Detection Of Early-stage Cancer Research Articles

Prediction of tissue-of-origin of early stage cancers using serum miRNomes.

Noninvasive detection of early stage cancers with accurate prediction of tumor tissue-of-origin could improve patient prognosis. Because miRNA profiles differ between organs, circulating miRNomics represent a promising method for early detection of cancers, but this has not been shown conclusively. A serum miRNA profile (miRNomes)-based classifier was evaluated for its ability to discriminate cancer types using advanced machine learning. The training set comprised 7931 serum samples from patients with 13 types of solid cancers and 5013 noncancer samples. The validation set consisted of 1990 cancer and 1256 noncancer samples. The contribution of each miRNA to the cancer-type classification was evaluated, and those with a high contribution were identified. Cancer type was predicted with an accuracy of 0.88 (95% confidence interval [CI] = 0.87 to 0.90) in all stages and an accuracy of 0.90 (95% CI = 0.88 to 0.91) in resectable stages (stages 0-II). The F1 score for the discrimination of the 13 cancer types was 0.93. Optimal classification performance was achieved with at least 100 miRNAs that contributed the strongest to accurate prediction of cancer type. Assessment of tissue expression patterns of these miRNAs suggested that miRNAs secreted from the tumor environment could be used to establish cancer type-specific serum miRNomes. This study demonstrates that large-scale serum miRNomics in combination with machine learning could lead to the development of a blood-based cancer classification system. Further investigations of the regulating mechanisms of the miRNAs that contributed strongly to accurate prediction of cancer type could pave the way for the clinical use of circulating miRNA diagnostics.

Clinical Value of Growth Differentiation Factor 15 Detection in the Diagnosis of Early Liver Cancer Based on Data Mining.

The incidence of liver cancer is increasing year by year, and how to effectively diagnose early-stage liver cancer and improve the survival rate of liver cancer patients has become one of the current research topics of concern. Aiming at this problem, it is of great significance for the diagnosis of early liver cancer. With the in-depth research on the diagnosis of early-stage liver cancer, the research on growth differentiation factor 15 is gradually carried out, and its performance advantages are of great significance to solve the problem of detection and diagnosis of early-stage liver cancer. This study can improve the accuracy of early diagnosis of liver cancer. The purpose of this paper is to study the application of data mining in the study of clinical value of growth and differentiation factor 15 detection and diagnosis of early liver cancer. In this paper, the data mining algorithm is analyzed, the performance of the algorithm is experimentally analyzed, and the relevant theoretical formulas are used to explain. The results showed that the expression level of GDF-15 was significant in early primary liver cancer (tumor diameter <2.5 cm). Different from normal liver tissue (P < 0.01), there was a significant difference (P < 0.01) compared with adjacent tissue (P < 0.01). Serum GDF-15 can be used as a tumor marker for predicting early stage liver cancer. The high expression of GDF-15 in early stage liver cancer is an independent risk factor affecting the prognosis of liver cancer patients.

LCSCNet: A multi-level approach for lung cancer stage classification using 3D dense convolutional neural networks with concurrent squeeze-and-excitation module

Lung cancer, the deadliest disease worldwide, poses a massive threat to humankind. Various researchers have designed Computer-Aided-Diagnosis systems for the early-stage detection of lung cancer. However, patients are primarily diagnosed in advanced stages when treatment becomes complicated and dependent on multiple factors like size, nature, location of the tumor, and proper cancer staging. TNM (Tumor, Node, and Metastasis) staging provides all this information. This study aims to develop a novel and efficient approach to classify lung cancer stages based on TNM standards. We propose a multi-level 3D deep convolutional neural network, LCSCNet (Lung Cancer Stage Classification Network). The proposed network architecture consists of three similar classifier networks to classify three labels, T, N, and M-labels. First, we pre-process the data, in which the CT images are augmented, and the label files are processed to get the corresponding TNM labels. For the classification network, we implement a dense convolutional neural network with a concurrent squeeze & excitation module and asymmetric convolutions for classifying each label separately. The overall stage is determined by combining all three labels. The concurrent squeeze & excitation module helps the network focus on the essential information of the image, due to which the classification performance is enhanced. The asymmetric convolutions are introduced to reduce the computation complexity of the network. Two publicly available datasets are used for this study. We achieved average accuracies of 96.23% for T-Stage, 97.63% for N-Stage, and 96.92% for M-Stage classification. Furthermore, an overall stage classification accuracy of 97% is achieved.

