Detection Of Early-stage Cancer Research Articles

Electrochemical sensor for rapid diagnosis and early-stage detection of pancreatic cancer using Er-GQDs decorated MoS2 nanoflowers

In this study, for the first time, the erbium-doped graphene quantum dots (Er-GQDs) were decorated onto a three-dimensional MoS2 nanoflower (EGM) for electrochemically sensitive and selective detection of carbohydrate antigen, CA 19-9, in phosphate buffer saline (PBS) and biological fluids. The MoS2 nanoflowers (MoS2 NF) were hydrothermally prepared at 200 °C for 24 h, and then 2–8 nm of Er-GQDs were uniformly deposited onto 200 nm MoS2 NF. The MoS2 NF has a high electric conductivity for electrochemical performance, whereas Er-GQDs act as an effective anchor with anti-CA19-9 antibodies (CA19-9 Ab) for ultra-selectivity to CA19-9 antigen. The SPE||EGM/Ab immunosensor has substantial benefits by providing a long-lasting, ultra-sensitive, and highly selective platform for CA19-9 antigen detection. The dynamic ranges in PBS, human serum, and saliva are 5 × 10−5–250, 5 × 10−4–250, and 3 × 10−4–250 U mL−1, respectively, with limits of detection (LODs) of (0.18–2.95) × 10−4 U mL−1. Furthermore, the SPE||EGM/Ab immunosensor can detect CA19-9 in human serum with a recovery of 97–108 %. The non-invasive detection of CA19-9 in saliva is stably sensitive before and after 1 h of breakfast. Moreover, the SPE||EGM/Ab immunosensor has excellent selectivity toward CA19-9 over the other 17 interferences. Results elaborate that the SPE||EGM/Ab immunosensor is a promising electrochemical biosensing probe for detecting CA19-9 in human serum and saliva for clinical diagnosis and prognosis of pancreatic cancer in the early stage.

Skin Cancer Image Segmentation Based on Midpoint Analysis Approach.

Skin cancer affects people of all ages and is a common disease. The death toll from skin cancer rises with a late diagnosis. An automated mechanism for early-stage skin cancer detection is required to diminish the mortality rate. Visual examination with scanning or imaging screening is a common mechanism for detecting this disease, but due to its similarity to other diseases, this mechanism shows the least accuracy. This article introduces an innovative segmentation mechanism that operates on the ISIC dataset to divide skin images into critical and non-critical sections. The main objective of the research is to segment lesions from dermoscopic skin images. The suggested framework is completed in two steps. The first step is to pre-process the image; for this, we have applied a bottom hat filter for hair removal and image enhancement by applying DCT and color coefficient. In the next phase, a background subtraction method with midpoint analysis is applied for segmentation to extract the region of interest and achieves an accuracy of 95.30%. The ground truth for the validation of segmentation is accomplished by comparing the segmented images with validation data provided with the ISIC dataset.

Optical Nanobiosensor Based on Surface-Enhanced Raman Spectroscopy and Catalytic Hairpin Assembly for Early-Stage Lung Cancer Detection via Blood Circular RNA.

Lung cancer has become the leading cause of cancer-related deaths globally. However, early detection of lung cancer remains challenging, resulting in poor outcomes for the patients. Herein, we developed an optical biosensor integrating surface-enhanced Raman spectroscopy (SERS) with a catalyzed hairpin assembly (CHA) to detect circular RNA (circRNA) associated with tumor formation and progression (circSATB2). The signals of the Raman reporter were considerably enhanced by generating abundant SERS "hot spots" with a core-shell nanoprobe and 2D SERS substrate with calibration capabilities. This approach enabled the sensitive (limit of detection: 0.766 fM) and reliable quantitative detection of the target circRNA. Further, we used the developed biosensor to detect the circRNA in human serum samples, revealing that patients with lung cancer had higher circRNA concentrations than healthy subjects. Moreover, we characterized the unique circRNA concentration profiles of the early stages (IA and IB) and subtypes (IA1, IA2, and IA3) of lung cancer. These results demonstrate the potential of the proposed optical sensing nanoplatform as a liquid biopsy and prognostic tool for the early screening of lung cancer.

