Diagnosis Of Stage Research Articles

Laparoscopic Heller’s cardiomyotomy as the main method for treatment of achalasia cardia: an evaluation of treatment results

Achalasia is an esophageal motility disorder characterized by symptoms of dysphagia, regurgitation of undigested food, respiratory symptoms (nocturnal cough, recurrent aspiration, and pneumonia), chest pain, and weight loss. The aim: to evaluate the results of surgical treatment for achalasia cardia by the method of laparoscopic Heller’s cardiomyotomy in combination with Dor fundoplication. Materials and methods. To evaluate the treatment results of achalasia cardia by the method of laparoscopic Heller’s cardiomyotomy with Dor fundoplication, we analyzed 30 case histories of patients who were treated in the period from 2011 to 2022. Inclusion criteria were age 18 years or older, diagnosis of stage II, III cardia achalasia confirmed using instrumental examinations. Exclusion criterion was stage I achalasia. The mean age of the patients was 53.0 ± 15.3 years. In terms of sex, this group was almost equal, there were 14 (46.7 %) women and 16 (53.3 %) men. The average duration of the disease was 5.0 ± 1.2 years. To determine the degree of achalasia, we used the radiological classification of the distal esophageal morphology in achalasia and distinguished 4 stages. Based on this classification, stage II achalasia was detected in 24 (80.0 %) patients, and stage III – in 6 (20.0 %), p = 0.0007, U = 180.0. Results. All the patients were operated on as planned. The total duration of preoperative preparation in the hospital was 0.8 ± 0.2 days. The main method of treatment in this group was laparoscopic Heller’s cardiomyotomy with Dor fundoplication. Conclusions. Laparoscopic Heller’s cardiomyotomy with fundoplication is currently one of the most effective methods of treatment for achalasia cardia. Minimally invasive surgical treatment methods for achalasia are effective in most patients, however, recurrence of symptoms in the long term occurs in 10.0–20.0 %, leaving open the question of finding new effective methods for the treatment of this pathology.

Alzheimer's disease stage recognition from MRI and PET imaging data using Pareto-optimal quantum dynamic optimization

The threat posed by Alzheimer's disease (AD) to human health has grown significantly. However, the precise diagnosis and classification of AD stages remain a challenge. Neuroimaging methods such as structural magnetic resonance imaging (sMRI) and fluorodeoxyglucose positron emission tomography (FDG-PET) have been used to diagnose and categorize AD. However, feature selection approaches that are frequently used to extract additional data from multimodal imaging are prone to errors. This paper suggests using a static pulse-coupled neural network and a Laplacian pyramid to combine sMRI and FDG-PET data. After that, the fused images are used to train the Mobile Vision Transformer (MViT), optimized with Pareto-Optimal Quantum Dynamic Optimization for Neural Architecture Search, while the fused images are augmented to avoid overfitting and then classify unfused MRI and FDG-PET images obtained from the AD Neuroimaging Initiative (ADNI) and Open Access Series of Imaging Studies (OASIS) datasets into various stages of AD. The architectural hyperparameters of MViT are optimized using Quantum Dynamic Optimization, which ensures a Pareto-optimal solution. The Peak Signal-to-Noise Ratio (PSNR), the Mean Squared Error (MSE), and the Structured Similarity Indexing Method (SSIM) are used to measure the quality of the fused image. We found that the fused image was consistent in all metrics, having 0.64 SIMM, 35.60 PSNR, and 0.21 MSE for the FDG-PET image. In the classification of AD vs. cognitive normal (CN), AD vs. mild cognitive impairment (MCI), and CN vs. MCI, the precision of the proposed method is 94.73%, 92.98% and 89.36%, respectively. The sensitivity is 90. 70%, 90. 70%, and 90. 91% while the specificity is 100%, 100%, and 85. 71%, respectively, in the ADNI MRI test data.

Assessment of Serum Omentin-1 and Interleukin-6 in the Diagnosis of Early Stages of Diabetic Nephropathy: A Cross-Sectional Observational Study.

