Diagnosis Of Early-stage Cancer Research Articles

Breast Cancer Education and Empowerment in Ethiopia: Evaluating Community-Based Cancer Prevention Efforts Using the RE-AIM Framework.

Cancer prevention challenges in Ethiopia include limited community awareness and low uptake of screening, which are in part driven by a lack of culturally and linguistically relevant cancer education appropriate for the diverse indigenous communities of this never-colonized nation. In 2022, a comprehensive multi-media breast cancer (BC) awareness campaign was implemented, featuring local cancer experts and survivors, with community-based screening events in the towns of Adama and Mojo. The RE-AIM framework was used to evaluate and describe its reach, effectiveness, adoption, implementation, and maintenance. Educational pamphlets, videos, social media posts, and interviews were distributed in person and through local and national media networks, reaching tens of millions of people and resulting in 525 individuals screened, with one diagnosis of early-stage cancer. During the free screening events, an interview-administered survey of BC knowledge, attitudes, and screening practices was conducted to inform future cancer education for this population. Among 287 survey respondents, about half correctly identified swelling (46%) or changing nipples/discharge (48.4%) as signs of BC. Maintenance challenges include the lack of a national screening program. Educational resources and a mobile app, translated into the local language, encourage continued patient empowerment to perform breast self-exams. In the absence of established BC prevention programs, "pop-up" mobile screening events can be effective for mobilizing communities to get screened. The paper highlights challenges and lessons gleaned from this community-based BC awareness campaign and screening event to inform future cancer education initiatives in Ethiopia and similar resource-limited settings.

Abstract PO1-28-06: Development of a plasma-based real-time qPCR gene expression assay for targeted screening and diagnosis of early-stage breast cancer

Abstract Background Over 12 million US women undergo follow-up breast imaging and exams annually due to breast cancer suspicions, incurring an $8 billion total cost, of which $2.18 billion is attributed to false-positive breast biopsies. Recent updates published to the Mammography Quality Standards Act (MQSA) now recommend supplemental imaging for women with heterogeneously dense and extremely dense breasts, regardless of suspicious findings. Given sensitivity limitations associated with mammography in dense-breasted women, as low as 30-47%, and non-specificity associated with ultrasound and MRI, these recent recommendations are likely to further strain the healthcare system and place substantial burden on patients and providers. With an estimated 25 million dense-breasted women who regularly screen for breast cancer now considered at-risk, there is a critical need for affordable and highly accurate testing, offering better specificity without sacrificing sensitivity. To address this gap, we developed an affordable and accurate plasma-based gene expression assay using real-time qPCR for targeted screening and diagnosis of early-stage breast cancer. Methods In previous studies, 26 cross-correlated mRNA gene targets were discovered and independently validated for non-invasive breast cancer detection across five independent patient cohorts using microarray gene expression profiling, comprised of peripheral blood mononuclear cells (n=337) and saliva (n=20), demonstrating strong efficacy, reproducibility, and concordance across bio-fluids and assay platforms in all studies. A sixth independent validation cohort comprised of 203 plasma samples was subsequently obtained for real-time qPCR clinical assay development and validation in our CLIA laboratory. The CLIA validation cohort was designed to be representative of different diagnosis status, stage, and ethnicity, and included Caucasian, Black/African American, Hispanic, and Asian women. After normalizing mRNA expression and clustering analysis, the signature was refined to 8 target genes and was assessed in a cohort of 87 plasma samples [Table 1] to verify the assay’s diagnostic performance for stage I breast cancer detection, under locked laboratory protocols. We leveraged machine learning methods derived from XGBoost classification, a supervised-learning algorithm that uses sequentially built shallow decision trees to provide accurate results and avoidance of overfitting. Results The XGBoost model, selecting from the 8 mRNA gene targets and patient age, achieved >99% sensitivity, 89% specificity for stage I breast cancer detection in the held-out test set, with an overall diagnostic accuracy of 94.5%. Normalized data showed significant differences in gene expression between healthy controls and stage I patients, and distinct clustering was observed for the 8 mRNA gene signature, including patient age. Conclusions Our plasma-based real-time qPCR gene expression clinical assay, with machine learning, is positioned as a highly accurate non-invasive tool for targeted screening and diagnosis of early-stage breast cancer. Furthermore, a clinically validated assay in this space addresses the need for more targeted diagnostics, while serving as a promising tool for clinicians as they make critical care decisions across the breast health continuum, accelerating time to earlier and more precise intervention and treatment, mitigating unnecessary healthcare expenditures, and reducing the mental, emotional, and financial burden on patients and their families. Citation Format: Martin Keiser, Elizabeth Cormier-May, Matthew Alderdice, Joy Kavanagh, William Guesdon, Heather Healy, Nathalie Jean-Charles, Jay Harness. Development of a plasma-based real-time qPCR gene expression assay for targeted screening and diagnosis of early-stage breast cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO1-28-06.

