Early Cancer Detection Research Articles

Automated early ovarian cancer detection system based on bioinformatics

Ovarian cancer is a common gynecological tumor, with a high mortality rate and difficult clinical treatment. Early detection of ovarian cancer has significant diagnostic value. In response to the problem of poor diagnostic performance of traditional early diagnosis methods, this article designed an automated early ovarian cancer detection system to improve the detection of early ovarian cancer. The conventional early diagnosis methods include serum CA125 (carbohydrate antigen 125) detection and positron emission tomography/computed tomography (PET/CT) imaging. This article combined serum CA125 detection and PET/CT imaging to detect the CA125 level and maximum standardized uptake value (SUV) in patient’s serum. When the CA125 level exceeded 35U/ml and the maximum SUV value exceeded 2.5, the test was considered positive. This article selected 200 patients from Jingzhou Hospital for the experiment and compared the three detection methods. The average specificity of single serum CA125 detection, single PET/CT imaging, and automated detection in patients under 50 were 61.24%, 79.57%, and 97.79%, respectively. The automated early ovarian cancer detection system designed in this article can significantly improve the specificity of early ovarian cancer detection and has excellent application value for early ovarian cancer detection.

B-321 Enhancing Early Detection of Prostate Cancer Using PSA-Guided Screening in High-Risk Men Under 50

Abstract Background Prostate Cancer remains a significant global public health concern. It is the most common cancer and the second leading cause of cancer death among men in the United States. Current guidelines, including those by the USPSTF, ACS and ASCO offer varying recommendations on PSA screening. Present clinical practice, primarily targeting men aged 50-69. Our study aimed to assess the feasibility and validity of using PSA for targeted screening in men under 50 who are at high risk, enhancing early detection of prostate cancer. Methods This retrospective analysis utilized data from our hospital's prostate cancer screening program collected from 2022 to 2023, including104 individuals under 50. Participants were categorized based on PSA levels 4-10 ng/mL (n=71) and over10 ng/mL (n=33). We analyzed the number of cases diagnosed with prostate cancer and the correlation with PSA levels, Gleason Scores (GS), family history, and ethnicity/race. Results In the104 participants, 88 of them were diagnosed with prostate cancer, highlighting an 84.6% detection rate. Among PSA levels 4-10 ng/mL group, 36 out of 43 White or Caucasian and 13 out of 13 Black or African American men were identified as GS 6-10. In the group PSA over 10 ng/mL, 18 White or Caucasian and 6 Black or African American individuals with more aggressive disease (GS 6-10). Importantly, a substantial fraction of these cases had a first-degree relative with prostate cancer, emphasizing the influence of family history on PSA screening decision. Conclusions Our findings suggest that PSA-guided screening in men under 50 at high risk can enhance the early detection of prostate cancer. The significant presence of intermediate-risk prostate cancer (GS 6 or 7) in populations at higher risk due to ethnicity/race and family history. Our study highlights the potential benefits of revisiting current screening guidelines to lower the age threshold for high-risk populations.

B-330 Reducing Inappropriate Fecal Immunochemical Test

Abstract Background Colorectal cancer is the third most common cancer worldwide. The risk of colorectal cancer increases with age and predominantly affects individuals over the age of 50. Regular screening is crucial for early detection and prevention of colorectal cancer. Pre-cancerous polyps and colorectal cancers can bleed into the gastrointestinal tract. While the presence of blood in the stool cannot always be seen visually, it can be detected by the Fecal Immunochemical Test (FIT), currently the most sensitive method for directly measuring hemoglobin in this specimen type. In our province, FIT is recommended for testing average-risk asymptomatic patients aged 50 to 74 every 1-2 years. Patients aged 40 to 49 or 75 to 84 can be tested if they meet specific criteria. Like many other tests in the lab, FIT can be used inappropriately. The focus of this study was to examine the utilization of FIT in our institution. Methods Data were collected from our institution's laboratory information system for the FIT performed on-site from July to December 2023. Results We performed 91,545 FIT tests during the 6-month period with an overall positive rate of 6.8%. Of the total tests performed, 87.9% were for patients aged 50-74 years, 10.6% for patients aged 40-49 or 75-84 years, and 1.2% for patients outside the approved age range. Each year, we performed over 2,000 FIT tests for patients outside the accepted screening age, with an estimated cost of over 30,000 CAD. Conclusions Our results showed a significant number of FITs were ordered inappropriately by healthcare providers. Considering the relatively high cost of FIT kits, labor, reagents, etc., the overall cost per reportable is significant. Thus, we have started engaging healthcare providers involved in FIT testing to strategize approaches to improve utilization.