Introduction The pathogenesis of diabetic nephropathy highlights the progression of inflammation and fibrosis from tubular to glomerular damage during the early stages of kidney involvement in diabetic individuals. As urine albumin serves as a marker for glomerular function, its detection indicates a stage of diabetic nephropathy where the glomerulus is already compromised. Consequently, relying solely on urine albumin for diagnosis becomes questionable. In our pursuit of identifying innovative biomarkers for the early detection of diabetic nephropathy, this study was crafted to explore the relationship between chemokines, omentin-1, interleukin-6, and microalbuminuria. Materials and methods Our study cohort comprised 116 patients diagnosed with diabetes mellitus. In our study, participants were stratified into two groups based on their urine albumin levels: Group 1, characterized by urine albumin creatinine ratio <30 mg/gm and estimated glomerular filtration rate >90 ml/min, and Group 2, with urine albumin creatinine ratio ≥30 mg/gm and <300 mg/gm, and estimated glomerular filtration rate >60 ml/min and <90 ml/min.Serum creatinine, glycated hemoglobin (HbA1c), fasting blood sugar and post-prandial blood sugar, lipid profile, total protein, albumin, fasting insulin, omentin-1, and interleukin-6 were estimated. Result There was a significant difference in the medians of serum urea, creatinine, omentin-1, interleukin-6, urine albumin creatinine ratio, and estimated glomerular filtration rate levels in the two groups. There was no difference in fasting blood sugar, post-prandial blood sugar,HbA1c, serum lipids, fasting insulin, and homeostatic model assessment for insulin resistance. The receiver operating characteristic curve plotted for the newer biomarkers of diabetic nephropathy showed that there was a significant diagnostic utility in diabetic nephropathy detection of serum omentin (p=0.000), interleukin-6 (p=0.002), and interleukin-6: omentin-1 ratio (p=0.000), which correlated well with the routine test that is urine microalbumin estimation. Risk assessment demonstrated that type 2 diabetes mellitus patients with an interleukin-6: omentin-1 ratio ≥0.26 had significantly higher odds, with an odds ratio of 3.97, for developing diabetic nephropathy, which was statistically significant. Conversely, a ratio of ≤0.26 was associated with kidney protection among patients with type 2 diabetes mellitus. Conclusion Our findings revealed decreased levels of omentin-1 and increased levels of interleukin-6 in the group with diabetic nephropathy compared to those without diabetic nephropathy among patients with type 2 diabetes mellitus. Interleukin-6: omentin-1ratio of ≤0.26 was associated with kidney protection among patients with type 2 diabetes mellitus. Based on the results obtained from this study, we propose that measuring the serum interleukin-6: omentin-1 ratio in patients with type 2 diabetes mellitus may assist in identifying the early stages of diabetic nephropathy before the onset of microalbuminuria. Timely intervention in these patients predisposed to diabetic nephropathy can aid in better treatment outcomes in type 2 diabetes mellitus.

Gastric cancer detection based on cell‐free DNA in blood: A systematic review and meta‐analysis

AbstractObjectiveScreening and early diagnosis of gastric cancer (GC) are crucial for improved prognosis. However, gastroscopic screening is not feasible in large populations due to its high cost and invasive nature. The detection of circulating cell‐free DNA (cfDNA) provides an attractive minimally‐invasive alternative for screening of GC. In this systematic review and meta‐analysis, we evaluate the diagnostic value of cfDNA‐based markers for GC, including the detection of total concentration, mutations, and methylation alterations.MethodsWe performed a systematic search of four literature databases (PubMed, Embase, Web of Science, and Cochrane Library) for articles published before November 2022. The revised tool for the Quality Assessment of Diagnostic Accuracy Studies (QUADAS‐2) was used to evaluate the quality of included studies. PROSPERO registration number: CRD42021210830.ResultsA total of 15 original articles involving 2849 individuals were included in this meta‐analysis, comprising five studies on concentration, nine studies on methylation alterations, and one study on mutation biomarkers of cfDNA. Among these studies, seven selected early‐stage GC subjects. For the diagnoses of overall stages and early‐stage GC, the pooled sensitivities with 95% confidence interval were 0.74 (0.66–0.82) and 0.64 (0.51–0.76), and the pooled specificities were 0.92 (0.84–0.96) and 0.94 (0.87–0.98) with summary areas under the curve (SAUCs) of 0.89 (0.86–0.91) and 0.86 (0.83–0.89), respectively.ConclusionsThis meta‐analysis suggests that cfDNA‐based biomarkers show diagnostic value for GC early detection.

