Citations

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 models—namely, PSPNet, VGG-SegNet, ResNet-SegNet, VGG-UNet, and ResNet-UNet—were 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 tests—namely, the Mann–Whitney test, paired t-test, and Wilcoxon test—demonstrated 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.

Background National studies have reported racial and socioeconomic disparities in gastric cancer (GC) care. The current study evaluated adequate lymph node (LN) assessment (≥16 LNs) during resection for GC within a healthcare system servicing a socioeconomically disparate, mostly non-White population in the Southeast United States. Methods A retrospective cohort study of patients undergoing resection for GC between 2003-2019 was performed. Factors associated with adequate LN assessment including patient and tumor characteristics were analyzed. Results Among 202 patients, adequate LN assessment was performed in 97 (48%) patients. On univariable analysis, younger age, non-White race, lower Charlson Comorbidity Index (CCI), Medicaid or no insurance, D1+/D2 lymphadenectomy, clinical evidence of regional LN metastases, total gastrectomy, and receipt of neoadjuvant therapy were associated with adequate LN assessment. On multivariable analysis, non-White race (OR 2.79, 95% CI 1.38-5.65), CCI <4 (OR 2.14, 95% CI 1.15-3.96), and D1+/D2 lymphadenectomy (OR 3.63, 95% CI 1.96-6.74) were the only factors independently associated with adequate LN evaluation. Conclusions In the current study, non-White race, independent of socioeconomics, was significantly associated with adequate LN assessment. Future work is necessary to improve standardization and achieve higher rates of adequate LN assessment for all patients during resection for GC.

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.

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.

Background: Congenitally corrected transposition of the great arteries (ccTGA) is a rare disease of unknown cause. We aimed to better understand familial recurrence patterns. Methods: An international, multicentre, retrospective cohort study was conducted in 29 tertiary hospitals in 6 countries between 1990 and 2018, entailing investigation of 1043 unrelated ccTGA probands. Results: Laterality defects and atrioventricular block at diagnosis were observed in 29.9% and 9.3%, respectively. ccTGA was associated with primary ciliary dyskinesia in 11 patients. Parental consanguinity was noted in 3.4% cases. A congenital heart defect was diagnosed in 81 relatives from 69 families, 58% of them being first-degree relatives, including 28 siblings. The most prevalent defects in relatives were dextro-transposition of the great arteries (28.4%), laterality defects (13.6%), and ccTGA (11.1%); 36 new familial clusters were described, including 8 pedigrees with concordant familial aggregation of ccTGA, 19 pedigrees with familial co-segregation of ccTGA and dextro-transposition of the great arteries, and 9 familial co-segregation of ccTGA and laterality defects. In one family co-segregation of ccTGA, dextro-transposition of the great arteries and heterotaxy syndrome in 3 distinct relatives was found. In another family, twins both displayed ccTGA and primary ciliary dyskinesia. Conclusions: ccTGA is not always a sporadic congenital heart defect. Familial clusters as well as evidence of an association between ccTGA, dextro-transposition of the great arteries, laterality defects and in some cases primary ciliary dyskinesia, strongly suggest a common pathogenetic pathway involving laterality genes in the pathophysiology of ccTGA.

Abstract As outpatient parenteral antimicrobial therapy (OPAT) becomes more common, it may be difficult to stay current with recent related publications. A group of multidisciplinary OPAT clinicians reviewed and ranked all OPAT publications published in 2021. This article provides a high-level summary of the OPAT manuscripts that were voted the “top 10” publications of 2021.

