Optimizing outcomes in men with prostate cancer: the cardiovascular event lowering (CaELo) pathways.

INTRODUCTION: Risk of cardiovascular disease is higher among men with prostate cancer than men without, and prostate cancer treatments (especially those that are hormonally based) are associated with increased cardiovascular risk. MATERIALS AND METHODS: An 11-member panel of urologic, medical, and radiation oncologists (along with a men's health specialist and an endocrinologist/preventive cardiologist) met to discuss current practices and challenges in the management of cardiovascular risk in prostate cancer patients who are taking androgen deprivation therapies (ADT) including LHRH analogues, alone and in combination with androgen-targeted therapies (ATTs). RESULTS: The panel developed an assessment algorithm to categorize patients by risk and deploy a risk-adapted management strategy, in collaboration with other healthcare providers (the patient's healthcare "village"), with the goal of preventing as well as reducing cardiovascular events. The panel also developed a patient questionnaire for cardiovascular risk as well as a checklist to ensure that all aspects of cardiovascular disease risk reduction are completed and monitored. CONCLUSIONS: Prostate cancer patients receiving ADT with or without ATT need to be more zealously assessed for prevention and aggressively managed to reduce cardiovascular events. This can and should include participation from the entire multidisciplinary healthcare team.

A Proposal for the Comprehensive Care of Men on Androgen Deprivation Therapy: Recommendations From the Multidisciplinary Prostate Cancer 360 Working Group.

INTRODUCTION: To promote comprehensive care of patients throughout the androgen deprivation therapy (ADT) prescribing process, the Prostate Cancer 360 (PC360) Working Group developed monitoring and management recommendations intended to mitigate or prevent ADT-associated adverse events. METHODS: The PC360 Working Group included 14 interdisciplinary experts with a dedicated clinical interest in prostate cancer and ADT management. The working group defined challenges associated with ADT adverse event management and then collaboratively developed comprehensive care recommendations intended to be practical for ADT prescribers. RESULTS: The PC360 Working Group developed both overarching recommendations for ADT adverse event management and specific recommendations across 5 domains (cardiometabolic, bone, sexual, psychological, and lifestyle). The working group recommends an interdisciplinary, team-based approach wherein the ADT prescriber retains an oversight role for ADT management while empowering patients and their primary and specialty care providers to manage risk factors. The PC360 recommendations also emphasize the importance of proactive patient education that involves partners or other support providers. Recommended monitoring and assessment tools, risk factor management, and patient counseling points are also included for the 5 identified domains, with an emphasis on lifestyle and behavioral interventions that can improve quality of life and reduce the risk for ADT-associated complications. CONCLUSIONS: Comprehensive care of patients receiving ADT requires early and ongoing coordinated management of a variety of health domains, including cardiometabolic, bone, sexual, psychological health. Patient education and primary care provider involvement should begin prior to ADT initiation and continue throughout treatment to improve patient and partner quality of life.

Princeton IV consensus guidelines: PDE5 inhibitors and cardiac health.

