Gastric cancer (GC) presents challenges in predicting treatment responses due to its patient-specific heterogeneity. Recently, liquid biopsies have emerged as a valuable data modality, offering essential cellular and molecular insights while facilitating the capture of time-sensitive information. This study aimed to leverage artificial intelligence (AI) technology to analyze longitudinal liquid biopsy data.

We collected a dataset from longitudinal liquid biopsies of 91 patients at Peking Cancer Hospital, spanning from July 2019 to April 2022. This dataset included 1895 tumor-related cellular images and 1698 tumor marker indices. Subsequently, we introduced the Dynamic-Aware Model (DAM) to predict responses to GC treatment. DAM incorporates dynamic data through AI-engineered components, facilitating an in-depth longitudinal analysis.

Utilizing threefold cross-validation, DAM exhibited superior performance compared to traditional cell-counting methods, achieving an AUC of 0.807 in predicting GC treatment responses. In the test set, DAM maintained stable efficacy with an AUC of 0.802. Besides, DAM showed the capability to accurately predict treatment responses based on early treatment data. Moreover, DAM's visual analysis of attention mechanisms identified six dynamic visual features related to focus areas, which were strongly associated with treatment-response.

These findings represent a pioneering effort in applying AI technology to interpret longitudinal liquid biopsy data and employ visual analytics in GC. This approach provides a promising pathway toward precise response prediction and personalized treatment strategies for patients with GC.

Novel therapies have significantly improved survival for non-metastatic non-small cell lung cancer (NSCLC), however recurrence remains a challenge. Current treatment and surveillance strategies rely on imaging and clinical assessments with limited sensitivity in early detection of disease progression. Liquid biopsy-mediated detection of minimal residual disease (MRD) allows monitoring of tumor activity at the molecular level before clinical and radiologic progression. Here, we review the current evidence for MRD in the adaptive management and surveillance of non-metastatic NSCLC, focusing on the missing links that prevent its widespread clinical adoption.

Early detection of cancer is crucial for improving patient outcomes. Traditional modalities such as mammography and low-dose computed tomography are effective but exhibit inherent limitations, including radiation exposure and accessibility challenges. This review explores innovative, non-invasive cancer screening methods, focusing on liquid biopsy and volatile organic compound (VOC)-based detection platforms. Liquid biopsy analyzes circulating tumor DNA and other biomarkers in bodily fluids, offering potential for early detection and monitoring of treatment response. VOC-based detection leverages unique metabolic signatures emitted by cancer cells, detectable in exhaled breath or other bodily emissions, providing a rapid and patient-friendly screening option. We provide a comprehensive overview of these advanced multi-cancer detection techniques to enhance diagnostic accuracy, accessibility, and patient adherence, and ultimately enhance survival rates and patient outcomes.

Artificial Intelligence (AI) is capable of revolutionizing cancer therapy and advancing precision oncology via integrating genomics data and digitized health information. AI applications show promise in cancer prediction, prognosis, and treatment planning, particularly in radiomics, deep learning, and machine learning for early cancer diagnosis. However, widespread adoption requires comprehensive data and clinical validation. While AI has demonstrated advantages in treating common malignancies like lung and breast cancers, challenges remain in managing rare tumors due to limited datasets. AI's role in processing multi-omics data and supporting precision oncology decision-making is critical as genetic and health data become increasingly digitized.

This review article presents current knowledge on AI and associated technologies, which are being utilized in the diagnosis and therapy of cancer. The applications of AI in radiomics, deep learning, and machine learning for cancer screening and treatment planning are examined. The study also explores the capabilities and limitations of predictive AI in diagnosis and prognosis, as well as generative AI, such as advanced chatbots, in patient and provider interactions.

AI can improve the early diagnosis and treatment of high-incidence cancers like breast and lung cancer. However, its application in rare cancers is limited by insufficient data for training and validation. AI can effectively process large-scale multi-omics data from DNA and RNA sequencing, enhancing precision oncology. Predictive AI aids in risk assessment and prognosis, while generative AI tools improve patient-provider communication. Despite these advancements, further research and technological progress are needed to overcome existing challenges.

AI holds transformative potential for cancer therapy, particularly in precision oncology, early detection, and personalized treatment planning. However, challenges such as data limitations in rare cancers, the need for clinical validation, and regulatory considerations must be addressed. Future advancements in AI could significantly improve decision-support systems in oncology, ultimately enhancing patient care and quality of life. The review highlights both the opportunities and obstacles in integrating AI into cancer diagnostics and therapeutics, calling for continued research and regulatory oversight.

Breast cancer (BC) remains a leading cause of morbidity and mortality among women worldwide, necessitating the development of innovative diagnostic and monitoring strategies. Liquid biopsy (LB), a minimally invasive approach that analyzes circulating tumor cells (CTCs), cell-free DNA (cfDNA), circulating tumor DNA (ctDNA), extracellular vesicles (EVs), and other tumor-derived biomarkers in body fluids, has emerged as a transformative tool in BC management. This review comprehensively explores the role of LB in early detection, disease monitoring, treatment stratification, and resistance surveillance in BC. We discuss the latest advancements in LB technologies, including next-generation sequencing (NGS), digital PCR, and single-cell analysis, highlighting their sensitivity and specificity. Additionally, we examine the clinical utility of LB in guiding personalized therapy, particularly in the context of hormone receptor-positive, HER2positive, and triple-negative BC subtypes. Despite its promise, several challenges, including standardization, validation, and integration into clinical practice, remain to be addressed. By summarizing current evidence and future directions, this review underscores the potential of LB to revolutionize BC diagnosis and treatment, paving the way for a more precise and dynamic approach to disease management.

Aptamers have presented considerable potential for disease diagnoses but still face a great challenge from the discrepancy between their screening conditions and real application in body fluid matrices (BFMs). In this study, we investigated the impact of BFM intervention on aptamer screening based on CE efficient separation and introduced adaptive screening strategies directly under the BFM background. Five BFMs, including serum, tears, artificial cerebrospinal fluid, saliva, and embryonic fluid, demonstrated the potential differentiating effects on aptamer screening based on the characterization of their "fingerprint-like profile" and their interactions with ssDNA libraries. Besides, the matrix effects of the five BFMs were quantified by calculating kinetic parameters derived from binding investigations between disease marker protein-spiked BFMs and ssDNA libraries. Through quantitative evaluation, two tailored SELEX strategies were introduced in BFM-intervened aptamer screening: spiking foreign proteins to the BFMs, and targeting proteins naturally abundant in BFMs. As proof of concept, we completed aptamer screenings for prolactin-induced protein (serum, spiked target) and human lactoferrin (tears, natural target) via CE-SELEX that demonstrated its adaptability in "splitting" BFM interference and achieving a clear collection of target-ssDNA complex. This study highlighted the critical importance of incorporating BFMs into SELEX and provided two tailored strategies that are worthy of choice for effective aptamer discovery, advancing the practical utility of aptamers.

