Sensitivity For Cancer Detection Research Articles

Advanced neural network systems for analysis of blood-based biomarkers in metastatic disease detection

Neural network systems have become the gold provocative standard in blood-based biomarker analysis for metastatic disease diagnosis, providing higher sensitivity and specificity for early cancer detection. The lexical method also showed that recent developments in artificial intelligence and machine learning technologies allow for the complex interpretation of various types of biomarkers, such as circulating tumor cells, cell-free DNA, microRNAs, and proteins. Many biochemical patterns of biomarkers could be easily handled through sophisticated algorithms and depending on these active approaches in diagnosing metastatic outcomes more accurately at earlier stages. Literature analysis involved a targeted selection of scientific peer-reviewed articles dedicated to neural network utilization in blood-based biomarker screening for cancer. Doing a systematic evaluation critically assessed methodologies associated with artificial intelligence, types of biomarkers, methods of detecting biomarkers, and clinical validation strategies. Filtering was done based on several factors including the identification of new architectures for neural networks, new biomarker analysis methods, and clinical applications primarily focusing on the early detection of metastatic diseases. Analysis of reviewed studies showed a high diagnostic accuracy when neural network systems are used for biomarker analysis. Volumes in Cancer Computational Biology Vol reported that the machine learning models have achieved different sensitivity rates of over 90% including the ability to detect early-stage metastatic disease. The use of several categories of biomarkers and application-improved neural networks for their evaluation showed that the combined use of markers yields better diagnostic results than using single biomarkers. Neural network systems have significant potential to transform metastatic disease diagnosis using circulating biomarker detection. Using sophisticated computational models biomarker patterns are analyzed in detail and the diagnosis is made earlier, thus contributing to better treatment outcomes. Some complexities that are potential barriers include enforcing analysis protocols as standards, correcting and calibrating, and embracing routine technological practice. High-end artificial neural networks are the new frontier in blood-based biomarker analysis of metastatic disease diagnosis. Applying AI within the classic biomarker detection approach increases diagnostic reliability, detection timeliness, and the overall outlook for patient treatment. The advancement in neural network architectures as well as biomarker analysis techniques provides for further improvement in cancer diagnostics in the future.

Artificial Intelligence (AI) and Liquid Biopsy Transforming Early Detection of Liver Metastases in Gastrointestinal Cancers.

Liver metastases from Gastrointestinal (GI) cancers present significant challenges in oncology, often signaling poor prognosis. Traditional detection methods like imaging and tissue biopsies have limitations in sensitivity, specificity, and tumor heterogeneity represen-tation. The advent of artificial intelligence (AI) in healthcare, driven by advancements in ma-chine learning, algorithms, and data science, offers a promising frontier for early detection and management of liver metastases. This review explores the integration of AI and liquid biopsy technologies as transformative tools in the proactive detection of liver metastases aris-ing from GI malignancies. Liquid biopsy, a non-invasive method, analyzes circulating tumor cells (CTCs), cell-free DNA (cfDNA), and circulating tumor DNA (ctDNA) in bodily fluids. It provides a compre-hensive overview of tumor heterogeneity and enables real-time monitoring of tumor evolu-tion and treatment response. Despite its advantages, liquid biopsy faces challenges such as low sensitivity for early-stage metastases, reduced detectability due to liver filtration, and technical limitations. AI enhances the potential of liquid biopsies by improving diagnostic accuracy through ad-vanced algorithms like Convolutional Neural Networks (CNNs) and Natural Language Pro-cessing (NLP). These AI models analyze complex biomedical data, offering higher sensitivity and specificity in cancer detection. The synergy between AI and liquid biopsies promises early detection, better disease monitoring, and personalized treatment strategies. This review underscores the significant advancements AI and liquid biopsy technologies bring to oncological precision medicine, particularly in improving overall survival (OS) and disease-free survival (DFS) for patients with GI cancer metastases. As we transition into the era of precision medicine, the integration of these technologies holds the potential to redefine cancer care and patient management.

The Role of Diffusion-Weighted Magnetic Resonance Imaging in Differentiating Benign and Malignant Breast Lesions: A Comprehensive Review

