to be moderately resistant and moderately susceptible with the mean scores of 4.1 and 4.5, respectively. [ABSTRACT FROM AUTHOR]

Conventional methods for intraoperative histopathologic diagnosis are labor- and time-intensive and may delay decision-making during brain tumor surgery. Stimulated Raman scattering (SRS) microscopy, a label-free optical process, has been shown to rapidly detect brain tumor infiltration in fresh, unprocessed human tissues. Previously, the execution of SRS microscopy in a clinical setting has not been possible. We report the first demonstration of SRS microscopy in an operating room using a portable fiber-laser-based microscope in unprocessed specimens from 101 neurosurgical patients. Additionally, we introduce an image-processing method, stimulated Raman histology (SRH), which leverages SRS images to create virtual hematoxylin and eosin- stained slides, revealing essential diagnostic features. In a simulation of intraoperative pathologic consultation in 30 patients, the concordance of SRH and conventional histology for predicting diagnosis was nearly perfect (κ>0.89) and accuracy exceeded 92%. We also built and validated a multilayer perceptron based on quantified SRH image attributes that predicts brain tumor subtype with 90% accuracy. This study provides insight into how SRH can now be used to improve the surgical care of brain tumor patients., Chemistry and Chemical Biology