Volume 33, Issue 160 (September & October 2025)                   J Adv Med Biomed Res 2025, 33(160): 167-178 | Back to browse issues page

Ethics code: IR.UMSHA.REC.1403.773

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Vosoughi M, Keshtpour Amlashi Z, Hamidi O, Sedighi Pashaki A, Nikzad S. Random Survival Forest Model for Predicting Survival in Non-Metastatic Breast Cancer. J Adv Med Biomed Res 2025; 33 (160) :167-178
URL: http://journal.zums.ac.ir/article-1-7665-en.html
Random Survival Forest Model for Predicting Survival in Non-Metastatic Breast Cancer
Milad Vosoughi1 , Zahra Keshtpour Amlashi2 , Omid Hamidi3 , Abdolazim Sedighi Pashaki2 , Safoora Nikzad *4
1- Department of Medicine, Hamadan University of Medical Sciences, Hamadan, Iran
2- Cancer Research Center, Hamadan University of Medical Sciences, Hamadan, Iran
3- Department of Science, Hamedan University of Technology, Hamedan, Iran
4- Department of Medical Physics, Faculty of Medicine, Hamadan University of Medical Sciences, Hamadan, Iran , s.nikzad@umsha.ac.ir
Abstract:   (465 Views)

Background & Objective: Breast cancer is one of the most commonly diagnosed cancers in women worldwide and remains a major public health concern in both developed and developing countries. This study aimed to identify key prognostic factors influencing survival in patients with non-metastatic breast cancer. A predictive model was developed using the Random Survival Forest (RSF) method to enhance survival estimation and support clinical decision-making.
 Materials & Methods: In this retrospective cohort study, the medical records of 767 patients with non-metastatic breast cancer who were treated at the Mahdia Radiotherapy Center in Hamadan between 2006 and 2018 were reviewed. After excluding incomplete records, 442 patients remained for the final analysis. Demographic, clinical, and treatment-related data were extracted from the patients’ medical history. Both the Cox proportional hazards (PH) regression (Cox regression) model and the RSF model were applied to identify significant predictors of survival.
Results:  The mean age of the patients was 49.23 ± 10.85 years, and the mean survival time was 21.09 ± 2.27 months. One, three, and five-year survival rates were 98.8%, 97.5%, and 97.2%, respectively. Based on the RSF model, radiotherapy dose, recurrence, age, nodal stage (N), and overall stage were identified as the most influential predictors of survival. HER2 status, initial treatment approach, and surgical method were also included in the model. The RSF model achieved a C-index ranging from 0.63 to 0.73, outperforming the Cox regression model (C-index 0.54–0.66).
Conclusion:  The RSF model effectively identified key predictors of survival in non-metastatic breast cancer and may serve as a valuable tool for personalized clinical decision-making. These findings demonstrate the potential value of machine learning-based models in oncology research and patient management.

Keywords: Non-metastatic Breast Cancer, Survival Analysis, Cox Proportional Hazards (PH), Regression (Cox regression), Random Survival Forest (RSF)
     
Type of Study: Original Research Article | Subject: Epidemiologic Studies
Received: 2025/04/11 | Accepted: 2025/10/10 | Published: 2025/11/11
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