Kidney Stone Risk in Normocalcemic Hyperparathyroidism before and after Parathyroid Surgery.

The higher risk for kidney stone in patients with primary hyperparathyroidism is well-documented; stone risk in patients with normocalcemic primary hyperparathyroidism (NPHPT) remains unclear. We present a case of recurrent calcium kidney stones in a patient with severe idiopathic hypercalciuria and NPHPT. The surgical resection of the parathyroid adenoma failed to reduce kidney stone risk (based on the 24-hr urine study) and kidney stone burden (based on ultrasound). This unique case examines the impact of surgical resection of an ectopic parathyroid adenoma on stone risk in a patient with NPHPT and recurrent calcium kidney stones.

Investigation of Disparity of Urologic Fellowship Match Rates by Gender and US Versus International Medical Graduates Over the Past Decade.

OBJECTIVE: To investigate the influence of postgraduate medical education (US vs international) and gender on applicant matching for postgraduate training across different urologic sub-specialties. METHODS: Match statistics of 5 societies that participated in the AUA fellowship match between 2010 and 2024 were retrospectively reviewed. Societies included: Endourology Society (EUS), Society for Urological Oncology (SUO), American Society of Andrology (ASA), Society of Genitourinary Reconstructive Surgeons (GURS), and Society of Pediatric Urology (SPU). Candidates were classified based on gender (male/female) and their postgraduate medical education: local graduates from the United States or Canada (US/Ca) and international medical graduates (IMGs). The match odds were analyzed using the Chi-square test, while trends were assessed through the Mann-Kendall test. RESULTS: Overall, 2439 applicants applied for 1627 programs from 2010 to 2024, comprising 1998 males (81.8%), 399 females (16.4%), and 42 undisclosed (1.7%). There were 1486 US/Ca graduates (60.8%) and 953 IMGs (39.2%). Around 1471 (60.6%) applicants were matched with a program, compared to 958 (39.4%) unmatched. The likelihood of US/Ca graduates matching (83.8%) was significantly higher than IMGs (23.3%), OR= 17.5, 95% CI: (14.3, 21.5), P <.001. IMGs had the highest match rate with GURS (33.8%, 47/118) and the lowest with SPU (7%, 1/14). Female applicants had a significantly higher chance of matching 324/399 (81.2%) than male applicants 1139/1998 (57%), OR= 3.26, 95% CI: (2.5, 4.3), P <.001. US/Ca-to-IMGs ratios and the male-to-female ratios were stable throughout the match years. CONCLUSION: Compared to IMGs, U.S./Ca graduates had remarkably higher matching rates. Matching outcomes were also significantly better for female applicants. Further assessment of international involvement and diversity in urological subspecialty roles is warranted.

Lower urinary tract symptoms in elderly men: Considerations for prostate cancer testing.

PURPOSE: Both lower urinary tract symptoms (LUTS) and prostate cancer (PCa) are common in elderly men. While LUTS are generally due to a benign etiology, they may provoke an evaluation with prostate-specific antigen (PSA), which can lead to a cascade of further testing and possible overdiagnosis in patients with competing risks. There is limited patient and provider understanding of the relationship between LUTS and PCa risk, and a lack of clarity in how to evaluate these men to balance appropriate diagnosis of aggressive PCa with avoidance of overdiagnosis. METHODS: A literature review was performed using keywords to query the electronic database PubMed. All articles published before November 2023 were screened by title and abstract for articles relevant to our subject. RESULTS: Epidemiological studies suggest that LUTS and PCa are largely independent in elderly men. The best available tools to assess PCa risk include PSA permutations, novel biomarkers, and imaging, but there are limitations in older men based on lack of validation in the elderly and unclear applicability of traditional definitions of "clinically significant" disease. We present a three-tiered approach to evaluating these patients. CONCLUSION: Elderly men commonly have LUTS as well as a high likelihood of indolent PCa. A systematic and shared decision-making-based approach can help to balance objectives of appropriate detection of phenotypically dangerous disease and avoidance of over-testing and overdiagnosis.

