Development and validation of prediction models for health-related quality of life outcomes after breast cancer surgery and reconstruction
Year: 2026
Type: article
Study design: Observational Study
Abstract: Background Predictions of Health-Related Quality of Life (HRQoL) outcomes could support realistic recovery expectations after breast cancer (BC) surgery. We aimed to develop and validate prediction models for HRQoL outcomes after BC surgery. Methods We used three datasets of BC patients from Berlin, Germany; Ljubljana, Slovenia; and Rotterdam; Netherlands. We included non-metastasised patients who were surgically treated for an initial diagnosis of BC and completed pre- and postoperative validated questionnaires. We used linear mixed models to analyse 15 domains of the EORTC QLQ-C30 and EORTC QLQ-BR23 over a two-year horizon. Baseline domain score (measured pre-operatively), age, BMI, smoking, TN stage, receptor status, neoadjuvant chemotherapy, axillary surgery and surgery type (breast-conserving, mastectomy, and immediate implant-based reconstruction) were included as predictors. Predictive performance at validation was assessed by the proportion of variance explained (marginal R 2 ; mR 2 ). Results We included N=795 patients from Germany for development and N=623 from Slovenia and N=417 from Netherlands for validation. The largest proportion of variance was explained by the prediction models for sexual functioning (SF, mR 2 35%), physical functioning (PF, mR 2 29%), body image (BI, mR 2 26%), and cognitive functioning (CF, mR 2 25%). The models captured meaningfully different trends over time for different outcomes and surgery types. The predictive performance of the models was largely driven by the baseline domain score. Performance was reasonable at external validation, with r 2 values of 19–33% for PF, 10–17% for CF, 15–18% for BI, and 22–28% for SF, although some other outcomes (e.g. breast symptoms and role functioning) showed miscalibration, indicating a need for recalibration. Conclusion HRQoL after breast cancer surgery can be predicted using simple models with baseline domain scores and surgery type, demonstrating a new opportunity for Patient-Reported Outcome Measures (PROMs) in personalized care.
Authors: Elfi M. Verheul, Maria Margarete Karsten, Pimrapat Gebert, Lea Doppelbauer, Simona Borstnar, Sabine Siesling, Hester F. Lingsma, Noëlle J.M.C. Vrancken-Peeters, Anne M. Stiggelbout, Marc A.M. Mureau, Linetta B. Koppert, David van Klaveren, Marieke Bak, Liesbeth C. de Wreede, Kasper Frank, Laura Hartman, Elham Hedayati, Toby Hackmann, Lára R. Hallsson, Yassin Engelberts, Yufeng Liu, Daisy Monika Lal, Ida J. Korfage, Amara Callistus Nwosu, Claudia Cruz Oliveira, Barbara Perić, Roberto Pazo-Cid, Carlos Romero-Piqueras, Judith Rietjens, Dirk Snelders, Uwe Siebert, Gaby Sroczynski, Ewout Steyerberg, Erik van Mulligen, Tamara Wit, Michel W. Wouters
Institutions: Erasmus MC, Dutch Institute for Clinical Auditing, Erasmus University Rotterdam, Charité - Universitätsmedizin Berlin, Institute of Oncology Ljubljana, Netherlands Comprehensive Cancer Organisation, University of Twente, Erasmus MC Cancer Institute, Leiden University Medical Center, Technical University of Munich, Leiden University, Vejle Sygehus, Lillebaelt Hospital, Karolinska Institutet, UMIT TIROL - Private University for Health Sciences and Health Technology, Lancaster University, Marie Curie, University of Ljubljana, Universidad de Zaragoza, Hospital Universitario Miguel Servet, Instituto de Investigación Sanitaria Aragón, Delft University of Technology, Harvard University, Massachusetts General Hospital, University Medical Center Utrecht
Language: English
Topic: Cancer survivorship and care
Subfield: Oncology
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $3,810
OpenAlex ID: W7128483896