Early-stage Diagnosis Research Articles

High-throughput enrichment of portal venous circulating tumor cells for highly sensitive diagnosis of CA19-9-negative pancreatic cancer patients using inertial microfluidics

The carbohydrate antigen 19-9 (CA19-9) is commonly used as a representative biomarker for pancreatic cancer (PC); however, it lacks sensitivity and specificity for early-stage PC diagnosis. Furthermore, some patients with PC are negative for CA19-9 (<37 U/mL), which introduces additional limitations to their accurate diagnosis and treatment. Hence, improved methods to accurately detect PC stages in CA19-9-negative patients are warranted. In this study, tumor-proximal liquid biopsy and inertial microfluidics were coupled to enable high-throughput enrichment of portal venous circulating tumor cells (CTCs) and support the effective diagnosis of patients with early-stage PC. The proposed inertial microfluidic system was shown to provide size-based enrichment of CTCs using inertial focusing and Dean flow effects in slanted spiral channels. Notably, portal venous blood samples were found to have twice the yield of CTCs (21.4 cells per 5 mL) compared with peripheral blood (10.9 CTCs per 5 mL). A combination of peripheral and portal CTC data along with CA19-9 results showed to greatly improve the average accuracy of CA19-9-negative PC patients from 47.1% with regular CA19-9 tests up to 87.1%. Hence, portal venous CTC-based microfluidic biopsy can be used with high sensitivity and specificity for the diagnosis of early-stage PC, particularly in CA19-9-negative patients.

Prediagnostic plasma proteomics profile for hepatocellular carcinoma.

Proteomics may discover pathophysiological changes related to hepatocellular carcinoma, an aggressive and lethal type of cancer with low sensitivity for early stage diagnosis. We measured 1305 prediagnostic (median = 12.7 years) SomaScan proteins from 54 pairs of healthy individuals who subsequently developed hepatocellular carcinoma and matched non-hepatocellular carcinoma control individuals from the Nurses' Health Study (NHS) and the Health Professionals Follow-up Study (HPFS). Candidate proteins were validated in the independent, prospective UK Biobank Pharma Proteomics Project (UKB-PPP). In NHS and HPFS, we identified 56 elevated proteins in hepatocellular carcinoma with an absolute fold change of more than 1.2 and a Wald test P value less than .05 in conditional logistic regression analysis. Ingenuity pathway analysis identified enrichment of pathways associated with cell viability, adhesion, proteolysis, apoptosis, and inflammatory response. Four proteins-chitinase-3-like protein 1, growth differentiation factor 15, interleukin-1 receptor antagonist protein, and E-selectin-showed strong positive associations with hepatocellular carcinoma and were thus validated by enzyme-linked immunosorbent assay (odds ratio = 2.48-14.7, all P < .05) in the NHS and HPFS and by Olink platform (hazard ratio = 1.90-3.93, all P < .05) in the UKB-PPP. Adding these 4 proteins to a logistic regression model of traditional hepatocellular carcinoma risk factors increased the area under the curve from 0.67 to 0.87 in the NHS and HPFS. Consistently, model area under the curve was 0.88 for hepatocellular carcinoma risk prediction in the UKB-PPP. However, the limited number of hepatocellular carcinoma patients in the cohorts necessitates caution in interpreting our findings, emphasizing the need for further validation in high-risk populations.

2. Diagnosis of Chronic Pancreatitis

Chronic pancreatitis (CP) is a clinically defined, usually progressive chronic inflammatory disease of the pancreas based on a combination of signs and symptoms. Current criteria for diagnosis rely on overt moderate-severe morphologic changes on cross-sectional imaging. Late-stage disease is an irreversible condition associated with lost pancreatic function and complications that lead to poor quality of life (QOL). Historically treatment is symptomatic, supportive and often ineffective. This article describes our approach to the diagnosis of CP and discusses the limitations of current diagnostic criteria. Clinical unmet needs include reliably making a diagnosis of early stages of CP, accurately predicting disease progression and establishing biomarkers of different stages of CP. Addressing these needs will offer an opportunity for targeted approaches to prevent progression to end-stage disease.

Novel Variants Linked to the Prodromal Stage of Parkinson's Disease (PD) Patients.

