Tag: nevin manimala

Cutis. 2022 Sep;110(3):150-154. doi: 10.12788/cutis.0607.

ABSTRACT

Mutations of the BRAF oncogene occur in both melanomas and several other cancers. Our objective was to determine if mutant BRAF V600E expression in a population-based cohort of patients with melanoma was associated with the development of a second primary malignancy of any type. Using the resources of the Rochester Epidemiology Project, we retrospectively identified 380 patients aged 18 to 60 years who were diagnosed with an incident melanoma from 1970 through 2009. We reviewed individual medical records to identify second primary malignancies. We evaluated mutant BRAF V600E expression from available melanoma tissue specimens and assessed its association with the development of a second primary malignancy. BRAF V600E expression in melanomas is associated with an increased risk for basal cell carcinoma (BCC).

PMID:36446115 | DOI:10.12788/cutis.0607

Cutis. 2022 Oct;110(4):215-219. doi: 10.12788/cutis.0626.

ABSTRACT

Approximately 4.5% of adults in the United States identify as members of the lesbian, gay, bisexual, transgender (LGBT) community, and this population has a variety of health care disparities. Dermatologists have the potential to greatly impact the health of this community, but learning experiences in dermatology residency are lacking. In this study, we investigated LGBT education in dermatologic residency from the residents’ perspective and assessed preparedness of dermatology residents to care for this community. An online survey was distributed to current US dermatology residents through program coordinator and program director listserves and postings on dermatology social media groups. Descriptive statistics and a Kruskal-Wallis rank test were used for analysis. There were 114 respondents. This study demonstrated that many dermatology residents are not effectively taught LGBT health topics and feel unprepared to treat this community. Most dermatology residents desired increased training. Further research is needed to determine the best strategies for increasing LGBT learning experiences in dermatology residency programs.

PMID:36446104 | DOI:10.12788/cutis.0626

JMIR Form Res. 2022 Nov 8. doi: 10.2196/40825. Online ahead of print.

ABSTRACT

BACKGROUND: Across each state. the emergence of the COVID-19 pandemic in the United States was marked by policies and rhetoric that often correspond to the political party in power. These diverging responses have sparked broad ongoing discussion about how the political leadership of a state may affect not only the COVID-19 case numbers in a given state, but also the subjective individual experience of the pandemic.

OBJECTIVE: This study leverages state-level data from Google Search Trends and CDC daily case data in order to investigate the temporal relationship between increases in relative search volume for COVID-19 symptoms and corresponding increases in case data. I aimed to identify whether there are state-level differences in patterns of lag time across each of the four spikes in the data (RQ1) and whether the political climate in a given state is associated with these differences (RQ2).

METHODS: Using publicly available data from Google Trends and the Centers for Disease Control and Prevention, linear mixed modeling was utilized to account for random state-level intercepts. Lag time was operationalized as number of days between a peak (a sustained increase before a sustained decline) in symptom search data and a corresponding spike in case data and was calculated manually for each of the four spikes in individual states. Google offers a dataset that tracks the relative search incidence of more than 400 potential COVID-19 symptoms, which is normalized on a 0-100 scale. I used the CDC’s definition of the eleven most common COVID-19 symptoms and created a single construct variable that operationalizes symptom searches. To measure political climate, I considered the proportion of 2020 Trump popular votes in a state as well as a dummy variable for the political party that controls the governorship and a continuous variable measuring proportional party control of federal Congressional representatives.

RESULTS: The strongest overall fit was for a linear mixed model that included proportion of 2020 Trump votes as the predictive variable of interest and included controls for mean daily cases and deaths as well as population. Additional political climate variables were discarded for lack of model fit. Findings indicated evidence that there are statistically-significant differences in lag time by state but that no individual variable measuring political climate was a statistically-significant predictor of these differences.

CONCLUSIONS: Given that there will likely be future pandemics within this political climate, it is important to understand how political leadership affects perceptions of and corresponding responses to public health crises. Although this study did not fully model this relationship, I believe that future research can build on the state-level differences that I identified by approaching the analysis with a different theoretical model, method for calculating lag time, and/or level of geographic modeling.

PMID:36446048 | DOI:10.2196/40825

JCO Precis Oncol. 2022 Nov;6:e2200133. doi: 10.1200/PO.22.00133.

ABSTRACT

PURPOSE: CONCORDE is the first phase I drug-radiotherapy (RT) combination platform in non-small-cell lung cancer, designed to assess multiple different DNA damage response inhibitors in combination with radical thoracic RT. Time-to-event continuous reassessment method (TiTE-CRM) methodology will inform dose escalation individually for each different DNA damage response inhibitor-RT combination and a randomized calibration arm will aid attribution of toxicities. We report in detail the novel statistical design and implementation of the TiTE-CRM in the CONCORDE trial.

METHODS: Statistical parameters were calibrated following recommendations by Lee and Cheung. Simulations were performed to assess the operating characteristics of the chosen models and were written using modified code from the R package dfcrm.