Cancer is a leading cause of death worldwide, and despite the introduction of new therapeutic approaches for advanced cases aimed at improving patient survival, only a subset of patients benefits from a complete response. In this context, there is a growing need for new cancer biomarkers and therapeutic strategies, and the use of Extracellular Vesicles (EVs) has been widely explored in various approaches. As circulating lipid-bilayer particles carrying a variety of biological information from tumor cells, EVs can be employed as good biomarkers of diagnosis, prognosis, therapy evaluation, and as adjuvants in cancer treatment. In this review, we provide a brief overview of the different types of EVs and their biogenesis and discuss how tumor-derived EV cargo can serve as a potential biomarker in clinical settings through liquid biopsy. We also highlight recent advances in EV nanoengineering and their potential as adjuvants in cancer treatment. Finally, we discuss the key unknowns, gaps, and bottlenecks that must be addressed to fully integrate EVs into precision oncology.

Colorectal cancer (CRC) is still one of the leading causes of cancer deaths worldwide. Even though there has been progress in cancer immunotherapy, the results of applying immune checkpoint inhibitors (ICIs) have been unsatisfactory, especially in microsatellite stable (MSS) CRC. Single-agent ICIs that target programmed cell death-1 (PD-1)/ PD-L1, cytotoxic T-lymphocyte-associated protein 4 (CTLA-4), T cell Ig- and mucin-domain-containing molecule-3 (TIM-3), and lymphocyte activation gene (LAG)-3 have emerged as having specific benefits. However, many primary and secondary resistance mechanisms are available in the tumor microenvironment (TME) that prevent it from happening. Combination strategies, such as the use of anti-PD-1 and anti-CTLA-4, can be effective in overcoming these resistance pathways, but toxicities remain a significant concern. Moreover, ICIs have been integrated with various treatment modalities, including chemotherapy, radiotherapy, antibiotics, virotherapy, polyadenosine diphosphate-ribose polymerase (PARP) inhibitors, and heat shock protein 90 (HSP90) inhibitors. The outcomes observed in both preclinical and clinical settings have been encouraging. Interestingly, manipulating gut microbiota via fecal microbiota transplantation (FMT) has been identified as a new strategy to increase the efficacy of immunotherapy in CRC patients. Therefore, integrating ICIs with other treatment approaches holds promise in enhancing the prognosis of CRC patients. This review focuses on the unmet need for new biomarkers to select patients for combination therapies and the ongoing work to overcome resistance and immune checkpoint blockade.

Polyamidoamine dendrimer (PAMAM) are effective carriers that transport diagnostic imaging reagents and drugs to the tumor site. Their excellent bio-compatibility and bio-degradability reduce damage to healthy tissues, resulting in improved treatment efficacy. Dendrimer molecules are particularly useful in targeted drug delivery within malignant cells. This article reviews recent progress of PAMAM in imaging and treating breast cancer, lung cancer, hepatocellular cancer, colorectal cancer, gastric cancer, prostate cancer, and glioblastoma. This review aims to provide new and feasible ideas for cancer diagnosis imaging and treatment while also serving as a significant reference point for personalized tumor therapy based on PAMAM materials.

The 2024 Nobel Prize in Physiology or Medicine was awarded to the pioneers who reported that microRNAs (miRNAs) regulate and direct the switch between physiological and pathological pathways via their over- or underexpression. The discovery changed the medical landscape and there are many completed and on-going clinical studies based on miRNAs. MiRNAs occur at the femtomolar level in biological fluids and are typically quantified using amplification-based techniques. Experimental nanopores have illustrated potential for trace analysis including amplification-free miRNA quantification. We repurposed the MinION, the only commercially available nanopore array device, and developed unique probes and protocols to detect and measure miRNA copies in blood and urine. Here, we report that miRNA copies are proportional to the total RNA isolated from the biospecimen, and that three known miRNA cancer biomarkers, i.e., miR-21, miR-375, and miR-141, were more than 1.5-fold overexpressed in blood samples from breast, ovarian, prostate, pancreatic, lung, and colorectal cancer patients compared to healthy patients. In these cancer samples, miR-15b was not overexpressed, in agreement with earlier studies. In contrast to literature reports, sample variability was undetectable in this study. The potential and limitations of this ready-to-use MinION/Yenos platform for multiple-cancer early detection (MCED) using blood or urine are discussed.

In this study, we developed a biosensor using a gold screen-printed electrode (Au-SPE) functionalized with mercaptoundecanoic acid (MUA) and an antibody for detecting the vascular-endothelial cadherin (CD144) as a endothelial biomarker protein on extracellular vesicles (EVs) isolated from saliva. The MUA functionalization provides a stable platform for immobilizing the CD144 antibody, ensuring the detection of the target protein. This biosensor combines Au-SPE technology with an immunoassay, offering a rapid, sensitive, and non-invasive method for detection of CD144 carried by EVs. Characterization of saliva-derived EVs using transmission electron microscopy (TEM) and nanoparticle tracking analysis (NTA) confirmed their morphology and size, which fell within the expected range of 80-180 nm. NTA indicated a lower concentration of particles in saliva-EVs than in serum-EVs (controls), highlighting the need for sensitive detection of EV cargos in this type of EV. Immunodetection confirmed the presence of CD144 in both saliva and serum-derived EVs, with higher concentrations in serum. Functionalization of Au-SPEs with MUA and CD144 antibodies was confirmed by significant resistance changes, and atomic force microscopy (AFM) was used to verify the preservation of EV morphology and their capturing post-immune adsorption. A calibration curve demonstrated the high sensitivity of the biosensor prototype for detecting CD144-positive EVs, with a limit of detection (LOD) of 0.111 ng mL-1 and a limit of quantification (LOQ) of 0.37 ng mL-1, requiring only 3 μL of EV-sample. This biosensor shows potential as a novel method for detecting and studying endothelial biomarkers associated with cardiovascular disease in EVs isolated from saliva, a capability not currently available with existing tools. Furthermore, it provides a key platform for expanding research to other biomarkers and diseases by monitoring protein cargos in the EVs, enhancing its utility across diverse clinical applications.