Abstract LB393: Exosome based multiomics combined with cfDNA methylation reveals complementary signatures in blood based liquid biopsy that carry promise for minimally invasive colorectal cancer screening

Abstract Introduction: Colorectal cancer (CRC) ranks second in cancer morbidity, with approximately 60% of patients being diagnosed at an advanced stage. Here, we apply a multiomic strategy jointly leveraging plasma cell-free DNA (cfDNA) and multiple extracellular vesicle (EV)-derived analytes to enhance CRC diagnostic accuracy. Materials and Methods: Plasma was collected from 96 individuals, 48 CRC patients (stage II-IV) and 48 CRC-negative healthy controls. EVs and cfDNA were isolated from the same plasma sample to look at complementary information within the sample. We developed an EV RNA sequencing platform targeting mRNAs and long non-coding RNAs and sequenced to a depth of 50M reads per sample; cfDNA methylome profiling was sequenced to an equivalent depth. The Olink platform profiled proteins both directly from plasma and from isolated EVs. Expression of EV-derived RNA, splice variants, and proteins in addition to cfDNA methylation patterns were analyzed using Bio-Techne’s multiomic platform. Machine learning-based feature selection algorithms identified biomarker signatures from each analyte. Receiver-operator characteristic curves (ROC) were generated utilizing leave-one-out cross-validation of naïve Bayes classifier models to compute the area under the curve (AUC). Individual signatures were integrated to generate a multiomic classifier. Results: Differential gene expression (DEx) analysis identified ~1000 DEx genes with significant enrichment of CRC-relevant pathways. Splice variant analysis identified several genes previously implicated in CRC with differential isoform usage. Over 1.7 million differentially methylated bases were detected between CRC-positive and CRC-negative cohorts. Segmentation of the genome using out-of-bag reference data yielded ~1100 genomic segments, many of which overlap previously reported CRC biomarkers and identified unique information about the CRC status. Proteomic analysis identified several differentially abundant markers with high discriminatory power. Following feature selection analysis, AUCs obtained from top 10 features of RNA expression, splice variants, cfDNA methylation, and protein abundance with an integrative analysis identified a multi-analyte signature, with an overall AUC of 0.99. Conclusion: While biomarker signatures obtained from each analyte in this study resulted in effective separation of CRC status, the multiomic signature further improved the discriminatory power—underscoring the complementary nature of the signals. Thus, a multiomic biomarker discovery strategy leveraging cfDNA and EV cargo holds tremendous promise for minimally invasive screening and potential detection of early-stage cancers, which is currently under further investigation. Citation Format: Kyle Manning, Dulaney Miller, Yang Yang, Jeff Cole, Shuran Xing, Christopher Benway, Christian Ray, Sudipto Chakrabortty, Brian Haynes, Seth Yu, Johan Skog. Exosome based multiomics combined with cfDNA methylation reveals complementary signatures in blood based liquid biopsy that carry promise for minimally invasive colorectal cancer screening [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB393.

Abstract 2447: Identification of tumor marker gangliosides for early cancer detection: A mass spectrometry approach

Abstract Tumor-marker gangliosides (TMGs) have enormous potential for early-stage cancer detection as diagnostic biomarkers despite being relatively understudied in this context. AOA Dx is focused on the development of a mass spectrometry platform for the quantitation of TMGs in human serum, particularly disialogangliosides GD2 and GD3. The development of a mass spectrometry platform for TMG quantitation may allow for the identification of diagnostic signatures for early-stage cancer detection. Briefly, we monitored fragment moieties characteristic of gangliosides in an untargeted fashion by mass spectrometry. We then identified those differentially altered in cancerous groups compared to normal, and subsequently performed targeted MRM analysis. Through these investigations, we have identified multiple TMGs, specifically multiple types of GD2 and GD3 species characterized by unique lipid tails across different cancer types in human serum from confirmed cancer diagnoses. Other ganglioside classes including GMs and relatively understudied GTs were also identified in this study. Notably, the serum of melanoma and ovarian cancer patients exhibited significantly increased levels of several ganglioside species compared to age-matched healthy serum, highlighting the potential of TMGs as a promising avenue for cancer diagnosis. Further experiments will focus on refining this mass spectrometry method and expand demographic representation to confirm our initial findings and identify additional TMGs of interest. In addition, we intend to apply immune-based platforms such as thin-layer chromatography and ELISA, which have already shown promising results in the detection and quantitation of these compounds. Taken together, AOA Dx is advancing the development of a high-throughput mass spectrometry platform designed for the detection of TMGs, with a primary focus on GD2 and GD3. Our objective is the identification of a diagnostic disease signature for early-stage cancer detection. The validation of this type of diagnostic panel would allow for expedited diagnosis and treatment of cancers currently diagnosed at late stages, ultimately reducing healthcare costs and increasing survival rates. Future research will aim to validate these biomarkers in independent prospective studies. Citation Format: Rachel Culp-Hill, Collin Hill, Adele Blackler, William Ricketts. Identification of tumor marker gangliosides for early cancer detection: A mass spectrometry approach [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2447.