Background: Breast magnetic resonance imaging (MRI) has emerged as a vital tool for detecting and characterizing breast lesions, particularly in differentiating benign from malignant tumors. This review explores the role of diffusion-weighted imaging (DWI) in breast MRI, highlighting its advantages over conventional imaging techniques. Methods: A systematic literature search was conducted across multiple databases, including PubMed and Scopus, to gather studies assessing DWI's sensitivity and specificity in breast cancer detection. Results: The findings indicate that DWI provides high diagnostic accuracy, with a pooled sensitivity of 86.5% and specificity of 83.5%, comparable to dynamic contrast-enhanced MRI (DCE-MRI). Notably, utilizing apparent diffusion coefficient (ADC) thresholds can minimize unnecessary biopsies without compromising sensitivity. However, challenges remain in achieving standardization in DWI protocols, particularly regarding b-value selection and imaging techniques, which impact ADC accuracy. Advanced methodologies such as intravoxel incoherent motion (IVIM) and diffusion kurtosis imaging (DKI) have shown promise in enhancing diagnostic capabilities by providing deeper insights into tissue microstructure. Conclusions: This review underscores the importance of ongoing standardization efforts and collaboration among institutions to improve the reliability and applicability of DWI in clinical settings. In conclusion, DWI represents a transformative approach to breast cancer management, allowing for enhanced differentiation of lesions while reducing the need for invasive procedures. Further research and standardization are essential to optimize its use in routine clinical practice.

Non-invasive multiple cancer screening using trained detection canines and artificial intelligence: a prospective double-blind study

The specificity and sensitivity of a simple non-invasive multi-cancer screening method in detecting breast, lung, prostate, and colorectal cancer in breath samples were evaluated in a double-blind study. Breath samples of 1386 participants (59.7% males, median age 56.0 years) who underwent screening for cancer using gold-standard screening methods, or a biopsy for a suspected malignancy were collected. The samples were analyzed using a bio-hybrid platform comprising trained detection canines and artificial intelligence tools. According to cancer screening/biopsy results, 1048 (75.6%) were negative for cancer and 338 (24.4%) were positive. Among the 338 positive samples, 261 (77.2%) were positive for one of the four cancer types that the bio-hybrid platform was trained to detect, with an overall sensitivity and specificity of 93.9% (95% confidence interval [CI] 90.3-96.2%) and 94.3% (95% CI 92.7%-95.5%), respectively. The sensitivity of each cancer type was similar; breast: 95.0% (95% CI 87.8-98.0%), lung: 95.0% (95% CI 87.8-98.0%), colorectal: 90.0% (95% CI 74.4-96.5%), prostate: 93.0% (95% CI 84.6-97.0%). The sensitivity of 14 other malignant tumors that the bio-hybrid platform was not trained to detect, but identified, was 81.8% (95% CI 71.8%-88.8%). Early cancer (0–2) detection sensitivity was 94.8% (95% CI 91.0%-97.1%). This bio-hybrid multi-cancer screening platform demonstrated high sensitivity and specificity and enables early-stage cancer detection.

Abstract A057: Cancer-induced epigenomic changes in circulating immune cells enable breast, colorectal and lung cancer detection

Abstract Background: Epigenomic changes via the measurement of 5-hydroxymethylation cytosine (5hmC) in DNA have been shown to enable detection in a variety of cancers including pancreatic and ovarian cancer in plasma-derived cell-free DNA (cfDNA). Here, we investigated whether blood-based 5hmC profiling of genomic DNA (gDNA) derived from cells of the buffy coat (BC) possess signals that enable the detection of breast, colorectal and lung cancers. Method: Blood samples collected in Streck tubes from cancer patients or non-cancer controls were processed to obtain buffy coat and plasma samples. gDNA and cfDNA was isolated from buffy coat and plasma, respectively. gDNA or cfDNA was then subjected to 5hmC enrichment by click chemistry followed by preparation of sequencing libraries. Sequencing was performed to obtain genome-wide 5hmC profiles of buffy coat gDNA and cfDNA samples. Results: Differential hydroxymethylation analysis of BC gDNA revealed increased 5hmC over genes associated with myeloid lineage in breast, colorectal and lung cancers relative to non-cancer controls. Flow cytometry analysis confirmed increases in myeloid cells in cancer samples relative to non-cancer controls. 5hmC patterns in BC gDNA showed a larger number of cancer-associated changes compared to cfDNA, in particular this was found in early-stage disease. 5hmC features from BC gDNA or plasma-derived cfDNA were used to build prediction models that yielded cancer classification with overall sensitivity of 51.3% and 52.6% at 98% specificity, for BC or cfDNA, respectively. For early-stage cancer, the sensitivity at 98% specificity was 46.5% for the BC and 28.2% for cfDNA model. A final prediction model incorporating features from both tissue sources achieved superior classification (65.8% sensitivity) at 98% specificity, compared with using features from either tissue source alone, particularly for early-stage cancer which achieved 53.5% sensitivity of cancer detection. Conclusion: Our results demonstrate that cancer impacts the 5hmC profiles of buffy coat cells which can be leveraged for cancer detection using blood. Citation Format: Gulfem D Guler, Giuliana P Mognol, Yuhong Ning, Carolina Fraire, David Haan, Michael Kesling, Roger Malta, Melissa Peters, Anna Bergamaschi, Tierney Phillips, Shimul Chowdhury, Wayne Volkmuth, Samuel Levy. Cancer-induced epigenomic changes in circulating immune cells enable breast, colorectal and lung cancer detection [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr A057.

