Second cancer risk after primary cancer treatment with three‐dimensional conformal, intensity‐modulated, or proton beam radiation therapy

Intensity-modulated radiation therapy (IMRT) allows dose to be concentrated in the tumor volume while sparing normal tissues. However, the downside to IMRT is the potential to increase the number of radiation-induced second cancers. The reasons for this potential are more monitor units and, therefore, a larger total-body dose because of leakage radiation and, because IMRT involves more fields, a bigger volume of normal tissue is exposed to lower radiation doses. Intensity-modulated radiation therapy may double the incidence of solid cancers in long-term survivors. This outcome may be acceptable in older patients if balanced by an improvement in local tumor control and reduced acute toxicity. On the other hand, the incidence of second cancers is much higher in children, so that doubling it may not be acceptable. IMRT represents a special case for children for three reasons. First, children are more sensitive to radiation-induced cancer than are adults. Second, radiation scattered from the treatment volume is more important in the small body of the child. Third, the question of genetic susceptibility arises because many childhood cancers involve a germline mutation. The levels of leakage radiation in current Linacs are not inevitable. Leakage can be reduced but at substantial cost. An alternative strategy is to replace X-rays with protons. However, this change is only an advantage if the proton machine employs a pencil scanning beam. Many proton facilities use passive modulation to produce a field of sufficient size, but the use of a scattering foil produces neutrons, which results in an effective dose to the patient higher than that characteristic of IMRT. The benefit of protons is only achieved if a scanning beam is used in which the doses are 10 times lower than with IMRT.

Purpose Radiotherapy reduces the absolute risk of breast cancer mortality by a few percentage points in suitable women but can cause a second cancer or heart disease decades later. We estimated the absolute long-term risks of modern breast cancer radiotherapy. Methods First, a systematic literature review was performed of lung and heart doses in breast cancer regimens published during 2010 to 2015. Second, individual patient data meta-analyses of 40,781 women randomly assigned to breast cancer radiotherapy versus no radiotherapy in 75 trials yielded rate ratios (RRs) for second primary cancers and cause-specific mortality and excess RRs (ERRs) per Gy for incident lung cancer and cardiac mortality. Smoking status was unavailable. Third, the lung or heart ERRs per Gy in the trials and the 2010 to 2015 doses were combined and applied to current smoker and nonsmoker lung cancer and cardiac mortality rates in population-based data. Results Average doses from 647 regimens published during 2010 to 2015 were 5.7 Gy for whole lung and 4.4 Gy for whole heart. The median year of irradiation was 2010 (interquartile range [IQR], 2008 to 2011). Meta-analyses yielded lung cancer incidence ≥ 10 years after radiotherapy RR of 2.10 (95% CI, 1.48 to 2.98; P < .001) on the basis of 134 cancers, indicating 0.11 (95% CI, 0.05 to 0.20) ERR per Gy whole-lung dose. For cardiac mortality, RR was 1.30 (95% CI, 1.15 to 1.46; P < .001) on the basis of 1,253 cardiac deaths. Detailed analyses indicated 0.04 (95% CI, 0.02 to 0.06) ERR per Gy whole-heart dose. Estimated absolute risks from modern radiotherapy were as follows: lung cancer, approximately 4% for long-term continuing smokers and 0.3% for nonsmokers; and cardiac mortality, approximately 1% for smokers and 0.3% for nonsmokers. Conclusion For long-term smokers, the absolute risks of modern radiotherapy may outweigh the benefits, yet for most nonsmokers (and ex-smokers), the benefits of radiotherapy far outweigh the risks. Hence, smoking can determine the net effect of radiotherapy on mortality, but smoking cessation substantially reduces radiotherapy risk.

Propensity scores (PS) are an increasingly popular method to adjust for confounding in observational studies. Propensity score methods have theoretical advantages over conventional covariate adjustment, but their relative performance in real-word scenarios is poorly characterized. We used datasets from 4 large-scale cardiovascular observational studies (PROMETHEUS, ADAPT-DES [the Assessment of Dual AntiPlatelet Therapy with Drug-Eluting Stents], THIN [The Health Improvement Network], and CHARM [Candesartan in Heart Failure-Assessment of Reduction in Mortality and Morbidity]) to compare the performance of conventional covariate adjustment with 4 common PS methods: matching, stratification, inverse probability weighting, and use of PS as a covariate. We found that stratification performed poorly with few outcome events, and inverse probability weighting gave imprecise estimates of treatment effect and undue influence to a small number of observations when substantial confounding was present. Covariate adjustment and matching performed well in all of our examples, although matching tended to give less precise estimates in some cases. PS methods are not necessarily superior to conventional covariate adjustment, and care should be taken to select the most suitable method.