Full-size Image Research Articles

Efficient Guided Grad-CAM Tuned Patch Neural Network for Accurate Anomaly Detection in Full Images

Deep learning-based anomaly detection in images has recently gained popularity as an investigative field with many global submissions. To simplify complex data analysis, researchers in the deep learning subfield of machine learning employ Artificial Neural Networks (ANNs) with many hidden layers. Finding data occurrences that significantly differ from generalizable to most data sets is the primary goal of anomaly detection. Many medical imaging applications use convolutional neural networks (CNNs) to examine anomalies automatically. While CNN structures are reliable feature extractors, they encounter challenges when simultaneously classifying and segmenting spots that need removal from scans. We suggest a separate and integration system to solve these issues, separated into two distinct departments: classification and segmentation. Initially, many network architecturesare taught independently for each abnormality, and these networks’ main components are combined. A sharedcomponent of the branched structure functions for all abnormalities. The final structure has two branches: onehas distinct sub-networks, each intended to classify a particular abnormality, and the other for segmenting various abnormalities. Deep CNNs training directly on high-resolution images necessitate input layer image compression, which results in the loss of information necessary for detecting medical abnormalities. A guided GradCAM (GCAM) tuned patch neural network is applied to full-size images for anomaly localization. Therefore, the suggested approach merges the pre-trained deep CNNs with class activation mappings and area suggestion systems to construct abnormality sensors and then fine-tunes the CNNs on picture patches, focusing on medical abnormalities instead of training on whole images. A mammogram data set was used to test the deep patch classifier, which had a 99% overall classification accuracy. A Brain tumor image data set was used to test the integrateddetector’s ability to detect abnormalities, and it did so with an average precision of 0.99.

Deep learning model for differentiating nasal cavity masses based on nasal endoscopy images

BackgroundNasal polyps and inverted papillomas often look similar. Clinically, it is difficult to distinguish the masses by endoscopic examination. Therefore, in this study, we aimed to develop a deep learning algorithm for computer-aided diagnosis of nasal endoscopic images, which may provide a more accurate clinical diagnosis before pathologic confirmation of the nasal masses.MethodsBy performing deep learning of nasal endoscope images, we evaluated our computer-aided diagnosis system’s assessment ability for nasal polyps and inverted papilloma and the feasibility of their clinical application. We used curriculum learning pre-trained with patches of nasal endoscopic images and full-sized images. The proposed model’s performance for classifying nasal polyps, inverted papilloma, and normal tissue was analyzed using five-fold cross-validation.ResultsThe normal scores for our best-performing network were 0.9520 for recall, 0.7900 for precision, 0.8648 for F1-score, 0.97 for the area under the curve, and 0.8273 for accuracy. For nasal polyps, the best performance was 0.8162, 0.8496, 0.8409, 0.89, and 0.8273, respectively, for recall, precision, F1-score, area under the curve, and accuracy. Finally, for inverted papilloma, the best performance was obtained for recall, precision, F1-score, area under the curve, and accuracy values of 0.5172, 0.8125, 0.6122, 0.83, and 0.8273, respectively.ConclusionAlthough there were some misclassifications, the results of gradient-weighted class activation mapping were generally consistent with the areas under the curve determined by otolaryngologists. These results suggest that the convolutional neural network is highly reliable in resolving lesion locations in nasal endoscopic images.

Vislocas: Vision transformers for identifying protein subcellular mis-localization signatures of different cancer subtypes from immunohistochemistry images

Proteins must be sorted to specific subcellular compartments to perform their functions. Abnormal protein subcellular localizations are related to many diseases. Although many efforts have been made in predicting protein subcellular localization from various static information, including sequences, structures and interactions, such static information cannot predict protein mis-localization events in diseases. On the contrary, the IHC (immunohistochemistry) images, which have been widely applied in clinical diagnosis, contains information that can be used to find protein mis-localization events in disease states. In this study, we create the Vislocas method, which is capable of finding mis-localized proteins from IHC images as markers of cancer subtypes. By combining CNNs and vision transformer encoders, Vislocas can automatically extract image features at both global and local level. Vislocas can be trained with full-sized IHC images from scratch. It is the first attempt to create an end-to-end IHC image-based protein subcellular location predictor. Vislocas achieved comparable or better performances than state-of-the-art methods. We applied Vislocas to find significant protein mis-localization events in different subtypes of glioma, melanoma and skin cancer. The mis-localized proteins, which were found purely from IHC images by Vislocas, are in consistency with clinical or experimental results in literatures. All codes of Vislocas have been deposited in a Github repository (https://github.com/JingwenWen99/Vislocas). All datasets of Vislocas have been deposited in Zenodo (https://zenodo.org/records/10632698).