Possibility of determining high quantitative fecal occult blood on stool surface using hyperspectral imaging

BackgroundFecal immunochemical tests are commonly performed for colorectal cancer screening. Instant fecal occult blood measurement in toilet bowel movements would improve convenience. Hyperspectral imaging (HSI) enables the nondestructive evaluation of materials that are difficult to assess visually. This study aimed to determine whether HSI could be used to identify fecal occult blood on stool surfaces.MethodsThe study included 100 patients who underwent colonoscopy, divided into groups A and B (50 patients, each) for creating a discriminant algorithm and validating the accuracy of the algorithm, respectively. In group A, 100 areas were randomly selected from the stool surface, and the fecal occult blood quantitative values were measured and photographed using a hyperspectral camera (cutoff: > 400 ng/mL). A discriminant algorithm image was created to extract spectral feature differences obtained from HSI via machine learning. In group B, 250 random areas were evaluated and compared to fecal occult blood quantitative values, measuring sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV).ResultsGroups A and B comprised 28 and 26 patients with cancer, respectively. Cancer detection sensitivity at the 400 ng/mL cutoff was 67.9% and 42.3% in groups A and B, respectively. The discriminant algorithm image exhibited high accuracy in group A (sensitivity; 77.1%, specificity; 96.9%, accuracy; 90.0%, PPV; 93.1%, NPV; 88.7%). In group B, the sensitivity, specificity, accuracy, PPV, and NPV were 83.3, 92.9, 90.8, 76.3, and 95.3%, respectively.ConclusionHSI can effectively discriminate high quantitative fecal occult blood, highlighting its potential for improved colorectal cancer screening.

Abbreviated Breast MRI as a Supplement to Mammography in Family Risk History of Breast Cancer within the Croatian National Breast Screening Program.

To evaluate the diagnostic performance of abbreviated breast MRI compared with mammography in women with a family history of breast cancer included in the Croatian National Breast Screening Program. 178 women with a family history of breast cancer aged 50 to 69 underwent abbreviated breast MRI and mammography. Radiological findings for each method were categorized according to the BI-RADS classification. The gold standard for assessing the diagnostic accuracy of breast MRI and mammography, in terms of suspicious BI-RADS 4 and BI-RADS 5 findings, was the histopathological diagnosis. Performance measures, including cancer detection rates, specificity, sensitivity, and positive and negative predictive values, were calculated for both imaging methods. Twelve new cases of breast cancer were detected, with seven (58.3%) identified only by abbreviated breast MRI, four (33.3%) detected by both mammography and breast MRI, and one (8.3%) diagnosed only by mammography. Diagnostic accuracy parameters for abbreviated breast MRI were 91.67% sensitivity, 94.58% specificity, 55.0% positive predictive value (PPV), and 99.37% negative predictive value (NPV), while for mammography, the corresponding values were 41.67%, 96.39%, 45.46%, and 95.81%, respectively. Abbreviated breast MRI is a useful supplement to screening mammography in women with a family history of breast cancer. Considering the results of the conducted research, it is recommended to assess whether women with a family history of breast cancer have an increased risk and subsequently provide annual abbreviated breast MRI in addition to mammography for early detection of breast cancer.

Multifunctional gold nanoparticles for cancer theranostics.

The diagnosis and treatment of cancer can often be challenging requiring more attractive options. Some types of cancers are more aggressive than others and symptoms for many cancers are subtle, especially in the early stages. Nanotechnology provides high sensitivity, specificity and multimodal capability for cancer detection, treatment and monitoring. In particular, metal nanoparticles (NPs) such as gold nanoparticles (AuNPs) are attractive nanosystems for researchers interested in bioimaging and therapy. The size, shape and surface of AuNPs can be modified for improving targeting and accumulation in cancer cells, for example through introduction of ligands and surface charge. The interactions of AuNPs with electromagnetic radiation (e.g., visible-near-infrared, X-rays) can be used for photothermal therapy and radiation therapy, through heat generated from light absorption and emission of Auger electrons, respectively. The subsequent expansion and high X-ray attenuation from AuNPs can be used for enhancing contrast for tumor detection (e.g., using photoacoustic, computed tomography imaging). Multi-functionality can be further extended through covalent/non-covalent functionalization, for loading additional imaging/therapeutic molecules for combination therapy and multimodal imaging. In order to cover the important aspects for designing and using AuNPs for cancer theranostics, this review focuses on the synthesis, functionalization and characterization methods that are important for AuNPs, and presents their unique properties and different applications in cancer theranostics.

