Using machine learning to predict radiation pneumonitis in patients with stage I non-small cell lung cancer treated with stereotactic body radiation therapy

Abstract

Purpose/Objectives To develop a patient-specific “big data” clinical decision tool to predict pneumonitis in Stage I non-small cell lung cancer (NSCLC) patients after stereotactic body radiation therapy (SBRT). Materials/Methods 61 features were recorded for 201 consecutive patients with Stage I NSCLC treated with SBRT, in whom 8 (4.0%) developed radiation pneumonitis (RP). Pneumonitis thresholds were found for each feature individually using decision stumps. The performance of three different algorithms (Decision Trees, Random Forests, RUSBoost) was evaluated. Learning curves were developed and the training error analyzed and compared to the testing error in order to evaluate the factors needed to obtain a cross-validated error smaller than 0.1. These included the addition of new features, increasing the complexity of the algorithm and enlarging the sample size and number of events. Results In the univariate analysis, the most important feature selected was the diffusion capacity of the lung for carbon monoxide (DLCO adj%). On multivariate analysis, the three most important features selected were the dose to 15 cc of the heart, dose to 4 cc of the trachea or bronchus, and race. Higher accuracy could be achieved if the RUSBoost algorithm was used with regularization. To predict radiation pneumonitis within an error smaller than 10%, we estimate that a sample size of 800 patients is required. Conclusion Clinically relevant thresholds that put patients at risk of developing radiation pneumonitis were determined in a cohort of 201 stage I NSCLC patients treated with SBRT. The consistency of these thresholds can provide radiation oncologists with an estimate of their reliability and may inform treatment planning and patient counseling. The accuracy of the classification is limited by the number of patients in the study and not by the features gathered or the complexity of the algorithm.

IMRT QA using machine learning: A multi‐institutional validation

“…Machine learning is a subfield of data science that focuses on designing algorithms that can learn from and make predictions on data. Machine learning applications in radiotherapy have emerged increasingly in recent years, with applications including predictive modeling of treatment outcome in radiation oncology,1, 2, 3, 4, 5, 6, 7 treatment optimization,8, 9, 10, 11 error detection and prevention,12, 13, 14, 15 and treatment machine quality assurance (QA) 16, 17, 18, 19. These machine learning techniques have provided physicians and physicists information for more effective and accurate treatment delivery as well as the ability to achieve personalized treatment.…”

Exploratory analysis using machine learning to predict for chest wall pain in patients with stage I non‐small‐cell lung cancer treated with stereotactic body radiation therapy

“…A cutoff of >0.75 was used for the generalization score, meaning a similar split of the data would result at least 75% of the time. The generalization score is used to characterize out‐of‐sample performance of the univariate dosimetric thresholds, and it quantifies how well these thresholds should perform for data that the algorithm has not encountered 26. This analysis was performed under the conditional assumption that the true distribution of patients satisfying the threshold is represented by the patients not developing CWS.…”

“…Machine learning has previously been used in radiation oncology for a variety of problems, from quality assurance to outcome prediction 20, 21, 22, 23, 24, 25, 26. In circumstances where the event being analyzed is relatively uncommon, machine learning algorithms are advantageous in magnifying events.…”

“…This is achieved by developing models that can learn from and make predictions of a given dataset. Examples include hierarchical clustering models which can iterate quickly through different features and cutoffs in order to identify potentially predictive factors based on how effectively events are separated from nonevents 26. The use of these computational algorithms to mine raw data can filter out noise and identify the pertinent factors when the number of events is smaller than the number of features.…”

Role of artificial intelligence in the care of patients with nonsmall cell lung cancer

“…Using this data, ML systems can be designed to automatically select everything from the optimal angle for the radiation beam to dose‐volume histogram predictions . Data can further be used to track the levels of radiation and toxicity, to aid in the development of clinical decision support tools and prevent radiation‐induced complications such as pneumonitis . Overall, the exploitation of this data can be used to create integrative predictive models that offer personalized radiation treatments with safety and efficiency …”