Joachim Widder1, Arjen van der Schaaf2, Philippe Lambin3, Corrie A M Marijnen4, Jean-Philippe Pignol5, Coen R Rasch6, Ben J Slotman7, Marcel Verheij8, Johannes A Langendijk2. 1. Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands. Electronic address: j.widder@umcg.nl. 2. Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands. 3. Department of Radiation Oncology, School for Oncology and Developmental Biology (GROW), Maastricht University Medical Center, Maastricht, The Netherlands. 4. Department of Radiation Oncology, Leiden University Medical Center, Leiden, The Netherlands. 5. Department of Radiation Oncology, Erasmus Medical Center Cancer Institute, Rotterdam, The Netherlands. 6. Department of Radiation Oncology, Academic Medical Center, Amsterdam, The Netherlands. 7. Department of Radiation Oncology, VU Medical Center, Amsterdam, The Netherlands. 8. Department of Radiation Oncology, Netherlands Cancer Institute, Amsterdam, The Netherlands.
Abstract
PURPOSE: Reducing dose to normal tissues is the advantage of protons versus photons. We aimed to describe a method for translating this reduction into a clinically relevant benefit. METHODS AND MATERIALS: Dutch scientific and health care governance bodies have recently issued landmark reports regarding generation of relevant evidence for new technologies in health care including proton therapy. An approach based on normal tissue complication probability (NTCP) models has been adopted to select patients who are most likely to experience fewer (serious) adverse events achievable by state-of-the-art proton treatment. RESULTS: By analogy with biologically targeted therapies, the technology needs to be tested in enriched cohorts of patients exhibiting the decisive predictive marker: difference in normal tissue dosimetric signatures between proton and photon treatment plans. Expected clinical benefit is then estimated by virtue of multifactorial NTCP models. In this sense, high-tech radiation therapy falls under precision medicine. As a consequence, randomizing nonenriched populations between photons and protons is predictably inefficient and likely to produce confusing results. CONCLUSIONS: Validating NTCP models in appropriately composed cohorts treated with protons should be the primary research agenda leading to urgently needed evidence for proton therapy.
PURPOSE: Reducing dose to normal tissues is the advantage of protons versus photons. We aimed to describe a method for translating this reduction into a clinically relevant benefit. METHODS AND MATERIALS: Dutch scientific and health care governance bodies have recently issued landmark reports regarding generation of relevant evidence for new technologies in health care including proton therapy. An approach based on normal tissue complication probability (NTCP) models has been adopted to select patients who are most likely to experience fewer (serious) adverse events achievable by state-of-the-art proton treatment. RESULTS: By analogy with biologically targeted therapies, the technology needs to be tested in enriched cohorts of patients exhibiting the decisive predictive marker: difference in normal tissue dosimetric signatures between proton and photon treatment plans. Expected clinical benefit is then estimated by virtue of multifactorial NTCP models. In this sense, high-tech radiation therapy falls under precision medicine. As a consequence, randomizing nonenriched populations between photons and protons is predictably inefficient and likely to produce confusing results. CONCLUSIONS: Validating NTCP models in appropriately composed cohorts treated with protons should be the primary research agenda leading to urgently needed evidence for proton therapy.
Authors: Neil G Burnet; Ranald I Mackay; Ed Smith; Amy L Chadwick; Gillian A Whitfield; David J Thomson; Matthew Lowe; Norman F Kirkby; Adrian M Crellin; Karen J Kirkby Journal: Br J Radiol Date: 2020-01-14 Impact factor: 3.039
Authors: David C Hall; Alexei V Trofimov; Brian A Winey; Norbert J Liebsch; Harald Paganetti Journal: Int J Radiat Oncol Biol Phys Date: 2017-02-14 Impact factor: 7.038
Authors: Pierre Blanchard; Andrew J Wong; G Brandon Gunn; Adam S Garden; Abdallah S R Mohamed; David I Rosenthal; Joseph Crutison; Richard Wu; Xiaodong Zhang; X Ronald Zhu; Radhe Mohan; Mayankkumar V Amin; C David Fuller; Steven J Frank Journal: Radiother Oncol Date: 2016-09-15 Impact factor: 6.280
Authors: Ibrahim Chamseddine; Yejin Kim; Brian De; Issam El Naqa; Dan G Duda; John Wolfgang; Jennifer Pursley; Harald Paganetti; Jennifer Wo; Theodore Hong; Eugene J Koay; Clemens Grassberger Journal: JCO Clin Cancer Inform Date: 2022-02
Authors: Pierre Blanchard; Adam S Garden; G Brandon Gunn; David I Rosenthal; William H Morrison; Mike Hernandez; Joseph Crutison; Jack J Lee; Rong Ye; C David Fuller; Abdallah S R Mohamed; Kate A Hutcheson; Emma B Holliday; Nikhil G Thaker; Erich M Sturgis; Merrill S Kies; X Ronald Zhu; Radhe Mohan; Steven J Frank Journal: Radiother Oncol Date: 2016-06-21 Impact factor: 6.280
Authors: Bismarck C L Odei; Dustin Boothe; Sameer R Keole; Carlos E Vargas; Robert L Foote; Steven E Schild; Jonathan B Ashman Journal: Int J Part Ther Date: 2017-03-14