Contemporary Update on Clinical and Experimental Prostate Cancer Biomarkers: A Multi-Omics-Focused Approach to Detection and Risk Stratification.

Prostate cancer remains a significant health challenge, being the most prevalent non-cutaneous cancer in men worldwide. This review discusses the critical advancements in biomarker discovery using single-omics and multi-omics approaches. Multi-omics, integrating genomic, transcriptomic, proteomic, metabolomic, and epigenomic data, offers a comprehensive understanding of the molecular heterogeneity of prostate cancer, leading to the identification of novel biomarkers and therapeutic targets. This holistic approach not only enhances the specificity and sensitivity of prostate cancer detection but also supports the development of personalized treatment strategies. Key studies highlighted include the identification of novel genes, genetic mutations, peptides, metabolites, and potential biomarkers through multi-omics analyses, which have shown promise in improving prostate cancer management. The integration of multi-omics in clinical practice can potentially revolutionize prostate cancer prognosis and treatment, paving the way for precision medicine. This review underscores the importance of continued research and the application of multi-omics to overcome current challenges in prostate cancer diagnosis and therapy.

Research advances in tumor diagnosis and early detection

This review explores recent advances in tumor diagnosis and early detection, focusing on cutting-edge developments in molecular diagnostic technologies, imaging techniques, and the integration of multi-omics data. Current tumor diagnostic methods have limitations in terms of sensitivity and specificity, particularly for early tumor detection. However, with continuous progress in research and emerging technologies, especially the advent of liquid biopsy, which enables the detection of circulating tumor DNA (ctDNA), exosomes, and tumor-educated platelets (TEPs), the sensitivity and accuracy of early cancer detection have significantly improved. Moreover, the combined application of artificial intelligence and high-resolution imaging technology has enhanced the precision of diagnosis. Despite these advances, challenges, such as the high cost of technology and difficulties in data integration, continue to impede widespread clinical adoption. Therefore, I believe that future research should prioritize the innovation of these technologies to improve their applicability for early detection across various cancer types, ultimately contributing to the advancement of personalized therapy.

Advances in Oral Exfoliative Cytology: From Cancer Diagnosis to Systemic Disease Detection.

Oral exfoliative cytology has emerged as a valuable tool in the early detection of oral cancer and other systemic diseases. This review comprehensively examines the current applications and recent advancements in oral exfoliative cytology techniques. We analyzed published literature from the past decade, focusing on methodological improvements, diagnostic accuracy, and emerging applications. Key findings include: (1) Enhanced cell collection and preparation methods have significantly improved sample quality and diagnostic reliability. (2) Integration of molecular markers and DNA analysis with traditional cytomorphological assessment has increased diagnostic sensitivity and specificity for oral cancer detection. (3) Novel applications in systemic disease detection, including diabetes and iron overload disorders, demonstrate the expanding utility of this technique. (4) Computer-assisted analysis and deep learning algorithms show promise in improving diagnostic accuracy and efficiency. Despite these advancements, challenges remain in standardization and widespread clinical implementation. This review provides a critical evaluation of oral exfoliative cytology's current status and future potential in oral and systemic disease diagnosis.

Artificial intelligence software for analysing chest X-ray images to identify suspected lung cancer: an evidence synthesis early value assessment.

