Tumor Targeting for Lung Cancer Radiotherapy Using Machine Learning Techniques

Abstract

Accurate lung tumor targeting in real time plays a fundamental role in image-guide radiotherapy of lung cancers. Precise tumor targeting is required for both respiratory gating and tracking. Gating is considered as the current state of the art for precise lung cancer radiotherapy, which irradiates the tumor when it moves into a predefined gating window. Tracking seems to be a next-generation technique, and it operates in a more aggressive fashion by following the tumor position with radiation beam in real time. Existing methods for gating and tracking often rely on observed motion patterns of external surrogates or implanted fiducial markers. However, external surrogates suffer from certain degrees of inaccuracy, and implanted fiducial markers are in limited uses due to the risk of pneumothorax. Therefore, direct tumor targeting techniques without implanting fiducial markers are desired. Previous studies in fluoroscopic markerless targeting are mainly based on template matching methods, which may fail when tumor boundary is unclear in fluoroscopic images. In this paper, we propose a novel framework of markerless gating and tracking based on machine learning algorithms. Specifically, gating is treated as a two-class classification problem, which is solved by principal component analysis (PCA) and artificial neural network (ANN). Further, we formulate the tracking problem as a regression task, which employs the correlation between the tumor position and nearby surrogate anatomic features in the image. Four regression methods were tested in this study: 1-degree and 2-degree linear regression, artificial neural network (ANN), and support vector machine (SVM). Finally, we demonstrate the superb performance of the proposed markerless gating and tracking algorithms on 10 fluoroscopic image sequences of 9 patients. For gating, the target coverage (the precision) ranges from 90% to 99%, with mean of 96.5%. For tracking, the mean localization error is about 2.1 pixels and the maximum error at 95% confidence level is about 4.6 pixels (pixel size is about 0.5 mm).