Early Cancer Detection Research Articles

Telomere Length and Telomerase Activity as Potential Biomarkers for Gastrointestinal Cancer.

Gastrointestinal (GI) cancers, such as colorectal and gastric cancers, pose significant global health challenges due to their high rates of incidence and mortality. Even with advancements in treatment and early detection, many patients still face poor outcomes, highlighting the critical need for new biomarkers and therapeutic targets. Telomere length (TL) and telomerase activity (TA) have gained attention in this context. Telomeres, protective nucleotide sequences at chromosome ends, shorten with each cell division, leading to cellular aging. Telomerase, a ribonucleoprotein enzyme, counteracts this shortening by adding telomeric repeats, a process tightly regulated in normal cells but often dysregulated in cancer. This review critically evaluates the role of TL and TA in the pathogenesis of GI cancers, examining their potential as diagnostic, prognostic, and predictive biomarkers. It explores how alterations in telomere biology contribute to the initiation and progression of GI tumors and assesses the therapeutic implications of targeting telomerase. By integrating findings from diverse studies, this review aims to elucidate the intricate relationship between telomere dynamics and gastrointestinal carcinogenesis, offering insights into how TL and TA could be leveraged to enhance the early detection, treatment, and prognosis of GI cancers.

Integrated triple signal amplification strategy for ultrasensitive electrochemical detection of gastric cancer-related microRNA utilizing MoS2-based nanozyme, hybridization chain reaction, and horseradish peroxidase

Early diagnosis and treatment of gastric cancer (GC) play a vital role in improving efficacy, reducing mortality and prolonging patients’ lives. Given the importance of early detection of gastric cancer, an electrochemical biosensor was developed for the ultrasensitive detection of miR-19b-3p by integrating MoS2-based nanozymes, hybridization chain reaction (HCR) with enzyme catalyzed reaction. The as-prepared MoS2-based nanocomposites were used as substrate materials to construct nanoprobes, which can simultaneously load probe DNA and HCR initiator for signal amplification. Moreover, the MoS2-based nanocomposites are also employed as nanozymes to amplify electrochemical response. The presence of miR-19b-3p induced the assembly of MoS2-based nanoprobes on the electrode surface, which can activate in-situ HCR reaction to load a large number of horseradish peroxidase (HRP) for signal amplification. Coupling with the co-catalytic ability of HRP and MoS2-based nanozymes, the designed electrochemical biosensor can detect as low as 0.7 aM miR-19b-3p. More importantly, this biosensor can efficiently analyze miR-19b-3p in clinical samples from healthy people and gastric cancer patients due to its excellent sensitivity and selectivity, suggesting that this biosensor has a potential application in early diagnosis of disease.

Pioneering Prostate Cancer Diagnosis: Evaluating AI Against Traditional Methods - A Systematic Literature Review

ABSTRACT Prostate cancer is one of the most common forms of men's cancer in the world and the understanding of its epidemiology is crucial to effective prevention, early detection and management strategies. This systematic review highlights the potential of artificial intelligence-based diagnostic methods to revolutionize the early diagnosis of prostate cancer. A comprehensive literature search was carried out using databases such as PubMed, Google Scholar and Scopus. After removing duplicates, titles and abstracts were examined in 279 studies and the following full texts were examined in 128 studies. We have ten studies to review in total. Therefore, we chose to review data for all 10 identified studies that applied AI techniques to detect ca prostate. The Paige Prostate Alpha AI system significantly enhances pathologists' diagnostic capabilities, leading to more accurate and effective prostate cancer detection. These limitations indicate that further research is required to address these issues and validate the results in a more diverse and controlled environment. AI-based diagnostic methods have shown significant promise in enhancing the early detection of prostate cancer. As technology continues to advance, integrating AI with traditional diagnostic approaches could lead to more effective, efficient, and accurate prostate cancer screening and diagnosis. Future studies should focus on large-scale clinical trials and real-world applications to validate these findings and facilitate the adoption of AI in clinical settings. Keywords: Prostate Cancer, Artificial Intelligent, Deep Machine Learning, MRI

A novel colonoscope with an extra-wide field of view increases polyp detection rate compared with standard colonoscope: Prospective model-based trial.

