Transformer-based representation learning and multiple-instance learning for cancer diagnosis exclusively from raw sequencing fragments of bisulfite-treated plasma cell-free DNA.

Early cancer diagnosis from bisulfite-treated cell-free DNA (cfDNA) fragments requires tedious data analytical procedures. Here, we present a deep-learning-based approach for early cancer interception and diagnosis (DECIDIA) that can achieve accurate cancer diagnosis exclusively from bisulfite-treated cfDNA sequencing fragments. DECIDIA relies on transformer-based representation learning of DNA fragments and weakly supervised multiple-instance learning for classification. We systematically evaluate the performance of DECIDIA for cancer diagnosis and cancer type prediction on a curated dataset of 5389 samples that consist of colorectal cancer (CRC; n = 1574), hepatocellular cell carcinoma (HCC; n = 1181), lung cancer (n = 654), and non-cancer control (n = 1980). DECIDIA achieved an area under the receiver operating curve (AUROC) of 0.980 (95% CI, 0.976-0.984) in 10-fold cross-validation settings on the CRC dataset by differentiating cancer patients from cancer-free controls, outperforming benchmarked methods that are based on methylation intensities. Noticeably, DECIDIA achieved an AUROC of 0.910 (95% CI, 0.896-0.924) on the externally independent HCC testing set in distinguishing HCC patients from cancer-free controls, although there was no HCC data used in model development. In the settings of cancer-type classification, we observed that DECIDIA achieved a micro-average AUROC of 0.963 (95% CI, 0.960-0.966) and an overall accuracy of 82.8% (95% CI, 81.8-83.9). In addition, we distilled four sequence signatures from the raw sequencing reads that exhibited differential patterns in cancer versus control and among different cancer types. Our approach represents a new paradigm towards eliminating the tedious data analytical procedures for liquid biopsy that uses bisulfite-treated cfDNA methylome.

Large language model produces high accurate diagnosis of cancer from end-motif profiles of cell-free DNA.

Instruction-tuned large language models (LLMs) demonstrate exceptional ability to align with human intentions. We present an LLM-based model-instruction-tuned LLM for assessment of cancer (iLLMAC)-that can detect cancer using cell-free deoxyribonucleic acid (cfDNA) end-motif profiles. Developed on plasma cfDNA sequencing data from 1135 cancer patients and 1106 controls across three datasets, iLLMAC achieved area under the receiver operating curve (AUROC) of 0.866 [95% confidence interval (CI), 0.773-0.959] for cancer diagnosis and 0.924 (95% CI, 0.841-1.0) for hepatocellular carcinoma (HCC) detection using 16 end-motifs. Performance increased with more motifs, reaching 0.886 (95% CI, 0.794-0.977) and 0.956 (95% CI, 0.89-1.0) for cancer diagnosis and HCC detection, respectively, with 64 end-motifs. On an external-testing set, iLLMAC achieved AUROC of 0.912 (95% CI, 0.849-0.976) for cancer diagnosis and 0.938 (95% CI, 0.885-0.992) for HCC detection with 64 end-motifs, significantly outperforming benchmarked methods. Furthermore, iLLMAC achieved high classification performance on datasets with bisulfite and 5-hydroxymethylcytosine sequencing. Our study highlights the effectiveness of LLM-based instruction-tuning for cfDNA-based cancer detection.

Recent advances in living cell nucleic acid probes based on nanomaterials for early cancer diagnosis.

The early diagnosis of cancer is vital for effective treatment and improved prognosis. Tumor biomarkers, which can be used for the early diagnosis, treatment, and prognostic evaluation of cancer, have emerged as a topic of intense research interest in recent years. Nucleic acid, as a type of tumor biomarker, contains vital genetic information, which is of great significance for the occurrence and development of cancer. Currently, living cell nucleic acid probes, which enable the in situ imaging and dynamic monitoring of nucleic acids, have become a rapidly developing field. This review focuses on living cell nucleic acid probes that can be used for the early diagnosis of tumors. We describe the fundamental design of the probe in terms of three units and focus on the roles of different nanomaterials in probe delivery.

