Early Diagnosis Research Articles

On the Improvements of the Performance of Bayes-Net Classification for Diagnosis of Diabetes: A Cross-validation Approach

Introduction: This study intends to support medical professionals in the early and precise detection and diagnosis of type1 and type2 Diabetes. Many complexities occur if Diabetes is unidentified and remains untreated. 9.3% of individuals worldwide have Diabetes as of 2019, according to data from the World Health Organization. The anticipated increase would be close to 11% of the global population by 2045. Diabetes mellitus is a chronic, one of the deadliest diseases that causes Body blood glucose levels to be above average level knowing a blood sugar. Diabetes can be type1 (T1D) and type2(T2D). An autoimmune condition called Type 1 diabetes is frequently found in young children. Adults are frequently diagnosed with type 2 diabetes, which is not autoimmune. A branch of artificial intelligence called machine learning has the potential to be successful in the field of predictions. Method: This research aims to design a machine learning model to predict Diabetes in a patient. The collective performance of the four algorithms is evaluated on various measures like accuracy, precision, sensitivity, and specificity. Therefore, four machine learning classification algorithms, namely Bayes-Net, Naïve-Bayes, J48, and Random Forest, were used to predict Diabetes in the early stage using 10–fold cross validation. Result: For experiments, a standard PIMA Indian diabetes dataset is used. According to the results, Bayes-Net performs better than other algorithms, with an accuracy rate of 87.72%. Conclusion: Diabetes Mellitus may harm kidneys, nerves, eyes, and other organs if it is not diagnosed in time. Therefore, early diagnosis and treatment could save countless lives. The process is tedious because patients visit a diagnostic facility and see a doctor, but various machine learning and deep learning approaches solve this clinical problem earlier.

SEBORRHEIC DERMATITIS, SCOPING REVIEW

Introduction: Seborrheic dermatitis (SD) is a common skin disease, where its signs and symptoms may differ according to skin color, associated medical conditions, environmental factors, and vehicle preference. Seborrheic dermatitis is a common skin condition in infants, adolescents and adults, characterized by scaling, erythema and itching, especially on the scalp, face, chest, back, armpits and groin. Objective: to detail the current information related to seborrheic dermatitis, etiology, epidemiology, pathophysiology, histopathology, clinical presentation, evaluation, differential diagnosis, treatment and prognosis. Methodology: a total of 29 articles were analyzed in this review, including review and original articles, as well as clinical cases, of which 23 bibliographies were used because the other articles were not relevant for this study. The sources of information were PubMed, Google Scholar and Cochrane; the terms used to search for information in Spanish, Portuguese and English were: sebopsoriasis, seborrheic dermatitis, seborrheic eczema, dandruff and pityriasis capitis. Results: The most relevant results indicate that seborrheic dermatitis has a significant global prevalence, affecting up to 5% of the population, with a higher incidence in individuals with HIV-AIDS. Its pathogenesis has been identified as involving a combination of factors, including Malassezia spp. colonization of the skin, alteration of the cutaneous microbiota and a deregulated immune response. The most effective treatments are topical, particularly antifungal agents such as ketoconazole and low potency corticosteroids, although treatment should be tailored to the individual characteristics of each patient. Despite being a chronic disease in many cases, early diagnosis and proper management allow control of symptoms and improve the quality of life of patients. Conclusions: Seborrheic dermatitis is a common skin disease, with significant prevalence in various ages and groups, especially in people with HIV-AIDS. Its pathogenesis involves factors such as colonization by Malassezia spp, alterations in the cutaneous microbiota and immune dysregulation. Although it may be self-limiting in some cases, in others it presents a chronic course with relapses. The most effective treatment includes topical antifungal and anti-inflammatory agents, adapted to the severity and location of the lesions. Early diagnosis and appropriate treatment are key to control symptoms and improve the quality of life of patients. KEY WORDS: dermatitis, seborrheic, eczema, skin.

Establishment and application of a sandwich ELISA method for measuring Toxoplasma gondii circulating fructose-1,6-bisphosphate aldolase (ALD) protein in cats.

