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Buchner JA, Kofler F, Mayinger MC, Brunner TB, Wittig A, Menze B, Zimmer C, Meyer B, Guckenberger M, Andratschke N, Shafie RE, Rogers S, Schulze K, Blanck O, Zamboglou C, Grosu A, Combs SE, Bernhardt D, Wiestler B, Peeken JC. What MRI Sequences are Necessary for Automated Neural Network-Based Metastasis Segmentation - An Ablation Study. Int J Radiat Oncol Biol Phys 2023; 117:e704-e705. [PMID: 37786065 DOI: 10.1016/j.ijrobp.2023.06.2195] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
PURPOSE/OBJECTIVE(S) Brain metastasis (BM) delineation is a time-consuming process in both daily clinical practice and research. Automated BM segmentation algorithms can be used to assist in this task. Most approaches to brain tumor segmentation, such as algorithms trained on the BraTS challenge, use four magnetic resonance imaging (MRI) sequences as input, making them susceptible to missing or corrupted sequences and increase the number of sequences necessary for MRI RT planning. The goal of this project is to compare neural networks with different combinations of input sequences for the segmentation of the contrast-enhancing metastasis and the surrounding FLAIR hyperintense edema. All models were tested in a multicenter international external test cohort. This allows us to determine which MRI sequences are needed for effective automated segmentations. MATERIALS/METHODS In total, we had T1-weighted sequences without (T1) and with contrast enhancement (T1-CE), T2-weighted sequences (T2), and T2 fluid-attenuated inversion recovery (FLAIR) sequences from 339 patients with at least one brain metastasis from seven centers available. Preprocessing yielded co-registered, skull-stripped sequences with an isotropic resolution of 1 millimeter. The contrast-enhancing metastasis as well as the surrounding FLAIR hyperintense edema were manually segmented to create reference labels. A baseline 3D U-Net with all four sequences as well as six additional U-Nets with different clinically plausible combinations (T1-CE; T1; FLAIR; T1-CE+FLAIR; T1-CE+T1+FLAIR; T1-CE+T1) of input sequences were trained on a cohort of 239 patients from two centers and subsequently tested on an external cohort of 100 patients from the remaining five centers. RESULTS All models that included T1-CE in their selected sequences showed similar performance for metastasis segmentation with a median Dice similarity coefficient (DSC) of 0.93-0.96. T1-CE alone likewise achieved a performance of 0.96 (IQR 0.93-0.97). The model trained with only FLAIR performed worse (DSC = 0.73, IQR 0.54-0.84). For edema segmentation, models that included both T1-CE and FLAIR performed best (median DSC = 0.93), while the remaining four models without simultaneous inclusion of these two sequences (T1-CE; T1; FLAIR; T1-CE+T1) reached a median DSC of 0.81-0.89. CONCLUSION Automatic segmentation of brain metastases with less than four input sequences is feasible with minimal or no loss of quality. A T1-CE-only protocol suffices for metastasis segmentation. In contrast, for edema segmentation, the combination of T1-CE and FLAIR seems to be important. Missing either T1-CE or FLAIR decreases performance. These findings may improve future imaging routines by omitting unnecessary sequences, thus speeding up procedures in daily clinical practice while allowing for optimal neural network-based target definitions.
