Abstract: The Knowledge-Based System for Diagnosing Breast Cancer aims to support medical students in enhancing their education regarding diagnosis and counseling. The system facilitates the analysis of biopsy images under a microscope, determination of tumor type, selection of appropriate treatment methods, and identification of disease-related questions. According to the Ministry of Health's annual report in Gaza, there were 7,069 cases of breast cancer between 2009 and 2014, with 1,502 cases reported in 2014. In an era dominated by visual (...) information, where 65% of the population are visual learners, the Knowledge-Based System serves as an effective tool to aid long-term information retention by utilizing visual and textual resources. The system features a user-friendly interface, enabling students to diagnose the disease and expand their understanding. Furthermore, it can be accessed via smartphones. The Knowledge-Based System comprises three primary components: Firstly, it provides specific patient-oriented questions to familiarize students with the questioning process. Secondly, it facilitates the analysis of biopsy sample images to determine their integrity and identify potential infections. Lastly, the system incorporates animated videos illustrating various approaches for treating breast cancer. (shrink)

Abstract: Breast cancer is reported to be the most common cancer type among women worldwide and it is the second highest women fatality rate amongst all cancer types. Notwithstanding all the progresses made in prevention and early intervention, early prognosis and survival prediction rates are still not sufficient. In this paper, we propose an ANN model which outperforms all the previous supervised learning methods by reaching 99.57 in terms of accuracy in Wisconsin Breast Cancer dataset. Experimental (...) results on Haberman’s Breast Cancer Survival dataset show the superiority of proposed method by reaching 88.24 % in terms of accuracy. The results are the best reported ones obtained from Artificial Neural Network using JNN environment without any preprocessing of the dataset. (shrink)

Abstract: Colon cancer is a prevalent and life-threatening disease, necessitating accurate and timely diagnosis for effective treatment and improved patient outcomes. This research paper presents the development of a knowledge-based system for diagnosing colon cancer using the CLIPS language. Knowledge-based systems offer the potential to assist healthcare professionals in making informed diagnoses by leveraging expert knowledge and reasoning mechanisms. The methodology involves acquiring and structuring medical knowledge specific to colon cancer, followed by the implementation of a knowledge- (...) based system using CLIPS. The system's design integrates relevant medical databases and employs advanced reasoning mechanisms to facilitate accurate diagnosis. Evaluation results demonstrate the system's effectiveness, with high accuracy and sensitivity rates compared to existing methods. The findings highlight the potential of knowledge-based systems in improving clinical practice and patient care. The research contributes to the growing field of medical informatics by demonstrating the feasibility and effectiveness of CLIPS-based knowledge systems for colon cancer diagnosis. Future research can focus on enhancing system performance and expanding its scope to encompass other types of cancer diagnoses. (shrink)

The aim of this paper is to assess the relevance of somatic evolution by natural selection to our understanding of cancer development. I do so in two steps. In the first part of the paper, I ask to what extent cancer cells meet the formal requirements for evolution by natural selection, relying on Godfrey-Smith’s (2009) framework of Darwinian populations. I argue that although they meet the minimal requirements for natural selection, cancer cells are not paradigmatic Darwinian populations. (...) In the second part of the paper, I examine the most important examples of adaptation in cancer cells. I argue that they are not significant accumulations of evolutionary changes, and that as a consequence natural selection plays a lesser role in their explanation. Their explanation, I argue, is best sought in the previously existing wiring of the healthy cells. (shrink)

Globally, breast cancer is the most common cancer in women worldwide, it represents about 25.0% of all cancer cases. In developing countries, breast cancer is mainly discovered at a late stage, which has a negative impact on the prognosis. To determine the extent of, and the factors contributing to, delay in breast cancer presentation, and to evaluate the mammography screening practice in breast cancer Libyan women. A descriptive cross-sectional study was carried out on five (...) hundred and one breast cancer patients who attended the Oncology Outpatient Clinic of the Medical Department at Tripoli University Hospital and Tripoli Central Hospital during a period between February and August 2019, after the application of inclusion and exclusion criteria enrolled in the study. The mean age of the patients was 47.35±01.0 years, 55.1% of them were diagnosed in late stage (III and IV), and 30.9% of the patients were presented late (≥3 months). The time interval between the initial breast symptom and first presentation to a clinic ranged between 2nd day and up to three years, with a median time of 1.5 months, and the main factor of the delayed presentation to a clinic was a lack of knowledge about symptoms (33.5%). The study revealed a significant association between delayed presentation and older age (P<0.001), low educational level (P<0.001), low family income (P<0.001), unemployed women (P< 0.002), postmenopausal women (P<0.001), patients with negative family history (P<0.05). This study concluded that delayed presentation is found in less than a third of the study population, and lack of knowledge about breast cancer signs and symptoms is a common factor. Age, educational status, family income, unemployed women, menopausal status, and family history of breast cancer were all significant factors for delayed presentation. Improving women's awareness about breast cancer is needed and applying the policy of regular screening mammograms is a useful tool for early diagnosis and early treatment in Libya. (shrink)