A deep learning framework for identifying and segmenting three vessels in fetal heart ultrasound images

BackgroundCongenital heart disease (CHD) is one of the most common birth defects in the world. It is the leading cause of infant mortality, necessitating an early diagnosis for timely intervention. Prenatal screening using ultrasound is the primary method for CHD detection. However, its effectiveness is heavily reliant on the expertise of physicians, leading to subjective interpretations and potential underdiagnosis. Therefore, a method for automatic analysis of fetal cardiac ultrasound images is highly desired to assist an objective and effective CHD diagnosis.MethodIn this study, we propose a deep learning-based framework for the identification and segmentation of the three vessels—the pulmonary artery, aorta, and superior vena cava—in the ultrasound three vessel view (3VV) of the fetal heart. In the first stage of the framework, the object detection model Yolov5 is employed to identify the three vessels and localize the Region of Interest (ROI) within the original full-sized ultrasound images. Subsequently, a modified Deeplabv3 equipped with our novel AMFF (Attentional Multi-scale Feature Fusion) module is applied in the second stage to segment the three vessels within the cropped ROI images.ResultsWe evaluated our method with a dataset consisting of 511 fetal heart 3VV images. Compared to existing models, our framework exhibits superior performance in the segmentation of all the three vessels, demonstrating the Dice coefficients of 85.55%, 89.12%, and 77.54% for PA, Ao and SVC respectively.ConclusionsOur experimental results show that our proposed framework can automatically and accurately detect and segment the three vessels in fetal heart 3VV images. This method has the potential to assist sonographers in enhancing the precision of vessel assessment during fetal heart examinations.

Deep Learning Combined with Radiologist's Intervention Achieves Accurate Segmentation of Hepatocellular Carcinoma in Dual-Phase Magnetic Resonance Images.

Segmentation of hepatocellular carcinoma (HCC) is crucial; however, manual segmentation is subjective and time-consuming. Accurate and automatic lesion contouring for HCC is desirable in clinical practice. In response to this need, our study introduced a segmentation approach for HCC combining deep convolutional neural networks (DCNNs) and radiologist intervention in magnetic resonance imaging (MRI). We sought to design a segmentation method with a deep learning method that automatically segments using manual location information for moderately experienced radiologists. In addition, we verified the viability of this method to assist radiologists in accurate and fast lesion segmentation. In our study, we developed a semiautomatic approach for segmenting HCC using DCNN in conjunction with radiologist intervention in dual-phase gadolinium-ethoxybenzyl-diethylenetriamine penta-acetic acid- (Gd-EOB-DTPA-) enhanced MRI. We developed a DCNN and deep fusion network (DFN) trained on full-size images, namely, DCNN-F and DFN-F. Furthermore, DFN was applied to the image blocks containing tumor lesions that were roughly contoured by a radiologist with 10 years of experience in abdominal MRI, and this method was named DFN-R. Another radiologist with five years of experience (moderate experience) performed tumor lesion contouring for comparison with our proposed methods. The ground truth image was contoured by an experienced radiologist and reviewed by an independent experienced radiologist. The mean DSC of DCNN-F, DFN-F, and DFN-R was 0.69 ± 0.20 (median, 0.72), 0.74 ± 0.21 (median, 0.77), and 0.83 ± 0.13 (median, 0.88), respectively. The mean DSC of the segmentation by the radiologist with moderate experience was 0.79 ± 0.11 (median, 0.83), which was lower than the performance of DFN-R. Deep learning using dual-phase MRI shows great potential for HCC lesion segmentation. The radiologist-aided semiautomated method (DFN-R) achieved improved performance compared to manual contouring by the radiologist with moderate experience, although the difference was not statistically significant.