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Affiliation(s)
- J A Buchner
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - F Kofler
- Helmholtz AI, Helmholtz Zentrum Munich, Munich, Germany; Department of Informatics, Technical University of Munich, Munich, Germany
| | - M C Mayinger
- Department of Radiation Oncology, University Hospital of Zurich, University of Zurich, Zurich, Switzerland
| | - T B Brunner
- Medical University of Graz, Dept. of Radiation Oncology, Graz, Austria; Department of Radiation Oncology, University Hospital Magdeburg, Magdeburg, Germany
| | - A Wittig
- Department of Radiotherapy and Radiation Oncology, University Hospital Jena, Friedrich-Schiller University, Jena, Germany
| | - B Menze
- Department of Informatics, Technical University of Munich, Munich, Germany
| | - C Zimmer
- Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - B Meyer
- Department of Neurosurgery, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - M Guckenberger
- Department of Radiation Oncology, University Hospital of Zurich, University of Zurich, Zurich, Switzerland
| | - N Andratschke
- Department of Radiation Oncology, University Hospital of Zurich, University of Zurich, Zurich, Switzerland
| | - R El Shafie
- Heidelberg Institute for Radiation Oncology (HIRO), National Center for Radiation Oncology (NCRO), Heidelberg, Germany; Department of Radiation Oncology, University Medical Center Göttingen, Göttingen, Germany
| | - S Rogers
- Radiation Oncology Center KSA-KSB, Kantonsspital Aarau, Aarau, Switzerland
| | - K Schulze
- Department of Radiation Oncology, General Hospital Fulda, Fulda, Germany
| | - O Blanck
- Department of Radiation Oncology, University Medical Center Schleswig-Holstein, Campus Kiel, Kiel, Germany
| | - C Zamboglou
- Department of Radiation Oncology, German Oncology Center, European University of Cyprus, Limassol, Cyprus; Department of Radiation Oncology, University of Freiburg - Medical Center, Freiburg, Germany
| | - A Grosu
- Department of Radiation Oncology, University of Freiburg - Medical Center, Freiburg, Germany; German Cancer Consortium (DKTK), Partner Site Freiburg, Freiburg, Germany
| | - S E Combs
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany; Institute of Radiation Medicine (IRM), Department of Radiation Sciences (DRS), Helmholtz Center Munich, Munich, Germany
| | - D Bernhardt
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany; German Cancer Consortium (DKTK), partner site Munich, Munich, Germany
| | - B Wiestler
- Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany; TranslaTUM - Central Institute for Translational Cancer Research, Technical University of Munich, Munich, Germany
| | - J C Peeken
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany; Institute of Radiation Medicine (IRM), Department of Radiation Sciences (DRS), Helmholtz Center Munich, Munich, Germany
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Bernhardt D, Peeken JC, Kehl V, Eitz K, Guckenberger M, Andratschke N, Mayinger MC, Lindel K, Dieckmann K, El Shafie R, Debus J, Riesterer O, Rogers S, Blanck O, Wolff R, Grosu A, Bilger A, Henkenberens C, Schulze K, Gani C, Müller AC, Radlanski K, Janssen S, Ferentinos K, Combs SE. Post-Operative Stereotactic Radiotherapy for Resected Brain Metastases: Results of the Multicenter Analysis (AURORA) of the German Working Group "Stereotactic Radiotherapy". Int J Radiat Oncol Biol Phys 2023; 117:e87-e88. [PMID: 37786203 DOI: 10.1016/j.ijrobp.2023.06.842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
PURPOSE/OBJECTIVE(S) While the results of prospective studies support the use of postoperative stereotactic radiotherapy (RT) to the resection cavity (RC) as the standard of care after surgery, there are several issues that need to be investigated such as factors for improving local control, risk of leptomeningeal disease and radiation necrosis. Further, the optimal dose and fractionation is still under debate. MATERIALS/METHODS The working group "Stereotactic Radiotherapy" of the German Society of Radiation Oncology (DEGRO) analyzed its multi-institutional database with 661 patients who received postoperative stereotactic RT to the RC. Treatment was performed at 13 centers between 2008 and 2021. Patient characteristics, treatment details, and follow-up data including overall survival (OS), local control (LC) were evaluated. Cox Regression and Kaplan-Meier curves with Log-rank Tests were calculated for selected variables. RESULTS In this retrospective study, overall survival was 61.5% at 1 year, 47.6% at 2 years, and 35.5% at 3 years, and local control was 84.6% at 1 year, 74.8% at 2 years, and 72.8% at 3 years. 96% of patients were treated with hypofractionated stereotactic radiotherapy (HSRT), only 26 patients received single fraction radiosurgery (4%). Prognostic factors associated with overall survival were Karnofsky Performance Status, RPA and GPA class, controlled primary tumor and absence of extracranial metastases, whereas prognostic factor associated with local control was planning target volume (23 mL or less). CONCLUSION HSRT is the most common fractionation form in the treatment of RCs in this multicenter analysis. This approach results in excellent OS and LC outcomes. OS in patients with resected brain metastases is mainly influenced by performance status. In regard to local control, RT of large cavities remain a challenge with significantly worse outcome.