In the last decade, the cases of breast and cervical cancer have been positioned at the top rank of cancer statistics worldwide. Consequently, many husbands become family caregivers (FCGs) and get the burden of cancer caregiving. Being blind and incompetent, they need supportive care from healthcare professionals (HCPs). To support them, HCPs provide various healthcare information to meet their needs. Further, their demand for a specific type of healthcare information may reflect their need for emotional support from (...) the HCPs to reduce the burden. This study aims to examine the predictors of the need for emotional support among FCGs of female cancer patients by analyzing the demanded healthcare information. The mindsponge theory was used in conceptual development and interpretation. Bayesian Mindsponge Framework (BMF) analytics were used for statistical analysis on a dataset of 48 husbands and 12 significant others in five congested communities of Surabaya, Indonesia. Results showed that among the six types of healthcare information, the demanded cancer-specific information and information on cancer physical needs significantly predict the need for emotional support among respondents. Meanwhile, FCGs with a higher demand for information on alternative therapies are less likely to need emotional support. Other types of information have ambiguous effects on the need for emotional support. This study reveals that the demanded cancer-specific information, e.g., cancer prognosis or likely outcome, and information on cancer physical needs must be prioritized in information disclosure to meet the needs for emotional support among FCGs of female cancer patients. (shrink)

Cancer research is experiencing ‘paradigm instability’, since there are two rival theories of carcinogenesis which confront themselves, namely the somatic mutation theory and the tissue organization field theory. Despite this theoretical uncertainty, a huge quantity of data is available thanks to the improvement of genome sequencing techniques. Some authors think that the development of new statistical tools will be able to overcome the lack of a shared theoretical perspective on cancer by amalgamating as many data as possible. We (...) think instead that a deeper understanding of cancer can be achieved by means of more theoretical work, rather than by merely accumulating more data. To support our thesis, we introduce the analytic view of theory development, which rests on the concept of plausibility, and make clear in what sense plausibility and probability are distinct concepts. Then, the concept of plausibility is used to point out the ineliminable role played by the epistemic subject in the development of statistical tools and in the process of theory assessment. We then move to address a central issue in cancer research, namely the relevance of computational tools developed by bioinformaticists to detect driver mutations in the debate between the two main rival theories of carcinogenesis. Finally, we briefly extend our considerations on the role that plausibility plays in evidence amalgamation from cancer research to the more general issue of the divergences between frequentists and Bayesians in the philosophy of medicine and statistics. We argue that taking into account plausibility-based considerations can lead to clarify some epistemological shortcomings that afflict both these perspectives. (shrink)

Cancer biology features the ascription of normal functions to parts of cancers. At least some ascriptions of function in cancer biology track local normality of parts within the global abnormality of the aberration to which those parts belong. That is, cancer biologists identify as functions activities that, in some sense, parts of cancers are supposed to perform, despite cancers themselves having no purpose. The present paper provides a theory to accommodate these normal function ascriptions—I call it the (...) Modeling Account of Normal Function (MA). MA comprises two claims. First, normal functions are activities whose performance by the function-bearing part contributes to the self-maintenance of the whole system and, thereby, results in the continued presence of that part. Second, MA holds that models of system-level activities that are (partly) constitutive of self-maintenance are improved by including a representation of the relevant function-bearing part and by making reference to the activity/activities that part performs which contribute(s) to those system-level activities. I contrast MA with two other accounts that seek to explicate the ascription of normal functions in biology, namely, the organizational account and the selected effects account. Both struggle to extend to cancer biology. However, I offer ecumenical readings which allow them to recover some ascriptions of normal function to parts of cancers. So, though I contend that MA excels in this respect, the purpose of this paper is served if it provides materials for bridging the gap between cancer biology, philosophy of cancer, and the literature on function. (shrink)

Breast cancer remains a significant health concern worldwide, necessitating the development of effective diagnostic tools. In this study, we employ a neural network-based approach to analyze the Wisconsin Breast Cancer dataset, sourced from Kaggle, comprising 570 samples and 30 features. Our proposed model features six layers (1 input, 1 hidden, 1 output), and through rigorous training and validation, we achieve a remarkable accuracy rate of 99.57% and an average error of 0.000170 as shown in the image below. Furthermore, (...) our investigation identifies the most influential features in breast cancer diagnosis, shedding light on the key determinants of malignancy. Notably, we find that factors such as fractal dimension_se, symmetry worst, compactness_worst, symmetry_se, and smoothness_se play pivotal roles in distinguishing between benign and malignant cases. This research contributes to the ongoing efforts to enhance breast cancer diagnosis, providing valuable insights into feature importance and showcasing the potential of neural networks in medical applications. Our findings have implications for improving early detection and treatment strategies, ultimately contributing to improved patient outcomes. (shrink)

In this paper, we developed an Artificial Neural Network (ANN) for detect the absence or presence of lung cancer in human body. Symptoms were used to diagnose the lung cancer, these symptoms such as Yellow fingers, Anxiety, Chronic Disease, Fatigue, Allergy, Wheezing, Coughing, Shortness of Breath, Swallowing Difficulty and Chest pain. They were used and other information about the person as input variables for our ANN. Our ANN established, trained, and validated using data set, which its title is (...) “survey lung cancer”. Model evaluation showed that the ANN model is able to detect the absence or presence of lung cancer with 96.67 % accuracy. (shrink)

Cancer care has transitioned from clinical-based to home-based care to support longterm care in a more familiar and comfortable environment. This care transition has put family caregivers (FCGs) in a strategic position as care providers. Cancer care at home involves psychological and emotional treatment at some point, making FCGs deal with the stress of cancer patients frequently. Due to their limited care competencies, they need supportive care from healthcare professionals in cancer stress management. This study aims (...) to examine how types of demanded healthcare information affect the FCG’s role in reducing the stress of female cancer patients. The mindsponge theory was used in conceptual development and interpretation. Bayesian Mindsponge Framework (BMF) analytics were used for statistical analysis on a dataset of 48 spouses (husbands) and 12 other family members in five congested communities of Surabaya, Indonesia. Results showed that among the six types of healthcare information, FCGs with higher demand for cancer-specific information were more likely to need support in reducing the stress of female cancer patients. Meanwhile, FCGs with a higher demand for information about support services were less likely to need support to reduce cancer patients’ stress. Other types of healthcare information have ambiguous effects on the need for support in reducing cancer-induced stress. This study reveals that the demanded cancer-specific information, e.g., cancer prognosis or likely outcome, must be prioritized to assist FCG’s role in managing cancer stress. (shrink)

