Yun Liu,1 Lai Xu,2 Yakun Fang,2 Chao Yan3 

1Department of Nephrology, Qilu Hospital (Qingdao), Cheeloo College of Medicine, Shandong University, Qingdao, 266035, People’s Republic of China; 2Department of Obstetrics, Qingdao Municipal Hospital, Qingdao, 266071, People’s Republic of China; 3Department of Radiation Oncology, Qilu Hospital (Qingdao), Cheeloo College of Medicine, Shandong University, Qingdao, 266035, People’s Republic of China

Correspondence: Chao Yan, Department of Emergency, Qilu Hospital (Qingdao), Cheeloo College of Medicine, Shandong University, No. 758 Hefei Road, Shibei District, Qingdao City, Shandong Province, 266035, People’s Republic of China, Tel +86 0532 88735360, Email yc028979@qlyyqd.com

Background: In view of the differences in the clinical efficacy of 125I radioactive particle brachytherapy for head and neck tumors, this study aims to systematically analyze the key factors affecting its efficacy, and build a reliable prediction model to provide a scientific basis for clinical precise evaluation and personalized treatment plan formulation.
Methods: Retrospective analysis of 174 patients (2020– 2024) divided into training (n=122) and validation (n=52) sets. Efficacy was assessed using RECIST criteria. Lasso Logistic regression identified independent factors, and a nomogram model was constructed and evaluated.
Results: The study confirmed that patients’ age, tumor stage, tumor diameter, particle implantation dose and serum tumor marker level were independent factors affecting the clinical efficacy (P< 0.05). The nomogram prediction model has excellent performance, and the c-index values in the training set and the validation set are 0.867 and 0.725, respectively, showing good discrimination ability; The results of calibration curve showed that the predicted value was in good agreement with the actual value, and the average absolute errors of the two groups were 0.114 and 0.133, respectively; In Hosmer lemeshow test, the training set χ2=7.422 (P=0.491), the validation set χ2=12.086 (P=0.147), suggesting that the model fitting effect is ideal; The area under the ROC curve in the training set and the validation set was 0.860 (95% CI:0.767– 0.953) and 0.750 (95% CI:0.501– 0.999), respectively, which showed high sensitivity and specificity.
Conclusion: The model effectively predicts 125I brachytherapy outcomes, aiding clinical evaluation and supporting precision treatment for head and neck tumors.

Keywords: brachytherapy, head and neck tumors, influencing factor, 125I radioactive particles, lasso-logistic regression, prediction models