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Major research efforts in liver cancer have been devoted to increasing the efficacy and effectiveness of surveillance for hepatocellular carcinoma (HCC). As with other cancers, surveillance programmes aim to detect tumours at an early stage, facilitate curative-intent treatment, and reduce cancer-related mortality. HCC surveillance is supported by a large randomised-controlled trial in patients with chronic HBV infection and several cohort studies in cirrhosis; however, effectiveness in clinical practice is limited by several barriers, including inadequate risk stratification, underuse of surveillance, and suboptimal accuracy of screening tests. There are several proposed strategies to address these limitations, including risk stratification algorithms and biomarkers to better identity at-risk individuals, interventions to increase surveillance, and emerging imaging- and blood-based surveillance tests with improved sensitivity and specificity for early HCC detection. Beyond clinical validation, data are needed to establish clinical utility, i.e. increased early tumour detection and reduced HCC-related mortality. If successful, these data could facilitate a precision screening paradigm in which surveillance strategies are tailored to individual HCC risk to maximise overall surveillance value. However, practical and logistical considerations must be considered when designing and implementing these validation efforts. To address these issues, ILCA (the International Liver Cancer Association) adjourned a single topic workshop on HCC risk stratification and surveillance in June 2022. Herein, we present a white paper on these topics, including the status of the field, ongoing research efforts, and barriers to the translation of emerging strategies. [ABSTRACT FROM AUTHOR]

Cancer is one of the leading causes of death worldwide producing an estimated cost of $161.2 billion in the US in 2017 only. Early detection of cancer can not only reduce cancer mortality rates but also dramatically reduce healthcare costs given that the 17 million new cancer cases in 2018 are estimated to grow 27.5 million new cases by 2040. Analytical devices based upon paper substrates can provide effective, rapid, and extremely low‐cost alternatives for early cancer detection compared to existing testing methods. However, low concentrations of biomarkers in body fluids as well as the possible association of any given biomarker with multiple diseases remains one of the greatest challenges to widespread adoption of these paper‐based devices. Fortunately, recent advances have opened the possibility of detecting multiple biomarkers within the same device, which can be predictive of a patient's condition with unprecedented cost‐effectiveness. Accordingly, this review highlights the recent advancements in paper‐based analytical devices with a multiplexing focus. The primary areas of interest include lateral flow assay and microfluidic paper‐based assay formats, signal amplification approaches to enhance the sensitivity for a specific cancer type, along with current challenges and future outlook for the detection of multiple cancer biomarkers. [ABSTRACT FROM AUTHOR]

Background: The incidence of skin cancers has been increasing steadily over the last decades. Although there have been significant breakthroughs in the management of skin cancers with the introduction of novel diagnostic tools and innovative therapies, skin cancer mortality, morbidity and costs heavily burden the society. Objective: Members of the European Association of Dermato‐Oncology, European Academy of Dermatology and Venereology, International Dermoscopy Society, European Dermatology Forum, European Board of Dermatovenereology of the European Union of Medical Specialists and EORTC Cutaneous Lymphoma Task Force have joined this effort to emphasize the fundamental role that the specialist in Dermatology–Venereology has in the diagnosis and management of different types of skin cancer. We review the role of dermatologists in the prevention, diagnosis, treatment and follow‐up of patients with melanoma, non‐melanoma skin cancers and cutaneous lymphomas, and discuss approaches to optimize their involvement in effectively addressing the current needs and priorities of dermato‐oncology. Discussion: Dermatologists play a crucial role in virtually all aspects of skin cancer management including the implementation of primary and secondary prevention, the formation of standardized pathways of care for patients, the establishment of specialized skin cancer treatment centres, the coordination of an efficient multidisciplinary team and the setting up of specific follow‐up plans for patients. Conclusion: Skin cancers represent an important health issue for modern societies. The role of dermatologists is central to improving patient care and outcomes. In view of the emerging diagnostic methods and treatments for early and advanced skin cancer, and considering the increasingly diverse skills, knowledge and expertise needed for managing this heterogeneous group of diseases, dermato‐oncology should be considered as a specific subspecialty of Dermatology–Venereology. [ABSTRACT FROM AUTHOR]

Background: Many individuals undergoing cancer treatment experience substantial financial hardship, often referred to as financial toxicity (FT). Those undergoing prostate cancer treatment may experience FT and its impact can exacerbate disparate health outcomes. Localized prostate cancer treatment options include: radiation, surgery, and/or active surveillance. Quality of life tradeoffs and costs differ between treatment options. In this project, our aim was to quantify direct healthcare costs to support patients and clinicians as they discuss prostate cancer treatment options. We provide the transparent steps to estimate healthcare costs associated with treatment for localized prostate cancer among the privately insured population using a large claims dataset. Methods: To quantify the costs associated with their prostate cancer treatment, we used data from the Truven Health Analytics MarketScan Commercial Claims and Encounters, including MarketScan Medicaid, and peer reviewed literature. Strategies to estimate costs included: (1) identifying the problem, (2) engaging a multidisciplinary team, (3) reviewing the literature and identifying the database, (4) identifying outcomes, (5) defining the cohort, and (6) designing the analytic plan. The costs consist of patient, clinician, and system/facility costs, at 1-year, 3-years, and 5-years following diagnosis. Results: We outline our specific strategies to estimate costs, including: defining complex research questions, defining the study population, defining initial prostate cancer treatment, linking facility and provider level related costs, and developing a shared understanding of definitions on our research team. Discussion and next steps: Analyses are underway. We plan to include these costs in a prostate cancer patient decision aid alongside other clinical tradeoffs. [ABSTRACT FROM AUTHOR]

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About five million people lose their lives every year to lung cancer, making it one of the leading causes of mortality worldwide. In the last few years, a lot of methods of detection of lung cancer were improved however these could not efficiently diagnose cancer. In this paper, a convolutional neural network (CNN) of robust deep learning is developed. CNN precision raises the deeper that is, however, it causes overfitting or vanishing gradient problems simultaneously. To solve the issue, the CNN used resort to parallel CNN. It used Pictures the LIDC-IDRI consortium image collection has thoracic CT images that have been annotated for lung cancer diagnosis and screening purposes. The presented model includes result models of CNN which are integrated via ensemble methods and it compare with every model. Results of the simulation illustrate that the presented method's accuracy has developed by 2.18 percent in comparison with the method of the main paper [ABSTRACT FROM AUTHOR]

The Air Quality Index (AQI) in India is steadily deteriorating, leading to a rise in the mortality rate due to Lung Cancer. This decline in air quality can be attributed to various factors such as PM 2.5, PM 10, and Ozone (O3). To establish a relationship between AQI and Lung Cancer, several predictive models including Linear Regression, KNN, Decision Tree, ANN, Random Forest Regression, and XGBoost Regression were employed to estimate pollutant levels and Air Quality Index in India. The models relied on publicly available state-wise Air Pollution Dataset. Among all the models, the XGBoost Regression displayed the highest accuracy, with pollutant level estimations reaching an accuracy range of 81% to 98% during training and testing. The second-highest accuracy range was achieved by Random Forest. The paper also explores the impact of increasing pollution levels on the rising mortality rate among lung cancer patients in India. [ABSTRACT FROM AUTHOR]