Consistent predictive ability of prostate-specific antigen density prediction model for clinically significant prostate cancer across age strata.

BACKGROUND: Prebiopsy prostate-specific antigen density (PSAD) is a well-known predictor of clinically significant prostate cancer (csPCa). Since prostate-specific antigen (PSA) and prostate volume (PV) increase normally with aging, PSAD thresholds may vary. The purpose of the study was to determine if PSAD was predictive of csPCa in different age strata. METHODS: We retrospectively reviewed our institutional database for patients who underwent multiparametric magnetic resonance imaging (MRI) between January 2016 and December 2021. We included patients who had post-MRI prostate biopsies. Based on age, we divided our cohort into four subgroups (groups 1-4): <55, 55-64, 65-74, and ≥75 years old. PSAD accuracy was estimated by the area under the curve (AUC) as a predictive model for differentiating csPCa between the groups. CsPCa was defined as a Gleason Grade Group 2 or higher. Three different PSAD thresholds (0.1, 0.15, and 0.2) were tested across the groups for sensitivity, specificity, and positive predictive value (PPV) and negative predictive value (NPV). Chi-square and analysis of variance tests were used for bivariate analysis. All analys were completed using R 4.3 (R Core Team, 2023). RESULTS: Among 1913 patients, 883 (46.1%) had prostate biopsies. In groups 1, 2, 3, and 4, there were 62 (7%), 321 (36.4%), 404 (45.8%), and 96 (10.9%) patients, respectively. Median PSA was 5.6 (interquartile range 3.4-8.1), 6.2 (4.8-9), 6.8 (5.1-9.7), and 9 (5.6-13), respectively (p < 0.01). Median PV was 42.3 (30-62), 51 (36-77), 55.5 (38-85.9), and 59.3 (42-110) mL, respectively (p < 0.01). No difference was observed in median PSAD between age groups 1-4 (0.1 [0.07-0.16], 0.11 [0.08-0.18], 0.1 [0.07-0.19], and 0.1 [0.07-0.2]), respectively (p = 0.393). CsPCa was diagnosed in 241 (27.3%) patients, of which 10 (16.1%), 65 (20.2%), 121 (30%), and 45 (46.7%) were in groups 1-4, respectively (p < 0.001). For groups 1-4, the PSAD AUC for predicting csPCa was 0.75, 0.68, 0.71, and 0.74. While testing PSAD threshold of 0.15 across the different age groups (1-4), the PPV vs. NPV was 39.1 vs. 93.2, 33.6 vs. 87, 50.9 vs. 80.8, and 66.1 vs. 64.7, respectively. CONCLUSIONS: PSAD prediction model was found to be similar among different age groups. In young patients, PSAD had a high NPV but low PPV. With increasing age, the opposite trend was observed, likely due to higher disease prevalence. While PSAD thresholds may be less useful in older patients to rule out higher-grade prostate cancer, the clinical consequences of these diagnoses require a case-by-case evaluation.

Optimising postoperative care: Same-day discharge after transurethral resection of the prostate.

INTRODUCTION: This study aims to assess the feasibility and safety of same-day discharge after transurethral resection of the prostate. MATERIALS AND METHODS: Five years of records were retrospectively analysed. Length of stay categorised patients into Groups 1 (same-day discharge) and 2 (standard-length discharge). Logistic regression analysis was performed, controlling for clinicodemographic factors. Student's t-test compared continuous bladder irrigation and catheter dwell times. RESULTS: A total of 459 patients were identified between 2016 and 2021, 280 in Group 1 and 179 in Group 2, with median ages of 71.0 (interquartile range 36-92) and 72.0 (interquartile range 47-101) years (p = 0.067), respectively. Same-day discharge rates notably increased post-2018 (p = 0.025). Median prostate tissue resected in Group 2 was 7.1g (3.4-12.4g) and in Group 1 was 4.9g (2.4-10.2g; p = 0.034). While continuous bladder irrigation >1 hour was significantly lower in Group 1 than Group 2 (96.8% versus 27.4%; p = 0.0001), catheter dwell times were comparable (70.1 and 70.8 hours, respectively). Control-adjusted results showed a 40% reduction in emergency department representation odds for Group 1 compared with Group 2 (odds ratio = 0.60; 95% confidence interval = 0.37-0.99; p = 0.04). Length of stay was not significantly associated with hospital readmissions (p = 0.11). Continuous bladder irrigation for <1 hour in Group 1 was associated with a reduced emergency department representation (odds ratio = 0.43; 95% confidence interval = 0.197-0.980) but not readmission (odds ratio = 0.413; 95% confidence interval = 0.166-1.104). CONCLUSIONS: Same-day discharge post-transurethral resection of the prostate may be a viable and safe option for carefully selected patients.

