Feasibility analysis of predicting expression of Ki67 in pancreatic cystic neoplasm based on radiomics
This study aims to predict expression of Ki67 molecular marker in pancreatic cystic neoplasm using radiomics. We firstly manually segmented tumor area in multi-detector computed tomography (MDCT) images. Then 409 high-throughput features were automatically extracted and the least absolute shrinkage selection operator (LASSO) regression model was used for feature selection. After 200 bootstrapping repetitions of LASSO, 20 most frequently selected features made up the optimal feature set. Then 200 bootstrapping repetitions of support vector machine (SVM) classifier with 10-fold cross-validation were used to avoid overfitting and accurately predict the Ki67 expression. The highest prediction accuracy could achieve 85.29% and the highest area under the receiver operating characteristic curve (AUC) was 91.54% with a sensitivity (SENS) of 81.88% and a specificity (SPEC) of 86.75%. According to the results of experiment, the feasibility of predicting expression of Ki67 in pancreatic cystic neoplasm based on radiomics was verified.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2019 |
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| Erschienen: |
2019 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:36 |
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| Enthalten in: |
Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi - 36(2019), 1 vom: 25. Feb., Seite 1-6 |
| Sprache: |
Chinesisch |
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| Beteiligte Personen: |
Wei, Ran [VerfasserIn] |
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| Links: |
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| Themen: |
English Abstract |
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| Anmerkungen: |
Date Revised 18.09.2023 published: Print Citation Status PubMed-not-MEDLINE |
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| doi: |
10.7507/1001-5515.201805014 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM29510662X |
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| 245 | 1 | 0 | |a Feasibility analysis of predicting expression of Ki67 in pancreatic cystic neoplasm based on radiomics |
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| 500 | |a Date Revised 18.09.2023 | ||
| 500 | |a published: Print | ||
| 500 | |a Citation Status PubMed-not-MEDLINE | ||
| 520 | |a This study aims to predict expression of Ki67 molecular marker in pancreatic cystic neoplasm using radiomics. We firstly manually segmented tumor area in multi-detector computed tomography (MDCT) images. Then 409 high-throughput features were automatically extracted and the least absolute shrinkage selection operator (LASSO) regression model was used for feature selection. After 200 bootstrapping repetitions of LASSO, 20 most frequently selected features made up the optimal feature set. Then 200 bootstrapping repetitions of support vector machine (SVM) classifier with 10-fold cross-validation were used to avoid overfitting and accurately predict the Ki67 expression. The highest prediction accuracy could achieve 85.29% and the highest area under the receiver operating characteristic curve (AUC) was 91.54% with a sensitivity (SENS) of 81.88% and a specificity (SPEC) of 86.75%. According to the results of experiment, the feasibility of predicting expression of Ki67 in pancreatic cystic neoplasm based on radiomics was verified | ||
| 650 | 4 | |a English Abstract | |
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Ki67 molecular marker | |
| 650 | 4 | |a multi-detector computed tomography | |
| 650 | 4 | |a pancreatic cystic neoplasms | |
| 650 | 4 | |a radiomics | |
| 700 | 1 | |a Lin, Kanru |e verfasserin |4 aut | |
| 700 | 1 | |a Guo, Yi |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Ji |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Yuanyuan |e verfasserin |4 aut | |
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