RESULTS: The results of the simulation work showed that the proposed statistical model setup can answer the research questions under a wide range of potential scenarios. The proposed models work well under varying levels of recruitment and with multiple adaptations to the original methodology.

CONCLUSION: The results demonstrate how TiTE-CRM methodology may be used in practice in a complex dose-finding platform study. We propose that this novel phase I design has potential to overcome some of the logistical barriers that for many years have prevented timely development of novel drug-RT combinations.

PMID:36446040 | DOI:10.1200/PO.22.00133

JCO Precis Oncol. 2022 Nov;6:e2200460. doi: 10.1200/PO.22.00460.

ABSTRACT

PURPOSE: Genetic studies of prostate cancer susceptibility have predominantly focused on non-Hispanic White men, despite the observation that Black men are more likely to develop prostate cancer and die from the disease. Therefore, we sought to identify genetic variants in Black patients diagnosed with early-onset prostate cancer.

METHODS: Whole-exome sequencing of germline DNA from a population-based cohort of Black men diagnosed with prostate cancer at age 62 years or younger was performed. Analysis was focused on a panel of DNA damage repair (DDR) genes and HOXB13. All discovered variants were ranked according to their pathogenic potential based upon REVEL score, evidence from existing literature, and prevalence in the cohort. Logistic regression was used to investigate associations between mutation status and relevant clinical characteristics.

RESULTS: Among 743 Black prostate cancer patients, we identified 26 unique pathogenic (P) or likely pathogenic (LP) variants in 14 genes (including HOXB13, BRCA1/2, BRIP1, ATM, CHEK2, and PALB2) among 30 men, or approximately 4.0% of the patient population. We also identified 33 unique variants of unknown significance in 16 genes among 39 men. Because of the rarity of these variants in the population, most associations between clinical characteristics did not achieve statistical significance. However, our results suggest that carriers for P or LP (P/LP) variants were more likely to have a first-degree relative diagnosed with DDR gene-associated cancer, have a higher prostate-specific antigen at time of diagnosis, and be diagnosed with metastatic disease.

CONCLUSION: Variants in DDR genes and HOXB13 may be important cancer risk factors for Black men diagnosed with early-onset prostate cancer, and are more frequently observed in men with a family history of cancer.

PMID:36446039 | DOI:10.1200/PO.22.00460

J Neurosurg Pediatr. 2022 Nov 11:1-10. doi: 10.3171/2022.10.PEDS22281. Online ahead of print.

ABSTRACT

OBJECTIVE: Pediatric patients are at risk of persistent hydrocephalus after posterior fossa tumor resection. The relationship between surgery-related factors and postoperative symptomatic hydrocephalus has not been elucidated. The objective of this study was to analyze features influencing postoperative hydrocephalus in Chinese children.

METHODS: The authors retrospectively evaluated 197 patients younger than 15 years of age who underwent posterior fossa tumor resection at their institution from January 2015 to June 2021. The outcome was whether children underwent CSF diversion within 6 months of resection. Preoperative characteristics, surgery-related factors, and postoperative features were included to identify independent prognosticators. A new logistic model containing independent prognosticators was developed and compared with the modified Canadian Preoperative Prediction Rule for Hydrocephalus (mCPPRH).

RESULTS: In this study, 30 patients (15.2%) underwent CSF diversion within 6 months after tumor resection. Tumor location and consistency, intracranial or spinal tumor metastasis determined by perioperative cerebral and spinal MRI, intraoperative blood loss, ventricular blood as determined on postoperative CT, and pathology were statistically significant variables in the univariate analysis. The only two independent predictors of postoperative symptomatic hydrocephalus were tumor metastasis (OR 3.463, 95% CI 1.137-10.549; p = 0.029) and postoperative ventricular blood (OR 4.212, 95% CI 1.595-11.122; p = 0.004). The final logistic model comprising tumor metastasis and postoperative ventricular blood was found to have better discrimination than the mCPPRH.

CONCLUSIONS: Tumor characteristics and surgery-related features were associated with postoperative symptomatic hydrocephalus. Tumor metastasis and postoperative ventricular blood were found to be important prognosticators of persistent hydrocephalus.

PMID:36446021 | DOI:10.3171/2022.10.PEDS22281

IEEE Trans Med Imaging. 2022 Nov 29;PP. doi: 10.1109/TMI.2022.3225422. Online ahead of print.

ABSTRACT

Anomaly detection in fundus images remains challenging due to the fact that fundus images often contain diverse types of lesions with various properties in locations, sizes, shapes, and colors. Current methods achieve anomaly detection mainly through reconstructing or separating the fundus image background from a fundus image under the guidance of a set of normal fundus images. The reconstruction methods, however, ignore the constraint from lesions. The separation methods primarily model the diverse lesions with pixel-based independent and identical distributed (i.i.d.) properties, neglecting the individualized variations of different types of lesions and their structural properties. And hence, these methods may have difficulty to well distinguish lesions from fundus image backgrounds especially with the normal personalized variations (NPV). To address these challenges, we propose a patch-based non-i.i.d. mixture of Gaussian (MoG) to model diverse lesions for adapting to their statistical distribution variations in different fundus images and their patch-like structural properties. Further, we particularly introduce the weighted Schatten p-norm as the metric of low-rank decomposition for enhancing the accuracy of the learned fundus image backgrounds and reducing false-positives caused by NPV. With the individualized modeling of the diverse lesions and the background learning, fundus image backgrounds and NPV are finely learned and subsequently distinguished from diverse lesions, to ultimately improve the anomaly detection. The proposed method is evaluated on two real-world databases and one artificial database, outperforming the state-of-the-art methods.