Toxoplasmosis is an important public health concern. Cats play a crucial role in increasing the risk of toxoplasmosis transmission to humans. Early diagnosis in cats is essential for the prevention and control of toxoplasmosis. In this study, we found that T. gondii aldolase (ALD) could be an effective diagnostic antigen, and then the recombinant ALD protein was expressed using the pET SUMO protein expression system, the mouse monoclonal antibody (MoAb) and rabbit polyclonal antibody (PoAb) of ALD were successfully produced, respectively. Furthermore, a reliable sandwich enzyme-linked immunosorbent assay (sELISA) was developed to detect circulating ALD in the sera of experimentally and naturally infected cats. rALD sELISA could detect T. gondii infection from 7DPI (post-infection day) to 14DPI with 100 % sensitivity and specificity, but could not detect T. gondii infection after 21DPI, indicating that it is a good early diagnosis tool. The detection limit was 7.8 ng/ml, the coefficients of variation (CV) of repeated tests within batches and between batches were confirmed to be less than 10 %. The results of 70 cat clinical serum samples detected by rALD sELISA were in almost perfect agreement beyond chance with those of a commercial ELISA kit (Cohen's kappa coefficient = 0.883). This sandwich ELISA method has high accuracy and can be used for early diagnosis of toxoplasmosis in cats.

Clinical value of serum LncRNA MIAT in early diagnosis and prognosis assessment of traumatic brain injury

ObjectiveThis study aims to explore the clinical significance of long non-coding RNA, myocardial infarction-associated transcript (MIAT), in patients with traumatic brain injury (TBI). MethodsRetrospective inclusion of TBI patients meeting clinical criteria with complete data, alongside healthy controls. RT-qPCR was used to detect the expression of the serum MIAT. Based on the Glasgow Coma Scale (GCS) scores, patients were categorized into mild, moderate, and severe TBI groups. The potential risk factors for severity were examined using logistic regression analysis. The one-year prognosis for TBI was determined using the Glasgow Outcome Scale (GOS) score. The correlation of MIAT levels with GCS scores and GOS scores was determined using Pearson correlation analysis. The effect of MIAT on the severity and poor prognosis was assessed using the receiver operating characteristic curve. Lastly, the dual-luciferase reporter assay confirmed the relationship between the MIAT and miR-221-3p. Results110 patients with TBI and 106healthy controls were included. Serum MIAT levels were strikingly higher in patients with TBI compared to controls, whereas miR-221-3p levels were lower. As the severity of TBI increases, the expression of MIAT levels gradually elevates. A notable negative correlation was observed between serum MIAT levels and both the GCS and GOS scores. MIAT levels were effective in distinguishing patients with moderate TBI from those with mild or severe TBI, with a sensitivity of 82.35% and 88.64% and a specificity of 86.67% and 86.27%. Furthermore, elevated MIAT levels, with a sensitivity of 85.00% and a specificity of 75.56%, can predict the clinical outcomes of patients with TBI. miR-221-3p levels were negatively correlated with MIAT expression in patients with TBI, and MIAT directly targeted miR-221-3p. ConclusionSerum MIAT could serve as a diagnostic marker of severity and may predict poor prognosis in patients with TBI. This study proposes fresh perspectives on the pursuit of biomarkers and the management of patients with TBI.

ENSMBL-PASD: Spearheading early autism spectrum disorder detection through advanced genomic computational frameworks utilizing ensemble learning models.

Autism spectrum disorder (ASD) is a complex neurodevelopmental condition influenced by various genetic and environmental factors. Currently, there is no definitive clinical test, such as a blood analysis or brain scan, for early diagnosis. The objective of this study is to develop a computational model that predicts ASD driver genes in the early stages using genomic data, aiming to enhance early diagnosis and intervention. This study utilized a benchmark genomic dataset, which was processed using feature extraction techniques to identify relevant genetic patterns. Several ensemble classification methods, including Extreme Gradient Boosting, Random Forest, Light Gradient Boosting Machine, ExtraTrees, and a stacked ensemble of classifiers, were applied to assess the predictive power of the genomic features. TheEnsemble Model Predictor for Autism Spectrum Disorder (eNSMBL-PASD) model was rigorously validated using multiple performance metrics such as accuracy, sensitivity, specificity, and Mathew's correlation coefficient. The proposed model demonstrated superior performance across various validation techniques. The self-consistency test achieved 100% accuracy, while the independent set and cross-validation tests yielded 91% and 87% accuracy, respectively. These results highlight the model's robustness and reliability in predicting ASD-related genes. The eNSMBL-PASD model provides a promising tool for the early detection of ASD by identifying genetic markers associated with the disorder. In the future, this model has the potential to assist healthcare professionals, particularly doctors and psychologists, in diagnosing and formulating treatment plans for ASD at its earliest stages.

