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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)
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- Skarping, Ida (author)
- Lund University,Lunds universitet,LUCC: Lunds universitets cancercentrum,Övriga starka forskningsmiljöer,Bröstcancer,Sektion I,Institutionen för kliniska vetenskaper, Lund,Medicinska fakulteten,LUCC: Lund University Cancer Centre,Other Strong Research Environments,Breastcancer,Section I,Department of Clinical Sciences, Lund,Faculty of Medicine,Skåne University Hospital
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- Dihge, Looket (author)
- Lund University,Lunds universitet,LUCC: Lunds universitets cancercentrum,Övriga starka forskningsmiljöer,Bröstcancerbehandling,Sektion I,Institutionen för kliniska vetenskaper, Lund,Medicinska fakulteten,LUCC: Lund University Cancer Centre,Other Strong Research Environments,Breast cancer treatment,Section I,Department of Clinical Sciences, Lund,Faculty of Medicine,Skåne University Hospital
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- Bendahl, Pär Ola (author)
- Lund University,Lunds universitet,LUCC: Lunds universitets cancercentrum,Övriga starka forskningsmiljöer,Bröstcancerbehandling,Sektion I,Institutionen för kliniska vetenskaper, Lund,Medicinska fakulteten,LUCC: Lund University Cancer Centre,Other Strong Research Environments,Breast cancer treatment,Section I,Department of Clinical Sciences, Lund,Faculty of Medicine
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- (creator_code:org_t)
- 2022-02-24
- 2022
- English.
- In: Diagnostics. - : MDPI AG. - 2075-4418. ; 12:3
- Journal article (peer-reviewed)
Abstract
Subject headings
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- 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).
Subject headings
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Cancer och onkologi (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Cancer and Oncology (hsv//eng)
Keyword
- Artificial neural network
- Axilla
- Breast neoplasm
- Lymph nodes
- Staging
- Validation study
Publication and Content Type
- art (subject category)
- ref (subject category)
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- By the author/editor
- Skarping, Ida
- Dihge, Looket
- Bendahl, Pär Ola
- Huss, Linnea
- Ellbrant, Julia
- Ohlsson, Mattias
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- Rydén, Lisa
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- MEDICAL AND HEALTH SCIENCES
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