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Updated: Mar 2, 2026

Dynamic Lung Tumor Tracking for Stereotactic Ablative Body Radiation Therapy
Published on: June 7, 2015
SU-E-J-127: An Initial Application of Evaluating Lung Tumor Motion Throughout Radiotherapy
Purpose:
We present a novel method for evaluating lung tumor motion incorporating planning CT scan, imaging for patient localization, and during treatment delivery.
Methods:
Tumor motion is evaluated at three stages of the treatment process. Following the acquisition of 4D CT scans for treatment planning, tumors are contoured at one phase and registered to other phases to obtain tumor motion patterns. Tumor motion prior to each treatment is evaluated by identifying tumors directly on every raw projection of the daily localization CBCT scans. Finally, tumor motion during treatment is evaluated by analyzing MV treatment beam images. Every frame of image is evaluated as three components, each with separate DRRs: all non-moving structures, the tumor, and other moving structures. The locations of three components are determined though a registration process. In order to quantify tumor motion, the cumulated probability, the percentage of time when the tumor motion is equal to or smaller than a given range, is evaluated at every stage.
Results:
This methodology has been applied to five lung cancer patients undergoing radiotherapy. More than 5400 frames from MV treatment beam images and 24 CBCT scans were acquired from 12 fractions. Both the average tumor position and the cumulated probability with the range were evaluated. The tumor motion ranges are generally larger than those obtained from 4D CT. Significant variation in motion patterns was observed as well.
Conclusions:
Information obtained from 4DCT is insufficient for motion assessment. In contrast, our novel methodology is capable of accurately characterizing lung tumor motion on a daily basis, without the need for implanted fiducial markers and without additional imaging dose. It provides daily verification that the tumor range is within prior estimates and covered by the treatment planning volume. This research is supported by CPRIT Individual Investigator Award RP110329.
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