Circulating tumor cells in solid malignancies: From advanced isolation technologies to biological understanding and clinical relevance in early diagnosis and prognosis.

Circulating tumor cells (CTCs) are shed from primary tumors and travel through the body via circulation, eventually settling to form micrometastases under favorable conditions. Numerous studies have identified CTCs as a negative prognostic indicator for survival across various cancer types. CTCs mirror the current heterogeneity and genetic and biological state of tumors, making their study invaluable for understanding tumor progression, cell senescence, and cancer dormancy. However, their isolation and characterization still poses a major challenge that limits their clinical translation. A wide array of methods, each with different levels of specificity, utility, cost, and sensitivity, have been developed to isolate and characterize CTCs. Moreover, innovative techniques are emerging to address the limitations of existing methods. In this review, we provide insights into CTC biology addressing spectra of markers employed for molecular analysis and functional characterization. It also emphasizes current label-dependent and label-independent isolation procedures, addressing their strengths and limitations. SIGNIFICANCE: A comprehensive overview of CTC biology, their molecular and functional characterization, along with their current clinical utility will help in understanding the present-day extent to which the clinical potential of CTCs is getting tapped in personalized medicine.

Integrated U-Net segmentation and gated recurrent unit classification for accurate brain tumor diagnosis from magnetic resonance imaging images

Early diagnosis and proper grouping of tumors in the brain are critical for successful therapy and positive outcomes for patients. This work proposes a complete technique for identifying brain tumors that employ sophisticated artificial intelligence methodologies and achieve an accuracy rate of 97.18%. The work makes use of the brain tumor magnetic resonance imaging (MRI) collection in Kaggle, which has 723 MRI scans classified as glioma, meningioma, pituitary tumor, and no tumor. These images are initially preprocessed, which includes scaling to a homogeneous size normalizing, and removal of noise to ensure uniformity and clarity. To improve the information set, generative adversarial networks (GANs) are used to perform data augmentation, producing artificial pictures that improve the database variety and resilience. To achieve exact cancer localization, the U-Net construction, recognized for its encoder-decoder design and skip links, is used to divide up tumor areas across images generated by MRI. The image segments are then input into gated recurrent units (GRUs), to analyze a collection of features to capture periods and differences between segments. The last classification is accomplished using an entirely linked layer and then a softmax stimulation, which provides the tumors classes. This method helps for medical experiments and clinical methods.

In vivo prospective randomised study of the wear of dental restorations using an intraoral scanner and its correlation with visual assessment.

Objectives: To evaluate the efficacy of the True Definition®intraoral scanner in quantifying the wear of the glass ionomer restorative material (KetacTM Universal and KetacTM Molar) over 1 year. We also studied the correlation between visual and digital assessments of restoration wear. Materials and Methods: This was a clinical follow-up study of a post-marketed material with a prospective, controlled, randomised, split-mouth, and blinded assessment design. Intraoral optical impression and visual assessment were carried out over three appointments over 12 months, starting with 36 patients.Results: According to the visual indices, all restorations in this study were clinically healthy. However, in the digital measurement of wear, 94.74% and 94.44% of the restorations during the T0-T6 and T6-T12 observation periods, respectively, showed deterioration greater than 41 microns. Moreover, in the analysis of agreement between measurement techniques, no agreement was obtained in the two analysed time periods: T0-T6 yielded a kappa (k) value of 0.000, and T6-T12 yielded k=0.0030. Discordant results were obtained in the correlation analysis. In T0-T6, the results were not considered statistically significant (p=0.838); however, the results obtained during T6-T12 showed a correlation (p-value <0.001).Conclusions: The wear of dental materials as observed by the human eye did not agree with that observed by intraoral scanning. The scanner effectively measures wear, detecting details that are beyond the capability of the human eye and conventional photographs. The surface deterioration of the restorations at both observation times can be considered non-physiological, potentially leading to premature occlusal alterations and accelerated physiological ageing.Clinical Relevance: Early diagnosis is crucial for avoiding alterations in the function of the stomatognathic system due to the wear of dental restorations. Additionally, since most of the tools applied are qualitative in nature, such as visual inspection, it is essential to find a standardised and precise tool that offers diagnosis, monitoring, and records of the evolution of tooth wear. This study has been registered at https://www.ClinicalTrials.gov, under the identifier NCT06275581.