The symptoms of most neurodegenerative diseases, including Parkinson's disease (PD), usually do not occur until substantial neuronal loss occurs. This makes the process of early diagnosis very challenging. Hence, this research used variant call format (VCF) analysis to detect variants and novel genes that could be used as prognostic indicators in the early diagnosis of prodromal PD. Data were obtained from the Parkinson's Progression Markers Initiative (PPMI), and we analyzed prodromal patients with gVCF data collected in the 2021 cohort. A total of 304 participants were included, including 100 healthy controls, 146 prodromal genetic individuals, 21 prodromal hyposmia individuals, and 37 prodromal individuals with RBD. A pipeline was developed to process the samples from gVCF to reach variant annotation and pathway and disease association analysis. Novel variant percentages were detected in the analyzed prodromal subgroups. The prodromal subgroup analysis revealed novel variations of 1.0%, 1.2%, 0.6%, 0.3%, 0.5%, and 0.4% for the genetic male, genetic female, hyposmia male, hyposmia female, RBD male, and RBD female groups, respectively. Interestingly, 12 potentially novel loci (MTF2, PIK3CA, ADD1, SYBU, IRS2, USP8, PIGL, FASN, MYLK2, USP25, EP300, and PPP6R2) that were recently detected in PD patients were detected in the prodromal stage of PD. Genetic biomarkers are crucial for the early detection of Parkinson's disease and its prodromal stage. The novel PD genes detected in prodromal patients could aid in the use of gene biomarkers for early diagnosis of the prodromal stage without relying only on phenotypic traits.

Plasma Metabolite Profiling in the Search for Early-Stage Biomarkers for Lung Cancer: Some Important Breakthroughs.

Lung cancer is the leading cause of cancer-related mortality worldwide. In order to improve its overall survival, early diagnosis is required. Since current screening methods still face some pitfalls, such as high false positive rates for low-dose computed tomography, researchers are still looking for early biomarkers to complement existing screening techniques in order to provide a safe, faster, and more accurate diagnosis. Biomarkers are biological molecules found in body fluids, such as plasma, that can be used to diagnose a condition or disease. Metabolomics has already been shown to be a powerful tool in the search for cancer biomarkers since cancer cells are characterized by impaired metabolism, resulting in an adapted plasma metabolite profile. The metabolite profile can be determined using nuclear magnetic resonance, or NMR. Although metabolomics and NMR metabolite profiling of blood plasma are still under investigation, there is already evidence for its potential for early-stage lung cancer diagnosis, therapy response, and follow-up monitoring. This review highlights some key breakthroughs in this research field, where the most significant biomarkers will be discussed in relation to their metabolic pathways and in light of the altered cancer metabolism.

A Perspective Review: Analyzing Collagen Alterations in Ovarian Cancer by High-Resolution Optical Microscopy.

High-grade serous ovarian cancer (HGSOC) is the predominant subtype of ovarian cancer (OC), occurring in more than 80% of patients diagnosed with this malignancy. Histological and genetic analysis have confirmed the secretory epithelial of the fallopian tube (FT) as a major site of origin of HGSOC. Although there have been significant strides in our understanding of this disease, early stage detection and diagnosis are still rare. Current clinical imaging modalities lack the ability to detect early stage pathogenesis in the fallopian tubes and the ovaries. However, there are several microscopic imaging techniques used to analyze the structural modifications in the extracellular matrix (ECM) protein collagen in ex vivo FT and ovarian tissues that potentially can be modified to fit the clinical setting. In this perspective, we evaluate and compare the myriad of optical tools available to visualize these alterations and the invaluable insights these data provide on HGSOC initiation. We also discuss the clinical implications of these findings and how these data may help novel tools for early diagnosis of HGSOC.

Esophageal cancer screening, early detection and treatment: Current insights and future directions.