Sociodemographic variation in the utilization of minimally invasive surgical approaches for pancreatic cancer

Minimally invasive pancreatic surgery (MIPS), when selectively utilized, has been shown to hasten recovery with outcomes comparable to open approaches, but access may not be equitable. This study explored variation in utilization of MIPS for pancreatic cancer. The National Cancer Database was queried to identify patients diagnosed with a primary pancreatic neoplasm from 2010 to 2020. Study participants had diagnoses of clinical or pathologic stage 1-3 disease and received curative-intent surgery. Multivariable analyses assessed the association between surgical approach and patient and disease factors. Inclusion criteria identified 73,137 patients: 51,408 underwent open surgery and 21,729 received MIPS. In our multivariable analysis, Black race was associated with reduced odds of MIPS (AOR 0.88; p=0.02), while older age (AOR 1.17; p=0.01), later year of diagnosis (AOR 1.57; p<0.001), and private insurance coverage (AOR 1.30; p=0.05) were associated with increased odds. When patients with adenocarcinoma were analyzed in isolation, disparities in MIPS utilization persisted even when controlling for disease stage. Sociodemographic factors like age, race, and insurance coverage appear to vary in the utilization of MIPS technologies for the treatment of pancreatic malignancy. Addressing variation with robust mixed methods approaches in the future is proposed to incorporate prospective interventions with highly annotated outcomes for additional study.

Asian American sub-ethnic disparities and trends in epithelial ovarian cancer diagnosis, treatment and survival

ABSTRACT Objectives Studies on ovarian cancer (OC) diagnosis, treatment and survival across disaggregated Asian sub-ethnic groups are sparse. Few studies have also conducted trend analyses of these outcomes within and across Asian groups. Methods Using logistic, Cox, and Joinpoint regression analyses of the 2000–2018 Surveillance, Epidemiology, and End Results (SEER) data, we examined disparities and trends in OC advanced stage diagnosis, receipt of treatments and the 5-year cause-specific survival across seven Asian sub-ethnic groups. Results There were 6491 OC patients across seven Asian sub-ethnic groups (mean [SD] age, 57.29 [13.90] years). There were 1583(24.39%) Filipino, 1183(18.23%) Chinese, and 761(11.72%) Asian Indian or Pakistani (AIP) patients. The majority (52.49%) were diagnosed with OC with at an advanced stage. AIP were more likely to have advanced stage diagnosis than other subgroups (ORs, 95%CIs: 0.77, 0.62−0.96 [Filipino]; 0.76, 0.60−0.95 [Chinese]; 0.71, 0.54−0.94 [Japanese]; 0.74, 0.56−0.98 [Vietnamese] and 0.66, 0.53−0.83 [Other Asians]). The Filipinos were least likely to receive surgery but most likely to undergo chemotherapy. Japanese patients had the worst 5-year OC cause-specific survival (50.29%, 95%CI: 46.20%−54.74%). Based on the aggregated analyses, there was a significantly decreased trend in advanced-stage diagnosis and an increased trend in receipt of chemotherapy. Trends in OC outcomes for several subethnicities differed from those observed in aggregated analyses. Conclusion In this cohort study of 6491 patients, OC diagnosis, treatment, survival, and trends differed across Asian American ethnic subgroups. Such differences must be considered in future research and interventions to ensure all Asian American subethnicities equally benefit from the advancements in OC care and control.

Fuzzy S-Transform is used for Identifying Image Borders of the Medial Model Mycosic Fungoides

In order to identify mycosis fungoides in medical photos, the researchers used an algorithm. There are several procedures that the detection system needs to take in order to identify cell mycosis fungoides. Mycosis fungoides image features have been studied using the new fuzzy transform because of the function's significance in accurate stage analysis. The statistical properties that were taken into consideration were energy, homogeneity, contrast, correlation, median, mean, entropy, and homogeneity. It has been confirmed that these statistical traits may be used to differentiate across various mycosis fungoides time periods.. We relied on the persistence function since it provides more precise examination of affected regions. Orthogonal conversion was found to be effective in assessing pixel area without changing image properties, allowing for the diagnosis of various illness stages.

