Radiotherapy techniques have evolved beyond IMRT toward possibly even more conformal techniques, such as stereotactic body radiotherapy, volumetric-arc-based therapy (VMAT), and proton radiotherapy. Their comparative implications toward second cancer risks are still unknown and will require further investigation. For example, the region of normal tissue receiving low dose exposure may be even greater with VMAT. One of the oft-cited advantages of proton radiotherapy is the resultant decrease in integral dose from protons, but some have speculated that the increased scatter dose of neutrons led to large uncertainties regarding second cancer risks for patients treated with proton radiotherapy.32 This issue is of particular concern for pediatric patients, where the risk of second cancers for nearby irradiated organs and its relationship with dose and irradiated volume is well established.34 For a clear clinical example, it is known that proton radiotherapy may potentially provide a significant dosimetric advantage in craniospinal irradiation for pediatric medulloblastoma patients. Do these potential dosimetric advantages and theoretical late effects’ benefits outweigh the potential financial and logistic burdens for patients and families when determining the radiotherapy technology to treat medulloblastoma patients?35,36

The unclear implications for second cancer risk of novel radiotherapy techniques despite their rapid adoption and utilization for treating patients emphasizes the critical value of dosimetric and modeling investigations. An example of the predicted risks associated with high-dose fractionated radiotherapy is shown in Figure 3. These clinical challenges are often dynamically evolving questions. For example, many radiation oncologists point out that the large radiation fields used in the past to treat Hodgkin’s lymphoma patients in the long-term epidemiologic studies such as the Late Effects Study Group are markedly different from the smaller, more conformal, and lower radiation dose fields used to treat lymphoma patients today. In breast cancer patients, the utilization of the prone technique for breast radiotherapy likely leads to lower doses of irradiated lung tissue,29 possibly leading to a lower predicted risk of subsequent secondary lung cancers.30 Until large epidemiologic studies with long-term follow-up report their results for these newer techniques, we will be dependent on dosimetric and modeling studies to provide important quantitative predictive information that may guide important clinical decisions.


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(To view a larger version of Figure 3, click here.)

Often, a first-order approximation can be made using linear risk modeling such as the BEIR VII model, which is simpler in formalism. However, the accuracy at estimating second cancer risk may be limited, as demonstrated by a recent study on estimating secondary cancer risks from modern breast radiotherapy techniques using Monte Carle dose calculations. This study showed that using linear risk models such as the BEIR VII model and more modern nonlinear risk models could result in a broad range of predicted second cancer risks.37 Fortunately, the field of carcinogenesis modeling and quantitative risk assessment has advanced in sophistication in recent years.

Advances in carcinogenesis modeling

Biologically motivated mathematical modeling of carcinogenesis has a history spanning several decades.38,39 Many biologically based models can be characterized as short term, in that they focus on those processes occurring during and shortly (ie, about 1 month or less) after irradiation.40–60 Many short-term models are motivated toward radiation-induced cancer risk estimation at low radiation doses,61 but some have also been adapted to predict radiotherapy-induced second cancers.42,59,60,62 The main advantage of this class of models is that they provide a detailed initial dose response for short-term endpoints, which are used as surrogates for carcinogenesis. The main disadvantage is that the possibly substantial modulations of the magnitude and shape of this initial dose response during the lengthy period (multiple years-decades in humans) between irradiation and manifestation of typical solid tumors are not considered; a simple proportional hazards assumption, plausible at low doses, becomes questionable at the high doses responsible for some second cancers.

A classical example of a short-term carcinogenesis formalism is the linear–quadratic exponential model, which assumes that radiation-induced carcinogenesis is primarily governed by a balance of cell mutation (also commonly referred to as initiation) and cell killing (often referred to as inactivation).63 The linear–quadratic exponential equation uses the classic linear–quadratic form both for radiation-induced initiation (ie, aD + bD2, where D is radiation dose and a and b are adjustable parameters) and for radiation-induced inactivation (exp[−αD − βD2], where α and β are adjustable parameters). It has been applied to data from Japanese atomic bomb survivors61 and to radiotherapy-treated cancer patients.62

At low and intermediate radiation doses (ie, up to a few Gy), the equation predicts that the radiogenic cancer risk is an increasing function of dose. This is because at such doses the risk is dominated by initiation, and inactivation has a limited effect because the majority of cells can survive the exposure. At high doses (eg, much greater than 5 Gy), however, the exponential inactivation term leads to a very small predicted radiogenic risk, because essentially all radiation-initiated cells would be inactivated by the radiation.