Detection Of Colorectal Cancer Research Articles

Serum ferritin and risk of colonic neoplasia: Implications for the workup and treatment of iron deficiency.

Iron deficiency is the most common extraintestinal sign of colonic neoplasia, including colorectal cancer (CRC) and other lower gastrointestinal pathology. Both upper endoscopy and colonoscopy is usually recommended in the work-up of patients with unexplained iron deficiency, particularly in men and postmenopausal women. As the incidence of early-onset CRC (age <50 years) rises in the United States, there is an increasing need to identify risk predictors to aid in the early detection of CRC. It remains unknown if serum ferritin (SF), and what specific threshold, can be used as a marker to stratify those at risk for CRC and other lower gastrointestinal pathology. In this current review of the literature, we aimed to review guidelines for diagnostic workup of colonic neoplasia in the setting of iron deficiency and examine the association and specific thresholds of SF and risk of CRC by age. Some of the published findings are conflicting, and conclusions specific to younger patients are limited. Though further investigation is warranted, the cumulative findings suggest that SF, in addition to considering the clinical context and screening guidelines, may have potential utility in the assessment of colonic neoplasia.

The Yield of Colonoscopy in the Evaluation of Constipation: An Age-Based Analysis of Outcome

Background: Chronic constipation, a prevalent gastrointestinal complaint, exhibits rising incidence and diverse clinical implications, especially among the aging population. This study aims to assess colonoscopy performance in chronic constipation across age groups, comprehensively evaluating diagnostic yield and comparing results with average-risk controls. Methods: A retrospective analysis was conducted on 50,578 colonoscopy procedures performed over 12 years, including 5478 constipated patients. An average-risk control group (n = 4100) was included. Data extracted from electronic medical records covered demographics, operational aspects, and colonoscopy findings. The primary outcome measures included the diagnosis rate of colorectal cancer (CRC), polyp detection rate (PDR), and inflammatory bowel disease (IBD) diagnoses in constipated patients versus controls, with age-based and multivariate analyses. Results: Constipated patients exhibiting lower rates of adequate bowel preparation (92.7% vs. 85.3%; p &lt; 0.001) and a lower cecal intubation rate. No significant variances between CRC and PDR were observed between constipated and controls, except for a potential of a slightly elevated CRC risk in constipated patients older than 80 (2.50% vs. 0% in controls; p = 0.07). Multivariate analysis demonstrated, across all age groups, that constipation did not confer an increased risk for CRC or polyp detection. Younger constipated patients exhibited a higher rate of IBD diagnoses (1.7% vs. 0.1% in controls; p &lt; 0.001). Conclusions: Constipation did not confer an increased risk for CRC or polyps, among any age groups, except for a potential signal of elevated CRC risk in patients older than 80; additionally, it was associated with higher rates of IBD in younger patients.

A novel colorectal cancer screening framework with feature interpretability to identify high-risk populations for colonoscopy.

Risk assessment is of paramount importance for the detection and treatment of colorectal cancer. We developed and validated a feature interpretability screening framework to identify high-risk populations and recommend colonoscopy for them. We utilized a training cohort consisting of 1252605 participants who underwent colonoscopies in Shanghai from 2013 to 2015 to develop the screening framework. We incorporated Shapley additive explanation values into feature selection to provide interpretability for the framework. Two sampling methods were separately employed to mitigate potential model bias caused by class imbalance. Furthermore, we employed various machine learning algorithms to construct risk assessment models and compared their performance. We tested the screening models on an external validation cohort of 359462 samples and conducted comprehensive evaluation and statistical analysis of the validation results. The external validation results demonstrated that the models in the proposed framework achieved sensitivity over 0.734, specificity over 0.790, and area under the receiver operating characteristic curve ranging from 0.808 to 0.859. In the predictions of the best-performing model, the prevalence rates of colorectal cancer were 0.059% and 1.056% in the low- and high-risk groups, respectively. If colonoscopies were performed only on the high-risk group predicted by the model, only 14.36% of total colonoscopies would be needed to detect 74.86% of colorectal cancer cases. We developed and validated a novel framework to identify populations at high risk for colorectal cancer. Those classified as high risk should undergo colonoscopy for further diagnosis.

Approaches and considerations in the endoscopic treatment of T1 colorectal cancer.