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Affiliation(s)
- D Bernhardt
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany; German Cancer Consortium (DKTK), Partner Site Munich, Germany, Munich, Germany
| | - J C Peeken
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany; Institute of Radiation Medicine (IRM), Department of Radiation Sciences (DRS), Helmholtz Center Munich, Munich, Germany
| | - V Kehl
- Institute for AI and Informatics in Medicine, Munich, NA, Germany
| | - K Eitz
- Department of Radiation Oncology - Klinikum rechts der Isar, Technical University of Munich (TUM), Munich, Germany
| | - M Guckenberger
- Department of Radiation Oncology, University Hospital Zurich (USZ), University of Zurich (UZH), Zurich, Switzerland
| | - N Andratschke
- Department of Radiation Oncology, University Hospital of Zurich, University of Zurich, Zurich, Switzerland
| | - M C Mayinger
- Department of Radiation Oncology, University Hospital of Zurich, University of Zurich, Zurich, Switzerland
| | - K Lindel
- Municipal Hospital, Department for Radiation Oncology, Karlsruhe, Germany
| | - K Dieckmann
- Department of Radiation Oncology, Vienna, Austria
| | - R El Shafie
- 8Department of Radiation Oncology, University Hospital Göttingen, Göttingen, Germany
| | - J Debus
- CCU Translational Radiation Oncology, German Cancer Consortium (DKTK) Core-Center Heidelberg, National Center for Tumor Diseases (NCT), Heidelberg University Hospital (UKHD) and German Cancer Research Center (DKFZ), Heidelberg, Germany; Radiation Oncology University Hospital Heidelberg, Heidelberg, Germany
| | - O Riesterer
- Center for Radiation Oncology KSA-KSB, Cantonal Hospital Aarau, Aarau, Switzerland
| | - S Rogers
- Radiation Oncology Center KSA-KSB, Kantonsspital Aarau, Aarau, Switzerland
| | - O Blanck
- Department of Radiation Oncology, University Medical Center Schleswig-Holstein, Campus Kiel, Kiel, Germany
| | - R Wolff
- University Hospital Frankfurt, Department of Neurosurgery, Frankfurt, Germany
| | - A Grosu
- German Cancer Consortium (DKTK), Partner Site Freiburg, Freiburg, Germany; Department of Radiation Oncology, Medical Center, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - A Bilger
- Department of Radiation Oncology, Medical Center, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - C Henkenberens
- Department of Radiotherapy and Special Oncology, Medical School Hannover, Hannover, Germany
| | - K Schulze
- Klinikum Fulda, 36251 Bad Hersfeld, Germany
| | - C Gani
- Department of Radiation Oncology, University Hospital and Medical Faculty Tübingen, Eberhard Karls University Tübingen, Tübingen, Germany
| | - A C Müller
- Department of Radiotherapy, Klinikum Ludwigsburg, Ludwigsburg, Germany
| | - K Radlanski
- Radiation Oncology and Radiotherapy, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - S Janssen
- Department of Radiation Oncology, University of Lübeck, Lübeck, Germany
| | - K Ferentinos
- Radiation Oncology Department, German Oncology Center, Limassol, Cyprus
| | - S E Combs
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany; Institute of Innovative Radiotherapy (iRT), Department of Radiation Sciences (DRS), Helmholtz Zentrum München, Neuherberg, Germany
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Kluge A, Ehrbar S, Grehn M, Fleckenstein J, Baus WW, Siebert FA, Schweikard A, Andratschke N, Mayinger MC, Boda-Heggemann J, Buergy D, Celik E, Krug D, Kovacs B, Saguner AM, Rudic B, Bergengruen P, Boldt LH, Stauber A, Zaman A, Bonnemeier H, Dunst J, Budach V, Blanck O, Mehrhof F. Treatment Planning for Cardiac Radioablation: Multicenter Multiplatform Benchmarking for the XXX Trial. Int J Radiat Oncol Biol Phys 2022; 114:360-372. [PMID: 35716847 DOI: 10.1016/j.ijrobp.2022.06.056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2022] [Revised: 05/15/2022] [Accepted: 06/05/2022] [Indexed: 10/18/2022]
Abstract
PURPOSE Cardiac radioablation is a novel treatment option for patients with refractory ventricular tachycardia (VT) unsuitable for catheter ablation. The quality of treatment planning depends on dose specifications, platform capabilities, and experience of the treating staff. To harmonize the treatment planning, benchmarking of this process is necessary for multicenter clinical studies such as the XXX trial. METHODS AND MATERIALS Planning computed tomography data and consensus structures from three patients were sent to five academic centers for independent plan development using a variety of platforms and techniques with the XXX study protocol serving as guideline. Three-dimensional dose distributions and treatment plan details were collected and analyzed. In addition, an objective relative plan quality ranking system for VT treatments was established. RESULTS For each case, three coplanar volumetric modulated arc (VMAT) plans for C-arm linear accelerators (LINAC) and three non-coplanar treatment plans for robotic arm LINAC were generated. All plans were suitable for clinical applications with minor deviations from study guidelines in most centers. Eleven of 18 treatment plans showed maximal one minor deviation each for target and cardiac substructures. However, dose-volume histograms showed substantial differences: in one case, the PTV≥30Gy ranged from 0.0% to 79.9% and the RIVA V14Gy ranged from 4.0% to 45.4%. Overall, the VMAT plans had steeper dose gradients in the high dose region, while the plans for the robotic arm LINAC had smaller low dose regions. Thereby, VMAT plans required only about half as many monitor units, resulting in shorter delivery times, possibly an important factor in treatment outcome. CONCLUSIONS Cardiac radioablation is feasible with robotic arm and C-arm LINAC systems with comparable plan quality. Although cross-center training and best practice guidelines have been provided, further recommendations, especially for cardiac substructures, and ranking of dose guidelines will be helpful to optimize cardiac radioablation outcomes.