Harnessing artificial intelligence for prostate cancer management.

Prostate cancer (PCa) is a common malignancy in males. The pathology review of PCa is crucial for clinical decision-making, but traditional pathology review is labor intensive and subjective to some extent. Digital pathology and whole-slide imaging enable the application of artificial intelligence (AI) in pathology. This review highlights the success of AI in detecting and grading PCa, predicting patient outcomes, and identifying molecular subtypes. We propose that AI-based methods could collaborate with pathologists to reduce workload and assist clinicians in formulating treatment recommendations. We also introduce the general process and challenges in developing AI pathology models for PCa. Importantly, we summarize publicly available datasets and open-source codes to facilitate the utilization of existing data and the comparison of the performance of different models to improve future studies.

Ductal, intraductal, and cribriform carcinoma of the prostate: Molecular characteristics and clinical management.

Prostatic acinar adenocarcinoma accounts for approximately 95% of prostate cancer (CaP) cases. The remaining 5% of histologic subtypes of CaP are known to be more aggressive and have recently garnered substantial attention. These histologic subtypes - namely, prostatic ductal adenocarcinoma (PDA), intraductal carcinoma of the prostate (IDC-P), and cribriform carcinoma of the prostate (CC-P) - typically exhibit distinct growth characteristics, genomic features, and unique oncologic outcomes. For example, PTEN mutations, which cause uncontrolled cell growth, are frequently present in IDC-P and CC-P. Germline mutations in homologous DNA recombination repair (HRR) genes (e.g., BRCA1, BRCA2, ATM, PALB2, and CHEK2) are discovered in 40% of patients with IDC-P, while only 9% of patients without ductal involvement had a germline mutation. CC-P is associated with deletions in common tumor suppressor genes, including PTEN, TP53, NKX3-1, MAP3K7, RB1, and CHD1. Evidence suggests abiraterone may be superior to docetaxel as a first-line treatment for patients with IDC-P. To address these and other critical pathological attributes, this review examines the molecular pathology, genetics, treatments, and oncologic outcomes associated with CC-P, PDA, and IDC-P with the objective of creating a comprehensive resource with a centralized repository of information on PDA, IDC-P, and CC-P.

Less qualitative multiparametric magnetic resonance imaging in prostate cancer can underestimate extraprostatic extension in higher grade tumors.