Lung cancer is one of the most common types of cancer in the United Kingdom. It is often diagnosed late. The 5-year survival rate for lung cancer is below 10%. Early diagnosis may improve survival. Software that has an artificial intelligence-developed algorithm might be useful in assisting with the identification of suspected lung cancer. This review sought to identify evidence on adjunct artificial intelligence software for analysing chest X-rays for suspected lung cancer, and to develop a conceptual cost-effectiveness model to inform discussion of what would be required to develop a fully executable cost-effectiveness model for future economic evaluation. The data sources were MEDLINE All, EMBASE, Cochrane Database of Systematic Reviews, Cochrane CENTRAL, Epistemonikos, ACM Digital Library, World Health Organization International Clinical Trials Registry Platform, clinical experts, Tufts Cost-Effectiveness Analysis Registry, company submissions and clinical experts. Searches were conducted from 25 November 2022 to 18 January 2023. Rapid evidence synthesis methods were employed. Data from companies were scrutinised. The eligibility criteria were (1) primary care populations referred for chest X-ray due to symptoms suggestive of lung cancer or reasons unrelated to lung cancer; (2) study designs that compared radiology specialist assessing chest X-ray with adjunct artificial intelligence software versus radiology specialists alone and (3) outcomes relating to test accuracy, practical implications of using artificial intelligence software and patient-related outcomes. A conceptual decision-analytic model was developed to inform a potential full cost-effectiveness evaluation of adjunct artificial intelligence software for analysing chest X-ray images to identify suspected lung cancer. None of the studies identified in the searches or submitted by the companies met the inclusion criteria of the review. Contextual information from six studies that did not meet the inclusion criteria provided some evidence that sensitivity for lung cancer detection (but not nodule detection) might be higher when chest X-rays are interpreted by radiology specialists in combination with artificial intelligence software than when they are interpreted by radiology specialists alone. No significant differences were observed for specificity, positive predictive value or number of cancers detected. None of the six studies provided evidence on the clinical effectiveness of adjunct artificial intelligence software. The conceptual model highlighted a paucity of input data along the course of the diagnostic pathway and identified key assumptions required for evidence linkage. This review employed rapid evidence synthesis methods. This included only one reviewer conducting all elements of the review, and targeted searches that were conducted in English only. No eligible studies were identified. There is currently no evidence applicable to this review on the use of adjunct artificial intelligence software for the detection of suspected lung cancer on chest X-ray in either people referred from primary care with symptoms of lung cancer or people referred from primary care for other reasons. Future research is required to understand the accuracy of adjunct artificial intelligence software to detect lung nodules and cancers, as well as its impact on clinical decision-making and patient outcomes. Research generating key input parameters for the conceptual model will enable refinement of the model structure, and conversion to a full working model, to analyse the cost-effectiveness of artificial intelligence software for this indication. This study is registered as PROSPERO CRD42023384164. This award was funded by the National Institute for Health and Care Research (NIHR) Evidence Synthesis programme (NIHR award ref: NIHR135755) and is published in full in Health Technology Assessment; Vol. 28, No. 50. See the NIHR Funding and Awards website for further award information.

Introduction of one-view tomosynthesis in population-based mammography screening: Impact on detection rate, interval cancer rate and false-positive rate.

To assess performance endpoints of a combination of digital breast tomosynthesis (DBT) and full-field digital mammography (FFDM) compared with FFDM only in breast cancer screening. This was a prospective population-based screening study, including eligible (50-69 years) women attending the Capital Region Mammography Screening Program in Denmark. All attending women were offered FFDM. A subgroup was consecutively allocated to a screening room with DBT. All FFDM and DBT underwent independent double reading, and all women were followed up for 2 years after screening date or until next screening date, whichever came first. 6353 DBT + FFDM and 395 835 FFDM were included in the analysis and were undertaken in 196 267 women in the period from 1 November 2012 to 12 December 2018. Addition of DBT increased sensitivity: 89.9% (95% confidence interval (CI): 81.0-95.5) for DBT + FFDM and 70.1% (95% CI: 68.6-71.6) for FFDM only, p < 0.001. Specificity remained similar: 98.2% (95% CI: 97.9-98.5) for DBT + FFDM and 98.3% (95% CI: 98.2-98.3) for FFDM only, p = 0.9. Screen-detected cancer rate increased statistically significantly: 11.18/1000 for DBT + FFDM and 6.49/1000 for FFDM only, p < 0.001. False-positive rate was unchanged: 1.75% for DBT + FFDM and 1.73% for FFDM only, p = 0.9. Positive predictive value for recall was 39.0% (95% CI: 31.9-46.5) for DBT + FFDM and 27.3% (95% CI: 26.4-28.2), for FFDM only, p < 0.0005. The interval cancer rate decreased: 1.26/1000 for DBT + FFDM and 2.76/1000 for FFDM only, p = 0.02. DBT + FFDM yielded a statistically significant increase in cancer detection and program sensitivity.

An attention-based neural network for lung cancer classification and gradient in MRI

Accurate lung cancer classification in magnetic resonance imaging (MRI) remains challenging due to the difficulty in detecting cancerous patterns. In response, this study introduces an attention-based VGG19 neural network for enhanced classification performance. Leveraging the VGG19 architecture's deep learning capabilities, our model incorporates attention mechanisms to selectively emphasize salient features during training. The attention-based approach addresses the challenge of discerning subtle patterns indicative of malignancy, significantly improving classification accuracy. We train and evaluate the model on a diverse dataset, ensuring its capacity to generalize across various patient cases. The attention mechanism proves effective in prioritizing critical regions within MRI scans, enhancing sensitivity and specificity in lung cancer detection. Additionally, we employ gradient analysis to interpret the decision-making process, providing valuable insights into influential features. Results demonstrate the proposed model's superiority over baseline approaches, showcasing its efficacy in inaccurate lung cancer classification. The attention-based VGG19 neural network not only advances classification capabilities but also offers interpretability crucial for gaining trust in automated diagnostic systems. This research contributes a robust solution to a pressing medical imaging challenge, holding promise for practical implementation in clinical settings to support radiologists in timely and accurate lung cancer diagnosis.