Breast Cancer Stage and Size Detected with Film versus Digital Mammography in New South Wales, Australia: A Population-Based Study Using Routinely Collected Data.

Digital mammography has replaced film mammography in breast-screening programs globally, including Australia. This led to an increase in the rate of detection, but whether there was increased detection of clinically important cancers is uncertain. In this population-wide retrospective cohort study in New South Wales, Australia spanning 2004 to 2016 and including 4,631,656 screens, there were 22,965 cancers in women screened with film (n = 11,040) or digital mammography (n = 11,925). We examined the change in tumor characteristics overall and how these rates changed over time, accounting for changes in background rates using an interrupted time-series. Comparisons were made with unscreened women (n = 26,326) during this time. We found increased detection of in situ cancer (3.36 per 10,000 screens), localized invasive, and smaller-sized breast cancers attributable to the change in mammography technology, whereas screen-detected intermediate-sized and metastatic breast cancers decreased. Rates of early-stage and intermediate-sized interval cancers increased, and late-stage (-1.62 per 10,000 screens) and large interval cancers decreased. In unscreened women, there were small increases in the temporal trends of cancers across all stages. At least some of the increased detection of smaller early-stage cancers may have translated into a reduction in larger and late-stage cancers, indicating beneficial detection of cancers that would have otherwise progressed. However, the increased detection of smaller early-stage and small cancers may also have increased over-diagnosis of lesions that would otherwise have not caused harm. Robust evaluation of potential benefits and harms is needed after changes to screening programs. See related In the Spotlight, p. 638.

Periodic magnetic modulation enhanced electrochemical analysis for highly sensitive determination of genomic DNA methylation

DNA methylation aberrations have a strong correlation with cancer in early detection, diagnosis, and prognosis, which make them possible candidate biomarkers. Electrochemical biosensors offer rapid protocols for detecting DNA methylation status with minimal pretreatment of samples. However, the inevitable presence of background current in the time domain, including electrochemical noise and variations, limits the detection performance of these biosensors, especially for low concentration analytes. Here, we propose an ultrasensitive frequency-domain electrochemical analysis strategy to effectively separate the weak signals from background current. To achieve this, we employed periodic magnetic field modulation of magnetic beads (MBs) on and off the electrode surface to generate a periodic electrochemical signal for subsequent frequency-domain analysis. By capturing labeled MBs with as low as 0.5 pg of DNA, we successfully demonstrated a highly sensitive electrochemical method for determination of genome-wide DNA methylation levels. We also validated the effectiveness of this methodology using DNA samples extracted from three types of hepatocellular carcinoma (HCC) cell lines. The results revealed varying genomic methylation levels among different HCC cell lines, indicating the potential application of this approach for early-stage cancer detection in terms of DNA methylation status.

Abstract IA017: A liquid biopsy fingerprint of disease via nanoengineering

Abstract Serum biomarkers are often insufficiently sensitive or specific to facilitate cancer screening or diagnostic testing. In many cancers, including high-grade serous carcinoma (HGSC), biomarkers fail to detect early-stage cancer detection or to substantially impact mortality rates. We developed a perception-based sensing method that captures a biomarker-agnostic ‘disease fingerprint’ of HGSC using serum. Instead of measuring individual biomarkers, the method collects large data sets of molecular binding interactions to a diverse array of moderately-selective sensors, which were used to train machine learning algorithms. In an initial study using serum from 264 patients, we built a prediction model of nanosensor responses that reliably identified HGSC substantially better than CA125. We believe that the improvement of such a method, using larger nanosensor arrays, more sophisticated AI algorithms, and larger patient cohorts, could improve the detection of HGSC and facilitate biomarker discovery efforts. Citation Format: Daniel A. Heller, Mijin Kim, Lakshmi Ramanathan, Kara Long Roche. A liquid biopsy fingerprint of disease via nanoengineering [abstract]. In: Proceedings of the AACR Special Conference on Ovarian Cancer; 2023 Oct 5-7; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(5 Suppl_2):Abstract nr IA017.

