| Autor: |
Yiren Wang, Huaiwen Zhang, Huan Wang, Yiheng Hu, Zhongjian Wen, Hairui Deng, Delong Huang, Li Xiang, Yun Zheng, Lu Yang, Lei Su, Yunfei Li, Fang Liu, Peng Wang, Shengmin Guo, Haowen Pang, Ping Zhou |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
BMC Cancer, Vol 24, Iss 1, Pp 1-15 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
1471-2407 |
| DOI: |
10.1186/s12885-024-13235-0 |
| Popis: |
Abstract Objective This study aimed to develop and validate a predictive model for assessing the efficacy of neoadjuvant chemotherapy (NACT) in nasopharyngeal carcinoma (NPC) by integrating radiomics and pathomics features using a particle swarm optimization-supported support vector machine (PSO-SVM). Methods A retrospective multi-center study was conducted, which included 389 NPC patients who received NACT from three institutions. Radiomics features were extracted from magnetic resonance imaging scans, while pathomics features were derived from histopathological images. A total of 2,667 radiomics features and 254 pathomics features were initially extracted. Feature selection involved intra-class correlation coefficient evaluation, Mann-Whitney U test, Spearman correlation analysis, and least absolute shrinkage and selection operator regression. The PSO-SVM model was constructed and validated using 10-fold cross-validation on the training set and further evaluated using an external validation set. Model performance was assessed using the area under the curve (AUC) of the receiver operating characteristic curve, calibration curves, and decision curve analysis. Results Eight significant predictive features (five radiomics and three pathomics) were identified. The PSO-SVM radiopathomics model achieved superior performance compared to models based solely on radiomics or pathomics features. The AUCs for the PSO-SVM radiopathomics model were 0.917 (95% CI: 0.887–0.948) in internal validation and 0.814 (95% CI: 0.742–0.887) in external validation. Calibration curves demonstrated good agreement between predicted probabilities and actual outcomes. Decision curve analysis showed that the PSO-SVM radiopathomics model provided higher clinical net benefit over a wider range of risk thresholds compared to other models. Conclusion The PSO-SVM radiopathomics model effectively integrates radiomics and pathomics features, offering enhanced predictive accuracy and clinical utility for assessing NACT efficacy in NPC. The multi-center approach and robust validation underscore its potential for personalized treatment planning, supporting improved clinical decision-making for NPC patients. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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