Machine learning and modeling: Data, validation, communication challenges

Naqa, Issam El; Ruan, Dan; Valdés, Gilmer; Dekker, André; McNutt, Todd; Ge, Yaorong; Wu, Qiuwen; Oh, Jung Hun; Thor, Maria; Smith, W. P.; Rao, Arvind; Fuller, Clifton D.; Xiao, Ying; Manion, Frank J.; Schipper, Matthew J.; Mayo, Charles S.; Moran, Jean M.; Haken, R. Ten

doi:10.1002/mp.12811

Cited by 76 publications

(68 citation statements)

References 95 publications

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“…In contrast, ML-based NN algorithms (ANN, CNN, and deep learning NN) are associated with high predictive accuracy and commonly outperformed other ML model in many applications. 36,37 The ANN model accuracy was assessed with MSE having a value of 0.0001 mm 2 (RMSE = 0.0097 mm) as shown in Fig. 5 for individual MLC leaf position.…”

Section: Discussionmentioning

confidence: 99%

Prediction of the individual multileaf collimator positional deviations during dynamic IMRT delivery priori with artificial neural network

2020

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Purposes: Multileaf collimator (MLC) positional accuracy during dynamic intensity modulation radiotherapy (IMRT) delivery is crucial for safe and accurate patient treatment. The deviations of individual leaf positions from its intended positions can lead to errors in the dose delivered to the patient and hence may adversely affect the treatment outcome. In this study, we propose a state-ofthe-art machine learning (ML) method based on an artificial neural network (ANN) for accurately predicting the MLC leaf positional deviations during the dynamic IMRT treatment delivery priori using log file data. Methods: Data of ten patients treated with sliding window dynamic IMRT delivery were retrospectively retrieved from a single-institution database. The patients' plans were redelivered with no patient on the couch using a Varian linear accelerator equipped with a Millennium 120 HD MLC system. Then the machine recorded log files data, a total of over 400 files containing 360 800 control points, were collected. A total of 14 parameters were extracted from the planning data in the log files such as leaf planned positions, dose fraction, leaf velocity, leaf moving status, leaf gap, and others. Next, we developed a feed-forward ANN architecture mapping the input parameters with the output to predict the MLC leaf positional deviations during the delivery priori. The proposed model was trained on 70% of the total data using the delivered leaf positional data as a target response. The trained model was then validated and tested on 30% of the available data. The model accuracy was evaluated using the mean squared error (MSE), regression plot, and error histogram. Results: The deviations between the individual MLC planned and delivered positions can reach up to a few millimeters, with a maximum deviation of 1.2 mm. The predicted leaf positions at control points closely matched the delivered positions for all MLC leaves during the treatment delivery. The ANN model achieved a maximum MSE of 0.0001 mm 2 (root MSE of 0.0097 mm) in predicting the leaf positions at control points of test data for each leaf. The correlation coefficient, that measures the goodness of fit, was perfect (R = 0.999) in all plots indicating an excellent agreement between the predicted and delivered MLC positions for the training, validation, and test data. Conclusions: We successfully demonstrated a proposed ANN-based method capable of accurately predicting the individual MLC leaf positional deviations during the dynamic IMRT delivery priori. Our ML model based on ANN outperformed the reported accuracy in the literature of various ML models. The results of this study could be extended to actual application in the dose calculation/optimization, hence enhancing the gamma passing rate for patient-specific IMRT quality assurance.

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Section: Discussionmentioning

confidence: 99%

Prediction of the individual multileaf collimator positional deviations during dynamic IMRT delivery priori with artificial neural network

2020

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“…In fact, most models reported above have accuracy that oscillates around area under ROC curve of 0.70 which is suboptimal for their clinical use. Therefore, a conscientious effort needs to be done in the field to collect better and bigger datasets but many challenges wait ahead . Possible ways to remove these obstacles include, but are not limited to, standardization of processes and data structures, information sharing across institutions, distributed learning, transfer learning, or synthetic data generation using generative adversarial networks .…”

Section: Discussionmentioning

confidence: 99%

Machine learning for radiation outcome modeling and prediction

2020

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Aims This review paper intends to summarize the application of machine learning to radiotherapy outcome modeling based on structured and un‐structured radiation oncology datasets. Materials and methods The most appropriate machine learning approaches for structured datasets in terms of accuracy and interpretability are identified. For un‐structured datasets, deep learning algorithms are explored and a critical view of the use of these approaches in radiation oncology is also provided. Conclusions We discuss the challenges in radiotherapy outcome prediction, and suggest to improve radiation outcome modeling by developing appropriate machine learning approaches where both accuracy and interpretability are taken into account.

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“…Another approach is the utilization of distributed (rapid) learning presented in Eurocat [69,70], where algorithms instead of data are shared across the different institutions. With all these exciting breakthroughs in ML and their potential in oncology, one still needs to be careful when wielding such methods and meticulously design the data validation experiments to avoid pitfalls of overfitting and misinformation [71].…”

Section: Discussionmentioning

confidence: 99%

Machine Learning and Imaging Informatics in Oncology

et al. 2018

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In the era of personalized and precision medicine, informatics technologies utilizing machine learning (ML) and quantitative imaging are witnessing a rapidly increasing role in medicine in general and in oncology in particular. This expanding role ranges from computer-aided diagnosis to decision support of treatments with the potential to transform the current landscape of cancer management. In this review, we aim to provide an overview of ML methodologies and imaging informatics techniques and their recent application in modern oncology. We will review example applications of ML in oncology from the literature, identify current challenges and highlight future potentials.

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Machine learning and modeling: Data, validation, communication challenges

Cited by 76 publications

References 95 publications

Prediction of the individual multileaf collimator positional deviations during dynamic IMRT delivery priori with artificial neural network

Prediction of the individual multileaf collimator positional deviations during dynamic IMRT delivery priori with artificial neural network

Machine learning for radiation outcome modeling and prediction

Machine Learning and Imaging Informatics in Oncology

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