Applying Artificial Neural Networks to Develop a Decision Support Tool for Tis–4N0M0 Non–Small-Cell Lung Cancer Treated With Stereotactic Body Radiotherapy

PURPOSE: Clear evidence indicating whether surgery or stereotactic body radiation therapy (SBRT) is best for non–small-cell lung cancer (NSCLC) is lacking. SBRT has many advantages. We used artificial neural networks (NNs) to predict treatment outcomes for patients with NSCLC receiving SBRT, aiming to aid in decision making. PATIENTS AND METHODS: Among consecutive patients receiving SBRT between 2005 and 2019 in our institution, we retrospectively identified those with Tis–T4N0M0 NSCLC. We constructed two NNs for prediction of overall survival (OS) and cancer progression in the first 5 years after SBRT, which were tested using an internal and an external test data set. We performed risk group stratification, wherein 5-year OS and cancer progression were stratified into three groups. RESULTS: In total, 692 patients in our institution and 100 patients randomly chosen in the external institution were enrolled. The NNs resulted in concordance indexes for OS of 0.76 (95% CI, 0.73 to 0.79), 0.68 (95% CI, 0.60 to 0.75), and 0.69 (95% CI, 0.61 to 0.76) and area under the curve for cancer progression of 0.80 (95% CI, 0.75 to 0.84), 0.72 (95% CI, 0.60 to 0.83), and 0.70 (95% CI, 0.57 to 0.81) in the training, internal test, and external test data sets, respectively. The survival and cumulative incidence curves were significantly stratified. NNs selected low-risk cancer progression groups of 5.6%, 6.9%, and 7.0% in the training, internal test, and external test data sets, respectively, suggesting that 48% of patients with peripheral Tis–4N0M0 NSCLC can be at low-risk for cancer progression. CONCLUSION: Predictions of SBRT outcomes using NNs were useful for Tis–4N0M0 NSCLC. Our results are anticipated to open new avenues for NN predictions and provide decision-making guidance for patients and physicians.