Response Prediction in Lung SBRT with Artificial Intelligence.

OBJECTIVE Lung cancer is the leading cause of cancer-related death worldwide. Although the majority of patients have locally advanced or metastatic disease at diagnosis, the incidence of early-stage non-small-cell lung cancer is expected to increase due to the wider use of thoracic CT scans. Primary tumor control and distant metastasis rates in early-stage lung cancer are similar for stereotactic body radiation therapy (SBRT) and surgery. Overall survival (OS) is lower for SBRT compared to surgery. Although some studies provide guidance on which cases will have a good response to SBRT, there is still no standard guideline. SBRT results are not the same in cases at the same stage or with the same metastatic burden. It is thought that there may be other parameters other than stage or tumor burden that affect the response. It is aimed to predict the response to SBRT with artificial intelligence in early-stage lung cancer, recurrent lung cancer, and lung metastases. METHODS Between September 2016 and April 2021, 137 cases and 148 lesions in which SBRT was applied by Eşkişehir Osmangazi University Faculty of Medicine Radiation Oncology Department were evaluated. To create a balanced data set, Synthetic Minority Oversampling Technique technique was used and 200 lesions were evaluated. Logistic Regression (LR), multilayer perceptron Classifier, Extreme Gradient Boosting, Support Vector Classifier, Random Forest Classifier, and Gaussian Naive Bayes algorithms were used. The data sets are divided into 85% training and 15% prediction sets. Models were created using the training set and validated using the prediction set. RESULTS Complete response was obtained in 41 tumors out of 148 tumors. The median OS after SBRT is 18 (2-61) months, and progression-free survival is 16 (0-61) months. Important variables are tumor diameter, NLR, presence of biopsy at diagnosis, tumor location and type, diagnosis, and histopathology. LR algorithm was determined as the best estimating algorithm with 80% accuracy (Confidence Interval, CI: 0.65-0.94, ROC AUC: 0.60), 66% sensitive and 90% specificity. CONCLUSION In order to use the current algorithm in clinical practice, it is necessary to increase the diversity of data and the number of patients by sharing data between centers. [ABSTRACT FROM AUTHOR]