Esophageal cancer ranks among the most prevalent malignant tumors globally, primarily due to its highly aggressive nature and poor survival rates. According to the 2020 global cancer statistics, there were approximately 604000 new cases of esophageal cancer, resulting in 544000 deaths. The 5-year survival rate hovers around a mere 15%-25%. Notably, distinct variations exist in the risk factors associated with the two primary histological types, influencing their worldwide incidence and distribution. Squamous cell carcinoma displays a high incidence in specific regions, such as certain areas in China, where it meets the cost-effectiveness criteria for widespread endoscopy-based early diagnosis within the local population. Conversely, adenocarcinoma (EAC) represents the most common histological subtype of esophageal cancer in Europe and the United States. The role of early diagnosis in cases of EAC originating from Barrett's esophagus (BE) remains a subject of controversy. The effectiveness of early detection for EAC, particularly those arising from BE, continues to be a debated topic. The variations in how early-stage esophageal carcinoma is treated in different regions are largely due to the differing rates of early-stage cancer diagnoses. In areas with higher incidences, such as China and Japan, early diagnosis is more common, which has led to the advancement of endoscopic methods as definitive treatments. These techniques have demonstrated remarkable efficacy with minimal complications while preserving esophageal functionality. Early screening, prompt diagnosis, and timely treatment are key strategies that can significantly lower both the occurrence and death rates associated with esophageal cancer.

Optimised ResNet50 for Multi-Class Classification of Brain Tumors

Categorizing brain cancers, including glioma, meningioma, and pituitary tumors, based on magnetic resonance imaging (MRI) images presents a significant challenge. Deep learning and machine learning techniques have shown promise in enhancing image categorization. To address this challenge, we leverage the power of the optimized ResNet50 model. Our approach involves classifying medical images using Convolutional Neural Network (CNN) features, which are then compared with the ResNet50 model. The primary goal is to detect brain tumors at an early stage using an advanced deep-learning model. We utilize an accessible dataset from Figshare, containing MRI images of the three distinct categories of brain tumors. Existing brain tumor models face limitations in handling multi-class problems and early-stage diagnosis. Therefore, we propose a fully automated approach employing Convolutional Neural Networks (CNN) to extract diverse properties from brain MRI scans. This method aims to provide accurate tumor diagnosis, even with a high number of classes and limited information in MRI data. Our proposed model involves the creation of identification blocks within a four-layered primary architecture, followed by testing and assessment of the interconnected layers. The results demonstrate that our model outperforms existing methods, achieving an impressive overall classification accuracy of 99.03%,

3D amide proton transfer-weighted imaging may be useful for diagnosing early-stage breast cancer: a prospective monocentric study

BackgroundWe investigated the value of three-dimensional amide proton transfer-weighted imaging (3D-APTWI) in the diagnosis of early-stage breast cancer (BC) and its correlation with the immunohistochemical characteristics of malignant lesions.MethodsSeventy-eight women underwent APTWI and dynamic contrast-enhanced (DCE)-MRI. Pathological results were categorized as either benign (n = 43) or malignant (n = 37) lesions. The parameters of APTWI and DCE-MRI were compared between the benign and malignant groups. The diagnostic value of 3D-APTWI was evaluated using the area under the receiver operating characteristic curve (ROC-AUC) to establish a diagnostic threshold. Pearson’s correlation was used to analyze the correlation between the magnetization transfer asymmetry (MTRasym) and immunohistochemical characteristics.ResultsThe MTRasym and time-to-peak of malignancies were significantly lower than those of benign lesions (all p < 0.010). The volume transfer constant, rate constant, and wash-in and wash-out rates of malignancies were all significantly greater than those of benign lesions (all p < 0.010). ROC-AUCs of 3D-APTWI, DCE-MRI, and 3D-APTWI+DCE to differential diagnosis between early-stage BC and benign lesions were 0.816, 0.745, and 0.858, respectively. Only the difference between AUCAPT+DCE and AUCDCE was significant (p < 0.010). When a threshold of MTRasym for malignancy for 2.42%, the sensitivity and specificity of 3D-APTWI for BC diagnosis were 86.5% and 67.6%, respectively; MTRasym was modestly positively correlated with pathological grade (r = 0.476, p = 0.003) and Ki-67 (r = 0.419, p = 0.020).Conclusions3D-APTWI may be used as a supplementary method for patients with contraindications of DCE-MRI. MTRasym can imply the proliferation activities of early-stage BC.Relevance statement3D-APTWI can be an alternative diagnostic method for patients with early-stage BC who are not suitable for contrast injection.Key points• 3D-APTWI reflects the changes in the microenvironment of early-stage breast cancer.• Combined 3D-APTWI is superior to DCE-MRI alone for early-stage breast cancer diagnosis.• 3D-APTWI improves the diagnostic accuracy of early-stage breast cancer.Graphical