Improvement of Breast Cancer Detection Using Dual-Layer Spectral CT.

This study aimed to investigate the diagnostic performance of breast mass detection on monoenergetic image data at 40 keV (MonoE40) and on iodine maps (IM) compared with conventional image data (CI). In this prospective single-center case-control study, 50 breast cancer patients were examined using contrast-enhanced dual-layer spectral CT. For qualitative and quantitative comparison of MonoE40 and IM with CI image data, four blinded, independent readers assessed 300 randomized single slices (two slices for each imaging type per case) with or without cancerous lesions for the presence of a breast mass. Detection sensitivity and specificity were calculated and readers rated their subjective diagnostic certainty. For statistical analysis of sensitivity and specificity, a paired t-test and ANOVA were used (significance level p = 0.05). A total of 50 female patients (median age 51 years, range 28-83 years) participated. IM had the highest overall scores in sensitivity and specificity for breast cancer detection, with 0.97 ± 0.06 and 0.95 ± 0.07, respectively, compared with 0.90 ± 0.04 and 0.92 ± 0.06 in CI. MonoE40 yielded a sensitivity of 0.96 ± 0.02 and specificity of 0.94 ± 0.08. All differences in sensitivity and specificity between MonoE or IM and CI were statistically significant (p < 0.001). The superiority of IM sensitivity and specificity was most pronounced in patients with dense breasts. Spectral CT improved the detection of breast cancer with higher sensitivity and specificity compared to conventional image data in our study.

Assessment of plasma cell-free DNA fragmentation for multi-cancer early detection: An independent clinical validation study.

e15037 Background: Cell-free DNA (cfDNA) in the circulation has gained significant attention due to its potential applications for non-invasive early cancer detection. Fragmentomics of cfDNA is emerging as a promising field of biomarker research, exhibiting the capability to sensitively detect multiple cancer types. In previous retrospective research, we developed an approach called PatternWGS for comprehensive analysis of cfDNA fragmentation patterns and presented a multi-cancer detection model. In this study, we aim to evaluate the performance of PatternWGS in an independent validation cohort including lung, breast, colorectal, liver, and gastric cancers from a new clinical center. Methods: Participants with confirmed diagnosis of lung, breast, colorectal or liver cancer were enrolled in the cancer group. Non-cancer participants without known presence of malignancies were recruited from the same clinical center. Plasma samples from patients with lung (n=47), breast (n=19), colorectal (n=14), liver (n=8) and gastric (n=6) cancers, along with 101 non-cancer individuals were collected in this study. cfDNA was isolated from plasma and stored within a month prior to the whole genome sequencing. The characteristics of cfDNA fragmentation integrating fragment size profiles, motif patterns, genomic coverage distributions were utilized to evaluate the performance for cancer signal detection. In addition, the performance of estimated cfDNA tumor fraction, using copy number aberrations (ichorCNA), was compared with our approach. Results: 94 patients with one of five types of cancers and 101 non-cancer controls were prospectively enrolled between July 2023 and November 2023. Among the cancer group, 64% were at early stages (Stage I: 48%, Stage II: 16%). A significantly higher proportion of shorter fragments was observed in cancer group, and the degree increased with increasing cancer stage. Overall sensitivity across cancer types and stages was 72.3%, and the specificity for non-cancer controls was 80%. As expected, the sensitivity of cancer detection is higher in late-stage patients compared to those in the early-stages (61% in stage I, 80% in stage II, 84.6% in stage III, and 91.7% in stage IV). The sensitivity of detecting lung (72% had stage I cancer), breast, colorectal, liver, and gastric cancers reached 57.4%, 84.2%, 85.7%, 87.5% and 100% respectively. The detection performance using cfDNA tumor fraction by ichorCNA demonstrated a relatively low overall sensitivity of 28.7% (24.6% for stage I-II, 44% for stage III-IV). Conclusions: This independent clinical validation study, which focused on exploring plasma cfDNA fragmentation, demonstrated ideal performance in early-stage cancer detection. The cfDNA fragmentation-based approach exhibited a superior sensitivity in detecting early cancer signals compared with copy number aberrations-based approach.