BACKGROUND: In 1999, 1 year after the approval of the first oral phosphodiesterase type 5 (PDE5) inhibitor for the treatment of erectile dysfunction (ED), the first Princeton Consensus Conference was held to address the clinical management of men with ED who also had cardiovascular disease. These issues were readdressed in the second and third conferences. In the 13 years since the last Princeton Consensus Conference, the experience with PDE5 inhibitors is more robust, and recent new data have emerged regarding not only safety and drug-drug interactions, but also a potential cardioprotective effect of these drugs. AIM: In March 2023, an interdisciplinary group of scientists and practitioners met for the fourth Princeton Consensus Guidelines at the Huntington Medical Research Institutes in Pasadena, California, to readdress the cardiovascular workup of men presenting with ED as well as the approach to treatment of ED in men with known cardiovascular disease. METHOD: A series of lectures from experts in the field followed by Delphi-type discussions were developed to reach consensus. OUTCOMES: Consensus was reached regarding a number of issues related to erectile dysfunction and the interaction with cardiovascular health and phosphodiesterase-5 inhibitors. RESULTS: An algorithm based on recent recommendations of the American College of Cardiology and American Heart Association, including the use of computed tomography coronary artery calcium scoring, was integrated into the evaluation of men presenting with ED. Additionally, the issue of nitrate use was further considered in an algorithm regarding the treatment of ED patients with coronary artery disease. Other topics included the psychological effect of ED and the benefits of treating it; the mechanism of action of the PDE5 inhibitors; drug-drug interactions; optimizing use of a PDE5 inhibitors; rare adverse events; potential cardiovascular benefits observed in recent retrospective studies; adulteration of dietary supplements with PDE5 inhibitors; the pros and cons of over-the-counter PDE5 inhibitors; non-PDE5 inhibitor therapy for ED including restorative therapies such as stem cells, platelet-rich plasma, and shock therapy; other non-PDE5 inhibitor therapies, including injection therapy and penile prostheses; the issue of safety and effectiveness of PDE5 inhibitors in women; and recommendations for future studies in the field of sexual dysfunction and PDE5 inhibitor use were discussed. CLINICAL IMPLICATIONS: Algorithms and tables were developed to help guide the clinician in dealing with the interaction of ED and cardiovascular risk and disease. STRENGTHS AND LIMITATIONS: Strengths include the expertise of the participants and consensus recommendations. Limitations included that participants were from the United States only for this particular meeting. CONCLUSION: The issue of the intersection between cardiovascular health and sexual health remains an important topic with new studies suggesting the cardiovascular safety of PDE5 inhibitors.

Artificial intelligence-based preventive, personalized and precision medicine for cardiovascular disease/stroke risk assessment in rheumatoid arthritis patients: a narrative review.

The challenges associated with diagnosing and treating cardiovascular disease (CVD)/Stroke in Rheumatoid arthritis (RA) arise from the delayed onset of symptoms. Existing clinical risk scores are inadequate in predicting cardiac events, and conventional risk factors alone do not accurately classify many individuals at risk. Several CVD biomarkers consider the multiple pathways involved in the development of atherosclerosis, which is the primary cause of CVD/Stroke in RA. To enhance the accuracy of CVD/Stroke risk assessment in the RA framework, a proposed approach involves combining genomic-based biomarkers (GBBM) derived from plasma and/or serum samples with innovative non-invasive radiomic-based biomarkers (RBBM), such as measurements of synovial fluid, plaque area, and plaque burden. This review presents two hypotheses: (i) RBBM and GBBM biomarkers exhibit a significant correlation and can precisely detect the severity of CVD/Stroke in RA patients. (ii) Artificial Intelligence (AI)-based preventive, precision, and personalized (aiP3) CVD/Stroke risk AtheroEdge™ model (AtheroPoint™, CA, USA) that utilizes deep learning (DL) to accurately classify the risk of CVD/stroke in RA framework. The authors conducted a comprehensive search using the PRISMA technique, identifying 153 studies that assessed the features/biomarkers of RBBM and GBBM for CVD/Stroke. The study demonstrates how DL models can be integrated into the AtheroEdge™-aiP3 framework to determine the risk of CVD/Stroke in RA patients. The findings of this review suggest that the combination of RBBM with GBBM introduces a new dimension to the assessment of CVD/Stroke risk in the RA framework. Synovial fluid levels that are higher than normal lead to an increase in the plaque burden. Additionally, the review provides recommendations for novel, unbiased, and pruned DL algorithms that can predict CVD/Stroke risk within a RA framework that is preventive, precise, and personalized.

Early Detection of Prostate Cancer: AUA/SUO Guideline Part II: Considerations for a Prostate Biopsy.

PURPOSE: The summary presented herein covers recommendations on the early detection of prostate cancer and provides a framework to facilitate clinical decision-making in the implementation of prostate cancer screening, biopsy, and follow-up. This is Part II of a two-part series focusing on initial and repeat biopsies, and biopsy technique. Please refer to Part I for discussion of initial prostate cancer screening recommendations. MATERIALS AND METHODS: The systematic review utilized to inform this guideline was conducted by an independent methodological consultant. The systematic review was based on searches in Ovid MEDLINE and Embase and Cochrane Database of Systematic Reviews (January 1, 2000-November 21, 2022). Searches were supplemented by reviewing reference lists of relevant articles. RESULTS: The Early Detection of Prostate Cancer Panel developed evidence- and consensus-based guideline statements to provide guidance in prostate cancer screening, initial and repeat biopsies, and biopsy technique. CONCLUSIONS: The evaluation of prostate cancer risk should be focused on the detection of clinically significant prostate cancer (Grade Group 2 or higher [GG2+]). The use of laboratory biomarkers, prostate MRI, and biopsy techniques described herein may improve detection and safety when a prostate biopsy is deemed necessary following prostate cancer screening.

