Identification of sentinel lymph node macrometastasis in breast cancer by deep learning based on clinicopathological characteristics
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...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-11-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-78040-y |
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| author | Daqu Zhang Miriam Svensson Patrik Edén Looket Dihge |
| author_facet | Daqu Zhang Miriam Svensson Patrik Edén Looket Dihge |
| author_sort | Daqu Zhang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-75ac2e84a7b143b286e6edf1cb6334fd |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-75ac2e84a7b143b286e6edf1cb6334fd2025-08-20T02:49:58ZengNature PortfolioScientific Reports2045-23222024-11-0114111510.1038/s41598-024-78040-yIdentification of sentinel lymph node macrometastasis in breast cancer by deep learning based on clinicopathological characteristicsDaqu Zhang0Miriam Svensson1Patrik Edén2Looket Dihge3Division of Computational Science for Health and Environment, Center for Environmental and Climate Science, Lund UniversityDepartment of Clinical Sciences Lund, Division of Surgery, Lund UniversityDivision of Computational Science for Health and Environment, Center for Environmental and Climate Science, Lund UniversityDepartment of Clinical Sciences Lund, Division of Surgery, Lund UniversityAbstract 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.https://doi.org/10.1038/s41598-024-78040-yBreast cancerLymphatic metastasisSentinel lymph nodeDeep learningClinical decision support |
| spellingShingle | Daqu Zhang Miriam Svensson Patrik Edén Looket Dihge Identification of sentinel lymph node macrometastasis in breast cancer by deep learning based on clinicopathological characteristics Scientific Reports Breast cancer Lymphatic metastasis Sentinel lymph node Deep learning Clinical decision support |
| title | Identification of sentinel lymph node macrometastasis in breast cancer by deep learning based on clinicopathological characteristics |
| title_full | Identification of sentinel lymph node macrometastasis in breast cancer by deep learning based on clinicopathological characteristics |
| title_fullStr | Identification of sentinel lymph node macrometastasis in breast cancer by deep learning based on clinicopathological characteristics |
| title_full_unstemmed | Identification of sentinel lymph node macrometastasis in breast cancer by deep learning based on clinicopathological characteristics |
| title_short | Identification of sentinel lymph node macrometastasis in breast cancer by deep learning based on clinicopathological characteristics |
| title_sort | identification of sentinel lymph node macrometastasis in breast cancer by deep learning based on clinicopathological characteristics |
| topic | Breast cancer Lymphatic metastasis Sentinel lymph node Deep learning Clinical decision support |
| url | https://doi.org/10.1038/s41598-024-78040-y |
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