Confirmation and characterization of the first case of acetolactate synthase (ALS)-inhibitor resistance in Japanese brome (Bromus japonicus) in the US.

BACKGROUND: Japanese brome (Bromus japonicus Thumb.) is one of the problematic annual weeds in winter wheat (Triticum aestivum L.) and is generally controlled by acetolactate synthase (ALS) inhibitors. Repeated use of the ALS inhibitor propoxycarbazone-Na resulted in the evolution of resistance to this herbicide in three B. japonicus populations, i.e., R1, R2, and R3 in Kansas (KS). However, the level of resistance and mechanism conferring resistance in these populations is unknown. The objectives of this research were to (i) evaluate the level of resistance to propoxycarbazone-Na in R1, R2, and R3 in comparison with a known susceptible population (S1), (ii) investigate the mechanism of resistance involved in conferring ALS-inhibitor resistance, and (iii) investigate the cross-resistance to other ALS inhibitors. RESULTS: Dose-response (0 to 16x; x = 44 g ai ha-1 of propoxycarbazone-Na) assay indicated 167, 125, and 667-fold resistance in R1, R2 and R3 populations, respectively, compared to S1 population. ALS gene sequencing confirmed the mutations resulting in amino acid substitutions, i.e., Pro-197-Thr (R3, R1)/Ser (R2, R1) bestowing resistance to these ALS inhibitors. Such amino acid substitutions also showed differential cross-resistance to sulfosulfuron, mesosulfuron-methyl, pyroxsulam, and imazamox among resistant populations. Pretreatment with malathion (a cytochrome P450 enzyme-inhibitor) followed by imazamox treatment suggested cross-resistance to this herbicide possibly via metabolism only in R3 population. CONCLUSION: Overall, these results confirm the first case of target-site based resistance to ALS inhibitors in B. japonicus in the US, highlighting the need for exploring herbicides with alternative modes of action to enhance weed control in winter wheat. © 2024 Society of Chemical Industry.

Identification and Characterization of Mesotrione-Resistant Grain Sorghum [Sorghum bicolor (L.) Moench]: A Viable Option for Postemergence Grass Weed Control.

Mesotrione is effective in controlling a wide spectrum of weeds in corn but not registered for postemergence use in sorghum because of crop injury. We screened a sorghum germplasm collection and identified two mesotrione-resistant sorghum genotypes (G-1 and G-10) and one susceptible genotype (S-1) in an in vitro plate assay. A mesotrione dose-response assay under greenhouse and field conditions confirmed that G-1 and G-10 are highly resistant compared to S-1. We found enhanced metabolism of mesotrione in G-1 and G-10 using HPLC assay, and a significant reduction in biomass accumulation was found in G-1 and G-10 plants pretreated with cytochrome P450 (CYP)-inhibitors malathion or piperonyl butoxide, indicating the involvement of CYPs in the metabolism of mesotrione. Genetic analyses using F1 and F2 progenies generated by crossing G-1 and G-10 separately with S-1 revealed that mesotrione resistance in sorghum is controlled by a single dominant gene along with several genes with minor effects.

Rapid Automated Analysis of Skull Base Tumor Specimens Using Intraoperative Optical Imaging and Artificial Intelligence.

BACKGROUND: Accurate specimen analysis of skull base tumors is essential for providing personalized surgical treatment strategies. Intraoperative specimen interpretation can be challenging because of the wide range of skull base pathologies and lack of intraoperative pathology resources. OBJECTIVE: To develop an independent and parallel intraoperative workflow that can provide rapid and accurate skull base tumor specimen analysis using label-free optical imaging and artificial intelligence. METHODS: We used a fiber laser-based, label-free, nonconsumptive, high-resolution microscopy method (<60 seconds per 1 × 1 mm2), called stimulated Raman histology (SRH), to image a consecutive, multicenter cohort of patients with skull base tumor. SRH images were then used to train a convolutional neural network model using 3 representation learning strategies: cross-entropy, self-supervised contrastive learning, and supervised contrastive learning. Our trained convolutional neural network models were tested on a held-out, multicenter SRH data set. RESULTS: SRH was able to image the diagnostic features of both benign and malignant skull base tumors. Of the 3 representation learning strategies, supervised contrastive learning most effectively learned the distinctive and diagnostic SRH image features for each of the skull base tumor types. In our multicenter testing set, cross-entropy achieved an overall diagnostic accuracy of 91.5%, self-supervised contrastive learning 83.9%, and supervised contrastive learning 96.6%. Our trained model was able to segment tumor-normal margins and detect regions of microscopic tumor infiltration in meningioma SRH images. CONCLUSION: SRH with trained artificial intelligence models can provide rapid and accurate intraoperative analysis of skull base tumor specimens to inform surgical decision-making.