Background: Colorectal cancer screening by fecal occult blood testing has been an important public health test and shown to reduce colorectal cancer-related mortality. However, the low participation rate in colorectal cancer screening by the general public remains a problematic public health issue. This fact could be attributed to the complex and unpleasant operation of the screening tool.Objective: This study aimed to validate a novel toilet paper-based point-of-care test (ie, JustWipe) as a public health instrument to detect fecal occult blood and provide detailed results from the evaluation of the analytic characteristics in the clinical validation.Methods: The mechanism of fecal specimen collection by the toilet-paper device was verified with repeatability and reproducibility tests. We also evaluated the analytical characteristics of the test reagents. For clinical validation, we conducted comparisons between JustWipe and other fecal occult blood tests. The first comparison was between JustWipe and typical fecal occult blood testing in a central laboratory setting with 70 fecal specimens from the hospital. For the second comparison, a total of 58 volunteers were recruited, and JustWipe was compared with the commercially available Hemoccult SENSA in a point-of-care setting.Results: Adequate amounts of fecal specimens were collected using the toilet-paper device with small day-to-day and person-to-person variations. The limit of detection of the test reagent was evaluated to be 3.75 µg of hemoglobin per milliliter of reagent. Moreover, the test reagent also showed high repeatability (100%) on different days and high reproducibility (>96%) among different users. The overall agreement between JustWipe and a typical fecal occult blood test in a central laboratory setting was 82.9%. In the setting of point-of-care tests, the overall agreement between JustWipe and Hemoccult SENSA was 89.7%. Moreover, the usability questionnaire showed that the novel test tool had high scores in operation friendliness (87.3/100), ease of reading results (97.4/100), and information usefulness (96.1/100).Conclusions: We developed and validated a toilet paper-based fecal occult blood test for use as a point-of-care test for the rapid (in 60 seconds) and easy testing of fecal occult blood. These favorable characteristics render it a promising tool for colorectal cancer screening as a public health instrument. [ABSTRACT FROM AUTHOR]

Gastric cancer, which is considered a major health concern, is the sixth most frequent cancer and the second leading cause of cancer-related mortality across the globe. The present survey aimed to systematically review the anti-gastric cancer effect of all organic and inorganic nanoparticles (NPs) in in vitro, in vivo, and clinical trials. The investigation followed the PRISMA guidelines, and the findings were recorded in the CAMARADES-NC3Rs Preclinical Systematic Review and Meta-Analysis Facility database. A detailed search was conducted on various English databases, such as Scopus, Web of Science, EMBASE, PubMed, and Google Scholar, with no specified publication time frame to obtain papers regarding the anti-gastric cancer properties of nanoparticles. The search process was performed using the following terms: "Nanoparticles," "Gastric cancer," "Antigastric cancer," "Metal nanoparticles," "Organic nanoparticles," "Inorganic nanoparticles, "in vitro," "Clinical," and "in vivo,". Out of 11,189 papers, 31 articles, including 19 (45.5%) in vitro, 3 (13.6%) in vivo, 3 (13.6%) clinical trials, and 6 (27.3%) in vitro/in vivo, up to 2023, met the inclusion criteria for discussion in this systematic review. The most widely used NPs were found to be organic nanoparticles, such as polylactic acid and poly lactic-co-glycolic acid (16, 80.0%), followed by inorganic nanoparticles, such as silver NPs (13, 41.9.0%). This review study highlighted the high anti-gastric cancer potential of a wide range of organic and non-organic NPs through their activity via some mechanisms, such as the induction of apoptosis, gene therapy, and drug delivery. Nonetheless, further studies, especially in clinical settings, are needed to confirm their anti-gastric effects and accurate mechanisms. [ABSTRACT FROM AUTHOR]

EUSOBI and 30 national breast radiology bodies support mammography for population-based screening, demonstrated to reduce breast cancer (BC) mortality and treatment impact. According to the International Agency for Research on Cancer, the reduction in mortality is 40 % for women aged 50-69 years taking up the invitation while the probability of false-positive needle biopsy is <1 % per round and overdiagnosis is only 1-10 % for a 20-year screening. Mortality reduction was also observed for the age groups 40-49 years and 70-74 years, although with "limited evidence". Thus, we firstly recommend biennial screening mammography for average-risk women aged 50-69 years; extension up to 73 or 75 years, biennially, is a second priority, from 40-45 to 49 years, annually, a third priority. Screening with thermography or other optical tools as alternatives to mammography is discouraged. Preference should be given to population screening programmes on a territorial basis, with double reading. Adoption of digital mammography (not film-screen or phosphor-plate computer radiography) is a priority, which also improves sensitivity in dense breasts. Radiologists qualified as screening readers should be involved in programmes. Digital breast tomosynthesis is also set to become "routine mammography" in the screening setting in the next future. Dedicated pathways for high-risk women offering breast MRI according to national or international guidelines and recommendations are encouraged.Key Points: • EUSOBI and 30 national breast radiology bodies support screening mammography. • A first priority is double-reading biennial mammography for women aged 50-69 years. • Extension to 73-75 and from 40-45 to 49 years is also encouraged. • Digital mammography (not film-screen or computer radiography) should be used. • DBT is set to become "routine mammography" in the screening setting in the next future. [ABSTRACT FROM AUTHOR]

During the COVID-19 pandemic, cancer patients were among the most vulnerable patient groups to the SARS-CoV-2 infection effects. This paper aimed to conduct an umbrella review and meta-meta-analysis to determine the severity of disease in cancer patients affected by COVID-19. The umbrella review and meta-meta-analysis were undertaken according to the PRISMA and MOOSE guidelines. The PubMed/Medline, Web of Science, and Scopus databases were searched for published papers from the start of the pandemic through July 18, 2022. The pooled effect sizes (ES) and odds ratios (ORs) were calculated using a random effect model in the 95% confidence interval (CI) for ICU (Intensive Care Unit) admissions and mortality in cancer patients infected with SARS-CoV-2. Egger's linear regression test, schematic illustrations of funnel plots, and Begg and Mazumdar's rank correlation tests were used to quantify the possibility of publication bias. The pooled ES was calculated based on 1,031,783 participants, and mortality was significantly increased in cancer patients affected by COVID-19 (OR = 2.02, %95 CI: 1.74–2.35, p < 0.001). The pooled ES for ICU admission was also significantly increased in cancer patients infected with SARS-CoV-2 (OR = 1.84, %95 CI: 1.44–2.34, p < 0.001). As a result, this synthesis of systematic reviews and meta-analyses by the meta-meta-analysis method revealed that disease severity is higher in cancer patients affected by COVID-19. Since cancer patients are a more sensitive and specific patient group, they should be evaluated more carefully, especially during the COVID-19 pandemic and other pandemics that may occur in the future. [ABSTRACT FROM AUTHOR]

Colorectal cancer is a malignant tumor with high morbidity and mortality worldwide. Histone lactylation, a new protein post-translational modification, can be used as a potential biomarker for the diagnosis and treatment of colorectal cancer by regulating the structure of chromatin and gene transcription. This paper focuses on the role and mechanism of histone lactylation modification in the development of colorectal cancer. Additionally, the latest research progress and clinical experimental results are presented. This paper further shows that histone lactylation manipulates gene expression by affecting nuclear transcription factors and other epigenetic modifications involved in various life processes and subsequently regulates cell physiological function, revealing its regulatory mechanism and the functions of related enzymes. The relationships between histone lactylation and energy metabolic pathways such as glycolysis and oxidative phosphorylation are discussed in detail. The final part of this paper illustrates the important role of this modification in terms of the occurrence and development of colorectal cancer, cell metabolic reprogramming, and epigenetic regulation, supporting the clinical application of the use of histone lactylation as a biomarker. [ABSTRACT FROM AUTHOR]

[Display omitted] • This article focuses on immunotherapy, a new treatment for tumors with low side effects, and suggests the direction of improvement. • Programmed cell death (apoptosis, necroptosis, ferroptosis, pyroptosis and autophagy) induced by nanomedicine was reviewed in this paper. • This paper focuses on the role of nanomedicine-induced programmed cell death in cancer therapy. • This article reviews the improvement of the limitations of immunotherapy with nanomedicine-induced PCD. • In this paper, the development prospect of tumor therapy and immunotherapy of PCD mediated by nanomedical drugs is prospected. There has been widespread concern about the high cancer mortality rate and the shortcomings of conventional cancer treatments. Immunotherapy is a novel oncology therapy with high efficiency and low side effects, which is a revolutionary direction for clinical oncology treatment. However, its clinical effectiveness is uneven. Based on the redefinition and reclassification of programmed cell death (PCD) (divided into necroptosis, ferroptosis, pyroptosis, and autophagy), the role of nanomedicine-induced PCD in cancer therapy has also received significant attention. Clinical and preclinical studies have begun to combine PCD with immunotherapy. In this article, we present recent research in tumor immunotherapy, provide an overview of how nanomedicine-induced PCD is involved in tumor therapy, and review how nanomedicine-induced PCD can improve the limitations of immunotherapy to enhance tumor immunotherapy. The future development of nanomedicine-mediated PCD tumor therapy and tumor immunotherapy is also proposed Key scientific concepts of overview Nanomedicine-induced PCD is a prospective method of tumor immunotherapy. Nanomedicines increase tumor site penetration and targeting ability, and nanomedicine-mediated PCD activation can stimulate powerful anti-tumor immune effects, which has a good contribution to immunotherapy of tumors. [ABSTRACT FROM AUTHOR]