B-352 Effects of COVID-19 on Breast Cancer Positivity: A Retrospective and Predictive Study

Abstract Background The COVID-19 pandemic has had a significant impact on the healthcare system, resulting in overall reduction in access to healthcare services, including the suspension of screening programs and postponement of surgeries and medical treatments. The aim of this study was to evaluate the effects of the pandemic on breast cancer positivity from cases received at a laboratory in São Paulo, Brazil. Methods A retrospective analysis of a database from a pathology laboratory in São Paulo, Brazil, was performed during the 2017-2023 period and classified as following: pre-pandemic (January 2017-February 2020), pandemic (March 2020 - April-2022), and post-pandemic (May 2022-December 2023). Cases were classified as negative or positive for malignancies. Positive anatomopathological examinations (breast biopsies and surgical specimens) considered primary, metastatic mesenchymal or endothelial malignancies. The Mann-Kendall test was used to evaluate the trend, and the PACF (Partial Autocorrelation Function) correlogram was used to analyze historical memory for definition of the autoregressive model. The augmented Dickey-Fuller hypothesis test was applied to analyze whether the series is stationary or not, and the first differences test was applied. The ARIMA model was used for the estimates (1,1,1). Statistics were performed using the gretl software and pymannkendall library in the Python language. Results A total of 74,973 samples were evaluated during the 2017-2023 period. In the three-year pre-pandemic period, 32,644 tests were recorded, with an average of 29.34% positive cases. From the analysis of the pre-pandemic time series, there was a positive trend towards an increase in cases (p < 0.0001, z=6.1360, Tau=0.6955). In a non-pandemic scenario, from March 2020 to February 2021, the number of new tests expected would be 11,535; however, in the pandemic period, only 8,904 were observed, a 22.81% reduction on expected cases. According to the historical average positivity rate in the pre-pandemic period, the expected and observed total were, respectively, 3,384 and 2,566 positive cases for malignancy. Conclusions This study reveals a significant decrease of approximately 22.81% in new breast anatomopathological diagnoses during the COVID-19 pandemic in a laboratory in São Paulo, Brazil. For comparison purposes, we identified a 24.17% of missed opportunities on diagnoses of new malignancy-positive cases, compared to the pre-pandemic period. These findings highlight the need for strategies to mitigate the negative effects of the pandemic on the early detection and treatment of breast cancer.

Comprehensive Details on Human Papillomavirus (HPV) Infections and Associated Malignancies: Awareness about Prophylaxis & Treatment Options Available for HPV Infections

Human Papilloma virus (HPV) role has now been widely accepted as a causative agent for cervical as well as head and neck squamous cell carcinomas. Screening and early detection of cervical cancer remains the key element to eradicate this deadly disease. Since it has a long natural history and disease progression, this gave an opportunity for early detection through screening of precancerous lesions which was adopted in high income countries (HIC) like UK which has led to the significant reduction of the cases. Unfortunately, Low- and middle-income countries are facing huge burden because of the lack of effective screening program. FDA approved Cervarix, Gardasil and Gardasil 9 are used as prophylaxis for cervical cancer & genital warts but screening is still essential after vaccination because these vaccines are not effective against all types of HPV types. HPV 16 & HPV 18 are highly oncogenic types and accounts for 70% of cervical cancers. The use of vaccine reduces the risk of getting HPV infection and decrease the rate of morbidity and mortality due to the cervical cancer. In addition to vaccines, many peptides or protein-based & cell or DNA-based vaccines are still in clinical trials; these prevention and management available in vaccines are likely to come up with significant benefits of health in future. Although it’s usually cured by immune system but presence of certain cofactors such as smoking, HIV etc. reduces the probability of an individual to eradicate the infection effectively. The aim of this study is to review literature regarding HPV related infections and malignancies. Moreover, it will make individuals aware about prophylaxis & treatment options available for HPV infection. This review article evaluates the HPV related diseases, there screening, evaluation and management currently available. Bangladesh Journal of Medical Science Vol. 23 No. 04 October’24 Page : 967-983