Multidimensional Cell-Free DNA Fragmentomic Assay for Detection of Early-Stage Lung Cancer.

Rationale: Cell-free DNA (cfDNA) analysis holds promise for early detection of lung cancer and benefits patients with higher survival. However, the detection sensitivity of previous cfDNA-based studies was still low to suffice for clinical use, especially for early-stage tumors. Objectives: Establish an accurate and affordable approach for early-stage lung cancer detection by integrating cfDNA fragmentomics and machine learning models. Methods: This study included 350 participants without cancer and 432 participants with cancer. The participants' plasma cfDNA samples were profiled by whole-genome sequencing. Multiple cfDNA features and machine learning models were compared in the training cohort to achieve an optimal model. Model performance was evaluated in three validation cohorts. Measurements and Main Results: A stacked ensemble model integrating five cfDNA features and five machine learning algorithms constructed in the training cohort (cancer: 113; healthy: 113) outperformed all the models built on individual feature-algorithm combinations. This integrated model yielded superior sensitivities of 91.4% at 95.7% specificity for cohort validation I (area under the curve [AUC], 0.984), 84.7% at 98.6% specificity for validation II (AUC, 0.987), and 92.5% at 94.2% specificity for additional validation (AUC, 0.974), respectively. The model's high performance remained consistent when sequencing depth was down to 0.5× (AUC, 0.966-0.971). Furthermore, our model is sensitive to identifying early pathological features (83.2% sensitivity for stage I, 85.0% sensitivity for <1 cm tumor at the 0.66 cutoff). Conclusions: We have established a stacked ensemble model using cfDNA fragmentomics features and achieved superior sensitivity for detecting early-stage lung cancer, which could promote early diagnosis and benefit more patients.

PSMA1-mediated ultrasmall gold nanoparticles facilitate tumor targeting and MR/CT/NIRF multimodal detection of early-stage prostate cancer

Prostate-specific membrane antigen (PSMA) is a prominent biomarker for prostate cancer (PCa) diagnosis. Safe contrast agents able to render the expression and distribution of PSMA would facilitate early accurate screening and prognostic prediction of PCa. However, current Gd-containing nanoparticles are often limited by nonspecific redistribution in mononuclear phagocyte system (MPS) and inadequate perfusion to target sites. Besides, intrinsic defects of magnetic resonance (MR) equipment also hamper their use for precisely depicting PSMA details. Herein, we devised a novel noninvasive MR/CT/NIRF multimodal contrast agent (AGGP) coordinated to a high-affinity PSMA ligand (PSMA1) to specifically detect and quantify PSMA expression in PCa lesions, which exhibited formidable tripe-modal signal augments, preferential PSMA targeting, effective MPS escaping and profitable renal-clearable behavior in living mice. Biocompatibility and histopathological studies substantiated high security of AGGP in vivo, opening the door to future opportunities for improving early-stage PCa detection and clinical implementation of more effective multifunctional nanotherapeutics.

Peptide-based optical biosensors: A promising approach for early-stage cancer detection

Cancer is the leading cause of 10 million deaths worldwide in 2020, which is exponentially increasing per year. The early detection and effective treatment of cancer can save millions of lives worldwide. Cancer diagnostic methods are scarce due to their high cost, laborious instrumentation, high maintenance, time-consuming, and complex nature. Furthermore, current cancer detection methods are also inconvenient and necessitate the use of technical experts. However, peptide-based optical biosensors are fast, and compact with a low detection limit and high selectivity, hence having a lot of potential for diagnosing different types of cancer. The present work aims to provide an overview of various types of peptide-based optical biosensors (OBS) for the early-stage detection of cancer. Here, the peptides with high specificity, biocompatibility, small size, and numerous conjugation possibilities incorporated with nanomaterials, are reported for the fabrication of optical biosensors to diagnose cancer. Peptide-based optical biosensors are known to overcome the limitations of the conventional method by providing high sensitivity, and portability, with rapid and real-time detection of all types of cancer cells. Moreover, in comparison to conventional methods, cancer can be detected in a quite few million tumor tissues using optical biosensors. This review mainly focused on the optical biosensors such as colorimetric, fluorescence, surface plasmon resonance (SPR), chemiluminescence (CL), and photoelectrochemical derived from the different types of peptides, including their working principle and conjugation with peptides. We have also discussed the advantages and disadvantages of current peptide-based optical biosensors for early-stage cancer detection, as well as future prospects.