Current status and prospects of herbicide-resistant grain sorghum (Sorghum bicolor).

Grain sorghum is a versatile crop, which can thrive under limited water and other inputs. However, crop loss from weed infestation continues to be a major constraint in grain sorghum production. Particularly, post-emergence grass weed control is a great challenge in grain sorghum due to the lack of herbicide options. Unlike in other major crops, such as maize or soybean, herbicide-resistant sorghum technology that can facilitate weed control throughout crop growing season is not available to growers yet. The development of herbicide-resistant sorghum can have potential to improve weed management, including post-emergence grass weed control. One of the major concerns in the development of such technology in sorghum is escape of resistance traits into weedy relatives of sorghum (e.g. shattercane and johnsongrass). This review focuses on sources of herbicide resistance in sorghum, the status of the development of herbicide-resistant sorghum technologies, overview of breeding methods, and limitations in the development of such sorghum technology as well as economic benefits for sorghum growers. © 2021 Society of Chemical Industry.

Detection of glioma infiltration at the tumor margin using quantitative stimulated Raman scattering histology.

In the management of diffuse gliomas, the identification and removal of tumor at the infiltrative margin remains a central challenge. Prior work has demonstrated that fluorescence labeling tools and radiographic imaging are useful surgical adjuvants with macroscopic resolution. However, they lose sensitivity at the tumor margin and have limited clinical utility for lower grade histologies. Fiber-laser based stimulated Raman histology (SRH) is an optical imaging technique that provides microscopic tissue characterization of unprocessed tissues. It remains unknown whether SRH of tissues taken from the infiltrative glioma margin will identify microscopic residual disease. Here we acquired glioma margin specimens for SRH, histology, and tumor specific tissue characterization. Generalized linear mixed models were used to evaluate agreement. We find that SRH identified residual tumor in 82 of 167 margin specimens (49%), compared to IHC confirming residual tumor in 72 of 128 samples (56%), and H&E confirming residual tumor in 82 of 169 samples (49%). Intraobserver agreements between all 3 modalities were confirmed. These data demonstrate that SRH detects residual microscopic tumor at the infiltrative glioma margin and may be a promising tool to enhance extent of resection.

A single gene inherited trait confers metabolic resistance to chlorsulfuron in grain sorghum (Sorghum bicolor).