Four enhanced algorithms for full size image hiding in chest x-ray images

Four enhanced algorithms for full size image hiding in chest x-ray images

Pan-Sharpening Network of Multi-Spectral Remote Sensing Images Using Two-Stream Attention Feature Extractor and Multi-Detail Injection (TAMINet)

Achieving a balance between spectral resolution and spatial resolution in multi-spectral remote sensing images is challenging due to physical constraints. Consequently, pan-sharpening technology was developed to address this challenge. While significant progress was recently achieved in deep-learning-based pan-sharpening techniques, most existing deep learning approaches face two primary limitations: (1) convolutional neural networks (CNNs) struggle with long-range dependency issues, and (2) significant detail loss during deep network training. Moreover, despite these methods’ pan-sharpening capabilities, their generalization to full-sized raw images remains problematic due to scaling disparities, rendering them less practical. To tackle these issues, we introduce in this study a multi-spectral remote sensing image fusion network, termed TAMINet, which leverages a two-stream coordinate attention mechanism and multi-detail injection. Initially, a two-stream feature extractor augmented with the coordinate attention (CA) block is employed to derive modal-specific features from low-resolution multi-spectral (LRMS) images and panchromatic (PAN) images. This is followed by feature-domain fusion and pan-sharpening image reconstruction. Crucially, a multi-detail injection approach is incorporated during fusion and reconstruction, ensuring the reintroduction of details lost earlier in the process, which minimizes high-frequency detail loss. Finally, a novel hybrid loss function is proposed that incorporates spatial loss, spectral loss, and an additional loss component to enhance performance. The proposed methodology’s effectiveness was validated through experiments on WorldView-2 satellite images, IKONOS, and QuickBird, benchmarked against current state-of-the-art techniques. Experimental findings reveal that TAMINet significantly elevates the pan-sharpening performance for large-scale images, underscoring its potential to enhance multi-spectral remote sensing image quality.

A Two-Branch Neural Network for Short-Axis PET Image Quality Enhancement.

The axial field of view (FOV) is a key factor that affects the quality of PET images. Due to hardware FOV restrictions, conventional short-axis PET scanners with FOVs of 20 to 35 cm can acquire only low-quality PET (LQ-PET) images in fast scanning times (2-3 minutes). To overcome hardware restrictions and improve PET image quality for better clinical diagnoses, several deep learning-based algorithms have been proposed. However, these approaches use simple convolution layers with residual learning and local attention, which insufficiently extract and fuse long-range contextual information. To this end, we propose a novel two-branch network architecture with swin transformer units and graph convolution operation, namely SW-GCN. The proposed SW-GCN provides additional spatial- and channel-wise flexibility to handle different types of input information flow. Specifically, considering the high computational cost of calculating self-attention weights in full-size PET images, in our designed spatial adaptive branch, we take the self-attention mechanism within each local partition window and introduce global information interactions between nonoverlapping windows by shifting operations to prevent the aforementioned problem. In addition, the convolutional network structure tends to consider the information in each channel equally during the feature extraction process. In our designed channel adaptive branch, we use a Watts Strogatz topology structure to connect each feature map to its most relevant features in each graph convolutional layer, substantially reducing information redundancy. Moreover, ensemble learning is adopted in our SW-GCN for mapping distinct features from the two well-designed branches to the enhanced PET images. We carried out extensive experiments on three single-bed position scans for 386 patients. The test results demonstrate that our proposed SW-GCN approach outperforms state-of-the-art methods in both quantitative and qualitative evaluations.