Background and study aims Colonoscopy, the gold standard for early detection of colorectal cancer, may miss polyps especially those hidden behind folds. This prospective study compared polyp detection and performance of a novel colonoscope with extra-wide field of view (EFOV) of 230 degrees (partially retrograde) to a standard colonoscope (SC, 170 degrees) in a colon model. Patients and methods A 3D printed colon model was used featuring 12 polyps placed throughout different colon segments, with several located on the proximal side of haustral folds. Endoscopists were instructed to identify polyps, first inserting the SC immediately followed by the EFOV device, and to place a snare to simulate a polypectomy. A standardized survey was used to record operator impressions. Results Twenty-nine experienced endoscopists participated in this study. On average, 5.3 vs 9.6 polyps were detected with the standard and EFOV colonoscopes, respectively ( P < 0.001). Five of 29 operators (17.2%) detected all 12 polyps with the EFOV device, whereas no operator detected all polyps with the SC. The success rate for snare placement was 100% for both endoscopes with similar times (mean of 14 vs 15 seconds for SC and EFOV, respectively). EFOV handling and optical performance were rated as equally good or better by all endoscopists. Conclusions Use of a colonoscope with novel optics significantly improved polyp detection compared with a standard colonoscope in this non-randomized model-based study, with favorable performance and usability ratings for the EFOV instrument. Clinical studies are needed to confirm these encouraging preliminary results.

Screening Model for Bladder Cancer Early Detection With Serum miRNAs Based on Machine Learning: A Mixed-Cohort Study Based on 16,189 Participants.

Early detection of bladder cancer (BCa) can have a positive impact on patients' prognosis. However, there is currently no widely accepted method for early screening of BCa. We aimed to develop an efficient, clinically applicable, and noninvasive method for the early screening of BCa by detecting specific serum miRNA levels. A mixed-cohort (including BCa, 12 different other cancers, benign disease patients, and health population) study was conducted using a sample size of 16,189. Five machine learning algorithms were utilized to develop screening models for BCa using the training dataset. The performance of the model was evaluated using receiver operating characteristic curve and decision curve analysis on the testing dataset, and subsequently, the model with the best predictive power was selected. Furthermore, the selected model's screening performance was evaluated using both the validation set and external set. The BCaS3miR model, utilizing only three serum miRNAs (miR-6087, miR-1343-3p, and miR-5100) and based on the KNN algorithm, is the superior screening model chosen for BCa. BCaS3miR consistently performed well in both the testing, validation, and external sets, exceeding 90% sensitivity and specificity levels. The area under the curve was 0.990 (95% CI: 0.984-0.991), 0.964 (95% CI: 0.936-0.984), and 0.917 (95% CI: 0.836-0.953) in the testing, validation, and external set. The subgroup analysis revealed that the BCaS3miR model demonstrated outstanding screening accuracy in various clinical subgroups of BCa. In addition, we developed a BCa screening scoring model (BCaSS) based on the levels of miR-1343-3p/miR-6087 and miR-5100/miR-6087. The screening effect of BCaSS is investigated and the findings indicate that it has predictability and distinct advantages. Using a mixed cohort with the largest known sample size to date, we have developed effective screening models for BCa, namely BCaS3miR and BCaSS. The models demonstrated remarkable screening accuracy, indicating potential for the early detection of BCa.

Assessing racial and ethnic disparities in time to surgery for patients with oral cavity cancers.

