Increased Nerve Density Adversely Affects Outcome in Oral Cancer.

PURPOSE: Perineural invasion (PNI) in oral cavity squamous cell carcinoma (OSCC) is associated with poor survival. Because of the risk of recurrence, patients with PNI receive additional therapies after surgical resection. Mechanistic studies have shown that nerves in the tumor microenvironment promote aggressive tumor growth. Therefore, in this study, we evaluated whether nerve density (ND) influences tumor growth and patient survival. Moreover, we assessed the reliability of artificial intelligence (AI) in evaluating ND. EXPERIMENTAL DESIGN: To investigate whether increased ND in OSCC influences patient outcome, we performed survival analyses. Tissue sections of OSCC from 142 patients were stained with hematoxylin and eosin and IHC stains to detect nerves and tumor. ND within the tumor bulk and in the adjacent 2 mm was quantified; normalized ND (NND; bulk ND/adjacent ND) was calculated. The impact of ND on tumor growth was evaluated in chick chorioallantoic-dorsal root ganglia (CAM-DRG) and murine surgical denervation models. Cancer cells were grafted and tumor size quantified. Automated nerve detection, applying the Halo AI platform, was compared with manual assessment. RESULTS: Disease-specific survival decreased with higher intratumoral ND and NND in tongue SCC. Moreover, NND was associated with worst pattern-of-invasion and PNI. Increasing the number of DRG, in the CAM-DRG model, increased tumor size. Reduction of ND by denervation in a murine model decreased tumor growth. Automated and manual detection of nerves showed high concordance, with an F1 score of 0.977. CONCLUSIONS: High ND enhances tumor growth, and NND is an important prognostic factor that could influence treatment selection for aggressive OSCC. See related commentary by Hondermarck and Jiang, p. 2342.

AngioNet: a convolutional neural network for vessel segmentation in X-ray angiography.

Coronary Artery Disease (CAD) is commonly diagnosed using X-ray angiography, in which images are taken as radio-opaque dye is flushed through the coronary vessels to visualize the severity of vessel narrowing, or stenosis. Cardiologists typically use visual estimation to approximate the percent diameter reduction of the stenosis, and this directs therapies like stent placement. A fully automatic method to segment the vessels would eliminate potential subjectivity and provide a quantitative and systematic measurement of diameter reduction. Here, we have designed a convolutional neural network, AngioNet, for vessel segmentation in X-ray angiography images. The main innovation in this network is the introduction of an Angiographic Processing Network (APN) which significantly improves segmentation performance on multiple network backbones, with the best performance using Deeplabv3+ (Dice score 0.864, pixel accuracy 0.983, sensitivity 0.918, specificity 0.987). The purpose of the APN is to create an end-to-end pipeline for image pre-processing and segmentation, learning the best possible pre-processing filters to improve segmentation. We have also demonstrated the interchangeability of our network in measuring vessel diameter with Quantitative Coronary Angiography. Our results indicate that AngioNet is a powerful tool for automatic angiographic vessel segmentation that could facilitate systematic anatomical assessment of coronary stenosis in the clinical workflow.