Keratinocyte Carcinoma: Canadian Landscape and an Evidence-based Approach to Follow-up

Dermatologists play a vital role in the early detection, prevention and effective management of skin cancer in patients with a prior history of the disease. Regular monitoring and timely interventions greatly enhance the overall prognosis and quality of life for patients with skin cancer. Dermatologists possess the requisite expertise to accurately diagnose and oversee the management of cutaneous skin cancers. Skin cancer screening via total body skin exam (TBSE) is often considered one of the safest, easiest, and most cost-effective tests in medicine. Despite dermatologists’ ability to offer such invaluable care for this patient population, offering routine skin checks for all patients with a prior history of skin cancer becomes exceptionally challenging given the high demand for dermatology care across Canada. It is important for dermatologists to maximize the efficiency of care during TBSEs by adhering to evidence-based guidelines when determining the frequency and duration of follow-up. These guidelines also provide a solid foundation for discussions with patients regarding the rationale for discharge back to their primary care provider.

B-240 Multi-omic blood test for early detection of cancer and pre-cancer

Abstract Background The earlier diagnosis of cancer is vital to improve patient survival. Many liquid biopsy technologies are focused on single tumor-derived biomarkers, which limits test sensitivity, especially for early-stage cancers that do not shed sufficient genetic material into the bloodstream. The Dxcover liquid biopsy employs Multi-Omic Spectral Analysis (MOSA) that interrogates a blood sample with Infrared (IR) radiation and produces a distinctive signature that represents the whole biomolecular profile of the sample. This multi-omic blood test analyzes the full complement of tumor and immune-derived markers present within blood derivatives and could facilitate the earlier detection of cancer. The technology can be tuned for high sensitivity (rule out) or high specificity (rule in) depending on the desired application, clinical priorities and international healthcare markets. Methods Three clinical studies have been completed, assessing the technology for the detection of: (1) brain cancer, (2) multiple cancers, and (3) colorectal cancer (CRC) and pre-cancerous lesions. In the first study, we recruited 988 patients prospectively with non-specific symptoms associated with a brain tumor. Secondly, we carried out a large-scale multi-cancer evaluation (n = 2092 patients), targeting eight different cancers. Moreover, the CREATE (ColoREctal cancer & Adenoma Test Evaluation) feasibility study (3) examined 100 CRC, 92 adenoma samples and 104 colonoscopy screening control patients. Results In study 1, the brain cancer algorithm detected 96% of the patients with brain cancer, and 100% of glioblastomas, the most aggressive primary brain tumor. In the multi-cancer study (2), area under the receiver operating characteristic curve values were calculated for the eight cancer types versus symptomatic non-cancer controls: brain (0.90), breast (0.76), colorectal (0.91), kidney (0.91), lung (0.91), ovarian (0.86), pancreatic (0.84) and prostate (0.86). We also assessed the test performance when all eight cancer types were pooled to classify ‘any cancer’ against non-cancer patients. The cancer versus asymptomatic non-cancer classification detected 64% of stage I cancers when specificity was 99% (overall sensitivity 57%). When tuned for higher sensitivity, this model identified 99% of Stage I cancers (with specificity 59%). In the CREATE study, the test reported promising results for pre-cancer detection; at 90% specificity, the test reported sensitivities of 80% for CRC and 59% for advanced adenomas. Conclusions A rapid liquid biopsy that is sensitive to pre-cancerous lesions and early-stage cancer could substantially improve patient outcomes. There is a low barrier to integrating this blood test into existing diagnostic pathways as the technology is rapid, simple to use, and sample preparation is minimal. In addition, the spectroscopic liquid biopsy has the potential to be combined with other orthogonal tests, such as cell-free DNA, which could provide an efficient route to diagnosis. Cancer treatment can be more effective when given earlier, and this low-cost strategy has the potential to improve patient prognosis.