The detection of early colorectal cancer (CRC) is increasing through the implementation of screening programs. This increased detection enhances the likelihood of minimally invasive surgery and significantly lowers the risk of recurrence, thereby improving patient survival and reducing mortality rates. T1 CRC, the earliest stage, is treated endoscopically in cases with a low risk of lymph node metastasis (LNM). The advantages of endoscopic treatment compared with surgery include minimal invasiveness and limited tissue disruption, which reduce morbidity and mortality, preserve bowel function to avoid colectomy, accelerate recovery, and improve cost-effectiveness. However, T1 CRC has a risk of LNM. Thus, selection of the appropriate treatment between endoscopic treatment and surgery, while avoiding overtreatment, is challenging considering the potential for complete resection, LNM, and recurrence risk.

Diagnostic performance of volatile organic compounds analysis and electronic noses for detecting colorectal cancer: a systematic review and meta-analysis

IntroductionThe detection of Volatile Organic Compounds (VOCs) could provide a potential diagnostic modality for the early detection and surveillance of colorectal cancers. However, the overall diagnostic accuracy of the proposed tests remains uncertain.ObjectiveThis systematic review is to ascertain the diagnostic accuracy of using VOC analysis techniques and electronic noses (e-noses) as noninvasive diagnostic methods for colorectal cancer within the realm of clinical practice.MethodsA systematic search was undertaken on PubMed, EMBASE, Web of Science, and the Cochrane Library to scrutinize pertinent studies published from their inception to September 1, 2023. Only studies conducted on human subjects were included. Meta-analysis was performed using a bivariate model to obtain summary estimates of sensitivity, specificity, and positive and negative likelihood ratios. The Quality Assessment of Diagnostic Accuracy Studies 2 tool was deployed for quality assessment. The protocol for this systematic review was registered in PROSPERO, and PRISMA guidelines were used for the identification, screening, eligibility, and selection process.ResultsThis review encompassed 32 studies, 22 studies for VOC analysis and 9 studies for e-nose, one for both, with a total of 4688 subjects in the analysis. The pooled sensitivity and specificity of VOC analysis for CRC detection were 0.88 (95% CI, 0.83-0.92) and 0.85 (95% CI, 0.78-0.90), respectively. In the case of e-nose, the pooled sensitivity was 0.87 (95% CI, 0.83-0.90), and the pooled specificity was 0.78 (95% CI, 0.62-0.88). The area under the receiver operating characteristic analysis (ROC) curve for VOC analysis and e-noses were 0.93 (95% CI, 0.90-0.95) and 0.90 (95% CI, 0.87-0.92), respectively.ConclusionThe outcomes of this review substantiate the commendable accuracy of VOC analysis and e-nose technology in detecting CRC. VOC analysis has a higher specificity than e-nose for the diagnosis of CRC and a sensitivity comparable to that of e-nose. However, numerous limitations, including a modest sample size, absence of standardized collection methods, lack of external validation, and a notable risk of bias, were identified. Consequently, there exists an imperative need for expansive, multi-center clinical studies to elucidate the applicability and reproducibility of VOC analysis or e-nose in the noninvasive diagnosis of colorectal cancer.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/#recordDetails, identifier CRD42023398465.

Association of lncRNA MEG3 rs941576 polymorphism, expression profile, and its related targets with the risk of obesity-related colorectal cancer: potential clinical insights