Worm-Based Diagnosis Combining Microfluidics toward Early Cancer Screening.

Early cancer diagnosis increases therapy efficiency and saves huge medical costs. Traditional blood-based cancer markers and endoscopy procedures demonstrate limited capability in the diagnosis. Reliable, non-invasive, and cost-effective methods are in high demand across the world. Worm-based diagnosis, utilizing the chemosensory neuronal system of C. elegans, emerges as a non-invasive approach for early cancer diagnosis with high sensitivity. It facilitates effectiveness in large-scale cancer screening for the foreseeable future. Here, we review the progress of a unique route of early cancer diagnosis based on the chemosensory neuronal system of C. elegans. We first introduce the basic procedures of the chemotaxis assay of C. elegans: synchronization, behavior assay, immobilization, and counting. Then, we review the progress of each procedure and the various cancer types for which this method has achieved early diagnosis. For each procedure, we list examples of microfluidics technologies that have improved the automation, throughput, and efficiency of each step or module. Finally, we envision that microfluidics technologies combined with the chemotaxis assay of C. elegans can lead to an automated, cost-effective, non-invasive early cancer screening technology, with the development of more mature microfluidic modules as well as systematic integration of functional modules.

Dutch, UK and US professionals' perceptions of screening for Barrett's esophagus and esophageal adenocarcinoma: a concept mapping study.

BACKGROUND: Novel, less-invasive technologies to screen for Barrett's esophagus (BE) may enable a paradigm shift in early detection strategies for esophageal adenocarcinoma (EAC). Understanding professionals' perspectives on screening is important to determine how to proceed. We aimed to explore and compare professionals' perceptions of screening for BE and EAC screening in three countries. METHODS: In this study, 29 Dutch, 20 British and 18 American health care professionals (clinicians, researchers and policy makers) participated in concept mapping: a mixed-methods consensus building methodology. Statements on perceived barriers, facilitators, advantages, disadvantages, implications or worries associated with screening for BE and EAC were collected in asynchronous digital brainstorm sessions. Subsequently, participants sorted the statements into groups according to thematic similarity and assessed the relevance of each statement in evaluating the acceptability of BE and EAC screening. Multidimensional scaling and cluster analysis were used to map the associations between generated statements. RESULTS: Professionals across three countries identified eight consistent themes that relate to their perceptions of screening for BE and EAC: (1) Benefits, (2) Harms, (3) Clinical effectiveness concerns, (4) Screening population, (5) Screening modality, (6) Resources, (7) Ownership, and (8) Public communication. Dutch and American professionals prioritized the potential health benefits of screening but also questioned clinical impact. In contrast, British participants prioritized identification of the screening population and suitable test. CONCLUSIONS: Most professionals see potential in less-invasive screening tests for BE and EAC but underline the need to define the target screening population and determine benefits and harms before widely employing them. Successful implementation will require thoughtful consideration of the involvement of general practitioners, readiness of endoscopy and pathology services, balanced public communication, and country-specific regulations.

Identifying the Common Cell-Free DNA Biomarkers across Seven Major Cancer Types.

Blood-based detection of circulating cell-free DNA (cfDNA) is a non-invasive and easily accessible method for early cancer detection. Despite the extensive utility of cfDNA, there are still many challenges to developing clinical biomarkers. For example, cfDNA with genetic alterations often composes a small portion of the DNA circulating in plasma, which can be confounded by cfDNA contributed by normal cells. Therefore, filtering out the potential false-positive cfDNA mutations from healthy populations will be important for cancer-based biomarkers. Additionally, many low-frequency genetic alterations are easily overlooked in a small number of cfDNA-based cancer tests. We hypothesize that the combination of diverse types of cancer studies on cfDNA will provide us with a new perspective on the identification of low-frequency genetic variants across cancer types for promoting early diagnosis. By building a standardized computational pipeline for 1358 cfDNA samples across seven cancer types, we prioritized 129 shard genetic variants in the major cancer types. Further functional analysis of the 129 variants found that they are mainly enriched in ribosome pathways such as cotranslational protein targeting the membrane, some of which are tumour suppressors, oncogenes, and genes related to cancer initiation. In summary, our integrative analysis revealed the important roles of ribosome proteins as common biomarkers in early cancer diagnosis.