Deep learning pneumoconiosis staging and diagnosis system based on multi-stage joint approach

BackgroundPneumoconiosis has a significant impact on the quality of patient survival due to its difficult staging diagnosis and poor prognosis. This study aimed to develop a computer-aided diagnostic system for the screening and staging of pneumoconiosis based on a multi-stage joint deep learning approach using X-ray chest radiographs of pneumoconiosis patients.MethodsIn this study, a total of 498 medical chest radiographs were obtained from the Department of Radiology of West China Fourth Hospital. The dataset was randomly divided into a training set and a test set at a ratio of 4:1. Following histogram equalization for image enhancement, the images were segmented using the U-Net model, and staging was predicted using a convolutional neural network classification model. We first used Efficient-Net for multi-classification staging diagnosis, but the results showed that stage I/II of pneumoconiosis was difficult to diagnose. Therefore, based on clinical practice we continued to improve the model by using the Res-Net 34 Multi-stage joint method.ResultsOf the 498 cases collected, the classification model using the Efficient-Net achieved an accuracy of 83% with a Quadratic Weighted Kappa (QWK) score of 0.889. The classification model using the multi-stage joint approach of Res-Net 34 achieved an accuracy of 89% with an area under the curve (AUC) of 0.98 and a high QWK score of 0.94.ConclusionsIn this study, the diagnostic accuracy of pneumoconiosis staging was significantly improved by an innovative combined multi-stage approach, which provided a reference for clinical application and pneumoconiosis screening.

Effects of housing stability and contemporary mortgage lending bias on breast cancer stage at diagnosis among older women in the United States.

Interventions aimed at upstream factors contributing to late-stage diagnoses could reduce disparities and improve breast cancer outcomes. This study examines the association between measures of housing stability and contemporary mortgage lending bias on breast cancer stage at diagnosis among older women in the United States. We studied 67,588 women aged 66-90 from the SEER-Medicare linked database (2010-2015). The primary outcome was breast cancer stage at diagnosis. Multinomial regression models adjusted for individual and neighborhood socio-economic factors were performed using a three-category outcome (stage 0, early stage, and late stage). Key census tract-level independent variables were residence in the same house as the previous year, owner-occupied homes, and an index of contemporary mortgage lending bias. In models adjusted for individual factors, higher levels of mortgage lending bias were associated with later stage diagnosis (RR = 1.10, 95% CI 1.02-1.20; RR = 1.31, 95% CI 1.16-1.49; RR = 1.41, 95% CI 1.24-1.60 for least to high, respectively). In models adjusted for individual and neighborhood socio-economic factors, moderate and high levels of mortgage lending bias were associated with later stage diagnosis (RR = 1.16, 95% CI 1.02-1.33 for moderate and RR = 1.18, 95% CI 1.02-1.37 for high). Owner occupancy and tenure were not associated with later stage diagnosis in adjusted models. Contemporary mortgage lending bias demonstrated a significant gradient relationship with later stage at diagnosis of breast cancer. Policy interventions aimed at reducing place-based mortgage disinvestment and its impacts on local resources and opportunities should be considered as part of an overall strategy to decrease late-stage breast cancer diagnosis and improve prognosis.

Blood and urine biomarkers for the diagnosis of early stages of knee osteoarthritis: A systematic review.

To identify biomarkers in human blood or urine at an early stage of knee osteoarthritis (OA) and to elucidate if any can accurately differentiate between healthy controls and early knee OA patients and be considered as a candidate for widespread clinical use for early diagnosis of the disease. Medline, Embase and Web of Science were screened to identify comparative studies measuring differences in blood or urine biomarkers between healthy controls and knee OA patients at an early stage (grade 1 or 2 Kellgren-Laurence). Two independent reviewers screened the abstracts for eligibility, reviewed the full texts, assessed the methodological quality and extracted the data. The Joanna Briggs Institute critical appraisal tool for diagnostic test accuracy studies was used to assess the quality of the included studies. Due to relevant heterogeneity, meta-analysis was not appropriate. Five studies met the eligibility criteria. The examined biomarkers were adropin, collagen type II metabolite, C-terminal cross-linked telopeptide of type II collagen, C-terminal cross-linked telopeptide of type I collagen, cartilage oligomeric matrix protein, matrix metalloproteinase 3, N-terminal propeptide of procollagen type IIA, type I procollagen N-terminal propeptides, N-terminal osteocalcin, angiopoietin-2, follistatin, granulocyte colony-stimulating factor, hepatocyte growth factor, interleukin-8, leptin, platelet-derived growth factor-BB, platelet endothelial cell adhesion molecule-1, vascular endothelial growth factor and calprotectin and totalling 19 biomarkers. All of the biomarkers were studied only once in the selected papers. There is no reliable biomarker available to differentiate between early knee OA in patients and healthy controls, but a potential role of a cluster of biomarkers to close this gap. There are several limitations, including inappropriate study designs, small sample sizes, nonconsecutive patient groups and inadequate statistical methods for evaluating biomarker performance in studies included. Level III.

