External validation of a multivariable prediction model for positive resection margins in breast-conserving surgery
Abstract Objectives Positive resection margins after breast-conserving surgery (BCS) most often demands a repeat surgery. To preoperatively identify patients at risk of positive margins, a multivariable model has been developed that predicts positive margins after BCS with a high accuracy. This stud...
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| Format: | Article |
| Language: | English |
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BMC
2025-01-01
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| Series: | BMC Research Notes |
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| Online Access: | https://doi.org/10.1186/s13104-025-07103-8 |
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| _version_ | 1832572089746325504 |
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| author | Irina Palimaru Manhoobi Julia Ellbrant Pär-Ola Bendahl Søren Redsted Anne Bodilsen Trine Tramm Peer Christiansen Lisa Rydén |
| author_facet | Irina Palimaru Manhoobi Julia Ellbrant Pär-Ola Bendahl Søren Redsted Anne Bodilsen Trine Tramm Peer Christiansen Lisa Rydén |
| author_sort | Irina Palimaru Manhoobi |
| collection | DOAJ |
| description | Abstract Objectives Positive resection margins after breast-conserving surgery (BCS) most often demands a repeat surgery. To preoperatively identify patients at risk of positive margins, a multivariable model has been developed that predicts positive margins after BCS with a high accuracy. This study aimed to externally validate this prediction model to explore its generalizability and assess if additional preoperatively available variables can further improve its predictive accuracy. The validation cohort included 225 patients with invasive breast cancer who underwent BCS at Aarhus University Hospital, Aarhus, Denmark during 2020–2022. Receiver operating characteristic (ROC) and calibration analysis were used to validate the prediction model. Univariable logistic regression was used to evaluate if additional variables available in the validation cohort were associated with positive margins and backward elimination to explore if these variables could further improve the model´s predictive accuracy. Results The AUC of the model was 0.60 (95% CI: 0.50–0.70) indicating a lower discriminative capacity in the external cohort. We found weak evidence for an association between increased preoperative breast density on mammography and positive resection margins after BCS (p = 0.027), but the AUC of the model did not improve, when mammographic breast density was included as an additional variable in the model. |
| format | Article |
| id | doaj-art-b2096f3024f84acc99aafa306b383db8 |
| institution | Kabale University |
| issn | 1756-0500 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Research Notes |
| spelling | doaj-art-b2096f3024f84acc99aafa306b383db82025-02-02T12:07:07ZengBMCBMC Research Notes1756-05002025-01-011811810.1186/s13104-025-07103-8External validation of a multivariable prediction model for positive resection margins in breast-conserving surgeryIrina Palimaru Manhoobi0Julia Ellbrant1Pär-Ola Bendahl2Søren Redsted3Anne Bodilsen4Trine Tramm5Peer Christiansen6Lisa Rydén7Department of Radiology, Aarhus University HospitalDepartment of Surgery, Department of Clinical Sciences, Division of Surgery, Skåne University Hospital, Lund UniversityDepartment of Clinical Sciences, Division of Oncology, Lund UniversityDepartment of Radiology, Aarhus University HospitalDepartment of Abdominal Surgery, Aarhus University HospitalDepartment of Pathology, Aarhus University HospitalDepartment of Plastic and Breast Surgery, Aarhus University HospitalDepartment of Surgery, Department of Clinical Sciences, Division of Surgery, Skåne University Hospital, Lund UniversityAbstract Objectives Positive resection margins after breast-conserving surgery (BCS) most often demands a repeat surgery. To preoperatively identify patients at risk of positive margins, a multivariable model has been developed that predicts positive margins after BCS with a high accuracy. This study aimed to externally validate this prediction model to explore its generalizability and assess if additional preoperatively available variables can further improve its predictive accuracy. The validation cohort included 225 patients with invasive breast cancer who underwent BCS at Aarhus University Hospital, Aarhus, Denmark during 2020–2022. Receiver operating characteristic (ROC) and calibration analysis were used to validate the prediction model. Univariable logistic regression was used to evaluate if additional variables available in the validation cohort were associated with positive margins and backward elimination to explore if these variables could further improve the model´s predictive accuracy. Results The AUC of the model was 0.60 (95% CI: 0.50–0.70) indicating a lower discriminative capacity in the external cohort. We found weak evidence for an association between increased preoperative breast density on mammography and positive resection margins after BCS (p = 0.027), but the AUC of the model did not improve, when mammographic breast density was included as an additional variable in the model.https://doi.org/10.1186/s13104-025-07103-8Breast-conserving surgeryPrediction modelExternal validationPositive resection marginsInvasive breast cancer |
| spellingShingle | Irina Palimaru Manhoobi Julia Ellbrant Pär-Ola Bendahl Søren Redsted Anne Bodilsen Trine Tramm Peer Christiansen Lisa Rydén External validation of a multivariable prediction model for positive resection margins in breast-conserving surgery BMC Research Notes Breast-conserving surgery Prediction model External validation Positive resection margins Invasive breast cancer |
| title | External validation of a multivariable prediction model for positive resection margins in breast-conserving surgery |
| title_full | External validation of a multivariable prediction model for positive resection margins in breast-conserving surgery |
| title_fullStr | External validation of a multivariable prediction model for positive resection margins in breast-conserving surgery |
| title_full_unstemmed | External validation of a multivariable prediction model for positive resection margins in breast-conserving surgery |
| title_short | External validation of a multivariable prediction model for positive resection margins in breast-conserving surgery |
| title_sort | external validation of a multivariable prediction model for positive resection margins in breast conserving surgery |
| topic | Breast-conserving surgery Prediction model External validation Positive resection margins Invasive breast cancer |
| url | https://doi.org/10.1186/s13104-025-07103-8 |
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