High-tech in radiation oncology: should there be a ceiling?

Abstract

Purpose To analyze some of the limitations to improvement of the outcome of radiotherapy (RT) expected from the introduction of sophisticated treatment planning and delivery technology. Methods and materials Several recent examples from the literature were analyzed in some detail. Mathematical modeling techniques were used to assess the likely clinical impact of new technologies or biologic principles. The findings of recent randomized controlled trials of RT for prostate, breast, and rectal cancer were analyzed from the perspective of cost-effectiveness and therapeutic gain. Results The main findings of the analyses may be summarized as follows. Dosimetric precision should aim for a <2% patient-to-patient variability in the delivered dose. Imprecision in clinical target volume definition remains an obstacle for high-precision RT. Functional imaging and novel biologic assays may facilitate a move from a clinical target volume to the real target volume. Improved target volume coverage is mainly important if RT has high effectiveness. Radiation oncology is increasingly becoming evidence based. However, there is still a long way to go. Hypofractionation in adjuvant RT for breast cancer may represent a favorable balance between cost and benefit. Treatment complications are potentially associated with both suffering and high cost. The identification of high-risk patients would improve the cost-effectiveness of high-tech RT aimed at avoiding complications. Conformal RT may allow the introduction of hypofractionation, which, again, could potentially save resources. With improvement in surgery and more screening-detected cancer cases, the number needed to treat increases, and this will directly affect the cost-effectiveness of high-tech RT unless efficient patient selection can be developed. Conclusion Sustained technological refinement is only likely to be cost-effective if the clinical and biologic understanding of patient-to-patient variability in the risk of specific types of failure and the optimal multimodality approach to handle these risks is developed at the same time. Mathematical modeling together with methods from health technology assessment and health economics are useful complements to standard methods from evidence-based medicine. Progress in functional imaging and in basic and clinical cancer biology is likely to provide the tools required for individualized risk-adapted RT.