Ultrasound Assessment of Changes on the Lungs during Respiratory Distress Syndrome in Newborns with Low Body Weight

Introduction: Respiratory distress syndrome (RDS) is a common pathology of premature newborns and one of the most common causes of hospitalization in intensive care units, as well as mortality among them. Aims of Study: To study the possibilities of ultrasound imaging for respiratory distress syndrome of varying severity in premature newborns. Patients and Methods: Lung Ultrasound was performed in 72 premature infants in whom an X-ray diagnosis of RDS was established. Results: In accordance with the radiological data of RDS I degree diagnosed in 13 (18,1±4,5%) patients, RDS II degree – in 31 (43,0±5,8%), RDS III degree – in 19 (26,4±5,2%) and RDS IV degree – in 9 (12,5±3,9%) premature infants. The results of lung ultrasound and chest x-ray in the diagnosis of stage I and IV RDS coincided in all cases. Moreover, the correlation coefficient between them had a high degree (0.924 and 0.950, respectively). Ultrasound revealed stage II RDS in 21 out of 31 preterm infants diagnosed by radiography. By ultrasound of the lungs, stage III RDS was diagnosed in 4 more premature infants than by X-ray (23 and 19). Conclusions: 1. The integrated use of lung ultrasound and chest X-ray significantly improves the quality of diagnosis and monitoring of lung lesions in premature infants with RDS with a significant reduction in radiation exposure to the patient. 2. Ultrasound is particularly useful for monitoring established radiographic lung pathology (eg, RDS, pneumonia) to reduce radiation exposure.

Immunological Signatures for Early Detection of Human Head and Neck Squamous Cell Carcinoma through RNA Transcriptome Analysis of Blood Platelets.

Although there has been a reduction in head and neck squamous cell carcinoma occurrence, it continues to be a serious global health concern. The lack of precise early diagnostic biomarkers and postponed diagnosis in the later stages are notable constraints that contribute to poor survival rates and emphasize the need for innovative diagnostic methods. In this study, we employed machine learning alongside weighted gene co-expression network analysis (WGCNA) and network biology to investigate the gene expression patterns of blood platelets, identifying transcriptomic markers for HNSCC diagnosis. Our comprehensive examination of publicly available gene expression datasets revealed nine genes with significantly elevated expression in samples from individuals diagnosed with HNSCC. These potential diagnostic markers were further assessed using TCGA and GTEx datasets, demonstrating high accuracy in distinguishing between HNSCC and non-cancerous samples. The findings indicate that these gene signatures could revolutionize early HNSCC identification. Additionally, the study highlights the significance of tumor-educated platelets (TEPs), which carry RNA signatures indicative of tumor-derived material, offering a non-invasive source for early-detection biomarkers. Despite using platelet and tumor samples from different individuals, our results suggest that TEPs reflect the transcriptomic and epigenetic landscape of tumors. Future research should aim to directly correlate tumor and platelet samples from the same patients to further elucidate this relationship. This study underscores the potential of these biomarkers in transforming early diagnosis and personalized treatment strategies for HNSCC, advocating for further research to validate their predictive and therapeutic potential.