249 Defining the Role of Hedgehog Signaling in Breast Cancer Risk of non-Hispanic Black Women

OBJECTIVES/GOALS: The molecular basis of increased risk of triple negative breast cancer in non-Hispanic Black women represents a critical knowledge gap that this research is designed to address; successful completion of this work could lead to better prevention, earlier stage diagnoses, and possible discovery of novel therapeutic strategies for this population. METHODS/STUDY POPULATION: We have recently generated a living tissue cohort of 11 non-Hispanic Black and 25 non-Hispanic White women who underwent breast surgery at Mayo Clinic. Gene expression profiling of normal breast tissue from this cohort has identified a pattern of gene expression differences that have been associated with the development of basal breast cancer and are also reflective of Hedgehog (Hh) signaling. We will identify protein-based biomarkers for Hedgehog signaling within normal breast tissue using immunohistochemistry methods. We will culture primary human mammary epithelial cells and further separate luminal and myoepithelial cells using flow cytometry to then decipher Hedgehog signaling. RESULTS/ANTICIPATED RESULTS: We anticipate identifying and localizing protein-based biomarkers for Hedgehog signaling within myoepithelial cells of non-Hispanic Black women. Using our findings, we aim to create a biomarker risk model for triple negative breast cancer and validate this model within a separate and larger cohort of women to predict breast cancer risk. DISCUSSION/SIGNIFICANCE: In addition to immediate benefits from improved risk prediction, the proposed work has the potential to provide new insight into the driving forces underlying basal breast carcinogenesis and the distinct biological differences that distinguish non-Hispanic Black women from non-Hispanic White women.

Hybrid Wi-Fi and PLC network for efficient e-health communication in hospitals: a prototype

E-health is being adapted in modern hospitals as a significant addition to the existing healthcare services. To this end, modern hospitals urgently require a mobile, high-capacity, secure, and cost-effective communication infrastructure. In this paper, we explore potential applications of a hybrid broadband power line communication (PLC) and Wi-Fi in an indoor hospital scenario. It utilizes the existing power line cables and Wi-Fi plug-and-play devices for indoor broadband communication. Broadband power line (BPL) adaptors with Wi-Fi outputs are used to build an access network in hospitals, particularly in areas where the wireless router signal is poor. The Tenda PH10 AV1,000 AC Wi-Fi power line adapter is a set of BPL adapters that offer operational bandwidth of up to 1,000 Mbps. These adapters are based on the HomePlug AV2 protocol and can provide a data rate up to 200 Mbps on the physical layer. An experiment using the PLC Wi-Fi kit is carried out to show that a Wi-Fi and PLC hybrid network is the best candidate to provide wide range of practical applications in a hospital including, but not limited to, telemedicine, electronic medical records, early-stage disease diagnosis, health management, real-time monitoring, and remote surgeries.

Nanopore Testing: A Paradigm Shift in Ovarian Cancer Screening

Nanopore Testing: A Paradigm Shift in Ovarian Cancer Screening

How to Enhance the Diagnosis of Early Stages of Chronic Obstructive Pulmonary Disease (COPD)? The Role of Mobile Spirometry in COPD Screening and Diagnosis—A Systematic Review

COPD is the third leading cause of death worldwide. Its diagnosis can be made with spirometry, which is underused due to its limited accessibility. Portable spirometry holds promise for enhancing the efficacy of COPD diagnoses. The study aimed to estimate COPD prevalence diagnosed with a portable spirometer in high-risk patients and compare it with COPD prevalence based on data from conventional, on-site spirometry. We also evaluated the strategy of a proactive approach to identify COPD in high-risk individuals. We conducted a systematic review of original studies on COPD targeted screening and diagnosis with portable and conventional spirometers selected from 8496 publications initially found in three databases: Cochrane, PubMed, and Embase. The inclusion criteria were met by 28 studies. COPD prevalence evaluated with the use of portable spirometers reached 20.27% and was lower compared to that estimated with the use of conventional spirometers (24.67%). In 11 included studies, postbronchodilator tests were performed with portable spirometers, which enabled a bedside COPD diagnosis. Portable spirometers can be successfully used in COPD targeted screening and diagnosis and thus enhance the detection of COPD at early stages.