Using artificial intelligence based imaging to predict lymph node metastasis in non-small cell lung cancer: a systematic review and meta-analysis.

Lung cancer, especially non-small cell lung cancer (NSCLC), is one of the most-deadly malignancies worldwide. Lung cancer has a worse 5-year survival rate than many primary malignancies. Thus, the early detection and prognosis prediction of lung cancer are crucial. The early detection and prognosis prediction of lung cancer have improved with the widespread use of artificial intelligence (AI) technologies. This meta-analysis examined the accuracy and efficacy of AI-based models in predicting lymph node metastasis (LNM) in NSCLC patients using imaging data. Our findings could help clinicians predict patient prognosis and select alternative therapies. We searched the PubMed, Web of Science, Cochrane Library, and Embase databases for relevant articles published up to January 31, 2024. Two reviewers individually evaluated all the retrieved articles to assess their eligibility for inclusion in the meta-analysis. The systematic assessment and meta-analysis comprised articles that satisfied the inclusion criteria (e.g., randomized or non-randomized trials, and observational studies) and exclusion criteria (e.g., articles not published in English), and provided data for the quantitative synthesis. The quality of the included articles was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2). The pooled sensitivity, specificity, and area under the curve (AUC) were used to evaluate the ability of AI-based imaging models to predict LNM in NSCLC patients. Sources of heterogeneity were investigated using meta-regression. Covariates, including country, sample size, imaging modality, model validation technique, and model algorithm, were examined in the subgroup analysis. The final meta-analysis comprised 11 retrospective studies of 6,088 NSCLC patients, of whom 1,483 had LNM. The pooled sensitivity, specificity, and AUC of the AI-based imaging model for predicting LNM in NSCLC patients were 0.87 [95% confidence interval (CI): 0.80-0.91], 0.85 (95% CI: 0.78-0.89), and 0.92 (95% CI: 0.90-0.94). Based on the QUADAS-2 results, a risk of bias was detected in the patient selection and diagnostic tests of the included articles. However, the quality of the included articles was generally acceptable. The pooled sensitivity and specificity were heterogeneous (I2>75%). The meta-regression and subgroup analyses showed that imaging modality [computed tomography (CT) or positron emission tomography (PET)/CT], and the neural network method model design significantly affected heterogeneity of this study. Models employing sample size data from a single center and the least absolute shrinkage and selection operator (LASSO) method had greater sensitivity than other techniques. Using the Deek' s funnel plot, no publishing bias was found. The results of the sensitivity analysis showed that deleting each article one by one did not change the findings. Imaging data models based on AI algorithms have good diagnostic accuracy in predicting LNM in patients with NSCLC and could be applied in clinical settings.

Prospective cohort for early detection of liver cancer (Pearl): a study protocol

IntroductionHepatocellular carcinoma (HCC) is the fastest-rising and fourth most common cause of cancer death worldwide. Liver cirrhosis is the largest underlying risk factor for HCC. Therefore, patients with cirrhosis should...

Multidimensional Fragmentomics Enables Early and Accurate Detection of Colorectal Cancer.