Inclusive innovations in services: leveraging digital technologies for inclusive innovations in healthcare services

PurposeThis paper aims to provide insights into the potential of digital technologies-based innovations for more inclusive healthcare by alleviating the affordability, accessibility and availability barriers to utilization of healthcare services. Also, it aims to provide insights into the potential of digital technologies-based innovations for more inclusive services, broadly.Design/methodology/approachA conceptual framework is inductively developed by analyzing real-world examples of digital technologies-based innovations for more inclusive healthcare through the lenses of economics of information in digital form and certain characteristics of services.FindingsConcurrent implementation of digital technologies-based healthcare innovations with innovations and/or modifications in service processes can enable greater inclusivity by alleviating the affordability, accessibility and availability barriers to utilization of healthcare services.Research limitations/implicationsIssues relating to inequities in healthcare, as a social problem, are the focus of research at multiple levels (e.g. global, national, regional and local) in several academic disciplines. In relation to the scope of the problems and challenges pertaining to providing quality healthcare to the unserved and underserved segments of society, worldwide, the contribution of the proposed framework to practice is modest. However, by highlighting the promise and potential of digital technologies-based innovations as solutions for alleviating barriers to affordability, accessibility and availability of healthcare services during various stages (prevention, detection, diagnosis, treatment and post-treatment follow-up) with illustrative vignettes and developing a framework, the article offers insights for future research. For instance, in reference to mission-driven social enterprises that operate in the product-market space for inclusive innovations under resource constraints, a resourcefulness-based view of the social enterprise constitutes a potential avenue for theory development and research.Practical implicationsGiven the conceptual nature of the article, the implications for practice are limited to cognitive implications. Action implications (instrumental implications or implications for practice) are outside of the scope of the article.Social implicationsInnovations that are economically viable, environmentally sustainable and socially impactful is one of the important issues of our times.Originality/valueThe proposed framework provides insights into the potential of digital technologies-based innovations for more inclusive healthcare by alleviating the affordability, accessibility and availability barriers in the context of emerging and less developed country markets and base of the pyramid segments of society in these markets.

Failure of the PD-1 Blocking Agent Pembrolizumab to Benefit a Patient with Renal Squamous Cell Cancer.

Renal squamous cell carcinoma (SCC) is a neoplasm with an extremely rare occurrence compared to other renal malignancies. The classic presentation includes a palpable mass and flank pain; however, the presentation is seldom non-specific. Our study describes the significance of programmed death ligand-1 (PD-L1) expression in renal cancer and its association with clinical outcomes, alongside available treatment options. An 80-year-old female with a history of hypertension and cerebral aneurysm presented with right flank pain and blood in urine and was diagnosed with pyelonephritis and left renal mass/phlegmon. A biopsy revealed SCC of the kidney with metastasis to the lung and aortocaval lymph node. Positron emission tomography (PET) scan confirmed malignancy in the kidney and lung. Treatment with pemrolizumab and carboplatin plus paclitaxel was initiated but poorly tolerated as the haemoglobin dropped rapidly. SCC poses a diagnostic challenge due to its rarity and non-specific symptoms, often leading to advanced stage diagnosis. PD-L1 expression is pivotal in assessing tumour aggressiveness and prognosis. PD-L1 inhibitors offer promise, but their efficacy in renal SCC warrants further investigation. Radical nephrectomy and systemic chemotherapy show potential in advanced cases, necessitating vigilant management of treatment-related side effects. This case emphasises the need for ongoing research to refine therapeutic approaches and enhance outcomes in renal SCC patients. PD-L1 expression is pivotal in assessing tumour aggressiveness and prognosis of renal cell carcinoma.PD-L1 inhibitors hold promise as a therapeutic intervention in renal squamous cancer.Radical nephrectomy and systemic chemotherapy show potential in managing advanced renal cancer.

Estimating mandibular growth stage based on cervical vertebral maturation in lateral cephalometric radiographs using artificial intelligence

