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Prediction of lung dose‐volume parameters of the patients with esophageal cancer undergoing radiotherapy based on artificial neural network

Fahui Li

Published 2025 in Journal of Applied Clinical Medical Physics

ABSTRACT

Abstract Objective Considering the complex and nonlinear relationship between characteristic parameters of tumors (location and morphology) and lung dose‐volume parameters of patients with esophageal cancer (EPC) undergoing intensity‐modulated radiation therapy (IMRT), the model identification method of artificial neural network (ANN) was adopted to build the model to predict lung dose‐volume parameters under different characteristic parameters of tumors. The goal is to enhance the efficiency and quality of radiotherapy planning and reduce the risk of radiation‐induced lung injury (RILI) by providing pre‐planning dose predictions. Methods 1). In the previous research work, a retrospective analysis was done on 103 cases of patients with EPC who were treated by radiotherapy, in which a linear regression model was employed and had proved the association between tumor morphology [including tumor relative length (L) and “tumor axial cross‐sectional area” (S)] and lung dose‐volume parameters (including V 5, V 10, V 20 and V 30 of lungs). 2). Building on earlier research, this study uses curve regression analysis to further explore the effect of tumor relative position (P) on lung dose‐volume parameters of patients with EPC undergoing IMRT. 3). A backpropagation (BP) neural network model was constructed with P, L, and S as inputs and lung dose‐volume parameters as outputs to establish their quantitative relationship. 4). Genetic algorithm (GA) was combined with BP neural network to optimize the initial weights and thresholds of BP neural network and avoid the model from falling into local optima, so as to improve the performance of BP neural network model. Results 1). Quadratic regression models were applied to analyze the relationship of P with V 5 and V 10 of the lung, yielding adjusted R 2 values of 0.177 and 0.081, respectively, with statistically significant regression coefficients (p < 0.01). The results suggest that as P increases, V 5 and V 10 initially increase, then decrease. Linear regression models were applied to analyze the relationship of P with V 20 and V 30 of the lung, yielding adjusted R 2 values of 0.06 and 0.072, respectively, with significant regression coefficients (p < 0.01). The results show that both V 20 and V 30 decrease as P increases. 2). The finding of this study indicates that the modeling method of BP neural network can realize the prediction of lung dose‐volume parameters under different characteristic parameters of tumors. The GA‐BP model showed slight improvements in prediction accuracy (PA) and error index (EI) compared to the BP model alone. For both models, prediction accuracy is highest for V 5, followed by V 10 and V 20, with V 30 being the least accurate. Notably, both the BP and GA‐BP neural networks achieve outstanding recognition accuracy for V 5, with EI and PA values of 0.121 and 88.61% for BP, and 0.113 and 89.00% for GA‐BP, respectively. 3). The re‐optimization of four test cases with under‐predicted whole‐lung V 5 and V 10 values resulted in substantial improvements over the original plans. Conclusion: The ANN model developed in this study can effectively predict lung dose‐volume parameters for patients with EPC undergoing IMRT. It provides pre‐planning guidance that enhances the efficiency and quality of radiotherapy planning and reducing the risk of RILI.

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