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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)

Skarping, Ida LU orcid ; Dihge, Looket LU ; Bendahl, Pär Ola LU ; Huss, Linnea LU ; Ellbrant, Julia LU ; Ohlsson, Mattias LU orcid and Rydén, Lisa LU orcid (2022) In Diagnostics 12(3).
Abstract

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... (More)

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).

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author
; ; ; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Artificial neural network, Axilla, Breast neoplasm, Lymph nodes, Staging, Validation study
in
Diagnostics
volume
12
issue
3
article number
582
publisher
MDPI AG
external identifiers
  • pmid:35328135
  • scopus:85125627767
ISSN
2075-4418
DOI
10.3390/diagnostics12030582
language
English
LU publication?
yes
id
752fdb2a-2c61-449b-9f77-33694c899f7b
date added to LUP
2022-05-12 16:14:10
date last changed
2024-06-13 12:26:18
@article{752fdb2a-2c61-449b-9f77-33694c899f7b,
  abstract     = {{<p>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).</p>}},
  author       = {{Skarping, Ida and Dihge, Looket and Bendahl, Pär Ola and Huss, Linnea and Ellbrant, Julia and Ohlsson, Mattias and Rydén, Lisa}},
  issn         = {{2075-4418}},
  keywords     = {{Artificial neural network; Axilla; Breast neoplasm; Lymph nodes; Staging; Validation study}},
  language     = {{eng}},
  number       = {{3}},
  publisher    = {{MDPI AG}},
  series       = {{Diagnostics}},
  title        = {{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)}},
  url          = {{http://dx.doi.org/10.3390/diagnostics12030582}},
  doi          = {{10.3390/diagnostics12030582}},
  volume       = {{12}},
  year         = {{2022}},
}