The identification of novel screening tools is imperative to empower the early detection of colorectal cancer (CRC). The influence of the long non-coding RNA maternally expressed gene 3 (MEG3) rs941576 single nucleotide polymorphism on CRC susceptibility remains uninvestigated. This research appraised MEG3 rs941576 association with the risk and clinical features of CRC and obesity-related CRC and its impact on serum MEG3 expression and its targets miR-27a/insulin-like growth factor 1 (IGF1)/IGF binding protein 3 (IGFBP3) and miR-181a/sirtuin 1 (SIRT1), along with the potential of these markers in obesity-related CRC diagnosis. 130 CRC patients (60 non-obese and 70 obese) and 120 cancer-free controls (64 non-obese and 56 obese) were enrolled. MEG3 targets were selected using bioinformatics analysis. MEG3 rs941576 was associated with magnified CRC risk in overall (OR (95% CI) 4.69(1.51–14.57), P = 0.0018) and stratified age and gender groups, but not with obesity-related CRC risk or MEG3/downstream targets’ expression. Escalated miR-27a and IGFBP3 and reduced IGF1 serum levels were concomitant with MEG3 downregulation in overall CRC patients versus controls and obese versus non-obese CRC patients. Serum miR-181a and SIRT1 were upregulated in CRC patients versus controls but weren’t altered in the obese versus non-obese comparison. Serum miR-181a and miR-27a were superior in overall and obesity-related CRC diagnosis, respectively; meanwhile, IGF1 was superior in distinguishing obese from non-obese CRC patients. Only serum miR-27a was associated with obesity-related CRC risk in multivariate logistic analysis. Among overall CRC patients, MEG3 rs941576 was associated with lymph node (LN) metastasis and tumor stage, serum MEG3 was negatively correlated with tumor stage, while SIRT1 was correlated with the anatomical site. Significant correlations were recorded between MEG3 and anatomical site, SIRT1 and tumor stage, and miR-27a/IGFBP3 and LN metastasis among obese CRC patients, while IGF1 was correlated with tumor stage and LN metastasis among non-obese CRC patients. Conclusively, this study advocates MEG3 rs941576 as a novel genetic marker of CRC susceptibility and prognosis. Our findings accentuate circulating MEG3/miR-27a/IGF1/IGFBP3, especially miR-27a as valuable markers for the early detection of obesity-related CRC. This axis along with SIRT1 could benefit obesity-related CRC prognosis.

Integrating artificial intelligence techniques for advancements in colorectal cancer management: navigating past and predicting future direction.

Artificial Intelligence (AI) in the last few years has emerged as a valuable tool in managing colorectal cancer, revolutionizing its management at different stages. In early detection and diagnosis, AI leverages its prowess in imaging analysis, scrutinizing CT scans, MRI, and colonoscopy views to identify polyps and tumors. This ability enables timely and accurate diagnoses, initiating treatment at earlier stages. AI has helped in personalized treatment planning because of its ability to integrate diverse patient data, including tumor characteristics, medical history, and genetic information. Integrating AI into clinical decision support systems guarantees evidence-based treatment strategy suggestions in multidisciplinary clinical settings, thus improving patient outcomes. This narrative review explores the multifaceted role of AI, spanning early detection of colorectal cancer, personalized treatment planning, polyp detection, lymph node evaluation, cancer staging, robotic colorectal surgery, and training of colorectal surgeons.

Identification and validation of the surface proteins FIBG, PDGF-β, and TGF-β on serum extracellular vesicles for non-invasive detection of colorectal cancer: experimental study.

The absence of non-invasive biomarkers for the early diagnosis of colorectal cancer (CRC) has contributed to poor prognosis. Extracellular vesicles (EVs) have emerged as promising candidates for cancer monitoring using liquid biopsy. However, the complexity of EVs isolation procedures and absence of clear targets for detecting serum-derived EVs have hindered the clinical application of EVs in early CRC diagnosis. In the discovery phase, we conducted a comprehensive 4D-DIA proteomic analysis of serum-derived EVs samples from 37 individuals, performing an initial screening of EVs surface proteins. In the technical validation phase, we developed an extraction-free CRC-EVArray microarray to assess the expression of these potential EVs surface proteins in a multicenter study comprising 404 individuals. In the application phase, we evaluated the diagnostic efficacy of the CRC-EVArray model based on machine-learning algorithms. Through 4D-DIA proteomic analysis, we identified 7 potential EVs surface proteins showing significantly differential expression in CRC patients compared to healthy controls. Utilizing our developed high-throughput CRC-EVArray microarray, we further confirmed the differential expression of 3 EVs surface proteins, FIBG, PDGF-β and TGF-β, in a large sample population. Moreover, we established an optimal CRC-EVArray model using the NNET algorithm, demonstrating superior diagnostic efficacy with an AUC of 0.882 in the train set and 0.937 in the test set. Additionally, we predicted the functions and potential origins of these EVs-derived proteins through a series of multi-omics approaches. Our systematic exploration of surface protein expression profiles on serum-derived EVs has identified FIBG, PDGF-β, and TGF-β as novel diagnostic biomarkers for CRC. And the development of CRC-EVArray diagnostic model based on these findings provided an effective tool for the large-scale CRC screening, thus facilitating its translation into clinical practice.