MAIN CONCLUSION: This study confirms a high level of metabolic resistance to the herbicide chlorsulfuron, inherited by a single dominant gene in a sorghum genotype (GL-1). Chlorsulfuron, an acetolactate synthase (ALS)-inhibitor, effectively controls post-emergence grass and broadleaf weeds but is not registered for use in sorghum because of crop injury. The objectives of this study were to characterize the inheritance and mechanism of chlorsulfuron resistance in the sorghum genotype GL-1. Chlorsulfuron dose-response experiments were conducted using GL-1 along with BTx623 (susceptible check), and Pioneer 84G62 (commercial sorghum hybrid). The F1 and F2 progeny were generated by crossing GL-1 with BTx623. To assess if the target site alterations bestow resistance, the ALS gene, the molecular target of chlorsulfuron, was sequenced from GL-1. The role of cytochrome P450 (CYP) in metabolizing chlorsulfuron, using malathion, a CYP-inhibitor was tested. The chlorsulfuron dose-response assay indicated that GL-1 and F1 progeny were ~ 20-fold more resistant to chlorsulfuron relative to BTx623. The F2 progenies segregated 3:1 (resistance: susceptibility) suggesting that chlorsulfuron resistance in GL-1 is a single dominant trait. No mutations in the ALS gene were detected in the GL-1; however, a significant reduction in biomass accumulation was found in plants pre-treated with malathion indicating that metabolism of chlorsulfuron contributes to resistance in GL-1. Also, GL-1 is highly susceptible to other herbicides (e.g., mesotrione and tembotrione) compared to Pioneer 84G62, suggesting the existence of a negative cross-resistance in GL-1. Overall, these results confirm a high level of metabolic resistance to chlorsulfuron inherited by a single dominant gene in GL-1 sorghum. These results have potential for developing chlorsulfuron-tolerant sorghum hybrids, with the ability to improve post-emergence weed control.

Rapid, label-free detection of diffuse glioma recurrence using intraoperative stimulated Raman histology and deep neural networks.

BACKGROUND: Detection of glioma recurrence remains a challenge in modern neuro-oncology. Noninvasive radiographic imaging is unable to definitively differentiate true recurrence versus pseudoprogression. Even in biopsied tissue, it can be challenging to differentiate recurrent tumor and treatment effect. We hypothesized that intraoperative stimulated Raman histology (SRH) and deep neural networks can be used to improve the intraoperative detection of glioma recurrence. METHODS: We used fiber laser-based SRH, a label-free, nonconsumptive, high-resolution microscopy method (<60 sec per 1 × 1 mm2) to image a cohort of patients (n = 35) with suspected recurrent gliomas who underwent biopsy or resection. The SRH images were then used to train a convolutional neural network (CNN) and develop an inference algorithm to detect viable recurrent glioma. Following network training, the performance of the CNN was tested for diagnostic accuracy in a retrospective cohort (n = 48). RESULTS: Using patch-level CNN predictions, the inference algorithm returns a single Bernoulli distribution for the probability of tumor recurrence for each surgical specimen or patient. The external SRH validation dataset consisted of 48 patients (recurrent, 30; pseudoprogression, 18), and we achieved a diagnostic accuracy of 95.8%. CONCLUSION: SRH with CNN-based diagnosis can be used to improve the intraoperative detection of glioma recurrence in near-real time. Our results provide insight into how optical imaging and computer vision can be combined to augment conventional diagnostic methods and improve the quality of specimen sampling at glioma recurrence.

Use of the MyProstateScore Test to Rule Out Clinically Significant Cancer: Validation of a Straightforward Clinical Testing Approach.

PURPOSE: The MyProstateScore test was validated for improved detection of clinically significant (grade group ≥2) prostate cancer relative to prostate specific antigen based risk calculators. We sought to validate an optimal MyProstateScore threshold for clinical use in ruling out grade group ≥2 cancer in men referred for biopsy. MATERIALS AND METHODS: Biopsy naïve men provided post-digital rectal examination urine prior to biopsy. MyProstateScore was calculated using the validated, locked multivariable model including only serum prostate specific antigen, urinary prostate cancer antigen 3 and urinary TMPRSS2:ERG. The MyProstateScore threshold approximating 95% sensitivity for grade group ≥2 cancer was identified in a training cohort, and performance was measured in 2 external validation cohorts. We assessed the 1) overall biopsy referral population and 2) population meeting guideline based testing criteria (ie, prostate specific antigen 3-10, or <3 with suspicious digital rectal examination). RESULTS: Validation cohorts were prospectively enrolled from academic (977 patients, median prostate specific antigen 4.5, IQR 3.1-6.0) and community (548, median prostate specific antigen 4.9, IQR 3.7-6.8) settings. In the overall validation population (1,525 patients), 338 men (22%) had grade group ≥2 cancer on biopsy. The MyProstateScore threshold of 10 provided 97% sensitivity and 98% negative predictive value for grade group ≥2 cancer. MyProstateScore testing would have prevented 387 unnecessary biopsies (33%), while missing only 10 grade group ≥2 cancers (3.0%). In 1,242 patients meeting guideline based criteria, MyProstateScore ≤10 provided 96% sensitivity and 97% negative predictive value, and would have prevented 32% of unnecessary biopsies, missing 3.7% of grade group ≥2 cancers. CONCLUSIONS: In a large, clinically pertinent biopsy referral population, MyProstateScore ≤10 provided exceptional sensitivity and negative predictive value for ruling out grade group ≥2 cancer. This straightforward secondary testing approach would reduce the use of more costly and invasive procedures after screening with prostate specific antigen.