Colorectal cancer is frequently diagnosed in advanced stages, highlighting the need for developing approaches for early detection. Liquid biopsy using cell-free DNA (cfDNA) fragmentomics is a promising approach, but the clinical application is hindered by complexity and cost. This study aimed to develop an integrated model using cfDNA fragmentomics for accurate, cost-effective early-stage colorectal cancer detection. Plasma cfDNA was extracted and sequenced from a training cohort of 360 participants, including 176 patients with colorectal cancer and 184 healthy controls. An ensemble stacked model comprising five machine learning models was employed to distinguish patients with colorectal cancer from healthy controls using five cfDNA fragmentomic features. The model was validated in an independent cohort of 236 participants (117 patients with colorectal cancer and 119 controls) and a prospective cohort of 242 participants (129 patients with colorectal cancer and 113 controls). The ensemble stacked model showed remarkable discriminatory power between patients with colorectal cancer and controls, outperforming all base models and achieving a high area under the receiver operating characteristic curve of 0.986 in the validation cohort. It reached 94.88% sensitivity and 98% specificity for detecting colorectal cancer in the validation cohort, with sensitivity increasing as the cancer progressed. The model also demonstrated consistently high accuracy in within-run and between-run tests and across various conditions in healthy individuals. In the prospective cohort, it achieved 91.47% sensitivity and 95.58% specificity. This integrated model capitalizes on the multiplex nature of cfDNA fragmentomics to achieve high sensitivity and robustness, offering significant promise for early colorectal cancer detection and broad patient benefit. Significance: The development of a minimally invasive, efficient approach for early colorectal cancer detection using advanced machine learning to analyze cfDNA fragment patterns could expedite diagnosis and improve treatment outcomes for patients. See related commentary by Rolfo and Russo, p. 3128.

Mapping local evidence on early recognition and management of people with potential cancer symptoms: a narrative review

Aim Early cancer detection has potential to improve outcomes. However, many patients in South Africa present when the disease is at an advanced stage. The World Health Organization recommends two approaches to early cancer detection: screening asymptomatic individuals, and early recognition and management of symptomatic individuals. This paper focuses on the latter. For people with potential cancer symptoms, the journey to diagnosis is complex and influenced by multiple factors. Most symptomatic people will self-present to primary health care clinics, where primary health care providers are pivotal in triage. Methods This article presents local insights into cancer awareness measurement tools: community-level cancer symptom awareness, lay beliefs, and symptom appraisal; factors influencing the journey from symptom discovery to diagnosis; primary health care provider challenges in assessing symptomatic individuals, and interventions to support symptom assessment and help-seeking. It draws on findings from the African Women Awareness of CANcer (AWACAN) project and a narrative review of relevant published articles on journeys to cancer diagnosis in SA (2013-2023). Findings Very few cancer awareness measurement tools have been locally validated, hampering comparison and limiting opportunities for intervention development and evaluation. The AWACAN study developed and validated a local cancer awareness measurement tool for breast and cervical cancer. Studies show that most people in SA need information on cancer risk, symptoms, and pathways to care. Barriers to accessing health care include financial, infrastructural, safety, stigma, and previous health facility experiences. Primary health care providers require support for symptom assessment and referral systems. There is limited local work on developing and evaluating interventions to improve timely cancer diagnosis. Conclusions This paper underscores the importance of prioritising early recognition and management of people with symptomatic cancer as part of a comprehensive cancer control plan, providing insights for improving the journey to diagnosis.

The role of cytoreductive nephrectomy in the era of immune checkpoint inhibitors: A review of current evidence and ongoing trials.

Renal cell carcinoma (RCC) was diagnosed in over 400 000 individuals globally in 2020, making it a significant global health concern. The incidence of RCC varies by region and overall mortality rates have been declining. This decline is attributed in part to advancements in early cancer detection through imaging and the development of more effective systemic therapies. Cytoreductive nephrectomy (CN) was adopted as a standard treatment for metastatic RCC (mRCC) based on clinical experience and early clinical trials. However, the treatment landscape has shifted with the introduction of tyrosine kinase inhibitors (TKI) in 2007 and, more recently, immune checkpoint inhibitors (ICIs). Dual ICI therapy and combinations of ICIs with TKIs are collectively referred to as immuno-combination therapies and have become standard first-line treatments. This review examines the evolving role of CN in the era of immuno-combination therapies, with a focus on patient selection and the timing of surgery. The immunogenic nature of RCC, characterized by spontaneous tumor regression and immune cell infiltration, suggests a potential benefit from combining CN with ICI therapy to enhance treatment outcomes. This is supported by several clinical studies that reported improved outcomes; however, these were limited by their retrospective nature. Ongoing clinical trials, such as NORDIC-SUN, PROBE, and SEVURO-CN, are expected to provide critical insights into the role of CN in the ICI era. Their findings will ultimately guide future clinical decision-making and further refine treatment strategies for mRCC.