109 Background: Despite advances, challenges remain in the early detection and management of oral cancers. Studies have indicated that worse outcomes can be linked to disparities in sex, race, and ethnicity. Research indicates that despite a greater incidence of oral cancer among White patients, the five-year survival rate for Black patients is 52%, compared to 70% for their counterparts (1). While existing research focuses on racial and ethnic disparities in the time to diagnosis and prognosis, there is limited literature that discusses disparities in time to treat. As such, our study aims to evaluate potential racial, ethnic, and/or gender disparities in the time between diagnosis and surgery. Methods: We conducted a retrospective cohort study evaluating 185 patients with oral cancer at the University of Miami. Patient information was obtained from electronic medical records. We performed analysis of variance (ANOVA) with post hoc tests. Levene's test was used to assess the homogeneity of variances assumption. All statistical analyses were performed using SPSS version 26.0 (IBM Corp., Armonk, NY, USA), and p-values less than 0.05 were considered statistically significant. Results: The mean age at diagnosis was 68.50 years (SD =13.079 years), with the majority of patients (42%) diagnosed as Stage 1. Our cohort was 46% female and 54% male. 62% identified as White and Non-Hispanic/Latinx, 29% as White and Hispanic/Latinx, 6% as Black, and 3% as other race/ethnicities. We also assessed time to treat as a function of gender, race and ethnicity. We found there was no significant difference between men and women (x̄= 148 vs 136 days) in time to surgery. We also found no difference between Hispanic/Latinx patients and non Hispanic/Latinx patients (x̄= 163 vs 137 days). Although not statistically significant, there was a notable trend in the data that showed a longer time to surgery. Black patients had a much greater time to surgical intervention compared to White patients (x̄= 308 days vs 134 days) (Table). At the time of data cutoff, nearly 81% of patients were alive. Conclusions: In conclusion, despite lack of statistical significant, our study found higher trends to time to surgery between Hispanic/Latinx and non-Hispanic/Latinx, and even more so a much greater difference in time to surgery between Black and White patients. Further research is needed to elucidate these potential systematic, socio-economic or cultural barriers to ensure equitable access. 1. Surveillance, Epidemiology, and End Results (SEER) Program, National Cancer Institute. Days between diagnosis and surgery. Ethnicity Mean Standard Deviation N Black or African American 308.44 737.375 9 Other 20.33 .577 3 White 134.37 467.579 147 Total 142.08 481.112 159

Early detection of pancreatic cancer in the era of precision medicine.

Pancreatic ductal adenocarcinoma (PDAC) is the third leading cause of cancer-related mortality and it is often diagnosed at advanced stages due to non-specific clinical presentation. Disease detection at localized disease stage followed by surgical resection remains the only potentially curative treatment. In this era of precision medicine, a multifaceted approach to early detection of PDAC includes targeted screening in high-risk populations, serum biomarkers and "liquid biopsies", and artificial intelligence augmented tumor detection from radiologic examinations. In this review, we will review these emerging techniques in the early detection of PDAC.

MA08.11 Incidental Lung Nodule Program: A New Approach for Early Detection and Management of Lung Cancer

MA08.11 Incidental Lung Nodule Program: A New Approach for Early Detection and Management of Lung Cancer

In vivo self-assembled bispecific fluorescence probe for early detection of bladder cancer and metastasis

In vivo self-assembled bispecific fluorescence probe for early detection of bladder cancer and metastasis

The Next Frontier for Colorectal Cancer Screening: Blood-Based Tests.

Colorectal cancer is associated with substantial morbidity and mortality worldwide. Detection of early colorectal cancer is possible through approved screening tests but overall adherence is quite low. Implementation of effective noninvasive options could increase screening uptake and prevent a significant number of deaths. Noninvasive early cancer detection can potentially be achieved using a liquid biopsy. In this issue of Cancer Research, Cao and colleagues report on a novel multidimensional fragmentomics assay, named FRAGTECT, for colorectal cancer detection in circulating cell-free DNA with promising results. See related article by Cao et al., p. 3286.

S558 Test Performance and Lesion Characteristics in a Large Clinical Validation Study of a Blood-Based Screening Test for the Early Detection of Colorectal Cancer

S558 Test Performance and Lesion Characteristics in a Large Clinical Validation Study of a Blood-Based Screening Test for the Early Detection of Colorectal Cancer

Serum human epididymis Protein-4 outperforms conventional biomarkers in the early detection of non-small cell lung cancer

Serum human epididymis Protein-4 outperforms conventional biomarkers in the early detection of non-small cell lung cancer

EP.04C.07 A Novel NGS Assay Using cfDNA Methylation for Early Detection of Lung Cancer in a Screen-Eligible Population

EP.04C.07 A Novel NGS Assay Using cfDNA Methylation for Early Detection of Lung Cancer in a Screen-Eligible Population