The problem of cancer is examined from the metaphysical standpoint of essence and ground. An essentialist definition of cancer is assumed that would be valid in all possible worlds in which cancer could logically exist. The grounds of cancer are then examined and elucidated. Two grounding cancer properties are identified and discussed: symmetry- breaking and computational intelligence. Each examination leads to concrete conclusions for novel therapeutic approaches and a more fundamental understanding of what cancer (...) is at bottom. Other possible cancer grounding properties related to evolution, adaptability and stochastic features are identified for future work. This approach is novel and offers new solutions to the problem of cancer. (shrink)

Abstract: Early detection of breast cancer is vital for improving patient outcomes and reducing mortality rates. Technological advancements have significantly enhanced the accuracy and efficiency of screening methods. This paper explores recent innovations in early detection, focusing on the evolution of digital mammography, the benefits of 3D mammography (tomosynthesis), and the application of advanced imaging techniques such as molecular imaging and MRI. It also examines the role of artificial intelligence (AI) in diagnostic tools, showing how machine learning algorithms are (...) improving imaging analysis and reducing diagnostic errors. Furthermore, the paper discusses advancements in genetic screening, liquid biopsies, and biomarkers, emphasizing their potential in identifying high-risk individuals and supporting personalized treatment plans. The paper provides a forward-looking perspective on the future of breast cancer detection, addressing both the opportunities and challenges associated with the adoption of these cutting-edge technologies to improve patient outcomes. (shrink)

The Knowledge Based System for Diagnosing Breast Cancer is used to assist medical students to improve their education on diagnosis and counseling the process of analyzing the biopsy image of the microscope, determining the type of tumor and the treatment method for each case and identifying the disease related questions. According to the Ministry of Health in its annual report in Gaza, between 2009 and 2014 there are 7069 cases of breast cancer, and in 2014 there are 1502 (...) cases of breast cancer . We are now in the age of visual information where 65% of the populations are visual learners; the Knowledge Based System is the easiest way to ensure students remember the information in the long term, using visual and textual information. The Knowledge Based System has an easy-to-use interface to help students diagnose the disease and enhance their information about the disease; the system can be used on smart phones. This Knowledge Based System can be divided into three main parts: The first part is specific questions for the patient, to help student to know questions type. The second part is the process of analyzing the biopsy sample image, to see if the biopsy image is intact or infected. The last part is the video shows ways to treat breast cancer in the form of animation. (shrink)

Cancer biologists ascribe normal functions to parts of cancer. Normal functions are activities that parts of systems are in some minimal sense supposed to perform. Cancer biologists’ finding normality within the abnormality of cancer pose difficulties for two main approaches to normal function. One approach claims that normal functions are activities that parts are selected for. However, some parts of cancers that have normal functions aren’t selected to perform them. The other approach claims that normal functions (...) are part-activities typical for the system and that contribute to survival/reproduction. However, cancers are too heterogeneous to establish what’s typical across a type. (shrink)

Adaptation and stress are two main concepts useful for better understanding the phases of illness and health-related human behavior. The two faces of adaptation, adaptation as a process and adaptation as a product, have raised the question of how long the adaptation process will take in cancer trajectories. The care setting transition from clinical-based into home-based cancer care has stressed the role of family caregivers (FCG) in cancer management. This study examines how types of demanded healthcare information (...) affect the FCG’s role in catalyzing the adaptation of female cancer patients. The mindsponge theory was used in conceptual development and results interpretation. Bayesian Mindsponge Framework (BMF) analytics was used for statistical analysis on a dataset of 48 spouses (husbands) and 12 other family members in five congested communities of Surabaya, Indonesia. Results showed that among the six types of demanded healthcare information, FCGs with a higher tendency to demand cancer-specific information are more likely to need support in catalyzing the adaptation of female cancer patients. Meanwhile, FCGs with a higher demand for information on alternative therapies are less likely to need support in catalyzing cancer adaptation. Other types of healthcare information have ambiguous effects on the need for support in cancer adaptation assistance. This study reveals that the demanded cancer-specific information, e.g., cancer’s prognosis or likely outcome, must be prioritized to assist FCG’s role in catalyzing adaptation among female cancer patients. Cancer prognosis and outcomes reflect the future of cancer care results. (shrink)

Although there is a growing recognition that older adults and those with extensive comorbid conditions undergo cancer screening too frequently, there is little information about patients’ perceptions regarding cessation of cancer screening. Information on older adults’ views of screening cessation would be helpful both for clinicians and for those designing interventions to reduce overscreening.