The Diagnostic Utility of Host RNA Biosignatures in Adult Patients With Sepsis: A Systematic Review and Meta-Analysis.

Sepsis is a life-threatening medical emergency, with a profound healthcare burden globally. Its pathophysiology is complex, heterogeneous and temporally dynamic, making diagnosis challenging. Medical management is predicated on early diagnosis and timely intervention. Transcriptomics is one of the novel "-omics" technologies being evaluated for recognition of sepsis. Our objective was to evaluate the performance of host gene expression biosignatures for the diagnosis of all-cause sepsis in adults. PubMed/Ovid Medline, Ovid Embase, and Cochrane databases from inception to June 2023. We included studies evaluating the performance of host gene expression biosignatures in adults who were diagnosed with sepsis using existing clinical definitions. Controls where applicable were patients without clinical sepsis. Data including population demographics, sample size, study design, tissue specimen, type of transcriptome, health status of comparator group, and performance of transcriptomic biomarkers were independently extracted by at least two reviewers. Meta-analysis to describe the performance of host gene expression biosignatures for the diagnosis of sepsis in adult patients was performed using the random-effects model. Risk of bias was assessed according to the Quality Assessment of Diagnostic Accuracy Studies-2 tool. A total of 117 studies (n = 17,469), comprising 132 separate patient datasets, were included in our final analysis. Performance of transcriptomics for the diagnosis of sepsis against pooled controls showed area under the receiver operating characteristic curve (AUC, 0.86; 95% CI, 0.84-0.88). Studies using healthy controls showed AUC 0.87 (95% CI, 0.84-0.89), while studies using controls with systemic inflammatory response syndrome (SIRS) had AUC 0.84 (95% CI, 0.78-0.90). Transcripts with excellent discrimination against SIRS controls include UrSepsisModel, a 210 differentially expressed genes biosignature, microRNA-143, and Septicyte laboratory. Transcriptomics is a promising approach for the accurate diagnosis of sepsis in adults and demonstrates good discriminatory ability against both healthy and SIRS control subjects.

A multifunctional biosensor for selective identification, sensitive detection and efficient photothermal sterilization of Salmonella typhimurium and Staphylococcus aureus.

The foodborne pathogens, e.g., Salmonella typhimurium (S. typ) and Staphylococcus aureus (S. aureus), pose a serious threat to human health. Accurate identification, rapid detection and efficient inactivation are crucial in the early diagnosis and treatment of S. typ and S. aureus. To date, however, the majority of studies have only concentrated on the construction of single-function biological platform for detection or inactivation of S. typ and S. aureus. Therefore, it is imperative to develop a multifunctional surface-enhanced Raman scattering (SERS) biosensor that can effectively sterilize S. typ and S. aureus while simultaneously achieving sensitive detection and selective identification. Herein, we designed and constructed a multifunctional SERS biosensor based on sandwich structure of "capture probe/bacteria/signal probe" in order to simultaneously identify, detect and kill S. typ and S. aureus. Aptamer-modified ZnO/Ag was used as a capture probe to accurately identify and capture the target bacteria in complex environments. Au@Ag-4-MPBA-Aptamer was employed as signal probe to provide the corresponding bacterial SERS "fingerprint" information. The SERS enhancement mechanism of the sandwich-structure ZnO/Ag-Au@Ag SERS substrate was discussed. The sandwich-type SERS biosensor exhibited the strong localized surface plasmon resonance (LSPR) effect and the detection limit for S. typ and S. aureus was as low as 10cfu/mL. Furthermore, the sandwich-type SERS biosensor offered excellent photothermal conversion efficiency (54.32%), enabling photothermal killing of target bacteria when exposed to laser irradiation. A dual enhancement strategy based on a sandwich structure was proposed to maximize the sensitivity of SERS signals using synergistic action of electromagnetic enhancement and chemical enhancement. SERS enhancement factor (EF) was as high as 4.67×105. In addition, the sandwich-type SERS biosensor not only exhibited negligible cytotoxicity, but also was proved to be a promising tool for photothermally inactivate of S. typ and S. aureus in food samples.

Clinical implications of human Parvovirus B19 infection on autoimmunity and autoimmune diseases.