Problem-dependent attention and effort in neural networks with applications to image resolution and model selection

This paper introduces two new ensemble-based methods to reduce the data and computation costs of image classification. They can be used with any set of classifiers and do not require additional training. In the first approach, data usage is reduced by only analyzing a full-sized image if the model has low confidence in classifying a low-resolution pixelated version. When applied on the best performing classifiers considered here, data usage is reduced by 61.2% on MNIST, 69.6% on KMNIST, 56.3% on FashionMNIST, 84.6% on SVHN, 40.6% on ImageNet, and 27.6% on ImageNet-V2, all with a less than 5% reduction in accuracy. However, for CIFAR-10, the pixelated data are not particularly informative, and the ensemble approach increases data usage while reducing accuracy. In the second approach, compute costs are reduced by only using a complex model if a simpler model has low confidence in its classification. Computation cost is reduced by 82.1% on MNIST, 47.6% on KMNIST, 72.3% on FashionMNIST, 86.9% on SVHN, 89.2% on ImageNet, and 81.5% on ImageNet-V2, all with a less than 5% reduction in accuracy; for CIFAR-10 the corresponding improvements are smaller at 13.5%. When cost is not an object, choosing the projection from the most confident model for each observation increases validation accuracy to 81.0% from 79.3% for ImageNet and to 69.4% from 67.5% for ImageNet-V2. Code available at:https://github.com/carohlfs/imavis.

Improved Fracture Segmentation from Unwrapped Drill-Core Images Using an Innovative Two-Stage Segmentation Approach

While machine learning (ML) provides a great tool for image analysis, obtaining accurate fracture segmentation from high-resolution core images is challenging. A major reason is that the segmentation quality of large and detailed objects, such as fractures, is limited by the capacity of the segmentation branch. This challenge can be seen in the Mask Region-based Convolutional Neural Network (Mask R-CNN), which is a common and well-validated instance segmentation model. This study proposes a two-stage segmentation approach using Mask R-CNN to improve fracture segmentation from unwrapped-core images. Two CNN models are used: the first model processes full-size unwrapped-core images to detect and segment fractures; the second model performs a more detailed segmentation by processing smaller regions of the images that include the fractures detected by the first model. In addition, the procedure uses a new architecture of Mask R-CNN with a point-based rendering (PointRend) neural network module that can increase segmentation accuracy. The method is evaluated on approximately 47 m of core from four boreholes and results in an improvement to previous fracture segmentation methods. It achieves an increase in the average intersection over union of approximately 27% from the baseline (one-stage segmentation with standard Mask R-CNN). The enhanced fracture segmentation provides a mean for obtaining an accurate fracture aperture with an average error of less than 1 mm, which represents a reduction of 0.5 mm from the baseline method. This work presents a novel contribution towards developing an ML-based workflow for core-image analysis.

Crosslinked polymer electrolyte constructed by metal-oxo clusters for solid lithium metal batteries

By using structurally well-defined metal-oxo clusters as crosslinkable nanofillers, the authors develop a hybrid inorganic/organic crosslinked polymer electrolyte. With the favorable electrochemical and mechanical properties, as well as the enhanced lithium dendrite-suppressing capability, the metal-oxo cluster crosslinked polymer electrolyte can significantly improve the capacities, rate capabilities, and cycling stabilities of solid lithium metal batteries. Download : Download high-res image (175KB)Download : Download full-size image

Synthesizing multi-frame high-resolution fluorescein angiography images from retinal fundus images using generative adversarial networks

BackgroundFundus fluorescein angiography (FA) can be used to diagnose fundus diseases by observing dynamic fluorescein changes that reflect vascular circulation in the fundus. As FA may pose a risk to patients, generative adversarial networks have been used to convert retinal fundus images into fluorescein angiography images. However, the available methods focus on generating FA images of a single phase, and the resolution of the generated FA images is low, being unsuitable for accurately diagnosing fundus diseases.MethodsWe propose a network that generates multi-frame high-resolution FA images. This network consists of a low-resolution GAN (LrGAN) and a high-resolution GAN (HrGAN), where LrGAN generates low-resolution and full-size FA images with global intensity information, HrGAN takes the FA images generated by LrGAN as input to generate multi-frame high-resolution FA patches. Finally, the FA patches are merged into full-size FA images.ResultsOur approach combines supervised and unsupervised learning methods and achieves better quantitative and qualitative results than using either method alone. Structural similarity (SSIM), normalized cross-correlation (NCC) and peak signal-to-noise ratio (PSNR) were used as quantitative metrics to evaluate the performance of the proposed method. The experimental results show that our method achieves better quantitative results with structural similarity of 0.7126, normalized cross-correlation of 0.6799, and peak signal-to-noise ratio of 15.77. In addition, ablation experiments also demonstrate that using a shared encoder and residual channel attention module in HrGAN is helpful for the generation of high-resolution images.ConclusionsOverall, our method has higher performance for generating retinal vessel details and leaky structures in multiple critical phases, showing a promising clinical diagnostic value.