BACKGROUND: Multiparametric magnetic resonance imaging (mpMRI) is increasingly used for risk stratification and preoperative staging of prostate cancer. It remains unclear how Grade Group (GG) interacts with the ability of mpMRI to determine the presence of extraprostatic extension (EPE) on surgical pathology. METHODS: A retrospective review of a robotic assisted laparoscopic radical prostatectomy (RALP) database from 2016-2020 was performed. Radiology mpMRI reports by multiple attending radiologists and without clear standardization or quality control were retrospectively assessed for EPE findings and compared with surgical pathology reports. The data were stratified by biopsy-based GG and a multivariable cluster analysis was performed to incorporate additional preoperative variables (age at diagnosis, PSA, etc.). Hazard ratios were calculated to determine how mpMRI findings and radiographic EPE relate to positive surgical margins. RESULTS: 289 patients underwent at least one mpMRI prior to RALP. Preoperative mpMRI demonstrated sensitivity of 39.3% and specificity of 88.8% for pathological EPE and had a negative predictive value (NPV) of 49.5%, and positive predictive value (PPV) of 84.0%. Stratification of NPV by GG yielded the following values: GG 1-5 (49.5%), GG 3-5 (40.8%), GG 4-5 (43.4%), and GG 5 (30.4%). Additionally, positive EPE on preoperative mpMRI was associated with a significantly decreased risk of positive surgical margins (RR: 0.655; 95% CI: 0.557-0.771). CONCLUSIONS: NPV of prostate mpMRI for EPE may be decreased for higher grade tumors. A detailed reference reading and image quality optimization may improve performance. However, urologists should exercise caution in nerve sparing approaches in these patients.

Epidemiology of and Risk Factors in Postoperative Complications from Robotically Assisted Laparoscopic Radical Prostatectomy in Contemporary National Surgical Quality Improvement Program Data.

Introduction: For localized clinically significant prostate cancer (csPCa), robotically assisted laparoscopic radical prostatectomy (RALP) is the gold standard surgical treatment. Despite low overall complication rate, continued quality assurance (QA) efforts to minimize complications of RALP are important, particularly given movement toward same-day discharge. In 2019, National Surgical Quality Improvement Program (NSQIP) began collecting RALP-specific data. In this study, we assessed pre- and perioperative factors associated with postoperative complications for RALP to further QA efforts. Materials and Methods: Surgical records of csPCa patients who underwent RALP were retrieved from the 2019 to 2021 NSQIP database, including new RALP-specific data. Multivariate logistic regression evaluated the association between risk factors and outcomes specific to RALP and pelvic lymph node dissection (PLND). Input variables included American Society of Anesthesiologists (ASA) class, age, operative time, and body mass index (BMI). Variables from the extended dataset with PLND information included number of nodes evaluated, perioperative antibiotics, postoperative venous thromboembolism (VTE) prophylaxis, history of prior pelvic surgery, and history of prior radiotherapy (RT). Outcomes of interest were any surgical complication, infection, pulmonary embolism, deep venous thrombosis, acute kidney injury, pneumonia, lymphocele, and urinary/anastomotic leak (UAL). Results: A total of 11,811 patients were included with 6.1% experiencing any complication. Prior RT, prior pelvic surgery, older age, higher BMI, lack of perioperative antibiotic therapy, longer operative time, PLND, and number of lymph nodes dissected were associated with higher risk of postoperative complications. Regarding procedure-specific complications, there were increased odds of UAL with prior RT, prior pelvic surgery, longer operative time, and higher BMI. Odds of developing lymphocele increased with prior pelvic surgery, performance of PLND, and increased number of nodes evaluated. Conclusion: In contemporary NSQIP data, RALP is associated with low complication rates; however, these rates have increased compared with historical studies. Attention to and counseling regarding risk factors for peri- and postoperative complications are important to set expectations and minimize risk of unplanned return to a health care setting after discharge.

Less qualitative multiparametric magnetic resonance imaging in prostate cancer can underestimate extraprostatic extension in higher grade tumors