Disparities in Cancer Stage Outcomes by Catchment Areas for a Comprehensive Cancer Center

The National Cancer Institute comprehensive cancer centers (CCCs) lack spatial and temporal evaluation of their self-designated catchment areas. To identify disparities in cancer stage at diagnosis within and outside a CCC's catchment area across a 10-year period using spatial and statistical analyses. This cross-sectional, population-based study conducted between 2010 and 2019 utilized cancer registry data for the Johns Hopkins Sidney Kimmel CCC (SKCCC). Eligible participants included patients with cancer in the contiguous US who received treatment for cancer, a diagnosis of cancer, or both at SKCCC. Patients were geocoded to zip code tabulation areas (ZCTAs). Individual-level variables included sociodemographic characteristics, smoking and alcohol use, treatment type, cancer site, and insurance type. Data analysis was performed between March and July 2023. Distance between SKCCC and ZCTAs were computed to generate a catchment area of the closest 75% of patients and outer zones in 5% increments for comparison. The primary outcome was cancer stage at diagnosis, defined as early-stage, late-stage, or unknown stage. Multinomial logistic regression was used to determine associations of catchment area with stage at diagnosis. This study had a total of 94 007 participants (46 009 male [48.94%] and 47 998 female [51.06%]; 30 195 aged 22-45 years [32.12%]; 4209 Asian [4.48%]; 2408 Hispanic [2.56%]; 16 004 non-Hispanic Black [17.02%]; 69 052 non-Hispanic White [73.45%]; and 2334 with other or unknown race or ethnicity [2.48%]), including 47 245 patients (50.26%) who received a diagnosis of early-stage cancer, 19 491 (20.73%) who received a diagnosis of late-stage cancer , and 27 271 (29.01%) with unknown stage. Living outside the main catchment area was associated with higher odds of late-stage cancers for those who received only a diagnosis (odds ratio [OR], 1.50; 95% CI, 1.10-2.05) or only treatment (OR, 1.44; 95% CI, 1.28-1.61) at SKCCC. Non-Hispanic Black patients (OR, 1.16; 95% CI, 1.10-1.23) and those with Medicaid (OR, 1.65; 95% CI, 1.46-1.86) and no insurance at time of treatment (OR, 2.12; 95% CI, 1.79-2.51) also had higher odds of receiving a late-stage cancer diagnosis. In this cross-sectional study of CCC data from 2010 to 2019, patients residing outside the main catchment area, non-Hispanic Black patients, and patients with Medicaid or no insurance had higher odds of late-stage diagnoses. These findings suggest that disadvantaged populations and those living outside of the main catchment area of a CCC may face barriers to screening and treatment. Care-sharing agreements among CCCs could address these issues.

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.

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.

