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New 5D Model May Predict Motion of Lung Tumors During Respiration

New 5D Model May Predict Motion of Lung Tumors During Respiration

ST. LOUIS-A new 5-dimensional (5D) mathematical model seems to accurately predict the motion of lung tumors on computed tomography (CT) scans obtained while patients breathe, according to a preliminary report presented at the 46th Annual Meeting of the American Society for Therapeutic Radiology and Oncology (ASTRO) (abstract 1050). Being able to quantitate the motion of internal organs and tumors during respiration would make it possible to compensate for or counteract the effects of breathing, thereby improving the accuracy of radiation treatment planning, Daniel A. Low, PhD, told Oncology News International in an interview. "A method that modeled the motion as a function of a noninvasive external measurement would be a valuable aid for reducing the effective motion during irradiation. For example, the linear accelerator can be programmed to activate only during a predetermined phase of breathing, based on advanced treatment planning optimization techniques," he explained. Limitations of 4D CT Most groups studying the motion of lung tumors view breathing as a function of time (the fourth dimension in 4D CT), which assumes that breathing is predictably cyclic and that tidal volume is the same from one breath to the next, noted Dr. Low, an associate professor of radiation oncology at the Washington University School of Medicine, St. Louis, Missouri. "The reality is, [lung cancer patients' breathing] is absolutely not that," he said. "These patients do not breathe, for the most part, in little sine waves. They have severe lung disease, independent of cancer, they have emphysema, and they have serious underlying physiologic damage caused by smoking ... they are not normal breathers." In addition, most radiation oncology groups use coached breathing, in which patients are instructed when and how to breathe during scanning. "We have very specifically avoided coached breathing because we want to understand how well these models work when a patient is not concentrating on the breathing process," Dr. Low said. Instead, patients are simply told not to move during scanning. Developing the Model The investigators first assessed the performance of a 4D CT process in which multislice CT scans were sorted (gated) by tidal volume. Each of 12 patients with lung cancer underwent 15 consecutive CT scans while spirometry measurements were obtained. The scans were sorted and reconstructed to create a 4D dataset. Analyses showed near-perfect correlation between tidal volume and the motion of internal objects, which was assessed from internal air content on the CT scan. In addition, the reconstructed 4D dataset had high accuracy and high precision (mean, 6%). They then developed a mathematical breathing-motion model based on 5 dimensions-the spatial location of the object (x, y, and z) during a reference breathing phase, tidal volume, and airflow (see Figure 1). To test the model, they compared the actual positions of 40 moving objects in the lung on CT scans obtained during breathing with the predicted positions from the model (see Figures 2-4). This comparison revealed "incredibly good fits," Dr. Low said. The objects tracked moved 4 to 11 mm during breathing, and the mean discrepancy between actual and predicted locations was about .5 mm. Although the model had been well tested in only one patient at the time of the ASTRO meeting, its excellent performance was especially noteworthy because that patient had widely ranging tidal volumes, according to Dr. Low. "This patient was not a regular breather by any means. The patient breathed little breaths, big breaths, medium breaths, and our model just did not care. It did just fine," he said, noting that models that assume constant tidal volume would have been inaccurate by large margins. Still, the findings using the new model must be viewed as preliminary, Dr. Low cautioned, noting that his team is now rigorously testing the model to see how it performs across patients and sessions. "The clinical applications of this model are exciting for radiation therapy. They may allow us to accurately determine if the tumor motion is small enough not to require special treatment, or to provide a treatment that fully accounts for the motion," he concluded, adding that he and his colleagues "plan on using this technology to remove breathing motion artifacts from nuclear medicine images." He noted that the model might also be useful in the assessment and management of patients with other lung diseases, such as emphysema.

 
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