Platelets, traditionally recognized for their role in hemostasis, have emerged as pivotal players in cancer biology. They actively contribute to tumor proliferation, angiogenesis, immune evasion, and metastasis and thus play a significant role in cancer progression. Tumor-educated platelets (TEPs) acquire protumorigenic phenotypes through RNA, protein, and receptor profile alterations driven by interactions with tumors and their microenvironment. These modifications enable TEPs to enhance tumor growth and dissemination and to play a critical role throughout the metastatic process. Moreover, TEPs are promising biomarkers that can easily be analyzed in liquid biopsies. Since they dynamically mirror tumor activity through transcriptomic and proteomic changes, their analysis offers a non-invasive method for determining cancer detection and diagnosis, patient prognosis, therapy monitoring, and personalization of treatment. Their demonstrated accuracy in identifying cancer types and predicting treatment responses underscores their ability to provide real-time insights into tumor biology, including in urological malignancies. Their diagnostic potential and their accessibility as blood-sourced biomarkers position TEPs as transformative tools in advancing personalized oncology. Here, we focus on the role of TEPs in urological tumors, exploring their applications in early cancer detection, disease monitoring, and the design of tailored therapeutic strategies.

Liquid biopsies are an emerging, noninvasive tool for cancer diagnostics, utilizing biological fluids for molecular profiling. Nevertheless, the current methods often lack the sensitivity and specificity necessary for early detection and real-time monitoring. This work explores an advanced approach to improving liquid biopsy techniques through machine learning analysis of the Raman spectra measured to classify distinct exosome solutions by their cancer origin. This was accomplished by conducting principal component analysis (PCA) of the Raman spectra of exosomes from three cancer cell lines (COLO205, A375, and LNCaP) to extract chemically significant features. This reduced set of features was then utilized to train a linear discriminant analysis (LDA) classifier to predict the source of the exosomes. Furthermore, we investigated differences in the lipid composition in these exosomes by their spectra. This spectral similarity analysis revealed differences in lipid profiles between the different cancer cell lines as well as identified the predominant lipids across all exosomes. Our PCA-LDA framework achieved 93.3% overall accuracy and F1 scores of 98.2%, 91.1%, and 91.0% for COLO205, A375, and LNCaP, respectively. Our results from spectral similarity analysis were also shown to support previous findings of lipid dynamics due to cancer pathology and pertaining to exosome function and structure. These findings underscore the benefits of enhancing Raman spectroscopy analysis with machine learning, laying the groundwork for the development of early noninvasive cancer diagnostics and personalized treatment strategies. This work potentially establishes the foundation for refining the classification model and optimizing exosome extraction and detection from clinical samples for clinical translation.

Acute leukemia (AL), classified as acute myeloid leukemia (AML) and acute lymphocytic leukemia (ALL), is a hematologic malignancy caused by the uncontrolled proliferation of leucocytes in the bone marrow. Early detection of AL is crucial for clinical treatment. Detection methods of AL are currently blood tests, bone marrow tests, imaging, and spinal fluid tests. However, these tests have drawbacks, such as high cost and time consumption. Liquid biopsy using biological fluids such as blood or serum is an emerging technique for noninvasive cancer detection and monitoring. Surface-enhanced Raman spectroscopy (SERS), which enhanced Raman signals by the interaction of plasmonic nanostructures with the analyte, is a highly sensitive and specific detection method with simple sample preparation that has been used in combination with machine learning techniques to analyze liquid biopsy. In this study, we developed a SERS-based liquid biopsy approach that enables accurate classification of AML and ALL subtypes and the prediction of disease recurrence. SERS spectra of serum samples from 24 healthy individuals, 43 AML patients, and 18 ALL patients were obtained using an Ag-based SERS substrate and clustered using hierarchical cluster analysis (HCA). The spectra were then classified using three commonly used classifiers, namely, support vector machine (SVM), random forest (RF), and k-nearest neighbor (kNN). Our findings demonstrate that the RF classifier has the highest accuracy values, with 96.1, 95.5, and 98.5% for classifying three groups and predicting the recurrence of AML and ALL, respectively. The combination of SERS-based serum analysis with machine learning algorithms represents a remarkable advancement in the realm of hematological disease diagnostics, particularly for AML and ALL. This approach not only facilitates the precise differentiation of disease subtypes but also introduces the novel capability of prognosticating disease recurrence.

Blood-based profiling of tumor DNA ("liquid biopsy") offers great prospects for non-invasive early cancer diagnosis and clinical guidance, but requires further computational advances to become a robust quantitative assay of tumor clonal evolution. We propose new methods to better characterize tumor clonal dynamics from circulating tumor DNA (ctDNA), through application to two specific tasks: (i) applying longitudinal ctDNA data to refine phylogeny models of clonal evolution, and (ii) quantifying changes in clonal frequencies that may be indicative of treatment response or tumor progression. We pose these through a probabilistic framework for optimally identifying markers and using them to characterize clonal evolution.

We first estimate a density over clonal tree models using bootstrap samples over pre-treatment tissue-based sequence data. We then refine these models over successive longitudinal samples. We use the resulting framework for modeling and refining tree densities to pose a set of optimization problems for selecting ctDNA markers to maximize measures of utility for reducing uncertainty in phylogeny models and quantifying clonal frequencies given the models. We tested our methods on synthetic data and showed them to be effective at refining tree densities and inferring clonal frequencies. Application to real tumor data further demonstrated the methods' effectiveness in refining a lineage model and assessing its clonal frequencies. The work shows the power of computational methods to improve marker selection, clonal lineage reconstruction, and clonal dynamics profiling for more precise and quantitative assays of somatic evolution and tumor progression.

https://github.com/CMUSchwartzLab/Mase-phi.git. (DOI: 10.5281/zenodo.14776163).

