Three primary research avenues seek to improve treatment outcome by optimizing the radiation dose distribution in NSCLC. First is the development of improved radiation delivery technology such as intensity modulated radiation therapy (IMRT) and image guided radiation therapy (IGRT), which may allow clinicians to safely decrease the margins required for target coverage and respiratory motion management. The second avenue attempts to improve target selection and delineation to reduce treatment volumes without compromising tumor control. Much interest is currently being devoted to the impact of multi-modality imaging, in particular CT-PET, in this context 22,23
, but attention to microscopic patterns of spread and patterns of nodal spread remains critical in this endeavor. The third avenue aims to improve the therapeutic ratio through an optimization of the dose distribution in the normal lung tissue.
There are a wealth of data in the literature regarding dose-volume factors as predictors of pulmonary toxicity 24–26
, and contemporary efforts at dose escalation frequently evaluate these parameters in an attempt to prospectively validate them 27
. One limitation of dose-volume analysis, which is perhaps under-appreciated, is the definition of the lung volumes themselves, specifically as it pertains to functional versus nonfunctional subvolumes. This fact is well appreciated by thoracic surgeons, who will routinely obtain lung scintigraphy scans to elucidate the contribution of a particular area of lung to overall pulmonary function28
, and also utilize the concept of “volume reduction” surgery in patients with severe emphysema to deliberately remove non-ventilating lung, and improve the ventilation-perfusion ratio. However, it is often overlooked by radiation oncologists, who will use the available data on normal tissue complication probability (NTCP) to guide them in design of a treatment plan, without recognizing that one of the fundamental premises of these models, “that all lung is created equal,” is erroneous. In fact, the differential contribution of different areas of lung tissue to overall pulmonary function has two major implications to the treatment planning process; one, that areas which are highly active in gas exchange should be spared to the maximum extent possible, and two, that areas which are functionally inactive or minimally active may potentially be exploited as “dead space”, allowing escalation of dose through these already non- or minimally ventilatory areas. This concept is being increasingly recognized as a potentially significant one, and efforts at integrating this functional information are now appearing in the literature.29
While direct estimation of pneumonitis risk in a specific patient from published NTCP model parameters may not be very reliable, the estimated relative change in risk resulting from the various dose distributions in this study is probably more robust. In line with other authors 19–21,30
, we feel that the parallel architecture model reflects the pathophysiology of symptomatic pneumonitis more closely than other models, such as the mean dose model or the Lyman-Kutcher-Burman model. The model parameters we used in this study have been applied in previous clinical outcome studies and have been found to produce clinically meaningful stratification of patients and dose plans into risk groups. The absolute reduction in the risk of symptomatic pneumonitis estimated here, in the order of 8 percentage points, may seem relatively modest. However, it should be noted that we are operating in the region of the relatively shallow foot of the sigmoid curve linking fdam
with complication risk. Clearly, in a higher-risk situation, we would expect a larger absolute risk reduction. The observed changes provide an important proof-of-principle but may also prove sufficient to allow the addition of concurrent chemotherapy or use of hypofractionated radiation schedules.
This remains a work in progress. We currently have several clinical protocols developing this technique as a potential tool to enhance radiotherapy delivery in lung cancer, and recognize that several limitations exist. First, we are generating plans based on a single time point HPH-MRI, and the ventilatory-competent lung volumes are likely to change over time. We clearly recognize this as a limitation; however, when a patient is seen in the clinic with a specific set of pulmonary function values and an HPH-MRI is obtained concurrently, one can be reasonably certain that depositing the highest doses of radiation to the least or non-ventilatory lung would have a high likelihood of leaving that patient with overall ventilatory capacity similar to that observed at baseline; in other words the patient is not very likely to be significantly “worse-off”. In central tumors, where considerable atelectasis exists, re-aeration may occur and this could require replanning with a repeat HPH-MRI; the advantage of integrating treatment delivery using the conformal avoidance IMRT/IGRT paradigm is that the daily imaging inherent with cone-beam or MVCT would detect such re-aeration, thereby permitting timely replanning. Second, the use of simple thresholding to identify ventilated lung volume represents a first iteration of what is undoubtedly a complex process, which is dependent upon factors such as gravity-dependent variations in ventilation in the anterior to posterior direction for the supine patient position as well as variations in the sensitivity of the rf-coil used to detect the MRI signal. Future work is refining both the automated algorithm used to detect the ventilated space in the lungs using regional algorithms, as well as improving the rf-coil homogeneity for thoracic lung imaging. Third, HPH-MRI only provides ventilatory information and does not integrate perfusion information; it is possible in extreme cases to produce a ventilation-perfusion mismatch which could actually compromise a patient’s lung function. Methods to assess perfusion with MR are currently in development, and could address this limitation in the future. Finally, the ultimate limitation is actually integrating this in a clinical trial and studying outcomes prospectively.