Hydrogen bond-modulated copper nanocluster-aggregation-induced enhanced ECL with layered double hydroxides-sensing interface for miRNA-135b detection in colorectal cancer

Hydrogen bond-modulated copper nanocluster-aggregation-induced enhanced ECL with layered double hydroxides-sensing interface for miRNA-135b detection in colorectal cancer

Stable hemoglobin concentration with fecal immunochemical test at high temperatures in a Caribbean colorectal cancer screening program

Background and aimsHigh temperatures may reduce fecal immunochemical test (FIT) positivity and colorectal cancer (CRC) detection sensitivity. We investigated the effect of temperature on hemoglobin concentration [Hb], in the FOB Gold®. Additionally, we examined FIT pick-up, storage, return times and specimen collection. Materials and MethodsIn vitro experiments with buffer containing FIT devices, inoculated with Hb-spiked stool. For 7 days, 144 samples were stored in groups of 36 at 4 °C, 22 °C, 30 °C, and 50 °C. Additionally, 54 samples were stored in groups of 18 at 34 °C, 42 °C and 50 °C for 20 h. Paired t-tests and repeated measure ANOVA assessed [Hb] change. Sixty-five screening participants completed a FIT-handling questionnaire. ResultsAfter 7 days, mean [Hb] was stable at 30 °C (0.8 µg/g;95 %CI: −1.5 to 3.1;p = 0.50). For 50 °C, [Hb] decreased within 2 days (−21.3 µg/g;95 %CI:-30.2 to −12.5;p < 0.001) and after 20 h (−63.0 µg/g;95 %CI:-88.7 to −37.3;p < 0.001), respectively. All other temperature categories showed significant mean [Hb] increase. Same-day FIT return was reported by 80 %. Eighty-seven percent experienced specimen collection as easy and 33 % kept the FIT refrigerated after collection. ConclusionsThe FOB Gold® is suitable for CRC screening in tropical climates. Although most respondents indicated same-day sample return, we recommend avoiding FIT storage above 30 °C for ≥ 7 days.

Colorectal Cancer Detection via Metabolites and Machine Learning

Today, colorectal cancer (CRC) diagnosis is performed using colonoscopy, which is the current, most effective screening method. However, colonoscopy poses risks of harm to the patient and is an invasive process. Recent research has proven metabolomics as a potential, non-invasive detection method, which can use identified biomarkers to detect potential cancer in a patient’s body. The aim of this study is to develop a machine-learning (ML) model based on chemical descriptors that will recognize CRC-associated metabolites. We selected a set of metabolites found as the biomarkers of CRC, confirmed that they participate in cancer-related pathways, and used them for training a machine-learning model for the diagnostics of CRC. Using a set of selective metabolites and random compounds, we developed a range of ML models. The best performing ML model trained on Stage 0–2 CRC metabolite data predicted a metabolite class with 89.55% accuracy. The best performing ML model trained on Stage 3–4 CRC metabolite data predicted a metabolite class with 95.21% accuracy. Lastly, the best-performing ML model trained on Stage 0–4 CRC metabolite data predicted a metabolite class with 93.04% accuracy. These models were then tested on independent datasets, including random and unrelated-disease metabolites. In addition, six pathways related to these CRC metabolites were also distinguished: aminoacyl-tRNA biosynthesis; glyoxylate and dicarboxylate metabolism; glycine, serine, and threonine metabolism; phenylalanine, tyrosine, and tryptophan biosynthesis; arginine biosynthesis; and alanine, aspartate, and glutamate metabolism. Thus, in this research study, we created machine-learning models based on metabolite-related descriptors that may be helpful in developing a non-invasive diagnosis method for CRC.

Circulating-tumour DNA methylation of HAND1 gene: a promising biomarker in early detection of colorectal cancer