PMID:36446017 | DOI:10.1109/TMI.2022.3225422

IEEE Trans Image Process. 2022 Nov 29;PP. doi: 10.1109/TIP.2022.3223793. Online ahead of print.

ABSTRACT

This study presents a high-accuracy, efficient, and physically induced method for 3D point cloud registration, which is the core of many important 3D vision problems. In contrast to existing physics-based methods that merely consider spatial point information and ignore surface geometry, we explore geometry aware rigid-body dynamics to regulate the particle (point) motion, which results in more precise and robust registration. Our proposed method consists of four major modules. First, we leverage the graph signal processing (GSP) framework to define a new signature, i.e., point response intensity for each point, by which we succeed in describing the local surface variation, resampling keypoints, and distinguishing different particles. Then, to address the shortcomings of current physics-based approaches that are sensitive to outliers, we accommodate the defined point response intensity to median absolute deviation (MAD) in robust statistics and adopt the X84 principle for adaptive outlier depression, ensuring a robust and stable registration. Subsequently, we propose a novel geometric invariant under rigid transformations to incorporate higher-order features of point clouds, which is further embedded for force modeling to guide the correspondence between pairwise scans credibly. Finally, we introduce an adaptive simulated annealing (ASA) method to search for the global optimum and substantially accelerate the registration process. We perform comprehensive experiments to evaluate the proposed method on various datasets captured from range scanners to LiDAR. Results demonstrate that our proposed method outperforms representative state-of-the-art approaches in terms of accuracy and is more suitable for registering large-scale point clouds. Furthermore, it is considerably faster and more robust than most competitors. [Our implementation will be released at https://github.com/zikai1/GraphReg].

PMID:36446012 | DOI:10.1109/TIP.2022.3223793

IEEE Trans Cybern. 2022 Nov 29;PP. doi: 10.1109/TCYB.2022.3221544. Online ahead of print.

ABSTRACT

Convolutional neural networks (CNNs) have attracted much research attention and achieved great improvements in single-image dehazing. However, previous learning-based dehazing methods are mainly trained on synthetic data, which greatly degrades their generalization capability on natural hazy images. To address this issue, this article proposes a semi-supervised learning approach for single-image dehazing, where both synthetic and realistic images are leveraged during training. Considering the situation that it is hard to obtain the realistic pairs of hazy and haze-free images, how to utilize the realistic data is not a trivial work. In this article, a domain alignment module is introduced to narrow the distribution distance between synthetic data and realistic hazy images in a latent feature space. Meanwhile, a haze-aware attention module is designed to describe haze densities of different regions in the image, thus adaptively responds for different hazy areas. Furthermore, the dark channel prior is introduced to the framework to improve the quality of the unsupervised learning results by considering the statistical characters of haze-free images. Such a semi-supervised design can significantly address the domain shift issue between the synthetic and realistic data, and improve generalization performance in the real world. Experiments indicate that the proposed method obtains state-of-the-art performance on both public synthetic and realistic hazy images with better visual results.

PMID:36445999 | DOI:10.1109/TCYB.2022.3221544

IEEE Trans Pattern Anal Mach Intell. 2022 Nov 29;PP. doi: 10.1109/TPAMI.2022.3225418. Online ahead of print.

ABSTRACT

This paper presents a generic probabilistic framework for estimating the statistical dependency and finding the anatomical correspondences among an arbitrary number of medical images. The method builds on a novel formulation of the N-dimensional joint intensity distribution by representing the common anatomy as latent variables and estimating the appearance model with nonparametric estimators. Through connection to maximum likelihood and the expectation-maximization algorithm, an information-theoretic metric called X-metric and a co-registration algorithm named X-CoReg are induced, allowing groupwise registration of the N observed images with computational complexity of O(N). Moreover, the method naturally extends for a weakly-supervised scenario where anatomical labels of certain images are provided. This leads to a combined-computing framework implemented with deep learning, which performs registration and segmentation simultaneously and collaboratively in an end-to-end fashion. Extensive experiments were conducted to demonstrate the versatility and applicability of our model, including multimodal groupwise registration, motion correction for dynamic contrast enhanced magnetic resonance images, and deep combined computing for multimodal medical images. Results show the superiority of our method in various applications in terms of both accuracy and efficiency, highlighting the advantage of the proposed representation of the imaging process.

PMID:36445992 | DOI:10.1109/TPAMI.2022.3225418