BackgroundTo investigate the effect of machine learning methods on predicting the Overall Survival (OS) for non-small cell lung cancer based on radiomics features analysis.MethodsA total of 339 radiomic features were extracted from the segmented tumor volumes of pretreatment computed tomography (CT) images. These radiomic features quantify the tumor phenotypic characteristics on the medical images using tumor shape and size, the intensity statistics and the textures. The performance of 5 feature selection methods and 8 machine learning methods were investigated for OS prediction. The predicted performance was evaluated with concordance index between predicted and true OS for the non-small cell lung cancer patients. The survival curves were evaluated by the Kaplan-Meier algorithm and compared by the log-rank tests.ResultsThe gradient boosting linear models based on Cox’s partial likelihood method using the concordance index feature selection method obtained the best performance (Concordance Index: 0.68, 95% Confidence Interval: 0.62~ 0.74).ConclusionsThe preliminary results demonstrated that certain machine learning and radiomics analysis method could predict OS of non-small cell lung cancer accuracy.Electronic supplementary materialThe online version of this article (10.1186/s13014-018-1140-9) contains supplementary material, which is available to authorized users.

Background The dynamic tracking of tumors with radiation beams in radiation therapy requires the prediction of real-time target locations prior to beam delivery, as treatment involving radiation beams and gating tracking results in time latency. Objective In this study, a deep learning model that was based on a temporal convolutional neural network was developed to predict internal target locations by using multiple external markers. Methods Respiratory signals from 69 treatment fractions of 21 patients with cancer who were treated with the CyberKnife Synchrony device (Accuray Incorporated) were used to train and test the model. The reported model’s performance was evaluated by comparing the model to a long short-term memory model in terms of the root mean square errors (RMSEs) of real and predicted respiratory signals. The effect of the number of external markers was also investigated. Results The average RMSEs of predicted (ahead time=400 ms) respiratory motion in the superior-inferior, anterior-posterior, and left-right directions and in 3D space were 0.49 mm, 0.28 mm, 0.25 mm, and 0.67 mm, respectively. Conclusions The experiment results demonstrated that the temporal convolutional neural network–based respiratory prediction model could predict respiratory signals with submillimeter accuracy.

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BACKGROUND Dynamic tracking of tumor with radiation beam in radiation therapy requires prediction of real-time target location ahead of beam delivery as the treatment with beam or gating tracking brings in time latency. OBJECTIVE A deep learning model based on a temporal convolutional neural network (TCN) using multiple external makers was developed to predict internal target location through multiple external markers in this study. METHODS The respiratory signals from 69 treatment fractions of 21 cancer patients treated with the Cyberknife Synchrony device were used to train and test the model. The reported model’s performance was evaluated through comparing with a long short term memory model in terms of root-mean-square-error (RMSE) between real and predicted respiratory signals. Besides, the effect of external marker number was also investigated. RESULTS The average RMSEs (mm) for 480-ms ahead of prediction using TCN model in the superior–inferior (SI), anterior–posterior (AP) and left–right (LR) and radial directions were 0.49, 0.28, 0.25 and 0.67, respectively. CONCLUSIONS The experiment results demonstrated that the TCN respiratory prediction model could predict the respiratory signals with sub-millimeter accuracy.