Emad Abd Al Rahman¹, Nur Intan Raihana Ruhaiyem^1,*, Majed Bouchahma², Kamarul Imran Musa³

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3007-3028, 2023, DOI:10.32604/iasc.2023.032580

Abstract This study offers a framework for a breast cancer computer-aided treatment prediction (CATP) system. The rising death rate among women due to breast cancer is a worldwide health concern that can only be addressed by early diagnosis and frequent screening. Mammography has been the most utilized breast imaging technique to date. Radiologists have begun to use computer-aided detection and diagnosis (CAD) systems to improve the accuracy of breast cancer diagnosis by minimizing human errors. Despite the progress of artificial intelligence (AI) in the medical field, this study indicates that systems that can anticipate a treatment plan once a patient has… More >

Fei Wang¹, Jihyun Ma², Nan Zhao³, Chi Lin^3,*, Haixing Jiang^1,*

Oncologie, Vol.24, No.4, pp. 865-895, 2022, DOI:10.32604/oncologie.2022.025447

Abstract Objective: The characteristics of clinical features and prognoses among patients with different racial backgrounds have not been clearly studied. We thus investigated the clinical characteristics and overall survival (OS) differences among Asian, White, and Black patients with pancreatic neuroendocrine tumors (pNETs). Materials and Methods: The Surveillance, Epidemiology, and End Results (SEER) database was queried to identify patients with pNETs between 1983 and 2015. We performed univariable (UVA) and multivariable logistic regression (MVA) to assess the association between variables and race category. A Kaplan-Meier (KM) plot was used to calculate the OS rates. The Cox proportional hazard regression was used to… More >