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Identification of sentinel lymph node macrometastasis in breast cancer by deep learning based on clinicopathological characteristics

Zhang, Daqu LU ; Svensson, Miriam LU ; Edén, Patrik LU and Dihge, Looket LU (2024) In Scientific Reports 14.
Abstract
The axillary lymph node status remains an important prognostic factor in breast cancer, and nodal staging using sentinel lymph node biopsy (SLNB) is routine. Randomized clinical trials provide evidence supporting de-escalation of axillary surgery and omission of SLNB in patients at low risk. However, identifying sentinel lymph node macrometastases (macro-SLNMs) is crucial for planning treatment tailored to the individual patient. This study is the first to explore the capacity of deep learning (DL) models to identify macro-SLNMs based on preoperative clinicopathological characteristics. We trained and validated five multivariable models using a population-based cohort of 18,185 patients. DL models outperform logistic regression, with... (More)
The axillary lymph node status remains an important prognostic factor in breast cancer, and nodal staging using sentinel lymph node biopsy (SLNB) is routine. Randomized clinical trials provide evidence supporting de-escalation of axillary surgery and omission of SLNB in patients at low risk. However, identifying sentinel lymph node macrometastases (macro-SLNMs) is crucial for planning treatment tailored to the individual patient. This study is the first to explore the capacity of deep learning (DL) models to identify macro-SLNMs based on preoperative clinicopathological characteristics. We trained and validated five multivariable models using a population-based cohort of 18,185 patients. DL models outperform logistic regression, with Transformer showing the strongest results, under the constraint that the sensitivity is no less than 90%, reflecting the sensitivity of SLNB. This highlights the feasibility of noninvasive macro-SLNM prediction using DL. Feature importance analysis revealed that patients with similar characteristics exhibited different nodal status predictions, indicating the need for additional predictors for further improvement. (Less)
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author
; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Breast cancer, Lymphatic metastasis, Sentinel lymph node, Deep learning, Clinical decision support
in
Scientific Reports
volume
14
article number
26970
publisher
Nature Publishing Group
external identifiers
  • pmid:39505964
  • scopus:85208689335
ISSN
2045-2322
DOI
10.1038/s41598-024-78040-y
project
Applications of Deep Learning in Breast Cancer Research
language
English
LU publication?
yes
id
0f5d6595-bcff-405d-9a97-51fc5fd1f07c
date added to LUP
2024-11-27 13:26:00
date last changed
2025-04-04 14:31:21
@article{0f5d6595-bcff-405d-9a97-51fc5fd1f07c,
  abstract     = {{The axillary lymph node status remains an important prognostic factor in breast cancer, and nodal staging using sentinel lymph node biopsy (SLNB) is routine. Randomized clinical trials provide evidence supporting de-escalation of axillary surgery and omission of SLNB in patients at low risk. However, identifying sentinel lymph node macrometastases (macro-SLNMs) is crucial for planning treatment tailored to the individual patient. This study is the first to explore the capacity of deep learning (DL) models to identify macro-SLNMs based on preoperative clinicopathological characteristics. We trained and validated five multivariable models using a population-based cohort of 18,185 patients. DL models outperform logistic regression, with Transformer showing the strongest results, under the constraint that the sensitivity is no less than 90%, reflecting the sensitivity of SLNB. This highlights the feasibility of noninvasive macro-SLNM prediction using DL. Feature importance analysis revealed that patients with similar characteristics exhibited different nodal status predictions, indicating the need for additional predictors for further improvement.}},
  author       = {{Zhang, Daqu and Svensson, Miriam and Edén, Patrik and Dihge, Looket}},
  issn         = {{2045-2322}},
  keywords     = {{Breast cancer; Lymphatic metastasis; Sentinel lymph node; Deep learning; Clinical decision support}},
  language     = {{eng}},
  month        = {{11}},
  publisher    = {{Nature Publishing Group}},
  series       = {{Scientific Reports}},
  title        = {{Identification of sentinel lymph node macrometastasis in breast cancer by deep learning based on clinicopathological characteristics}},
  url          = {{http://dx.doi.org/10.1038/s41598-024-78040-y}},
  doi          = {{10.1038/s41598-024-78040-y}},
  volume       = {{14}},
  year         = {{2024}},
}