To emphasize what we have outlined in a preceding paper, we consider the following: that human consciousness model should take into consideration “spirit” role, i.e. the mind-body-spirit as integral aspect, which view is neglected in the so-called Freudian mental model. In this paper, we consider two approaches to cancer treatment derived from such an integral triune view of human consciousness, including (a) healing frequency approach as advised by Royal Rife and David Hawkins, and also (b) relational therapy, based on (...) recent research on the healing role of love and compassion. (shrink)

Fear of cancer is mostly related to cancer recurrence, metastasis, additional cancer, and diagnostic tests. Its legacy as a lethal disease has raised fear of approaching death. Currently, cancer’s total suffering and the worsening phenomena have raised fear, especially among female patients. Family caregivers (FCGs) who are responsible for the day-to-day cancer care at home need to help the patients deal with this fear frequently. Due to the limited care competencies, they need supportive care from (...) healthcare professionals in cancer fear management. This study aims to assess how types of demanded healthcare information affect the FCG’s role in reducing the fear of female cancer patients. The mindsponge theory was used in conceptual development and interpretation. Bayesian Mindsponge Framework (BMF) analytics were used for statistical analysis on a dataset of 48 spouses (husbands) and 12 other family members in five congested communities of Surabaya, Indonesia. Results showed that among the six types of healthcare information, FCGs with higher demand on cancer-specific information and information on cancer physical needs were more likely to need support in reducing the fear of female cancer patients. Meanwhile, FCGs with a higher demand for information on support services were less likely to need support to reduce cancer patients’ fear. Other types of healthcare information have ambiguous effects on the need for support in reducing cancer-induced fear. This study reveals that the demanded cancer-specific information, e.g., cancer’s prognosis or likely outcome and information on cancer physical needs, need to be met in priority to assist FCG’s role in cancer fear management. (shrink)

Lung cancer is a serious and deadly disease that affects the lungs, which are responsible for taking in oxygen and expelling carbon dioxide from the body. The disease can develop in any part of the lungs and is usually caused by smoking or exposure to certain chemicals. The main Objective: of this expert system is to provide an accurate diagnosis of lung cancer and the appropriate treatment options. In this paper, Methods: we present the design and implementation of (...) an expert system that can assist physicians in diagnosing lung cancer and determining the best course of treatment. The system provides an overview of the disease, its causes, symptoms, diagnosis, and treatment, along with relevant medical information. Results: The system was developed using the Clips expert system language and evaluated by medical professionals who found it to be an effective tool for diagnosing and treating lung cancer. Conclusion: The proposed expert system has the potential to improve patient outcomes and reduce healthcare costs associated with the treatment of lung cancer. (shrink)

Many researchers consider cancer to have molecular causes, namely mutated genes that result in abnormal cell proliferation (e.g. Weinberg 1998). For others, the causes of cancer are to be found not at the molecular level but at the tissue level where carcinogenesis consists of disrupted tissue organization with downward causation effects on cells and cellular components (e.g. Sonnenschein and Soto 2008). In this contribution, I ponder how to make sense of such downward causation claims. Adopting a manipulationist account (...) of causation (Woodward 2003), I propose a formal definition of downward causation and discuss further requirements (in light of Baumgartner 2009). I then show that such an account cannot be mobilized in support of non-reductive physicalism (contrary to Raatikainen 2010). However, I also argue that such downward causation claims might point at particularly interesting dynamic properties of causal relationships that might prove salient in characterizing causal relationships (following Woodward 2010). (shrink)

The “Expert System for Diagnosing Breast Cancer" is used to assist medical students to improve their education on diagnosis and counseling the process of analyzing the biopsy image of the microscope, determining the type of tumor and the treatment method for each case and identifying the disease related questions. According to the Ministry of Health in its annual report in Gaza, between 2009 and 2014 there are 7069 cases of breast cancer, and in 2014 there are 1502 cases (...) of breast cancer . We are now in the age of visual information where 65% of the populations are visual learners; the expert system is the easiest way to ensure students remember the information in the long term, using visual and textual information. The expert system has an easy-to-use interface to help students diagnose the disease and enhance their information about the disease; the system can be used on smart phones. This expert system can be divided into three main parts: The first part is specific questions for the patient, to help student to know questions type. The second part is the process of analyzing the biopsy sample image, to see if the biopsy image is intact or infected. The last part is the video shows ways to treat breast cancer in the form of animation. (shrink)

Abstract: Lung cancer is the top reason of cancer-associated deaths globally. Surgery is the typical treatment for early-stage non-small cell lung cancer (NSCLC). Advancement in the knowledge of the biology of non-small cell lung cancer has shown molecular evidence used for systemic cancer therapy aiming metastatic disease, with a significant impact on patients’ overall survival (OS) and eminence of life. Though, a biopsy of overt metastases is an invasive technique restricted to assured positions and not (...) effortlessly satisfactory in the clinic. The examination of peripheral blood samples of cancer patients embodies a new basis of cancer-derived material, recognized as liquid biopsy, and its constituents (circulating tumour cells (CTCS), circulating free DNA (cfDNA), exosomes, and tumour-educated platelets (TEP)) may be gotten from nearly any body liquids. These constituents have shown to imitate features of the status of both the primary and metastatic diseases, aiding the clinicians to go towards a tailored medicine. In this paper, the reasons of lung cancer will be recognized and the risk elements that initiated the increase of infection, for instance Smoking, Disclosure to secondhand smoke, Disclosure to radon gas, Disclosure to asbestos and other compounds, Family past history of lung cancer, and decrease of the spread of disease and approaches of handling and prevention of lung cancer. (shrink)

Abstract: Early detection of breast cancer plays a pivotal role in improving patient prognosis and reducing mortality rates. Recent technological advancements have significantly enhanced the accuracy and effectiveness of breast cancer screening methods. This paper explores the latest innovations in early detection, including the evolution of digital mammography, the impact of 3D mammography (tomosynthesis), and the use of advanced imaging techniques such as molecular imaging and MRI. Furthermore, the integration of artificial intelligence (AI) in diagnostic tools is discussed, (...) highlighting how machine learning algorithms are refining imaging analyses and reducing diagnostic errors. Advances in genetic screening, liquid biopsies, and biomarkers are also examined, showcasing their potential in identifying high-risk individuals and enabling personalized treatment plans. This paper provides insights into the future of breast cancer detection, outlining both the opportunities and challenges that lie ahead in adopting these innovative technologies to improve patient outcomes. (shrink)