Label-Free Optical Technologies to Enhance Noninvasive Endoscopic Imaging of Early-Stage Cancers.

White light endoscopic imaging allows for the examination of internal human organs and is essential in the detection and treatment of early-stage cancers. To facilitate diagnosis of precancerous changes and early-stage cancers, label-free optical technologies that provide enhanced malignancy-specific contrast and depth information have been extensively researched. The rapid development of technology in the past two decades has enabled integration of these optical technologies into clinical endoscopy. In recent years, the significant advantages of using these adjunct optical devices have been shown, suggesting readiness for clinical translation. In this review, we provide an overview of the working principles and miniaturization considerations and summarize the clinical and preclinical demonstrations of several such techniques for early-stage cancer detection. We also offer an outlook for the integration of multiple technologies and the use of computer-aided diagnosis in clinical endoscopy.

Breast cancer screening and early diagnosis in China: a systematic review and meta-analysis on 10.72 million women

BackgroundThe incidence of breast cancer among Chinese women has gradually increased in recent years. This study aims to analyze the situation of breast cancer screening programs in China and compare the cancer detection rates (CDRs), early-stage cancer detection rates (ECDRs), and the proportions of early-stage cancer among different programs.MethodsWe conducted a systematic review and meta-analysis of studies in multiple literature databases. Studies that were published between January 1, 2010 and June 30, 2023 were retrieved. A random effects model was employed to pool the single group rate, and subgroup analyses were carried out based on screening model, time, process, age, population, and follow-up method.ResultsA total of 35 studies, including 47 databases, satisfied the inclusion criteria. Compared with opportunistic screening, the CDR (1.32‰, 95% CI: 1.10‰–1.56‰) and the ECDR (0.82‰, 95% CI: 0.66‰–0.99‰) were lower for population screening, but the proportion of early-stage breast cancer (80.17%, 95% CI: 71.40%–87.83%) was higher. In subgroup analysis, the CDR of population screening was higher in the urban group (2.28‰, 95% CI: 1.70‰–2.94‰), in the breast ultrasonography (BUS) in parallel with mammography (MAM) group (3.29‰, 95% CI: 2.48‰–4.21‰), and in the second screening follow-up group (2.47‰, 95% CI: 1.64‰–3.47‰), and the proportion of early-stage breast cancer was 85.70% (95% CI: 68.73%–97.29%), 88.18% (95% CI: 84.53%–91.46%), and 90.05% (95% CI: 84.07%–94.95%), respectively.ConclusionThere were significant differences between opportunistic and population screening programs. The results of these population screening studies were influenced by the screening process, age, population, and follow-up method. In the future, China should carry out more high-quality and systematic population-based screening programs to improve screening coverage and service.

Detection of early-stage gastrointestinal cancers by micronuclei DNA from erythrocytes.

382 Background: Gastrointestinal (GI) cancers account for over a third of cancer deaths. However, their early detection remains a challenge. Micronuclei (MN) are extranuclear bodies containing damaged chromosome segments indicative of genomic instability. Increased MN frequency in hematopoietic cells is observed in patients with solid tumors. We have developed a method (WO2021/228246 A1) for purifying and characterizing micronuclei DNA (mnDNA) in erythrocytes from peripheral blood. Here, we evaluated the potential of mnDNA for early-stage GI cancer detection, including colorectal (CC) and gastric (GC) cancers. Methods: Peripheral blood (1 ml) was collected from healthy donors (HD) and cancer patients mnDNA isolation and purification from erythrocytes. Participants were randomly divided into training, validation and independent test cohorts in a 7:2:1 ratio, maintaining similar cancer types, CC/GC/HD ratio, gender and age distribution. Distinct mnDNA features between cancer patients and HDs were identified using sequencing data. Logistic regression algorithms were applied using these features for cancer detection. Results: The study enrolled 1046 participants, including 419 HDs, 366 CC and 261 GC cases. Of these, over half were diagnosed with early-stage diseases; TNM stage I accounted for 38.7%, stage II for 22.8%, stage III for 30.5% and stage IV for 8.0%. The cancer detection model with mnDNA features yielded an overall accuracy of 84.8%, with a sensitivity of 84.1% and specificity of 85.7%. For individual caner types, sensitivity was 89.2% for CC and 76.9% for GC. For early-stage (stage I-II) CC and GC, the model demonstrated sensitivities of 88.9% and 76.5%, respectively, at 85.7% specificity. Conclusions: Unlike cell-free DNA, mnDNA are chromosomal fragments in the cytoplasm. The abundance of erythrocytes in peripheral blood provides an easily accessible source for mnDNA enrichment. This pilot study illustrates the potential of mnDNA as a tool for early GI cancer detection, contributing to large-scale efforts towards developing an effective GI cancer screening test.

