The utilization of functional magnetic resonance imaging (fMRI) is critical in the preoperative planning phase of brain tumor surgery because it allows for a delicate balance between maximizing tumor resection and maintaining brain function. A decade of fMRI development was examined in this study, with a particular emphasis on its use in diagnosing and assessing the efficacy of brain cancer treatments. We examined the foundational principles, practical implementations, and verification of fMRI via direct brain stimulation, with particular emphasis on its capacity to detect cerebral regions affected by tumors that are eloquent in nature. Recently, fMRI has undergone significant progress, allowing for its integration into clinical workflows to facilitate precise mapping of brain functions. This extensive analysis encompasses the scrutiny of resting-state fMRI (Rs-fMRI) as a method of capturing functional connectivity, thereby providing significant insights into the management of patients with brain tumors. Methodological advancements, clinical applicability, and future orientations of fMRI are highlighted in this review, which emphasizes the substantial influence of the technique on neurosurgical planning and patient outcomes.

Background: Globally, breast cancer (BC) is the most commonly detected neoplasm in women. Breast self-examination (BSE) is an effective screening technique that enables women to learn about the composition of their breasts and assist in the early identification of any potential breast abnormalities. Objective: This study aimed to assess the degree of BSE knowledge and attention among Jordanian females who are students or professionals in medical disciplines. Methods: Participants’ knowledge about BSE and related issues was assessed using a self-administered questionnaire. The study invites participation from all females aged 18 and above, through both an online and in-person survey. The study extended invitations to female university students in Jordan across academic levels I, II, III, IV, V, and VI. A scoring system was employed, and the statistical analyses were performed using IBM SPSS Statistics (Version 20.0). Results: The study had 946 female participants, with 98.41% of them being single. Low BSE practice was reported among 90.49% of the participants (n = 856) and this demonstrated a weak understanding of BC disease, including its possible risks, methods of detection, diagnosis, treatment, signs and symptoms, as well as knowledge about mammography and other related information. Only 27.27% (n = 258) of participants practice BSE once a month and on a regular basis. Conclusion: BC is considered the most prevalent malignant condition and the second largest cause of cancer-related deaths for women in Jordan. Screening strategies are essential for promptly identifying breast cancer and reducing the associated illness and death rates. It is recommended that women commence performing BSE starting at the age of 18. Furthermore, it is essential to incorporate a learning outcome in the cancer chapters that are directly relevant to the subject of BC and emphasize the significance of BSE for students pursuing a career in the medical area.

As an alternative to task-based functional magnetic resonance imaging (T-fMRI), resting-state functional magnetic resonance imaging (Rs-fMRI) is suggested for preoperative mapping of patients with brain tumours, with an emphasis on treatment guidance and neurodegeneration prediction. A systematic review was conducted of 18 recent studies involving 1035 patients with brain tumours and Rs-fMRI protocols. This was accomplished by searching the electronic databases PubMed, Scopus, and Web of Science. For clinical benefit, we compared Rs-fMRI to standard T-fMRI and intraoperative direct cortical stimulation (DCS). The results of Rs-fMRI and T-fMRI were compared and their correlation with intraoperative DCS results was examined through a systematic review. Our exhaustive investigation demonstrated that Rs-fMRI is a dependable and sensitive preoperative mapping technique that detects neural networks in the brain with precision and identifies crucial functional regions in agreement with intraoperative DCS. Rs-fMRI comes in handy, especially in situations where T-fMRI proves to be difficult because of patient-specific factors. Additionally, our exhaustive investigation demonstrated that Rs-fMRI is a valuable tool in the preoperative screening and evaluation of brain tumours. Furthermore, its capability to assess brain function, forecast surgical results, and enhance decision-making may render it applicable in the clinical management of brain tumours.

Ovarian cancer is the most common cause of death among gynecological malignancies, due to its multiple subtypes that pose significant diagnostic challenges. In general, improving classification accuracy leads to earlier diagnosis and more successful treatment. This study presents a new methodology based on the DenseNet121 model architecture with the integration of the GAM Attention module, which combines ductal and spatial attention mechanisms. This module aims to improve the model’s ability to focus on the most relevant regions and features in ovarian MRI images, thereby improving classification accuracy. Additionally, a Bayesian optimization algorithm is used to find the best set of hyperparameters to train the proposed model. The performance of the proposed model was tested on the KAUH-OCM ovarian cancer MRI dataset obtained from King Abdullah University Hospital (KAUH). The results demonstrated that the proposed model outperformed previously trained models (DenseNet121, VGG16, VGG19, ResNet50, and InceptionV3) with an accuracy of 91.84%, a specificity of 96.76%, and an F1 score of 91.78%. Furthermore, the proposed model was also tested on the KAUH-OCCTD ovarian cancer CT dataset and the polycystic ovary syndrome (PCOS) ultrasound dataset, achieving high accuracy rates of 97.27% and 100%, respectively. These results demonstrate the potential of the proposed model to revolutionize ovarian cancer diagnosis with high accuracy. These findings may guide gynecologists in providing better treatment strategies for these patients.

Aim: This study aimed to evaluate and compare the diagnostic performance of computed tomography (CT) and magnetic resonance imaging (MRI) in the detection of endometrial cancer, using a deep learning approach. Methods: Two endometrial image sets were obtained from King Abdullah University Hospital: the KAUH Endometrial Cancer MRI dataset (KAUH-ECMD) and the KAUH Endometrial Cancer CT dataset (KAUH-ECCTD), collected from 300 patients aged between 22 and 85. A hybrid deep learning model combining ResNet50 and Vision Transformer (ViT) was applied to classify the images into three categories: benign, malignant, and normal. Results: The proposed ViTNet model achieved an accuracy of 90.24% in detecting endometrial cancer using MRI images and 86.99% using CT images. The MRI-based approach demonstrated superior diagnostic performance in detecting endometrial cancer compared to CT-based classification. Conclusion: Deep learning models utilizing MRI and CT images demonstrate high accuracy in classifying endometrial cases. MRI in particular shows promise in supporting diagnostic workflows. Future work will focus on further validating the model's ability to evaluate depth of invasion and other prognostic features.