PurposeTreatment plans are used for patients under radiotherapy in clinics. Before execution, these plans are checked for safety and quality by human experts. A few of them were identified with flaws and needed further improvement. To automate this checking process, an unsupervised learning method based on an autoencoder was proposed.MethodsFirst, features were extracted from the treatment plan by human experts. Then, these features were assembled and used for model learning. After network optimization, a reconstruction error between the predicted and target signals was obtained. Finally, the questionable plans were identified based on the value of the reconstruction error. A large value of the reconstruction error indicates a longer distance from the standard distribution of normal plans. A total of 576 treatment plans for breast cancer patients were used for the test. Among them, 19 were questionable plans identified by human experts. To evaluate the performance of the autoencoder, it was compared with four baseline detection algorithms, namely, local outlier factor (LOF), hierarchical density-based spatial clustering of applications with noise (HDBSCAN), one-class support vector machine (OC-SVM), and principal component analysis (PCA).ResultsThe results showed that the autoencoder achieved the best performance than the other four baseline algorithms. The AUC value of the autoencoder was 0.9985, while the second one was 0.9535 (LOF). While maintaining 100% recall, the average accuracy and precision of the results by the autoencoder were 0.9658 and 0.5143, respectively. While maintaining 100% recall, the average accuracy and precision of the results by LOF were 0.8090 and 0.1472, respectively.ConclusionThe autoencoder can effectively identify questionable plans from a large group of normal plans. There is no need to label the data and prepare the training data for model learning. The autoencoder provides an effective way to carry out an automatic plan checking in radiotherapy.

Background Magnetic resonance imaging (MRI) is currently used for online target monitoring and plan adaptation in modern image-guided radiotherapy. However, storing a large amount of data accumulated during patient treatment becomes an issue. In this study, the feasibility to compress MRI images accumulated in MR-guided radiotherapy using video encoders was investigated. Methods Two sorting algorithms were employed to reorder the slices in multiple MRI sets for the input sequence of video encoder. Three cropping algorithms were used to auto-segment regions of interest for separate data storage. Four video encoders, motion-JPEG (M-JPEG), MPEG-4 (MP4), Advanced Video Coding (AVC or H.264) and High Efficiency Video Coding (HEVC or H.265) were investigated. The compression performance of video encoders was evaluated by compression ratio and time, while the restoration accuracy of video encoders was evaluated by mean square error (MSE), peak signal-to-noise ratio (PSNR), and video quality matrix (VQM). The performances of all combinations of video encoders, sorting methods, and cropping algorithms were investigated and their effects were statistically analyzed. Results The compression ratios of MP4, H.264 and H.265 with both sorting methods were improved by 26% and 5%, 42% and 27%, 72% and 43%, respectively, comparing to those of M-JPEG. The slice-prioritized sorting method showed a higher compression ratio than that of the location-prioritized sorting method for MP4 (P=0.00000), H.264 (P=0.00012) and H.265 (P=0.00000), respectively. The compression ratios of H.265 were improved significantly with the applications of morphology algorithm (P=0.01890 and P=0.00530), flood-fill algorithm (P=0.00510 and P=0.00020) and level-set algorithm (P=0.02800 and P=0.00830) for both sorting methods. Among the four video encoders, H.265 showed the best compression ratio and restoration accuracy. Conclusions The compression ratio and restoration accuracy of video encoders using inter-frame coding (MP4, H.264 and H.265) were higher than that of video encoders using intra-frame coding (M-JPEG). It is feasible to implement video encoders using inter-frame coding for high-performance MRI data storage in MR-guided radiotherapy.