Parvovirus B19 (B19V) is a human pathogen from the Parvoviridae family that primarily targets and replicates in erythroid progenitor cells (EPCs). While its symptoms are typically self-limiting in healthy individuals, B19V can cause or exacerbate autoimmune diseases in vulnerable patients. This review integrates the involvement of B19V in the development and worsening of several autoimmune diseases, including systemic lupus erythematosus (SLE), rheumatoid arthritis (RA), juvenile idiopathic arthritis (JIA), hematological disorders (thalassemia, anemia, and thrombocytopenia), vasculitis, antiphospholipid syndrome (APS), dermatological disease (systemic sclerosis, psoriasis), autoimmune thyroid disease, myocarditis, and myasthenia gravis, and autoinflammatory disease of adult-onset Still's disease (AOSD). B19V contributes to autoimmunity and autoimmune disease onset and progression through mechanisms such as molecular mimicry, immune system disruption, and chronic infection. By summarizing findings from in vitro experiments, clinical case studies, seroprevalence data, and biopsy results, this review highlights the critical connection between B19V and autoimmune disease development. Recognizing the role of B19V in the early diagnosis and management of these conditions is essential, as its presence may influence the disease course and severity. Greater awareness among healthcare professionals and the public is necessary to address the impact of B19V, leading to more accurate diagnoses and better-informed treatment approaches for autoimmune diseases linked to the virus.

Olfactory testing in infants with perinatal asphyxia: Enhancing encephalopathy risk stratification for future health outcomes.

Perinatal asphyxia (PA) is a leading cause of neonatal morbidity and mortality, often resulting in long-term neurodevelopmental challenges. Despite advancements in perinatal care, predicting long-term outcomes remains difficult. Early diagnosis is essential for timely interventions to reduce brain injury, with tools such as Magnetic Resonance Imaging, brain ultrasound, and emerging biomarkers playing a possible key role. Olfaction, one of the earliest senses to develop, may provide valuable insights into long-term neurodevelopmental outcomes following PA due to its intricate neural connections with regions responsible for memory, emotion, and homeostasis. Newborns demonstrate early olfactory abilities, such as recognizing maternal odors, which are vital for bonding, feeding, and emotional regulation. These responses are processed by a network of brain regions, including the olfactory bulb (OB), piriform cortex, amygdala, and orbitofrontal cortex. Hypoxic injury to these regions, particularly the OB, may disrupt olfactory processing in infants with PA, potentially affecting their cognitive and social development. Investigating the relationship between olfactory system development and perinatal brain injury could lead to innovative diagnostic and therapeutic approaches. Further research, including clinical and animal studies, is necessary to fully explore the potential of olfactory assessments in predicting outcomes after PA. This educational review explores and discusses the potential of olfaction as a predictor of long-term outcomes and a tool for risk stratification following PA, opening new pathways for interventions and improved care.

VG@nAu-based fluorescent biosensor for grading Alzheimer's disease by detecting P-tau181 protein in clinical samples.

Alzheimer's disease (AD) is a neurodegenerative disorder with a very long duration, posing a serious threat to people's life and health. To date, no medicine that can cure or reverse the disease has been developed or reported, so early diagnosis and timely intervention are essential. The concentration of Phosphorylated tau181 (P-tau181) in blood has been approved by FDA as a standard for assisting clinical diagnosis of AD. In this study, a fluorescence biosensor based on vertical graphene-modified nano-gold film (VG@nAu) was developed for the grading of AD diagnosis by detecting P-tau181 protein in the blood. The VG@nAu substrate that produces hundreds of times IR800 molecular compared with a glass substrate, which is prepared by sputtering nano-gold onto vertical graphene surface. The detection limit of this fluorescence biosensor for P-tau181 is 0.82pg/mL. The sensor also shows excellent selectivity and stability. Moreover, we analyzed the levels of P-tau181 in 16 clinical samples divided into four groups (HC group, SCD group, MCI group, and AD group) and found significant differences in P-tau181 levels. The grouping result based on the detection results using this biosensor is consistent with the grouping results of the clinical doctors. This study achieves ultrasensitive detection of P-tau181 protein by constructing a fluorescent biosensor, which validates the feasibility of the VG@nAu-based fluorescence biosensor for the grading of AD diagnosis. This research provides a promising tool for ultrasensitive detecting biomarkers in clinical samples, indicating significant potential for its use in related diagnosing disease.

