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Photo of Mattias Ohlsson

Mattias Ohlsson


Photo of Mattias Ohlsson

Retrospective validation study of an artificial neural network-based preoperative decision-support tool for noninvasive lymph node staging (NILS) in women with primary breast cancer (ISRCTN14341750)


  • Ida Skarping
  • Julia Ellbrant
  • Looket Dihge
  • Mattias Ohlsson
  • Linnea Huss
  • Pär Ola Bendahl
  • Lisa Rydén

Summary, in English

Background: Surgical sentinel lymph node biopsy (SLNB) is routinely used to reliably stage axillary lymph nodes in early breast cancer (BC). However, SLNB may be associated with postoperative arm morbidities. For most patients with BC undergoing SLNB, the findings are benign, and the procedure is currently questioned. A decision-support tool for the prediction of benign sentinel lymph nodes based on preoperatively available data has been developed using artificial neural network modelling. Methods: This was a retrospective geographical and temporal validation study of the noninvasive lymph node staging (NILS) model, based on preoperatively available data from 586 women consecutively diagnosed with primary BC at two sites. Ten preoperative clinicopathological characteristics from each patient were entered into the web-based calculator, and the probability of benign lymph nodes was predicted. The performance of the NILS model was assessed in terms of discrimination with the area under the receiver operating characteristic curve (AUC) and calibration, that is, comparison of the observed and predicted event rates of benign axillary nodal status (N0) using calibration slope and intercept. The primary endpoint was axillary nodal status (discrimination, benign [N0] vs. metastatic axillary nodal status [N+]) determined by the NILS model compared to nodal status by definitive pathology. Results: The mean age of the women in the cohort was 65 years, and most of them (93%) had luminal cancers. Approximately three-fourths of the patients had no metastases in SLNB (N0 74% and 73%, respectively). The AUC for the predicted probabilities for the whole cohort was 0.6741 (95% confidence interval: 0.6255–0.7227). More than one in four patients (n = 151, 26%) were identified as candidates for SLNB omission when applying the predefined cut-off for lymph node-negative status from the development cohort. The NILS model showed the best calibration in patients with a predicted high probability of healthy axilla. Conclusion: The performance of the NILS model was satisfactory. In approximately every fourth patient, SLNB could potentially be omitted. Considering the shift from postoperatively to preoperatively available predictors in this validation study, we have demonstrated the robustness of the NILS model. The clinical usability of the web interface will be evaluated before its clinical implementation. Trial registration: Registered in the ISRCTN registry with study ID ISRCTN14341750. Date of registration 23/11/2018.


  • LUCC: Lund University Cancer Centre
  • Surgery (Lund)
  • Breast Cancer Surgery
  • Department of Astronomy and Theoretical Physics - Undergoing reorganization
  • eSSENCE: The e-Science Collaboration
  • LU Profile Area: Natural and Artificial Cognition
  • Clinical Sciences, Helsingborg
  • Personalized Breast Cancer Treatment
  • The Liquid Biopsy and Tumor Progression in Breast Cancer

Publishing year





BMC Cancer





Document type

Journal article


BioMed Central (BMC)


  • Cancer and Oncology
  • Surgery


  • Artificial neural network
  • Axillary lymph nodes
  • Breast neoplasm
  • Decision support tool
  • Sentinel lymph node biopsy
  • Staging
  • Validation



Research group

  • Breast Cancer Surgery
  • Personalized Breast Cancer Treatment
  • The Liquid Biopsy and Tumor Progression in Breast Cancer


  • ISSN: 1471-2407