Evaluation of diagnostic value of Doppler ultrasound in the diagnosis of malignant breast masses.

Breast cancer is the leading cause of cancer mortality in women, and it is on the rise in Iran. Therefore, an early-stage diagnosis of breast cancer is of critical importance. Because ultrasound is one of the available, inexpensive, and minimally invasive techniques for distinguishing malignant from benign masses, a comparison of conventional ultrasound, color Doppler, and spectral Doppler findings can be useful. The purpose of this study was to determine the diagnostic value of sonographic indices, specifically Doppler parameters, in identifying the nature of breast masses. This is a cross-sectional study, with diagnostic value analysis. Before undergoing a biopsy, 80 patients with breast masses underwent B-mode and Doppler breast ultrasound. The ultrasound findings were then compared to pathologic results to determine which groups were malignant or benign. The resulting data were analyzed using statistical tests and diagnostic values with SPSS 22 software. B-mode grey-scale ultrasound indices such as mass shape, mass margin, mass orientation, and posterior features, as well as Doppler indices such as vascularity, RI (Resistive Index), PI (Pulsatility Index), and PSV (Peak Systolic Velocity), were found to be statistically significant with pathological findings. Color Doppler revealed vascularity in 65% of benign and 84% of malignant masses. The diagnostic value results revealed that mass shape, mass margin, mass orientation, and posterior features all play a significant role in predicting lesion malignancy, with a sensitivity of 92%, 58%, 64%, 56%, and specificity of 59%, 66%, 82%, and 84%, respectively. The RI, PI, and PSV indices were significantly higher in malignant masses, and all of them had remarkable diagnostic values in predicting malignancy, with a (Area Under The Curve) AUC of 0.863, 0.882, 0.702, a sensitivity of 84% and 84%, 68%, and a specificity of 83%, 86%, and 62%, respectively, at the optimal cut-off points (0.65, 1.32, 12.40) obtained from the Receiver Operating Characteristics (ROC) curves.

Site-specific patterns of early-stage cancer diagnosis during the COVID-19 pandemic.

The COVID-19 pandemic caused widespread disruptions in cancer care. We hypothesized that the greatest disruptions in diagnosis occurred in screen-detected cancers. We identified patients (≥18 years of age) with newly diagnosed cancer from 2019 to 2020 in the US National Cancer Database and calculated the change in proportion of early-stage to late-stage cancers using a weighted linear regression. Disruptions in early-stage diagnosis were greater than in late-stage diagnosis (17% vs 12.5%). Melanoma demonstrated the greatest relative decrease in early-stage vs late-stage diagnosis (22.9% vs 9.2%), whereas the decrease was similar for pancreatic cancer. Compared with breast cancer, cervical, melanoma, prostate, colorectal, and lung cancers showed the greatest disruptions in early-stage diagnosis. Uninsured patients experienced greater disruptions than privately insured patients. Disruptions in cancer diagnosis in 2020 had a larger impact on early-stage disease, particularly screen-detected cancers. Our study supports emerging evidence that primary care visits may play a critical role in early melanoma detection.

Retrospective descriptive analysis of the demographic and clinicopathological presentation of breast cancer patients in Kasr Al-Ainy Hospital over 5 years