Stratifying Risk for Pancreatic Cancer by Multiplexed Blood Test.

Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal disease, for which mortality closely parallels incidence. So far, the available techniques for PDAC detection are either too invasive or not sensitive enough. To overcome this limitation, here we present a multiplexed point-of-care test that provides a "risk score" for each subject under investigation, by combining systemic inflammatory response biomarkers, standard laboratory tests, and the most recent nanoparticle-enabled blood (NEB) tests. The former parameters are routinely evaluated in clinical practice, whereas NEB tests have been recently proven as promising tools to assist in PDAC diagnosis. Our results revealed that PDAC patients and healthy subjects can be distinguished accurately (i.e., 88.9% specificity, 93.6% sensitivity) by the presented multiplexed point-of-care test, in a quick, non-invasive, and highly cost-efficient way. Furthermore, the test allows for the definition of a "risk threshold", which can help clinicians to trace the optimal diagnostic and therapeutic care pathway for each patient. For these reasons, we envision that this work may accelerate progress in the early detection of PDAC and contribute to the design of screening programs for high-risk populations.

Generalized pancreatic cancer diagnosis via multiple instance learning and anatomically-guided shape normalization.

Pancreatic cancer is a highly malignant cancer type with a high mortality rate. As no obvious symptoms are associated with this cancer type, most of the diagnoses are made when the patients are already in a late stage. In this work, we propose an automated method for effective early diagnosis of pancreatic cancer based on multiple instance learning with contrast-enhanced CT images. In this method, diagnosis stability and generalizability were improved through shape normalization based on anatomical structures as well as instance-level contrastive learning. Specifically, anatomically-guided shape normalization were developed to reconstruct the pancreatic regions of interest by spatial transformations, account for larger tumor parts in these regions, and hence enhance the extraction of pancreatic features. Moreover, instance-level contrastive learning was employed to aggregate different types of tumor features within the multiple instance learning framework. This learning approach can maintain the tumor feature integrity and enhance the diagnosis stability. Finally, a balance-adjustment strategy was designed to alleviate the class imbalance problem caused by the scarcity of tumor samples. Extensive experimental results demonstrated remarkable performance of our method when conducted cross-validation on an in-house dataset with 310 patients and independent test on two unseen datasets (a private test set with 316 and a publicly-available test set with 281). The proposed strategies also led to significant improvements in generalizability. Besides, the clinical significance of the proposed method was further verified through two independent test results in which tumors smaller than 2 cm in diameter were identified at accuracies of 80.9% and 90.1%, respectively. Overall, our method provides a potentially successful tool for early diagnosis of pancreatic cancer. Our source codes will be released at https://github.com/SJTUBME-QianLab/MIL_PAdiagnosis.

Earlier cancer diagnosis in primary care: a feasibility economic analysis of ThinkCancer!

BACKGROUND: UK cancer survival rates are much lower compared with other high-income countries. In primary care, there are opportunities for GPs and other healthcare professionals to act more quickly in response to presented symptoms that might represent cancer. ThinkCancer! is a complex behaviour change intervention aimed at primary care practice teams to improve the timely diagnosis of cancer. AIM: To explore the costs of delivering the ThinkCancer! intervention to expedite cancer diagnosis in primary care. DESIGN & SETTING: Feasibility economic analysis using a micro-costing approach, which was undertaken in 19 general practices in Wales, UK. METHOD: From an NHS perspective, micro-costing methodology was used to determine whether it was feasible to gather sufficient economic data to cost the ThinkCancer! INTERVENTION: Owing to the COVID-19 pandemic, ThinkCancer! was mainly delivered remotely online in a digital format. Budget impact analysis (BIA) and sensitivity analysis were conducted to explore the costs of face-to-face delivery of the ThinkCancer! intervention as intended pre-COVID-19. RESULTS: The total costs of delivering the ThinkCancer! intervention across 19 general practices in Wales was £25 030, with an average cost per practice of £1317 (standard deviation [SD]: 578.2). Findings from the BIA indicated a total cost of £34 630 for face-to-face delivery. CONCLUSION: Data collection methods were successful in gathering sufficient health economics data to cost the ThinkCancer! INTERVENTION: Results of this feasibility study will be used to inform a future definitive economic evaluation alongside a pragmatic randomised controlled trial (RCT).