Dual consistency semi-supervised nuclei detection via global regularization and local adversarial learning

Nuclei detection is a fundamental analytical step in digital histopathology image analysis. Since labeling the centroids for each nucleus in histopathology images is extremely time-consuming, researchers attempt to explore consistency-based approaches for efficient semi-supervised nuclei detection. However, existing methods can only be used for detection on small patches mostly containing one nucleus and contextual information among neighboring nuclei is not considered. On the contrary, using the whole image to achieve nuclei detection in a semi-supervised manner may suffer from a large amount of background noise and thus cannot yield optimal performance. To address these problems, we propose a novel semi-supervised learning method for nuclei detection on a full-size histopathology image via global consistency regularization and local consistency adversarial learning. Specifically, the proposed dual consistency semi-supervised learning can improve the efficiency in inference and learn the context-aware nuclei features by global consistency regularization. Meanwhile, local consistency adversarial learning is introduced to focus on nuclei regions and reinforce the local spatial contiguity of prediction maps. We have evaluated the proposed dual consistency semi-supervised method on public CRCHisto and collected SemiBCN datasets, and the results show that with the synergy of global consistency regularization and local consistency adversarial learning, our method delivers a significant improvement over the state-of-the-art methods.

Retinal Blood-Vessel Extraction Using Weighted Kernel Fuzzy C-Means Clustering and Dilation-Based Functions.

Automated blood-vessel extraction is essential in diagnosing Diabetic Retinopathy (DR) and other eye-related diseases. However, the traditional methods for extracting blood vessels tend to provide low accuracy when dealing with difficult situations, such as extracting both micro and large blood vessels simultaneously with low-intensity images and blood vessels with DR. This paper proposes a complete preprocessing method to enhance original retinal images before transferring the enhanced images to a novel blood-vessel extraction method by a combined three extraction stages. The first stage focuses on the fast extraction of retinal blood vessels using Weighted Kernel Fuzzy C-Means (WKFCM) Clustering to draw the vessel feature from the retinal background. The second stage focuses on the accuracy of full-size images to achieve regional vessel feature recognition of large and micro blood vessels and to minimize false extraction. This stage implements the mathematical dilation operator from a trained model called Dilation-Based Function (DBF). Finally, an optimal parameter threshold is empirically determined in the third stage to remove non-vessel features in the binary image and improve the overall vessel extraction results. According to evaluations of the method via the datasets DRIVE, STARE, and DiaretDB0, the proposed WKFCM-DBF method achieved sensitivities, specificities, and accuracy performances of 98.12%, 98.20%, and 98.16%, 98.42%, 98.80%, and 98.51%, and 98.89%, 98.10%, and 98.09%, respectively.

MAMIQA: No-Reference Image Quality Assessment Based on Multiscale Attention Mechanism With Natural Scene Statistics

No-Reference Image Quality Assessment aims to evaluate the perceptual quality of an image, according to human perception. Many recent studies use Transformers to assign different self-attention mechanisms to distinguish regions of an image, simulating the perception of the human visual system (HVS). However, the quadratic computational complexity caused by the self-attention mechanism is time-consuming and expensive. Meanwhile, the image resizing in the feature extraction stage loses the full-size image quality. To address these issues, we propose a lightweight attention mechanism using decomposed large-kernel convolutions to extract multiscale features, and a novel feature enhancement module to simulate HVS. We also propose to compensate the information loss caused by image resizing, with supplementary features from natural scene statistics. Experimental results on five standard datasets show that the proposed method surpasses the SOTA, while significantly reducing the computational costs.