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Affiliation(s)
- Anne Kluge
- Klinik für Radioonkologie und Strahlentherapie, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Stefanie Ehrbar
- Klinik für Radio-Onkologie, UniversitätsSpital Zürich, University of Zurich, Zürich, CH
| | - Melanie Grehn
- Department of Radiation Oncology, University Medical Center Schleswig-Holstein, Kiel, Germany
| | - Jens Fleckenstein
- Department of Radiation Oncology, Universitätsmedizin Mannheim, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Wolfgang W Baus
- Department of Radiation Oncology and Cyberknife Center, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Frank-Andre Siebert
- Department of Radiation Oncology, University Medical Center Schleswig-Holstein, Kiel, Germany
| | - Achim Schweikard
- University of Lübeck, Institute for Robotic and Cognitive Systems, Lübeck, Germany
| | - Nicolaus Andratschke
- Klinik für Radio-Onkologie, UniversitätsSpital Zürich, University of Zurich, Zürich, CH
| | - Michael C Mayinger
- Klinik für Radio-Onkologie, UniversitätsSpital Zürich, University of Zurich, Zürich, CH
| | - Judit Boda-Heggemann
- Department of Radiation Oncology, Universitätsmedizin Mannheim, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Daniel Buergy
- Department of Radiation Oncology, Universitätsmedizin Mannheim, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Eren Celik
- Department of Radiation Oncology and Cyberknife Center, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - David Krug
- Department of Radiation Oncology, University Medical Center Schleswig-Holstein, Kiel, Germany
| | - Boldizsar Kovacs
- Universitäres Herzzentrum, Klinik für Kardiologie, UniversitätsSpital Zürich, University of Zurich, Zürich, CH
| | - Ardan M Saguner
- Universitäres Herzzentrum, Klinik für Kardiologie, UniversitätsSpital Zürich, University of Zurich, Zürich, CH
| | - Boris Rudic
- Medizinische Klinik, Universitätsmedizin Mannheim and German Center for Cardiovascular Research (DZHK), Partner Site Heidelberg/Mannheim, Mannheim, Germany
| | - Paula Bergengruen
- Klinik für Radioonkologie und Strahlentherapie, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Leif-Hendrik Boldt
- Med. Klinik m.S. Kardiologie, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Annina Stauber
- Department of Radiation Oncology and Cyberknife Center, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Adrian Zaman
- Klinik für Innere Medizin III, Abteilung für Elektrophysiologie und Rhythmologie, Universitätsklinikum Schleswig-Holstein, Kiel, Germany
| | - Hendrik Bonnemeier
- Klinik für Innere Medizin III, Abteilung für Elektrophysiologie und Rhythmologie, Universitätsklinikum Schleswig-Holstein, Kiel, Germany
| | - Jürgen Dunst
- Department of Radiation Oncology, University Medical Center Schleswig-Holstein, Kiel, Germany
| | - Volker Budach
- Klinik für Radioonkologie und Strahlentherapie, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Oliver Blanck
- Department of Radiation Oncology, University Medical Center Schleswig-Holstein, Kiel, Germany
| | - Felix Mehrhof
- Klinik für Radioonkologie und Strahlentherapie, Charité - Universitätsmedizin Berlin, Berlin, Germany.