Revising the Suspected-Cancer Guidelines: Impacts on Patients' Primary Care Contacts and Costs.

OBJECTIVES: This study aimed to explore the impact of revising suspected-cancer referral guidelines on primary care contacts and costs. METHODS: Participants had incident cancer (colorectal, n = 2000; ovary, n = 763; and pancreas, n = 597) codes in the Clinical Practice Research Datalink or England cancer registry. Difference-in-differences analyses explored guideline impacts on contact days and nonzero costs between the first cancer feature and diagnosis. Participants were controls ("old National Institute for Health and Care Excellence [NICE]") or "new NICE" if their index feature was introduced during guideline revision. Model assumptions were inspected visually and by falsification tests. Sensitivity analyses reclassified participants who subsequently presented with features in the original guidelines as "old NICE." For colorectal cancer, sensitivity analysis (n = 3481) adjusted for multimorbidity burden. RESULTS: Median contact days and costs were, respectively, 4 (interquartile range [IQR] 2-7) and £117.69 (IQR £53.23-£206.65) for colorectal, 5 (IQR 3-9) and £156.92 (IQR £78.46-£272.29) for ovary, and 7 (IQR 4-13) and £230.64 (IQR £120.78-£408.34) for pancreas. Revising ovary guidelines may have decreased contact days (incidence rate ratio [IRR] 0.74; 95% confidence interval 0.55-1.00; P = .05) with unchanged costs, but parallel trends assumptions were violated. Costs decreased by 13% (equivalent to -£28.05, -£50.43 to -£5.67) after colorectal guidance revision but only in sensitivity analyses adjusting for multimorbidity. Contact days and costs remained unchanged after pancreas guidance revision. CONCLUSIONS: The main analyses of symptomatic patients suggested that prediagnosis primary care costs remained unchanged after guidance revision for pancreatic cancer. For colorectal cancer, contact days and costs decreased in analyses adjusting for multimorbidity. Revising ovarian cancer guidelines may have decreased primary care contact days but not costs, suggesting increased resource-use intensity; nevertheless, there is evidence of confounding.

Magneto-Nanomechanical Array Biosensor for Ultrasensitive Detection of Oncogenic Exosomes for Early Diagnosis of Cancers.

Exosomes are a class of nanoscale vesicles secreted by cells, which contain abundant information closely related to parental cells. The ultrasensitive detection of cancer-derived exosomes is highly significant for early non-invasive diagnosis of cancer. Here, an ultrasensitive nanomechanical sensor is reported, which uses a magnetic-driven microcantilever array to selectively detect oncogenic exosomes. A magnetic force, which can produce a far greater deflection of microcantilever than that produced by the intermolecular interaction force even with very low concentrations of target substances, is introduced. This method reduced the detection limit to less than 10 exosomes mL-1 . Direct detection of exosomes in the serum of patients with breast cancer and in healthy people showed a significant difference. This work improved the sensitivity by five orders of magnitude as compared to that of traditional nanomechanical sensing based on surface stress mode. This method can be applied parallelly for highly sensitive detection of other microorganisms (such as bacteria and viruses) by using different probe molecules, which can provide a supersensitive detection approach for cancer diagnosis, food safety, and SARS-CoV-2 infection.

A method for early diagnosis of lung cancer from tumor originated DNA fragments using plasma cfDNA methylome and fragmentome profiles.