Cancers are genetically diversified, so anticancer treatments have different levels of efficacy on people due to genetic differences. The main objective of this work is to predict the anticancer drug efficiency for colorectal cancer patients to reduce the mortality rates and provides immune energy for the patients. This paper proposes a novel anti-cancer drug efficacy system in colorectal cancer patients. The input data gene is normalized with the Min–Max normalization technique that normalizes the data in distinct scales. Subsequently, proposes an improved entropy-based feature to evaluate the uncertainty distribution of data, in which it induces weight to overcome the issue of computational complexity. Along with this feature, a correlation-based feature and statistical features are also retrieved. Subsequently, proposes a Recursive Feature Elimination with Hybrid Machine Learning (RFEHML) mechanism for selecting the appropriate feature set by eliminating the recursive features with the aid of hybrid Machine Learning strategies that combine decision tree and logistic regression. Also, the Gini impurity is employed for ranking the feature and selecting the maximum importance score by eliminating the least acquired importance score. Further, proposes a hybrid model for predicting the drug efficiency with the trained feature set. The hybrid model comprises of Long Short-Term Memory (LSTM) and Updated Rectified Linear Unit-Deep Convolutional Neural Network (UReLU-DCNN) model, in which DCNN is modified by updating the activation function at the fully connected layer. Consequently, the learned feature predicts the drug efficacy of anti-cancer in colorectal cancer patients by determining whether the patient is a responder or non-responder of the drug. Finally, the performance of the proposed RFEHML model is compared with other traditional approaches. It is found that the developed method has higher accuracy for each learning percentage, with values of 60LP = 92.48%, 70LP = 94.28%, 80LP = 95.24%, and 90LP = 96.86%, respectively. [ABSTRACT FROM AUTHOR]

Cancer incidence numbers are routinely recorded by national or regional population-based cancer registries (PBCRs). However, in most southern European countries, the local PBCRs cover only a fraction of the country. Therefore, national cancer incidence can be only obtained through estimation methods. In this paper, we predict incidence rates in areas without cancer registry using multivariate spatial models modelling jointly cancer incidence and mortality. To evaluate the proposal, we use cancer incidence and mortality data from all the German states. We also conduct a simulation study by mimicking the real case of Spain considering different scenarios depending on the similarity of spatial patterns between incidence and mortality, the levels of lethality, and varying the amount of incidence data available. The new proposal provides good interval estimates in regions without PBCRs and reduces the relative error in estimating national incidence compared to one of the most widely used methodologies. [ABSTRACT FROM AUTHOR]

Objectives: Despite the rising risk factor exposure and non-communicable disease (NCD) mortality across the Middle East and the North African (MENA) region, public health policy responses have been slow and appear discordant with the social, economic and political circumstances in each country. Good health policy and outcomes are intimately linked to a research-active culture, particularly in NCD. In this study we present the results of a comprehensive analysis of NCD research with particular a focus on cancer, diabetes and cardiovascular disease in 10 key countries that represent a spectrum across MENA between 1991 and 2018. Methods: The study uses a well validated bibliometric approach to undertake a quantitative analysis of research output in the ten leading countries in biomedical research in the MENA region on the basis of articles and reviews in the Web of Science database. We used filters for each of the three NCDs and biomedical research to identify relevant papers in the WoS. The countries selected for the analyses were based on the volume of research outputs during the period of analysis and stability, included Egypt, Iran, Jordan, Kuwait, Lebanon, Oman, Qatar, Saudi Arabia, Turkey and the United Arab Emirates. Results: A total of 495,108 biomedical papers were found in 12,341 journals for the ten MENA countries (here we consider Turkey in the context of MENA). For all three NCDs, Turkey's output is consistently the highest. Iran has had considerable growth in research output to occupy second place across all three NCDs. It appears that, relative to their wealth (measured by GDP), some MENA countries, particularly Oman, Qatar, Kuwait and the United Arab Emirates, are substantially under-investing in biomedical research. In terms of investment on particular NCDs, we note the relatively greater commitment on cancer research compared with diabetes or cardiovascular disease in most MENA countries, despite cardiovascular disease causing the greatest health-related burden. When considering the citation impact of research outputs, there have been marked rises in citation scores in Qatar, Lebanon, United Arab Emirates and Oman. However, Turkey, which has the largest biomedical research output in the Middle East has the lowest citation scores overall. The level of intra-regional collaboration in NCD research is highly variable. Saudi Arabia and Egypt are the dominant research collaborators across the MENA region. However, Turkey and Iran, which are amongst the leading research-active countries in the area, show little evidence of collaboration. With respect to international collaboration, the United States and United Kingdom are the dominant research partners across the region followed by Germany and France. Conclusion: The increase in research activity in NCDs across the MENA region countries during the time period of analysis may signal both an increasing focus on NCDs which reflects general global trends, and greater investment in research in some countries. However, there are several risks to the sustainability of these improvements that have been identified in particular countries within the region. For example, a lack of suitably trained researchers, low political commitment and poor financial support, and minimal international collaboration which is essential for wider global impact. [ABSTRACT FROM AUTHOR]

Venous thromboembolism (VTE) is a main contributor to morbidity and mortality in cancer patients. Biomarkers with the potential to predict cancer-associated VTE are continually sought. Of these, markers of thrombin generation present a likely option. The present systematic review examines the ability of three widely used biomarkers of thrombin generation: prothrombin fragment 1.2 (F1.2), thrombin-antithrombin complex (TAT), and ex vivo thrombin generation, to predict VTE in both solid and hematologic adult cancer patients. Relevant studies were identified in the PubMed and Embase databases, and the review conformed to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. Each study was evaluated using the quality assessment tool from the National Heart, Lung, and Blood Institute. The review protocol was published on PROSPERO with identifier CRD42022362339. In total, 24 papers were included in the review: 11 reporting data on F1.2, 9 on TAT, and 12 on ex vivo thrombin generation. The quality ratings of the included studies varied from good (n = 13), fair (n = 8), to poor (n = 3) with a high heterogenicity. However, F1.2, TAT complex, and ex vivo thrombin generation were all found to be associated with the development of VTE. This association was most pronounced for F1.2. Furthermore, the determination of F1.2 was able to improve the precision of several established risk assessment scores. In conclusion, markers of thrombin generation were found to be elevated in cancer patients with VTE, and particularly, F1.2 was found to be a promising predictor of cancer-associated VTE. [ABSTRACT FROM AUTHOR]

Purpose: To reduce lung cancer mortality, individuals at high-risk should receive a low-dose computed tomography screening annually. To increase the likelihood of screening, interventions that promote shared decision-making are needed. The goal of this study was to investigate the feasibility, acceptability, usability, and preliminary effectiveness of a computer-based decision aid. Methods: Thirty-three participants were recruited through primary-care clinics in a small southeastern-US city. Participants used a computer-based decision aid ("Is Lung Cancer Screening for You?") during a clinic appointment. Paper surveys collected self-reported feasibility, acceptability, and usability data. A research coordinator was present to observe each patient's and health-care provider's interactions, and to assess the fidelity of shared decision-making. Results: The decision aid was feasible, acceptable for use in a clinic setting, and easy for participants to use. Patients had low decisional conflict following use of the decision aid and had high screening intention and actual screening rates. Shared decision-making discussions using the decision aid were nearly 6 min on average. Conclusion: Computer-based decision aids are feasible for promoting shared lung cancer–screening decisions. A more robust study is warranted to measure the added value of a computer-based version of this aid versus a paper-based aid. [ABSTRACT FROM AUTHOR]

