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Cervical cancer is a common disease that can be carried by women. It is the 3rd leading cause of female cancer in Malaysia and the 4th most common type of cancer for women globally. Referring to the HPV Information Centre in 2018, it is estimated that 1682 women are diagnosed and 944 die from having cervical cells. In 2018, 569, 847 of the 18,078,957 cases were categorised as cervical cancer, which is 3.2%. With the advancement in science and technology, cervical cancer can be detected at an early stage by conducting a Pap smear test. This test will filter abnormal cervical cells and detect precancerous changes in cervical cells based on the colour and shape properties of their nuclei and cytoplasm. The problem is that performing the procedure manually can be time-consuming and cause inconsistencies and errors even further because the cervical cell itself does not show an obvious difference in texture and colour from normal cells. This paper will focus on colour contrast enhancement of the cervical cell using a few methods such as Contrast Stretching (CS), CLAHE, Histogram Equalization (HE), Image Adjustment, and Multi Scale Retinex (MSR). From this research, the cervical cell colour contrast can be enhanced to a better level and the detection of cervical cells can happen faster and more accurately. Hence, the errors in detecting can be reduced again so they can be treated soon. [ABSTRACT FROM AUTHOR]

This paper presents a multi-objective model for scheduling Intensity Modulated Radiotherapy Treatment (IMRT) in patients with lung cancer, based on Genetic Algorithms (GA). The suggested approach is used to minimise the fitness function defined as the mean squared error by optimizing the weight between layers and biases. When compared to existing algorithms, the suggested GA-based Firefly Algorithm (FA) technique was shown to have the lowest mean squared error of 0.0014. The GA schedules, using real data acquired at the Cancer Center in collaboration, are effective. The suggested GA-based FA is discussed and assessment results are displayed. [ABSTRACT FROM AUTHOR]

Patient-reported outcome measures (PROMs) have been increasingly integrated into patient-facing technologies to engage and empower patients in cancer self-management at home. However, researchers and developers face several challenges in selecting the best-suited PROMs for patient-facing technologies, due to the complex nature of the disease, the multitude of PROMs with high psychometric quality, and the lack of clear standards for PROM utilization. In this paper, we have discussed these challenges, illustrated by breast cancer instruments. [ABSTRACT FROM AUTHOR]

Prediction of mortality risk for post-operative lung resection patients with scores may recommend the physicians to change the course of treatment to increase the life expectancy. Numerous post-operative scores such as biochemical, demographic and spirometric variables were used to predict the mortality risk and treatment post-surgery. Such large number of variables misleads the treatment as well as increase the complexity of decision making processes. In this paper, the prediction accuracy and complexity of support vector machine classification model before and after feature ranking was studied. Four feature ranking algorithms such as oneR, gain ratio, information gain and symmetrical uncertainty were used to rank the feature subsets. The classification accuracy and complexity before the feature selection was 84.89% and 78,264 kernels evaluated, respectively. However, after feature selection, the accuracy improved to 85.14% and complexity significantly reduced to ~40% due to the reduction in number of kernels evaluated. These results highlighted the importance of feature selection to predict the life expectancy of post-operative lung cancer patients. [ABSTRACT FROM AUTHOR]

Radioactive Carbon-14 and Fluorine-18 isotopes were bombarded with alpha particles within an energy range of (10–100MeV) the electronic and nuclear stopping power was calculated using SRIM2013 program. After that, we used the Matlab program and the Igor program to compute the total stopping force in non-calculated energy values in the SRIM program, and the work of interpolated to calculate the total stopping power of the radioactive Carbon-14 and Fluorine-18 isotope used in the treatment of cancer. [ABSTRACT FROM AUTHOR]

Breast cancer is the most frequent cancer in women, although it may affect either gender. It is responsible for an unfathomable amount of deaths globally. In a particularly low-resource developing country, there is a lack of knowledge and facilities, particularly in rural regions, as well as a high percentage of breast cancer discovered in the late stages. Early identification of breast cancer, on the other hand, can help raise the chances of survival. With a rising number of patients, manual examination of medical photographs has become tiresome, time consuming, and impractical. With advances in machine learning, it is now feasible to develop an automatic and efficient Computer Aided Diagnosis (CAD) system and make the entire process of identifying a malignant tumor better resources efficient and time saving through correct use. [ABSTRACT FROM AUTHOR]

Complementary and alternative medicine (CAM) is either used to complement standard oncological therapies to help in alleviating patients' symptoms and side effects from chemotherapy, thus improving their quality of life (QOL), including the patient's emotional, social, and physical wellbeing, since pain is the most frequent distress amongst oncological patients, or to directly fight cancer as an alternative therapy to conventional treatments. Common practices of many complementary and alternative treatments emphasise on good nutrition and prophylaxis. Thus, CAM acts by boosting the natural immunity of the body to induce necrosis of carcinogen cells; nevertheless, further scientific research is required to identify and quantify the improvements in oncological patients' health due to the complementary and alternative therapies used. Furthermore, it is also important to discern the effects due to CAM itself, as opposed to those that occur because of other concurrent treatments, and to understand their safety profile, also considering its interaction with conventional oncological therapies. In this study, evidence-based information is analysed and discussed on CAM used for cancer, such as Cannabidiol (CBD), Graviola (soursop), Origanum Vulgare, Zamzam Water (ZW), Traditional Chinese Medicine (TCM), and Paris Polyphylla. Findings suggest that various complementary medical therapies can help to improve oncological patients' wellbeing, when undergone along with standard oncological treatments, e.g., CBD proved beneficial for improving a variety of symptoms, such as pain and nausea. Similarly, alternative therapies, such as TCM, are approved to be used in China to treat various tumours. Furthermore, the side effects of conventional treatments and dissatisfaction were identified amongst the push factors to use CAM, whilst pull factors included the positive aspects associated with CAM, such as fewer side effects, stronger immunity, and an enhanced QOL. [ABSTRACT FROM AUTHOR]