Early detection is critical for minimizing mortality from cancer. Plasma cell-free DNA (cfDNA) contains the signatures of tumor DNA, allowing us to quantify the signature and diagnose early-stage tumors. Here, we report a novel tumor fragment quantification method, TOF (Tumor Originated Fragment) for the diagnosis of lung cancer by quantifying and analyzing both the plasma cfDNA methylation patterns and fragmentomic signatures. TOF utilizes the amount of ctDNA predicted from the methylation density information of each cfDNA read mapped on 6243 lung-tumor-specific CpG markers. The 6243 tumor-specific markers were derived from lung tumor tissues by comparing them with corresponding normal tissues and healthy blood from public methylation data. TOF also utilizes two cfDNA fragmentomic signatures: 1) the short fragment ratio, and 2) the 5' end-motif profile. We used 298 plasma samples to analyze cfDNA signatures using enzymatic methyl-sequencing data from 201 lung cancer patients and 97 healthy controls. The TOF score showed 0.98 of the area under the curve in correctly classifying lung cancer from normal samples. The TOF score resolution was high enough to clearly differentiate even the early-stage non-small cell lung cancer patients from the healthy controls. The same was true for small cell lung cancer patients.

Neutrophil membrane-coated immunomagnetic nanoparticles for efficient isolation and analysis of circulating tumor cells.

The isolation and analysis of scarce circulating tumor cells (CTCs) with immunomagnetic nanoparticles (IMNs) have shown promising outcomes in noninvasive cancer diagnosis. However, the IMNs adsorb nonspecific proteins after entering into biofluids and the formed protein coronas cover surface targeting ligands, limiting the detection efficiency of IMNs. In addition, the interaction between surface targeting ligands and white blood cells (WBCs) significantly limits the purity of CTCs isolated by IMNs. Furthermore, the interfacial collision of nanoparticles and cells has negative effects on the viability of isolated CTCs. All of these limitations synthetically restrict the isolation and analysis of rare CTCs for early diagnosis and precision medicine. Here, we proposed that surface functionalization of IMNs with neutrophil membranes can simultaneously reduce nonspecific protein adsorption, enhance the interaction with CTCs, reduce the distraction from WBCs, and improve the viability of isolated CTCs. In spiked blood samples, our neutrophil membrane-coated IMNs (Neu-IMNs) exhibited a superior separation efficiency from 41.36% to 96.82% and an improved purity from 40.25% to 90.68% when compared to bare IMNs. Additionally, we successfully isolated CTCs in 19 out of total 20 blood samples from breast cancer patients using Neu-IMNs and further confirmed the feasibility of the isolated CTCs for downstream cell sequencing. Our work provides a new perspective on engineered IMNs for efficient isolation and analysis of CTCs, paving the way for early noninvasive diagnosis of cancer.

In situ electrochemical reductive construction of metal oxide/metal-organic framework heterojunction nanoarrays for hydrogen peroxide sensing.

Transition metal oxide/metal-organic framework heterojunctions (TMO@MOF) that combine the large specific surface area of MOFs with TMOs' high catalytic activity and multifunctionality, show excellent performances in various catalytic reactions. Nevertheless, the present preparation approaches of TMO@MOF heterojunctions are too complex to control, stimulating interests in developing simple and highly controllable methods for preparing such heterojunction. In this study, we propose an in situ electrochemical reduction approach to fabricating Cu2O nanoparticle (NP)@CuHHTP heterojunction nanoarrays with a graphene-like conductive MOF CuHHTP (HHTP is 2,3,6,7,10,11-hexahydroxytriphenylene). We have discovered that size-controlled Cu2O nanoparticles could be in situ grown on CuHHTP by applying different electrochemical reduction potentials. Also, the obtained Cu2O NP@CuHHTP heterojunction nanoarrays show high H2O2 sensitivity of 8150.6 µA·mM-1·cm2 and satisfactory detection performances in application of measuring H2O2 concentrations in urine and serum samples. This study offers promising guidance for the synthesis of MOF-based heterojunctions for early cancer diagnosis.

Development of Electrochemical Biosensor for miR204-Based Cancer Diagnosis.