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Boda-Heggemann J, Blanck O, Mehrhof F, Ernst F, Buergy D, Fleckenstein J, Tülümen E, Krug D, Siebert FA, Zaman A, Kluge AK, Parwani AS, Andratschke N, Mayinger MC, Ehrbar S, Saguner AM, Celik E, Baus WW, Stauber A, Vogel L, Schweikard A, Budach V, Dunst J, Boldt LH, Bonnemeier H, Rudic B. Interdisciplinary Clinical Target Volume Generation for Cardiac Radioablation: Multicenter Benchmarking for the RAdiosurgery for VENtricular TAchycardia (RAVENTA) Trial. Int J Radiat Oncol Biol Phys 2021; 110:745-756. [DOI: 10.1016/j.ijrobp.2021.01.028] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2020] [Revised: 01/19/2021] [Accepted: 01/21/2021] [Indexed: 02/05/2023]
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Pigorsch SU, Kampfer S, Oechsner M, Mayinger MC, Mozes P, Devecka M, Kessel KK, Combs SE, Wilkens JJ. Report on planning comparison of VMAT, IMRT and helical tomotherapy for the ESCALOX-trial pre-study. Radiat Oncol 2020; 15:253. [PMID: 33138837 PMCID: PMC7607845 DOI: 10.1186/s13014-020-01693-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2020] [Accepted: 10/21/2020] [Indexed: 12/12/2022] Open
Abstract
Background The ESCALOX trial was designed as a multicenter, randomized prospective dose escalation study for head and neck cancer. Therefore, feasibility of treatment planning via different treatment planning systems (TPS) and radiotherapy (RT) techniques is essential. We hypothesized the comparability of dose distributions for simultaneous integrated boost (SIB) volumes respecting the constraints by different TPS and RT techniques. Methods CT data sets of the first six patients (all male, mean age: 61.3 years) of the pre-study (up to 77 Gy) were used for comparison of IMRT, VMAT, and helical tomotherapy (HT). Oropharynx was the primary tumor location. Normalization of the three step SIB (77 Gy, 70 Gy, 56 Gy) was D95% = 77 Gy. Coverage (CVF), healthy tissue conformity index (HTCI), conformation number (CN), and dose homogeneity (HI) were compared for PTVs and conformation index (COIN) for parotids. Results All RT techniques achieved good coverage. For SIB77Gy, CVF was best for IMRT and VMAT, HT achieved highest CN followed by VMAT and IMRT. HT reached good HTCI value, and HI compared to both other techniques. For SIB70Gy, CVF was best by IMRT. HTCI favored HT, consequently CN as well. HI was slightly better for HT. For SIB56Gy, CVF resulted comparably. Conformity favors VMAT as seen by HTCI and CN. Dmean of ipsilateral and contralateral parotids favor HT. Conclusion Different TPS for dose escalation reliably achieved high plan quality. Despite the very good results of HT planning for coverage, conformity, and homogeneity, the TPS also achieved acceptable results for IMRT and VMAT. Trial registration ClinicalTrials.gov Identifier: NCT 01212354, EudraCT-No.: 2010-021139-15. ARO: ARO 14-01
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Affiliation(s)
- Steffi U Pigorsch
- Department of Radiation Oncology, Technical University of Munich (TUM), School of Medicine, Klinikum Rechts Der Isar, Ismaninger Straße 22, 81675, Munich, Germany.