Image-based motion artifact reduction on liver dynamic contrast enhanced MRI

Liver MRI images often suffer from degraded quality due to ghosting or blurring artifacts caused by patient respiratory or bulk motion. In this study, we developed a two-stage deep learning model to reduce motion artifact on dynamic contrast enhanced (DCE) liver MRIs. The stage-I network utilized a deep residual network with a densely connected multi-resolution block (DRN-DCMB) network to remove most motion artifacts. The stage-II network applied the generative adversarial network (GAN) and perceptual loss compensation to preserve image structural features. The stage-I network served as the generator of GAN and its pretrained parameters in stage-I were further updated via backpropagation during stage-II training. The stage-I network was trained using small image patches with simulated motion artifacts including image-space rotational and translational motion, and K-space based centric and interleaved linear motion, sinusoidal, and rotational motion to mimic liver motion patterns. The stage-II network training used full-size images with the same types of simulated motion. The liver DCE-MRI image volumes without obvious motion artifacts in 10 patients were used for the training process, of which 1020 images of 8 patients were used for training and 240 images of 2 patients for validation. Finally, the whole two-stage deep learning model was tested with simulated motion images (312 clean images from 5 test patients) and patient images with real motion artifacts (28 motion images from 12 patients). The resulted images after two-stage processing demonstrated reduced motion artifacts while preserved anatomic details without image blurriness, with SSIM of 0.935 ± 0.092, MSE of 60.7 ± 9.0 × 10-3, and PSNR of 32.054 ± 2.219.

Ultra-High-Resolution UAV-Based Detection of Alternaria solani Infections in Potato Fields

Automatic detection of foliar diseases in potato fields, such as early blight caused by Alternaria solani, could allow farmers to reduce the application of plant protection products while minimizing production losses. UAV-based, high resolution, NIR-sensitive cameras offer the advantage of a detailed top-down perspective, with high-contrast images ideally suited for detecting Alternaria solani lesions. A field experiment was conducted with 8 plots housing 256 infected plants which were monitored 6 times over a 16-day period with a UAV. A modified RGB camera, sensitive to NIR, was combined with a superzoom lens to obtain ultra-high-resolution images with a spatial resolution of 0.3 mm/px. More than 15,000 lesions were annotated with points in two full size images corresponding to 1250 cropped tiles of 256 by 256 pixels. A deep learning U-Net model was trained to predict the density of Alternaria solani lesions for every pixel. In this way, density maps were calculated to indicate disease hotspots as a guide for the farmer.

39 Treatment efficacy of aromatase inhibitors for leiomyosarcoma of the uterus

The objective of this study was to assess whether aromatase inhibitors (AIs) improve overall and survival in patients with uterine leiomyosarcoma (uLMS). We conducted an institutional review board-approved, retrospective cohort study at Kaiser Permanente, Riverside Medical Center. All patients who received a diagnosis of uLMS on pathology from 2012–2021 were reviewed. Demographics and outcomes were compared between patients that received AIs versus those that did not. The study included a total 17 patients with uLMS, including five who received an AI (29%). A Log-rank (Mantel-Cox) test demonstrated a significant difference in overall survival (OS) between patients who received Anastrozole versus patients who did not receive hormonal treatment, p=0.0305, hazard ratio (HR) 0.165, 95% confidence interval (CI) [0.032, 0.844] (Fig. 1).Download : Download high-res image (104KB)Download : Download full-size image Uterine leiomyosarcoma (uLMS) is a rare and aggressive malignancy that generally portends a poor prognosis. Generally speaking, surgical therapy is the standard of care for early-stage disease. Despite complete resection of the tumor, the risk of recurrence of leiomyosarcoma remains high due to its high-grade histopathology. Hormonal therapy has been successful in decreasing rates of recurrence and improving survival in a subset of patients with uLMS. More specifically, aromatase inhibitors (AIs) have been utilized as post-operative adjuvant therapy for patients with estrogen receptor (ER) and progesterone receptor (PR) positive stage I uLMS and for patients with recurrent, metastatic and unresectable disease. Our study demonstrates that there may be a role for AIs in the treatment of recurrent uLMS. Due to its high recurrence rate and low response to chemotherapy and radiation, other forms of treatment need to be explored and identified.