Characterization, Genetic Analyses, and Identification of QTLs Conferring Metabolic Resistance to a 4-Hydroxyphenylpyruvate Dioxygenase Inhibitor in Sorghum (Sorghum bicolor).

Postemergence grass weed control continues to be a major challenge in grain sorghum [Sorghum bicolor (L.) Moench], primarily due to lack of herbicide options registered for use in this crop. The development of herbicide-resistant sorghum technology to facilitate broad-spectrum postemergence weed control can be an economical and viable solution. The 4-hydroxyphenylpyruvate dioxygenase-inhibitor herbicides (e.g., mesotrione or tembotrione) can control a broad spectrum of weeds including grasses, which, however, are not registered for postemergence application in sorghum due to crop injury. In this study, we identified two tembotrione-resistant sorghum genotypes (G-200, G-350) and one susceptible genotype (S-1) by screening 317 sorghum lines from a sorghum association panel (SAP). These tembotrione-resistant and tembotrione-susceptible genotypes were evaluated in a tembotrione dose-response [0, 5.75, 11.5, 23, 46, 92 (label recommended dose), 184, 368, and 736 g ai ha-1] assay. Compared with S-1, the genotypes G-200 and G-350 exhibited 10- and seven fold more resistance to tembotrione, respectively. To understand the inheritance of tembotrione-resistant trait, crosses were performed using S-1 and G-200 or G-350 to generate F1 and F2 progeny. The F1 and F2 progeny were assessed for their response to tembotrione treatment. Genetic analyses of the F1 and F2 progeny demonstrated that the tembotrione resistance in G-200 and G-350 is a partially dominant polygenic trait. Furthermore, cytochrome P450 (CYP)-inhibitor assay using malathion and piperonyl butoxide suggested possible CYP-mediated metabolism of tembotrione in G-200 and G-350. Genotype-by-sequencing based quantitative trait loci (QTL) mapping revealed QTLs associated with tembotrione resistance in G-200 and G-350 genotypes. Overall, the genotypes G-200 and G-350 confer a high level of metabolic resistance to tembotrione and controlled by a polygenic trait. There is an enormous potential to introgress the tembotrione resistance into breeding lines to develop agronomically desirable sorghum hybrids.

Role of Cytochrome P450 Enzymes in Plant Stress Response.

Cytochrome P450s (CYPs) are the largest enzyme family involved in NADPH- and/or O2-dependent hydroxylation reactions across all the domains of life. In plants and animals, CYPs play a central role in the detoxification of xenobiotics. In addition to this function, CYPs act as versatile catalysts and play a crucial role in the biosynthesis of secondary metabolites, antioxidants, and phytohormones in higher plants. The molecular and biochemical processes catalyzed by CYPs have been well characterized, however, the relationship between the biochemical process catalyzed by CYPs and its effect on several plant functions was not well established. The advent of next-generation sequencing opened new avenues to unravel the involvement of CYPs in several plant functions such as plant stress response. The expression of several CYP genes are regulated in response to environmental stresses, and they also play a prominent role in the crosstalk between abiotic and biotic stress responses. CYPs have an enormous potential to be used as a candidate for engineering crop species resilient to biotic and abiotic stresses. The objective of this review is to summarize the latest research on the role of CYPs in plant stress response.

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

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.

