Contouring variation affects estimates of normal tissue complication probability for breast fibrosis after radiotherapy
Tanwiwat Jaikuna,
Eliana Vasquez Osorio,
David Azria,
Jenny Chang-Claude,
Maria Carmen De Santis,
Sara Gutiérrez-Enríquez,
Marcel van Herk,
Peter Hoskin,
Maarten Lambrecht,
Zoe Lingard,
Petra Seibold,
Alejandro Seoane,
Elena Sperk,
R Paul Symonds,
Christopher J. Talbot,
Tiziana Rancati,
Tim Rattay,
Victoria Reyes,
Barry S. Rosenstein,
Dirk de Ruysscher,
Ana Vega,
Liv Veldeman,
Adam Webb,
Catharine M.L. West,
Marianne C. Aznar
Affiliations
Tanwiwat Jaikuna
Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Christie NHS Foundation Trust Hospital, Manchester, United Kingdom; Division of Radiation Oncology, Department of Radiology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
Eliana Vasquez Osorio
Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Christie NHS Foundation Trust Hospital, Manchester, United Kingdom
David Azria
Department of Radiation Oncology, Montpellier Cancer Institute, Université Montpellier, Inserm, U1194, France
Jenny Chang-Claude
Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany; University Cancer Center Hamburg (UCCH), University Medical Center Hamburg-Eppendorf, Germany
Maria Carmen De Santis
Radiation Oncology, Fondazione IRCCS Isituto Nazionale dei Tumori, Milan, Italy
Sara Gutiérrez-Enríquez
Hereditary Cancer Genetics Group, Vall d’Hebron Institute of Oncology (VHIO), Vall d’Hebron Hospital Campus, Barcelona, Spain
Marcel van Herk
Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Christie NHS Foundation Trust Hospital, Manchester, United Kingdom
Peter Hoskin
Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Christie NHS Foundation Trust Hospital, Manchester, United Kingdom
Maarten Lambrecht
KU Leuven, Department of Radiation Oncology, Leuven, Belgium
Zoe Lingard
Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Christie NHS Foundation Trust Hospital, Manchester, United Kingdom
Petra Seibold
Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
Alejandro Seoane
Medical Physics Department, Vall d'Hebron Hospital Universitari, Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain
Elena Sperk
Department of Radiation Oncology, Mannheim Cancer Center, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
R Paul Symonds
Leicester Cancer Research Centre, University of Leicester, United Kingdom
Christopher J. Talbot
Leicester Cancer Research Centre, University of Leicester, United Kingdom
Tiziana Rancati
Data Science Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
Tim Rattay
Leicester Cancer Research Centre, University of Leicester, United Kingdom
Department of Radiation Oncology, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, USA
Dirk de Ruysscher
Maastricht University Medical Center, Department of Radiation Oncology (Maastro Clinic), GROW School for Oncology and Developmental Biology, Maastricht, the Netherlands
Ana Vega
Fundación Pública Galega de Medicina Xenómica, Grupo de Medicina Xenómica (USC), Santiago de Compostela, Spain; Instituto de Investigación Sanitaria de, Santiago de Compostela, Spain; Biomedical Network on Rare Diseases (CIBERER), Spain
Liv Veldeman
Ghent University Hospital, Department of Radiation Oncology, Ghent, Belgium
Adam Webb
Department of Genetics and Genome Biology, University of Leicester, United Kingdom
Catharine M.L. West
Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Christie NHS Foundation Trust Hospital, Manchester, United Kingdom
Marianne C. Aznar
Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Christie NHS Foundation Trust Hospital, Manchester, United Kingdom; Corresponding author. Radiotherapy Related Research, Department 58, The Christie NHS foundation trust, Wilmslow road, Manchester, M20 4BX, United Kingdom.
Background: Normal tissue complication probability (NTCP) models can be useful to estimate the risk of fibrosis after breast-conserving surgery (BCS) and radiotherapy (RT) to the breast. However, they are subject to uncertainties. We present the impact of contouring variation on the prediction of fibrosis. Materials and methods: 280 breast cancer patients treated BCS-RT were included. Nine Clinical Target Volume (CTV) contours were created for each patient: i) CTV_crop (reference), cropped 5 mm from the skin and ii) CTV_skin, uncropped and including the skin, iii) segmenting the 95% isodose (Iso95%) and iv) 3 different auto-contouring atlases generating uncropped and cropped contours (Atlas_skin/Atlas_crop). To illustrate the impact of contour variation on NTCP estimates, we applied two equations predicting fibrosis grade ≥ 2 at 5 years, based on Lyman-Kutcher-Burman (LKB) and Relative Seriality (RS) models, respectively, to each contour. Differences were evaluated using repeated-measures ANOVA. For completeness, the association between observed fibrosis events and NTCP estimates was also evaluated using logistic regression. Results: There were minimal differences between contours when the same contouring approach was followed (cropped and uncropped). CTV_skin and Atlas_skin contours had lower NTCP estimates (−3.92%, IQR 4.00, p < 0.05) compared to CTV_crop. No significant difference was observed for Atlas_crop and Iso95% contours compared to CTV_crop. For the whole cohort, NTCP estimates varied between 5.3% and 49.5% (LKB) or 2.2% and 49.6% (RS) depending on the choice of contours. NTCP estimates for individual patients varied by up to a factor of 4. Estimates from “skin” contours showed higher agreement with observed events. Conclusion: Contour variations can lead to significantly different NTCP estimates for breast fibrosis, highlighting the importance of standardising breast contours before developing and/or applying NTCP models.