Radiotherapy is the main treatment strategy for nasopharyngeal carcinoma. A major factor affecting radiotherapy outcome is the accuracy of target delineation. Target delineation is time-consuming, and the results can vary depending on the experience of the oncologist. Using deep learning methods to automate target delineation may increase its efficiency. We used a modified deep learning model called U-Net to automatically segment and delineate tumor targets in patients with nasopharyngeal carcinoma. Patients were randomly divided into a training set (302 patients), validation set (100 patients), and test set (100 patients). The U-Net model was trained using labeled computed tomography images from the training set. The U-Net was able to delineate nasopharyngeal carcinoma tumors with an overall dice similarity coefficient of 65.86% for lymph nodes and 74.00% for primary tumor, with respective Hausdorff distances of 32.10 and 12.85 mm. Delineation accuracy decreased with increasing cancer stage. Automatic delineation took approximately 2.6 hours, compared to 3 hours, using an entirely manual procedure. Deep learning models can therefore improve accuracy, consistency, and efficiency of target delineation in T stage, but additional physician input may be required for lymph nodes.

Optomotor response/reflex (OMR) assays are emerging as a powerful and versatile tool for phenotypic study and new drug discovery for eye and brain disorders. Yet efficient OMR assessment for visual performance in mice remains a challenge. Existing OMR testing devices for mice require a lengthy procedure and may be subject to bias due to use of artificial criteria. We developed an optimized staircase protocol that utilizes mouse head pausing behavior as a novel indicator for the absence of OMR, to allow rapid and unambiguous vision assessment. It provided a highly sensitive and reliable method that can be easily implemented into automated or manual OMR systems to allow quick and unbiased assessment for visual acuity and contrast sensitivity in mice. The sensitivity and quantitative capacity of the protocol were validated using wild type mice and an inherited mouse model of retinal degeneration – mice carrying rhodopsin deficiency and exhibiting progressive loss of photoreceptors. Our OMR system with this protocol was capable of detecting progressive visual function decline that was closely correlated with the loss of photoreceptors in rhodopsin deficient mice. It provides significant advances over the existing methods in the currently available OMR devices in terms of sensitivity, accuracy and efficiency.

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