Plasma cell-free DNA methylome-based liquid biopsy for accurate gastric cancer detection.

Early detection plays a critical role in mitigating mortality rates linked to gastric cancer. However, current clinical screening methods exhibit suboptimal efficacy. Methylation alterations identified from cell-free DNA (cfDNA) present a promising biomarker for early cancer detection. Our study focused on identifying gastric cancer-specific markers from cfDNA methylation to facilitate early detection. We enrolled 150 gastric cancer patients and 100 healthy controls in this study, and undertook genome-wide methylation profiling of cfDNA using cell-free methylated DNA immunoprecipitation and high-throughput sequencing. We identified 21 differentially methylated regions (DMRs) between the gastric tumor and nontumor groups using multiple algorithms. Subsequently, using the 21 DMRs, we developed a gastric cancer detection model by random forest algorithm in the discovery set, and validated the model in an independent set. The model was able to accurately discriminate gastric cancer with a sensitivity and specificity of 93.90% and 95.15% in the discovery set, respectively, and 88.38% and 94.23% in the validation set, respectively. These results underscore the efficacy and accuracy of cfDNA-derived methylation markers in distinguishing early stage gastric cancer. This study highlighted the significance of cfDNA methylation alterations in early gastric cancer detection.

Efficacy of ColonFlag as a Complete Blood Count-Based Machine Learning Algorithm for Early Detection of Colorectal Cancer: A Systematic Review.

Colorectal cancer (CRC) screening is essential to reduce incidence and mortality rates. However, participation in screening remains suboptimal. ColonFlag, a machine learning algorithm using complete blood count (CBC), identifies individuals at high CRC risk using routinely performed tests. This study aims to review the existing literature assessing the efficacy of ColonFlag across diverse populations in multiple countries. The Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) were followed in reporting this systematic review. Searches were conducted on PubMed, Cochrane, ScienceDirect, and Google Scholar for English articles, using keywords related to CBC, machine learning, ColonFlag, and CRC, covering the first development study from 2016 to August 2023. The Cochrane Prediction Model Risk of Bias Assessment Tool (PROBAST) was used to assess the risk of bias. A total of 949 articles were identified during the literature search. Ten studies were found to be eligible. ColonFlag yielded Area Under the Curve (AUC) values ranging from 0.736 to 0.82. The sensitivity and specificity ranged from 3.91% to 35.4% and 82.73% to 94%, respectively. The positive predictive values ranged between 2.6% and 9.1%, while the negative predictive values ranged from 97.6% to 99.9%. ColonFlag performed better in shorter time windows, tumors located more proximally, in advanced stages, and in cases of CRC compared to adenoma. While ColonFlag exhibits low sensitivity compared to established screening methods such as the fecal immunochemical test (FIT) or colonoscopy, its potential to detect CRC before clinical diagnosis suggests an opportunity for identifying more cases than regular screening alone.

التشخيص المتأخر لسرطان القولون والمستقيم بمركز طبرق الطبي

Colorectal cancer (CRC) is a leading cause of cancer-related mortality worldwide, with delayed presentation posing a significant challenge to early diagnosis and improved survival rates. Understanding the causes of late presentation and its impact on disease progression is crucial for developing effective interventions. This study aims to explore the relationship between delayed presentation of colorectal cancer and its effects on disease staging and patient outcomes at Tobruk Medical Center. A retrospective analysis of 103 patients with pathologically confirmed colorectal cancer diagnosed between January 2017 and January 2022 was conducted. Data on demographic characteristics, diagnostic timelines, and tumor staging were analyzed, categorizing delays as long (≥ 60 days) or short (< 60 days) based on the time from symptom onset to first medical consultation. The study revealed that 76.7% of patients experienced long delays in seeking medical care, which often resulted in advanced-stage disease at the time of diagnosis. Acute complications, such as intestinal obstruction, were observed in 59 patients, while 15 presented with distant metastases (Stage III or IV). These findings emphasize the urgent need for strategies to enhance early detection of colorectal cancer through public awareness campaigns, regular screening programs, and improved diagnostic protocols in healthcare facilities. Addressing these factors could significantly reduce complications and improve survival rates for CRC patients. Keywords: Colorectal cancer, delayed presentation, disease progression, early detection, Tobruk Medical Center.