Incidence, mortality, and global burden of retinoblastoma in 204 countries worldwide from 1990 to 2021: Data and systematic analysis from the Global Burden of Disease Study 2021.

Retinoblastoma (Rb), the primary intraocular malignancy in children, poses significant risks, yet its overall burden remains inadequately assessed. This study aims to analyze global Rb trends using Global Burden of Disease, Injuries, and Risk Factors study (GBD) 2021 data. GBD 2021 data was analyzed to assess Rb incidence, mortality, and disability-adjusted life years (DALYs) from 1990 to 2021. Average annual percentage changes (AAPCs) were calculated across genders, age groups (0-9 years), and geographic regions categorized by socio-demographic index (SDI) quintiles. From 1990 to 2021, the global Rb age-standardized incidence rate (ASIR) increased from 0.08 (per 100,000, range: 0.05 to 0.10) to 0.09 (per 100,000, range: 0.06 to 0.13). ASIR was not significantly correlated with SDI (R = -0.095, P = 0.18), while age-standardized DALYs rate (R = -0.693, P < 0.001) and age-standardized mortality rate (ASMR) (R = -0.71, P < 0.001) were significantly and negatively correlated with SDI. Increases in ASIR were concentrated in Asia, Europe, and northern and southern Africa. The highest standardized DALYs and ASMR were noted in certain countries in Asia, Europe, and South Africa. Among age groups, the highest disease burdens were observed in the "0-6 days" and "2-4 years" groups. There were no significant gender differences in Rb burden globally. Despite global progress, regions with lower SDI face elevated Rb burden and mortality. Females exhibit higher burdens during infancy, necessitating further investigation. Effective Rb management in resource-limited areas requires international collaboration focused on health education, early diagnosis, and prenatal screening for high-risk families.

DNA nanotechnology-based strategies for gastric cancer diagnosis and therapy.

Gastric cancer (GC) is a formidable adversary in the field of oncology. The low early diagnosis rate of GC results in a low overall survival rate. Therefore, early accurate diagnosis and effective treatment are the key to reduce the mortality of GC. With the advent of nanotechnology, researchers continue to explore new possibilities for accurate diagnosis and effective treatment. One such breakthrough is the application of DNA nanotechnology. In this paper, the application of exciting DNA nanomaterials in the diagnosis and treatment of GC is discussed in depth. Firstly, the biomarkers related to GC and the diagnostic strategies related to DNA nanotechnology are summarized. Second, the latest research progress of DNA nanomaterials in the GC targeted therapy are summarized. Finally, the challenges and opportunities of DNA nanomaterials in the research and clinical application of GC are prospected.

Incidence trends, demographic disparities, and survival outcomes of gastrointestinal malignancies in young adults: A focus on stomach and pancreatic cancers.

339 Background: The incidence of gastrointestinal (GI) cancers has been rising among younger populations. While prior research has highlighted the increasing incidence of colorectal cancer (CRC) in adolescents and young adults (AYA), defined as individuals aged 15-39, there is less focus on non-CRC GI malignancies, such as stomach and pancreatic cancers, within this age group. This study aims to further elucidate the incidence, mortality, and demographic disparities in the AYA population for stomach and pancreatic cancers compared to older populations (&gt;50 years old). Methods: Data was extracted from the SEER database spanning from 2000 to 2020. Demographic, clinical, and secondary cancer characteristics were summarized across the sample, stratified by cancer type. Overall survival (OS) and disease-specific survival (DSS) were analyzed using the Kaplan-Meier method, with comparisons performed via the log-rank test. Univariate and multivariate analyses were conducted to identify predictors of survival outcomes. Results: We identified 19,983 AYA patients diagnosed with non-CRC GI malignancies (including esophageal, stomach, small intestine, anal, hepatobiliary, and pancreatic cancers) between 2000 and 2020. Among these patients, the majority were male (58%), White (47%), and resided in metropolitan areas (90%). The median age at diagnosis was 38 years. Pancreatic cancer was 20% more common in the AYA group compared to those over 50 years old (12%), while stomach cancer was also more prevalent in the AYA group (27% vs. 17%). Survival outcomes were significantly better in the AYA group, with a median OS of 24 months compared to 9 months in the older population, and a median DSS of 52 months versus 13 months (p&lt;0.001). Predictors of worse OS and DSS included male sex, African American race, income below $45,000/year, lack of surgical intervention, and delayed initiation of treatment. Conclusions: The incidence of stomach and pancreatic cancers is notably higher in the AYA population compared to older adults, with AYA patients demonstrating better overall and disease-specific survival outcomes. However, certain demographic factors, such as male sex, African American race, and lower income, are associated with worse survival, underscoring the need for targeted interventions to address these disparities. Early diagnosis and timely treatment are critical to improving outcomes in this younger population.