Cancer ranks among the most lethal diseases worldwide. Tissue biopsy is currently the primary method for the diagnosis and biological analysis of various solid tumors. However, this method has some disadvantages related to insufficient tissue specimen collection and intratumoral heterogeneity. Liquid biopsy is a noninvasive approach for identifying cancer-related biomarkers in peripheral blood, which allows for repetitive sampling across multiple time points. In the field of liquid biopsy, representative biomarkers include circulating tumor cells (CTCs), circulating tumor DNA (ctDNA), and exosomes. Many studies have evaluated the prognostic and predictive roles of CTCs and ctDNA in various solid tumors. Although these studies have limitations, the results of most studies appear to consistently demonstrate the correlations of high CTC counts and ctDNA mutations with lower survival rates in cancer patients. Similarly, a reduction in CTC counts throughout therapy may be a potential prognostic indicator related to treatment response in advanced cancer patients. Moreover, the biochemical characteristics of CTCs and ctDNA can provide information about tumor biology as well as resistance mechanisms against targeted therapy. This review discusses the current clinical applications of liquid biopsy in cancer patients, emphasizing its possible utility in outcome prediction and treatment decision-making.

Cancer, characterized by the uncontrolled proliferation of cells, is one of the leading causes of death globally, with approximately one in five people developing the disease in their lifetime. While many driver genes were identified decades ago, and most cancers can be classified based on morphology and progression, there is still a significant gap in knowledge about genetic aberrations and nuclear DNA damage. The study of two critical groups of genes-tumor suppressors, which inhibit proliferation and promote apoptosis, and oncogenes, which regulate proliferation and survival-can help to understand the genomic causes behind tumorigenesis, leading to more personalized approaches to diagnosis and treatment. Aberration of tumor suppressors, which undergo two-hit and loss-of-function mutations, and oncogenes, activated forms of proto-oncogenes that experience one-hit and gain-of-function mutations, are responsible for the dysregulation of key signaling pathways that regulate cell division, such as p53, Rb, Ras/Raf/ERK/MAPK, PI3K/AKT, and Wnt/β-catenin. Modern breakthroughs in genomics research, like next-generation sequencing, have provided efficient strategies for mapping unique genomic changes that contribute to tumor heterogeneity. Novel therapeutic approaches have enabled personalized medicine, helping address genetic variability in tumor suppressors and oncogenes. This comprehensive review examines the molecular mechanisms behind tumor-suppressor genes and oncogenes, the key signaling pathways they regulate, epigenetic modifications, tumor heterogeneity, and the drug resistance mechanisms that drive carcinogenesis. Moreover, the review explores the clinical application of sequencing techniques, multiomics, diagnostic procedures, pharmacogenomics, and personalized treatment and prevention options, discussing future directions for emerging technologies.

Pancreatic ductal adenocarcinoma (PDAC) is a leading cause of cancer-related mortality, primarily due to late stage at diagnosis. This review examines the multifaceted applications of liquid biopsy and circulating tumor DNA (ctDNA) analysis in the diagnosis and management of PDAC. We review the current literature on the technological advancements in liquid biopsy analysis such as next generation sequencing (NGS) and digital droplet PCR (ddPCR) as well as multi-omics technologies, highlighting their potential for accurate molecular subtyping through ctDNA analysis. This review highlights the significant role of ctDNA in the assessment of tumor behavior, disease subtyping, prediction and monitoring of treatment response, and evaluation of minimal residual disease. We discuss the implications of integrating liquid biopsy techniques into clinical practice as well as its challenges and limitations. By drawing insights from recent studies, this review aims to provide a comprehensive overview of how liquid biopsy and ctDNA analysis can enhance early disease management strategies in PDAC. We underscore the need for additional prospective studies and clinical trials to validate its feasibility and accuracy in order to establish clinical utility, with the ultimate goal of routine incorporation into practice to improve patient outcomes and transform the treatment landscape for PDAC.

Breast cancer management has traditionally relied on tissue biopsies and imaging, which offer limited insights into the disease. However, the discovery of circulating tumor DNA (ctDNA) and minimal residual disease (MRD) detection has revolutionized our approach to breast cancer. ctDNA, which is fragmented tumor DNA found in the bloodstream, provides a minimally invasive way to understand the tumor's genomic landscape, revealing heterogeneity and critical mutations that biopsies may miss. MRD, which indicates cancer cells that remain after treatment, can now be detected using ctDNA and other advanced methods, improving our ability to predict disease recurrence. This allows for personalized adjuvant therapies based on individual MRD levels, avoiding unnecessary treatments for patients with low MRD. This review discusses how ctDNA and MRD represent a paradigm shift towards personalized, genomically guided cancer care, which has the potential to significantly improve patient outcomes in breast cancer.

We aim to compare the diagnostic accuracy of the different methodologies used in the detection of cell-free human papillomavirus (HPV) DNA in HPV-associated head and neck squamous cell carcinoma detection using bivariate analysis methods.

Pubmed, Embase, and Scopus were queried using a broad search strategy to search for relevant studies.

Test characteristics were extracted from 33 studies following literature screening, and underwent analyses utilizing a bivariate approach. Summary statistics were identified for each type of methodology, and forest plots and summary receiver operating characteristic curves were constructed. Bias was estimated using Deek's Funnel Plot and the QUADAS-2 tool.

In terms of diagnostic accuracy, digital droplet polymerase chain reaction (ddPCR) based testing exhibited the highest diagnostics odds ratio at 138 (59.5, 318), followed closely by next-generation sequencing (NGS) at 120 (39.7, 362), then by polymerase chain reaction (PCR) at 31.4 (14.4, 68.6), and quantitative PCR at 8.74 (4.63, 16.5).

NGS and ddPCR are comparable in overall diagnostic accuracy, bringing into question their relative roles in diagnosis and screening. Cost-effective ddPCR assays may serve as useful diagnostic and screening tests in the clinic with their low false positive rates and high sensitivity. However, NGS assays also offer high sensitivity and companion metrics, suggesting they may have a more precise role in disease monitoring. Importantly, assay development and benchmarking need further standardization to improve comparison between assays. Finally, saliva-based testing needs to be further investigated using NGS and ddPCR to further understand its limitations in disease detection and monitoring.

Lung cancer is frequently detected during visits to the emergency department (ED). The ED is crucial for identifying likely cases of lung cancer and coordinating the subsequent care for these patients.

This scoping review aims to explore the definitions of emergency presentations (EPs) of lung cancer, along with mortality rates, cancer stage, and treatments for patients diagnosed with lung cancer following an EP.

We conducted a scoping review of the literature on EPs of lung cancer, identifying 27 relevant articles out of 1338 initially screened.

Most studies originated from the United Kingdom, collectively reporting over 270,000 EPs of lung cancer. The majority of included studies provided strong evidence. Key findings revealed higher mortality rates among patients diagnosed with lung cancer through emergency presentations, with a significant proportion presenting at advanced stages. Patients with EPs were less likely to undergo surgical removal or receive radiotherapy. Lastly, only 66.7% of the studies defined an EP, with great heterogeneity among EP definitions. Methodological differences precluded meta-analysis.