Abstract: This research explores the application of artificial neural networks (ANNs) in predicting cancer using a synthetically generated dataset designed for research purposes. The dataset comprises 10,000 pseudo-patient records, each characterized by gender, age, smoking history, fatigue, and allergy status, along with a binary indicator for the presence or absence of cancer. The 'Gender,' 'Smoking,' 'Fatigue,' and 'Allergy' attributes are binary, while 'Age' spans a range from 18 to 100 years. The study employs a three-layer ANN architecture to (...) develop a predictive model. The achieved accuracy of 97.24% and a low loss value of 0.02 indicate promising performance. Further analysis involves a comprehensive examination of the confusion matrix, precision, recall, and F1 score metrics, along with the ROC curve and AUC score, to provide a detailed understanding of the model's strengths and weaknesses. Additionally, considerations for cross-validation, hyperparameter tuning, and interpretability are discussed. It is emphasized that while the synthetic nature of the dataset allows for controlled experimentation, results should be cautiously interpreted and validated on authentic clinical data before extrapolation to real-world medical scenarios. This research serves as a valuable contribution to the exploration of predictive models for cancer detection, encouraging continued investigation and development in the field. (shrink)

Among all aspects, the unmet needs of healthcare information from the healthcare and illness-related domains have the potency to predict the unmet needs of emotional support from the emotional and relational domains in this population. The current study aims to examine the predictors of the needs for emotional support among FCGs of cancer patients by analyzing the demanded healthcare information, i.e., cancer-specific information, caregiver-specific information, therapy-specific information, information on cancer physical needs, information on alternative therapies, and information (...) on support services. (shrink)

The current study aims to examine how types of demanded healthcare information affect the FCG’s role in catalyzing the adaptation of female cancer patients (i.e., cancer-specific information, caregiver-specific information, therapy-specific information, information on cancer physical needs, information on alternative therapies, and information on support services).

This article, based on a case study, examines issues of confidentiality and family impact in a diagnosis of Huntington's disease. Genetic-based illness transcends individualistic models of patient autonomy because it also involves other family members, requiring a new approach to patient confidentiality.

Fact/value holism has become commonplace in philosophy of science, especially in feminist literature. However, that facts are bearers of empirical content, while values are not, remains a firmly-held distinction. I support a more thorough-going holism: both facts and values can function as empirical claims, related in a seamless, semantic web. I address a counterexample from Kourany where facts and values seem importantly discontinuous, namely, the simultaneous support by the Nazis of scientifically sound cancer research and morally unsound political policies. (...) I conclude that even by the criteria available at the time, Nazi cancer research was empirically weak, and the weaknesses in their research are continuous with their moral failures in just the ways predicted by the holism I support. (shrink)

In this paper, we developed an Artificial Neural Network (ANN) for detect the absence or presence of lung cancer in human body. Symptoms were used to diagnose the lung cancer, these symptoms such as Yellow fingers, Anxiety, Chronic Disease, Fatigue, Allergy, Wheezing, Coughing, Shortness of Breath, Swallowing Difficulty and Chest pain. They were used and other information about the person as input variables for our ANN. Our ANN established, trained, and validated using data set, which its title is (...) “survey lung cancer”. Model evaluation showed that the ANN model is able to detect the absence or presence of lung cancer with 96.67 % accuracy. -/- . (shrink)

Background: Liver cancer is one of the malignant tumors worldwide, while the prevention and control situation is grim at present, and the diffusion of its early screening technology still faces some challenges. This study aims to investigate the influencing mechanism of perceived ease of use, organizational support mechanism, and industry competitive pressure on hepatic early screening technologies use by physicians, so as to promote the wider use of corresponding technologies. -/- Methods: Under the theoretical guidance of technology-organization-environment framework and (...) mindsponge theory, this study took hepatic contrast-enhanced ultrasound as an example, and conducted a cross-sectional questionnaire by randomly selecting physicians from Fujian and Jiangxi provinces in China with a high and low incidence of liver cancer, respectively. Structural equation modeling was used to determine the correlation among perceived ease of use, organizational support mechanism, and industry competitive pressure, as well as their impact on the physicians’ behavior toward contrast-enhanced ultrasound use. -/- Results: The hypothesis model fits well with the data (χ2/df = 1.863, GFI = 0.937, AGFI = 0.908, RMSEA = 0.054, NFI = 0.959, IFI = 0.980, CFI = 0.980). Under technology-organization-environment framework, the perceived ease of use (β = 0.171, p < 0.05), organizational support mechanism (β = 0.423, p < 0.01), industry competitive pressure (β = 0.159, p < 0.05) significantly influenced physicians’ use of hepatic contrast-enhanced ultrasound. Besides, perceived ease of use and organizational support mechanism (β = 0.216, p < 0.01), perceived ease of use and industry competitive pressure (β = 0.671, p < 0.01), organizational support mechanism and industry competitive pressure (β = 0.330, p < 0.01) were all associated significantly. -/- Conclusion: From the lens of information processing (mindsponge theory) and technology-organization-environment framework, this study clarified the social and psychological influencing mechanism of perceived ease of use, organizational support mechanism, and industry competitive pressure on physicians’ use of hepatic contrast-enhanced ultrasound. The results will directly propose recommendations for expanding hepatic contrast-enhanced ultrasound utilization and indirectly promoting other appropriate and effective health technologies diffusion within the integrated health system. (shrink)