BackgroundColorectal cancer (CRC) is one of the significant global health concerns with an increase in cases. Regular screening tests are crucial for early detection as it is often asymptomatic in the initial stages. Liquid biopsies, a non-invasive approach that examines biomarkers in biofluids, offer a promising future in diagnosing and screening cancer. Circulating-tumour DNA (ctDNA) is the genetic material in biofluids released into the circulatory system by cells. ctDNA is a promising marker for monitoring patients since cancer cells display distinct DNA methylation patterns compared to normal cells. The potential of our research to contribute to early detection and improved patient outcomes is significant.AimsThe primary objective of this research project was to explore the HAND1 methylation levels in plasma ctDNA as a potential biomarker for diagnosing CRC and evaluate the methylation level of the well-established gene SPET9 to compare it with the methylation level of HAND1.Materials and methodsPlasma samples were collected from 30 CRC patients and 15 healthy individuals, with CRC samples obtained pre-treatment. ctDNA was extracted and treated with bisulfite for methylation status assessment. Quantitative methylation-specific PCR (qMS-PCR) was performed for HAND1 and SEPT9, using β-actin (ACTB gene) as a reference. The study aims to evaluate the potential of these genes as diagnostic biomarkers for CRC, contributing to early detection and improved patient outcomes.ResultsOur study yielded significant results: 90% of CRC patients (27 out of 30) had hypermethylation in the SEPT9 gene, and 83% (25 out of 30) exhibited hypermethylation in the HAND1 gene. The methylation levels of both genes were significantly higher in CRC patients than in healthy donors. These findings underscore the potential of SEPT9 and HAND1 methylation as promising biomarkers for diagnosing CRC, potentially leading to early detection and improved patient outcomes.ConclusionThese findings highlight the potential of SEPT9 and HAND1 methylation as promising biomarkers for diagnosing CRC. However, further research and validation studies are needed to confirm these findings and to explore their clinical utility in CRC diagnosis and management.

Identification and clinicopathological analysis of potential p73-regulated biomarkers in colorectal cancer via integrative bioinformatics.

This study aims to decipher crucial biomarkers regulated by p73 for the early detection of colorectal cancer (CRC) by employing a combination of integrative bioinformatics and expression profiling techniques. The transcriptome profile of HCT116 cell line p53 p73 and p53 p73 knockdown was performed to identify differentially expressed genes (DEGs). This was corroborated with three CRC tissue expression datasets available in Gene Expression Omnibus. Further analysis involved KEGG and Gene ontology to elucidate the functional roles of DEGs. The protein-protein interaction (PPI) network was constructed using Cytoscape to identify hub genes. Kaplan-Meier (KM) plots along with GEPIA and UALCAN database analysis provided the insights into the prognostic and diagnostic significance of these hub genes. Machine/deep learning algorithms were employed to perform TNM-stage classification. Transcriptome profiling revealed 1289 upregulated and 1897 downregulated genes. When intersected with employed CRC datasets, 284 DEGs were obtained. Comprehensive analysis using gene ontology and KEGG revealed enrichment of the DEGs in metabolic process, fatty acid biosynthesis, etc. The PPI network constructed using these 284 genes assisted in identifying 20 hub genes. Kaplan-Meier, GEPIA, and UALCAN analyses uncovered the clinicopathological relevance of these hub genes. Conclusively, the deep learning model achieved TNM-stage classification accuracy of 0.78 and 0.75 using 284 DEGs and 20 hub genes, respectively. The study represents a pioneer endeavor amalgamating transcriptomics, publicly available tissue datasets, and machine learning to unveil key CRC-associated genes. These genes are found relevant regarding the patients' prognosis and diagnosis. The unveiled biomarkers exhibit robustness in TNM-stage prediction, thereby laying the foundation for future clinical applications and therapeutic interventions in CRC management.

Evaluation of the “Burgenland PREvention trial of colorectal cancer Disease with ImmunologiCal Testing” (B-PREDICT)—a population-based colorectal cancer screening program

BackgroundThe colorectal cancer (CRC) screening program B-PREDICT is a population based invited two stage screening project using a faecal immunochemical test (FIT) for initial screening followed by a colonoscopy for those with a positive FIT. B-PREDICT was compared with the opportunistic screening colonoscopy (OPP-COL), performed in course of the nationwide screening program.MethodsWithin B-PREDICT all residents of the Austrian federal state Burgenland, aged between 40 and 80 are annually invited to FIT testing. All individuals who underwent initial colonoscopy in Burgenland between 01/2003 and 12/2014, were included in this study. Individuals from the FIT-triggered invited screening program B-PREDICT were compared with those from the non-FIT triggered OPP-COL.Results15 133 individuals from B-PREDICT were compared to 10 045 individuals with OPP-COL. CRC detection rates were 1.34% (CI-95%, [1.15; 1.52]) in B-PREDICT compared to 0.54% in OPP-COL (95%-CI, [0.39; 0.68] p < 0.001). The decrease in the age standardized incidence rates of CRC was more pronounced in the population screened with FIT than in the general population screened with colonoscopy. Changes in incidence rates per year were -4.4% (95%-CI, [-5.1; -3.7]) vs. -1.8% (95%-CI, [-1.9; -1.6] p < 0.001).ConclusionsB-PREDICT shows a two-fold higher detection rate of CRC as well as HRA compared to OPP-COL.

