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)

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 t...

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Bibliographic Details
Published in:Diagnostics (Basel) 2022-02, Vol.12 (3), p.582
Main Authors: Skarping, Ida, Dihge, Looket, Bendahl, Pär-Ola, Huss, Linnea, Ellbrant, Julia, Ohlsson, Mattias, Rydén, Lisa
Format: Article
Language:English
Subjects:
Artificial neural network
Axilla
Biopsy
Breast cancer
Breast neoplasm
Cancer and Oncology
Cancer och onkologi
Cancer therapies
Clinical Medicine
Consent
Decision making
Estrogens
Ethics
Hospitals
Klinisk medicin
Localization
Lymph nodes
Lymphatic system
Mammography
Medical and Health Sciences
Medicin och hälsovetenskap
Metastasis
Neural networks
Patients
Staging
Study Protocol
Surgery
Ultrasonic imaging
Validation studies
Validation study
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Description
Summary: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).
ISSN:2075-4418
2075-4418
DOI:10.3390/diagnostics12030582