Machine Learning-Based Survival Prediction in Cancer Patients Treated with Nimotuzumab: A Real-World Data Mining Approach
Authors
-
Carmen Viada
Center of Molecular Immunology/Havana/Cuba
https://orcid.org/0000-0002-1604-3545
-
Mayra Ramos-Suzarte
Center of Molecular Immunology/Havana/Cuba
https://orcid.org/0000-0002-9058-3224
-
Lázara García
Center of Molecular Immunology/Havana/Cuba
https://orcid.org/0000-0003-4289-4041
- Aliuska Frias Center of Molecular Immunology/Havana/Cuba
- Yanela Santiestaban Center of Molecular Immunology/Havana/Cuba
- Yuliannis Santiesteban Center of Molecular Immunology/Havana/Cuba
- Mabel Alvarez Center of Molecular Immunology/Havana/Cuba
- Marta Fors Universidad de Las Américas, Quito, Ecuador
DOI:
https://doi.org/10.70099/BJ/2025.02.02.7Keywords:
Nimotuzumab, EGFR-targeted therapy, Real-world data, Survival prediction, Machine learning in oncology, Decision tree analysis, Personalized cancer therapy, Head and neck squamous cell carcinoma, Glioma, Esophageal cancer, Biomarker-driven treatment, Clinical trial data mining, AI-based patient stratification, EGFR monoclonal antibody comparison, Clinical trial data integrationAbstract
Nimotuzumab, a humanized monoclonal antibody targeting the epidermal growth factor receptor (EGFR), has demonstrated clinical benefit in various epithelial tumors. However, selecting the right patient populations remains a challenge, particularly when considering real-world treatment scenarios and diverse tumor types. This study aimed to identify clinical and demographic subgroups of patients with the highest survival benefit from Nimotuzumab, using decision tree models applied to integrated clinical trial data.A total of 1,871 patients diagnosed with head and neck, brain, and esophageal cancers were included from 19 studies (phases I to IV and observational). Survival-related variables were analyzed using decision tree algorithms, a machine learning method suited for revealing complex, non-linear patterns among clinical features. The models stratified patients based on baseline characteristics and treatment outcomes.
The results revealed distinct predictive profiles for each cancer type. In head and neck cancer, survival was best predicted by race, disease status, use of radiotherapy, performance status, and toxic habits. In brain tumors, the most influential variables were age, performance status, and histological subtype. In esophageal cancer, survival was mainly determined by sex, histological diagnosis, and race. These variables consistently emerged as key decision nodes in the trained models.
The novelty of this study lies in its use of a machine learning approach across multiple tumor types treated with the same targeted therapy, Nimotuzumab. It is also among the first to apply such methodology to real-world data from low- and middle-income countries. By generating interpretable decision trees, the study offers a practical tool for identifying patient subgroups that are most likely to benefit from EGFR-targeted treatment, thereby supporting more efficient, personalized cancer care.
These findings reinforce the value of integrating data mining and artificial intelligence techniques into oncology to enhance treatment selection and improve patient outcomes.
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