Gender Disparity in Referral for Definitive Care of Malignant Pleural Effusions.

BACKGROUND: Gender disparities exist in cancer care. Malignant pleural effusions (MPEs) carry a poor prognosis and are managed by different physicians. This study sought to evaluate referral patterns and gender differences for definitive treatment and outcomes of MPE patients. MATERIALS AND METHODS: Patients diagnosed with MPE from 1999 to 2015 at a quaternary care hospital were retrospectively reviewed to obtain patient history, referral to thoracic surgery for definitive management, and outcomes. Analysis was performed using chi-squared/Fisher's exact test, logistic regression models, and multivariate analysis. RESULTS: 224/686 patients (32.7%) were referred to thoracic surgery. No survival difference existed between referral and nonreferral groups or referred patients who received or did not receive pleurodesis. 405 patients (59.0%) were women. Women were statistically significantly less likely to be referred than men (27.9% versus 39.5%, P = 0.0014). This disparity persisted when comorbidities were controlled for (P = 0.0004) and when gynecologic cancers (e.g., uterine, ovarian, but not including breast; 55 female patients) were excluded from analysis (28.9% versus 39.5%, P = 0.0049). Women had statistically significantly more thoracenteses (3.34 versus 2.19, P < 0.0001) and improved survival compared with males (median survival = 136 d versus 54; P = 0.0004). CONCLUSIONS: Gender disparity exists in referral patterns for definitive management of MPE; women are less likely to be referred than men. Women have longer survival and a greater number of thoracenteses performed, despite a lower referral rate for definitive care. Further research is needed to understand the differences in referral rates and outcomes between men and women.

Posterior Fossa Craniotomy for Adherent Fourth Ventricle Neurocysticercosis.

BACKGROUND AND IMPORTANCE: Neurocysticercosis (NCC) is an infectious helminthic disease often presenting in patients who have immigration or travel history from areas where NCC is endemic. Fourth ventricle cysts from NCC pose a unique treatment challenge, as there is little consensus on the best treatment. This case study describes the treatment of a patient with fourth ventricle neurocysticercosis (FVNCC), examines the therapeutic decision-making, and provides a video of a posterior fossa craniotomy (PFC) resection of a degenerative cyst. CLINICAL PRESENTATION: The patient presented with headache, dizziness, nausea, and memory difficulties. A fourth ventricle cyst consistent with NCC was found on magnetic resonance imaging, and serum enzyme-linked immunosorbent assay (ELISA) confirmed the diagnosis. The cyst was removed utilizing an open PFC followed by antihelminthic therapy and corticosteroids. There was resolution of symptoms at 9 mo postoperatively. CONCLUSION: Several treatment modalities have been proposed for isolated cysts in the fourth ventricle, including medication, ventriculoperitoneal shunt, endoscopic removal, and PFC. The treatment decision is complex, and there is little guidance on the best treatment choices. In this article, we describe treatment via PFC for an adherent FVNCC cyst.

A machine learning approach to predict early outcomes after pituitary adenoma surgery.

