Skin cancer mortality rates continue to rise, and survival analysis is increasingly needed to
understand who is at risk and what interventions improve outcomes. However, current …

The Proportional Hazards Model (PHM) under a Condition-Based Maintenance (CBM)
policy is used by asset-intensive industries to predict failure rate, reliability function, and …

Epithelial ovarian cancer (EOC) presents considerable difficulties in prognostication and
treatment strategy development. Bevacizumab, an anti-angiogenic medication, has …

Survival analysis is a family of statistical methods for analyzing event occurrence times. We
adopt a mixed-integer optimization approach to estimation of sparse Cox proportional …

Rationale and Objectives This study aimed to develop a deep learning (DL) prognostic
model to evaluate the significance of intra-and peritumoral radiomics in predicting outcomes …

Background The time until an event happens is the outcome variable of interest in the
statistical data analysis method known as survival analysis. Some researchers have created …

Discovering molecular biomarkers for predicting patient survival outcomes is an essential
step toward improving prognosis and therapeutic decision-making in the treatment of severe …

Cancer is one of the leading factors in global mortality,, with head and neck squamous cell
carcinoma (HNSCC) being the seventh most common cancer worldwide [2, 3]. Survival …

Colorectal cancer and cancers in the head and neck region still pose a big problem in
medicine and in the healthcare sector. In 2021 alone 11 121 deaths could be accounted for …

Survival analysis is a family of statistical methods for analyzing event occurrence times. In
this paper, we address the mixed-integer optimization approach to sparse estimation of the …