23 Identifying HPV vaccination initiation hot and cold spots and associated disparities in Texas

Over 90% of HPV-associated cancers are preventable through HPV vaccination. Only 43.5% of Texas adolescents, however, have completed the vaccine series, and the state ranks 47th in vaccination rates across the country. The goal of this study was to examine the geospatial uptake of the HPV vaccine in Texas and the associated disparities. HPV vaccine data from 2017–2021 was obtained from the Texas Department of State Health Services. Zip code-level estimates of individuals 9 years and older who received at least one HPV vaccine since 2017 in the Texas Immunization Information System (ImmTrac2) database were used to create a choropleth map. Through the Environmental Science Research Institute's (ESRI) ArcGIS Pro Version 2.2.0 software, the Cluster and Outlier Analysis Tool was used to determine a “neighborhood” around each zip code. Zip codes were subsequently demarcated as hot or cold spots (or neither) based off Local Moran's I score calculations, meaning they had a statistically significant greater or lesser concentration of vaccinations than would be expected from a random distribution. After identifying hot spots and cold spots of vaccine initiation, we explored the demographic neighborhood characteristics. 346 statistically significant hot and 374 cold spot zip codes of HPV vaccine initiation were identified (Fig. 1). The remainder of the zip codes (1115) were outliers or not part of statistically significant clusters. Hot spot zip codes, or areas of high vaccine initiation, were found along the Gulf coast, in large cities, and in less developed areas near the Texas panhandle. Cold spot zip codes were in the northern Texas panhandle, along the Texas–Louisiana border, and on the periphery of major cities. Compared to cold spot zips, hot spots had significantly higher percentages of children on public insurance, had higher poverty rates, and had higher percentages of African American and Hispanic residents.Download : Download high-res image (217KB)Download : Download full-size image In Texas, areas with higher poverty rates, increased use of public insurance, and higher percentages of minority populations had significantly higher rates of HPV vaccine initiation. This may be due to differences in patient interest in the vaccine, to vaccine access through government insurance, or to outreach efforts that have promoted the vaccine in especially vulnerable populations.

25 Comparison of three testing strategies for germline genetic testing for homologous recombination deficiency mutations in women with epithelial ovarian cancer

One in five women with epithelial ovarian cancer (EOC) will have an inherited germline mutation, yet testing rates are low. Point-of-care testing, where abbreviated counseling with in-office testing is offered, may increase testing rates. We aimed to compare testing rates among three different testing strategies used in our practice over time. All patients with EOC were identified in the electronic medical record (EMR) using diagnosis codes at an urban academic gynecologic oncology practice from 1/2015 to 12/2020. IRB approval was obtained. EMR review was performed. Trends in genetic counseling and testing rates were analyzed across three different practice models: embedded genetic counselor in the clinic (before 5/2017), referral out for genetic counseling (5/2017–10/2017) and point-of-care office testing (after 10/2017). Descriptive statistics and chi-square tests were performed. 431 patients with EOC patients were identified. Patients were predominantly white (88%), older women (average age 67.9), with Medicare (58%) or private insurance (39%), and advanced stage disease (63% Stage III, 17% Stage IV) of high-grade serous (77%) histology. Germline BRCA1 or 2 mutations were identified in 10.5% and 4.5% of patients, and 10.6% of patients had a different homologous recombination deficiency mutation. Significant differences in genetic testing rates were identified by strategy (p<0.0001). During the embedded genetic counselor period, 66% (173/263) of patients underwent genetic testing. Testing dropped to 13% (13/104) when patients were referred out, then improved to 65% (160/245) with the initiation of point-of-care testing (Fig. 1). 80% of patients eventually received germline genetic testing; 44% of patients initially not tested (40/91) underwent catch-up testing after initiation of point-of-care testing.Download : Download high-res image (101KB)Download : Download full-size image Germline genetic testing rates for women with EOC were comparable for embedded genetic counselor and point-of-care testing strategies. Point-of-care testing has allowed catch-up testing over time, whereas the referral out strategy presented a substantial barrier. To achieve guideline-concordant care, point-of-care testing with post-test counseling represents a feasible strategy for gynecologic oncology practices where access to an embedded genetic counselor is not possible.