Trajectories of premorbid healthcare contact days in veterans with pancreatic cancer.

706 Background: Early detection of pancreatic cancer is a key clinical priority. Although prior work has evaluated machine learning models to identify at-risk patients for pancreatic cancer screening, this is limited by complexity and cost. "Contact days’’— days spent receiving healthcare outside the home for any reason or length of time—have previously been used to measure the ‘’time toxicity’’ of cancer care post-diagnosis. We sought to characterize trajectories of premorbid contact days, alongside standardized risk scores and healthcare costs to explore their use as potential markers for early pancreatic cancer diagnosis. Methods: We identified decedents with pancreatic adenocarcinoma diagnosed between 2012 and 2022 who had at least one primary care visit in the Veterans Affairs (VA) system in each of the two years prior to diagnosis using the VA Clinical Cancer Registry (VACCR) and the Corporate Data Warehouse (CDW). We grouped patients into two cohorts: (1) surgical (receipt of curative intent surgery), and (2) non-surgical and collected sociodemographic and clinical characteristics. During a 2-year premorbid period we calculated monthly contact days (24 data points) and plotted them as percentages (e.g., 3 contact days in a month=10%) and visualized their trajectory using cubic smoothing splines. We plotted trajectories for each sociodemographic and clinical subgroup and visually evaluated differences. We similarly plotted monthly Care Assessment Need (CAN) scores, a validated VA risk score identifying patients at high risk of hospitalization/death, and monthly VA healthcare costs. Results: We included 8,654 patients (649 surgical - median age 69 years, 74% white, 78% stage 1-2 and 8,005 non-surgical - median age 71 years, 72% white, 21% stage 1-2). For both groups, trajectories of contact days were similar. Baseline contact days (-24 to -12 months) were 7-8% (≈2 days/month). Contact days started to rise on average 9 months prior to diagnosis at an approximate rate of 1% per month, reaching 15-20% at diagnosis (≈5-6 days/month). In the surgical group, patients with a Charlson comorbidity score of ≥2, urban residence, and Black race had higher baseline contact days. Those &lt;65 years and rural residence had a steeper slope of rise in contact days. CAN score trajectories closely mirrored contact day trajectories in both groups. Cost trajectories were similar in both groups (≈$1000/month at baseline), rose gradually to ≈$2000/month by -3 months, and then rose sharply to &gt;$6000/month 2 months before diagnosis. Conclusions: In this decade-long national study of Veterans diagnosed with pancreatic cancer, we found that contact days and CAN scores, but not costs, start to rise 9 months prior to diagnosis. These data provide the first evidence supporting their further evaluation in pancreatic cancer risk models which may aid early diagnosis. The similar trajectories in patients who go on to receive curative-intent surgery or not may imply they may be markers for pancreas cancer diagnosis but perhaps not outcomes.

A systematic review of the impact of artificial intelligence in pancreatic cancer detection.