A set of molecular biomarkers as an indicator of cancer risk.

e12563 Background: Biomarkers are molecular indicators of a biological status and are often used to determine the presence or absence of cancer Molecular changes in both in of cancer cells and their surrounding environment including, blood other body fluids, and tissues that react to the cancer provide biomarker materials that may be effective predictor of disease risk or disease outcome. Effective biomarkers for the disease associated risk and early detection of cancer improves potential patient outcome. An emerging theme is to develop multi-omics panels of biomarker with sufficient sensitivity and specificity for the pre-symptomatic detection of cancer. We applied approximately 300 genomics and proteomics-based genomics (PbG) biomarkers, to identify a panel of biomarkers associated with different types of cancers using patient and donor RNA samples collected from platelets Platelet RNA mirrors the RNA of nucleated cells and can be an important indicator of tissue states particularly in under pro-inflammatory conditions [PMC9555784]. Methods: Publicly available RNA-seq data of N = 2351 blood platelet samples, (including n= 390 asymptomatic controls) from 18 different tumor types was analyzed to identify the panel of biomarkers associated with highest risk cancers. Approximately 300 genomic and PbG signatures (developed from gene expression-based signatures on patients stratified according to protein expression (IHC) and survival. The predictive value of each gene signature (including commercial once ROR, OncotypeDx, etc.) was evaluated by logistic regression modeling in univariate and multivariate settings. Results: Out of 300 biomarkers, we found a set of PbG biomarkers including ratio of BRG1 expression between nuclear and cytoplasm, cytoplasmic C-terminal-binding protein 2 (CtBP2) in express in cytoplasm and ratio of nucleus and cytoplasm, Nucleus Tripartite motif-containing 28 (TRIM28), Vascular endothelial growth factor (VEGF), Mitogen-activated protein kinases (MAPK), androgen receptor (AR) express in Nuclear vs cytoplasm, Glycoprotein 78 (Gp78) in cytoplasm, E2F3, and a member of the epidermal growth factor (EGF) receptor family (ERBB2) are highly predictive (0.77 (0.76 ~ 0.79)) of cancer associated risk on a multivariate training/validation setting. The BRG1 biomarker has the highest odds ratio (11.33) within model, known for coordinating and maintaining key signaling pathways that promote oncogenesis in different cancer types including leukemia, breast, and prostate cancer. Cancer Risk model = 3.4 - 1.01 ERBB2 + 0.3 GP78_Cyto - 3.2 AR_Nucl + 0.7 CtBP2_NucVsCyto - 0.3 E2F3 - 0.99 AR_NucVsCyto + 0.87 VEGF - 1.9 MAPK + 2.43 BRG1_NucVsCyto + 1.35 TRIM28_Nucl + 1.23 CtBP2_Cyto. Conclusions: Utilizing novel genomic, and proteomic technologies, risk prediction and early detection of cancer could identify patients at a time when outcomes are superior and treatment is manageable. Further evaluation of this model is recommended.

A fragment-based algorithm for identifying pan-cancer differential methylation markers from cfDNA.

e22507 Background: Methylation-based sequencing has been developed as a promising approach for Multi-Cancer Early Detection (MCED). Conventionally, cancer-specific methylation markers were identified by comparing paired tumor and adjacent normal tissues using traditional algorithm DSS (Dispersion shrinkage for sequencing data). However, the clinical applicability is limited by the unsatisfactory sensitivity in early-stage disease. Hence, we included blood specific markers derived from paired tissue and blood samples and developed a novel fragment-based algorithm in cfDNA with a better signal to noise ratio (SNR) than DSS to improve the sensitivity of the assay. Methods: A total of 391 plasma samples and 433 tissue samples from 7 cancer types were subjected to whole genome bisulfite sequencing (WGBS) (Table). The proportion of stage I-III and stage IV was 3.5:1 among 7 cancer types. The average sequencing depth was 30X. For each bin, the proportion of fully methylated (M) / un-methylated (U) fragments, defined with methylation level ≥85% / ≤15%, was computed individually. One-sided Wilcoxon rank sum test was employed to test whether cancer cfDNA have significantly (FDR &lt; 0.001) higher proportion of U/M fragments than control cfDNA. Results: To evaluate the performance of fragment-based algorithm in cfDNA, the false positive was assessed by resampling from healthy subjects. The healthy cfDNA were randomly assigned 20 times as pseudo case-control groups of 30 /170. The number of markers among healthy samples was recorded as NFalse Positive (NFP), while the number of markers between cancer and healthy samples was recorded as NPositive (NP). SNR was defined as (NP-NFP)/NFP. We calculated SNR using the fragment-based algorithm and achieved SNR of 135.2 for colorectal cancer, compared to SNR of 2.2 using DSS. The improved SNR was also reproduced in other cancer types. Concordance between plasma and tissue markers was 30.09%-95.92% (median 66.72%) among 7 cancer types, which was related to the proportion of ctDNA for each cancer type. Plasma markers which show weak signals in tissue are defined as blood-specific markers. These markers were enriched in immune-related pathways, suggesting a correlation to the immune response of cancer patients. Conclusions: We developed a novel fragment-based algorithm that achieves much higher SNR than DSS in cfDNA. We also identified blood-specific methylation markers which may improve the sensitivity of early cancer detection in clinical. [Table: see text]