It has been known for long time that the cosmic sound wave was there since the early epoch of the Universe. Signatures of its existence are abound. However, such an acoustic model of cosmology is rarely developed fully into a complete framework from the notion of space, cancer therapy up to the sky. This paper may be the first attempt towards such a complete description of the Universe based on classical wave equation of sound. It is argued that one (...) can arrive at a consistent description of space, elementary particles, Sachs-Wolfe acoustic theorem, up to a novel approach for cancer therapy, starting from this simple classical wave equation of sound. We also discuss a plausible extension of Acoustic Sachs-Wolfe theorem based on its analogue with Klein-Gordon equation to become Acoustic Sachs-Wolfe-Christianto-Smarandache-Umniyati (ASWoCSU) equation. It is our hope that the new proposed equation can be verified with observation data. But we admit that our model is still in its infancy, more researches are needed to fill all the missing details. (shrink)

The current study aims to examine how types of demanded healthcare information affect the FCG’s role in reducing the fear of female cancer patients (i.e., cancer-specific information, caregiver-specific information, therapy-specific information, information on cancer physical needs, information on alternative therapies, and information on support services).

Inflammation is activated by diverse triggers that induce the expression of cytokines and adhesion molecules, which permit a succession of molecules and cells to deliver stimuli and functions that help the immune system clear the primary cause of tissue damage, whether this is an infection, a tumor, or a trauma. During inflammation, short-term changes in the expression and secretion of strong mediators of inflammation occur, while long-term changes occur to specific groups of cells. Long-term changes include cellular transdifferentiation for some (...) types of cells that need to regenerate damaged tissue, as well as death for specific immune cells that can be detrimental to tissue integrity if they remain active beyond the boundaries of essential function. The transcriptional regulator NFκB enables some of the fundamental gene expression changes during inflammation, as well as during tissue development. During recurrence of malignant disease, cell stress-induced alterations enable the growth of cancer cell clones that are substantially resistant to therapeutic intervention and to the immune system. A number of those alterations occur due to significant defects in feedback signal cascades that control the activity of NFκB. Specifically, cell stress contributes to feedback defects as it overrides modules that otherwise control inflammation to protect host tissue. NFκB is involved in both the suppression and promotion of cancer, and the key distinctive feature that determines its net effect remains unclear. This paper aims to provide a clear answer to at least one aspect of this question, namely the mechanism that enables a divergent response of cancer cells to critical inflammatory stimuli and to cell stress in general. (shrink)

In this thesis, I give a metascientific account of causality in medicine. I begin with two historical cases of causal discovery. These are the discovery of the causation of Burkitt’s lymphoma by the Epstein-Barr virus, and of the various viral causes suggested for cervical cancer. These historical cases then support a philosophical discussion of causality in medicine. This begins with an introduction to the Russo- Williamson thesis (RWT), and discussion of a range of counter-arguments against it. Despite these, I (...) argue that the RWT is historically workable, given a small number of modifications. I then expand Russo and Williamson’s account. I first develop their suggestion that causal relationships in medicine require some kind of evidence of mechanism. I begin with a number of accounts of mechanisms and produce a range of consensus features of them. I then develop this consensus position by reference to the two historical case studies with an eye to their operational competence. In particular, I suggest that it is mechanistic models and their representations which we are concerned with in medicine, rather than the mechanism as it exists in the world. -/- I then employ these mechanistic models to give an account of the sorts of evidence used in formulating and evaluating causal claims. Again, I use the two human viral oncogenesis cases to give this account. I characterise and distinguish evidence of mechanism from evidence of difference-making, and relate this to mechanistic models. I then suggest the relationship between types of evidence presents us with a means of tackling the reference-class problem. This sets the scene for the final chapter. Here, I suggest the manner in which these two different classes of evidence become integrated is also reflected in the way that developing research programmes change as their associated causal claims develop. (shrink)

Abstract—Breast cancer is considered the second most common cancer in women compared to all other cancers. It is fatal in less than half of all cases and is the main cause of mortality in women. It accounts for 16% of all cancer mortalities worldwide. Early diagnosis of breast cancer increases the chance of recovery. Data mining techniques can be utilized in the early diagnosis of breast cancer. In this paper, an academic experimental breast cancer (...) dataset is used to perform a data mining practical experiment using the Waikato Environment for Knowledge Analysis (WEKA) tool. The WEKA Java application represents a rich resource for conducting performance metrics during the execution of experiments. Pre-processing and feature extraction are used to optimize the data. The classification process used in this study was summarized through thirteen experiments. Additionally, 10 experiments using various different classification algorithms were conducted. The introduced algorithms were: Naïve Bayes, Logistic Regression, Lazy IBK (Instance-Bases learning with parameter K), Lazy Kstar, Lazy Locally Weighted Learner, Rules ZeroR, Decision Stump, Decision Trees J48, Random Forest and Random Trees. The process of producing a predictive model was automated with the use of classification accuracy. Further, several experiments on classification of Wisconsin Diagnostic Breast Cancer and Wisconsin Breast Cancer, were conducted to compare the success rates of the different methods. Results conclude that Lazy IBK classifier k-NN can achieve 98% accuracy among other classifiers. The main advantages of the study were the compactness of using 13 different data mining models and 10 different performance measurements, and plotting figures of classifications errors. (shrink)