685 Background: Pancreatic cancer is the fourth leading cause of cancer deaths in the United States, and early detection remains a significant challenge. Screening the general population is not feasible, but the rise of artificial intelligence (AI) has introduced new possibilities for improving early diagnosis and patient outcomes. Methods: A systematic literature search was conducted using PubMed, Google Scholar, and MEDLINE using MeSH terms Artificial intelligence or AI, and diagnosis and pancreatic carcinoma or pancreatic adenocarcinoma. Prisma guidelines were adhered to, and a total of 19 studies resulted, 6 of them were retrospective studies, 4 of them were literature reviews, and 4 of them were randomized trials. The rest were duplicates. The inclusion criteria for this study is AI being used in the diagnosis, only in pancreatic cancer, within the last 5 years, only in English, and only retrospective studies were included. Results: One study used Digital Imaging Processing (DIP) for analyzing Endoscopic ultrasound (EUS) images from 153 pancreatic cancer patients, yielding a sensitivity of 97.98% and a specificity of 94.32%. Two studies explored Computer Aided Diagnosis (CAD) models applied to PET/CT and EUS images, achieving a sensitivity of 95.23% and specificity of 97.51% in PET/CT scans and 83.3% and 93.3% in EUS images, respectively. Another study used a Faster R-CNN model to analyze CT images from 338 pancreatic cancer patients showed high diagnostic accuracy in much less time. Additionally, two studies utilized Natural Language Processing (NLP) for identifying family histories of pancreatic cancer and detecting pancreatic cysts, with the latter achieving sensitivity and specificity rates of 99.9% and 98.8%. Conclusions: The current strategies for early diagnosis of pancreatic cancer focus on serum biomarkers and EUS-guided Fine Needle Aspiration (EUS-FNA) and sensitivity varies but depends on the physician's expertise. AI is showing promise in improving pancreatic cancer diagnosis by enhancing early detection and accuracy. Techniques like deep learning, NLP-based models, Faster R-CNN, and CAD systems analyze medical data and images more effectively than manual methods. AI holds the potential to shape the future of pancreatic cancer diagnosis and improve patient outcomes.

Multiomics Analysis Reveals Molecular Changes during Early Progression of Precancerous Lesions to Lung Adenocarcinoma in Never-Smokers.

Lung cancer is the most common cause of cancer mortality globally, and the prevalence of lung adenocarcinoma, the most common lung cancer subtype, has increased sharply in East Asia. Early diagnosis leads to better survival rates, but this requires an improved understanding of the molecular changes during early tumorigenesis, particularly in nonsmokers. In this study, we performed whole-exome sequencing and RNA sequencing of samples from 94 East Asian patients with precancerous lesions [25 with atypical adenomatous hyperplasia (AAH); 69 with adenocarcinoma in situ (AIS)] and 73 patients with early invasive lesions [minimally invasive adenocarcinoma (MIA)]. Cellular analysis revealed that the activities of endothelial and stromal cells could be used to categorize tumors into molecular subtypes within pathologically defined types of lesions. The subtypes were linked with the radiologically defined type of lesions and corresponded to immune cell infiltration throughout the early progression of lung adenocarcinoma. Spatial transcriptomic analysis revealed the distribution of epithelial cells, endothelial cells, fibroblasts, and plasma cells within MIA samples. Characterization of the molecular lesion subtypes identified positively selected mutational patterns and suggested that angiogenesis in the late-stage AIS type potentially contributes to tissue invasion of the MIA type. This study offers a resource that may help improve early diagnosis and patient prognosis, and the findings suggest possible approaches for early disease interception. Significance: Integrative analysis of multiomics data revealed coordination between immune and nonimmune cells during early progression of precancerous lesions to lung adenocarcinomas and shed light on the molecular characteristics of clinically defined subtypes.

Individualized brain radiomics-based network tracks distinct subtypes and abnormal patterns in prodromal Parkinson's disease.

Individuals in the prodromal phase of Parkinson's disease (PD) exhibit significant heterogeneity and can be divided into distinct subtypes based on clinical symptoms, pathological mechanisms, and brain network patterns. However, little has been done regarding the valid subtyping of prodromal PD, which hinders the early diagnosis of PD. Therefore, we aimed to identify the subtypes of prodromal PD using the brain radiomics-based network and examine the unique patterns linked to the clinical presentations of each subtype. Individualized brain radiomics-based network was constructed for normal controls (NC; N = 110), prodromal PD patients (N = 262), and PD patients (N = 108). A data-driven clustering approach using the radiomics-based network was carried out to cluster prodromal PD patients into higher-/lower-risk subtypes. Then, the dissociated patterns of clinical manifestations, anatomical structure alterations, and gene expression between these two subtypes were evaluated. Clustering findings indicated that one prodromal PD subtype closely resembled the pattern of NCs (N-P; N = 159), while the other was similar to the pattern of PD (P-P; N = 103). Significant differences were observed between the subtypes in terms of multiple clinical measurements, neuroimaging for morphological changes, and gene enrichment for synaptic transmission. Identification of prodromal PD subtypes based on brain connectomes and a full understanding of heterogeneity at this phase could inform early and accurate PD diagnosis and effective neuroprotective interventions.