With increase in cancer burden worldwide and poor survival rates due to delayed diagnosis, it is pertinent to develop a device for early diagnosis. We report an electrochemical biosensor for quantification of miRNA-204 (miR-204) biomarker that is dysregulated in most of the cancers. The proposed methodology uses the gold nanoparticles-modified carbon screen-printed electrode for immobilization of single-stranded DNA probe against miR-204. Colloidal gold nanoparticles were synthesized using L-glutamic acid as reducing agent. Nanoparticles were characterized by UV-visible spectroscopy and transmission electron microscopy. Spherical gold nanoparticles were of 7-28 nm in size. Biosensor fabricated using these nanoparticles was characterized by cyclic voltammetry after spiking 0.1 fg/mL-0.1 µg/mL of miR-204 in fetal bovine serum. Response characteristics of the miR-204 biosensor displayed high sensitivity of 8.86 µA/µg/µL/cm2 with wide detection range of 15.5 aM to 15.5 nM. The low detection limit makes it suitable for early diagnosis and screening of cancer.

A Fluorescent "Turn-On" Clutch Probe for Plasma Cell-Free DNA Identification from Lung Cancer Patients.

Early diagnosis of cancer is of paramount significance for the therapeutic intervention of cancers. Although the detection of circulating cell-free DNA (cfDNA) has emerged as a promising, minimally invasive approach for early cancer diagnosis, there is an urgent need to develop a highly sensitive and rapid method to precisely identify plasma cfDNA from clinical samples. Herein, we report a robust fluorescent "turn-on" clutch probe based on non-emissive QDs-Ru complexes to rapidly recognize EGFR gene mutation in plasma cfDNA from lung cancer patients. In this system, the initially quenched emission of QDs is recovered while the red emission of Ru(II) complexes is switched on. This is because the Ru(II) complexes can specifically intercalate into the double-stranded DNA (dsDNA) to form Ru-dsDNA complexes and simultaneously liberate free QDs from the QDs-Ru complexes, which leads to the occurrence of an overlaid red fluorescence. In short, the fluorescent "turn-on" clutch probe offers a specific, rapid, and sensitive paradigm for the recognition of plasma cfDNA biomarkers from clinical samples, providing a convenient and low-cost approach for the early diagnosis of cancer and other gene-mutated diseases.

Effectiveness of Visual Inspection with Acetic Acid (VIA) Screening on Cervical Cancer Mortality and Incidence - A Systematic Review and Meta-Analysis.

OBJECTIVES: Despite being a cheap, easy, and commonly used technique for screening early development of cervical cancer, collective evidence on the effect of visual inspection with acetic acid (VIA) for reducing cervical cancer mortality and incidence are conflicting. We conducted a systematic review and meta-analysis to determine the effectiveness of VIA screening on cervical cancer mortality and incidence. METHODS: We searched PubMed, Embase, Cochrane library (Cochrane Database of Systematic Reviews & Cochrane Central Register of Controlled Trials), World Health Organization's (WHO) International Clinical Trials Registry Platform, and Google Scholar to identify studies conducted among women with no history of cervical cancer that assessed effectiveness of VIA on the cervical cancer mortality and incidence. Random effects model was used to estimate incident rate ratio and sensitivity analysis was conducted using Bayesian methods. RESULTS: Of the included 4 studies, three were cluster randomized trials from India and one was quasi-experimental study done in Thailand. Duration of follow-up ranged from 7 to 12 years. Based on 3 trials, pooled rate-ratio for cervical cancer mortality and all-cause mortality was 0.68 (95% CI: 0.56-0.81, I2=0%) and 0.91 (0.85-0.97, I2=57%), respectively. Pooled rate-ratio of invasive cervical cancer was 0.94 (95% CI: 0.67 - 1.30, I2=84%). Likewise, there was non-significant reduction in incidence of stage IB, >=stage II, and unknown stage cervical cancer. CONCLUSIONS: VIA screening may lead to reduction in cervical cancer and all-cause mortality in long run. However, the effectiveness of VIA in preventing invasive cervical cancer is inconclusive.

In vitro selection of DNA aptamers against human osteosarcoma.

