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Routine 3D Treatment Planning: Opportunities, Challenges, and Hazards

Routine 3D Treatment Planning: Opportunities, Challenges, and Hazards

Dr. Marks and coauthors provide a good description of their experience with image-based three-dimensional (3D) treatment planning for conformal radiotherapy. They focus on three particular areas: (1) physician acceptance of the technology, (2) challenges to quality assurance, and (3) physiologic imaging and its impact on planning.

Modern 3D treatment planning began with the early work in beam’s-eye view by McShan and Reinstein,[1,2] and with refinements and tools developed by charged particle radiotherapy research programs.[3,4] The initial planning software was created largely at academic centers, including the University of Michigan, Memorial Sloan-Kettering Cancer Center, University of Washington, and University of North Carolina.[5-8] Over the past 10 years, this technology has become widely available commercially.

Computed tomography (CT) simulation hardware and software has also greatly influenced the adoption of image-based planning. High-performance CT scanners, which provide volumetric anatomic data, are now common in radiation oncology departments. With customized, “user friendly” software, the process of 3D planning has become much more acceptable to clinicians.

The revolution in image-based planning is based on the advantage of clearly seeing what is to be treated and what is to be avoided. The identification of targets on the axial plane (or in principle coronal or saggital planes) led to beam’s-eye views to assist in aperture design and beam orientation. Designing radiation portals without computer graphics simply was not as accurate. The need for accuracy is clearly indicated with the current emphasis on dose escalation to achieve higher local control rates.

Physician Surveys

The Duke experience with 3D treatment planning is consistent with that of other groups. There are differences in the portal shape based on CT planning as well as differences in beam orientations in some, but not all cases. The physician time required for the planning process depends on the case—as noted in the Marks article; 3D treatment planning for prostate cancer requires less time, whereas brain, lung, and head and neck cancer cases can be quite labor intensive.

The image-based process not only alters the target area, but also affects the amount of normal tissue exposed to radiation. The authors maintain that 3D planning reduces the volume of normal tissue irradiated about 65% of the time. Clearly, for nonaxial beams such as those used in complex head and neck cases, computer-assisted planning is essential.

Physiologic Imaging

The authors emphasize further the need for physiologic imaging in support of planning. The Duke group has pioneered the use of positron-emission tomography (PET) and single-photon–

emission computed tomography (SPECT) in planning treatment for lung cancer. PET has been used to improve the identification of active tumors that are associated with increased metabolic activity. As Marks and coauthors note, this group’s inclusion of PET modified the treatment beam in 34% of cases—a very significant effect. In addition, their use of SPECT to identify and exclude better-functioning regions of the lung is also well directed. Such imaging can be employed to assess functional damage to lung postradiation, making it an important tool for better understanding normal tissue complication probabilities.

In a similar vein, Hamilton and colleagues at the University of Chicago have described the use of functional imaging to identify regions of brain function that must be avoided in planning stereotactic irradiation of intracranial lesions. Many of the aspects of biological imaging are nicely summarized in an article by Ling et al.[9]

Challenges for the Future

Dr. Marks concludes with a technical description of the potential pitfalls associated with computer-aided planning. These problems range from immobilization devices obscuring surface setup landmarks to the need for structuring the treatment process to deliver multiple-field treatments with accuracy and confidence. In my opinion, these risks are not as hazardous as might be implied.

Nevertheless, critical areas in need of improvement include methods of identifying the target and the normal functional areas to be avoided. Image processing has advanced to the point where 3D renderings of anatomy from high-resolution spiral CT scanners are feasible. Figures 1 and 2 show two such rendered images—enlarged lymph nodes in the left neck, and a lung tumor where spiculations are clearly visible as the lung parenchyma is made transparent during the visualization. Volume rendering may be an increasingly important visualization tool for radiation and surgical oncologists, as virtual reality technology permits visualization, surgical rehearsal, and radiation treatment simulation with ultrarealism.[9]

Currently, most 3D treatment planning allows margins for physiologic and setup uncertainties. As real-time imaging improves, it may be possible to monitor tumor position from moment to moment and gate radiation to further localize dose to the planning target volume without compromising dosimetric coverage. Image-guided planning will evolve into image-guided therapy.

Intensity modulated radiation delivery promises to provide highly conformal dose distributions, even when concave dose shapes are required to avoid critical structures.[11] Combined with advances in visualization and tumor localization, radiation oncology is undergoing rapid technologic change.

Conclusions

Marks et al provide a useful historical perspective on the evolution of 3D planning at Duke. The most valuable aspect they have investigated is the use of functional imaging in guiding treatment planning. With other advances in visualization and delivery of highly conformal dose distributions, opportunities to further advance the field are at hand.

References

1. McShan DL, Silverman A, Lanza DM, et al: A computerized three-dimensional treatment planning system utilizing interactive colour graphics. Br J Radiol 52(618):478-481, 1979.

2. Reinstein LE, McShan D, Webber BM, et al: A computer-assisted three-dimensional treatment planning system. Radiology 127(1):259-264, 1978.

3. Goitein M, Abrams M, Rowell D, et al: Multidimensional treatment planning: II. Beam’s eye-view, back projection, and projection through CT sections. Int J Radiat Oncol Biol Phys 9(6):789-797, 1983.

4: Sherouse GW, Chaney EL: The portable virtual simulator. Int J Radiat Oncol Biol Phys 21(2):475-482, 1991.

5. Lyman JT: Complication probability as assessed from dose-volume histograms. Radiat Res 8(suppl):S13-19, 1985.

6. Kalet IJ, Jacky JP: A research-oriented treatment planning program system. Comput Methods Programs Biomed 14(1):85-98, 1982.

7. Sailer SL, Chaney EL, Rosenman JG, et al: Treatment planning at the University of North Carolina at Chapel Hill. Semin Radiat Oncol 2(4):267-273, 1992.

8. Mohan R, Barest G, Brewster LJ, et al: A comprehensive three-dimensional radiation treatment planning system. Int J Radiat Oncol Biol Phys 15(2):481-495, 1988.

9. Ling CC, Humm J, Larson S, et al: Towards multidimensional radiotherapy (MD-CRT): Biological imaging and biological conformality. Int J Radiat Oncol Biol Phys 47(3):551-560, 2000.

10. Lee JS, Jani AB, Pelizzari CA, et al: Volumetric visualization of head and neck CT data for treatment planning. Int J Radiat Oncol Biol Phys 44(3):693-703, 1999.

11. Purdy JA: Future directions in 3-D treatment planning and delivery: A physicist’s perspective. Int J Radiat Oncol Biol Phys 46(1):3-6, 2000.

 
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