Hydroxymethylation profile of cell-free DNA is a biomarker for early colorectal cancer

Early detection of cancer will improve survival rates. The blood biomarker 5-hydroxymethylcytosine has been shown to discriminate cancer. In a large covariate-controlled study of over two thousand individual blood samples, we created, tested and explored the properties of a 5-hydroxymethylcytosine-based classifier to detect colorectal cancer (CRC). In an independent validation sample set, the classifier discriminated CRC samples from controls with an area under the receiver operating characteristic curve (AUC) of 90% (95% CI [87, 93]). Sensitivity was 55% at 95% specificity. Performance was similar for early stage 1 (AUC 89%; 95% CI [83, 94]) and late stage 4 CRC (AUC 94%; 95% CI [89, 98]). The classifier could detect CRC even when the proportion of tumor DNA in blood was undetectable by other methods. Expanding the classifier to include information about cell-free DNA fragment size and abundance across the genome led to gains in sensitivity (63% at 95% specificity), with similar overall performance (AUC 91%; 95% CI [89, 94]). We confirm that 5-hydroxymethylcytosine can be used to detect CRC, even in early-stage disease. Therefore, the inclusion of 5-hydroxymethylcytosine in multianalyte testing could improve sensitivity for the detection of early-stage cancer.

10P Detection of early-stage lung cancer using 5-hydroxymethylcytosine signatures in circulating cell-free DNA

10P Detection of early-stage lung cancer using 5-hydroxymethylcytosine signatures in circulating cell-free DNA

Data-driven analysis of a validated risk score for ovarian cancer identifies clinically distinct patterns during follow-up and treatment

BackgroundOvarian cancer is the eighth most common cancer among women and due to late detection prognosis is poor with an overall 5-year survival of 30–50%. Novel biomarkers are needed to reduce diagnostic surgery and enable detection of early-stage cancer by population screening. We have previously developed a risk score based on an 11-biomarker plasma protein assay to distinguish benign tumors (cysts) from malignant ovarian cancer in women with adnexal ovarian mass.MethodsProtein concentrations of 11 proteins were characterized in plasma from 1120 clinical samples with a custom version of the proximity extension assay. The performance of the assay was evaluated in terms of prediction accuracy based on receiver operating characteristics (ROC) and multiple hypothesis adjusted Fisher’s Exact tests on achieved sensitivity and specificity.ResultsThe assay’s performance is validated in two independent clinical cohorts with a sensitivity of 0.83/0.91 and specificity of 0.88/0.92. We also show that the risk score follows the clinical development and is reduced upon treatment, and increased with relapse and cancer progression. Data-driven modeling of the risk score patterns during a 2-year follow-up after diagnosis identifies four separate risk score trajectories linked to clinical development and survival. A Cox proportional hazard regression analysis of 5-year survival shows that at time of diagnosis the risk score is the second-strongest predictive variable for survival after tumor stage, whereas MUCIN-16 (CA-125) alone is not significantly predictive.ConclusionThe robust performance of the biomarker assay across clinical cohorts and the correlation with clinical development indicates its usefulness both in the diagnostic work-up of women with adnexal ovarian mass and for predicting their clinical course.

Multiplex Analysis of CircRNAs from Plasma Extracellular Vesicle-Enriched Samples for the Detection of Early-Stage Non-Small Cell Lung Cancer.

Background: The analysis of liquid biopsies brings new opportunities in the precision oncology field. Under this context, extracellular vesicle circular RNAs (EV-circRNAs) have gained interest as biomarkers for lung cancer (LC) detection. However, standardized and robust protocols need to be developed to boost their potential in the clinical setting. Although nCounter has been used for the analysis of other liquid biopsy substrates and biomarkers, it has never been employed for EV-circRNA analysis of LC patients. Methods: EVs were isolated from early-stage LC patients (n = 36) and controls (n = 30). Different volumes of plasma, together with different number of pre-amplification cycles, were tested to reach the best nCounter outcome. Differential expression analysis of circRNAs was performed, along with the testing of different machine learning (ML) methods for the development of a prognostic signature for LC. Results: A combination of 500 μL of plasma input with 10 cycles of pre-amplification was selected for the rest of the study. Eight circRNAs were found upregulated in LC. Further ML analysis selected a 10-circRNA signature able to discriminate LC from controls with AUC ROC of 0.86. Conclusions: This study validates the use of the nCounter platform for multiplexed EV-circRNA expression studies in LC patient samples, allowing the development of prognostic signatures.

