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BackgroundIn 2012, the United States Preventative Services Task Force issued new guidelines recommending that male U.S. residents, irrespective of race, no longer be screened for prostate cancer. In African American men, the incidence of prostate cancer is almost 60 % higher and the mortality rate is two to three times greater than in Caucasians. The purpose of this study is to reduce African American men's prostate cancer burden by demonstrating they need separate screening guidelines.MethodsWe performed a PubMed search using the keywords: African American, Prostate cancer, Outcomes, Molecular markers, Prostate-specific Antigen velocity, PSA density, and to derive data relevant to our hypothesis.ResultsIn our literature review, we identified several aspects of prostate cancer that are different in Caucasian and African American men. These included prostate cancer incidence and outcome, the clinical course of the disease, serum PSA levels, genetic differences, and social barriers. It's also important to note that the USPSTF guidelines were based on two studies, one of which reported that only 4 % of its participants were African American. The other did not report demographic information, but used participants from seven European countries with small African American populations.ConclusionGiven the above, we conclude that separate prostate cancer screening guidelines are greatly necessary to help save the lives of African Americans.

Background: Diagnosis and treatment management for head and neck squamous cell carcinoma (HNSCC) is guided by routine diagnostic head and neck computed tomography (CT) scans to identify tumor and lymph node features. The extracapsular extension (ECE) is a strong predictor of patients' survival outcomes with HNSCC. It is essential to detect the occurrence of ECE as it changes staging and treatment planning for patients. Current clinical ECE detection relies on visual identification and pathologic confirmation conducted by clinicians. However, manual annotation of the lymph node region is a required data preprocessing step in most of the current machine learning‐based ECE diagnosis studies. Purpose: In this paper, we propose a Gradient Mapping Guided Explainable Network (GMGENet) framework to perform ECE identification automatically without requiring annotated lymph node region information. Methods: The gradient‐weighted class activation mapping (Grad‐CAM) technique is applied to guide the deep learning algorithm to focus on the regions that are highly related to ECE. The proposed framework includes an extractor and a classifier. In a joint training process, informative volumes of interest (VOIs) are extracted by the extractor without labeled lymph node region information, and the classifier learns the pattern to classify the extracted VOIs into ECE positive and negative. Results: In evaluation, the proposed methods are well‐trained and tested using cross‐validation. GMGENet achieved test accuracy and area under the curve (AUC) of 92.2% and 89.3%, respectively. GMGENetV2 achieved 90.3% accuracy and 91.7% AUC in the test. The results were compared with different existing models and further confirmed and explained by generating ECE probability heatmaps via a Grad‐CAM technique. The presence or absence of ECE has been analyzed and correlated with ground truth histopathological findings. Conclusions: The proposed deep network can learn meaningful patterns to identify ECE without providing lymph node contours. The introduced ECE heatmaps will contribute to the clinical implementations of the proposed model and reveal unknown features to radiologists. The outcome of this study is expected to promote the implementation of explainable artificial intelligence‐assiste ECE detection. [ABSTRACT FROM AUTHOR]

Purpose: To establish a predictive model for the incidence of esophagitis for lung cancer patients treated with radiotherapy. Methods and Materials: 139 treatment charts of lung cancer patients treated with radiation therapy or combined chemo-radiotherapy from January 01, 2014 to June 30, 2017 at University of Mississippi Radiation Oncology were retrospectively reviewed. Mean esophagus dose (MED) and the endpoints of esophagitis grade 1 and 2 based on Radiation Therapy Oncology Group (RTOG) definitions were derived from the Pinnacle treatment planning system (TPS) and the EPIC electronic medical record (EMR) system, respectively. Binary logistic regression and Probit statistical analysis were used to determine the relationship between the probability of grade 1 and 2 esophagitis with the mean esophagus dose. Results and Conclusions: The regression model of the incidence of grade 1 and 2 esophagitis was established. The results suggest that MED is a good predictor of the risk of radiation-induced esophagitis. The mean esophagus doses associated with a 50% incidence probability ([TD.sub.50]) for grade 1 and 2 esophagitis were determined as 1,510 cGy and 4,594 cGy, respectively. The parameters, n, m and [TD.sub.50] as described in the Lyman Kutcher Burman (LKB) model were fitted and compared with other published findings. Our findings may be useful as additional clinical guidelines in treatment planning and plan evaluation, as well as obtaining informed patient consent. Key Words: Radiation Therapy, Lung Cancer, Dose Volume Effect, Esophagitis, Radiation Dosimetric Parameters, Mean Esophagus Dose, LKB Model, INTRODUCTION Since the late 1980s, radiation therapy was considered the standard of care for patients with lung cancer.[1] Although radiation is beneficial at killing the tumor cell, it is harmful [...]