BACKGROUND: Breast cancer is a common disease in women. Early detection and early treatment can reduce breast cancer mortality. Studies have shown that breast cancer microcalcifications is one of the important clinical manifestations of early breast cancer, and sometimes even the only manifestation. When the mammography image shows typical malignant microcalcification, it can be diagnosed as breast cancer without any other signs of malignancy. In the aided diagnosis of microcalcifications, it is a crucial step to automatically find and locate regions of interest containing microcalcifications. However, the existing feature extraction method for microcalcifications only extracts features in the time domain or wavelet domain, and does not completely represent all the information of the region of interest. An extraction method based on the combination of Dual-Tree Complex Wavelet Transform (DTCWT) and texture features is proposed in the paper. METHODS: First, the processing operations including denoising, enhancement, and edge detection were performed on mammograms. Sub-image segmentation is then performed. DTCWT features and texture features are extracted for each sub-image.DTCWT features are combined with texture features, and then genetic algorithm is used for feature optimization. The features are classified by the Extreme Learning Machine (ELM) to achieve rapid detection and automatic extraction of ROI with microcalcifications. The experimental results verify that the feature model proposed in this paper has the highest detection rate for ROI regions. The ROI region extracted by the proposed feature model was used as subsequent experimental data. Three different methods were used to detect the microcalcifications, including Top-hat, wavelet transform, and methods combining Top-Hat and wavelet transform. RESULTS: The method was applied to 100 mammograms from the mammograms database of women in Northeast China. In the automatic extraction of ROI, the accuracy, sensitivity, specificity, positive accuracy and negative accuracy of the proposed model combined with DTCWT were 95.92%, 96.71%, 92.20%, 93.65%, 96.33%, respectively. When the Top-hat algorithm was used for microcalcifications detection, the sensitivity reached 89.6%, and the false positive detection rate was 2.6. When the wavelet transform algorithm was used for microcalcifications detection, the sensitivity was 91.1%, and the false positive detection rate was 3.28. When the combined algorithm was used for microcalcifications detection, the sensitivity was 86.7%, and the false positive detection rate decreased to 1.35. CONCLUSIONS: The proposed model combined with DTCWT features achieves better result in the automatic extraction of ROI. Moreover, in the subsequent detection of microcalcifications based on three methods, the three methods achieved better results in sensitivity and false positive detection rate, respectively. [ABSTRACT FROM AUTHOR]

Cancer is the second leading cause of death in Armenia. Over the past two decades, the country has seen a significant rise in cancer morbidity and mortality. This review aims to provide up-to-date info about the state of cancer control in Armenia and identify priority areas of research. The paper analyzes published literature and local and international statistical reports on Armenia and similar countries to put numbers into context. While cancer detection, diagnosis, and treatment are improving, the prevalence of risk factors is still quite high and smoking is widespread. Early detection rates are low and several important screening programs are absent. Diagnosis and treatment methods are not standardized; there is a lack of treatment accessibility due to insufficient government coverage and limited availability of essential medicines. Overall, there is room for improvement in this sector, as research is limited and multidisciplinary approaches to the topic are rare. [ABSTRACT FROM AUTHOR]

Lung cancer (LC) is one of the leading causes for cancer-related deaths in the world, accounting for 28% of all cancer deaths in Europe. Screening for lung cancer can enable earlier detection of LC and reduce lung cancer mortality as was demonstrated in several large image-based screening studies such as the NELSON and the NLST. Based on these studies, screening is recommended in the US and in the UK a targeted lung health check program was initiated. In Europe lung cancer screening (LCS) has not been implemented due to limited data on cost-effectiveness in the different health care systems and questions on for example the selection of high-risk individuals, adherence to screening, management of indeterminate nodules, and risk of overdiagnosis. Liquid biomarkers are considered to have a high potential to address these questions by supporting pre- and post- Low Dose CT (LDCT) risk-assessment thereby improving the overall efficacy of LCS. A wide variety of biomarkers, including cfDNA, miRNA, proteins and inflammatory markers have been studied in the context of LCS. Despite the available data, biomarkers are currently not implemented or evaluated in screening studies or screening programs. As a result, it remains an open question which biomarker will actually improve a LCS program and do this against acceptable costs. In this paper we discuss the current status of different promising biomarkers and the challenges and opportunities of blood-based biomarkers in the context of lung cancer screening. [ABSTRACT FROM AUTHOR]

Background: Cancer is a chronic disease brought on by mutations to the genes that control our cells' functions and become themost common cause of mortality and comorbidities. Thus, this study aimed to assess the comprehensive and common mortality-related risk factors of lung cancer using more than thirty scientific research papers. Methods: Possible risk factors contributing to lung cancer mortality were assessed across 201 studies sourced from electronic databases, including Google Scholar, Cochrane Library, Web of Science (WOS), EMBASE, Medline/ PubMed, the Lung Cancer Open Research Dataset Challenge, and Scopus. Out of these, 32 studies meeting the eligibility criteria for meta-analysis were included. Due to the heterogeneous nature of the studies, a random-effects model was applied to estimate the pooled effects of covariates. Results: The overall prevalence of mortality rate was 10% with a 95% confidence interval of 6 and 16%. Twenty studies (62.50%) studies included in this study considered the ages of lung cancer patients as the risk factors for mortality. Whereas, eighteen (56.25%) and thirteen (40.63%) studies incorporated the gender and smoking status of patients respectively. The comorbidities of lung cancer mortality such as cardiovascular disease, hypertension, diabetes, and pneumonia were also involved in 7 (21.90%), 6 (18.75%), 5 (15.63%), and 2 (6.25%) studies, respectively. Patients of older age are more likely to die as compared to patients of younger age. Similarly, lung patients who had smoking practice were more likely to die as compared to patients who hadn't practiced smoking Conclusion: The mortality rate of lung cancer patients is considerably high. Older age, gender, stage, and comorbidities such as cardiovascular, hypertension, and diabetes have a significant positive effect on lung cancer mortality. The study results will contribute to future research, management, and prevention strategies for lung cancer. [ABSTRACT FROM AUTHOR]

Colorectal cancer (CRC) is the third most common malignancy and second leading cause of cancer-related mortality in the world. Adenoma detection rate (ADR), a quality indicator for colonoscopy, has gained prominence as it is inversely related to CRC incidence and mortality. As such, recent efforts have focused on developing novel colonoscopy devices and technologies to improve ADR. The main objective of this paper is to provide an overview of advancements in the fields of colonoscopy mechanical attachments, artificial intelligence-assisted colonoscopy, and colonoscopy optical enhancements with respect to ADR. We accomplished this by performing a comprehensive search of multiple electronic databases from inception to September 2023. This review is intended to be an introduction to colonoscopy devices and technologies. Numerous mechanical attachments and optical enhancements have been developed that have the potential to improve ADR and AI has gone from being an inaccessible concept to a feasible means for improving ADR. While these advances are exciting and portend a change in what will be considered standard colonoscopy, they continue to require refinement. Future studies should focus on combining modalities to further improve ADR and exploring the use of these technologies in other facets of colonoscopy. [ABSTRACT FROM AUTHOR]