Use of micronuclei DNA from erythrocytes in early-stage colorectal cancer detection.

25 Background: Current development in stool- and cell-free DNA (cfDNA)-based technologies have demonstrated promising potentials in colorectal cancer (CRC) diagnosis. However, early detection of CRC and advanced adenoma (AA) remains challenging. Micronuclei (MN) are extranuclear bodies containing chromatin segments resulting from errors in DNA repair. Elevated levels of MN+ erythrocytes have been studied in genotoxicity testing and cancer diagnosis. We have developed a method (WO2021/228246 A1) for purifying and sequencing micronuclei DNA (mnDNA) in erythrocytes from peripheral blood. Here, we compared mnDNA between healthy donors (HDs) and CRC patients and explored its application for early cancer diagnosis. Methods: Peripheral blood (1ml) was obtained from a training cohort of 987 individuals, a validation cohort of 246, and an independent test cohort of 193. mnDNA was isolated and sequenced from erythrocytes. Genome-wide analysis identified distinctive mnDNA features between HD and CRC. Predictive models using these features were developed to differentiate CRC and AA from HDs. A clinical trial comparing mnDNA with an approved multitarget stool DNA test (sDNA-FIT) was also conducted on 60 individuals (42 HDs, 6 AAs, and 12 CRCs). Results: Genome-wide analysis of mnDNA revealed substantial differences in distribution patterns between HDs and CRC patients. The predictive model built on mnDNA features achieved an AUC of 92.98% (95%CI: 87.85-98.12%), with an 88.64% sensitivity and 92.75% specificity in the independent test cohort. The model identified early-stage (stage I-II) CRC and AA with sensitivities of 83.33% or 69.56%, respectively. Comparing mnDNA to sDNA-FIT showed superior overall sensitivity (83.33% vs. 61.11%), AA sensitivity (83.33% vs. 16.66%), with comparable specificity (85.71% vs. 88.10%). Conclusions: Our results demonstrate that mnDNA from a small amount of peripheral blood enable accurate detection of AA and early-stage CRC. As a type of cytoplasmic DNA, mnDNA can provide a valuable tool for early cancer detection, offering different underlying mechanisms compared to current methods. A larger clinical study (NCT05875584) is ongoing and will further validate the application of mnDNA in early cancer detection.

Diagnosis, treatment, and prognosis of stage IB non-small cell lung cancer with visceral pleural invasion.

With the increasing implementation of early lung cancer screening and the increasing emphasis on physical examinations, the early-stage lung cancer detection rate continues to rise. Visceral pleural invasion (VPI), which denotes the tumor's breach of the elastic layer or reaching the surface of the visceral pleura, stands as a pivotal factor that impacts the prognosis of patients with non-small cell lung cancer (NSCLC) and directly influences the pathological staging of early-stage cases. According to the latest 9th edition of the TNM staging system for NSCLC, even when the tumor diameter is less than 3 cm, the final T stage remains T2a if VPI is present. There is considerable controversy within the guidelines regarding treatment options for stage IB NSCLC, especially among patients exhibiting VPI. Moreover, the precise determination of VPI is important in guiding treatment selection and prognostic evaluation in individuals with NSCLC. This article aims to provide a comprehensive review of the current status and advancements in studies pertaining to stage IB NSCLC accompanied by VPI.