Machine Learning based Lung Cancer Diagnostic System using Optimized Feature Subset Selection

Lung is a vital organ that plays a major role in respiration. Without breathing, one may not survive in this world. Hence lung is an important organ that acts as filter to absorb oxygen and supply it to heart where pumping takes place through blood vessel in the circulatory system .The pumped blood takes oxygen and other nutrients to every other parts of the body. Hence one must take care of lung. There are various diseases associated with lungs. Lung Cancer is a deadly disease that spread across the countries all over the world. An early detection of lung cancer has been proved to improve the survival rate of human life. There are various resources are available to detect the lung cancer disease. They are low dose CT-scans, X-rays, blood-based screening, pathology slide reading, biopsy’s test, survey data(clinical dataset) etc. helps to predict the disease well in advance. Our proposed work uses two clinical datasets that has various features to detect how likely the persons get affected from the lung disease. Dataset1 includes features such as age, gender, smoking, yellow fingers, anxiety, peer-pressure, chronic disease, fatigue, allergy, wheezing, alcohol, coughing, shortness of breath, swallowing difficulty, and chest pain. Also, the work has experimented with another dataset2 that represents causes of lung cancer due to exposure of pesticide. Our proposed diagnostic system consider all these features in total and perform feature selection to extract optimal feature subsets using cuckoo search algorithm then perform classification using machine learning algorithms such as Linear Support Vector Machine, Logistic Regression and Random Forest algorithm. It is observed that with the cuckoo search algorithm, dataset 1 achieves an accuracy of 100%, precision of 100%, recall of 100%, and F1-score of 100% by LR Classifier. The Linear SVC classifier achieves an accuracy of 90%, a precision of 88%, a recall of 86%, and an F1-score of 87%.The Random forest Classifier achieves an accuracy of 91%, precision of 86%, recall of 93%, and F1-score of 90%. For dataset 2, both the LR classifier and Linear SVC classifier outperform with an accuracy of 100%, precision of 100%, recall of 100%, and F1-score of 100%. Whereas Random Forest provides accuracy of 97%, precision of 97%, recall of 96%, and F1-score of 97%.

Telomere Length and Telomerase Activity as Potential Biomarkers for Gastrointestinal Cancer.

Gastrointestinal (GI) cancers, such as colorectal and gastric cancers, pose significant global health challenges due to their high rates of incidence and mortality. Even with advancements in treatment and early detection, many patients still face poor outcomes, highlighting the critical need for new biomarkers and therapeutic targets. Telomere length (TL) and telomerase activity (TA) have gained attention in this context. Telomeres, protective nucleotide sequences at chromosome ends, shorten with each cell division, leading to cellular aging. Telomerase, a ribonucleoprotein enzyme, counteracts this shortening by adding telomeric repeats, a process tightly regulated in normal cells but often dysregulated in cancer. This review critically evaluates the role of TL and TA in the pathogenesis of GI cancers, examining their potential as diagnostic, prognostic, and predictive biomarkers. It explores how alterations in telomere biology contribute to the initiation and progression of GI tumors and assesses the therapeutic implications of targeting telomerase. By integrating findings from diverse studies, this review aims to elucidate the intricate relationship between telomere dynamics and gastrointestinal carcinogenesis, offering insights into how TL and TA could be leveraged to enhance the early detection, treatment, and prognosis of GI cancers.