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

Implementing Indicators and Trajectories of Return to Work After Breast Cancer Diagnosis: A Mixed-Methods Study Using the French National Healthcare Insurance Database and Stakeholder Consultation

PurposeReturn to work (RTW) is important for quality of life after breast cancer but its analysis at the population-level remains limited in France. This study aimed to implement Electronic Healthcare Data (EHD)-based indicators and trajectories to measure RTW after breast cancer diagnosis, and to examine stakeholders’ perspectives regarding these indicators. MethodsWe followed a mixed-methods approach that consisted of (i) implementing RTW indicators and identifying clusters of trajectories using state sequence analysis with data from a representative sample of the French National Health Data System and (ii) exploring, through qualitative focus group and interviews, stakeholders’ perceptions on the interpretation, limitations, and utility of these indicators. ResultsWe extracted data from 317 women aged 25-55 years with a first diagnosis of early-stage breast cancer. The median number of sickness absence periods was 2 for a total of 434 days during the 3-year follow-up, and the median time to sustainable RTW was 240 days. Three clusters of RTW trajectories were identified: “early RTW” (49.5% of the population), “RTW after partial resumption” (37.5%) and “continuous compensation” (12.9%). Feedback from stakeholders highlighted the multi-factorial nature of RTW and underscored the added value of EHD for studying RTW, despite certain limitations. ConclusionsWe demonstrated the feasibility of calculating RTW indicators and identifying trajectories using the French National Health Data System. These indicators can serve as outcome measures in RTW promotion and provide a basis for designing targeted interventions for breast cancer survivors.

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.

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.

Diagnostic value of plasma-derived exosomal miR-223 for epithelial ovarian cancer

ObjectivesTo evaluate the diagnostic value of plasma exosomal miR-223 and its combination with CA125 for the diagnosis of early-stage epithelial ovarian cancer (EOC).Patients and methodsExosomes derived from the plasma of 78 EOC patients, 40 patients with epithelial benign ovarian tumors, and 52 healthy participants were isolated using the ultracentrifugation method and identified by transmission electron microscopy (TEM) and western blot.ResultsThe expression of exosomal miR-223 was significantly upregulated in the plasma of EOC patients compared to that in healthy subjects and patients with benign diseases. The combination of exosomal miR-223 and CA125 from plasma had an equivalent area under the ROC curve (AUC) to CA125 alone for discriminating between EOC and non-EOC cases, including healthy subjects and benign ovarian tumors. However, the AUC value of the combination was 0.944 (95% CI: 0.899–0.990) for differentially diagnosing early-stage EOC from healthy subjects, slightly higher than that of CA125 alone (0.928, 95% CI: 0.875–0.981), with a sensitivity and specificity of 0.9784 and 0.885, respectively.ConclusionOur data suggest that plasma exosomal miR-223 can be used as a complement to CA125 to increase the diagnostic power for differentiating early-stage EOC from healthy subjects.

Label-Free Optical Technologies to Enhance Noninvasive Endoscopic Imaging of Early-Stage Cancers.

White light endoscopic imaging allows for the examination of internal human organs and is essential in the detection and treatment of early-stage cancers. To facilitate diagnosis of precancerous changes and early-stage cancers, label-free optical technologies that provide enhanced malignancy-specific contrast and depth information have been extensively researched. The rapid development of technology in the past two decades has enabled integration of these optical technologies into clinical endoscopy. In recent years, the significant advantages of using these adjunct optical devices have been shown, suggesting readiness for clinical translation. In this review, we provide an overview of the working principles and miniaturization considerations and summarize the clinical and preclinical demonstrations of several such techniques for early-stage cancer detection. We also offer an outlook for the integration of multiple technologies and the use of computer-aided diagnosis in clinical endoscopy. Expected final online publication date for the Annual Review of Analytical Chemistry, Volume 17 is May 2024. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Urine Metabolic Profiling for Rapid Lung Cancer Screening: A Strategy Combining Rh-Doped SrTiO3-Assisted Laser Desorption/Ionization Mass Spectrometry and Machine Learning.