Optical Ring Resonator Based Biosensor with High Sensitivity for Label-Free Detection of Early-Stage Cancer

Optical Ring Resonator Based Biosensor with High Sensitivity for Label-Free Detection of Early-Stage Cancer

Exploring the evolution of T cell function and diversity across different stages of non-small cell lung cancer.

The immune system plays a key role in detecting and fighting cancerous tumors. T cells are a crucial component in both natural and therapeutic cancer immunoediting responses, but it is unclear if they are the primary agents of these processes. In this study, patients with lung lesions detected by CT scan were selected, and their peripheral blood samples were analyzed for T cell population and serum cytokines/chemokines. T cell subtypes (CD3, CD4, CD8, CD27, CD28, CD45, CD45RA, CD57, CCR7, and PD1) and serum cytokines/chemokines (IL-2, IL-6, IL-10, IFN-γ, TGF-β, TNFα, CXCL1, CXCL9, and CXCL12) were measured by flow cytometry and analysis before surgical resection or other cancer treatments. The frequency of T cell subpopulations in patients with lung cancer (n = 111) corresponded to those seen in patients with T cell exhaustion. As lung cancer progressed, the proportion of effector memory T cells decreased, while the proportion of naive T cells, PD-1, CD57+, CD28+CD27+, CD45RA+, and CD3+CD4+CCR7 increased. Circulating CD8+PD1+ T cells were positively correlated with intra-tumoral PD-L1 expression. Concurrently, serum levels of IL-2, TGF-β, and CXCL9 decreased, while IL-6, IL-10, IFN-γ, and CXCL12 increased during the progression of lung cancer. In conclusion, T cell dysfunction is associated with cancer progression, particularly in advanced-stage lung cancer, and cancer immunoediting will provide early-stage cancer detection and further therapeutic strategies.

Enhancing cancer detection and prevention mechanisms using advanced machine learning approaches

Cancer is a major global health challenge, emphasizing the critical need for early detection to enhance patient outcomes. This study thoroughly investigates the applications of advanced machine learning methods for cancer detection and prevention, aiming to develop robust algorithms that can accurately identify cancerous cells and assess cancer severity based on key parameters. The authors synthesize insights from previous cancer detection and prevention research through an in-depth literature review. The study sets specific objectives, including creating and evaluating innovative algorithms for classifying cancer cells.The authors employed machine learning techniques to analyze parameters, such as cell size, shape, nucleus characteristics, and additional factors like cell texture, mitosis count, tumour progression, metastasis, gene expression patterns, and biological markers. This methodology is distinguished by its effective use of diverse data types and automated feature extraction to improve cancer detection and prediction accuracy. Advanced machine learning methods enhance the precision and reliability of current cancer cell classification algorithms. The research underscores the importance of timely and accurate cancer detection, which enables early intervention and significantly improves patient survival rates. The results and discussion section meticulously analyzes the findings, demonstrating the approach's effectiveness in accurately identifying cancerous cells and assessing cancer severity. This study is a valuable resource for medical professionals, supporting early-stage cancer detection and classification of cancer cells at different stages. The proposed algorithms show promising results in improving the accuracy and efficiency of cancer detection systems, paving the way for future advancements in cancer research and offering essential insights for healthcare practitioners and researchers.

Resectability versus Operability in Early-Stage Non-Small Cell Lung Cancer.

With increased detection of early-stage non-small cell lung cancer (NSCLC) owing to screening, determining optimal management increasingly hinges on assessing resectability and operability. Resectability refers to the feasibility of achieving microscopically negative margins based on tumour size, location and degree of local invasion and achieving an anatomical lobar resection. Operability reflects the patient's tolerance for resection based on comorbidities, cardiopulmonary reserve and frailty. Standardized criteria help guide these assessments, but application variability contributes to practice inconsistencies. This review synthesizes a strategic approach to evaluating resectability and operability in contemporary practice. Standardization promises reduced care variability and optimized patient selection to maximize curative outcomes in this new era of early detection. Recent pivotal trials demonstrate equivalency of sublobar resection to lobectomy for small, peripheral, node-negative NSCLC, expanding options for parenchymal preservation in borderline surgical candidates. Furthermore, recent phase 3 trials have highlighted the benefit of chemoimmunotherapy as a neoadjuvant treatment with an excellent pathological response and a down staging of the tumour, improving the resectability of the early-stage NSCLC. A good assessment of the operability and resectability is paramount in order to offer the best course of treatment for our patients. European and American societies have issued recommendations to help clinicians assess the cardiopulmonary function and predict the extension of pulmonary resection that could afford the patient. This operability assessment is closely linked with the evaluated tumour resectability which will determine the extension of pulmonary resection that is needed for the patient in order to achieve a good oncological outcome. Some major progresses have been done recently to improve the operability and resectability of patients. For instance, prehabilitation program allows better postoperative morbidity. Some studies have shown a potential good oncological outcome with sublobar resection expending access to surgery for patient with reduced lung function. Some others have identified the neoadjuvant immunochemotherapy as a potential solution for downstaging tumours. Work-up of early-stage NSCLC is a key moment and has to be done thoroughly and in full knowledge of the recent findings in order to propose the most appropriate treatment for the patient.