Non-small Cell Lung Cancer (NCLC) is the predominant form of lung cancer, representing the primary cause of cancer-related mortality on a global scale. In recent decades, significant research has been conducted on tumor angiogenesis in treating NCLC, owing to its pivotal role in advancing cancer. Numerous anti-angiogenic pharmaceuticals, including Recombinant Endostatin (RE), have undergone extensive assessment in preclinical and clinical settings, yielding varied outcomes that frequently fall short of expectations. Nevertheless, there is a growing fascination with RE owing to its capacity to induce a circulatory normalizing window, thereby potentially enhancing the therapeutic effectiveness of the conventional treatment for NCLC. This work aimed to assess the potential benefits of RE in enhancing the effectiveness and safety of Platinumbased Radiotherapy (PRT) when administered through intravenous and pulmonary infusions for patients with stage IIIA/B-NCLC. This paper presents a comprehensive review of both experimental and clinical investigations that have examined the combined administration of REPRT as a therapeutic approach for NCLC treatment. Out of the total sample size of 14 individuals who were assessed, the treatment's combined effectiveness is 77.6%. This percentage indicates a substantial proportion of positive results about the therapeutic intervention. The disease control rate, encompassing patients who display complete or partial responses, exhibits a noteworthy rise of 89.6%. The results underscores the effectiveness of RE-PRT in successfully arresting or reversing the advancement of the NCLC [ABSTRACT FROM AUTHOR]

Breast cancer ranks among the most prevalent malignancies affecting the female population and is a prominent contributor to cancer-related mortality. Mathematical modeling is a significant tool that can be employed to comprehend the dynamics of breast cancer progression and dissemination and to formulate novel therapeutic approaches. This paper introduces a mathematical model of breast cancer that utilizes the Caputo–Fabrizio fractal-fractional derivative. The aim is to elucidate and comprehend the intricate dynamics governing breast cancer cells and cytotoxic T lymphocytes in the context of the fractional derivative. The derivative presented herein offers a broader perspective than the conventional derivative, as it incorporates the intricate fractal characteristics inherent in the process of tumor proliferation. The significance of this study lies in its contribution to a novel mathematical model for breast cancer, which incorporates the fractal characteristics of tumor development. The present model possesses the capability to investigate the impacts of diverse treatment strategies on the proliferation of breast cancer, as well as to formulate novel treatment strategies that exhibit enhanced efficacy. [ABSTRACT FROM AUTHOR]

The impact of cancer in Alberta is expected to grow considerably, largely driven by population growth and aging. The Future of Cancer Impact (FOCI) initiative offers an overview of the present state of cancer care in Alberta and highlights potential opportunities for research and innovation across the continuum. In this paper, we present a series of detailed projections and analyses regarding cancer epidemiological estimates in Alberta, Canada. Data on cancer incidence and mortality in Alberta (1998–2018) and limited-duration cancer prevalence in Alberta (2000–2019) were collected from the Alberta Cancer Registry. We used the Canproj package in the R software to project these epidemiological estimates up to the year 2040. To estimate the direct management costs, we ran a series of microsimulations using the OncoSim All Cancers Model. Our findings indicate that from 2020, the total number of annual new cancer cases and cancer-related deaths are projected to increase by 56% and 49% by 2040, respectively. From 2019, the five-year prevalence of all cancers in Alberta is projected to increase by 86% by 2040. In line with these trends, the overall direct cost of cancer management is estimated to increase by 53% in 2040. These estimates and projections are integral to future strategic planning and investment. [ABSTRACT FROM AUTHOR]

Lung cancer is the second most dangerous disease worldwide which is curable if its diagnosis is done in earlier stages. Even with the rise in cancer diseases, researches have shown that the mortality rate of cancer patients is less. For the treatment of cancer patients, Machine learning and Artificial Intelligence (AI) have been researched and employed for the early detection of this disease in the past years. Biomedical image processing and detection of data have been used in combination in order to devise new techniques. In this paper, image classification was performed and machine learning algorithms were applied on lung cancer disease dataset to calculate measures such as accuracy, sensitivity, etc. K-Nearest Neighbour (KNN), Random forest and Support Vector Machine (SVM) algorithms have been used to analyze the initial stage lung cancer by applying these on the lung cancer dataset. [ABSTRACT FROM AUTHOR]

There has been growing evidence suggesting that diabetes may be associated with increased liver cancer risk. However, studies conducted in Asian countries are limited. This project considered data of 968,738 adults pooled from 20 cohort studies of Asia Cohort Consortium to examine the association between baseline diabetes and liver cancer incidence and mortality. Cox proportional hazard model and competing risk approach was used for pooled data. Two‐stage meta‐analysis across studies was also done. There were 839,194 subjects with valid data regarding liver cancer incidence (5654 liver cancer cases [48.29/100,000 person‐years]), follow‐up time and baseline diabetes (44,781 with diabetes [5.3%]). There were 747,198 subjects with valid data regarding liver cancer mortality (5020 liver cancer deaths [44.03/100,000 person‐years]), follow‐up time and baseline diabetes (43,243 with diabetes [5.8%]). Hazard ratio (HR) (95% confidence interval [95%CI]) of liver cancer diagnosis in those with vs. without baseline diabetes was 1.97 (1.79, 2.16) (p <.0001) after adjusting for baseline age, gender, body mass index, tobacco smoking, alcohol use, and heterogeneity across studies (n = 586,072; events = 4620). Baseline diabetes was associated with increased cumulative incidence of death due to liver cancer (adjusted HR (95%CI) = 1.97 (1.79, 2.18); p <.0001) (n = 595,193; events = 4110). A two‐stage meta‐analytic approach showed similar results. This paper adds important population‐based evidence to current literature regarding the increased incidence and mortality of liver cancer in adults with diabetes. The analysis of data pooled from 20 studies of different Asian countries and the meta‐analysis across studies with large number of subjects makes the results robust. [ABSTRACT FROM AUTHOR]

Background: Screening participation remains suboptimal in cervical cancer (CC) and colorectal cancer (CRC) screening despite their effectiveness in reducing cancer-related morbidity and mortality. We investigated the effectiveness of an intervention by leveraging the high participation rate in breast cancer (BC) screening as an opportunity to offer self-sampling kits to nonparticipants in CC and CRC screening. Methods and findings: A pragmatic, unblinded, cluster-randomised, multiple period, crossover trial was conducted in 5 BC screening units in the Central Denmark Region (CDR) between September 1, 2021 and May 25, 2022. On each of 100 selected weekdays, 1 BC screening unit was randomly allocated as the intervention unit while the remaining units served as controls. Women aged 50 to 69 years attending BC screening at the intervention unit were offered administrative check-up on their CC screening status (ages 50 to 64 years) and CRC screening status (aged 50 to 69), and women with overdue screening were offered self-sampling. Women in the control group received only standard screening offers according to the organised programmes. The primary outcomes were differences between the intervention group and the control group in the total screening coverage for the 2 programmes and in screening participation among women with overdue screening, measured 6 months after the intervention. These were assessed using intention-to-treat analysis, reporting risk differences with 95% confidence intervals (CIs). A total of 27,116 women were included in the trial, with 5,618 (20.7%) in the intervention group and 21,498 (79.3%) in the control group. Six months after the intervention, total coverage was higher in the intervention group as compared with the control group in CC screening (88.3 versus 83.5, difference 4.8 percentage points, 95% CI [3.6, 6.0]; p < 0.001) and in CRC screening (79.8 versus 76.0, difference 3.8 percentage points, 95% CI [2.6, 5.1]; p < 0.001). Among women overdue with CC screening, participation in the intervention group was 32.0% compared with 6.1% in the control group (difference 25.8 percentage points, 95% CI [22.0, 29.6]; p < 0.001). In CRC screening, participation among women overdue with screening in the intervention group was 23.8% compared with 8.9% in the control group (difference 14.9 percentage points, 95% CI [12.3, 17.5]; p < 0.001). Women who did not participate in BC screening were not included in this study. Conclusions: Offering self-sampling to women overdue with CC and CRC screening when they attend BC screening was a feasible intervention, resulting in an increase in participation and total coverage. Other interventions are required to reach women who are not participating in BC screening. Trial registration: ClinicalTrials.govNCT05022511. The record of processing activities for research projects in the Central Denmark Region (R. No.: 1-16-02-217-21). Anne Dorte Lerche Helgestad and team investigate the effectiveness of an intervention by leveraging the high participation rate in breast cancer screening as an opportunity to engage non-participants in in cervical cancer and colorectal cancer screening. Author summary: Why was this study done?: Participation rates in cervical cancer (CC) and colorectal cancer (CRC) screening lag significantly behind those of breast cancer (BC) screening in Denmark, despite the proven preventive benefits in reducing cancer-related morbidity and mortality. Previous research has identified numerous barriers to nonparticipation across different screening programmes, prompting suggestions for combined screening services as a potential solution. Considering that women attending BC screening fall within the age range eligible for CC and CRC screening, leveraging the BC screening visit could serve as an opportune moment to engage in the other screening programmes. What did the researchers do and find?: We conducted a randomised controlled trial including 27,116 women aged 50 to 69 years attending BC screening, of whom 5,618 women received an intervention offering self-sampled CC and/or CRC screening if overdue for screening and within the eligible screening age range. The intervention proved effective in increasing coverage with 4.8 percentage points in CC screening and 3.8 percentage points in CRC screening compared to the control group. The intervention was feasible and well received by the women, without affecting their high satisfaction with BC screening. What do these findings mean?: This study suggests that combining preventive services may offer a pragmatic strategy to enhancing the effectiveness of public health interventions in various settings. The intervention strategy can be easily expanded and could increase screening participation if implemented on a larger scale. The main limitation of the study is that the intervention focuses solely on women attending BC screening, and a different approach is required for women not participating in this screening. [ABSTRACT FROM AUTHOR]