Objective: Lung cancer is the most common cause of morbidity and mortality. Platinum-based chemotherapy, which is the primary line of treatment, offers limited benefit due to drug resistance and side effects. Berberine (BBR), which is characterised by its potent and safe anticancer activity, represents a promising combination option in chemotherapy. To overcome the limitations in lung cancer chemotherapy, we investigated whether BBR and cisplatin (CIS) exert synergistic effects on non-small cell lung cancer cell line (A549) based on (...) cytotoxicity and apoptotic response markers. -/- Methods: The potential cytotoxic effects of the combination treatment were evaluated using the MTT and Chou-Talalay methods. Elisa assays were also performed to measure the levels of the pro-apoptotic protein Bax and the effector protein caspase (Cas)-3. -/- Results: The results showed that BBR alone reduced A549 cell viability in a dose-dependent manner and synergized with CIS (CI =0.34±0.05 at IC50 concentrations). Elisa results showed that the combined treatment (both at IC50 concentrations) modulated apoptotic signalling pathways in A549 cells. Bax and Cas3 protein levels were dramatically enhanced in A549 cells treated with CIS +BBR compared to control (0.5% DMSO) (p < 0.001). -/- Conclusion: Our results suggest that BBR can synergistically enhance the therapeutic effect of CIS in A549 cells. The potential therapeutic efficacy of BBR as part of a combination in current chemotherapy should be supported by in-depth research and clinical studies on the molecular mechanisms associated with cancer. (shrink)

The purpose of this paper is to present a critique of the current view that reduces cancer to a cellular problem caused by specific gene mutations and to propose, instead, that such a problem might become more intelligible, if it is understood as a phenomenon that results from the breakdown of the morphological plan or Gestalt of the organism. Such and organism, in Aristotelian terms, is characterized for presenting a specific morphe or logos (form) and for having a telos (...) (end) to fulfill. A malignant tumor represents an entity separated from both the organic logos and the organic telos. According to the basic postulates of Semiophysics – a blend of Aristotelian physics and Catastrophe Theory developed by René Thom – an organism is a source (original) form individuated by a dominant pregnance that corresponds to its morphogenetic field. Here it is suggested that cancer in aged individuals might result from the progressive exhaustion of developmental constraints that regulate the process of ontogeny, that it is expected to go from the fertilized non-differentiated zygote to the mature fully developed organism, because there is no further point ahead in the developmental pathway past the reproductive age. Cancer in young individuals (before their reproductive maturity) may then be consequence of the premature derangement of such fundamental developmental constraints. In all cases the result is the loss of morphological coherence within the organism. Thus representing a conflict between an organized morphology (the organism) and a part of such a morphology that drifts towards an amorphous state (the tumor). (shrink)

In this paper we present applications of the ACGT Master Ontology (MO) which is a new terminology resource for a transnational network providing data exchange in oncology, emphasizing the integration of both clinical and molecular data. The development of a new ontology was necessary due to problems with existing biomedical ontologies in oncology. The ACGT MO is a test case for the application of best practices in ontology development. This paper provides an overview of the application of the ontology within (...) the ACGT project thus far. (shrink)

Over the past year, technology companies have made headlines claiming that their artificially intelligent (AI) products can outperform clinicians at diagnosing breast cancer, brain tumours, and diabetic retinopathy. Claims such as these have influenced policy makers, and AI now forms a key component of the national health strategies in England, the United States, and China. While it is positive to see healthcare systems embracing data analytics and machine learning, concerns remain about the efficacy, ethics, and safety of some commercial, (...) AI health solutions. This paper argues that improved regulation and guidance is urgently required to mitigate risks, ensure transparency and best practice, Without this, patients, clinicians, and other stakeholders cannot be assured of an app’s efficacy, and safety. (shrink)

Breast cancer is the leading cause of death among women with cancer. Computer-aided diagnosis is an efficient method for assisting medical experts in early diagnosis, improving the chance of recovery. Employing artificial intelligence (AI) in the medical area is very crucial due to the sensitivity of this field. This means that the low accuracy of the classification methods used for cancer detection is a critical issue. This problem is accentuated when it comes to blurry mammogram images. In (...) this paper, convolutional neural networks (CNNs) are employed to present the traditional convolutional neural network (TCNN) and supported convolutional neural network (SCNN) approaches. The TCNN and SCNN approaches contribute by overcoming the shift and scaling problems included in blurry mammogram images. In addition, the flipped rotation-based approach (FRbA) is proposed to enhance the accuracy of the prediction process (classification of the type of cancerous mass) by taking into account the different directions of the cancerous mass to extract effective features to form the map of the tumour. The proposed approaches are implemented on the MIAS medical dataset using 200 mammogram breast images. Compared to similar approaches based on KNN and RF, the proposed approaches show better performance in terms of accuracy, sensitivity, spasticity, precision, recall, time of performance, and quality of image metrics. (shrink)

Objective: The use of neoadjuvant chemotherapy (NACT) in breast cancer is increasing. The management of locally advanced breast cancer differs due to the approach of the center to which the patient applied and the approach of the following physician. From this point of view, we aimed to evaluate the real life data of our region. -/- Methods: The study included 106 patients treated with neoadjuvant chemotherapy in the medical oncology clinic of two different university hospitals. Association between clinicopathological (...) features and pathological complete response (pCR) were analyzed. -/- Results: The pCR rate was higher in patients with negative hormone receptors and this difference was statistically significant (p:0.000). The rate of obtaining pCR increased as the NACT duration increased, and this increase was statistically significant. The mean NACT duration applied to the patients with pCR was 5.48 ± 0.22 months, and the mean NACT duration for those who could not obtain pCR was 5.01 ± 0.1 months (p:0.041). The recurrence rate of patients with pCR was 11.1%, while the recurrence rate of patients who could not obtain pCR was 31.6% (p:0.04). -/- Conclusions: Pathological response to chemotherapy is an important factor in determining prognosis. There appears to be a need for new biomarkers that allow the prediction of pCR and long-term outcomes. (shrink)