Dermatoglypics is one of the most unique subjects which helps in the investigation to identify individuals in medical and forensic sciences. Fingerprint patterns are developed during the third and fourth month of the growth of the foetus and do not change with time. These patterns represent genetic makeup and thus help to identify or predict diseases. Various diseases such as down‟s syndrome, turner syndrome, patau syndrome, etc. are studied using dermatoglyphics. A cross-sectional study was conducted on 48 individuals, in which 24 samples were collected from patients suffering from Cancer and other 24 control samples were collected from healthy individuals whose age group is between 25 to 60 years old, having no sign of symptoms and no family history of cancer. Fingerprints from individuals (both control and cases) were collected using the Ink Method. These patterns are divided into 4 types i.e., arches, loops, whorls and no patterns (as there is loss of fingerprint pattern in cancer patients). Study and analysis of fingerprint patterns in controls and cases helps us to understand their relation and how dermatoglyphics in cancer patients are affected. This study also helps for early prediction or detection, which in-turn helps for early therapy and thus helpful to save individuals' lives. This study also helps us to reduce risk factors and it is a simple, low cost and non-invasive method for screening the cancer. [ABSTRACT FROM AUTHOR]

Breast cancer has been one of the deadliest diseases and becomes the leading cause of cancer deaths in women worldwide. It becomes an issue to any countries as the number of victims developing breast cancer is increasing, and the survivability rate of cancerous women is still ambiguous. In this study, a hybrid classification model is developed for predicting breast cancer survivability. The hybrid classification model is a combination of three single classification models, namely Naive Bayes, J48 Decision Tree and RBF Network. Stacking method has been used to combine those models. This hybrid model has been tested using Breast Cancer Wisconsin dataset which gives result 96.14% of accuracy, 96.30% of precision, 96.10% of recall and 96.20% of f-measure. These results are outperformed each of the single classifiers. [ABSTRACT FROM AUTHOR]

Typical univariate measures of variation in chemotherapy protocols fail to capture and describe the full multi-dimensional complexity of treatment adjustments in real-world data. In this preliminary work, we propose novel visualisations of observed treatment events, as well as treatment-as-delivered relative to initial prescriptions, as a means of gaining insights into complex patterns of treatment variation in cancer patients. Simple clustering techniques were also used to confirm the utility of these visualisations and our ability to correlate observed variations with historical events. [ABSTRACT FROM AUTHOR]

"Infobuttons" spearheaded electronic health records (EHR) based decision support by offering automated knowledge resources to physicians. However, how such a mechanism could be leveraged to provide optimal resources to patients remains unanswered. Informatics approaches are expected to utilize more relevant information beyond EHR, such as patient-reported outcomes, to support clinical decisions. This pilot study is intended to explore how patient-reported outcomes version of the common terminology criteria for adverse events (PRO-CTCAE) in EHR can be incorporated and how to recommend tailored content to cancer patients via automated feedback. [ABSTRACT FROM AUTHOR]

Heavy metal exposure is regarded as the most serious threat to human health and the biological system, Increases and decreases in them cause a great deal of damage, including toxicity. Recently, an increase in cancer cases has been observed in Babil Governorate which is a major motivation to measure the concentration of heavy metals in human serum of blood. In this study, 15 samples were taken from lung cancer patients compared with 15 healthy peoples volunteers. The level of Pb found to be range (0.1211 - 0.1684 ppm, Mean±SD=0.14001±0.0144 ppm in healthy group) but was found (0.0895 - 0.1526 ppm, Mean±SD=0.1266±0.0193 ppm in lung cancer patients). The value of Cd found to be range (0.0460 - 0.0667 ppm Mean±SD=0.05922±0.0047 ppm in healthy group) but was found (0.0563 - 0.0861 ppm Mean±SD=0.0674±0.0070 ppm in patients). The value of Cu found to be range (0.4092 - 0.7415 ppm, Mean±SD=0.5362± 0.1023 ppm in healthy group) The value of Cu found (0.5403 - 1.4488 ppm, Mean±SD = 0.7685±0.2350 ppm in patients). These study concluded the level of Pb in patients and healthy group were found higher than acceptable limits 0.05 ppm, but was found the level of Copper and Cadmium less than acceptable limits at 1.59 and 0.12 ppm in patients and healthy. [ABSTRACT FROM AUTHOR]

The aim of this study was to measure AST, ALT, GGT, TP, LDH, and albumin in patients with breast cancer to assess ceruloplasmin, Cu, SA, and liver. Materials and Procedures: The research was completed.Materials and Methods: The research was conducted at Kirkuk Teaching Hospital. The study included 64 women who had BC. Their ages ranged from 30 to 56 years old. The control group consisted of 64 matched normal individuals. Patients had lower total protein and albumin concentrations than the control group. The findings suggest that serum sialic acid levels may be useful in the diagnosis of disese. [ABSTRACT FROM AUTHOR]

Breast cancer refers to the unpredictable growth and propagation of cells that originate in the breast tissue. In the present world breast cancer is the most common type of cancer in women. Research has been carried out by scientists from all over the world over a decade of time. As a result, reasons like, increasing age, puberty at an early age or menopause at older age, obesity, giving birth at an older age, non pregnancy and personal and family genetic history of health issues are known to be vital causing factors of breast cancer. This article focuses on finding the attributes which have high impact in causing the breast cancer by the concept of attribute reduction as an application of nano topology. [ABSTRACT FROM AUTHOR]