Feasibility analysis of predicting expression of estrogen receptor in breast cancer based on radiomics
This study aims to predict expression of estrogen receptor (ER) in breast cancer by radiomics. Firstly, breast cancer images are segmented automatically by phase-based active contour (PBAC) method. Secondly, high-throughput features of ultrasound images are extracted and quantized. A total of 404 high-throughput features are divided into three categories, such as morphology, texture and wavelet. Then, the features are selected by R language and genetic algorithm combining minimum-redundancy-maximum-relevance (mRMR) criterion. Finally, support vector machine (SVM) and AdaBoost are used as classifiers, achieving the goal of predicting ER by breast ultrasound image. One hundred and four cases of breast cancer patients were conducted in the experiment and optimal indicator was obtained using AdaBoost. The prediction accuracy of molecular marker ER could achieve 75.96% and the highest area under the receiver operating characteristic curve (AUC) was 79.39%. According to the results of experiment, the feasibility of predicting expression of ER in breast cancer using radiomics was verified.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2017 |
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| Erschienen: |
2017 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:34 |
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| Enthalten in: |
Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi - 34(2017), 4 vom: 25. Aug., Seite 597-601 |
| Sprache: |
Chinesisch |
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| Beteiligte Personen: |
Liu, Tongtong [VerfasserIn] |
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| Links: |
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| Themen: |
English Abstract |
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| Anmerkungen: |
Date Revised 02.07.2023 published: Print Citation Status PubMed-not-MEDLINE |
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| doi: |
10.7507/1001-5515.201611033 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM283924160 |
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| 100 | 1 | |a Liu, Tongtong |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Feasibility analysis of predicting expression of estrogen receptor in breast cancer based on radiomics |
| 264 | 1 | |c 2017 | |
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| 500 | |a Date Revised 02.07.2023 | ||
| 500 | |a published: Print | ||
| 500 | |a Citation Status PubMed-not-MEDLINE | ||
| 520 | |a This study aims to predict expression of estrogen receptor (ER) in breast cancer by radiomics. Firstly, breast cancer images are segmented automatically by phase-based active contour (PBAC) method. Secondly, high-throughput features of ultrasound images are extracted and quantized. A total of 404 high-throughput features are divided into three categories, such as morphology, texture and wavelet. Then, the features are selected by R language and genetic algorithm combining minimum-redundancy-maximum-relevance (mRMR) criterion. Finally, support vector machine (SVM) and AdaBoost are used as classifiers, achieving the goal of predicting ER by breast ultrasound image. One hundred and four cases of breast cancer patients were conducted in the experiment and optimal indicator was obtained using AdaBoost. The prediction accuracy of molecular marker ER could achieve 75.96% and the highest area under the receiver operating characteristic curve (AUC) was 79.39%. According to the results of experiment, the feasibility of predicting expression of ER in breast cancer using radiomics was verified | ||
| 650 | 4 | |a English Abstract | |
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a R language | |
| 650 | 4 | |a estrogen receptor | |
| 650 | 4 | |a high level features | |
| 650 | 4 | |a molecular marker | |
| 650 | 4 | |a radiomics | |
| 700 | 1 | |a Li, Jiawei |e verfasserin |4 aut | |
| 700 | 1 | |a Hu, Yuzhou |e verfasserin |4 aut | |
| 700 | 1 | |a Yu, Jinhua |e verfasserin |4 aut | |
| 700 | 1 | |a Guo, Yi |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Yuanyuan |e verfasserin |4 aut | |
| 700 | 1 | |a Chang, Cai |e verfasserin |4 aut | |
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