Although digital breast tomosynthesis has higher sensitivity than digital mammography and at least as high specificity, digital mammography remains the most common method for conducting mammographic screening. At the same time, mammography systems are now delivered “DBT-ready” and can be used for either digital mammography or digital breast tomosynthesis. In this paper, we ask whether it is ethically permissible to use such equipment for digital mammography, given its lower sensitivity. We argue it is not, and that clinics are ethically required (...) to use their DBTready equipment to screen with digital breast tomosynthesis whenever this is practically possible. Our argument relies on a comparison between digital breast tomosynthesis and a hypothesized improvement in the image quality of digital mammography. (shrink)

Objective: Hashimoto’s thyroiditis is the most common autoimmune thyroid disease. Papillary thyroid cancer is the most common thyroid cancer. Whether Hashimoto’s thyroiditis is a predisposing factor for papillary thyroid cancer remains unclear. In this study, the frequency of papillary thyroid cancer was investigated in patients with Hashimoto’s thyroiditis who underwent total thyroidectomy. -/- Methods: In this study, 534 patients were screened retrospectively. Preoperative thyroid function tests, anti-thyroid antibodies, ultrasonography findings, fine-needle aspiration biopsies, and thyroidectomy pathology results (...) were examined. According to the pathology, 139 patients had Hashimoto’s thyroiditis. Patients with Hashimoto’s thyroiditis (group 1) and non-Hashimoto’s thyroiditis (group 2) were compared. -/- Results: Papillary thyroid cancer was found in 70 patients (50.4%) in group 1 and 156 patients (39.5%) in group 2 (P=.026). The odds ratio was 1.59 (95% CI; 1.07-2.34). There was no difference for sex, age, tumor size, microcarcinoma–macrocarcinoma distribution, subtype, number of tumor focus, lymph node metastasis, vessel invasion, and extrathyroidal spread. -/- Conclusion: We found more papillary thyroid cancer and less capsular invasion in patients with Hashimoto’s thyroiditis, but we did not find any differences between groups in terms of age, gender, tumor size, vascular invasion, and metastasis. According to these findings, Hashimoto’s thyroiditis may be a risk factor for papillary thyroid cancer. (shrink)

Since its initial publication in 2018, Professor Anya Plutynski’s Explaining Cancer: Finding Order in Disorder has garnered a great deal of accolades.1 In 2021, The London School of Economics and Political Science conferred Professor Plutynski the Lakatos Award, recognizing the book’s significant contribution to the philosophy of science. On the heels of its recent reissuing as a paperback, it is an ideal time to revisit this remarkable work.

Most forms of human papillomavirus can create alterations on a woman's cervix that can lead to cervical cancer in the long run, while others can produce genital or epidermal tumors. Cervical cancer is a leading cause of morbidity and mortality among women in low- and middle-income countries. The prediction of cervical cancer still remains an open challenge as there are several risk factors affecting the cervix of the women. By considering the above, the cervical cancer risk (...) factor dataset from KAGGLE data warehouse is executed for predicting the cervical cancer risk classes. The cervical cancer data set is normalised with incomplete data and Pattern Calibration. Secondly, the interpretive data analysis is carried out, and the target feature's dispersion of the cervical cancer risk is visualised. Thirdly, several classifiers are fitted to the unprocessed data set, and the performance is measured with pre and post feature scaling. Fourth, oversampling methodologies are applied to the pre - processed data set. Fifth, the oversampled dataset by differment methods are applied to all the classifiers and the performance is compared with pre and post feature scaling. Sixth, Precision, recall, Fscore, accuracy, and running time are some of the metrics used in performance analysis. The code is written in Python and executed with Anaconda Navigator on the Spyder framework. The findings of the experiments reveal that the Random forest classifier tends to sustain 96% accuracy pre and post scaling for unporocessed dataset. Similarly the same classifier tends to sustain 98% accuracy for all the oversampling techniques. (shrink)

Roseoloviruses are ubiquitous worldwide. Its infection in children is very common causing a disease named as Roseola infantum. It appears that most of the children are still being suffered by the same disease without any proper treatment all over the world. And, the situation becomes worst when these latent viruses reactivated in future to develop disease complications and cancer in human. The Present review is an attempt to describe Roseoloviruses causing diseases including cancer in human in the light (...) of recent researches done so far in the past. (shrink)

Chloroquines are 4-aminoquinoline-based drugs mainly used to treat malaria. At pharmacological concentrations, they have significant effects on tissue homeostasis, targeting diverse signaling pathways in mammalian cells. A key target pathway is autophagy, which regulates macromolecule turnover in the cell. In addition to affecting cellular metabolism and bioenergetic flow equilibrium, autophagy plays a pivotal role at the interface between inflammation and cancer progression. Chloroquines consequently have critical effects in tissue metabolic activity and importantly, in key functions of the immune system. (...) In this article, we will review the work addressing the role of chloroquines in the homeostasis of mammalian tissue, and the potential strengths and weaknesses concerning their use in cancer therapy. (shrink)

I argue that ductal carcinoma in situ (DCIS), the tumor most commonly diagnosed by breast mammography, cannot be confidently classified as cancer, that is, as pathological. This is because there may not be dysfunction present in DCIS—as I argue based on its high prevalence and the small amount of risk it conveys—and thus DCIS may not count as a disease by dysfunction-requiring approaches, such as Boorse’s biostatistical theory and Wakefield’s harmful dysfunction account. Patients should decide about treatment for DCIS (...) based on the risks it poses and the risks and benefits of treatment, not on its disease status. (shrink)