Survival Analysis of Cancer Patients Based on Chemotherapy Response Data

Hanshal, Othman; Kzar, Marwah; Sagar, Bushra

doi:10.25673/123767

Proceedings of International Conference on Applied Innovation in IT · 2026/03/31 · Vol. 14 · Issue 1 · pp. 1483–1490

Survival Analysis of Cancer Patients Based on Chemotherapy Response Data

Othman A. Hanshal, Marwah Sami Kzar, Bushra Jabbar Abdul-Kareem and Anil Kumar Sagar

📄 Download PDF DOI: 10.25673/123767

Abstract

Background: Chemotherapy is still a key part of cancer treatment, but survival rates vary a lot and are mostly based on how well the treatment works. It is important to figure out how important response is for predicting outcomes and to use modern computational methods to improve patient stratification. To assess the influence of chemotherapy response on overall survival (OS) and progression-free survival (PFS) utilizing both classical and advanced survival models. A retrospective cohort study involving 420 cancer patients was conducted. According to RECIST v1.1, the response was classified as a complete response (CR), a partial response (PR), stable disease (SD), or progressive disease (PD). We used Kaplan-Meier and Cox regression to figure out how long people would live, and we used Accelerated Failure Time (AFT) and Random Survival Forest (RSF) models to check the results for sensitivity and robustness. CR and PR were independently correlated with markedly extended overall survival (OS) and progression-free survival (PFS) in contrast to progressive disease (PD). RSF showed better predictive accuracy (C-index=0.82) than Cox (C-index=0.76). Chemotherapy response is a strong sign of how long someone will live. The incorporation of sophisticated computational models improves forecasting and facilitates clinical application.

Keywords

Survival Analysis Chemotherapy Response Cox Regression Kaplan-Meier Accelerated Failure Time Random Survival Forest Oncology Prognosis.

References

A. Romero, J. A. García-Sáenz, M. Fuentes-Ferrer, J. L. Garcia-Asenjo, V. Furió, J. M. Román, and T. Caldés, “Correlation between response to neoadjuvant chemotherapy and survival in locally advanced breast cancer patients,” Annals of Oncology, vol. 24, no. 3, pp. 655-661, 2013.
H. Bonnefoi, S. Litière, M. Piccart, G. MacGrogan, P. Fumoleau, E. Brain, and D. A. Cameron, “Pathological complete response after neoadjuvant chemotherapy is an independent predictive factor irrespective of simplified breast cancer intrinsic subtypes: a landmark and two-step approach analyses from the EORTC 10994/BIG 1-00 phase III trial,” Annals of Oncology, vol. 25, no. 6, pp. 1128-1136, 2014.
Y. Zhao, X. Dong, R. Li, X. Ma, J. Song, Y. Li, and D. Zhang, “Evaluation of the pathological response and prognosis following neoadjuvant chemotherapy in molecular subtypes of breast cancer,” OncoTargets and Therapy, pp. 1511-1521, 2015.
C. Lu, K. Bera, X. Wang, P. Prasanna, J. Xu, A. Janowczyk, and A. Madabhushi, “A prognostic model for overall survival of patients with early-stage non-small cell lung cancer: a multicentre, retrospective study,” The Lancet Digital Health, vol. 2, no. 11, pp. e594-e606, 2020.
J. Liu, L. Tan, H. Zhang, C. Ma, G. Qin, S. Huang, and G. Wang, “Long-term survival outcomes of neoadjuvant chemotherapy in stage II-III HR+/HER2- breast cancer,” Scientific Reports, vol. 15, no. 1, p. 30201, 2025.
J. Wang and L. Zhao and Y. Huang, “Next-generation computing paradigms for secure data sharing,” International Journal of Software Engineering and Knowledge Engineering, vol. 35, no. 2, pp. 225-240, 2025, [Online]. Available: https://doi.org/10.1142/S0219649225500406.
V. Mehta and S. Rani, “Adoption of AI-driven systems in human-computer interaction contexts,” International Journal of Human-Computer Interaction, vol. 41, no. 6, pp. 701-718, 2025, [Online]. Available: https://doi.org/10.1080/10447318.2025.2480826.
Y. Jin et al., “Comparing random survival forests and Cox regression for survival prediction among breast cancer patients not achieving pathological complete response,” 2025.
M. Antonini, A. Mattar, T. M. Pereira, L. L. Oliveira, M. D. Teixeira, A. G. Amorim, and L. H. Gebrim, “Pathologic complete response and breast cancer survival post-neoadjuvant chemotherapy: a systematic review and meta-analysis of real-world data,” Heliyon, vol. 11, no. 10, 2025.
J. S. Deutsch, A. Cimino-Mathews, E. Thompson, M. Provencio, P. M. Forde, J. Spicer, and J. M. Taube, “Association between pathologic response and survival after neoadjuvant therapy in lung cancer,” Nature Medicine, vol. 30, no. 1, pp. 218-228, 2024.
G. Yang, H. Yue, X. Zhang, C. Zeng, L. Tan, and X. Zhang, “Comparison of neoadjuvant chemotherapy or chemoradiotherapy plus immunotherapy for locally resectable esophageal squamous cell carcinoma,” Frontiers in Immunology, vol. 15, p. 1336798, 2024.
A. M. Fowler, D. A. Mankoff, and B. N. Joe, “Imaging neoadjuvant therapy response in breast cancer,” Radiology, vol. 285, no. 2, pp. 358-375, 2017.
S. M. Noman, Y. M. Fadel, M. T. Henedak, N. A. Attia, M. Essam, S. Elmaasarawii, and W. Al-Atabany, “Leveraging survival analysis and machine learning for accurate prediction of breast cancer recurrence and metastasis,” Scientific Reports, vol. 15, no. 1, p. 3728, 2025.
D. Y. Oh, A. R. He, S. Qin, L. T. Chen, T. Okusaka, J. W. Kim, and J. W. Valle, “Durvalumab plus chemotherapy in advanced biliary tract cancer: 3-year overall survival update from the phase III TOPAZ-1 study,” Journal of Hepatology, 2025.
R. Sharma, P. Gupta, and A. Singh, “Human-computer interaction frameworks for secure digital adoption,” International Journal of Human-Computer Interaction, vol. 41, no. 7, pp. 845-862, 2025, [Online]. Available: https://doi.org/10.1080/10447318.2025.2495843.