ABSTRACT Background: Multiparametric magnetic resonance imaging (mpMRI) is increasingly used for risk stratification and preoperative staging of prostate cancer. It remains unclear how Grade Group (GG) interacts with the ability of mpMRI to determine the presence of extraprostatic extension (EPE) on surgical pathology. Methods: A retrospective review of a robotic assisted laparoscopic radical prostatectomy (RALP) database from 2016-2020 was performed. Radiology mpMRI reports by multiple attending radiologists and without clear standardization or quality control were retrospectively assessed for EPE findings and compared with surgical pathology reports. The data were stratified by biopsy-based GG and a multivariable cluster analysis was performed to incorporate additional preoperative variables (age at diagnosis, PSA, etc.). Hazard ratios were calculated to determine how mpMRI findings and radiographic EPE relate to positive surgical margins. Results: Two hundred and eighty nine patients underwent at least one mpMRI prior to RALP. Preoperative mpMRI demonstrated sensitivity of 39.3% and specificity of 88.8% for pathological EPE and had a negative predictive value (NPV) of 49.5%, and positive predictive value (PPV) of 84.0%. Stratification of NPV by GG yielded the following values: GG 1-5 (49.5%), GG 3-5 (40.8%), GG 4-5 (43.4%), and GG 5 (30.4%). Additionally, positive EPE on preoperative mpMRI was associated with a significantly decreased risk of positive surgical margins (RR: 0.655; 95% CI: 0.557-0.771). Conclusions: NPV of prostate mpMRI for EPE may be decreased for higher grade tumors. A detailed reference reading and image quality optimization may improve performance. However, urologists should exercise caution in nerve sparing approaches in these patients.

"Shared decision-making" for prostate cancer screening: Is it a marker of quality preventative healthcare?

BACKGROUND: "Shared decision-making" (SDM) is a cornerstone of prostate cancer (PCa) screening guidelines due to tradeoffs between clinical benefits and concerns for over-diagnosis and over-treatment. SDM requires effort by primary-care-providers (PCP) in an often busy clinical setting to understand patient preferences with the backdrop of patient risk factors. We hypothesized that SDM for PCa screening, given its prominence in guidelines and practical challenges, may be associated with quality preventative healthcare in terms of other appropriate cancer screening and encouragement of other preventative health behaviors. METHODS: From the 2020 Behavioral Risk Factor Surveillance Survey, 50-75 year old men who underwent PSA screening were assessed for their participation in SDM, PCa and colorectal cancer (CRC) screening, and other preventative health behaviors, like vaccination, exercise, and smoking status. Adjusted odds ratio of likelihood of PSA testing as a function of SDM was calculated. Likelihoods of SDM and PSA testing as a function of preventative health behaviors were also calculated. RESULTS: Screening rates were 62 % for PCa and 88 % for CRC. Rates of SDM were 39.1 % in those with PSA screening, and 16.2 % in those without. Odds of PSA screening were higher when SDM was present (AOR = 2.68). History of colonoscopy was associated with higher odds of SDM (AOR = 1.16) and PSA testing (AOR = 1.94). Health behaviors, like regular exercise, were associated with increased odds of SDM (AOR = 1.14) and PSA testing (AOR = 1.28). History of flu vaccination (AOR = 1.29) and pneumonia vaccination (AOR = 1.19) were associated with higher odds of SDM. Those who received the flu vaccine were also more likely to have PSA testing (AOR = 1.36). Smoking was negatively associated with SDM (AOR = 0.86) and PSA testing (AOR = 0.93). Older age was associated with higher rates of PSA screening (AOR = 1.03, CI = 1.03-1.03). Black men were more likely than white men to have SDM (AOR = 1.6, CI = 1.59 - 1.6) and decreased odds of PSA testing (AOR = 0.94, CI = 0.94 - 0.95). CONCLUSIONS: SDM was associated with higher odds of PSA screening, CRC screening, and other appropriate preventative health behaviors. Racial disparities exist in both SDM and PSA screening usage. SDM may be a trackable metric that can lead to wider preference-sensitive care and improved preventative care.

Renal Imaging in Stone Disease: Which Modality to Choose?

Numerous imaging modalities are available to the provider when diagnosing or surveilling kidney stones. The decision to order one over the other can be nuanced and especially confusing to non-urologic practitioners. This manuscript reviews the main modalities used to image stones in the modern era - renal bladder ultrasound, Kidney Ureter Bladder plain film radiography (KUB), magnetic resonance imaging (MRI), and non-contrast computerized tomography (NCCT). While NCCT has become the most popular and familiar modality for most practitioners, particularly in the acute setting, ultrasound is a cost-effective technology that is adept at monitoring interval stone development in patients and evaluating for the presence of hydronephrosis. KUB and MRI also occupy unique niches in the management of urolithiasis. In the correct clinical setting, each of these modalities has a role in the acute workup and management of suspected nephrolithiasis.