Artificial Intelligence Algorithm for Subclinical Breast Cancer Detection

Early breast cancer detection is associated with lower morbidity and mortality. To examine whether a commercial artificial intelligence (AI) algorithm for breast cancer detection could estimate the development of future cancer. This retrospective cohort study of 116 495 women aged 50 to 69 years with no prior history of breast cancer before they underwent at least 3 consecutive biennial screening examinations used scores from an AI algorithm (INSIGHT MMG, version 1.1.7.2; Lunit Inc; used September 28, 2022, to April 5, 2023) for breast cancer detection and screening data from multiple, consecutive rounds of mammography performed from September 13, 2004, to December 21, 2018, at 9 breast centers in Norway. The statistical analyses were performed from September 2023 to August 2024. Artificial intelligence algorithm score indicating suspicion for the presence of breast cancer. The algorithm provided a continuous cancer detection score for each examination ranging from 0 to 100, with increasing values indicating a higher likelihood of cancer being present on the current mammogram. Maximum AI algorithm score for cancer detection and absolute difference in score among breasts of women developing screening-detected cancer, women with interval cancer, and women who screened negative. The mean (SD) age at the first study round was 58.5 (4.5) years for 1265 women with screening-detected cancer in the third round, 57.4 (4.6) years for 342 women with interval cancer after 3 negative screening rounds, and 56.4 (4.9) years for 116 495 women without breast cancer all 3 screening rounds. The mean (SD) absolute differences in AI scores among breasts of women developing screening-detected cancer were 21.3 (28.1) at the first study round, 30.7 (32.5) at the second study round, and 79.0 (28.9) at the third study round. The mean (SD) differences prior to interval cancer were 19.7 (27.0) at the first study round, 21.0 (27.7) at the second study round, and 34.0 (33.6) at the third study round. The mean (SD) differences among women who did not develop breast cancer were 9.9 (17.5) at the first study round, 9.6 (17.4) at the second study round, and 9.3 (17.3) at the third study round. Areas under the receiver operating characteristic curve for the absolute difference were 0.63 (95% CI, 0.61-0.65) at the first study round, 0.72 (95% CI, 0.71-0.74) at the second study round, and 0.96 (95% CI, 0.95-0.96) at the third study round for screening-detected cancer and 0.64 (95% CI, 0.61-0.67) at the first study round, 0.65 (95% CI, 0.62-0.68) at the second study round, and 0.77 (95% CI, 0.74-0.79) at the third study round for interval cancers. In this retrospective cohort study of women undergoing screening mammography, mean absolute AI scores were higher for breasts developing vs not developing cancer 4 to 6 years before their eventual detection. These findings suggest that commercial AI algorithms developed for breast cancer detection may identify women at high risk of a future breast cancer, offering a pathway for personalized screening approaches that can lead to earlier cancer diagnosis.

EP.04B.05 Discordance Between Nodule and Lung Cancer Location in Early Lung Cancer Detection Cohorts

EP.04B.05 Discordance Between Nodule and Lung Cancer Location in Early Lung Cancer Detection Cohorts

Enhanced breast tumor localization with DRA antenna backscattering and GPR algorithm in microwave imaging

With the rapid rate of increasing breast cancer cases across the globe and restrictions of existing early stage detection techniques, microwave imaging based cancer and malignant cell diagnosing methodology is finding its way. Microwave imaging technology has higher accuracy of detection in the variation of tissue properties. In this article, a dielectric resonator based wide band antenna is designed and investigated for microwave imaging (MWI) of early breast cancer detection due to its improved accuracy. The designed antenna has compact size, broad frequency spectrum, high gain and broadside radiation characteristics. To attain the wider bandwidth from (6.5 GHz–12.5 GHz), two rectangular shaped dielectric slabs are used. Wide band microwave frequency band supports the high resolution images. A C-shaped defected ground structure is used to tune the impedance matching. Dielectric resonator based antenna has higher gain and reduces the squints in antenna impedance. Two-asymmetrical dielectric slabs contribute in the excitation of different resonating modes which in turn enhance the bandwidth. It also contributes to excite multiple resonance mode that in turn enhance the bandwidth. Antenna performance is first numerically and experimentally verified in free space. Then, simulation based performance is examined for the microwave imaging. Numerical phantom with equivalent tissue properties is designed with and without the tumor. Unhealthy cells have higher water percentage and larger dielectric constant as compared to healthy cells. It causes the different in the back scattered signal of antenna, which is analyzed to diagnose the tumor or cancer. Here, a maximum deviation of 16 dB is observed in the backscattered signal. Furthermore, surface of the breast tissue is scanned along the x-axis and y-axis at different angels. The collected signals are used to reconstruct the microwave image of the interior. Mean value data of backscattered signals is used to locate the existence of unhealthy cells and GPR algorithm is used to calculate the depth of a 4 mm tumor in breast tissue. Proposed structure has shown efficient performance for microwave imaging.