The role of low-dose computed tomography in lung cancer screening

Introduction: Lung cancer is a serious public health problem in Hungary, but currently there is no nationwide screening program for the early detection of the disease. Several technological developments have been carried out recently to improve the effectiveness of lung cancer screening. Low-dose computed tomography (LDCT) is one of these technologies. Objective: The objective of this study is to provide an overview on guidelines and recommendations related to the application of LDCT as a novel lung cancer screening modality and to summarize the scientific literature and screening practices of other countries. Method: We performed a targeted literature review to collect information about LDCT in lung cancer screening. We searched in publicly available databases for guidelines and recommendations as well as scientific publications on screening and early detection of lung cancer. Results: In our literature search, we identified 16 guidelines and recommendations for lung cancer screening and LDCT. Regarding the efficacy of LDCT lung cancer screening, 10 foreign randomized controlled trials and 2 Hungarian trials were reviewed. Information on screening practices of 10 European countries were identified. Discussion: Evidences suggest that LDCT screening improves the detection of lung cancer, especially at early stages, and reduces cancer-specific mortality. Conclusion: In summary, in the high-risk population, LDCT can be considered an effective screening modality for the early-stage detection of lung cancer and for reducing lung cancer mortality. The ongoing Hungarian and foreign pilot programs may provide futher evidence for the implementation of a nationwide LDCT lung cancer screening program.

Interval Advanced Gastric Cancer After Negative Endoscopy

Esophagogastroduodenoscopy (EGD) is effective in reducing gastric cancer mortality through detection of early-stage cancer in areas with a high prevalence of gastric cancer. Although the risk of post-endoscopy advanced gastric cancer (AGC) is low, interval AGC remains a concern. We investigated the characteristics and predictors of interval AGC after negative EGD. We included 1257 patients with gastric cancer within 6 to 36 months of a "cancer-negative" index EGD between 2005 and 2021 at a tertiary university hospital in South Korea. Observation time on the index EGD was used as a quality indicator. We compared the clinical and endoscopic characteristics and quality indicators between interval AGC and screen-detected early gastric cancer (EGC). Within 6 to 36 months of negative EGD, 102 AGCs (8.1%) and 1155 EGCs (91.9%) were identified. The percentage of patients with shorter observation time (<3 minutes) in the index EGD was higher in the interval AGC group than in the detected EGC group (P= .002). A multivariable analysis comparing screen-detected EGD and interval AGC was adjusted for age, sex, family history of gastric cancer, H.pylori status, endoscopic findings, and endoscopy-related factors including gastric observation time and interval time. A shorter observation time (<3 minutes) (odds ratio, 2.27; 95% confidence interval, 1.20-4.30), and interval time >2 years (odds ratio, 1.84; 95% confidence interval, 1.04-3.24) were associated with an increased risk of interval AGC. A shorter observation time during index EGD is an important predictor of interval AGC. Further, withdrawal time longer than 3 minutes may be a quality indicator for screening EGD.

A Road Map toward Field-Effect Transistor Biosensor Technology for Early Stage Cancer Detection.

Field effect transistor (FET)-based nanoelectronic biosensor devices provide a viable route for specific and sensitive detection of cancer biomarkers, which can be used for early stage cancer detection, monitoring the progress of the disease, and evaluating the effectiveness of therapies. On the road to implementation of FET-based devices in cancer diagnostics, several key issues need to be addressed: sensitivity, selectivity, operational conditions, anti-interference, reusability, reproducibility, disposability, large-scale production, and economic viability. To address these well-known issues, significant research efforts have been made recently. An overview of these efforts is provided here, highlighting the approaches and strategies presently engaged at each developmental stage, from the design and fabrication of devices to performance evaluation and data analysis. Specifically, this review discusses the multistep fabrication of FETs, choice of bioreceptors for relevant biomarkers, operational conditions, measurement configuration, and outlines strategies to improve the sensing performance and reach the level required for clinical applications. Finally, this review outlines the expected progress to the future generation of FET-based diagnostic devices and discusses their potential for detection of cancer biomarkers as well as biomarkers of other noncommunicable and communicable diseases.

