Cervix Motion in 50 Cervical Cancer Patients Assessed by Daily Cone Beam Computed Tomographic Imaging of a New Type of Marker.

PURPOSE

To evaluate a new type of marker and a new method of marker implantation and to assess interfraction cervix motion for a large population of patients with locally advanced cervical cancer by daily cone beam computed tomographic (CBCT) imaging.

METHODS AND MATERIALS

We investigated the position of markers in 50 patients treated in prone position during at least 23 fractions. To reduce streaking artifacts in the planning CT scan, a new type of polymeric marker was used and compared with conventional gold markers. In addition, a new method of implantation was used in an attempt to reduce marker loss. In each fraction, a CT scan was acquired before dose delivery and aligned to the bony anatomy of the planning CT scan, simulating the clinical setup protocol. First, sufficient visibility of the markers was verified. Then, systematic and random displacement of the marker centroids was recorded and analyzed in 3 directions with regard to the planning CT and the first CBCT (to evaluate the presence of a vaginal catheter in the planning CT). Streaking artifacts were quantified with the standard deviation of the mean squared intensity difference in a radius around the marker.

RESULTS

Marker loss was minimal during treatment: in only 3 of the 50 patients 1 marker was lost. Streaking artifacts for the new markers were reduced compared with conventional gold markers. For the planning CT, M/Σ/σ were 0.4/3.4/2.2 mm, 1.0/5.5/4.5 mm, and -3.9/5.1/3.6 mm for the left-right, anterior-posterior, and cranial-caudal directions, respectively. With regard to the first CBCT scan, M/Σ/σ were 0.8/2.8/2.1, 0.6/4.4/4.4, and -1.3/4.5/3.6 mm.

CONCLUSIONS

A new type of marker and implantation method was shown to have significantly reduced marker loss and streaking artifacts compared with gold fiducial markers. The recorded marker displacement confirms results reported in the existing literature but for a larger dataset.