Lung cancer ranks among the cancers with the highest global incidence rates and mortality. Swift and extensive screening is crucial for the early-stage diagnosis of lung cancer. Laser desorption/ionization mass spectrometry (LDI-MS) possesses clear advantages over traditional analytical methods for large-scale analysis due to its unique features, such as simple sample processing, rapid speed, and high-throughput performance. As n-type semiconductors, titanate-based perovskite materials can generate charge carriers under ultraviolet light irradiation, providing the capability for use as an LDI-MS substrate. In this study, we employ Rh-doped SrTiO3 (STO/Rh)-assisted LDI-MS combined with machine learning to establish a method for urine-based lung cancer screening. We directly analyzed urine metabolites from lung cancer patients (LCs), pneumonia patients (PNs), and healthy controls (HCs) without employing any pretreatment. Through the integration of machine learning, LCs are successfully distinguished from HCs and PNs, achieving impressive area under the curve (AUC) values of 0.940 for LCs vs HCs and 0.864 for LCs vs PNs. Furthermore, we identified 10 metabolites with significantly altered levels in LCs, leading to the discovery of related pathways through metabolic enrichment analysis. These results suggest the potential of this method for rapidly distinguishing LCs in clinical applications and promoting precision medicine.

Unmet Social Needs and Breast Cancer Screening Utilization and Stage at Presentation

Unmet social needs in local populations may hinder the development of targeted cancer control interventions aimed at improving screening utilization and early-stage breast cancer diagnosis to ultimately improve breast cancer survival disparities. To evaluate if (1) city-funded screening mammography is associated with utilization of screening mammography, (2) unmet social needs are associated with utilization of screening mammography, and (3) unmet social needs are associated with later-stage disease at diagnosis. This cohort study included patients with stages I-IV invasive ductal or lobular carcinoma treated at an academic medical center (including both an underserved safety-net hospital [SNH] and a National Cancer Institute-designated academic cancer center [ACC]) from 2020 to 2023. Eligible patients were aged 18 years or older and able to consent. Data were analyzed between July 2023 and September 2023. The Health Leads Social Needs Screening Toolkit, a screening tool that gathers information on the most common social need domains affecting patient health. Univariable and multivariable logistic regression was utilized to evaluate the following primary outcomes: (1) routine screening mammography and (2) American Joint Committee on Cancer 8th edition clinical stage at presentation. Of the 322 women who completed the Health Leads Social Needs Screening Toolkit, 201 (62%) self-identified as Hispanic, 63 (19%) as non-Hispanic Black, and 63 (19%) as non-Hispanic White. Two hundred fifty-five (76%) patients with access to city-funded screening mammography completed a screening mammogram. Patients who presented to the SNH were more likely to present with late-stage disease compared with early-stage disease (15 of 48 [31%] vs 50 of 274 [18%]; P = .04). On multivariable logistic regression, not completing a screening mammography was associated with having an increasing number of unmet social needs (OR, 0.74; 95% CI, 0.55-0.99; P = .047) and an increasing age at diagnosis (OR, 0.92; 95% CI, 0.89-0.96; P < .001). Moreover, increasing unmet social needs was significantly associated with late-stage diagnosis above and beyond screening mammography (OR, 1.38; 95% CI, 1.01-1.89; P = .04). In this cohort study, access to screening mammography did not translate to utilization of screening mammography, increasing unmet social needs were significantly associated with lower rates of screening mammography, and those with increasing unmet social needs were more likely to present with late-stage disease. This association transcended recruitment site (SNH vs ACC), indicating that patients in either hospital setting may benefit from unmet social needs screening to overcome access to care barriers associated with late-stage disease at diagnosis.

Surface-enhanced Raman spectroscopy of the filtrate portions of the blood serum samples of breast cancer patients obtained by using 30 kDa filtration device