Background Breast cancer is the most common type of cancer and the second leading cause of mortalities among women. Early-stage diagnosis of breast cancer increases the chances of survival, and therefore, reduces mortality rates. A national initiative was implemented in Egypt in 2019 when women above the age of 18 years were granted free screening at 3538 healthcare units, and 114 hospitals nationwide. Objective To describe the sociodemographic factors and clinicopathological presentation of breast cancer among the patients presented to Cairo University Hospital over 5 years. Patients and methods We conducted a descriptive historical study at Kasr Al-Ainy Teaching Hospital of Cairo University, we retrospectively assessed all women with primary breast cancer diagnosed over 5 years during the period from the July 1, 2017 to the July 1, 2022. Data was collected from medical records, operative notes, radiology reports, and pathology reports from the database system of the Oncology Department of Cairo University Hospitals (Kasr Al-Ainy). The patient data is put into a spreadsheet that focuses on the demographic and clinicopathological characteristics of the patients (age, size of the tumor, side of breast affected, histopathology findings, imaging characteristics, immunohistochemistry, and TNM staging of breast cancer). Results We enrolled a total of 509 female patients who were screened for early detection of breast cancer during the past 5 years in general surgery, radio diagnosis, and oncology departments, Kasr Al-Ainy Teaching Hospital; they showed a mean age of 53.7±11.7 years and ranged between 26 and 88 years. In the current study molecular subtyping showed that 24.4% were Luminal A, 34% were Luminal B1, 19.4% were Luminal B2, 9.2% were HER2-enriched and 13% were triple-negative breast cancer subtype. We found that neoadjuvant therapy was prescribed for 190 (37.3%) patients, 36 (19%) of those patients achieved pCR, while the remaining patients had residual disease in the postoperative specimen. Conclusion We concluded that in our center, Egyptian females are diagnosed with breast cancer earlier compared with developed countries; however, the current study reports approximately the same percentages of molecular subtypes, rates of pCR, and metastatic disease at the time of presentation compared with developed countries.

Adjuvant Ovarian Function Suppression in Premenopausal Hormone Receptor–Positive Breast Cancer

Few oncology studies have assessed the effectiveness of adjuvant ovarian function suppression (OFS) in observational settings for premenopausal hormone receptor-positive breast cancer. Target trial emulation is increasingly used for estimating treatment outcomes in observational cohorts. To describe hormone therapy and OFS treatment patterns (aim 1), examine the association between adding OFS to tamoxifen (TAM) or aromatase inhibitor (AI) and survival (aim 2), and examine the association between duration of hormone treatment (TAM or AI) plus OFS (H-OFS) and survival (aim 3). This population-based cohort study included all premenopausal, early-stage breast cancer diagnoses between 2010 and 2020 in Alberta, Canada. Target trial emulation was conducted. Eligibility criteria were directly modeled after the Suppression of Ovarian Function Trial (SOFT) and Tamoxifen and Exemestane Trial (TEXT). Participants were followed up for a maximum of 5 years. Data were analyzed from July 2022 through March 2023. For aim 2, exposures were receiving the following baseline treatments for 2 years: AI + OFS (AI-OFS), TAM + OFS (T-OFS), and TAM alone. For aim 3, exposures were a 2-year or greater and a less than 2-year duration of H-OFS. Recurrence-free survival was the primary outcome of interest. Marginal structural Cox models with inverse probability treatment and censoring weights were used to estimate hazard ratios (HRs), adjusted for baseline and time-varying confounding variables. Among 3434 female patients with premenopausal, early-stage breast cancer diagnoses (median [IQR] age, 45 [40-48] years), 2647 individuals satisfied SOFT and TEXT eligibility criteria. There were 2260 patients who initiated TAM, 232 patients who initiated T-OFS, and 155 patients who initiated AI-OFS; 192 patients received H-OFS for 2 or more years, and 195 patients received H-OFS for less than 2 years. The 5-year recurrence risks were not significantly lower in AI-OFS vs TAM (HR, 0.76; 95% CI, 0.38-1.33) or T-OFS vs TAM (HR, 0.87; 95% CI, 0.50-1.45) groups. Patients receiving H-OFS for 2 or more years had significantly better 5-year recurrence-free survival compared with those receiving H-OFS for less than 2 years (HR, 0.69; 95% CI, 0.54-0.90). This study found no significant reductions in recurrence risk for AI-OFS and T-OFS compared with TAM alone. H-OFS duration for at least 2 years was associated with significantly improved recurrence-free survival.

