Machine-learning methods based on the texture and non-texture features of MRI for the preoperative prediction of sentinel lymph node metastasis in breast cancer
2023 Translational Cancer Research. All rights reserved..
Background: The establishment of an accurate, stable, and non-invasive prediction model of sentinel lymph node (SLN) metastasis in breast cancer is difficult nowadays. The aim of this work is to identify the optimal machine learning model based on the three-dimensional (3D) image features of magnetic resonance imaging (MRI) for the preoperative prediction of SLN metastasis in breast cancer patients.
Methods: A total of 172 patients with histologically proven breast cancer were enrolled retrospectively, including 74 SLN metastasis patients and 98 non-SLN metastasis patients. All of them underwent diffusion-weighted imaging (DWI) magnetic resonance imaging (MRI) scan. Firstly, a total of 10,320 texture and four non-texture features were extracted from the region of interests (ROIs) of image. Twenty-four feature selection methods and 11 classification methods were then evaluated by using 10-fold cross-validation to identify the optimal machine learning model in terms of the mean area under the curve (AUC), accuracy (ACC), and stability.
Results: The result showed that the model based on the combination of minimum redundancy maximum relevance (MRMR) + random forest (RF) exhibited the optimal predictive performance (AUC: 0.97±0.03; ACC: 0.89±0.05; stability: 2.94). Moreover, we independently investigated the performance of feature selection methods and classification methods, and observed that L1-support vector machine (L1-SVM) (AUC: 0.80±0.08; ACC: 0.76±0.07) and sequential forward floating selection (SFFS) (stability: 3.04) presented the best average predictive performance and stability among all feature selection methods, respectively. RF (AUC: 0.85±0.11; ACC: 0.80±0.09) and SVM (stability: 8.43) showed the best average predictive performance and stability among all classification methods, respectively.
Conclusions: The identified model based on the 3D image features of MRI provides a non-invasive way for the preoperative prediction of SLN metastasis in breast cancer patients.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:12 |
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| Enthalten in: |
Translational cancer research - 12(2023), 12 vom: 31. Dez., Seite 3471-3485 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Wang, Jian [VerfasserIn] |
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| Links: |
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| Themen: |
Breast cancer |
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| Anmerkungen: |
Date Revised 10.01.2024 published: Print-Electronic Citation Status PubMed-not-MEDLINE |
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| doi: |
10.21037/tcr-22-2534 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM366835807 |
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| 500 | |a published: Print-Electronic | ||
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| 520 | |a 2023 Translational Cancer Research. All rights reserved. | ||
| 520 | |a Background: The establishment of an accurate, stable, and non-invasive prediction model of sentinel lymph node (SLN) metastasis in breast cancer is difficult nowadays. The aim of this work is to identify the optimal machine learning model based on the three-dimensional (3D) image features of magnetic resonance imaging (MRI) for the preoperative prediction of SLN metastasis in breast cancer patients | ||
| 520 | |a Methods: A total of 172 patients with histologically proven breast cancer were enrolled retrospectively, including 74 SLN metastasis patients and 98 non-SLN metastasis patients. All of them underwent diffusion-weighted imaging (DWI) magnetic resonance imaging (MRI) scan. Firstly, a total of 10,320 texture and four non-texture features were extracted from the region of interests (ROIs) of image. Twenty-four feature selection methods and 11 classification methods were then evaluated by using 10-fold cross-validation to identify the optimal machine learning model in terms of the mean area under the curve (AUC), accuracy (ACC), and stability | ||
| 520 | |a Results: The result showed that the model based on the combination of minimum redundancy maximum relevance (MRMR) + random forest (RF) exhibited the optimal predictive performance (AUC: 0.97±0.03; ACC: 0.89±0.05; stability: 2.94). Moreover, we independently investigated the performance of feature selection methods and classification methods, and observed that L1-support vector machine (L1-SVM) (AUC: 0.80±0.08; ACC: 0.76±0.07) and sequential forward floating selection (SFFS) (stability: 3.04) presented the best average predictive performance and stability among all feature selection methods, respectively. RF (AUC: 0.85±0.11; ACC: 0.80±0.09) and SVM (stability: 8.43) showed the best average predictive performance and stability among all classification methods, respectively | ||
| 520 | |a Conclusions: The identified model based on the 3D image features of MRI provides a non-invasive way for the preoperative prediction of SLN metastasis in breast cancer patients | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Machine learning | |
| 650 | 4 | |a breast cancer | |
| 650 | 4 | |a magnetic resonance imaging (MRI) | |
| 650 | 4 | |a preoperative prediction | |
| 650 | 4 | |a sentinel lymph node (SLN) | |
| 700 | 1 | |a Gao, Xinna |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Shuixing |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Yu |e verfasserin |4 aut | |
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