Artificial intelligence-augmented histopathologic review using image analysis to optimize DNA yield from formalin-fixed paraffin-embedded slides.

To achieve minimum DNA input requirements for next-generation sequencing (NGS), pathologists visually estimate macrodissection and slide count decisions. Unfortunately, misestimation may cause tissue waste and increased laboratory costs. We developed an artificial intelligence (AI)-augmented smart pathology review system (SmartPath) to empower pathologists with quantitative metrics for accurately determining tissue extraction parameters. SmartPath uses two deep learning architectures, a U-Net based network for cell segmentation and a multi-field-of-view convolutional network for tumor area segmentation, to extract features from digitized H&E-stained formalin-fixed paraffin-embedded slides. From the segmented tumor area, SmartPath suggests a macrodissection area. To predict DNA yield per slide, the extracted features from within the macrodissection area are correlated with known DNA yields to fit a regularized linear model (R = 0.85). Then, a pathologist-defined target yield divided by the predicted DNA yield per slide gives the number of slides to scrape. Following model development, an internal validation trial was conducted within the Tempus Labs molecular sequencing laboratory. We evaluated our system on 501 clinical colorectal cancer slides, where half received SmartPath-augmented review and half traditional pathologist review. The SmartPath cohort had 25% more DNA yields within a desired target range of 100-2000 ng. The number of extraction attempts was statistically unchanged between cohorts. The SmartPath system recommended fewer slides to scrape for large tissue sections, saving tissue in these cases. Conversely, SmartPath recommended more slides to scrape for samples with scant tissue sections, especially those with degraded DNA, helping prevent costly re-extraction due to insufficient extraction yield. A statistical analysis was performed to measure the impact of covariates on the results, offering insights on how to improve future applications of SmartPath. With these improvements, AI-augmented histopathologic review has the potential to decrease tissue waste, sequencing time, and laboratory costs by optimizing DNA yields, especially for samples with scant tissue and/or degraded DNA.

Pharmacological manipulation of the olfactory bulb modulates beta oscillations: testing model predictions.

The mammalian olfactory bulb (OB) generates gamma (40-100 Hz) and beta (15-30 Hz) local field potential (LFP) oscillations. Gamma oscillations arise at the peak of inhalation supported by dendrodendritic interactions between glutamatergic mitral cells (MCs) and GABAergic granule cells (GCs). Beta oscillations are induced by odorants in learning or odor sensitization paradigms, but their mechanism and function are still poorly understood. When centrifugal OB inputs are blocked, beta oscillations disappear, but gamma oscillations persist. Centrifugal inputs target primarily GABAergic interneurons in the GC layer (GCL) and regulate GC excitability, suggesting a causal link between beta oscillations and GC excitability. Our previous modeling work predicted that convergence of excitatory/inhibitory inputs onto MCs and centrifugal inputs onto GCs increase GC excitability sufficiently to produce beta oscillations primarily through voltage dependent calcium channel-mediated GABA release, independently of NMDA channels. We test some of the predictions of this model by examining the influence of NMDA and muscarinic acetylcholine (ACh) receptors, which affect GC excitability in different ways, on beta oscillations. A few minutes after intrabulbar infusion, scopolamine (muscarinic antagonist) suppressed odor-evoked beta in response to a strong stimulus but increased beta power in response to a weak stimulus, as predicted by our model. Pyriform cortex (PC) beta power was unchanged. Oxotremorine (muscarinic agonist) suppressed all oscillations, likely from overinhibition. APV, an NMDA receptor antagonist, suppressed gamma oscillations selectively (in OB and PC), lending support to the model's prediction that beta oscillations can be supported independently of NMDA receptors. NEW & NOTEWORTHY Olfactory bulb local field potential beta oscillations appear to be gated by GABAergic granule cell excitability. Reducing excitability with scopolamine reduces beta induced by strong odors but increases beta induced by weak odors. Beta oscillations rely on the same synapse as gamma oscillations but, unlike gamma, can persist in the absence of NMDA receptor activation. Pyriform cortex beta oscillations maintain power when olfactory bulb beta power is low, and the system maintains beta band coherence.

Granule cell excitability regulates gamma and beta oscillations in a model of the olfactory bulb dendrodendritic microcircuit.

Odors evoke gamma (40-100 Hz) and beta (20-30 Hz) oscillations in the local field potential (LFP) of the mammalian olfactory bulb (OB). Gamma (and possibly beta) oscillations arise from interactions in the dendrodendritic microcircuit between excitatory mitral cells (MCs) and inhibitory granule cells (GCs). When cortical descending inputs to the OB are blocked, beta oscillations are extinguished whereas gamma oscillations become larger. Much of this centrifugal input targets inhibitory interneurons in the GC layer and regulates the excitability of GCs, which suggests a causal link between the emergence of beta oscillations and GC excitability. We investigate the effect that GC excitability has on network oscillations in a computational model of the MC-GC dendrodendritic network with Ca(2+)-dependent graded inhibition. Results from our model suggest that when GC excitability is low, the graded inhibitory current mediated by NMDA channels and voltage-dependent Ca(2+) channels (VDCCs) is also low, allowing MC populations to fire in the gamma frequency range. When GC excitability is increased, the activation of NMDA receptors and other VDCCs is also increased, allowing the slow decay time constants of these channels to sustain beta-frequency oscillations. Our model argues that Ca(2+) flow through VDCCs alone could sustain beta oscillations and that the switch between gamma and beta oscillations can be triggered by an increase in the excitability state of a subpopulation of GCs.

Multi-Field-of-View Deep Learning Model Predicts Nonsmall Cell Lung Cancer Programmed Death-Ligand 1 Status from Whole-Slide Hematoxylin and Eosin Images.

BACKGROUND: Tumor programmed death-ligand 1 (PD-L1) status is useful in determining which patients may benefit from programmed death-1 (PD-1)/PD-L1 inhibitors. However, little is known about the association between PD-L1 status and tumor histopathological patterns. Using deep learning, we predicted PD-L1 status from hematoxylin and eosin (H and E) whole-slide images (WSIs) of nonsmall cell lung cancer (NSCLC) tumor samples. MATERIALS AND METHODS: One hundred and thirty NSCLC patients were randomly assigned to training (n = 48) or test (n = 82) cohorts. A pair of H and E and PD-L1-immunostained WSIs was obtained for each patient. A pathologist annotated PD-L1 positive and negative tumor regions on the training samples using immunostained WSIs for reference. From the H and E WSIs, over 145,000 training tiles were generated and used to train a multi-field-of-view deep learning model with a residual neural network backbone. RESULTS: The trained model accurately predicted tumor PD-L1 status on the held-out test cohort of H and E WSIs, which was balanced for PD-L1 status (area under the receiver operating characteristic curve [AUC] =0.80, P << 0.01). The model remained effective over a range of PD-L1 cutoff thresholds (AUC = 0.67-0.81, P ≤ 0.01) and when different proportions of the labels were randomly shuffled to simulate interpathologist disagreement (AUC = 0.63-0.77, P ≤ 0.03). CONCLUSIONS: A robust deep learning model was developed to predict tumor PD-L1 status from H and E WSIs in NSCLC. These results suggest that PD-L1 expression is correlated with the morphological features of the tumor microenvironment.