The objective of this paper was to examine potential factors contributing to smoking-related cancer mortality trends. Smoking associated cancer mortality data from 1970 to 2016 was extracted from the IARC Cancer Mortality Database and WHO Mortality Database. Data from the United States, Greece, Norway, Chile, and Colombia were analyzed due to variance in annual cigarette consumption per capita, the prevalence of current tobacco use, and geographical similarity. Norwegian males, Chilean males and females, and Colombian males and females saw decreases in cancer mortality rates during the study period; on the other hand, Greek males and females showed unchanged mortality rates, and Norwegian females showed a very slight increase in cancer mortality rates. United States age-standardized mortality rates (ASR) rose from 180.75 deaths per 100,000 persons in the year 1970 to the early 1980s before showing a steady and significant decline to 90.46 deaths per 100,000 persons in 2016. This study shows a strong correlation in developed countries (United States, Norway, and Greece) between advances in cancer therapies, early detection techniques, health screening, and public and legal health initiatives with a decrease in smoking-related cancer mortality rates. In developing countries (Chile and Colombia), widespread H. pylori testing, and treatment advances were found to be the main drivers of decreasing cancer mortality rates. [ABSTRACT FROM AUTHOR]

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The cancer is a serious health problem, which is The cancer death rate (cancer mortality) is 158.3 per 100,000 men and women per year (based on 2013–2017 deaths). Both clinical and translational studies have demonstrated that chronic inflammation is associated with Cancer progression. However, the precise mechanisms of inflammasome, and the pathways that mediate this phenomenon are not fully characterized. One of the most recently identified signaling pathways, whose activation seems to affect many metabolic disorders, is the "inflammasome" a multiprotein complex composed of NLRP3 (nucleotide-binding domain and leucine-rich repeat protein 3), ASC (apoptosis associated speck-like protein containing a CARD), and procaspase-1. NLRP3 inflammasome activation leads to the processing and secretion of the proinflammatory cytokines interleukin-1β (IL-1β) and IL-18. The goal of this paper is to review new insights on the effects of the NLRP3 inflammasome activation in the complex mechanisms of crosstalk between different organs, for a better understanding of the role of chronic inflammation in cancer pathogenesis. We will provide here a perspective on the current research on NLRP3 inflammasome, which may represent an innovative therapeutic target to reverse the malignancy condition consequences of the inflammation. AYM-qS3qtQKAqRzQ-vEXmk Video Abstract [ABSTRACT FROM AUTHOR]

Gastric Cancer is the most common malignancy of the digestive tract, which is the third leading cause of cancer-related mortality worldwide. The early prognosis methods, especially Machine Learning (ML)-based approaches are one of the main strategies against GC, which have become a necessity to identify and prognosticate the factors that affect the GC. They enable the specialists to accelerate the subsequent clinical management of patients, who suffer from the GC. This paper aims at creating an Ensemble Method inspired from ML to predict the most significant factors of the GC occurrence. The main objective of this research is to predict the probabilities of the GC occurrence and its associated deaths. To achieve this goal, the created EM benefits from some ML-based methods, including Least Absolute Shrinkage and Selection Operator (LASSO)/Ridge Regression, Elastic Net, Logistic Regression (LR), Random Forest (RF), Gradient Boosting Decision Trees (GBDTs), and Deep Neural Network (DNN). The purpose of the provided EM is to lessen the prediction errors for the large number of the patients' features. The main novelties of this research include: (i) A sequential EM created by a Stacking method to predict the probability of the GC and associated deaths; (ii) Benefiting from the significance level to make an accurate prediction; (iii) Employing two Chi-square tests to select the influent features; (iv) Tuning the parameters of the applied ML models to avoid over-fitting and intensifying the errors; (v) Applying different kinds of the regression methods to treat hyper-dimension cases; (vi) A new model for weighting the applied ML models. The outcomes of the implementation of the created EM in seven pioneer hospitals in the field of GC show that the designed EM generates more precise predictions with an accuracy of 97.9% and 76.3% to predict the GC and its associated deaths, respectively. Moreover, the obtained results from the Area Under Curve (AUC) validates and confirms the capability of the created EM to predict the probability of the GC and its related deaths with an accuracy of 98% and 90% to predict the GC and its associated deaths, respectively. [ABSTRACT FROM AUTHOR]

Colorectal cancer (CRC) is one of the leading causes of death in the United States and worldwide. Recent evidence has corroborated a strong correlation between poor diet and the development of CRC, and further research is being conducted to investigate the association between intestinal microbiome and the development of cancer. New studies have established links with certain foods and synthetic food compounds that may be effective in reducing the risk for carcinogenesis by providing protection against cancer cell proliferation and antagonizing oncogenic pathways. Prebiotics are gaining popularity as studies have demonstrated chemo-preventive as well as anticancer potential of prebiotics. This paper aims to discuss the wide definition and scope of prebiotics by reviewing the studies that provide insights into their effects on human health in the context of colorectal cancer. [ABSTRACT FROM AUTHOR]

Breast cancer is among the most common cancers found in women, causing cancer-related deaths and making it a severe public health issue. Early prediction of breast cancer can increase the chances of survival and promote early medical treatment. Moreover, the accurate classification of benign cases can prevent cancer patients from undergoing unnecessary treatments. Therefore, the accurate and early diagnosis of breast cancer and the classification into benign or malignant classes are much-needed research topics. This paper presents an effective feature engineering method to extract and modify features from data and the effects on different classifiers using the Wisconsin Breast Cancer Diagnosis Dataset. We then use the feature to compare six popular machine-learning models for classification. The models compared were Logistic Regression, Random Forest, Decision Tree, K-Neighbors, Multi-Layer Perception (MLP), and XGBoost. The results showed that the Decision Tree model, when applied to the proposed feature engineering, was the best performing, achieving an average accuracy of 98.64%. [ABSTRACT FROM AUTHOR]

Prostate cancer morbidity and mortality are increasing globally and in China, and the rate of metastasis is also rising, limiting the therapeutic effect and clinical prognosis of prostate cancer. CD151 is considered to be the first promoter of tumor metastasis in the tetraspanin superfamily. Previous research has linked CD151 to the progression of a number of malignancies, including prostate cancer. However, a recent study found that CD151 can inhibit the progression of prostate cancer. As a result, this paper examines existing research on CD151 and prostate cancer progression in order to clarify the relationship and provide a possible reference for future studies. [ABSTRACT FROM AUTHOR]