Abstract Almost all patients with multiple myeloma (MM) eventually relapse, either asymptomatically or with end-organ damage. However, it remains unclear whether initiating therapy at the time of biochemical progression (BP) improves the outcomes compared with initiating therapy at the clinical progression (CP) stage. Here, we retrospectively assessed 1347 patients with relapsed MM. Most progressions were BP (60.4%); 39.6% had CP. The most prevalent symptoms at relapse were new or evolving bone disease (80.9%), anemia (38.0%), and renal failure (12.7%). Patients with BP had longer median time from second-line treatment to the next treatment compared with patients who had CP (17.0 vs 9.6 months; P &lt; .001) as well as longer median overall survival from first relapse (59.4 vs 26.2 months; P &lt; .001). Male sex (odds ratio [OR], 1.49; 95% confidence interval [CI], 1.02-2.18; P = .04), plasma cell labeling index ≥2% (OR, 1.58; 95% CI, 1.02-2.45; P = .04), and extramedullary disease at diagnosis (OR, 1.84; 95% CI, 1.08-3.13; P = .03) were associated with higher risk of CP, whereas very good partial remission or better had decreased risk of CP (OR, 0.62; 95% CI, 0.43-0.91; P = .02). To conclude, patients with CP have inferior postprogression outcomes compared with patients who have BP. Patients with deeper response to first-line therapy are less likely to develop CP. The presence of a specific CRAB (C, hypercalcemia; R, renal failure; A, anemia; B, bone disease) symptom at diagnosis predicts for the development of similar CRAB symptoms at relapse.

Abstract Multiple myeloma (MM) is a plasma cell malignancy that is characterized by diverse clinical presentations. Although biochemical assessment of disease activity is commonly used to monitor treatment response, findings on magnetic resonance imaging and positron emission tomography (PET)/computed tomography (CT), among other imaging modalities, have proven to harbor prognostic value. We sought to corroborate these findings by examining the prognostic significance of fluorodeoxyglucose PET/CT scanning in the setting of newly diagnosed MM. We retrospectively analyzed 195 patients with a PET/CT available at diagnosis and at 6 months posttreatment to examine their value as an adjuvant metric to conventional hematologic responses in terms of time to next treatment (TTNT) and overall survival (OS). The median TTNT and OS for the entire cohort were 24.6 months (95% confidence interval [CI], 20.4-29.1) and 79 months (95% CI, 63.1-119.1), respectively. When comparing PET/CT negative (−) with PET/CT positive (+) patients, we found significantly prolonged median TTNT (55.2 vs 17.8 months, P &lt; .0001) and OS (unreached vs 60.8 months, P &lt; .0001) in the former group. We then examined the additive value of PET/CT on the hematologic response achieved at 6 months and found that PET/CT (−) is associated with significantly increased median TTNT and OS for the very good partial response (VGPR) group and the less than VGPR group. Importantly, PET/CT retained prognostic significance after adjusting for multiple other predictive variables. We conclude that a PET/CT (−) at 6 months confers a significant prognostic advantage for patients with newly diagnosed MM and adds significant value to the hematologic response assessment.

Abstract Background We evaluated the time to progression (TTP) and survival outcomes of second-line therapy for metastatic colorectal cancer among adults aged 70 years and older compared with younger adults following progression on first-line clinical trials. Methods Associations between clinical and disease characteristics, time to initial progression, and rate of receipt of second-line therapy were evaluated. TTP and overall survival (OS) were compared between older and younger adults in first- and second-line trials by Cox regression, adjusting for age, sex, Eastern Cooperative Oncology Group Performance Status, number of metastatic sites and presence of metastasis in the lung, liver, or peritoneum. All statistical tests were 2-sided. Results Older adults comprised 16.4% of patients on first-line trials (870 total older adults aged &gt;70 years; 4419 total younger adults aged ≤70 years, on first-line trials). Older adults and those with Eastern Cooperative Oncology Group Performance Status &gt;0 were less likely to receive second-line therapy than younger adults. Odds of receiving second-line therapy decreased by 11% for each additional decade of life in multivariable analysis (odds ratio = 1.11, 95% confidence interval = 1.02 to 1.21, P = .01). Older and younger adults enrolled in second-line trials experienced similar median TTP and median OS (median TTP = 5.1 vs 5.2 months, respectively; median OS = 11.6 vs 12.4 months, respectively). Conclusions Older adults were less likely to receive second-line therapy for metastatic colorectal cancer, though we did not observe a statistical difference in survival outcomes vs younger adults following second-line therapy. Further study should examine factors affecting decisions to treat older adults with second-line therapy. Inclusion of geriatric assessment may provide better criteria regarding the risks and benefits of second-line therapy.