Exploring factors associated with uptake of breast cancer screening among a subset of women in a South African metropolitan area: research to inform public health interventions

Aim Breast cancer represents a significant global health concern and remains a leading cause of cancer-related mortality, particularly among women in developing countries. Despite the South African Department of Health recommending early breast cancer detection to improve overall survival rates and enhance patients’ quality of life, there is lack of awareness among women regarding the importance of regular breast cancer screening and available options. This study investigates the factors associated with breast cancer screening uptake among a subset of women attending public health services in a South African metropolitan area. Methods A cross-sectional survey of adult women attending primary health care services at a community health centre was conducted in February-March 2020. Data on sociodemographics, health beliefs, access to information, and breast cancer screening uptake were subjected to descriptive and logistic regression analyses. Results Out of 252 women, 48.0% were older than 30 years, 69.4% were unemployed, and 56.0% self-reported breast cancer screening non-uptake. After adjusting for other variables, marital status, employment status, home language, and perceived breast cancer screening benefits were significantly associated with breast cancer screening uptake. Married women were twice (AoR: 2.3; CI: 1.19-4.58) as likely to self-report breast cancer screening than their unmarried counterparts. Employed women were almost thrice (AoR: 2.6; CI: 1.41-4.83) as likely to self-report breast cancer screening uptake relative to unemployed women. Women whose home language was Sesotho were almost twice (AoR: 1.9; CI: 1.05-3.43) as likely to self-report breast cancer screening than women reporting other home languages. Self-reported uptake of breast cancer screening was also more likely with every unit increase in perceived benefit scores (AoR: 1.1; CI: 1.02-1.16). Conclusions A significant proportion of women were in the age range where breast cancer screening is typically recommended, with more than half self-reporting breast cancer screening non-uptake. Results highlight the necessity of addressing socioeconomic, cultural and individual perceptions to improve breast cancer screening rates and promote early detection.

Structured multidisciplinary decision making and survival in the prospective observational Detecting Early Lung Cancer (DELUGE) in the Mississippi Delta cohort.

35 Background: Lung cancer screening (LCS) and incidental lung nodule programs (LNP) are associated with improved lung cancer survival (OS); multidisciplinary care (MDC) enhances guideline concordance. We evaluated the outcomes of patients with LCS- or LNP-detected lung cancer who received care with, versus without, structured program-based multidisciplinary decision-making within a large, tri-state regional community healthcare system. Methods: Prospective observational study of patients enrolled into LCS and LNP in the DELUGE cohort. Data on patients diagnosed with lung cancer was linked to a prospective institutional MDC database to classify care delivery as MDC or non-MDC. We compared patient demographic and clinical characteristics, including stage distribution with chi-square tests, OS with the log-rank test, and used proportional hazards models for death with hazard ratios adjusted for age, sex, race, smoking history, insurance status and histologic type. Results: From 2015 to 2023, 504 patients were diagnosed through LCS, 195 (39%) had MDC; 1714 through LNP, 712 (42%) had MDC; 2,218 in total through both early detection programs, 907 (41%) had MDC (Table). 89% of MDC recipients resided in metropolitan areas, compared to 60% of non-MDC patients (p&lt;0.0001). There was a median of 1 (Interquartile range: 1-2) comorbidity across all subsets. Adenocarcinoma was more common among the MDC subsets: 46% v 35% LCS, 56% v 43% LNP; small cell lung cancer was more common among the non-MDC subsets: 11% v 18% LCS, 9% v 14% LNP (p=0.0005). Clinical Stage I was more frequent among MDC v non-MDC subsets for LCS, LNP, and both (all p&lt;0.001, Table). OS was significantly higher in MDC v non-MDC (p&lt;0.0001), aggregate 3-year OS was 75% v 55% (LCS), 60% v 42% (LNP), 63% v 45% (both); and 5-year OS was 65% v 51% (LCS), 49% v 34% (LNP), 52% v 37% (both), respectively. Sensitivity analysis restricting the cohorts to metropolitan patients only or to patients who received care within the institutions where the MDC was active yielded similar results. Conclusions: Structured multidisciplinary decision-making was strongly associated with significant stage re-distribution and better OS among patients diagnosed with lung cancer through screening or lung nodule programs. The MDC structure needs to be embedded within early lung cancer detection programs for optimal results. Variable LCS: MDC? LNP: MDC? Both Programs: MDC? Yes; n=195 No; n=309 Yes; n=712 No; n=1002 Yes; n=907 No; n=1311 Median age, years 68 68 69 71 69 70 Female, % 49 48 51 50 50 49 Black race, % 13 20 30 22 26 22 Medicaid/uninsured, % 2 5 7 7 6 7 Actively smoking, % 70 74 46 52 51 57 Stage I v IV, % 66 v 11 47 v 21 52 v 19 33 v 34 55 v 17 36 v 31 Surgical resection, % 52 32 45 20 46 23 Adjusted HR for death Ref 1.9 (1.3, 2.7) Ref 1.6 (1.4, 1.9) Ref 1.7 (1.4, 1.9)