The existing 2D/3D strategies still have limitations in human liver and tumor segmentation efficiency. Therefore, this paper proposes a 2.5D network combing cascaded context module (CCM) and Ladder Atrous Spatial Pyramid Pooling (L-ASPP), named CCLNet, for automatic liver and tumor segmentation from CT. First, we utilize the 2.5D mode to improve the training efficiency; Second, we employ the ResNet-34 as the encoder to enhance the segmentation accuracy. Third, the L-ASPP module is used to enlarge the receptive field. Finally, the CCM captures more local and global feature information. We experimented on the LiTS17 and 3DIRCADb datasets. Experimental results prove that the method skillfully balances accuracy and cost, thus having good prospects in liver and liver segmentation in clinical assistance. [ABSTRACT FROM AUTHOR]

Simple Summary: Cancer remains a global burden, currently causing nearly one in six deaths worldwide. Accurate projections of cancer incidence and mortality are needed for effective and efficient policymaking, accurate resource allocation, and to assess the impact of newly introduced policies and measures. However, the COVID-19 pandemic disrupted public health systems and caused a significant number of cancers to remain undiagnosed, thus affecting the quality of official statistics and their usefulness for health studies. This paper addresses this issue by proposing novel cancer incidence/cancer mortality models based on population web-search habits and historical links with official health variables. The models are empirically estimated using data from one of the most vulnerable European Union (EU) members, Romania, a country that consistently reports lower survival rates than the EU average, and are further used to forecast cancer incidence and mortality rates in the country. Research findings have important policy implications, and the novel framework, owing to its generalizability, can be applied to the same task in other countries. Overall, the results indicate a continuation of the increasing trends in cancer incidence and mortality in Romania and thus underline the urgency to change the status quo in the Romanian public-health system. Cancer remains a leading cause of worldwide mortality and is a growing, multifaceted global burden. As a result, cancer prevention and cancer mortality reduction are counted among the most pressing public health issues of the twenty-first century. In turn, accurate projections of cancer incidence and mortality rates are paramount for robust policymaking, aimed at creating efficient and inclusive public health systems and also for establishing a baseline to assess the impact of newly introduced public health measures. Within the European Union (EU), Romania consistently reports higher mortality from all types of cancer than the EU average, caused by an inefficient and underfinanced public health system and lower economic development that in turn have created the phenomenon of "oncotourism". This paper aims to develop novel cancer incidence/cancer mortality models based on historical links between incidence and mortality occurrence as reflected in official statistics and population web-search habits. Subsequently, it employs estimates of the web query index to produce forecasts of cancer incidence and mortality rates in Romania. Various statistical and machine-learning models—the autoregressive integrated moving average model (ARIMA), the Exponential Smoothing State Space Model with Box-Cox Transformation, ARMA Errors, Trend, and Seasonal Components (TBATS), and a feed-forward neural network nonlinear autoregression model, or NNAR—are estimated through automated algorithms to assess in-sample fit and out-of-sample forecasting accuracy for web-query volume data. Forecasts are produced with the overperforming model in the out-of-sample context (i.e., NNAR) and fed into the novel incidence/mortality models. Results indicate a continuation of the increasing trends in cancer incidence and mortality in Romania by 2026, with projected levels for the age-standardized total cancer incidence of 313.8 and the age-standardized mortality rate of 233.8 representing an increase of 2%, and, respectively, 3% relative to the 2019 levels. Research findings thus indicate that, under the no-change hypothesis, cancer will remain a significant burden in Romania and highlight the need and urgency to improve the status quo in the Romanian public health system. [ABSTRACT FROM AUTHOR]

Brain tumor is a serious disease leading to a life-threatening condition when worsen resulting in brain cancer. With a rapid fall in 5-year survival rate, brain cancer is the root cause for a large number of deaths worldwide. Diagnosis of brain tumor and detection of nature of cancer with higher accuracy at earliest possible stage is very important for proper treatment that will help patients have a better chance of surviving the disease. As per our study in this paper we conducted comparative research on various machine learning methods for detection of brain tumor at an early stage using Magnetic Resonance Images (MRI). MRIs is one of the most preferable diagnostic techniques for Brain Tumor image diagnosing as it gives more detailed images of the brain structures and blood vessels without using ionized radiations and any physical movement of the patient. This paper majorly focuses on a comparative analysis of various approaches for classification and detection of brain tumor and at last analysing the recent best technique. The process of detection followed in research till date follows Image Pre-Processing, Image Segmentation, Feature Extraction, and Support Vector Machine Classification. Various sources of datasets have been used that includes limited amount of MRI Images. Our analysis is also focused on the limitations and shortcomings of current algorithms. Thus, by comparing various researches, we summarized that when support vector machine (SVM) was used to classify brain tumor images, accuracy was trustworthy for patients but usage of limited amount of dataset reduced the essence of the prediction as using larger dataset can surely bring worthy conversion. We sum up that CNN with Transfer Learning model when used for brain tumor detection increase the reliability of prediction as this model when used with Paediatric Pneumonia detection and Covid-19 detection gave impressive results with increased dataset volume, resulting in early detection of disease and rapid treatment. Thus, for better results, classification and detection of Brain Tumor using CNN with Transfer Learning will be a good option. [ABSTRACT FROM AUTHOR]

Pursuant to a Congressional act in 2008, the Department of Health and Human Services established the Interagency Breast Cancer and Environmental Research Coordinating Committee to address the burden of breast cancer in the United States. Subsequently, the Committee recommended researchers study the timing of exposure to breast cancer risk factors. Given the high breast cancer mortality rate on Staten Island, this paper presents a case-control study investigating breast cancer risk associated with puberty while living on Staten Island. The dataset combined New York City Department of Health and Mental Hygiene female death certificate information between 1985 and 2006, with life history information from newspaper obituaries. Data analyzed included: age, length of residence on Staten Island, birth on Staten Island, and residence on Staten Island during puberty. Cases were individuals who died of breast cancer and controls were individuals who died of non-malignant causes. Analysis included multivariate logistic regression on the full dataset and multiple replicates of randomized one case to two controls simulations. Results indicated that living on Staten Island during puberty (ages 9–19) was associated with an elevated risk of dying from breast cancer (odds ratio 1.35, p < 0.001, 95% CI = 1.18, 1.55). This paper suggests the importance of studying puberty as a window of susceptibility for breast cancer risk. [ABSTRACT FROM AUTHOR]

