Treatment Patterns and Survival of Elderly Patients With Breast Cancer Brain Metastases

Narrative Review of Multidisciplinary Management of Central Nervous Involvement in Patients with HER2-Positive Metastatic Breast Cancer: Focus on Elderly Patients

The tumor biology of human epidermal growth factor receptor 2 (HER2)-positive breast cancer (BC) promotes the development of central nervous system (CNS) metastases, with 25% of patients with HER2-positive BC developing CNS metastases. Furthermore, the incidence of HER2-positive BC brain metastases has increased in the last decades, likely because of the improved survival with targeted therapies and better detection methods. Brain metastases are detrimental to quality of life and survival and represent a challenging clinical problem, particularly in elderly women, who comprise a substantial proportion of patients diagnosed with BC and often have comorbidities or an age-related decline in organ function. Treatment options for patients with BC brain metastases include surgical resection, whole-brain radiation therapy, stereotactic radiosurgery, chemotherapy, and targeted agents. Ideally, local and systemic treatment decisions should be made by a multidisciplinary team, with input from several specialties, based on an individualized prognostic classification. In elderly patients with BC, additional age-associated conditions, such as geriatric syndromes or comorbidities, and the physiologic changes associated with aging, may impact their ability to tolerate cancer therapy and should be considered in the treatment decision-making process. This review describes the treatment options for elderly patients with HER2-positive BC and brain metastases, focusing on the importance of multidisciplinary management, the different points of view from the distinct disciplines, and the role of oncogeriatric and palliative care in this vulnerable patient group.

Brain Metastases in Elderly Patients-The Role of Surgery in the Context of Systemic Treatment

In patients with brain metastases (BM), advanced age is considered a negative prognostic factor. To address the potential reasons for that, we assessed 807 patients who had undergone BM resection; 315 patients aged at least 65 years (group A) were compared with 492 younger patients (group B). We analyzed the impact of the pre- and postoperative Karnofsky performance status (KPS), postoperative treatment structure and post-treatment survival. BM resection significantly improved KPS scores in both groups (p = 0.0001). Median survival after BM resection differed significantly between the groups (A: 5.81 vs. B: 8.12 months; p = 0.0015). In both groups, patients who received postoperative systemic treatment showed significantly longer overall survival (p = 0.00001). However, elderly patients less frequently received systemic treatment (p = 0.0001) and the subgroup of elderly patients receiving such therapies had a significantly higher postsurgical KPS score (p = 0.0007). In all patients receiving systemic treatment, age was no longer a negative prognostic factor. Resection of BM improves the functional status of elderly patients, thus enhancing the likeliness to receive systemic treatment, which, in turn, leads to longer overall survival. In the context of such a treatment structure, age alone is no longer a prognostic factor for survival.

Modeling and comparing data mining algorithms for prediction of recurrence of breast cancer

Breast cancer is the most common invasive cancer and the second leading cause of cancer death in women. and regrettably, this rate is increasing every year. One of the aspects of all cancers, including breast cancer, is the recurrence of the disease, which causes painful consequences to the patients. Moreover, the practical application of data mining in the field of breast cancer can help to provide some necessary information and knowledge required by physicians for accurate prediction of breast cancer recurrence and better decision-making. The main objective of this study is to compare different data mining algorithms to select the most accurate model for predicting breast cancer recurrence. This study is cross-sectional and data gathering of this research performed from June 2018 to June 2019 from the official statistics of Ministry of Health and Medical Education and the Iran Cancer Research Center for patients with breast cancer who had been followed for a minimum of 5 years from February 2014 to April 2019, including 5471 independent records. After initial pre-processing in dataset and variables, seven new and conventional data mining algorithms have been applied that each one represents one kind of data mining approach. Results show that the C5.0 algorithm possibly could be a helpful tool for the prediction of breast cancer recurrence at the stage of distant recurrence and nonrecurrence, especially in the first to third years. also, LN involvement rate, Her2 value, Tumor size, free or closed tumor margin were found to be the most important features in our dataset to predict breast cancer recurrence.