Accelerated hazard prediction based on age time-scale for women diagnosed with breast cancer using a deep learning method

• Published in: BMC medical informatics and decision making 2024-10, Vol.24 (1), p.314-13, Article 314
• Main Authors: Ramezani, Zahra, Charati, Jamshid Yazdani, Alizadeh-Navaei, Reza, Eslamijouybari, Mohammad
• Format: Article
• Language: English
• Subjects: Accelerated hazard model; Adult; Age; Age composition; Age Factors; Aged; Breast cancer; Breast feeding; Breast Neoplasms; Categories; Clinical trials; Deep Learning; Demographic variables; Demographics; Demography; Diagnosis; Disease management; Female; Flexibility; Genetics; Health aspects; Humans; Hysterectomy; Interval censoring; Machine learning; Medical prognosis; Medical science; Menstruation; Middle Aged; Patients; Prediction; Predictions; Prognosis; Proportional Hazards Models; Risk factors; Survival; Survival analysis; Time dependence; Time Factors; Tobacco; Variables; Womens health
• ISSN: 1472-6947; 1472-6947
• DOI: 10.1186/s12911-024-02725-7

Breast cancer is the most common cancer in women. Previous studies have investigated estimating and predicting the proportional hazard rates and survival in breast cancer. This study deals with predicting accelerated hazards (AH) rate based on age categories in breast cancer patients using deep learning methods. The AH has a time-dependent structure whose rate changes according to time and variable effects. We have collected data related to 1225 female patients with breast cancer at the Mandarin University of Medical Sciences. The patients' demographic and clinical characteristics including family history, age, history of tobacco use, hysterectomy, first menstruation age, gravida, number of breastfeeding, disease grade, marital status, and survival status have been recorded. Initially, we dealt with predicting three age groups of patients: ≤ 40, 41-60, and ≥ 61 years. Then, the prediction of accelerated risk value based on age categories for each breast cancer patient through deep learning and the importance of variables using LightGBM is discussed. Improving clinical management and treatment of breast cancer requires advanced methods such as time-dependent AH calculation. When the behavioral effect is assumed as a time scale change between hazard functions, the AH model is more appropriate for randomized clinical trials. The study results demonstrate the proper performance of the proposed model for predicting AH by age categories based on breast cancer patients' demographic and clinical characteristics.