Early Detection of Prostate Cancer: AUA/SUO Guideline Part I: Prostate Cancer Screening.

PURPOSE: The summary presented herein covers recommendations on the early detection of prostate cancer and provides a framework to facilitate clinical decision-making in the implementation of prostate cancer screening, biopsy, and follow-up. This is Part I of a two-part series that focuses on prostate cancer screening. Please refer to Part II for discussion of initial and repeat biopsies as well as biopsy technique. MATERIALS AND METHODS: The systematic review utilized to inform this guideline was conducted by an independent methodological consultant. The systematic review was based on searches in Ovid MEDLINE and Embase and Cochrane Database of Systematic Reviews (January 1, 2000-November 21, 2022). Searches were supplemented by reviewing reference lists of relevant articles. RESULTS: The Early Detection of Prostate Cancer Panel developed evidence- and consensus-based guideline statements to provide guidance in prostate cancer screening, initial and repeat biopsy, and biopsy technique. CONCLUSIONS: Prostate-specific antigen (PSA)-based prostate cancer screening in combination with shared decision-making (SDM) is recommended. Current data regarding risk from population-based cohorts provide a basis for longer screening intervals and tailored screening, and the use of available online risk calculators is encouraged.

Economics of Artificial Intelligence in Healthcare: Diagnosis vs. Treatment.

Motivation: The price of medical treatment continues to rise due to (i) an increasing population; (ii) an aging human growth; (iii) disease prevalence; (iv) a rise in the frequency of patients that utilize health care services; and (v) increase in the price. Objective: Artificial Intelligence (AI) is already well-known for its superiority in various healthcare applications, including the segmentation of lesions in images, speech recognition, smartphone personal assistants, navigation, ride-sharing apps, and many more. Our study is based on two hypotheses: (i) AI offers more economic solutions compared to conventional methods; (ii) AI treatment offers stronger economics compared to AI diagnosis. This novel study aims to evaluate AI technology in the context of healthcare costs, namely in the areas of diagnosis and treatment, and then compare it to the traditional or non-AI-based approaches. Methodology: PRISMA was used to select the best 200 studies for AI in healthcare with a primary focus on cost reduction, especially towards diagnosis and treatment. We defined the diagnosis and treatment architectures, investigated their characteristics, and categorized the roles that AI plays in the diagnostic and therapeutic paradigms. We experimented with various combinations of different assumptions by integrating AI and then comparing it against conventional costs. Lastly, we dwell on three powerful future concepts of AI, namely, pruning, bias, explainability, and regulatory approvals of AI systems. Conclusions: The model shows tremendous cost savings using AI tools in diagnosis and treatment. The economics of AI can be improved by incorporating pruning, reduction in AI bias, explainability, and regulatory approvals.

Cardiovascular/Stroke Risk Stratification in Diabetic Foot Infection Patients Using Deep Learning-Based Artificial Intelligence: An Investigative Study.

A diabetic foot infection (DFI) is among the most serious, incurable, and costly to treat conditions. The presence of a DFI renders machine learning (ML) systems extremely nonlinear, posing difficulties in CVD/stroke risk stratification. In addition, there is a limited number of well-explained ML paradigms due to comorbidity, sample size limits, and weak scientific and clinical validation methodologies. Deep neural networks (DNN) are potent machines for learning that generalize nonlinear situations. The objective of this article is to propose a novel investigation of deep learning (DL) solutions for predicting CVD/stroke risk in DFI patients. The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) search strategy was used for the selection of 207 studies. We hypothesize that a DFI is responsible for increased morbidity and mortality due to the worsening of atherosclerotic disease and affecting coronary artery disease (CAD). Since surrogate biomarkers for CAD, such as carotid artery disease, can be used for monitoring CVD, we can thus use a DL-based model, namely, Long Short-Term Memory (LSTM) and Recurrent Neural Networks (RNN) for CVD/stroke risk prediction in DFI patients, which combines covariates such as office and laboratory-based biomarkers, carotid ultrasound image phenotype (CUSIP) lesions, along with the DFI severity. We confirmed the viability of CVD/stroke risk stratification in the DFI patients. Strong designs were found in the research of the DL architectures for CVD/stroke risk stratification. Finally, we analyzed the AI bias and proposed strategies for the early diagnosis of CVD/stroke in DFI patients. Since DFI patients have an aggressive atherosclerotic disease, leading to prominent CVD/stroke risk, we, therefore, conclude that the DL paradigm is very effective for predicting the risk of CVD/stroke in DFI patients.

