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Mattias Ohlsson

Mattias Ohlsson

Professor

Mattias Ohlsson

The implementation of a noninvasive lymph node staging (NILS) preoperative prediction model is cost effective in primary breast cancer

Author

  • Ida Skarping
  • Kristoffer Nilsson
  • Looket Dihge
  • Adam Fridhammar
  • Mattias Ohlsson
  • Linnea Huss
  • Pär Ola Bendahl
  • Katarina Steen Carlsson
  • Lisa Rydén

Summary, in English

Purpose: The need for sentinel lymph node biopsy (SLNB) in clinically node-negative (cN0) patients is currently questioned. Our objective was to investigate the cost-effectiveness of a preoperative noninvasive lymph node staging (NILS) model (an artificial neural network model) for predicting pathological nodal status in patients with cN0 breast cancer (BC). Methods: A health-economic decision-analytic model was developed to evaluate the utility of the NILS model in reducing the proportion of cN0 patients with low predicted risk undergoing SLNB. The model used information from a national registry and published studies, and three sensitivity/specificity scenarios of the NILS model were evaluated. Subgroup analysis explored the outcomes of breast-conserving surgery (BCS) or mastectomy. The results are presented as cost (€) and quality-adjusted life years (QALYs) per 1000 patients. Results: All three scenarios of the NILS model reduced total costs (–€93,244 to –€398,941 per 1000 patients). The overall health benefit allowing for the impact of SLNB complications was a net health gain (7.0–26.9 QALYs per 1000 patients). Sensitivity analyses disregarding reduced quality of life from lymphedema showed a small loss in total health benefits (0.4–4.0 QALYs per 1000 patients) because of the reduction in total life years (0.6–6.5 life years per 1000 patients) after reduced adjuvant treatment. Subgroup analyses showed greater cost reductions and QALY gains in patients undergoing BCS. Conclusion: Implementing the NILS model to identify patients with low risk for nodal metastases was associated with substantial cost reductions and likely overall health gains, especially in patients undergoing BCS.

Department/s

  • Breastcancer
  • LUCC: Lund University Cancer Centre
  • Breast cancer prevention & intervention
  • Breast cancer treatment
  • Breast Cancer Surgery
  • eSSENCE: The e-Science Collaboration
  • Artificial Intelligence in CardioThoracic Sciences (AICTS)
  • Computational Biology and Biological Physics
  • Clinical Sciences, Helsingborg
  • Surgery
  • The Liquid Biopsy and Tumor Progression in Breast Cancer
  • Personalized Breast Cancer Treatment
  • Health Economics
  • EpiHealth: Epidemiology for Health
  • Surgery (Lund)

Publishing year

2022-08

Language

English

Pages

577-586

Publication/Series

Breast Cancer Research and Treatment

Volume

194

Issue

3

Document type

Journal article

Publisher

Springer

Topic

  • Cancer and Oncology

Keywords

  • Artificial neural network
  • Axillary lymph nodes
  • Breast neoplasm
  • Cost-effectiveness
  • Staging

Status

Published

Research group

  • Breast cancer prevention & intervention
  • Breast Cancer Surgery
  • Artificial Intelligence in CardioThoracic Sciences (AICTS)
  • Surgery
  • The Liquid Biopsy and Tumor Progression in Breast Cancer
  • Personalized Breast Cancer Treatment
  • Health Economics

ISBN/ISSN/Other

  • ISSN: 0167-6806