Predictive performance of different NTCP techniques for radiation-induced esophagitis in NSCLC patients receiving proton radiotherapy

Abstract

This study aimed to compare the predictive performance of different modeling methods in developing normal tissue complication probability (NTCP) models for predicting radiation-induced esophagitis (RE) in non–small cell lung cancer (NSCLC) patients receiving proton radiotherapy. The dataset was composed of 328 NSCLC patients receiving passive-scattering proton therapy and 41.6% of the patients experienced ≥ grade 2 RE. Five modeling methods were used to build NTCP models: standard Lyman–Kutcher–Burman (sLKB), generalized LKB (gLKB), multivariable logistic regression using two variable selection procedures-stepwise forward selection (Stepwise-MLR), and least absolute shrinkage and selection operator (LASSO-MLR), and support vector machines (SVM). Predictive performance was internally validated by a bootstrap approach for each modeling method. The overall performance, discriminative ability, and calibration were assessed using the Negelkerke R2, area under the receiver operator curve (AUC), and Hosmer–Lemeshow test, respectively. The LASSO-MLR model showed the best discriminative ability with an AUC value of 0.799 (95% confidence interval (CI): 0.763–0.854), and the best overall performance with a Negelkerke R2 value of 0.332 (95% CI: 0.266–0.486). Both of the optimism-corrected Negelkerke R2 values of the SVM and sLKB models were 0.301. The optimism-corrected AUC of the gLKB model (0.796) was higher than that of the SVM model (0.784). The sLKB model had the smallest optimism in the model variation and discriminative ability. In the context of classification and probability estimation for predicting the NTCP for radiation-induced esophagitis, the MLR model developed with LASSO provided the best predictive results. The simplest LKB modeling had similar or even better predictive performance than the most complex SVM modeling, and it was least likely to overfit the training data. The advanced machine learning approach might have limited applicability in clinical settings with a relatively small amount of data.

Machine learning in non-small cell lung cancer radiotherapy: A bibliometric analysis

“…A recently published study compared the predictive performance of different modeling methods in radiation-induced esophagitis. The researcher reported that the least absolute shrinkage and selection operator (LASSO-MLR) performed best in radiation-induced esophagitis prediction (AUC=0.79) ( 35 ). Furthermore, differentiation between radiation pneumonitis and immune checkpoint inhibitor (ICI)-induced pneumonitis is also very important when radiotherapy is combined with ICI treatment.…”

Evaluation of an automated clinical decision system with deep learning dose prediction and NTCP model for prostate cancer proton therapy

“…The far more interesting impact on patient selection is from the uncertainty of the NTCP model itself. While there is no consensus on the optimal NTCP model, we selected the LKB model because it is still the most commonly used NTCP model in radiotherapy, and its non-inferiority to the complex NTCP model has been demonstrated in our previous study (Chen et al 2022). We are planning to investigate the impact of the combined dose and model uncertainty on model-based patient selection in our future study.…”

SAPT: a synchrotron-based proton therapy facility in Shanghai

“…The real treatment plan is more complicated than that in the IEC62667 cases. Dose calculation [26], the beam model [27], and different techniques [28] are carefully considered by the clinical team of Shanghai Ruijin Hospital. Figure 13 shows a typical measurement result.…”