Despite methodological heterogeneity, our synthesis indicates that patients presenting acutely with undiagnosed lung cancer often present at advanced stages and experience high mortality rates. These findings underscore the need for further research to develop evidence-based interventions for improving outcomes among ED patients with suspected lung cancer.

Liquid biopsies are expected to advance cancer management, and particularly physical cues are gaining attention for indicating tumorigenesis and metastasis. Atomic force microscopy (AFM) has become a standard and important tool for detecting the mechanical properties of single living cells, but studies of developing AFM-based methods to efficiently measure the mechanical properties of circulating tumor cells (CTCs) in liquid biopsy for clinical utility are still scarce. Herein, we present a proof-of-concept study based on the complementary combination of AFM and microfluidics, which allows label-free sorting of individual CTCs and subsequent automated AFM measurements of the mechanical properties of CTCs. With the use of a microfluidic system containing contraction-expansion microchannels, specific cancer cell types were separated and harvested in a marker-independent manner. Subsequently, automated AFM indentation and force spectroscopy experiments were performed on the enriched cells under the precise guidance of the label-free identification of cells using a deep learning optical image recognition model. The effectiveness of the presented method was verified on three experimental sample systems, including mixed microspheres with different sizes, a mixture of different types of cancer cells, and a mixture of cancer cells and blood cells. The study illustrates a feasible framework based on the integration of AFM and microfluidics for non-destructive and efficient nanomechanical phenotyping of CTCs in bodily fluids, which offers additional possibilities for the clinical applications of AFM-based nanomechanical analysis and will also benefit the field of mechanobiology as well as cancer liquid biopsy.

Liquid biopsy (LB) offers a promising, minimally invasive alternative to traditional tissue biopsies in cancer care, enabling real-time monitoring and personalized treatment. Despite its potential, the routine implementation of LB in clinical practice faces significant challenges. This scoping review examines the barriers and facilitators influencing the implementation of liquid biopsies into standard cancer care.

Four academic databases (PubMed, Scopus, Embase, and Web of Science) were systematically searched without language restrictions. We included peer-reviewed articles that were published between January 2019 and March 2024 that focused on the implementation of LB in cancer care or described barriers and facilitators to its implementation. Data relevant to the review objective, including key article characteristics; barriers and facilitators of implementation; and recommendations for advancement or optimisation; were extracted and analysed using thematic and visual network analyses.

The majority of the included articles were narrative review articles (84%), with most from China (24.2%) and the United States (20%). Thematic analysis identified four main categories and their associated barriers and facilitators to the implementation of LB in cancer care: (1) Laboratory and personnel requirements; (2) Disease specificity; (3) Biomarker-based liquid biopsy; and (4) Policy and regulation. The majority of barriers identified were concentrated in the pre-analytical phase, highlighting the lack of standardization in LB technologies and outcomes.

Through a thematic analysis of the barriers and facilitators to LB implementation, we present an integrated tool designed to encourage the standardization of testing methods for clinical practice guidelines in the field.

Nanoparticles have been widely used in cancer diagnostics and treatment research due to their unique properties. Magnetic nanoparticles are popular in imaging techniques due to their ability to alter the magnetization field around them. Plasmonic nanoparticles are mainly applied in cancer treatments like photothermal therapy due to their ability to convert light into heat. While these nanoparticles are popular among their respective fields, magnetic-plasmonic core-shell nanoparticles (MPNPs) have gained popularity in recent years due to the combined magnetic and optical properties from the core and shell. MPNPs have stood out in cancer theranostics as a multimodal platform capable of serving as a contrast agent for imaging, a guidable drug carrier, and causing cellular ablation through photothermal energy conversion. In this review, we summarize the different properties of MPNPs and the most common synthesis approaches. We particularly discuss applications of MPNPs in cancer diagnosis and treatment based on different mechanisms using the magnetic and optical properties of the particles. Lastly, we look into current challenges they face for clinical applications and future perspectives using MPNPs for cancer detection and therapy.

Molecular diagnosis plays a significant role in detection of biomolecules linked to early stage cancer since it offers greater sensitivity and reliability for identification of biomarker level changes as the disease progresses. The application of vibrational spectroscopy in biomarker detection is defined by the fingerprint spectrum of a molecule originating from single-molecule vibrations. This characteristic makes surface enhanced Raman spectroscopy (SERS) a promising tool for identification of biomarkers. The performance of the SERS technique largely depends on the material being used as the SERS substrate. Graphene, with its large surface area and abundance of aromatic regions, is considered advantageous as SERS substrate. Combining graphene with metal nanomaterials considerably increases SERS signal intensity, thereby enhancing detection sensitivity. Therefore, this review emphasizes the significance of selecting graphene-metal nanohybrids as suitable SERS substrates for signal amplification. The detail understanding of the mechanism of graphene-metal hybrid in SERS based detection of early stage cancer is also presented. Furthermore, several examples demonstrated the application of graphene-metal hybrid nanomaterials in detecting biomarkers and cancer cell differentiation using SERS imaging.

Featuring a special section on cancer biomarkers, this issue of the Biomedical Journal highlights research on cfDNA, fecal miRNA, mitochondrial pathways, Epstein-Barr virus DNA, multi-biomarker panels combined with LC-MS-based methods, and circulating tumor cell (CTC) growth status as potential tools for cancer detection and management, including colorectal cancer, ovarian cancer, nasopharyngeal carcinoma, and hepatocellular carcinoma. Additionally, it explores the social stigma surrounding mpox, and discusses the achievements in miRNA research that earned the 2024 Nobel Prize in Physiology or Medicine. Advances in deep learning for trauma imaging are examined, alongside a review of decades of enterovirus research and current vaccination strategies. Other studies investigate the combined use of anthelmintic and antibiotic therapy for roundworm infection, and the application of a well-established Traditional Chinese Medicine herbal formula, commonly prescribed for gynecological conditions, against autoimmune hepatitis. Further articles elucidate the role of the nuclear receptor HNF4 in Schistosoma, mitochondrial dysfunction in acute kidney injury, the effects of astragalus polysaccharides on prostate cancer, and the use of deep learning to predict mortality from electrocardiograms.

