A Fast Neural Network Approach to Predict Lung Tumor Motion during Respiration for Radiation Therapy Applications

Abstract

During radiotherapy treatment for thoracic and abdomen cancers, for example, lung cancers, respiratory motion moves the target tumor and thus badly affects the accuracy of radiation dose delivery into the target. A real-time image-guided technique can be used to monitor such lung tumor motion for accurate dose delivery, but the system latency up to several hundred milliseconds for repositioning the radiation beam also affects the accuracy. In order to compensate the latency, neural network prediction technique with real-time retraining can be used. We have investigated real-time prediction of 3D time series of lung tumor motion on a classical linear model, perceptron model, and on a class of higher-order neural network model that has more attractive attributes regarding its optimization convergence and computational efficiency. The implemented static feed-forward neural architectures are compared when using gradient descent adaptation and primarily the Levenberg-Marquardt batch algorithm as the ones of the most common and most comprehensible learning algorithms. The proposed technique resulted in fast real-time retraining, so the total computational time on a PC platform was equal to or even less than the real treatment time. For one-second prediction horizon, the proposed techniques achieved accuracy less than one millimeter of 3D mean absolute error in one hundred seconds of total treatment time.

Feasibility of predicting tumor motion using online data acquired during treatment and a generalized neural network optimized with offline patient tumor trajectories

“…In this work, an average of 28 input‐output data pairs was used for each training batch (i.e., sliding window length). This is smaller than the number of data pairs required in the other studies that use the same number of training epochs (i.e., 800 per batch) for a static neural network …”

“…This is smaller than the number of data pairs required in the other studies that use the same number of training epochs (i.e., 800 per batch) for a static neural network. 72…”

Novel Mechanism of Classifying the Brain Tumor for Identifying its Critical State

“…This fact has been proven by Chadded [28] by using Gaussian mixture model. Bukovsky et al [29] have used neural network in order to perform identification followed by prediction of tumor. This process is a way advanced to classification process using feedforward neural network.…”

Application of artificial intelligence in respiratory medicine: Has the time arrived?

“…One application is to support lung movement tracking for the accurate delivery of radiotherapy to lung cancers. The system latency of existing techniques limits their accuracy while real‐time retraining of neural networks has been shown to improve precision of delivery …”

“…The system latency of existing techniques limits their accuracy while real-time retraining of neural networks has been shown to improve precision of delivery. 9 There are important considerations to ensure the safe and effective implementation of AI into health care. Data collection and usage must follow informed consent.…”