  Current image-guided prostate radiotherapy often relies on the use of
implanted fiducials or transducers for target localization. Fiducial or
transducer insertion requires an invasive procedure that adds cost and risks
for bleeding, infection, and discomfort to some patients. We are developing a
novel markerless prostate localization strategy using a pre-trained deep
learning model to interpret routine projection kV X-ray images without the need
for daily cone-beam computed tomography (CBCT). A deep learning model was first
trained by using several thousand annotated projection X-ray images. The
trained model is capable of identifying the location of the prostate target for
a given input X-ray projection image. To assess the accuracy of the approach,
three patients with prostate cancer received volumetric modulated arc therapy
(VMAT) were retrospectively studied. The results obtained by using the deep
learning model and the actual position of the prostate were compared
quantitatively. The deviations between the target positions obtained by the
deep learning model and the corresponding annotations ranged from 1.66 mm to
2.77 mm for anterior-posterior (AP) direction, and from 1.15 mm to 2.88 mm for
lateral direction. Target position provided by deep learning model for the kV
images acquired using OBI is found to be consistent that derived from the
fiducials. This study demonstrates, for the first time, that highly accurate
markerless prostate localization based on deep learning is achievable. The
strategy provides a clinically valuable solution to daily patient positioning
and real-time target tracking for image-guided radiotherapy (IGRT) and
interventions.