Real-world PET PSMA imaging patterns in a Chicago safety-net hospital.

e17009 Background: Prostate-specific membrane antigen positron emission tomography (PSMA PET) imaging is an FDA-approved imaging modality with increased sensitivity for prostate cancer (PCa) detection. It thus can more accurately stage patients to determine optimal initial treatment. However, patients with indications for these scans can still experience significant delays in obtaining them, mainly due to insurance denials. Such delays often disproportionately affect those with healthcare disparities, representing a substantial population at University of Illinois Chicago (UIC), a safety-net hospital. We sought to identify patterns, barriers, &amp; areas for improvement in obtaining this guideline-directed imaging modality. Methods: PCa patients with a PSMA PET scan ordered between January 2021 and June 2023 were included for analysis. Key medical and demographic data such as age, race/ethnicity, and insurance status were obtained. We also determined the time between ordering and obtaining the PSMA PET, and whether the scan led to classifying the patient’s disease as clinically localized, regional, or metastatic. We also determined whether subsequent treatment was curative or palliative intent. The dates and types of other imaging modalities, if obtained, were also recorded. Results: We identified 76 patients with PSMA PET scans ordered during the study timeframe. The mean age was 68.6 years, with a standard deviation of 7.3 years. 60.0% identified as Black or African American, 24.0% as White, 4.0% as Asian, and 1.3% as American Indian or Alaska Native. 40.8% had Illinois Medicare as their primary insurance, 27.6% had Medicaid, and 31.6% had another medical insurance. Most individuals (77.6%) had a delay in obtaining their PSMA PET scan, with a median time to scan of 23.0 days (range 6-136). All insurance types, including Illinois Medicare, Medicaid, and private insurance, had delays (74.2%, 81.0% and 79.2% of all PSMA PETs, respectively). Many delayed individuals also received conventional imaging (46.0%) within 3 months of the PSMA PET scan order as part of their workup, with 40.0% of PSMA PETs revealing new findings compared to the other scans. Ultimately, most of the imaged patients (54.0%) were determined to have regional or metastatic PCa. Delays in obtaining and receiving the results of scans affected individuals who underwent both curative (40.8%) as well as palliative intent (46.1%) treatment. Conclusions: Between January 2021 and June 2023, PCa patients had a median delay of approximately three weeks in obtaining a PSMA PET scan that was ordered urgently for the determination of their PCa treatment. Such delays can significantly impact health outcomes and often result in redundant conventional imaging. This retrospective study represents the first step in a quality improvement project to reduce delays to PSMA PETs and enable the development of a timely treatment plan to improve patient outcomes in our diverse patient population.

Effectiveness of adjunctive ultrasound after mammography in improving breast cancer screening findings from a study in Malaysian women

Background: Breast cancer is a significant health issue affecting women worldwide, including Malaysia, where the incidence of breast cancer is increasing. Early detection of breast cancer plays a crucial role in improving treatment outcomes and reducing mortality rates. Aims and Objectives: This study examines the impact of adjunctive breast ultrasound (BUS) on screening for Malaysian women, offering evidence to enhance future protocols and decisions in the early detection. Materials and Methods: This study involved 5807 women aged 40 and above undergoing breast screening at Beacon Hospital from January to December 2022. Data collected included mammography and BUS results, categorized by breast imaging reporting and data system (BI-RADS) and diagnostic criteria. Suspicious cases on both modalities had histopathology examination (HPE) for confirmation. A subset of 1690 cases underwent both mammography and BUS screening and was compared to those who only had mammography using BI-RADS scores. Results: This study revealed that the majority of mammograms, 94.69% (5499 cases), were categorized as BI-RADS category 0, 1, 2, and 3, while 5.3% (307 cases) were classified as BI-RADS 4 and 5. Among the cases that underwent ultrasound (1688 cases), 23.43% of them were categorized as BI-RADS 4–5. Using ultrasound in addition to mammography increased the number of cases (BI-RADS 4–5) by 33.48%, resulting in a total of 460 cases identified compared to 307 cases, detected by mammography alone. Significantly, 39% (74 cases) of the 190 cases with confirmed malignancies (BI-RADS 6) by HPE were initially classified as negative mammography (BI-RADS 0–3) but were later identified as BI-RADS 5 on ultrasound. Conclusion: Ultrasound as a supplementary screening method holds the potential for boosting breast cancer detection sensitivity and reducing false negatives, thereby aiding in early detection and better treatment outcomes.