OBJECTIVEPituitary adenomas occur in a heterogeneous patient population with diverse perioperative risk factors, endocrinopathies, and other tumor-related comorbidities. This heterogeneity makes predicting postoperative outcomes challenging when using traditional scoring systems. Modern machine learning algorithms can automatically identify the most predictive risk factors and learn complex risk-factor interactions using training data to build a robust predictive model that can generalize to new patient cohorts. The authors sought to build a predictive model using supervised machine learning to accurately predict early outcomes of pituitary adenoma surgery.METHODSA retrospective cohort of 400 consecutive pituitary adenoma patients was used. Patient variables/predictive features were limited to common patient characteristics to improve model implementation. Univariate and multivariate odds ratio analysis was performed to identify individual risk factors for common postoperative complications and to compare risk factors with model predictors. The study population was split into 300 training/validation patients and 100 testing patients to train and evaluate four machine learning models using binary classification accuracy for predicting early outcomes.RESULTSThe study included a total of 400 patients. The mean ± SD patient age was 53.9 ± 16.3 years, 59.8% of patients had nonfunctioning adenomas and 84.7% had macroadenomas, and the mean body mass index (BMI) was 32.6 ± 7.8 (58.0% obesity rate). Multivariate odds ratio analysis demonstrated that age < 40 years was associated with a 2.86 greater odds of postoperative diabetes insipidus and that nonobese patients (BMI < 30) were 2.2 times more likely to develop postoperative hyponatremia. Using broad criteria for a poor early postoperative outcome-major medical and early surgical complications, extended length of stay, emergency department admission, inpatient readmission, and death-31.0% of patients met criteria for a poor early outcome. After model training, a logistic regression model with elastic net (LR-EN) regularization best predicted early postoperative outcomes of pituitary adenoma surgery on the 100-patient testing set-sensitivity 68.0%, specificity 93.3%, overall accuracy 87.0%. The receiver operating characteristic and precision-recall curves for the LR-EN model had areas under the curve of 82.7 and 69.5, respectively. The most important predictive variables were lowest perioperative sodium, age, BMI, highest perioperative sodium, and Cushing's disease.CONCLUSIONSEarly postoperative outcomes of pituitary adenoma surgery can be predicted with 87% accuracy using a machine learning approach. These results provide insight into how predictive modeling using machine learning can be used to improve the perioperative management of pituitary adenoma patients.

MiPanda: A Resource for Analyzing and Visualizing Next-Generation Sequencing Transcriptomics Data.

The Michigan Portal for the Analysis of NGS data portal (http://mipanda.org) is an open-access online resource that provides the scientific community with access to the results of a large-scale computational analysis of thousands of high-throughput RNA sequencing (RNA-seq) samples. The portal provides access to gene expression profiles, enabling users to interrogate expression of genes across myriad normal and cancer tissues and cell lines. From these data, tissue- and cancer-specific expression patterns can be identified. Gene-gene coexpression profiles can also be interrogated. The current portal contains data for over 20,000 RNA-seq samples and will be continually updated.

Marker-aided selection and validation of various Pi gene combinations for rice blast resistance in elite rice variety ADT 43.

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 Pi and Pi1 were found to be moderately resistant and moderately susceptible with the mean scores of 4.1 and 4.5, respectively.

Rapid Intraoperative Diagnosis of Pediatric Brain Tumors Using Stimulated Raman Histology.

Accurate histopathologic diagnosis is essential for providing optimal surgical management of pediatric brain tumors. Current methods for intraoperative histology are time- and labor-intensive and often introduce artifact that limit interpretation. Stimulated Raman histology (SRH) is a novel label-free imaging technique that provides intraoperative histologic images of fresh, unprocessed surgical specimens. Here we evaluate the capacity of SRH for use in the intraoperative diagnosis of pediatric type brain tumors. SRH revealed key diagnostic features in fresh tissue specimens collected from 33 prospectively enrolled pediatric type brain tumor patients, preserving tumor cytology and histoarchitecture in all specimens. We simulated an intraoperative consultation for 25 patients with specimens imaged using both SRH and standard hematoxylin and eosin histology. SRH-based diagnoses achieved near-perfect diagnostic concordance (Cohen's kappa, κ > 0.90) and an accuracy of 92% to 96%. We then developed a quantitative histologic method using SRH images based on rapid image feature extraction. Nuclear density, tumor-associated macrophage infiltration, and nuclear morphology parameters from 3337 SRH fields of view were used to develop and validate a decision-tree machine-learning model. Using SRH image features, our model correctly classified 25 fresh pediatric type surgical specimens into normal versus lesional tissue and low-grade versus high-grade tumors with 100% accuracy. Our results provide insight into how SRH can deliver rapid diagnostic histologic data that could inform the surgical management of pediatric brain tumors.Significance: A new imaging method simplifies diagnosis and informs decision making during pediatric brain tumor surgery. Cancer Res; 78(1); 278-89. ©2017 AACR.