Vascular Implications of COVID-19: Role of Radiological Imaging, Artificial Intelligence, and Tissue Characterization: A Special Report.

The SARS-CoV-2 virus has caused a pandemic, infecting nearly 80 million people worldwide, with mortality exceeding six million. The average survival span is just 14 days from the time the symptoms become aggressive. The present study delineates the deep-driven vascular damage in the pulmonary, renal, coronary, and carotid vessels due to SARS-CoV-2. This special report addresses an important gap in the literature in understanding (i) the pathophysiology of vascular damage and the role of medical imaging in the visualization of the damage caused by SARS-CoV-2, and (ii) further understanding the severity of COVID-19 using artificial intelligence (AI)-based tissue characterization (TC). PRISMA was used to select 296 studies for AI-based TC. Radiological imaging techniques such as magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound were selected for imaging of the vasculature infected by COVID-19. Four kinds of hypotheses are presented for showing the vascular damage in radiological images due to COVID-19. Three kinds of AI models, namely, machine learning, deep learning, and transfer learning, are used for TC. Further, the study presents recommendations for improving AI-based architectures for vascular studies. We conclude that the process of vascular damage due to COVID-19 has similarities across vessel types, even though it results in multi-organ dysfunction. Although the mortality rate is ~2% of those infected, the long-term effect of COVID-19 needs monitoring to avoid deaths. AI seems to be penetrating the health care industry at warp speed, and we expect to see an emerging role in patient care, reduce the mortality and morbidity rate.

Deep Learning Paradigm for Cardiovascular Disease/Stroke Risk Stratification in Parkinson's Disease Affected by COVID-19: A Narrative Review.

Background and Motivation: Parkinson's disease (PD) is one of the most serious, non-curable, and expensive to treat. Recently, machine learning (ML) has shown to be able to predict cardiovascular/stroke risk in PD patients. The presence of COVID-19 causes the ML systems to become severely non-linear and poses challenges in cardiovascular/stroke risk stratification. Further, due to comorbidity, sample size constraints, and poor scientific and clinical validation techniques, there have been no well-explained ML paradigms. Deep neural networks are powerful learning machines that generalize non-linear conditions. This study presents a novel investigation of deep learning (DL) solutions for CVD/stroke risk prediction in PD patients affected by the COVID-19 framework. Method: The PRISMA search strategy was used for the selection of 292 studies closely associated with the effect of PD on CVD risk in the COVID-19 framework. We study the hypothesis that PD in the presence of COVID-19 can cause more harm to the heart and brain than in non-COVID-19 conditions. COVID-19 lung damage severity can be used as a covariate during DL training model designs. We, therefore, propose a DL model for the estimation of, (i) COVID-19 lesions in computed tomography (CT) scans and (ii) combining the covariates of PD, COVID-19 lesions, office and laboratory arterial atherosclerotic image-based biomarkers, and medicine usage for the PD patients for the design of DL point-based models for CVD/stroke risk stratification. Results: We validated the feasibility of CVD/stroke risk stratification in PD patients in the presence of a COVID-19 environment and this was also verified. DL architectures like long short-term memory (LSTM), and recurrent neural network (RNN) were studied for CVD/stroke risk stratification showing powerful designs. Lastly, we examined the artificial intelligence bias and provided recommendations for early detection of CVD/stroke in PD patients in the presence of COVID-19. Conclusion: The DL is a very powerful tool for predicting CVD/stroke risk in PD patients affected by COVID-19.

COVLIAS 2.0-cXAI: Cloud-Based Explainable Deep Learning System for COVID-19 Lesion Localization in Computed Tomography Scans.