A novel visible and near-infrared hyperspectral imaging platform for automated breast-cancer detection

Background: Breast cancer is a leading cause of cancer-related deaths among women worldwide. Early and accurate detection is crucial for improving patient outcomes. Our study utilizes Visible and Near-Infrared Hyperspectral Imaging (VIS-NIR HSI), a promising non-invasive technique, to detect cancerous regions in ex-vivo breast specimens based on their hyperspectral response.Methods: In this paper, we present a novel HSI platform integrated with fuzzy c-means clustering for automated breast cancer detection. We acquire hyperspectral data from breast tissue samples, and preprocess it to reduce noise and enhance hyperspectral features. Fuzzy c-means clustering is then applied to segment cancerous regions based on their spectral characteristics.Results: Our approach demonstrates promising results. We evaluated the quality of the clustering using metrics like Silhouette Index (SI), Davies-Bouldin Index (DBI), and Calinski-Harabasz Index (CHI). The clustering metrics results revealed an optimal number of 6 clusters for breast tissue classification, and the SI values ranged from 0.68 to 0.72, indicating well-separated clusters. Moreover, the CHI values showed that the clusters were well-defined, and the DBI values demonstrated low cluster dispersion. Additionally, the sensitivity, specificity, and accuracy of our system were evaluated on a dataset of breast tissue samples. We achieved an average sensitivity of 96.83%, specificity of 93.39%, and accuracy of 95.12%. These results indicate the effectiveness of our HSI-based approach in distinguishing cancerous and non-cancerous regions.Conclusions: The paper introduces a robust hyperspectral imaging platform coupled with fuzzy c-means clustering for automated breast cancer detection. The clustering metrics results support the reliability of our approach in effectively segmenting breast tissue samples. In addition, the system shows high sensitivity and specificity, making it a valuable tool for early-stage breast cancer diagnosis. This innovative approach holds great potential for improving breast cancer screening and, thereby, enhancing our understanding of the disease and its detection patterns.

Lipidomics reveals new lipid-based lung adenocarcinoma early diagnosis model.

Lung adenocarcinoma (LUAD) continues to pose a significant mortality risk with a lack of dependable biomarkers for early noninvasive cancer detection. Here, we find that aberrant lipid metabolism is significantly enriched in lung cancer cells. Further, we identified four signature lipids highly associated with LUAD and developed a lipid signature-based scoring model (LSRscore). Evaluation of LSRscore in a discovery cohort reveals a robust predictive capability for LUAD (AUC: 0.972), a result further validated in an independent cohort (AUC: 0.92). We highlight one lipid signature biomarker, PE(18:0/18:1), consistently exhibiting altered levels both in cancer tissue and in plasma of LUAD patients, demonstrating significant predictive power for early-stage LUAD. Transcriptome analysis reveals an association between increased PE(18:0/18:1) levels and dysregulated glycerophospholipid metabolism, which consistently displays strong prognostic value across two LUAD cohorts. The combined utility of LSRscore and PE(18:0/18:1) holds promise for early-stage diagnosis and prognosis of LUAD.

Impact of the COVID-19 pandemic on gastric surgery in Japan: A nationwide study using interrupted time-series analyses.

Changes in the number of surgeries for gastric cancer during the coronavirus disease 2019 (COVID-19) pandemic have been reported, but data are insufficient to understand the impact at the national level. This study aimed to determine the impact of the COVID-19 pandemic on gastric surgery in Japan. Insurance claims data registered from January 2015 to January 2021 were used. Changes in the number of endoscopic resections and gastrectomies for gastric cancer were estimated using an interrupted time-series analysis. The number of endoscopic resections significantly decreased in July 2020 (-1565; 95% confidence interval [CI]: -2022, -1108) and January 2021 (-539; 95% CI: -970, -109), and the number of laparoscopic surgeries significantly decreased in July 2020 (-795; 95% CI: -1097, -492), October 2020 (-313; 95% CI: -606, -19), and January 2021 (-507; 95% CI: -935, -78). Meanwhile, the number of open gastrectomies remained unchanged, and the number of robot-assisted gastrectomies steadily increased since their coverage by public health insurance in April 2018. The decreased number of endoscopic resections and laparoscopic surgeries in Japan suggests a decline in early-stage gastric cancer diagnosis, likely due to the suspension of gastric cancer screening and diagnostic testing during the pandemic. Meanwhile, the number of open and robot-assisted gastrectomies remained unchanged and increased, respectively, indicating that these applications were not affected by the pandemic-related medical crisis. These findings highlight that procedures for cancer diagnosis, including screening, should still be provided during pandemics.