Silicon nitride (Si3N4) leads to enhanced performance of silica-silver based plasmonic sensor for colorectal cancer detection under optimum radiation damping

Silicon Nitride (Si3N4) possesses large refractive index and transparent behaviour in a broad spectral range from ultraviolet to infrared. These properties of Si3N4 facilitate an efficient light-matter interaction leading to enhanced light confinement. In this work, we have investigated Si3N4-based plasmonic sensor for pathogenic colorectal tissues detection at a wavelength of 1000 nm. The sensor structure consists of fused silica prism and Ag–Si3N4 heterojunction. The figure-of-merit (FOM) of the sensor is optimized by judiciously coordinating the thickness of Ag layer (dM) and Si3N4 layer (dA) under optimum radiation damping (ORD). An optimized FOM as high as 6011 RIU−1 (at dM = 47.3 nm) has been achieved corresponding to dA = 8.6 nm under ORD. At ORD, the corresponding power loss ratio (PLR) is as high as 2.773, and the normalized electric field enhancement factor (FEF) is about 1.06. The magnitude of combined performance factor (CPF), which is the product of FOM, PLR and FEF, of the proposed sensor is as large as 17668.61 RIU−1. The proposed application of Si3N4 in plasmonic sensor in combination with ORD condition leads to substantially enhanced sensing performance and has been explored for the first time to the best of our knowledge.

Preclinical evaluation of AGTR1-Targeting molecular probe for colorectal cancer imaging in orthotopic and liver metastasis mouse models

Despite advancements in colorectal cancer (CRC) treatment, the prognosis remains unfavorable for patients with distant liver metastasis. Fluorescence molecular imaging with specific probes is increasingly used to guide CRC surgical resection in real-time and treatment planning. Here, we demonstrate the targeted imaging capacity of an MPA-PEG4-N3-Ang II probe labeled with near-infrared (NIR) fluorescent dye targeting the angiotensin II (Ang II) type 1 receptor (AGTR1) that is significantly upregulated in CRC. MPA-PEG4-N3-Ang II was highly selective and specific to in vitro tumor cells and in vivo tumors in a mouse CRC xenograft model. The favorable ex vivo imaging and in vivo biodistribution of MPA-PEG4-N3-Ang II afforded tumor-specific accumulation with low background and >10 contrast tumor-to-colorectal values in multiple subcutaneous CRC models at 8 h following injection. Biodistribution analysis confirmed the probe's high uptake in HT29 and HCT116 orthotopic and liver metastatic models of CRC with signal-to-noise ratio (SNR) values of tumor-to-colorectal and -liver fluorescence of 5.8 ± 0.6, 5.3 ± 0.7, and 2.7 ± 0.5, 2.6 ± 0.5, respectively, enabling high-contrast intraoperative tumor visualization for surgical navigation. Given its rapid tumor targeting, precise tumor boundary delineation, durable tumor retention and docking study, MPA-PEG4-N3-Ang II is a promising high-contrast imaging agent for the clinical detection of CRC.

Multilevel analysis of social determinants of advanced stage colorectal cancer diagnosis

The advanced stage at diagnosis of colorectal cancer (CRC) may be related to individual factors, socioeconomic conditions, and healthcare service availability. The objective of the study was to analyze the prevalence of advanced stage CRC at the time of diagnosis and its association with individual, contextual, socioeconomic, and healthcare service indicators. An observational, cross-sectional study was conducted, analyzing cases of malignant neoplasms of the colon and rectum in individuals of both sexes, aged between 18 and 99 years, diagnosed between 2010 and 2019 in Brazil (n = 69,047). Data were collected from the Hospital Cancer Registry (HCR), Atlas of Human Development in Brazil, and from the National Registry of Health Institutions (NRHI). A Multilevel Poisson Regression model with random intercept was used. The prevalence of advanced stage CRC at diagnosis was 65.6%. Advanced stage was associated with older age groups prevalence ratio (PR) 4.40 and younger age groups (PR 1.84), low Human Development Index (HDI) (PR 1.22), and low density of family health strategy teams (PR 1.10). The study highlights the unequal distribution of social determinants of health in the diagnosis CRC in Brazil, revealing the need to evaluate and redirect public policies aimed at improving early detection and prevention of CRC in the country.

