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Mattias Ohlsson
Professor
![Foto på Mattias Ohlsson](/sv/sites/cec.lu.se.sv/files/styles/lu_personal_page_desktop/public/2023-02/Mattias%20Ohlsson.jpg.webp?itok=4Daqlihc)
The implementation of a noninvasive lymph node staging (NILS) preoperative prediction model is cost effective in primary breast cancer
Författare
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.
Avdelning/ar
- Bröstcancer
- LUCC: Lunds universitets cancercentrum
- Bröstcancer - prevention & intervention
- Bröstcancerbehandling
- Bröstcancerkirurgi
- eSSENCE: The e-Science Collaboration
- Artificiell intelligens och thoraxkirurgisk vetenskap (AICTS)
- Beräkningsbiologi och biologisk fysik - Har omorganiserats
- Kliniska Vetenskaper, Helsingborg
- Kirurgi
- The Liquid Biopsy och Tumörprogression i Bröstcancer
- Individuell Bröstcancerbehandling
- Hälsoekonomi
- EpiHealth: Epidemiology for Health
- Kirurgi, Lund
Publiceringsår
2022-08
Språk
Engelska
Sidor
577-586
Publikation/Tidskrift/Serie
Breast Cancer Research and Treatment
Volym
194
Issue
3
Dokumenttyp
Artikel i tidskrift
Förlag
Springer
Ämne
- Cancer and Oncology
Nyckelord
- Artificial neural network
- Axillary lymph nodes
- Breast neoplasm
- Cost-effectiveness
- Staging
Aktiv
Published
Forskningsgrupp
- 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/Övrigt
- ISSN: 0167-6806