Background: The previous COVID-19 lung diagnosis system lacks both scientific validation and the role of explainable artificial intelligence (AI) for understanding lesion localization. This study presents a cloud-based explainable AI, the "COVLIAS 2.0-cXAI" system using four kinds of class activation maps (CAM) models. Methodology: Our cohort consisted of ~6000 CT slices from two sources (Croatia, 80 COVID-19 patients and Italy, 15 control patients). COVLIAS 2.0-cXAI design consisted of three stages: (i) automated lung segmentation using hybrid deep learning ResNet-UNet model by automatic adjustment of Hounsfield units, hyperparameter optimization, and parallel and distributed training, (ii) classification using three kinds of DenseNet (DN) models (DN-121, DN-169, DN-201), and (iii) validation using four kinds of CAM visualization techniques: gradient-weighted class activation mapping (Grad-CAM), Grad-CAM++, score-weighted CAM (Score-CAM), and FasterScore-CAM. The COVLIAS 2.0-cXAI was validated by three trained senior radiologists for its stability and reliability. The Friedman test was also performed on the scores of the three radiologists. Results: The ResNet-UNet segmentation model resulted in dice similarity of 0.96, Jaccard index of 0.93, a correlation coefficient of 0.99, with a figure-of-merit of 95.99%, while the classifier accuracies for the three DN nets (DN-121, DN-169, and DN-201) were 98%, 98%, and 99% with a loss of ~0.003, ~0.0025, and ~0.002 using 50 epochs, respectively. The mean AUC for all three DN models was 0.99 (p < 0.0001). The COVLIAS 2.0-cXAI showed 80% scans for mean alignment index (MAI) between heatmaps and gold standard, a score of four out of five, establishing the system for clinical settings. Conclusions: The COVLIAS 2.0-cXAI successfully showed a cloud-based explainable AI system for lesion localization in lung CT scans.

Eight pruning deep learning models for low storage and high-speed COVID-19 computed tomography lung segmentation and heatmap-based lesion localization: A multicenter study using COVLIAS 2.0.

BACKGROUND: COVLIAS 1.0: an automated lung segmentation was designed for COVID-19 diagnosis. It has issues related to storage space and speed. This study shows that COVLIAS 2.0 uses pruned AI (PAI) networks for improving both storage and speed, wiliest high performance on lung segmentation and lesion localization. METHOD: ology: The proposed study uses multicenter ∼9,000 CT slices from two different nations, namely, CroMed from Croatia (80 patients, experimental data), and NovMed from Italy (72 patients, validation data). We hypothesize that by using pruning and evolutionary optimization algorithms, the size of the AI models can be reduced significantly, ensuring optimal performance. Eight different pruning techniques (i) differential evolution (DE), (ii) genetic algorithm (GA), (iii) particle swarm optimization algorithm (PSO), and (iv) whale optimization algorithm (WO) in two deep learning frameworks (i) Fully connected network (FCN) and (ii) SegNet were designed. COVLIAS 2.0 was validated using "Unseen NovMed" and benchmarked against MedSeg. Statistical tests for stability and reliability were also conducted. RESULTS: Pruning algorithms (i) FCN-DE, (ii) FCN-GA, (iii) FCN-PSO, and (iv) FCN-WO showed improvement in storage by 92.4%, 95.3%, 98.7%, and 99.8% respectively when compared against solo FCN, and (v) SegNet-DE, (vi) SegNet-GA, (vii) SegNet-PSO, and (viii) SegNet-WO showed improvement by 97.1%, 97.9%, 98.8%, and 99.2% respectively when compared against solo SegNet. AUC > 0.94 (p < 0.0001) on CroMed and > 0.86 (p < 0.0001) on NovMed data set for all eight EA model. PAI <0.25 s per image. DenseNet-121-based Grad-CAM heatmaps showed validation on glass ground opacity lesions. CONCLUSIONS: Eight PAI networks that were successfully validated are five times faster, storage efficient, and could be used in clinical settings.

Cardiovascular Risk Stratification in Diabetic Retinopathy via Atherosclerotic Pathway in COVID-19/Non-COVID-19 Frameworks Using Artificial Intelligence Paradigm: A Narrative Review.

