Construction of an oligometastatic prediction model for nasopharyngeal carcinoma patients based on pathomics features and dynamic multi-swarm particle swarm optimization support vector machine.

OBJECTIVE

This study aimed to develop a risk prediction model for post-treatment oligometastasis in nasopharyngeal carcinoma (NPC) by integrating pathomics features and an improved Support vector machine (SVM) algorithm, offering precise early decision support.

METHODS

This study retrospectively included 462 NPC patients, without or with oligometastasis defined by ESTRO/EORTC criteria. Whole-slide images were scanned, and three representative H&E-stained regions were selected for pathomics feature extraction via CellProfiler software. Features screened by intraclass correlation coefficient, Mann-Whitney U test, Spearman correlation, minimum redundancy maximum relevance, and Least absolute shrinkage and selection operator regression. Based on these screened features, three models were built: Dynamic Multi-Swarm Particle Swarm Optimization SVM (DMS-PSO-SVM), Particle Swarm Optimization SVM (PSO-SVM), and a standard SVM. Model training and hyperparameter tuning were conducted on the training set (n=369), followed by evaluation on a validation set (n=93).

RESULTS

6 pathomics features were screened as important features. DMS-PSO-SVM yielded superior performance, with training-set AUC=0.880 and validation-set AUC=0.866, consistently outperforming both PSO-SVM (AUC=0.721) and standard SVM (AUC=0.718). Calibration curves showed good agreement for DMS-PSO-SVM (P>0.05) but indicated miscalibration in the standard SVM (P<0.05). Decision curve analysis further demonstrated that DMS-PSO-SVM offered higher net benefit across a wide range of risk thresholds.

CONCLUSION

Incorporating pathomics and DMS-PSO optimization significantly improved NPC oligometastasis prediction. This model showed high discriminative ability, calibration, and clinical utility, suggesting that pathomics and machine learning-based strategies could aid early recognition of high-risk patients and inform individualized treatment approaches. A demo of the DMS-PSO-SVM modeling algorithm code used in this study can be found on Github (https://github.com/Edward-E-S-Wang/DMS-PSO-SVM).