595P Multi-marker liquid biopsy for detection of early-stage ovarian cancer

Ovarian cancer is often not detected (∼70% of the cases) until it has spread to regional or distant sites, thus limiting curative options. In contrast, when detected in a localized state, the 5-year survival rate increases dramatically to ∼90% in comparison to the ∼30% survival rate for distant cases. Currently, only 15% of cases are detected at early stages highlighting the need for the development of non-invasive detection strategies such as liquid biopsies.

Clinical Performance of a Multivariate Index Assay in Detecting Early-Stage Ovarian Cancer in Filipino Women.

This study evaluated the clinical performance and overall utility of a multivariate index assay in detecting early-stage ovarian cancer in a Filipino population. This is a prospective cohort study among Filipino women undergoing assessment for an ovarian mass in a tertiary center. Patients diagnosed with early-stage ovarian cancer and who underwent a physical examination before level III specialist ultrasonographic and Doppler evaluation, multivariate index assay (MIA2G), and surgery for an adnexal mass were included in this study. Ovarian tumors were classified as high-risk for malignancy based on the IOTA-LR2 score. The ovarian imaging and biomarker results were correlated with the reference standard: surgico-pathologic findings. The MIA2G exhibited the best overall performance among individual classifiers with a sensitivity of 91.7% and NPV of 84.7%, with a concomitant higher sensitivity in early-stage disease, whether as an individual classifier (93.5%) or in serial combination with ultrasound (85.5%). The performance of biomarkers (specificity, positive predictive values, and AUROC) such as MIA2G and CA-125 significantly improved when combined with an ultrasound risk scoring approach (p < 0.01). MIA2G showed a higher sensitivity for detecting lesions among EOC and late-stage ovarian cancers than otherwise. The application of biomarkers for evaluating ovarian masses in our local setting is secondary to ultrasound but adopting multivariate index assays rather than CA-125 would increase the detection of early-stage ovarian cancers regardless of menopausal status. This is most relevant in areas where level III sonographers or gynecologic oncologists are limited and preoperative referrals to these specialists can improve the survival of our patients.

Early-Stage Detection of Ovarian Cancer Based on Clinical Data Using Machine Learning Approaches.

One of the common types of cancer for women is ovarian cancer. Still, at present, there are no drug therapies that can properly cure this deadly disease. However, early-stage detection could boost the life expectancy of the patients. The main aim of this work is to apply machine learning models along with statistical methods to the clinical data obtained from 349 patient individuals to conduct predictive analytics for early diagnosis. In statistical analysis, Student’s t-test as well as log fold changes of two groups are used to find the significant blood biomarkers. Furthermore, a set of machine learning models including Random Forest (RF), Support Vector Machine (SVM), Decision Tree (DT), Extreme Gradient Boosting Machine (XGBoost), Logistic Regression (LR), Gradient Boosting Machine (GBM) and Light Gradient Boosting Machine (LGBM) are used to build classification models to stratify benign-vs.-malignant ovarian cancer patients. Both of the analysis techniques recognized that the serumsamples carbohydrate antigen 125, carbohydrate antigen 19-9, carcinoembryonic antigen and human epididymis protein 4 are the top-most significant biomarkers as well as neutrophil ratio, thrombocytocrit, hematocrit blood samples, alanine aminotransferase, calcium, indirect bilirubin, uric acid, natriumas as general chemistry tests. Moreover, the results from predictive analysis suggest that the machine learning models can classify malignant patients from benign patients with accuracy as good as 91%. Since generally, early-stage detection is not available, machine learning detection could play a significant role in cancer diagnosis.

Does completion of radical hysterectomy improve oncological outcomes of women with clinical early-stage cervical cancer and intraoperative detection of nodal involvement?: protocol for a systematic review and meta-analysis

IntroductionThe management of women with clinical early-stage cervical cancer and lymph node involvement detected intraoperatively is heterogeneous and controversial. This paper presents the protocol of a systematic review and meta-analysis...

Novel multiplex stool-based assay for the detection of early-stage colon cancer in a Chinese population.