Background: Preclinical animal studies have suggested that myeloid cell–synthesized coagulation factor X dampens antitumor immunity and that rivaroxaban, a direct factor Xa inhibitor, can be used to promote tumor immunity. This study was aimed at assessing whether patients with atrial fibrillation taking direct factor Xa inhibitors have lower risk of cancer and cancer-related mortality than patients taking the direct thrombin inhibitor dabigatran. Methods and findings: This nationwide population-based cohort study in Denmark included adult patients with atrial fibrillation and without a history of cancer, who started taking a factor Xa inhibitor or dabigatran between 2011 and 2015. Data on medical history, outcomes, and drug use were acquired through Danish healthcare registries. The primary outcome was any cancer. Secondary outcomes were cancer-related mortality and all-cause mortality. Outcome events were assessed during 5 years of follow-up in an intention-to-treat analysis. The propensity score-based inverse probability of treatment weighting was used to compute cumulative incidence and subdistribution hazard ratios (SHRs) and corresponding 95% confidence intervals (CIs), with death as a competing event. Propensity scores were estimated using logistic regression and including in the model sex, age group at index date, comorbidities, and use of comedications. A total of 11,742 patients with atrial fibrillation starting a factor Xa inhibitor and 11,970 patients starting dabigatran were included. Mean age was 75.2 years (standard deviation [SD] 11.2) in the factor Xa cohort and 71.7 years (SD 11.1) in the dabigatran cohort. On the basis of the propensity score-weighted models, after 5 years of follow-up, no substantial difference in the cumulative incidence of cancer was observed between the factor Xa inhibitor (2,157/23,711; 9.11%, 95% CI [8.61%,9.63%]) and dabigatran (2,294/23,715; 9.68%, 95% CI [9.14%,10.25%]) groups (SHR 0.94, 95% CI [0.89,1.00], P value 0.0357). We observed no difference in cancer-related mortality (factor Xa inhibitors cohort 1,028/23,711; 4.33%, 95% CI [4.02%,4.68%]. Dabigatran cohort 1,001/23,715; 4.22%, 95% CI [3.83%,4.66%]; SHR 1.03, 95% CI [0.94,1.12]), but all-cause mortality was higher in the factor Xa inhibitor cohort (factor Xa inhibitors cohort 7,416/23,711; 31.31%, 95% CI [30.37%,32.29%]. Dabigatran cohort 6,531/23,715; 27.56%, 95% CI [26.69%,28.45%]; HR 1.17, 95% CI [1.13,1.21]). The main limitations of the study were the possibility of residual confounding and the short follow-up period. Conclusions: In this population based cohort study, factor Xa inhibitor use was not associated with an overall lower incidence of cancer or cancer-related mortality when compared to dabigatran. We did observe an increase in all-cause mortality in the factor Xa inhibitor cohort. Floris Bosch and colleagues investigate whether patients with atrial fibrillation taking direct Factor Xa inhibitors have a lower risk of cancer and cancer-related mortality than patients taking dabigatran. Author summary: Why was this study done?: A preclinical study in mice with breast cancer and fibrosarcoma showed that factor X dampens antitumor immunity and that factor Xa inhibitor promote tumor immunity. Whether factor Xa inhibition is associated with decreased cancer incidence and cancer-related mortality in humans is unknown. What did the researchers do and find?: We assessed cancer incidence during 5 years of follow-up in patients with atrial fibrillation in Denmark using a factor Xa inhibitor (n = 11,742) or a thrombin inhibitor (dabigatran) (n = 11,970) for stroke prevention. No substantial difference in the cumulative incidence of cancer was observed between the factor Xa inhibitor (9.11%, 95% CI [8.61%,9.63%]) and dabigatran (9.68%, 95% CI [9.14%,10.25%]) groups (SHR 0.94, 95% CI [0.89,1.00]. No difference in cancer-related mortality (SHR 1.03, 95% CI [0.94,1.12]) was observed, but all-cause mortality was higher in the factor Xa inhibitor cohort (HR 1.17, 95% CI [1.13,1.21]). What do these findings mean?: Factor Xa inhibitor use for atrial fibrillation did not appear to significantly reduce cancer risk compared to dabigatran use. The main limitations of the study were the possibility of residual confounding and the short follow-up period. [ABSTRACT FROM AUTHOR]

Lung cancer has emerged as a leading cause of global cancer-related mortality, necessitating effective early detection and classification methods. Recent advancements in deep learning algorithms have shown promise in detecting and classifying lung cancer from CT scan images. This paper proposes an ensemble lung cancer detection and classification model that integrates diverse models like BEiT, DenseNet, and Sequential CNN, leveraging ensemble methods such as AND, OR, Weighted Box Fusion, and Boosting. This study utilizes the Chest CT-Scan Images Dataset for testing. Various studies have explored ensemble methods, deep learning techniques, and hybrid models to address the challenges of accurate lung cancer diagnosis. These studies show that ensemble methods offer a powerful strategy for enhancing accuracy by combining the strengths of multiple classifiers to mitigate individual weaknesses, leading to improved overall performance. The results demonstrate that our ensemble approach outperforms single-model techniques and other ensemble methods, achieving a prediction accuracy of 98%. The proposed method enhances accuracy and provides a practical solution using existing datasets and resources. This research highlights the potential of ensemble methods as a transformative tool in advancing lung cancer diagnosis accuracy and efficacy. [ABSTRACT FROM AUTHOR]

Cancer is a disease in which some cells of the body grow uncontrollably and occasionally spread to other parts of the body. With a group of more than 100 different types, cancer can start almost anywhere in the body. Defective cells may form a mass called a tumor which can be cancerous (malignant), which grows and spreads to other parts of the body, or benign that can grow but not spread throughout the body. In 2021, more than 10 million people died of cancer worldwide (1 out of 6 deaths). This paper has thoroughly investigated the cancer status in the Occupied Palestinian Territories (OPT), in terms of its various types; incidence; mortality; sex, age, and geography distribution; and potential causes. In the OPT, with a population of 5.35 million, cancer mortality was 14% in 2016, being the second cause of death after cardiovascular diseases accounting 30.6% of all causes of death. Cancer mortality in the OPT increased by 136% from 2000 to 2016, and by 14% from 2016 to 2020. In addition to other types of cancer in the OPT, its main types are lung (highest in males), breast (highest in females), colorectal (highest in both sexes), and leukemia (highest in children). The high rates of different types of cancer in the OPT can be attributed to various causes, including those related to environmental pollution, nutrition, stress, and lifestyle factors (smoking, lack of activity, increased dependence on technologies, etc.), whereas only 10–30% of cancer cases are attributed to genetics. [ABSTRACT FROM AUTHOR]

Clinical and epidemiological evidence has recently revealed a link between coronary artery disease (CAD) and cancer. Shared risk factors and common biological pathways are probably involved in both pathological conditions. The aim of this paper was to evaluate whether and which conventional risk factors and novel circulating biomarkers could predict cancer incidence and death in patients with CAD. The study included 750 CAD patients, who underwent blood sampling for the evaluation of systemic inflammatory indexes (NLR and SII) and specific biomarkers of oxidative damage (leukocyte telomere length (LTL), mitochondrial DNA copy number (mtDNAcn)). Study participants were followed up for a mean of 5.4 ± 1.2 years. Sixty-seven patients (8.9%) developed cancer during the follow-up time, and nineteen (2.5%) died of cancer. Cox multivariable analysis revealed that age (HR = 1.071; 95% CI: 1.034–1.109; p < 0.001), smoking habit (HR = 1.994; 95% CI: 1.140–3.488; p = 0.016), obesity (HR = 1.708; 95% CI: 1.022–2.854; p = 0.041) and SII (HR = 1.002; 95% CI: 1.001–1.003; p = 0.045) were associated with cancer incidence, while only age (HR = 1.132; 95% CI: 1.052–1.219; p = 0.001) was a predictor of cancer death. Patients with lung and gastrointestinal cancers had significantly higher median mtDNAcn levels than those without cancer. Our study suggests that aggressive risk factor modification and suppression of chronic inflammation may be essential to preventing cancer in CAD patients. [ABSTRACT FROM AUTHOR]

Background and Objectives: Cancer organizations worldwide have recently released care guidelines for cancer patients with coronavirus disease 2019 (COVID-19). Several studies have reported higher mortality rates in cancer patients with COVID-19. However, drawing robust conclusions remains challenging due to a lack of research on clinical prognostic factors in this patient group. Methods: A comprehensive literature search was conducted using the PubMed, Embase, and Cochrane databases. We searched the keywords in the following combination: ("COVID-19" or "coronavirus" or "wuhan virus") and ("cancer"). The search was performed on August 1, 2020, and only papers written in English were included in this study. We collected data from 3,215 cancer patients with COVID-19 from 16 studies and analyzed overall mortality after COVID-19 infection in cancer patients compared to controls, as well as prognostic factors for severity and mortality after COVID-19 infection. The prognostic factors analyzed encompassed demographics, comorbidities, symptoms, cancer treatment within 4 weeks of COVID-19 diagnosis, and treatment for COVID-19 infection. Results: This meta-analysis evaluated mortality rates and related prognostic factors in cancer patients infected with COVID-19. First, 15 of the 16 studies reported mortality data; 663 patients died among a total of 3,215 people, resulting in a combined mortality rate of 21%. Second, the following poor prognostic factors were identified: male sex, older age (=65 years), respiratory symptoms (e.g., cough and dyspnea), and other comorbidities (e.g., cardiovascular disease, hypertension, and chronic obstructive pulmonary disease). Conclusion: The mortality of cancer patients infected with COVID-19 can reach about 20%. [ABSTRACT FROM AUTHOR]