Diabetes is one of the main causes of the rising cases of blindness in adults. This microvascular complication of diabetes is termed diabetic retinopathy (DR) and is associated with an expanding risk of cardiovascular events in diabetes patients. DR, in its various forms, is seen to be a powerful indicator of atherosclerosis. Further, the macrovascular complication of diabetes leads to coronary artery disease (CAD). Thus, the timely identification of cardiovascular disease (CVD) complications in DR patients is of utmost importance. Since CAD risk assessment is expensive for low-income countries, it is important to look for surrogate biomarkers for risk stratification of CVD in DR patients. Due to the common genetic makeup between the coronary and carotid arteries, low-cost, high-resolution imaging such as carotid B-mode ultrasound (US) can be used for arterial tissue characterization and risk stratification in DR patients. The advent of artificial intelligence (AI) techniques has facilitated the handling of large cohorts in a big data framework to identify atherosclerotic plaque features in arterial ultrasound. This enables timely CVD risk assessment and risk stratification of patients with DR. Thus, this review focuses on understanding the pathophysiology of DR, retinal and CAD imaging, the role of surrogate markers for CVD, and finally, the CVD risk stratification of DR patients. The review shows a step-by-step cyclic activity of how diabetes and atherosclerotic disease cause DR, leading to the worsening of CVD. We propose a solution to how AI can help in the identification of CVD risk. Lastly, we analyze the role of DR/CVD in the COVID-19 framework.

Cardiovascular/Stroke Risk Assessment in Patients with Erectile Dysfunction-A Role of Carotid Wall Arterial Imaging and Plaque Tissue Characterization Using Artificial Intelligence Paradigm: A Narrative Review.

PURPOSE: The role of erectile dysfunction (ED) has recently shown an association with the risk of stroke and coronary heart disease (CHD) via the atherosclerotic pathway. Cardiovascular disease (CVD)/stroke risk has been widely understood with the help of carotid artery disease (CTAD), a surrogate biomarker for CHD. The proposed study emphasizes artificial intelligence-based frameworks such as machine learning (ML) and deep learning (DL) that can accurately predict the severity of CVD/stroke risk using carotid wall arterial imaging in ED patients. METHODS: Using the PRISMA model, 231 of the best studies were selected. The proposed study mainly consists of two components: (i) the pathophysiology of ED and its link with coronary artery disease (COAD) and CHD in the ED framework and (ii) the ultrasonic-image morphological changes in the carotid arterial walls by quantifying the wall parameters and the characterization of the wall tissue by adapting the ML/DL-based methods, both for the prediction of the severity of CVD risk. The proposed study analyzes the hypothesis that ML/DL can lead to an accurate and early diagnosis of the CVD/stroke risk in ED patients. Our finding suggests that the routine ED patient practice can be amended for ML/DL-based CVD/stroke risk assessment using carotid wall arterial imaging leading to fast, reliable, and accurate CVD/stroke risk stratification. SUMMARY: We conclude that ML and DL methods are very powerful tools for the characterization of CVD/stroke in patients with varying ED conditions. We anticipate a rapid growth of these tools for early and better CVD/stroke risk management in ED patients.

COVLIAS 1.0Lesion vs. MedSeg: An Artificial Intelligence Framework for Automated Lesion Segmentation in COVID-19 Lung Computed Tomography Scans.

Background: COVID-19 is a disease with multiple variants, and is quickly spreading throughout the world. It is crucial to identify patients who are suspected of having COVID-19 early, because the vaccine is not readily available in certain parts of the world. Methodology: Lung computed tomography (CT) imaging can be used to diagnose COVID-19 as an alternative to the RT-PCR test in some cases. The occurrence of ground-glass opacities in the lung region is a characteristic of COVID-19 in chest CT scans, and these are daunting to locate and segment manually. The proposed study consists of a combination of solo deep learning (DL) and hybrid DL (HDL) models to tackle the lesion location and segmentation more quickly. One DL and four HDL models­namely, PSPNet, VGG-SegNet, ResNet-SegNet, VGG-UNet, and ResNet-UNet­were trained by an expert radiologist. The training scheme adopted a fivefold cross-validation strategy on a cohort of 3000 images selected from a set of 40 COVID-19-positive individuals. Results: The proposed variability study uses tracings from two trained radiologists as part of the validation. Five artificial intelligence (AI) models were benchmarked against MedSeg. The best AI model, ResNet-UNet, was superior to MedSeg by 9% and 15% for Dice and Jaccard, respectively, when compared against MD 1, and by 4% and 8%, respectively, when compared against MD 2. Statistical tests­namely, the Mann−Whitney test, paired t-test, and Wilcoxon test­demonstrated its stability and reliability, with p < 0.0001. The online system for each slice was <1 s. Conclusions: The AI models reliably located and segmented COVID-19 lesions in CT scans. The COVLIAS 1.0Lesion lesion locator passed the intervariability test.