IntroductionDetermining the right time for orthodontic treatment is one of the most important factors affecting the treatment plan and its outcome. The aim of this study is to estimate the mandibular growth stage based on cervical vertebral maturation (CVM) in lateral cephalometric radiographs using artificial intelligence. Unlike previous studies, which use conventional CVM stage naming, our proposed method directly correlates cervical vertebrae with mandibular growth slope.Methods and materialsTo conduct this study, first, information of people achieved in American Association of Orthodontics Foundation (AAOF) growth centers was assessed and after considering the entry and exit criteria, a total of 200 people, 108 women and 92 men, were included in the study. Then, the length of the mandible in the lateral cephalometric radiographs that were taken serially from the patients was calculated. The corresponding graphs were labeled based on the growth rate of the mandible in 3 stages; before the growth peak of puberty (pre-pubertal), during the growth peak of puberty (pubertal) and after the growth peak of puberty (post-pubertal). A total of 663 images were selected for evaluation using artificial intelligence. These images were evaluated with different deep learning-based artificial intelligence models considering the diagnostic measures of sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV). We also employed weighted kappa statistics.ResultsIn the diagnosis of pre-pubertal stage, the convolutional neural network (CNN) designed for this study has the higher sensitivity and NPV (0.84, 0.91 respectively) compared to ResNet-18 model. The ResNet-18 model had better performance in other diagnostic measures of the pre-pubertal stage and all measures in the pubertal and post-pubertal stages. The highest overall diagnostic accuracy was also obtained using ResNet-18 model with the amount of 87.5% compared to 81% in designed CNN.ConclusionThe artificial intelligence model trained in this study can receive images of cervical vertebrae and predict mandibular growth status by classifying it into one of three groups; before the growth spurt (pre-pubertal), during the growth spurt (pubertal), and after the growth spurt (post-pubertal). The highest accuracy is in post-pubertal stage with the designed networks.

Computer-aided diagnosis of cystic lung diseases using CT scans and deep learning.

Auxiliary diagnosis of different types of cystic lung diseases (CLDs) is important in the clinic and is instrumental in facilitating early and specific treatments. Current clinical methods heavily depend on accumulated experience, restricting their applicability in regions with less developed medical resources. Thus, how to realize the computer-aided diagnosis of CLDs is of great clinical value. This work proposed a deep learning-based method for automatically segmenting the lung parenchyma in computed tomography (CT) slice images and accurately diagnosing the CLDs using CT scans. A two-stage deep learning method was proposed for the automatic classification of normal cases and five different CLDs using CT scans. Lung parenchyma segmentation is the foundation of CT image analysis and auxiliary diagnosis. To meet the requirements of different sizes of the lung parenchyma, an adaptive region-growing and improved U-Net model was employed for mask acquisition and automatic segmentation. The former was achieved by a self-designed adaptive seed point selection method based on similarity measurement, and the latter introduced multiscale input and multichannel output into the original U-Net model and effectively achieved the lightweight design by adjusting the structure and parameters. After that, the middle 30 consecutive CT slice images of each sample were segmented to obtain lung parenchyma, which was employed for training and testing the proposed multichannel parallel input recursive MLP-Mixer network (MPIRMNet) model, achieving the computer-aided diagnosis of CLDs. A total of 4718 and 16290 CT slice images collected from 543 patients were employed to validate the proposed segmentation and classification methods, respectively. Experimental results showed that the improved U-Net model can accurately segment the lung parenchyma in CT slice images, with the Dice, precision, volumetric overlap error, and relative volume difference of 0.96±0.01, 0.93±0.04, 0.05±0.02, and 0.05±0.03, respectively. Meanwhile, the proposed MPIRMNet model achieved appreciable classification effect for normal cases and different CLDs, with the accuracy, sensitivity, specificity, and F1 score of 0.8823±0.0324, 0.8897±0.0325, 0.9746±0.0078, and 0.8831±0.0334, respectively. Compared with classical machine learning and convolutional neural networks-based methods for this task, the proposed classification method had a preferable performance, with a significant improvement of accuracy of 10.74%. The work introduced a two-stage deep learning method, which can achieve the segmentation of lung parenchyma and the classification of CLDs. Compared to previous diagnostic tasks targeting single CLD, this work can achieve various CLDs' diagnosis in the early stage, thereby achieving targeted treatment and increasing the potential and value of clinical applications.

Female Breast Melanomas Are Associated With Lower Stage Diagnosis and Decreased Mohs Utilization: A National Cohort Study.

Female Breast Melanomas Are Associated With Lower Stage Diagnosis and Decreased Mohs Utilization: A National Cohort Study.