Pancreatic cancer remains one of the most challenging malignancies to treat due to its late-stage diagnosis, aggressive progression, and high resistance to existing therapies. This review examines the latest advancements in early detection, and therapeutic strategies, with a focus on emerging biomarkers, tumor microenvironment (TME) modulation, and the integration of artificial intelligence (AI) in data analysis. We highlight promising biomarkers, including microRNAs (miRNAs) and circulating tumor DNA (ctDNA), that offer enhanced sensitivity and specificity for early-stage diagnosis when combined with multi-omics panels. A detailed analysis of the TME reveals how components such as cancer-associated fibroblasts (CAFs), immune cells, and the extracellular matrix (ECM) contribute to therapy resistance by creating immunosuppressive barriers. We also discuss therapeutic interventions that target these TME components, aiming to improve drug delivery and overcome immune evasion. Furthermore, AI-driven analyses are explored for their potential to interpret complex multi-omics data, enabling personalized treatment strategies and real-time monitoring of treatment response. We conclude by identifying key areas for future research, including the clinical validation of biomarkers, regulatory frameworks for AI applications, and equitable access to innovative therapies. This comprehensive approach underscores the need for integrated, personalized strategies to improve outcomes in pancreatic cancer.

Tracheal, bronchus, and lung (TBL) cancer attributable to ambient particulate matter pollution (APMP) is a growing global health concern, particularly in individuals aged 70 and above. This study aims to evaluate past trends, identify key drivers, and project future disease burden.

Data from the Global Burden of Disease Study 2021 was analyzed for TBL cancer-related disability-adjusted life years (DALYs) and mortality from 1990 to 2021, stratified by SDI regions. Statistical methods, including Joinpoint regression, age-period-cohort modeling, and decomposition analysis, were used to identify temporal trends and drivers of DALYs. Future projections were made using the Nordpred model.

From 1990 to 2021, global DALYs of TBL cancer due to APMP increased steadily (AAPC 0.75%). Population growth was the main driver, accounting for 79.37% of the increase, with epidemiological factors playing a varying role across regions. The highest DALY growth was observed in middle SDI regions (AAPC 2.99%), while high SDI regions saw a decline (AAPC -1.76%). Projections up to 2044 suggest a substantial increase in DALYs across all SDI regions, with the fastest growth expected among individuals aged 70-74, but DALY rates are projected to decline steadily.

Population growth is the primary factor driving the increase in DALYs associated with TBL cancer, with significant regional disparities. Projections suggest a continued rise in disease burden, particularly in lower SDI regions, underlining the urgency for targeted public health interventions and strategies to mitigate exposure and improve healthcare outcomes for at-risk populations.

Advances in neuro-oncology have transformed the diagnosis and management of brain tumors, which are among the most challenging malignancies due to their high mortality rates and complex neurological effects. Despite advancements in surgery and chemoradiotherapy, the prognosis for glioblastoma multiforme (GBM) and brain metastases remains poor, underscoring the need for innovative diagnostic strategies. This review highlights recent advancements in imaging techniques, liquid biopsies, and artificial intelligence (AI) applications addressing current diagnostic challenges. Advanced imaging techniques, including diffusion tensor imaging (DTI) and magnetic resonance spectroscopy (MRS), improve the differentiation of tumor progression from treatment-related changes. Additionally, novel positron emission tomography (PET) radiotracers, such as 18F-fluoropivalate, 18F-fluoroethyltyrosine, and 18F-fluluciclovine, facilitate metabolic profiling of high-grade gliomas. Liquid biopsy, a minimally invasive technique, enables real-time monitoring of biomarkers such as circulating tumor DNA (ctDNA), extracellular vesicles (EVs), circulating tumor cells (CTCs), and tumor-educated platelets (TEPs), enhancing diagnostic precision. AI-driven algorithms, such as convolutional neural networks, integrate diagnostic tools to improve accuracy, reduce interobserver variability, and accelerate clinical decision-making. These innovations advance personalized neuro-oncological care, offering new opportunities to improve outcomes for patients with central nervous system tumors. We advocate for future research integrating these tools into clinical workflows, addressing accessibility challenges, and standardizing methodologies to ensure broad applicability in neuro-oncology.

Pancreatic adenocarcinoma (PDAC) remains one of the most lethal cancers, with limited advancements in treatment efficacy due to high rates of chemoresistance. Circulating tumor cells (CTCs) derived from liquid biopsies offer a non-invasive approach to monitoring tumor evolution and identifying molecular mechanisms of resistance. This study aims to longitudinally collect, culture, and characterize CTCs from PDAC patients to elucidate resistance mechanisms and tumor-specific gene expression profiles.

Blood samples from 10 PDAC patients were collected across different treatment stages, yielding 16 CTC cultures. Differential gene expression, pathway dysregulation, and protein-protein interaction studies were utilized, highlighting patient-specific and disease progression-associated changes. Longitudinal comparisons within five patients provided further insights into dynamic molecular changes associated with therapeutic resistance.

CTC cultures exhibited the activation of key pathways implicated in PDAC progression and resistance, including TNFα/NF-kB, hedgehog signaling, and the epithelial-to-mesenchymal transition. Longitudinal samples revealed dynamic changes in signaling pathways, highlighting upregulated mechanisms of chemoresistance, including PI3K/Akt/mTOR and TGF-β pathways. Additionally, protein-protein interaction analysis emphasized the role of the immune system in PDAC progression and therapy response. Patient-specific gene expression patterns therefore suggest potential applications for precision medicine.

This proof-of-concept study demonstrates the feasibility of longitudinally capturing and analyzing CTCs from PDAC patients. The findings provide critical insights into molecular drivers of chemoresistance and highlight the potential of CTC profiling to inform personalized therapeutic strategies. Future large-scale studies are warranted to validate these findings and further explore CTC-based approaches in PDAC management.

The existence of cancer stem cells (CSCs) in various tumors has become increasingly clear in addition to their prominent role in therapy resistance, metastasis, and recurrence. For early diagnosis, disease progression monitoring, and targeting, there is a high demand for clinical-grade methods for quantitative measurement of CSCs from patient samples. Despite years of active research, standard measurement of CSCs has not yet reached clinical settings, especially in the case of solid tumors. This is because detecting this plastic heterogeneous population of cells is not straightforward. This review summarizes various techniques, highlighting their benefits and limitations in detecting CSCs from patient samples. In addition, methods designed to detect CSCs based on secreted and niche-associated signaling factors are reviewed. Spatial and single-cell methods for analyzing patient tumor tissues and noninvasive techniques such as liquid biopsy and in vivo imaging are discussed. Additionally, methods recently established in laboratories, preclinical studies, and clinical assays are covered. Finally, we discuss the characteristics of an ideal method as we look toward the future.

