No abstract

Myeloid cells constitute a significant part of the immune system in the context of cancer, exhibiting both immunostimulatory effects, through their role as antigen presenting cells, and immunosuppressive effects, through their polarization to myeloid-derived suppressor cells (MDSCs) and tumor-associated macrophages. While they are rarely sufficient to generate potent anti-tumor effects on their own, myeloid cells have the ability to interact with a variety of immune populations to aid in mounting an appropriate anti-tumor immune response. Therefore, myeloid therapies have gained momentum as a potential adjunct to current therapies such as immune checkpoint inhibitors (ICIs), dendritic cell vaccines, oncolytic viruses, and traditional chemoradiation to enhance therapeutic response. In this review, we outline critical pathways involved in the recruitment of the myeloid population to the tumor microenvironment and in their polarization to immunostimulatory or immunosuppressive phenotypes. We also emphasize existing strategies of modulating myeloid recruitment and polarization to improve anti-tumor immune responses. We then summarize current preclinical and clinical studies that highlight treatment outcomes of combining myeloid targeted therapies with other immune-based and traditional therapies. Despite promising results from reports of limited clinical trials thus far, there remain challenges in optimally harnessing the myeloid compartment as an adjunct to enhancing anti-tumor immune responses. Further large Phase II and ultimately Phase III clinical trials are needed to elucidate the treatment benefit of combination therapies in the fight against cancer.

Objective This study investigates the accuracy of an automated method to rapidly segment relevant temporal bone anatomy from cone beam computed tomography (CT) images. Implementation of this segmentation pipeline has potential to improve surgical safety and decrease operative time by augmenting preoperative planning and interfacing with image-guided robotic surgical systems. Study Design Descriptive study of predicted segmentations. Setting Academic institution. Methods We have developed a computational pipeline based on the symmetric normalization registration method that predicts segmentations of anatomic structures in temporal bone CT scans using a labeled atlas. To evaluate accuracy, we created a data set by manually labeling relevant anatomic structures (eg, ossicles, labyrinth, facial nerve, external auditory canal, dura) for 16 deidentified high-resolution cone beam temporal bone CT images. Automated segmentations from this pipeline were compared against ground-truth manual segmentations by using modified Hausdorff distances and Dice scores. Runtimes were documented to determine the computational requirements of this method. Results Modified Hausdorff distances and Dice scores between predicted and ground-truth labels were as follows: malleus (0.100 ± 0.054 mm; Dice, 0.827 ± 0.068), incus (0.100 ± 0.033 mm; Dice, 0.837 ± 0.068), stapes (0.157 ± 0.048 mm; Dice, 0.358 ± 0.100), labyrinth (0.169 ± 0.100 mm; Dice, 0.838 ± 0.060), and facial nerve (0.522 ± 0.278 mm; Dice, 0.567 ± 0.130). A quad-core 16GB RAM workstation completed this segmentation pipeline in 10 minutes. Conclusions We demonstrated submillimeter accuracy for automated segmentation of temporal bone anatomy when compared against hand-segmented ground truth using our template registration pipeline. This method is not dependent on the training data volume that plagues many complex deep learning models. Favorable runtime and low computational requirements underscore this method’s translational potential.

ObjectivePreoperative planning for otologic or neurotologic procedures often requires manual segmentation of relevant structures, which can be tedious and time‐consuming. Automated methods for segmenting multiple geometrically complex structures can not only streamline preoperative planning but also augment minimally invasive and/or robot‐assisted procedures in this space. This study evaluates a state‐of‐the‐art deep learning pipeline for semantic segmentation of temporal bone anatomy.Study DesignA descriptive study of a segmentation network.SettingAcademic institution.MethodsA total of 15 high‐resolution cone‐beam temporal bone computed tomography (CT) data sets were included in this study. All images were co‐registered, with relevant anatomical structures (eg, ossicles, inner ear, facial nerve, chorda tympani, bony labyrinth) manually segmented. Predicted segmentations from no new U‐Net (nnU‐Net), an open‐source 3‐dimensional semantic segmentation neural network, were compared against ground‐truth segmentations using modified Hausdorff distances (mHD) and Dice scores.ResultsFivefold cross‐validation with nnU‐Net between predicted and ground‐truth labels were as follows: malleus (mHD: 0.044 ± 0.024 mm, dice: 0.914 ± 0.035), incus (mHD: 0.051 ± 0.027 mm, dice: 0.916 ± 0.034), stapes (mHD: 0.147 ± 0.113 mm, dice: 0.560 ± 0.106), bony labyrinth (mHD: 0.038 ± 0.031 mm, dice: 0.952 ± 0.017), and facial nerve (mHD: 0.139 ± 0.072 mm, dice: 0.862 ± 0.039). Comparison against atlas‐based segmentation propagation showed significantly higher Dice scores for all structures (p < .05).ConclusionUsing an open‐source deep learning pipeline, we demonstrate consistently submillimeter accuracy for semantic CT segmentation of temporal bone anatomy compared to hand‐segmented labels. This pipeline has the potential to greatly improve preoperative planning workflows for a variety of otologic and neurotologic procedures and augment existing image guidance and robot‐assisted systems for the temporal bone.