Machine learning model for predicting the magnitude of tumor movement based on clinical information and CT-based radiomics features.

[PURPOSE] To accurately predicting and classifying the magnitude of tumor movement in lung cancer patients, a machine learning model based on clinical information and CT-based radiomics feature with artificial neural network (ANN) was developed.

[METHODS AND MATERIALS] CT images of lung tumor of 110 patients were enrolled, comprising 165 tumor sites. Manually extracted 18 clinical features, while 1218 radiomics features were calculated based on CT images. Superior-inferior (SI) direction tumor amplitude values were computed from in-house 4DCT. The three models for predicting tumor motion and classifying active or no motion management strategy based on ANN are as follows: i) C-model which based on clinical and image features; ii) R-model which based on radiomic features; iii) hybrid model that combines two previous models. were utilized for assessing the predictive accuracy of three models. For assessing classification performance, the area under the curve (AUC), specificity and sensitivity were calculated.

[RESULTS] In the test dataset, the C-model showed an MAE of 2.95 mm and an R value of 0.51, while the R-model demonstrated an MAE of 1.34 mm and an R value of 0.88. The hybrid model exhibited the best performance with an MAE of 1.23 mm and an R value of 0.91. Sensitivity and AUC were 0.79 and 0.95, 1.00 and 0.99, 1.00 and 1.00 for the C-model, R-model and hybrid model.

[CONCLUSION] Utilizing machine learning techniques based on clinical features combined with CT image information enables accurate prediction of lung cancer tumor motion range as well as effective classification of motion management strategies.