Cancer remains a major global health challenge with a high mortality rate, as evidenced by the rise in new cases every year. Conventional diagnostic methods like PET scans, MRIs, and biopsies, despite being widely used, suffer from significant drawbacks such as high radiation exposure, difficulty in distinguishing malignant from benign tumors, and invasiveness. Early detection, which is crucial for improving treatment outcomes and survival rates, is hindered by the asymptomatic nature of early-stage cancer and the limitations of current diagnostic tools. Cancer biomarkers, detectable in body fluids, offer valuable diagnostic information, and recent advances in nanotechnology have led to the development of highly sensitive nano-biosensors. This review explores recent advancements (2022-2024) in the field of ultrasensitive nano-biosensors, emphasizing the strategic integration of nanomaterials to enhance sensitivity and accuracy in cancer biomarker detection. It highlights how precise nanomaterial positioning in sensor components like electrodes and bioreceptors enables early cancer diagnosis at low biomarker concentrations. These innovations underscore the transformative potential of nanomaterials in revolutionizing early cancer diagnostics, improving patient care, and enhancing survival outcomes.

Pancreatic ductal adenocarcinoma (PDAC) is projected to be the second leading cause of cancer-related death by 2030. Early identification is rare, with a 5-year overall survival (OS) of less than 10%. Advances in the understanding of PDAC tumor biology are needed to improve these outcomes. Circulating tumor DNA (ctDNA) represents a promising novel biomarker in the identification and management of PDAC. Drawn from peripheral blood and analyzed using a variety of techniques, the detection of ctDNA in PDAC has been associated with shorter OS, minimal residual disease presence, and shorter recurrence-free survival. The use of ctDNA has also been examined as an indicator of therapeutic resistance, susceptibility to targeted therapy, and therapeutic response. While promising, ctDNA analysis is limited by its low rates of detection in some settings and lack of predictive ability in others. Many studies examining the utility of ctDNA for the management of PDAC have been relatively small retrospective cohort studies. The current findings will need to be validated by incorporation of ctDNA analysis into cancer registries and larger prospective studies. Given the current, rapid evolution in the field, it is possible that with time, ctDNA will be more routinely incorporated into the clinical management of PDAC.

Extracellular vesicles (EVs) have emerged as promising biomarkers in liquid biopsy, owing to their ubiquitous presence in bodily fluids and their ability to carry disease-related cargo. Recognizing their significance in disease diagnosis and treatment, substantial efforts have been dedicated to developing efficient methods for EV isolation, detection, and analysis. EVs, heterogeneous membrane-encapsulated vesicles secreted by all cells, contain bioactive substances capable of modulating recipient cell biology upon internalization, including proteins, lipids, DNA, and various RNAs. Their prevalence across bodily fluids has positioned them as pivotal mediators in physiological and pathological processes, notably in cancer, where they hold potential as straightforward tumor biomarkers. This review offers a comprehensive examination of advanced nanotechnology-based techniques for detecting lung cancer through EV analysis. It begins by providing a brief overview of exosomes and their role in lung cancer progression. Furthermore, this review explores the evolving landscape of EV isolation and cargo analysis, highlighting the importance of characterizing specific biomolecular signatures within EVs for improved diagnostic accuracy in lung cancer patients. Innovative strategies for enhancing the sensitivity and specificity of EV isolation and detection, including the integration of microfluidic platforms and multiplexed biosensing technologies are summarized. The discussion then extends to key challenges associated with EV-based liquid biopsies, such as the standardization of isolation and detection protocols and the establishment of robust analytical platforms for clinical translation. This review highlights the transformative impact of EV-based liquid biopsy in lung cancer diagnosis, heralding a new era of personalized medicine and improved patient care.

Cancer is an important disease that threatens human life and health. Many natural compounds from plants have been found to have a better inhibitory effect on cancer, and flavonoids are one of them. Tangeretin, a flavonoid, is widely present in a variety of citrus plants and has been shown to have a variety of biological activities that can inhibit tumor cells. Tangeretin can inhibit the growth, proliferation, and metastasis of cancer cells by acting on JAK/STAT (Janus Kinase/signal transducer and activator of transcription) and caspase-3 signal transduction and by regulating the cell cycle of tumor cells. Tangeretin can also work with other chemotherapy drugs, such as cisplatin, to reduce the drug resistance of cancer cells and improve the therapeutic effect of chemotherapy drugs. This review summarizes the effects of tangeretin on various cancers.

There is an unmet need for effective ovarian cancer screening and diagnostic approaches that enable earlier-stage cancer detection and increased overall survival. We have developed a high-performing accessible approach that evaluates cfDNA fragmentomes and protein biomarkers to detect ovarian cancer.

Programmed cell death (PCD) is closely related to the occurrence, development, and treatment of breast cancer. The aim of this study was to investigate the association between various programmed cell death patterns and the prognosis of breast cancer (BRCA) patients.

The levels of 19 different programmed cell deaths in breast cancer were assessed by ssGSEA analysis, and these PCD scores were summed to obtain the PCDS for each sample. The relationship of PCDS with immune as well as metabolism-related pathways was explored. PCD-associated subtypes were obtained by unsupervised consensus clustering analysis, and differentially expressed genes between subtypes were analyzed. The prognostic signature (PCDRS) were constructed by the best combination of 101 machine learning algorithm combinations, and the C-index of PCDRS was compared with 30 published signatures. In addition, we analyzed PCDRS in relation to immune as well as therapeutic responses. The distribution of genes in different cells was explored by single-cell analysis and spatial transcriptome analysis. Potential drugs targeting key genes were analyzed by Cmap. Finally, the expression levels of key genes in clinical tissues were verified by RT-PCR.

PCDS showed higher levels in cancer compared to normal. Different PCDS groups showed significant differences in immune and metabolism-related pathways. PCDRS, consisting of seven key genes, showed robust predictive ability over other signatures in different datasets. The high PCDRS group had a poorer prognosis and was strongly associated with a cancer-promoting tumor microenvironment. The low PCDRS group exhibited higher levels of anti-cancer immunity and responded better to immune checkpoint inhibitors as well as chemotherapy-related drugs. Clofibrate and imatinib could serve as potential small-molecule complexes targeting SLC7A5 and BCL2A1, respectively. The mRNA expression levels of seven genes were upregulated in clinical cancer tissues.