A new oral testosterone (TLANDO) treatment regimen without dose titration requirement for male hypogonadism.

BACKGROUND: Male hypogonadism (testosterone level < 300 ng/dl) is a clinical syndrome that results from failure of the testis to produce physiological levels of testosterone. Most marketed testosterone replacement therapy products often require multiple dose adjustment clinic visits to achieve the desired, eugonadal testosterone levels. OBJECTIVE: To evaluate the efficacy and safety of a novel oral testosterone undecanoate therapy for the treatment of hypogonadism. MATERIAL AND METHODS: Ninety-five (N = 95) hypogonadal men were enrolled in this open-label, single-arm, multicenter study in the United States (NCT03242590). Subjects received 225 mg of oral testosterone undecanoate (TLANDO) twice a day for 24 days without dose adjustment. Primary efficacy was percentages of subjects who achieved mean 24-h testosterone levels within the eugonadal range and secondary efficacies were evaluated based on the upper limit of lab normal range of testosterone concentration. RESULTS: Subjects enrolled were on average age of 56 years, with about 17% of subjects older than 65 years. The mean body mass index was 32.8 kg/m2 . The baseline mean total testosterone values were below the normal range (202 ± 74 ng/dl). Post-treatment with 450 mg testosterone undecanoate daily dose without dose adjustment, 80% of subjects (95% confidence interval of 72%-88%) achieved a testosterone Cavg in the normal range and restored testosterone levels to mean testosterone Cavg of 476 ± 184 ng/dl at steady state. Testosterone restoration was comparable to other approved testosterone replacement therapy products. TLANDO was well tolerated with no deaths, no drug-related serious adverse events, and no hepatic adverse events. DISCUSSION AND CONCLUSIONS: TLANDO restored testosterone levels to the normal range in the majority of hypogonadal males. This new oral testosterone replacement therapy can provide an option for no-titration oral testosterone replacement therapy. This therapy has the potential to improve patient compliance in testosterone replacement therapy.

Pharmacokinetics of testosterone therapies in relation to diurnal variation of serum testosterone levels as men age.

BACKGROUND: To improve symptoms associated with testosterone deficiency, many testosterone therapies are available that aim to restore serum testosterone (T) levels to the normal physiologic range. The magnitude, frequency, and duration between peak and trough T concentrations vary with route of administration, and none reflect normal endogenous daily diurnal T variations. OBJECTIVE: To compare pharmacokinetic profiles of serum T from approved T formulations with endogenous diurnal T variations in young and older men, and to consider whether there may be value in mimicking the diurnal T rhythmicity with exogenous testosterone therapies as men age. MATERIALS AND METHODS: A literature search of studies examining the diurnal variation of endogenous T in healthy men and men with testosterone deficiency was performed using PubMed in January 2020. Additional searches for serum T pharmacokinetic profiles of various testosterone therapy formulations were also conducted. Prescribing information for various T formulations was also reviewed. DISCUSSION AND CONCLUSION: Endogenous diurnal T variation is well described and appears to be blunted naturally as men age. Men with testosterone deficiency lack diurnal T variation and exhibit a flatter T profile compared with eugonadal men. Some T replacement options provide intraday T level variations similar to normal circadian secretion, and others provide a flatter exposure profile reflective of depot release. Others provide profiles that exceed the frequency and physiologic range of the natural diurnal variation of T. All exogenous T replacement dosing targets an increase in average T levels to within the normal physiologic range and improves symptoms associated with low T, but no single testosterone therapy can exactly mimic the normal diurnal T patterns seen in younger men and the blunted circadian T secretion of older men.