Predictive Modeling of Long-Term Survivors with Stage IV Breast Cancer Using the SEER-Medicare Dataset.

Importance: Treatment of women with stage IV breast cancer (BC) extends population-averaged survival by only a few months. Here, we develop a model for identifying individual circumstances where appropriate therapy will extend survival while minimizing adverse events.

Objective: Our goal is to develop high-confidence deep learning (DL) models to predict survival in individual stage IV breast cancer patients based on their unique circumstances generated by patient, cancer, treatment, and adverse event variables.

AI Survival Prediction Modeling: The Importance of Considering Treatments and Changes in Health Status over Time.

Background And Objectives: Deep learning (DL)-based models for predicting the survival of patients with local stages of breast cancer only use time-fixed covariates, i.e., patient and cancer data at the time of diagnosis.

Bone Marrow Stroma Co-cultivation Model of Breast Cancer Dormancy.

Breast cancer cells metastasize to the bone marrow before a primary tumor is detected. Most micrometastases die in this hostile microenvironment, but some survive and enter a state of dormancy and chemoresistance due to their close interaction with cells in the bone marrow hematopoietic niche. Over many years, some of the cells reawaken and result in metastatic disease that cannot be cured.

Correction: Wieder, R.; Adam, N. Racial Disparities in Breast Cancer Treatments and Adverse Events in the SEER-Medicare Data. 2023, , 4333.

[...

Temporal Association Rule Mining: Race-Based Patterns of Treatment-Adverse Events in Breast Cancer Patients Using SEER-Medicare Dataset.

Purpose: Disparities in the screening, treatment, and survival of African American (AA) patients with breast cancer extend to adverse events experienced with systemic therapy. However, data are limited and difficult to obtain. We addressed this challenge by applying temporal association rule (TAR) mining using the SEER-Medicare dataset for differences in the association of specific adverse events (AEs) and treatments (TRs) for breast cancer between AA and White women.