BACKGROUNDStool DNA (sDNA) methylation analysis is a promising, noninvasive approach for colorectal cancer screening; however, reliable biomarkers for detecting early-stage colon cancer (ECC) are lacking, particularly in the Chinese population.AIMTo identify a novel stool-based assay that can improve the effectiveness of ECC screening.METHODSA blinded case-control study was performed using archived stool samples from 125 ECC patients, and 125 control subjects with normal colonoscopy. The cohort was randomly divided into training and test sets at a 1.5:1 ratio. Targeted bisulfite sequencing (TBSeq) was conducted on five pairs of preoperative and postop-erative sDNA samples from ECC patients to identify DNA methylation biomarkers, which were validated using pyrosequencing. By logistic regression analysis, a multiplex stool-based assay was developed in the training set, and the detection performance was further assessed in the test set and combined set. The χ2 test was used to investigate the association of detection sensitivity with clinico-pathological features.RESULTSFollowing TBSeq, three hypermethylated cytosine-guanine sites were selected as biomarkers, including paired box 8, Ras-association domain family 1 and secreted frizzled-related protein 2, which differed between the groups and were involved in important cancer pathways. An sDNA panel containing the three biomarkers was constructed with a logistic model. Receiver operating characteristic (ROC) analysis revealed that this panel was superior to the fecal immunochemical test (FIT) or serum carcinoembryonic antigen for the detection of ECC. We further found that the combination of the sDNA panel with FIT could improve the screening effectiveness. In the combined set, the sensitivity, specificity and area under the ROC curve for this multiplex assay were 80.0%, 93.6% and 0.918, respectively, and the performance remained excellent in the subgroup analysis by tumor stage. In addition, the detection sensitivity did not differ with tumor site, tumor stage, histological differentiation, age or sex, but was significantly higher in T4 than in T1-3 stage tumors (P = 0.041).CONCLUSIONWe identified a novel multiplex stool-based assay combining sDNA methylation biomarkers and FIT, which could detect ECC with high sensitivity and specificity throughout the colon, showing a promising application perspective.

Nano-omics: nanotechnology-based multidimensional harvesting of the blood-circulating cancerome.

Over the past decade, the development of 'simple' blood tests that enable cancer screening, diagnosis or monitoring and facilitate the design of personalized therapies without the need for invasive tumour biopsy sampling has been a core ambition in cancer research. Data emerging from ongoing biomarker development efforts indicate that multiple markers, used individually or as part of a multimodal panel, are required to enhance the sensitivity and specificity of assays for early stage cancer detection. The discovery of cancer-associated molecular alterations that are reflected in blood at multiple dimensions (genome, epigenome, transcriptome, proteome and metabolome) and integration of the resultant multi-omics data have the potential to uncover novel biomarkers as well as to further elucidate the underlying molecular pathways. Herein, we review key advances in multi-omics liquid biopsy approaches and introduce the 'nano-omics' paradigm: the development and utilization of nanotechnology tools for the enrichment and subsequent omics analysis of the blood-circulating cancerome.

A concise review on potential cancer biomarkers and advanced manufacturing of smart platform-based biosensors for early-stage cancer diagnostics

Advanced diagnostic technologies for early-stage detection of cancer are essential for proper management and guided therapeutics to increase chances of survival for patients. Presently, most forms of cancer get diagnosed post metastasis throughout the body resulting in late-stage treatment leading to fatality which may have otherwise been avoided. In order to overcome this limitation in cancer management, accurate, rapid and effective clinical screening diagnostic methods for cancer detection need to be urgently developed. In this review, we have compiled the extensive research being carried out in recent times on fabrication of biosensors for cancer diagnostics, as these devices may be customised to detect a particular target bio analyte (disease biomarker) of a specific cancer type, with the help of a corresponding bioreceptor (i.e., nucleic acid, protein, enzyme) using electrochemical/optical/piezoelectric signals that can be analysed with a special emphasis on the latest advancement in Internet of Things (IoT) based sensing technologies. We have correspondingly enumerated the latest cancer biomarkers that have been identified and may be used for potential diagnosis as well as to determine the effectiveness of drugs at different target sites and anticancer chemotherapeutic medication using sensor-based technology. This review provides a restructured base for ongoing research on cancer diagnostics, which may potentially help develop accurate point-of-care rapid diagnostic screening methods, for effective monitoring, management and treatment of angiogenesis and cancer metastasis at an early stage and reduce fatalities.