| | - Severin Kampfer
- Department of Radiation Oncology, Technical University of Munich (TUM), School of Medicine, Klinikum Rechts Der Isar, Ismaninger Straße 22, 81675, Munich, Germany
| | - Markus Oechsner
- Department of Radiation Oncology, Technical University of Munich (TUM), School of Medicine, Klinikum Rechts Der Isar, Ismaninger Straße 22, 81675, Munich, Germany
| | - Michael C Mayinger
- Department of Radiation Oncology, University Hospital Zurich, Rämistrasse 100, Zurich, Switzerland
| | - Petra Mozes
- Department of Radiation Oncology, Technical University of Munich (TUM), School of Medicine, Klinikum Rechts Der Isar, Ismaninger Straße 22, 81675, Munich, Germany
| | - Michal Devecka
- Department of Radiation Oncology, Technical University of Munich (TUM), School of Medicine, Klinikum Rechts Der Isar, Ismaninger Straße 22, 81675, Munich, Germany
| | - Kerstin K Kessel
- Department of Radiation Oncology, Technical University of Munich (TUM), School of Medicine, Klinikum Rechts Der Isar, Ismaninger Straße 22, 81675, Munich, Germany
| | - Stephanie E Combs
- Department of Radiation Oncology, Technical University of Munich (TUM), School of Medicine, Klinikum Rechts Der Isar, Ismaninger Straße 22, 81675, Munich, Germany.,Institute of Radiation Medicine (IRM), Helmholtz Zentrum München, Ingolstädter Landstraße 1, Neuherberg, Germany
| | - Jan J Wilkens
- Department of Radiation Oncology, Technical University of Munich (TUM), School of Medicine, Klinikum Rechts Der Isar, Ismaninger Straße 22, 81675, Munich, Germany
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Borm KJ, Loos M, Oechsner M, Mayinger MC, Paepke D, Kiechle MB, Combs SE, Duma MN. Acute radiodermatitis in modern adjuvant 3D conformal radiotherapy for breast cancer - the impact of dose distribution and patient related factors. Radiat Oncol 2018; 13:218. [PMID: 30404664 PMCID: PMC6223003 DOI: 10.1186/s13014-018-1160-5] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2018] [Accepted: 10/22/2018] [Indexed: 12/20/2022] Open
Abstract
Purpose This study was performed to evaluate skin toxicity during modern three-dimensional conformal radiotherapy (3D-CRT) and to evaluate the importance of dose distribution and patient related factors. Material and methods This study comprises 255 patients with breast cancer treated with tangential three-dimensional conformal radiotherapy (3D-CRT) after breast conserving surgery between 03/2012 and 05/2017. The median prescribed dose was 50.4 Gy (range 50–50.4) and 92.2% of the patients received a sequential boost of 10–16 Gy. Adverse skin toxicities (according to CTCAE v. 4.03 and the occurrence of moist desquamations) were assessed at the end of treatment. The dose distribution in the skin (5 mm strip from the patient outline) and in the CTV was evaluated and correlated to the CTCAE scores and the occurrence of moist desquamation. Results 42.4% of the patients developed grade I, 55.7% grade II and 2% grade III skin toxicities. Moist desquamation was observed in 59 cases (23.1%). Dose distribution within the CTV and skin was homogenous with only small areas receiving 107% of the prescribed dose (median: 0.7 cm3) in the CTV and 105% (median 0.5 cm3) in the skin. On univariate analysis breast size as well as V107%(CTV), V105%(skin) and V80%(skin) correlated significantly (p < 0.05) with the incidence of skin toxicity. On multivariate analysis only V80%(skin) was confirmed as independent risk factor. Conclusion Modern tangential multi-field 3D-CRT allows a homogeneous dose distribution with similar skin toxicity as compared to studies performing IMRT. Dose distribution within the skin (V80%) might have a relevant impact on the severity of skin toxicity and the occurrence of moist desquamation.
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Affiliation(s)
- Kai J Borm
- Department of Radiotherapy, Klinikum Rechts der Isar, Technical University, Munich, Germany
| | | | - Markus Oechsner
- Department of Radiotherapy, Klinikum Rechts der Isar, Technical University, Munich, Germany
| | - Michael C Mayinger
- Department of Radiotherapy, Klinikum Rechts der Isar, Technical University, Munich, Germany
| | - Daniela Paepke
- Department of Gynecology and Obstetrics, Klinikum Rechts der Isar, Technical University, Munich, Germany
| | - Marion B Kiechle
- Department of Gynecology and Obstetrics, Klinikum Rechts der Isar, Technical University, Munich, Germany
| | - Stephanie E Combs
- Department of Radiotherapy, Klinikum Rechts der Isar, Technical University, Munich, Germany.,Deutsches Konsortium für Translationale Krebsforschung (DKTK)-Partner Site Munich, 81675, Munich, Germany.,Institute of Innovative Radiohterapy, Helmholtzzentrum München, Munich, Germany
| | - Marciana N Duma
- Department of Radiotherapy, Klinikum Rechts der Isar, Technical University, Munich, Germany. .,Institute of Innovative Radiohterapy, Helmholtzzentrum München, Munich, Germany. .,Department of Radiation Oncology, Klinikum rechts der Isar/ TU Munchen, Ismaninger Strasse 22, 81675, Munchen, Germany.
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