BACKGROUND: Osteosarcoma is a highly malignant bone tumor, most frequently occurring in the rapid bone growth phase. Effective treatment of this disease is hindered by the lack of specific probes for early diagnosis and the fast cancer widespread. METHODS: To find such probes, the cell-Systematic Evolution of Ligands by EXponential enrichment (cell-SELEX) methodology was implemented against the human osteosarcoma MG-63 cell line towards the selection of new specific aptamers. After 10 rounds of selection, the aptamer DNA pool was Sanger sequenced and the sequences were subjected to a bioinformatic analysis that included sequence alignment, phylogenetic relationship, and secondary structure prediction. RESULTS: A DNA aptamer (OS-7.9), with a dissociation constant (Kd) value in the nanomolar range (12.8 ± 0.9 nM), revealed high affinity against the target cells at the physiological temperature. Furthermore, the selected aptamer also recognized lung carcinoma and colon colorectal adenocarcinoma cell lines, which are reported as common metastasis sites of osteosarcoma. CONCLUSIONS: These results suggest that OS-7.9 could recognize a common protein expressed in these cancer cells, possibly becoming a potential molecular probe for early diagnosis and targeted therapies for metastatic disease. Moreover, to the best of our knowledge, this was the first attempt to generate a DNA aptamer (OS-7.9 aptamer) against the MG-63-cell line by cell-SELEX.

Implementing and Sustaining Early Cancer Diagnosis Initiatives in Canada: An Exploratory Qualitative Study.

BACKGROUND: The interval between suspected cancer and diagnosis for symptomatic patients is often fragmented, leading to diagnosis delays and increased patient stress. We conducted an exploratory qualitative study to explore barriers and facilitators to implementing and sustaining current initiatives across Canada that optimize early cancer diagnosis, with particular relevance for symptomatic patients. METHODS: The national study included a document review and key informant interviews with purposefully recruited participants. Data were analyzed by two researchers using descriptive statistics and thematic analysis. RESULTS: Twenty-two participants from eight provinces participated in key informant interviews and reported on 17 early cancer diagnosis initiatives. Most initiatives (88%) were in early phases of implementation. Two patient-facing and eight provider/organization barriers to implementation (e.g., lack of stakeholder buy-in and limited resources) and five facilitators for implementation and sustainability were identified. Opportunities to improve early cancer diagnosis initiatives included building relationships with stakeholders, co-creating initiatives, developing initiatives for Indigenous and underserved populations, optimizing efficiency and sustainability, and standardizing metrics to evaluate impact. CONCLUSION: Early cancer diagnosis initiatives in Canada are in early implementation phases. Lack of stakeholder buy-in and limited resources pose a challenge to sustainability. We present opportunities for funders and policymakers to optimize the use and potential impact of early cancer diagnosis initiatives.

Ex Vivo Vibration Spectroscopic Analysis of Colorectal Polyps for the Early Diagnosis of Colorectal Carcinoma.

Colorectal cancer is one of the most common and often fatal cancers in humans, but it has the highest chance of a cure if detected at an early precancerous stage. Carcinogenesis in the colon begins as an uncontrolled growth forming polyps. Some of these polyps can finally be converted to colon cancer. Early diagnosis of adenomatous polyps is the main approach for screening and preventing colorectal cancer, and vibration spectroscopy can be used for this purpose. This work is focused on evaluating FTIR and Raman spectroscopy as a tool in the ex vivo analysis of colorectal polyps, which could be important for the early diagnosis of colorectal carcinoma. Multivariate analyses (PCA and LDA) were used to assist the spectroscopic discrimination of normal colon tissue, as well as benign and malignant colon polyps. The spectra demonstrated evident differences in the characteristic bands of the main tissue constituents, i.e., proteins, nucleic acids, lipids, polysaccharides, etc. Suitable models for discriminating the three mentioned diagnostic groups were proposed based on multivariate analyses of the spectroscopic data. LDA classification was especially successful in the case of a combined set of 55 variables from the FTIR, FT Raman and dispersion Raman spectra. This model can be proposed for ex vivo colorectal cancer diagnostics in combination with the colonoscopic extraction of colon polyps for further testing. This pilot study is a precursor for the further evaluation of the diagnostic potential for the simultaneous in vivo application of colonoscopic Raman probes.