PCDRS can be used as a biomarker to assess the prognosis and treatment response of BRCA patients, which offers novel insights for prognostic monitoring and treatment personalization of BRCA patients.

Cancer has emerged as a global health crisis, claiming millions of lives annually. Dendrimers and dendronized nanoparticles, a novel class of nanoscale molecules with highly branched three-dimensional macromolecular structures, have gained significant attention in cancer treatment and diagnosis due to their unique properties. These dendritic macromolecules offer a precisely controlled branching architecture, enabling functionalization with specific targeting molecules to enhance the selective delivery of therapeutic agents to tumor cells while minimizing systemic toxicity. Through surface modifications and the incorporation of various components, dendrimers demonstrate remarkable adaptability as nanocarriers for biomedical imaging and theranostic applications. Surface functionalization strategies, including PEGylation and ligand attachment (e.g., folic acid, RGD peptide, lactobionic acid), further enhance biocompatibility and facilitate targeted tumor cell imaging. Leveraging their improved biocompatibility and target specificity, dendritic nanosystems offer heightened sensitivity and precision in cancer diagnostics. Notably, the encapsulation of metal nanoparticles within dendrimers, such as gold nanoparticles, has shown promise in enhancing tumor imaging capabilities. Ongoing advancements in nanotechnology are poised to increase the sophistication and complexity of dendrimer-based systems, highlighting their potential as nanocarriers in drug delivery platforms, with a growing number of clinical trials on the horizon. This review provides a comprehensive overview of the potential and future prospects of dendrimers and dendrimer-based nanocarriers in targeted cancer therapy and diagnosis, exploring their ability to enhance biocompatibility, reduce toxicity, and improve therapeutic outcomes across various malignancies.

Malignant pericardial effusions are associated with a poor prognosis. Pericardial fluid cytology and pericardial biopsy are the primary methods for diagnosis. This study aimed to conduct a multi-institutional analysis to compare the diagnostic sensitivity of cytology and biopsy, and to investigate potential explanations for false-negative results in cytology.

A retrospective review of pericardial fluid cytology cases with concurrent biopsy was conducted across four different institutions. Results were compared using standard statistical methods with attention to sensitivity and histologic distribution. False-negative cytology cases were investigated for further exploration.

A total of 309 cases were collected, of which 99 (32.0%) were confirmed malignant through repeat sampling or clinical history. Pericardial fluid cytology and biopsy identified 84 and 64 malignant cases, respectively. Our findings confirmed significantly higher sensitivity of cytology compared to biopsy (84.8% vs 65.7%). The most common sites of origin were lung, breast, and gastrointestinal, with adenocarcinoma being the most prevalent histologic subtype. Histologic review of 12 false-negative cytology cases revealed three key explanations; lymphoma was the most common missed diagnosis (33.3%); fibrinous pericarditis obscures neoplastic cells on the pericardial surface; and pericardial involvement can be seen without extension into the pericardial space.

This study demonstrated diagnostic superiority of pericardial fluid cytology over biopsy in the evaluation of malignant pericardial effusions. We identified several limitations in fluid cytology causing false negatives. In the context of an underlying malignancy with pericardial effusion, pathologists should consider immunohistochemistry studies to aid on the diagnosis.

Liquid biopsy technology is non-invasive and convenient, and is currently an emerging technology for cancer screening. Among them, clustered regularly interspaced short palindromic repeats (CRISPR)-CRISPR associated protein 12a (Cas12a) based nucleic acid detection technology has the advantages of high sensitivity, rapidity, and easy operation. However, CRISPR-Cas12a does not discriminate single-base mismatches of targets well enough to meet the needs of clinical detection. Herein, we developed the Triple-Mismatch Differentiating (TMD) assay. This assay amplified the small thermodynamic difference in mismatches at one site at the level of CRISPR-Cas12a activation to a significant thermodynamic difference at three sites at both the level of CRISPR-Cas12a activation and trans-cleavage, which greatly improves the ability of CRISPR-Cas12a to discriminate between base mismatches. Our manipulation greatly improved the specificity of the CRISPR-Cas12a system while maintaining its inherent sensitivity and simplicity, increasing the detection limit to 0.0001%. When testing samples from pancreatic cancer patients, our results were highly consistent with NGS sequencing results. We believe that the TMD assay will provide a new technology for early cancer detection and will be widely used in the clinical practice.

Cancer screening plays a critical role in early disease detection and improving outcomes. In Australia, established screening protocols for colorectal, breast and cervical cancer have significantly contributed to timely cancer detection. However, the recent introduction of multi-cancer early detection (MCED) tests arguably can disrupt current screening, yet the extent to which these tests provide additional benefits remains uncertain. We present the development and initial validation of a system dynamics (SD) model that estimates the additional cancer detections and costs associated with MCED tests.

This article describes the development of a simulation model built to evaluate the additional patient diagnoses and the economic impact of incorporating MCED testing alongside Australia's well-established standard of care (SOC) screening programs for colorectal, breast, cervical and lung cancers. The model was designed to estimate the additional number of patients diagnosed at each cancer stage (stage I, II, III, IV, or unknown) and the associated costs. This simulation model allows for the analysis of multiple scenarios under a plausible set of assumptions regarding population-level participation rates.

An SD model was developed to represent the existing SOC national cancer screening pathways and to integrate potential clinical pathways that could be introduced by MCED tests. The SD model was built to investigate three scenarios for the use of MCED testing: firstly, to explore the viability of MCED testing as a substitute among individuals who are not opting for SOC screening for any reason; secondly, to implement MCED testing exclusively for individuals ineligible for SOC screening, yet have high-risk characteristics; and thirdly, to employ MCED testing after SOC screening to serve as a triaging/confirmatory tool for individuals receiving inconclusive test results. The three primary scenarios were constructed by varying diagnostic accuracy and uptake rates of MCED tests.

The clinical utility and outcomes of MCED testing for screening and early detection still lack comprehensive evidence. Nonetheless, this simulation model facilitates a thorough analysis of MCED tests within the Australian healthcare context, providing insights into potential additional detections and costs to the healthcare system, which may help prioritise future evidence development. The adaptable yet novel SD model presented herein is anticipated to be of considerable interest to industry, policymakers, consumers and clinicians involved in informing clinical and economic decisions regarding integrating MCED tests as cancer screening and early detection tools. The expected results of applying this SD model will determine whether using MCED testing in conjunction with SOC screening offers any potential benefits, possibly guiding policy decisions and clinical practices towards the adoption of MCED tests.