The Surgical Management of Urolithiasis: A Review of the Literature.

The incidence of stone disease has increased significantly in the past 30 years, with a reported prevalence of 11% of the U.S. population in 2022, up from 9% in 2012 and 5.2% in 1994.1 While prevention is a vital aspect of management, many patients present with symptomatic urolithiasis requiring surgical management. Emerging advances in endoscopy and technology has led to a dynamic shift in the surgical management of stone disease. This paper will serve as a comprehensive review to inform urologic and non-urologic medical professionals alike, as well as the layperson, on the surgical treatment of nephrolithiasis, starting from the initial evaluation, laboratory and radiographic studies, and various surgical options. Additionally, the nuances of managing the pediatric and pregnant patient with nephrolithiasis will be explored. Using the most up-to-date urologic data, our aim is to provide a comprehensive resource for readers who interact with patients experiencing acute episodes of urolithiasis.

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.

The state of PSA counseling in male-to-female transgender patients in the U.S.

BACKGROUND: Morbidity and mortality from prostate cancer (PCa) are known to vary heavily based on socioeconomic and demographic risk factors. We sought to describe prescreening PSA (prostate-specific antigen) counseling (PPC) rates amongst male-to-female transgender (MtF-TG) patients and non-TG patients using the behavioral risk factor surveillance system (BRFSS). METHODS: We used the survey data from 2014, 2016, and 2018 BRFSS and included respondents aged 40-79 years who completed the "PCa screening" and "sexual orientation and gender identity" modules. We analyzed differences in age, education level, income level, marital status, and race/ethnicity using Pearson's χ2 tests. The association of PPC with MtF-TG status and other patient characteristics was evaluated using multivariate logistic regression. RESULTS: A total of 175,383 respondents were included, of which 0.3% identified as MtF-TG. Overall, 62.4% of respondents reported undergoing PPC. On univariate analysis, PPC rates were lower among MtF-TG respondents when compared to the non-TG group (58.3% vs. 62.4%, p = 0.03). MtF-TG respondents were also more likely to report lower education level (p < 0.01), lower-income level (p < 0.01), and were less likely to be white (p < 0.01) than non-TG respondents. However, multivariate analysis adjusting for these respondent features demonstrated an association between higher income and higher education levels with increased odds of PPC, but no association was demonstrated between MtF-TG status and PPC rates. PPC rates for the MtF-TG and non-TG populations did not change significantly over time. CONCLUSIONS: Although PPC was less frequently reported among MtF-TG respondents than in the non-TG group on univariate analysis, this association was not demonstrated when controlling for confounders, including education and income levels. Instead, on multivariate analysis, low education and income levels were more predictive of PPC rates. Further research is needed to ensure equivalent access to prescreening counseling for patients across the socioeconomic and gender identity spectrum.

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.

Role of Artificial Intelligence in Radiogenomics for Cancers in the Era of Precision Medicine.

Radiogenomics, a combination of "Radiomics" and "Genomics," using Artificial Intelligence (AI) has recently emerged as the state-of-the-art science in precision medicine, especially in oncology care. Radiogenomics syndicates large-scale quantifiable data extracted from radiological medical images enveloped with personalized genomic phenotypes. It fabricates a prediction model through various AI methods to stratify the risk of patients, monitor therapeutic approaches, and assess clinical outcomes. It has recently shown tremendous achievements in prognosis, treatment planning, survival prediction, heterogeneity analysis, reoccurrence, and progression-free survival for human cancer study. Although AI has shown immense performance in oncology care in various clinical aspects, it has several challenges and limitations. The proposed review provides an overview of radiogenomics with the viewpoints on the role of AI in terms of its promises for computational as well as oncological aspects and offers achievements and opportunities in the era of precision medicine. The review also presents various recommendations to diminish these obstacles.

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.