P3.06F.03 Unbiased Proteomics and Multi-Omics Discovery of a Peripheral Blood-Based Classifier for Early Lung Cancer Detection

P3.06F.03 Unbiased Proteomics and Multi-Omics Discovery of a Peripheral Blood-Based Classifier for Early Lung Cancer Detection

Assessing capacity for hereditary cancer risk assessment in metro Chicago.

307 Background: Since 2005, the United States Preventive Services Task Force (USPSTF) recommends that primary care (PC) clinicians identify and refer adults with a high-risk cancer family history to cancer genetic services (CGS), yet hereditary cancer risk assessment (HCRA) continues to be underutilized in PC. A population-based approach to HCRA in PC settings can expand access to cancer prevention and early detection to vulnerable populations and reduce disparities in cancer outcomes. The Chicago Cancer Screening and Testing Access Coalition (CCSTAC) is developing strategies to integrate HCRA in PC settings. We conducted an environmental scan to assess current capacity to deliver 1) HRCA in Federally Qualified Health Centers (FQHCs) and 2) follow-up CGS for high risk patients. Methods: In 2023, we administered two surveys to assess HCRA practices in Metro Chicago. We invited 22 FQHCs to complete a 21-item survey of current practices, readiness to implement, and barriers to HCRA. We also invited 21 CGS sites to complete a 25-item survey to understand staffing, utilization, referral patterns, and barriers to access. Surveys were administered electronically via Qualtrics. We conducted a descriptive analysis of survey data. Results: Twenty FQHCs (91%) completed the survey on HCRA in PC; 85% reported familiarity with USPSTF guidelines for HCRA. The majority (75%) reported no plans to implement HCRA. Clinic workflow, paucity of follow-up resources, cost, lack of provider knowledge, and absence of HCRA as a mandatory performance measure were cited as the most significant barriers to implementation. Genetic counselors (GCs) from 19 CGS hospital-based sites (90%) completed the survey on availability of genetic services. CGS were most often located in a cancer center (47%); no CGS were located in PC. Most referrals originated from oncology, surgery, or gastroenterology, with the lowest rate of referrals from community-based PC. Nearly half of CGS clinics (47%) reported capacity for at least a small increase in patient volume. Wait-time at the majority of CGS clinics was less than a month. GCs cited patient logistics (e.g. transportation, time off work), lack of referral information from providers, and systemic racism as significant barriers in patient access to CGS. Conclusions: After almost 20 years of evidence-based guidelines, use of HCRA is still very limited in the community-based PC setting. Our findings point to a need to bridge the gap between hospital-based CGS and FQHCs to ensure patients in vulnerable communities have access to guideline-concordant cancer prevention and screening strategies. Based on these findings, CCSTAC is designing strategies to integrate HCRA in primary care at FQHCs focused on provider education, well-designed clinic workflows, connections with existing CGS, and advocacy to align UDS and HEDIS measures with USPSTF guidelines.