Engineering of an EPHA2-Targeted Monobody for the Detection of Colorectal Cancer.

Colorectal cancer (CRC) is the third most common cancer worldwide, and is second only to lung cancer with respect to cancer-related deaths. Noninvasive molecular imaging using established markers is a new emerging method to diagnose CRC. The human ephrin receptor family type-A 2 (hEPHA2) oncoprotein is overexpressed at the early, but not late, stages of CRC. Previously, we reported development of an E1 monobody that is specific for hEPHA2-expressing cancer cells both in vitro and in vivo. Herein, we investigated the ability of the E1 monobody to detect hEPHA2 expressing colorectal tumors in a mouse model, as well as in CRC tissue. The expression of hEPHA2 on the surface of CRC cells was analyzed by western blotting and flow cytometry. The targeting efficacy of the E1 monobody for CRC cells was examined by flow cytometry, and immunofluorescence staining. E1 conjugated to the Renilla luciferase variant 8 (Rluc8) reporter protein was used for in vivo imaging in mice. Additionally, an enhanced green fluorescence protein (EGFP) conjugated E1 monobody was used to check the ability of the E1 monobody to target CRC tissue. The E1 monobody bound efficiently to hEPHA2-expressing CRC cell lines, and E1 conjugated to the Rluc8 reporter protein targeted tumor tissues in mice transplanted with HCT116 CRC tumor cells. Finally, E1-EGFP stained tumor tissues from human CRC patients, showing a pattern similar to that of an anti-hEPHA2 antibody. The E1 monobody has utility as an EPHA2 targeting agent for the detection of CRC.

SERS detection platform based on a nucleic acid aptamer-functionalized Au nano-dodecahedron array for efficient simultaneous testing of colorectal cancer-associated microRNAs

A surface-enhanced Raman scattering (SERS) detection platform was constructed based on Au nano-dodecahedrons (AuNDs) functionalized with nucleic acid aptamer-specific binding and self-assembly techniques. SERS labels were prepared by modifying Raman signaling molecules and complementary aptamer chains and were bound on the aptamer-functionalized AuNDs array. Using this protocol, the limits of detection (LODs) of miR-21 and miR-18a in the serum were 6.8 pM and 7.6 pM, respectively, and the detection time was 5 min. Additionally, miR-21 and miR-18a were detected in the serum of a mouse model of colorectal cancer. The results of this protocol were consistent with quantitative real-time polymerase chain reaction (qRT-PCR). This method provides an efficient and rapid method for the simultaneous testing of miRNAs, which has great potential clinical value for the early detection of colorectal cancer (CRC).

Development and external validation of a transfer learning-based system for the pathological diagnosis of colorectal cancer: a large emulated prospective study.

The progress in Colorectal cancer (CRC) screening and management has resulted in an unprecedented caseload for histopathological diagnosis. While artificial intelligence (AI) presents a potential solution, the predominant emphasis on slide-level aggregation performance without thorough verification of cancer in each location, impedes both explainability and transparency. Effectively addressing these challenges is crucial to ensuring the reliability and efficacy of AI in histology applications. In this study, we created an innovative AI algorithm using transfer learning from a polyp segmentation model in endoscopy. The algorithm precisely localized CRC targets within 0.25 mm² grids from whole slide imaging (WSI). We assessed the CRC detection capabilities at this fine granularity and examined the influence of AI on the diagnostic behavior of pathologists. The evaluation utilized an extensive dataset comprising 858 consecutive patient cases with 1418 WSIs obtained from an external center. Our results underscore a notable sensitivity of 90.25% and specificity of 96.60% at the grid level, accompanied by a commendable area under the curve (AUC) of 0.962. This translates to an impressive 99.39% sensitivity at the slide level, coupled with a negative likelihood ratio of <0.01, signifying the dependability of the AI system to preclude diagnostic considerations. The positive likelihood ratio of 26.54, surpassing 10 at the grid level, underscores the imperative for meticulous scrutiny of any AI-generated highlights. Consequently, all four participating pathologists demonstrated statistically significant diagnostic improvements with AI assistance. Our transfer learning approach has successfully yielded an algorithm that can be validated for CRC histological localizations in whole slide imaging. The outcome advocates for the integration of the AI system into histopathological diagnosis, serving either as a diagnostic exclusion application or a computer-aided detection (CADe) tool. This integration has the potential to alleviate the workload of pathologists and ultimately benefit patients.