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Mattias Ohlsson

Mattias Ohlsson

Professor

Mattias Ohlsson

The NILS Study Protocol : A Retrospective Validation Study of an Artificial Neural Network Based Preoperative Decision-Making Tool for Noninvasive Lymph Node Staging in Women with Primary Breast Cancer (ISRCTN14341750)

Author

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

Summary, in English

Newly diagnosed breast cancer (BC) patients with clinical T1–T2 N0 disease undergo sentinel-lymph-node (SLN) biopsy, although most of them have a benign SLN. The pilot noninvasive lymph node staging (NILS) artificial neural network (ANN) model to predict nodal status was published in 2019, showing the potential to identify patients with a low risk of SLN metastasis. The aim of this study is to assess the performance measures of the model after a web-based implementation for the prediction of a healthy SLN in clinically N0 BC patients. This retrospective study was designed to validate the NILS prediction model for SLN status using preoperatively available clinicopathological and radiological data. The model results in an estimated probability of a healthy SLN for each study participant. Our primary endpoint is to report on the performance of the NILS prediction model to distinguish between healthy and metastatic SLNs (N0 vs. N+) and compare the observed and predicted event rates of benign SLNs. After validation, the prediction model may assist medical professionals and BC patients in shared decision making on omitting SLN biopsies in patients predicted to be node-negative by the NILS model. This study was prospectively registered in the ISRCTN registry (identification number: 14341750).

Department/s

  • LUCC: Lund University Cancer Centre
  • Breastcancer
  • Breast cancer treatment
  • Clinical Sciences, Helsingborg
  • Anaesthesiology and Intensive Care Medicine
  • Computational Biology and Biological Physics
  • eSSENCE: The e-Science Collaboration
  • Surgery (Lund)

Publishing year

2022-03

Language

English

Publication/Series

Diagnostics

Volume

12

Issue

3

Document type

Journal article

Publisher

MDPI AG

Topic

  • Cancer and Oncology

Keywords

  • Artificial neural network
  • Axilla
  • Breast neoplasm
  • Lymph nodes
  • Staging
  • Validation study

Status

Published

Research group

  • Anaesthesiology and Intensive Care Medicine

ISBN/ISSN/Other

  • ISSN: 2075-4418