Integrated triple signal amplification strategy for ultrasensitive electrochemical detection of gastric cancer-related microRNA utilizing MoS2-based nanozyme, hybridization chain reaction, and horseradish peroxidase

Early diagnosis and treatment of gastric cancer (GC) play a vital role in improving efficacy, reducing mortality and prolonging patients’ lives. Given the importance of early detection of gastric cancer, an electrochemical biosensor was developed for the ultrasensitive detection of miR-19b-3p by integrating MoS2-based nanozymes, hybridization chain reaction (HCR) with enzyme catalyzed reaction. The as-prepared MoS2-based nanocomposites were used as substrate materials to construct nanoprobes, which can simultaneously load probe DNA and HCR initiator for signal amplification. Moreover, the MoS2-based nanocomposites are also employed as nanozymes to amplify electrochemical response. The presence of miR-19b-3p induced the assembly of MoS2-based nanoprobes on the electrode surface, which can activate in-situ HCR reaction to load a large number of horseradish peroxidase (HRP) for signal amplification. Coupling with the co-catalytic ability of HRP and MoS2-based nanozymes, the designed electrochemical biosensor can detect as low as 0.7 aM miR-19b-3p. More importantly, this biosensor can efficiently analyze miR-19b-3p in clinical samples from healthy people and gastric cancer patients due to its excellent sensitivity and selectivity, suggesting that this biosensor has a potential application in early diagnosis of disease.

Pioneering Prostate Cancer Diagnosis: Evaluating AI Against Traditional Methods - A Systematic Literature Review

ABSTRACT Prostate cancer is one of the most common forms of men's cancer in the world and the understanding of its epidemiology is crucial to effective prevention, early detection and management strategies. This systematic review highlights the potential of artificial intelligence-based diagnostic methods to revolutionize the early diagnosis of prostate cancer. A comprehensive literature search was carried out using databases such as PubMed, Google Scholar and Scopus. After removing duplicates, titles and abstracts were examined in 279 studies and the following full texts were examined in 128 studies. We have ten studies to review in total. Therefore, we chose to review data for all 10 identified studies that applied AI techniques to detect ca prostate. The Paige Prostate Alpha AI system significantly enhances pathologists' diagnostic capabilities, leading to more accurate and effective prostate cancer detection. These limitations indicate that further research is required to address these issues and validate the results in a more diverse and controlled environment. AI-based diagnostic methods have shown significant promise in enhancing the early detection of prostate cancer. As technology continues to advance, integrating AI with traditional diagnostic approaches could lead to more effective, efficient, and accurate prostate cancer screening and diagnosis. Future studies should focus on large-scale clinical trials and real-world applications to validate these findings and facilitate the adoption of AI in clinical settings. Keywords: Prostate Cancer, Artificial Intelligent, Deep Machine Learning, MRI

A novel colonoscope with an extra-wide field of view increases polyp detection rate compared with standard colonoscope: Prospective model-based trial.

Background and study aims Colonoscopy, the gold standard for early detection of colorectal cancer, may miss polyps especially those hidden behind folds. This prospective study compared polyp detection and performance of a novel colonoscope with extra-wide field of view (EFOV) of 230 degrees (partially retrograde) to a standard colonoscope (SC, 170 degrees) in a colon model. Patients and methods A 3D printed colon model was used featuring 12 polyps placed throughout different colon segments, with several located on the proximal side of haustral folds. Endoscopists were instructed to identify polyps, first inserting the SC immediately followed by the EFOV device, and to place a snare to simulate a polypectomy. A standardized survey was used to record operator impressions. Results Twenty-nine experienced endoscopists participated in this study. On average, 5.3 vs 9.6 polyps were detected with the standard and EFOV colonoscopes, respectively ( P < 0.001). Five of 29 operators (17.2%) detected all 12 polyps with the EFOV device, whereas no operator detected all polyps with the SC. The success rate for snare placement was 100% for both endoscopes with similar times (mean of 14 vs 15 seconds for SC and EFOV, respectively). EFOV handling and optical performance were rated as equally good or better by all endoscopists. Conclusions Use of a colonoscope with novel optics significantly improved polyp detection compared with a standard colonoscope in this non-randomized model-based study, with favorable performance and usability ratings for the EFOV instrument. Clinical studies are needed to confirm these encouraging preliminary results.