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Abstract The incidence of Alzheimer’s disease and related dementias (ADRD) and breast cancer increases with age. Despite being one of the most effective ways to diagnose breast cancer early, mammography in ADRD patients comes with an increased risk of treatment complications and false-positive results. Family caregivers are often involved in the decision-making process, and this study evaluates the relationship between dementia severity and caregiver preferences when making decisions about mammography with the patient alone, and with the patient and doctor. We included 181 caregivers from the Decisions about Cancer screening in Alzheimer’s Disease trial, which uses the Dementia Severity Rating Scale (DSRS) to assess dementia severity and a modified Control Preferences Scale (CPS) to assess each caregiver’s preferred decision-making approach. Multinomial logistic regression models evaluated the relationship between DSRS and CPS categories (active, passive, and collaborative), while controlling for the caregivers’ age, sex, race, education, and relationship to patient. Model 1 examined the caregivers’ preferred role with the patient, and it found a significant association between increased dementia severity and preference for a collaborative approach (p<0.001) or passive approach (p<0.05) compared to an active approach. Model 2 did not find a significant association between dementia severity and the caregivers’ preferred role when making decisions with the patient and doctor; however, those with increased age and education were more likely to prefer an active role. The association between dementia severity, caregiver characteristics, and decision-making preferences supports the need for approaches to support ADRD caregivers with medical decision making.

Breast cancer is one of the main causes of death among women worldwide. Early detection of this disease helps reduce the number of premature deaths. This research aims to design a method for identifying and diagnosing breast tumors based on ultrasound images. For this purpose, six techniques have been performed to detect and segment ultrasound images. Features of images are extracted using the fractal method. Moreover, k-nearest neighbor, support vector machine, decision tree, and Naïve Bayes classification techniques are used to classify images. Then, the convolutional neural network (CNN) architecture is designed to classify breast cancer based on ultrasound images directly. The presented model obtains the accuracy of the training set to 99.8%. Regarding the test results, this diagnosis validation is associated with 88.5% sensitivity. Based on the findings of this study, it can be concluded that the proposed high-potential CNN algorithm can be used to diagnose breast cancer from ultrasound images. The second presented CNN model can identify the original location of the tumor. The results show 92% of the images in the high-performance region with an AUC above 0.6. The proposed model can identify the tumor’s location and volume by morphological operations as a post-processing algorithm. These findings can also be used to monitor patients and prevent the growth of the infected area.

Breast cancer is the most common cancer in women worldwide, but there are also rarer types of breast neoplasms in clinical practice. One of these neoplasms is a phyllodes tumor. Due to the rare occurrence of phyllodes tumors and few studies of this pathology, there is today no information about the precise etiology and pathogenesis of this tumor. For the same reasons, it is very difficult to correctly and timely diagnose breast cancer, which requires both a highly qualified radiologist who first detects this disease in a patient and a pathologist who establishes a final morphological diagnosis. Existing studies, such as mammography and ultrasound, do not have reliable criteria for the diagnosis of phyllodes tumors and are unable to differentiate different histological types of these neoplasms, which further complicates the diagnosis of this pathology. Also, standards for the treatment of patients with this diagnosis have not been fully approved. The paper describes a clinical case of successful surgical treatment for a malignant phyllodes tumor of the left breast in a 47-year-old patient.

Breast cancer is one of the leading causes of mortality globally, but early diagnosis and treatment can increase the cancer survival rate. In this context, thermography is a suitable approach to help early diagnosis due to the temperature difference between cancerous tissues and healthy neighboring tissues. This work proposes an ensemble method for selecting models and features by combining a Genetic Algorithm (GA) and the Support Vector Machine (SVM) classifier to diagnose breast cancer. Our evaluation demonstrates that the approach presents a significant contribution to the early diagnosis of breast cancer, presenting results with 94.79% Area Under the Receiver Operating Characteristic Curve and 97.18% of Accuracy.

Mammography is a significant screening test for early detection of breast cancer, which increases the patient’s chances of complete recovery. In this paper, a clustering method is presented for the detection of breast cancer tumor locations and areas. To implement the clustering method, we used the growth region approach. This method detects similar pixels nearby. To find the best initial point for detection, it is essential to remove human interaction in clustering. Therefore, in this paper, the FCM-GA algorithm is used to find the best point for starting growth. Their results are compared with the manual selection method and Gaussian Mixture Model method for verification. The classification is performed to diagnose breast cancer type in two primary datasets of MIAS and BI-RADS using features of GLCM and probabilistic neural network (PNN). Results of clustering show that the presented FCM-GA method outperforms other methods. Moreover, the accuracy of the clustering method for FCM-GA is 94%, as the best approach used in this paper. Furthermore, the result shows that the PNN methods have high accuracy and sensitivity with the MIAS dataset.

Mammograms, commonly used to diagnose breast cancer, are difficult medical images to interpret. Computer aided diagnosis (CAD) systems have the potential to assist radiologists by locating suspicious regions in the mammograms for more detailed examination. One approach is for CAD systems to detect microcalcification. This approach uses classification of texture features and has applications for the detection of breast cancer as well as other abnormalties in medical images. The Support Vector Machine (SVM) has been shown to be effective in texture classification. SVM performs well in high dimensional space such as the space spanned by texture images. The kernel function in SVM algorithm implicitly performs feature extraction. Since SVM is basically suited for two-class classification problems, it is potentially a good choice for several different medical imaging which deal with abnormality detection. The main contribution of this thesis in the sense of texture classification is proposing a new texture classification algorithm by effectively employing external features within SVM kernel and introducing a new feature extraction method for texture classification.

Mammograms, commonly used to diagnose breast cancer, are difficult medical images to interpret. Computer aided diagnosis (CAD) systems have the potential to assist radiologists by locating suspicious regions in the mammograms for more detailed examination. One approach is for CAD systems to detect microcalcification. This approach uses classification of texture features and has applications for the detection of breast cancer as well as other abnormalties in medical images. The Support Vector Machine (SVM) has been shown to be effective in texture classification. SVM performs well in high dimensional space such as the space spanned by texture images. The kernel function in SVM algorithm implicitly performs feature extraction. Since SVM is basically suited for two-class classification problems, it is potentially a good choice for several different medical imaging which deal with abnormality detection. The main contribution of this thesis in the sense of texture classification is proposing a new texture classification algorithm by effectively employing external features within SVM kernel and introducing a new feature extraction method for texture classification.

Despite decades of research, diagnostic tests with specificity and accuracy for early breast cancer are yet unavailable. Major problems associated with poor diagnosis are either due to incompetency of reported biomarkers or small volume of patients under study. Moreover, heterogeneity of the disease further complicates the struggle of identifying effective biomarkers. Therefore, to improve the survival rate, look for new, sensitive and specific biomarkers for early breast cancer diagnosis is need of hour. In this study, we have reviewed recently reported serum biomarkers and categorized them based on their biomolecular nature such as protein, ctDNA, epigenetics regulation and miRNA. Potential role of these available biomarkers in early diagnosis of breast cancer has also been discussed. Based on the facts obtained from literature review, it is revealed that using any individual biomolecule as a biomarker is not sufficient to diagnose breast cancer at early stages rather it is suggested that a panel of proteins or miRNAs would offer better sensitivity and specificity. Whereas, unavailability of a potential ctDNA and epigenetics regulation candidate for diagnostic purpose is and suggest the use of more sophisticated techniques to unwound these regulations in serum especially at early stages of breast cancer.