Raman spectroscopy is reliable tool for analyzing and exploring early disease diagnosis related to body fluids, such as blood serum, which contain low molecular weight fraction (LMWF) and high molecular weight fraction (HMWF) proteins. The disease biomarkers consist of LMWF which are dominated by HMWF hence their analysis is difficult. In this study, in order to overcome this issue, centrifugal filter devices of 30 kDa were used to obtain filtrate and residue portions obtained from whole blood serum samples of control and breast cancer diagnosed patients. The filtrate portions obtained in this way are expected to contain the marker proteins of breast cancer of the size below this filter size. These may include prolactin, Microphage migration inhabitation factor (MIF), γ-Synuclein, BCSG1, Leptin, MUC1, RS/DJ-1 present in the centrifuged blood serum (filtrate portions) which are then analyzed by the SERS technique to recognize the SERS spectral characteristics associated with the progression of breast cancer in the samples of different stages as compared to the healthy ones. The key intention of this study is to achieve early-stage breast cancer diagnosis through the utilization of Surface Enhanced Raman Spectroscopy (SERS) after the centrifugation of healthy and breast cancer serum samples with Amicon ultra-filter devices of 30 kDa. The silver nanoparticles with high plasmon resonance are used as a substrate for SERS analysis. Principal Component Analysis (PCA) and Partial Least Discriminant Analysis (PLS-DA) models are utilized as spectral classification tools to assess and predict rapid, reliable, and non-destructive SERS-based analysis. Notably, they were particularly effective in distinguishing between different SERS spectral groups of the cancerous and non-cancerous samples. By comparing all these spectral data sets to each other PLSDA shows the 79 % accuracy, 76 % specificity, and 81 % sensitivity in samples with AUC value of AUC = 0.774 SERS has proven to be a valuable technique for the rapid identification of the SERS spectral features of blood serum and its filtrate fractions from both healthy individuals and those with breast cancer, aiding in disease diagnosis.

Serum Interleukins 8, 17, and 33 as Potential Biomarkers of Colon Cancer.

This research investigated the serum levels of three interleukins (IL8, IL17A, and IL33) and the possible relationships between them in healthy people and colon cancer patients at different stages. This study involved 82 participants, 42 of whom had colon cancer and 40 were healthy individuals. The cancer patients were classified into four groups according to the TNM staging classification of colon and rectal cancer. Serum levels of the interleukins were measured by the ELISA test. The data were analyzed statistically to compare the demographic characteristics, the interleukin levels across cancer stages, and the correlation between interleukins in both groups. The results showed that women had more early-stage colon cancer diagnoses, while men had more advanced-stage cancer diagnoses. Stage two colon cancer was more common in older people. Younger people, men, and those with early-stage colon cancer had higher levels of interleukins. The levels of IL8 and IL17A were higher in the cancer group, while the level of IL33 was higher in the healthy group. There was a strong correlation between IL8 and IL17A levels in both groups (p = 0.001). IL17A influenced the level of IL33 in the cancer group (p = 0.007). This study suggested that cytokine variation profiles could be useful for detecting colon cancer and predicting its outcome.

Eliminating Medicaid dental benefits and early-stage oral cancer diagnoses.

Despite the importance of regular dental visits for detecting oral cancer, millions of low-income adults lack access to dental services. In July 2009, California eliminated adult Medicaid dental benefits. We tested if this impacted oral cancer detection for Medicaid enrollees. We analyzed Surveillance, Epidemiology, and End Results-Medicaid data, which contains verified Medicaid enrollment status, to estimate a difference-in-differences model. Our design compares the change in early-stage (Stages 0-II) diagnoses before and after dropping dental benefits in California with the change in early-stage diagnoses among eight states that did not change Medicaid dental benefits. Patients were grouped by oropharyngeal cancer (OPC) and non-OPC (oral cavity cancer), type, and the length of Medicaid enrollment. We also assessed if the effect of dropping dental benefits varied by the number of dentists per capita. Dropping Medicaid dental benefits was associated with a 6.5%-point decline in early-stage diagnoses of non-OPC (95% CI = -14.5, -3.2, p = 0.008). This represented a 20% relative reduction from baseline rates. The effect was highest among beneficiaries with 3 months of continuous Medicaid enrollment prior to diagnosis who resided in counties with more dentists per capita. Specifically, dropping dental coverage was associated with a 1.25%-point decline in the probability of early-stage non-OPC diagnoses for every additional dentist per 5000 population (p = 0.006). Eliminating Medicaid dental benefits negatively impacted early detection of cancers of the oral cavity. Continued volatility of Medicaid dental coverage and provider shortages may be further delaying oral cancer diagnoses. Alternative approaches are needed to prevent advanced stage OPC.