Continuous Urine Output-Based Alert Identifies Cardiac Surgery–associated Acute Kidney Injury Earlier Than Serum Creatinine: A Prospective and Retrospective Observational Study

Objective(s)Acute kidney injury (AKI) is defined and staged by reduced urine output (UO) and increased serum creatinine (SCr). UO is typically measured manually and documented in the electronic health record, making early and reliable detection of oliguria-based AKI and electronic data extraction challenging. We investigated the diagnostic performance of continuous UO, enabled by active drain line clearance, based alerts (Accuryn AKI Alert), compared to AKI stage 2 SCr criteria and their association to length of stay (LOS), need for continuous renal replacement therapy (CRRT), and 30-day mortality. DesignProspective and retrospective observational study. SettingNine tertiary centers. ParticipantsCardiac surgery patients. InterventionsNone. Measurements and Main Results522 patients were analyzed. AKI stages 1, 2, and 3 were diagnosed in 32.18%, 30.46%, and 3.64% of patients based on UO compared to 33.72%, 4.60%, and 3.26% of patients using SCr, respectively. Continuous UO-based alerts diagnosed stage ≥1 AKI 33.6 (IQR= [15.43, 95.68]) hours before stage ≥2 identified by SCr. A SCr-based diagnosis of AKI stage ≥2 has been designated a Hospital Harm by the Centers for Medicare & Medicaid Services. Using this criterion as a benchmark, AKI alerts had a discriminative power of 0.78. AKI Alert stage 1 was significantly associated with increased ICU and hospital LOS and CRRT, and stage ≥2 alert with mortality. ConclusionsAKI Alert based on continuous UO, enabled by active drain line clearance, detected AKI stages 1 and 2 before SCr. Early AKI detection allows for early kidney optimization, potentially improving patient outcomes.

Clinical Approach to Advanced Mantle Cell Lymphoma: A Detailed Case Analysis

Mantle cell lymphoma (MCL) is a rare and aggressive subtype of non-Hodgkin lymphoma (NHL) originating from cells in the mantle zone. Herein, we present a case report of a 49-year-old male patient diagnosed with stage VI MCL, illustrating various diagnostic methods and procedures utilized in his evaluation. The patient presented with progressively enlarging cervical lymph nodes, fatigue, weight loss, anorexia, chronic diarrhea, abdominal pain, and rectal bleeding. Clinical examination revealed cutaneomucosal pallor, tachycardia, expiratory polypnea, bilateral jugulocarotid adenopathies, and splenomegaly. Laboratory investigations showed normocytic normochromic anemia, hyperleukocytosis, lymphocytic predominance, and thrombocytopenia. Peripheral blood smear analysis indicated a lymphoproliferative disorder, confirmed by flow cytometry revealing monotypic lymphoid cells. Bone marrow biopsy showed hypoplastic marrow with atypical cells. Immunohistochemistry confirmed mantle cell lymphoma localization. Subsequent thoraco-abdomino-pelvic CT scan revealed mediastinal and abdominal adenopathy with splenomegaly, leading to a diagnosis of stage IV MCL. The case highlights the importance of early and comprehensive diagnostic approaches in MCL management.

Exploring the Role of Vitamin D, Vitamin D-Dependent Proteins, and Vitamin D Receptor Gene Variation in Lung Cancer Risk.

Lung cancer has an unfavorable prognosis with a rate of low overall survival, caused by the difficulty of diagnosis in the early stages and resistance to therapy. In recent years, there have been new therapies that use specific molecular targets and are effective in increasing the survival chances of advanced cancer. Therefore, it is necessary to find more specific biomarkers that can identify early changes in carcinogenesis and allow the earliest possible treatment. Vitamin D (VD) plays an important role in immunity and carcinogenesis. Furthermore, the vitamin D receptor (VDR) regulates the expression of various genes involved in the physiological functions of the human organism. The genes encoding the VDR are extremely polymorphic and vary greatly between human populations. To date, there are significant associations between VDR polymorphism and several types of cancer, but the data on the involvement of VDR polymorphism in lung cancer are still conflicting. Therefore, in this review, our aim was to investigate the relationship between VDR single-nucleotide polymorphisms in humans and the degree of risk for developing lung cancer. The studies showcased different gene polymorphisms to be associated with an increased risk of lung cancer: TaqI, ApaI, BsmI, FokI, and Cdx2. In addition, there is a strong positive correlation between VD deficiency and lung cancer development. Still, due to a lack of awareness, the assessment of VD status and VDR polymorphism is rarely considered for the prediction of lung cancer evolution and their clinical applicability, despite the fact that studies have shown the highest risk for lung cancer given by TaqI gene polymorphisms and that VDR polymorphisms are associated with more aggressive cancer evolution.