Study of North Jordanian women's knowledge of breast cancer causes and medical imaging screening advantages

Background: among both industrialized and developing nations, breast cancer is the most prevalent type of cancer among women. The purpose of this research project is to evaluate adult females in northern Jordan's level of awareness regarding breast cancer risk factors and potential causes. A link between certain variables and their comprehension of breast cancer risk factors was established, enabling us to take appropriate measures to increase women's awareness. Methods: We conducted a cross-sectional study using a self-administered questionnaire to assess the awareness of breast cancer risk factors, treatment beliefs, and screening practices among adult females in north Jordan. There was a scoring system used. IBM SPSS Statistics (Version 20.0) was used to conduct all statistical analyses. Results: Only 18.11% of participants (n = 201) reported having little understanding of breast cancer risk factors; 36.31% of participants (n = 403) reported having fair knowledge; and 45.58% of participants (n = 506) reported having a good grasp of breast cancer risk factors. Conclusion: The study's findings show that North Jordanian women are well informed about breast cancer and its potential causes. To improve females' knowledge of breast cancer, however, we advocate for the need for ongoing medical education programs

Expired EBT3 Films’ Sensitivity for the Measurement of X-ray and UV Radiation: An Optical Analysis

The aim of this study is to compare the optical responses of external beam therapy 3 (EBT3) films exposed to X-rays and solar ultraviolet rays (SUV-rays), as a dose control technique in the clinical sector for various radiation types, energies, and absorbed doses up to 4 Gy. In this study, EBT3 films with three different expiry dates were prepared and cut into pieces of size 2 by 2 cm2. The first group was exposed to 90 kVp X-rays, while the second group was exposed to the SUV-rays at noon. The analysis was performed using a visible Jaz spectrometer and an EPSON Perfection V370 Photo scanner to obtain the absorbance, the net reflective optical density (ROD) and the red-green-blue (RGB) values of the samples. The results have shown that spectroscopic measurements of the exposed expired EBT3 films with these radiation sources are able to produce primary peaks and secondary peaks at λ = 641.74 nm and λ = 585.98 nm for X-rays, and at λ = 637.93 nm and λ = 584.45 nm for SUV-rays, respectively. According to these findings, compared to 2021 films that expired shortly before the trial start date; 2018 films responded better to the absorbed dose than 2016 films when exposed to both X-ray and SUV-rays. In terms of energy dependence, the expired EBT3 2018 had the largest net ROD value. Using L*a*b* indices extracted from the RGB data, and despite that EBT3 films have expiry dates according to the manufacturer; all the films exhibited a substantial colour change, indicating that these films are still usable for clinical and research purposes

Optical Response of Expired EBT3 Film for Absorbed Dose Measurement in X-ray and Electron Beam Range

The purpose of this study was to investigate the optical response of an expired External Beam Therapy (EBT3) film, which expired in 2018, using X-rays and electron beam doses. The film’s optical responses were evaluated for its usability in measuring different radiation sources, energy, and absorbed doses ranging up to 5 Gy. Pieces of the expired EBT3 film were irradiated with 90 kVp, 6 MV X-ray photons, and 6 MeV electron beam. The analysis was performed using the Jaz visible spectrometer and EPSON Perfection V370 Photo scanner to obtain the absorbance and the net relative optical density (ROD) of the film samples respectively. The results showed that spectroscopic measurements of the exposed expired EBT3 films under these radiation sources were able to produce primary secondary peaks at λ = 633.52 nm and λ = 582.3 nm respectively. The best wavelength subsets that presented the best MLR regression fitting for all experiments were 541.48 nm, 561.11 nm, and 600.28 nm. While, for the 6 MV photon and the 6 MeV electron beam they were 600.28 nm, 650.79 nm and 654.10 nm. In case of the irradiation with the 6 MV photon and the 6 MeV electron beam, expired EBT3 film showed no significant differences, which made it suitable for dosimetry in various sources of radiation. The individual calibration of radiation dose produces very high measurement accuracy with coefficient of determination, R2 above 0.99 and root mean square of error, RMSE of 0.038 Gy, 0.113 Gy, and 0.115 Gy for films irradiated with 90 kVp X-rays, 6 MV photon beam, and 6 MeV electron beam respectively. Hence, from the results, the expired EBT3 film used in this study showed promising usability of expired EBT3 films beyond their prescribed expiry dates

Early-Stage Detection of Ovarian Cancer Based on Clinical Data Using Machine Learning Approaches

One of the common types of cancer for women is ovarian cancer. Still, at present, there are no drug therapies that can properly cure this deadly disease. However, early-stage detection could boost the life expectancy of the patients. The main aim of this work is to apply machine learning models along with statistical methods to the clinical data obtained from 349 patient individuals to conduct predictive analytics for early diagnosis. In statistical analysis, Student’s t-test as well as log fold changes of two groups are used to find the significant blood biomarkers. Furthermore, a set of machine learning models including Random Forest (RF), Support Vector Machine (SVM), Decision Tree (DT), Extreme Gradient Boosting Machine (XGBoost), Logistic Regression (LR), Gradient Boosting Machine (GBM) and Light Gradient Boosting Machine (LGBM) are used to build classification models to stratify benign-vs.-malignant ovarian cancer patients. Both of the analysis techniques recognized that the serumsamples carbohydrate antigen 125, carbohydrate antigen 19-9, carcinoembryonic antigen and human epididymis protein 4 are the top-most significant biomarkers as well as neutrophil ratio, thrombocytocrit, hematocrit blood samples, alanine aminotransferase, calcium, indirect bilirubin, uric acid, natriumas as general chemistry tests. Moreover, the results from predictive analysis suggest that the machine learning models can classify malignant patients from benign patients with accuracy as good as 91%. Since generally, early-stage detection is not available, machine learning detection could play a significant role in cancer diagnosis