Comprehensive Analysis of microRNA Methylation Profiles and Determination of Their Functional Significance in Colorectal Cancer: A Study Protocol

Colorectal cancer (CRC) is a leading cause of cancer-associated fatalities globally. Despite the notable progress in diagnostics and patient care, challenges persist in areas such as early detection, prognostic variable identification, metastatic disease treatment, and personalised treatment options. In CRC, microRNAs, a type of short non-coding RNA, are found to be deregulated and can significantly influence its onset and progression. However, previous microRNA research primarily focused on expression levels to ascertain their biological significance, leaving the microRNA methylation landscapes in CRC patients largely unexplored. This study aims to investigate the genome-wide methylome profiles of microRNA and clarify their roles in CRC. We will analyse the microRNA methylation profiles from our in-house data of more than 100 CRCs and correlate the differentially methylated microRNAs with clinicopathological features. To understand the biological significance of methylated microRNAs, we will perform pathway enrichment analysis and molecular dynamics simulations to examine the binding of argonaut protein to the differentially methylated microRNAs and the structural changes involved. Furthermore, we will conduct functional studies to determine the roles of selected microRNAs. CRC cell lines with hypermethylated microRNA of interest will be treated with a demethylating agent, followed by cell-based assays. Subsequent transcriptome-wide microRNA gene target identification and protein profiling will be performed to interrogate the molecular pathways affected by demethylation of the microRNA of interest. Methylation signifies a major transformation in cancer detection, as widespread epigenetic changes could potentially provide superior early-stage cancer detection and classification compared to somatic mutations. Effective CRC diagnosis ensures timely and appropriate treatment for patients, thereby improving their quality of life. The insights gained from this study could be applied to individualized health diagnostics, disease prognostication, and treatment monitoring.

An Observation and Analysis the role of Convolutional Neural Network towards Lung Cancer Prediction

Lung cancer is one of the most serious and prevalent diseases, causing many deaths each year. Though CT scan images are mostly used in the diagnosis of cancer, the assessment of scans is an error-prone and time-consuming task. Machine learning and AI-based models can identify and classify types of lung cancer quite accurately, which helps in the early-stage detection of lung cancer that can increase the survival rate. In this paper, Convolutional Neural Network is used to classify Adenocarcinoma, squamous cell carcinoma and normal case CT scan images from the Chest CT Scan Images Dataset using different combinations of hidden layers and parameters in CNN models. The proposed model was trained on 1000 CT Scan Images of cancerous and non-cancerous cells to find the best combination of parameters in CNN to predict lung cancer accurately. The proposed system recorded the highest accuracy of 92.79%. In addition to that, the paper addresses 192 observations made using the CNN model.

A non-invasive method for concurrent detection of multiple early-stage cancers in women

Untargeted serum metabolomics was combined with machine learning-powered data analytics to develop a test for the concurrent detection of multiple cancers in women. A total of fifteen cancers were tested where the resulting metabolome data was sequentially analysed using two separate algorithms. The first algorithm successfully identified all the cancer-positive samples with an overall accuracy of > 99%. This result was particularly significant given that the samples tested were predominantly from early-stage cancers. Samples identified as cancer-positive were next analysed using a multi-class algorithm, which then enabled accurate discernment of the tissue of origin for the individual samples. Integration of serum metabolomics with appropriate data analytical tools, therefore, provides a powerful screening platform for early-stage cancers.