Si-Qi Qiu1, Merel Aarnink2, Marissa C van Maaren3, Monique D Dorrius4, Arkajyoti Bhattacharya5, Jeroen Veltman6, Caroline A H Klazen7, Jan H Korte8, Susanne H Estourgie9, Pieter Ott10, Wendy Kelder11, Huan-Cheng Zeng12, Hendrik Koffijberg2, Guo-Jun Zhang13, Gooitzen M van Dam14, Sabine Siesling15. 1. Department of Surgery, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands; The Breast Center, Cancer Hospital of Shantou University Medical College, Guangdong, China. 2. Department of Health Technology and Services Research, MIRA Institute for Biomedical Technology and Technical Medicine, University of Twente, Enschede, The Netherlands. 3. Department of Research, Netherlands Comprehensive Cancer Organisation, Utrecht, The Netherlands. 4. Department of Radiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands. 5. Department of Medical Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands. 6. Department of Radiology, ZiekenhuisgroepTwente, Almelo, The Netherlands. 7. Department of Radiology, Medisch Spectrum Twente, Enschede, The Netherlands. 8. Department of Radiology, Isala, Zwolle, The Netherlands. 9. Department of Surgery, Medisch Centrum Leeuwarden, Friesland, The Netherlands. 10. Department of Radiology, Martini Hospital, Groningen, The Netherlands. 11. Department of Surgery, Martini Hospital, Groningen, The Netherlands. 12. The Breast Center, Cancer Hospital of Shantou University Medical College, Guangdong, China. 13. Changjiang Scholar's Laboratory of Shantou University Medical College, Guangdong, China. 14. Department of Surgery, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands; Department of Nuclear Medicine and Molecular Imaging & Intensive Care, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands. 15. Department of Health Technology and Services Research, MIRA Institute for Biomedical Technology and Technical Medicine, University of Twente, Enschede, The Netherlands; Department of Research, Netherlands Comprehensive Cancer Organisation, Utrecht, The Netherlands. Electronic address: s.siesling@iknl.nl.
Abstract
PURPOSE: This study aimed to validate and update a model for predicting the risk of axillary lymph node (ALN) metastasis for assisting clinical decision-making. METHODS: We included breast cancer patients diagnosed at six Dutch hospitals between 2011 and 2015 to validate the original model which includes six variables: clinical tumor size, tumor grade, estrogen receptor status, lymph node longest axis, cortical thickness and hilum status as detected by ultrasonography. Subsequently, we updated the original model using generalized linear model (GLM) tree analysis and by adjusting its intercept and slope. The area under the receiver operator characteristic curve (AUC) and calibration curve were used to assess the original and updated models. Clinical usefulness of the model was evaluated by false-negative rates (FNRs) at different cut-off points for the predictive probability. RESULTS: Data from 1416 patients were analyzed. The AUC for the original model was 0.774. Patients were classified into four risk groups by GLM analysis, for which four updated models were created. The AUC for the updated models was 0.812. The calibration curves showed that the updated model predictions were better in agreement with actual observations than the original model predictions. FNRs of the updated models were lower than the preset 10% at all cut-off points when the predictive probability was less than 12.0%. CONCLUSIONS: The original model showed good performance in the Dutch validation population. The updated models resulted in more accurate ALN metastasis prediction and could be useful preoperative tools in selecting low-risk patients for omission of axillary surgery.
PURPOSE: This study aimed to validate and update a model for predicting the risk of axillary lymph node (ALN) metastasis for assisting clinical decision-making. METHODS: We included breast cancerpatients diagnosed at six Dutch hospitals between 2011 and 2015 to validate the original model which includes six variables: clinical tumor size, tumor grade, estrogen receptor status, lymph node longest axis, cortical thickness and hilum status as detected by ultrasonography. Subsequently, we updated the original model using generalized linear model (GLM) tree analysis and by adjusting its intercept and slope. The area under the receiver operator characteristic curve (AUC) and calibration curve were used to assess the original and updated models. Clinical usefulness of the model was evaluated by false-negative rates (FNRs) at different cut-off points for the predictive probability. RESULTS: Data from 1416 patients were analyzed. The AUC for the original model was 0.774. Patients were classified into four risk groups by GLM analysis, for which four updated models were created. The AUC for the updated models was 0.812. The calibration curves showed that the updated model predictions were better in agreement with actual observations than the original model predictions. FNRs of the updated models were lower than the preset 10% at all cut-off points when the predictive probability was less than 12.0%. CONCLUSIONS: The original model showed good performance in the Dutch validation population. The updated models resulted in more accurate ALN metastasis prediction and could be useful preoperative tools in selecting low-risk patients for omission of axillary surgery.
Authors: Rachelle Crescenzi; Paula M Donahue; Vaughn G Braxton; Allison O Scott; Helen B Mahany; Sarah K Lants; Manus J Donahue Journal: NMR Biomed Date: 2018-10-12 Impact factor: 4.044
Authors: Cornelia D van Steenbeek; Marissa C van Maaren; Sabine Siesling; Annemieke Witteveen; Xander A A M Verbeek; Hendrik Koffijberg Journal: BMC Med Res Methodol Date: 2019-06-08 Impact factor: 4.615