Deep Learning Radiomics to Predict Regional Lymph Node Staging for Hilar Cholangiocarcinoma
Copyright © 2021 Wang, Shao, Wang, Chen, Ying, Chai, Ruan, Tian, Cheng, Zhang, Zhang, Wang, Ding, Liang and Wu..
BACKGROUND: Our aim was to establish a deep learning radiomics method to preoperatively evaluate regional lymph node (LN) staging for hilar cholangiocarcinoma (HC) patients.
METHODS AND MATERIALS: Of the 179 enrolled HC patients, 90 were pathologically diagnosed with lymph node metastasis. Quantitative radiomic features and deep learning features were extracted. An LN metastasis status classifier was developed through integrating support vector machine, high-performance deep learning radiomics signature, and three clinical characteristics. An LN metastasis stratification classifier (N1 vs. N2) was also proposed with subgroup analysis.
RESULTS: The average areas under the receiver operating characteristic curve (AUCs) of the LN metastasis status classifier reached 0.866 in the training cohort and 0.870 in the external test cohorts. Meanwhile, the LN metastasis stratification classifier performed well in predicting the risk of LN metastasis, with an average AUC of 0.946.
CONCLUSIONS: Two classifiers derived from computed tomography images performed well in predicting LN staging in HC and will be reliable evaluation tools to improve decision-making.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2021 |
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| Erschienen: |
2021 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:11 |
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| Enthalten in: |
Frontiers in oncology - 11(2021) vom: 01., Seite 721460 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Wang, Yubizhuo [VerfasserIn] |
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| Links: |
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| Themen: |
Computed tomography |
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| Anmerkungen: |
Date Revised 28.04.2022 published: Electronic-eCollection Citation Status PubMed-not-MEDLINE |
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| doi: |
10.3389/fonc.2021.721460 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 520 | |a Copyright © 2021 Wang, Shao, Wang, Chen, Ying, Chai, Ruan, Tian, Cheng, Zhang, Zhang, Wang, Ding, Liang and Wu. | ||
| 520 | |a BACKGROUND: Our aim was to establish a deep learning radiomics method to preoperatively evaluate regional lymph node (LN) staging for hilar cholangiocarcinoma (HC) patients | ||
| 520 | |a METHODS AND MATERIALS: Of the 179 enrolled HC patients, 90 were pathologically diagnosed with lymph node metastasis. Quantitative radiomic features and deep learning features were extracted. An LN metastasis status classifier was developed through integrating support vector machine, high-performance deep learning radiomics signature, and three clinical characteristics. An LN metastasis stratification classifier (N1 vs. N2) was also proposed with subgroup analysis | ||
| 520 | |a RESULTS: The average areas under the receiver operating characteristic curve (AUCs) of the LN metastasis status classifier reached 0.866 in the training cohort and 0.870 in the external test cohorts. Meanwhile, the LN metastasis stratification classifier performed well in predicting the risk of LN metastasis, with an average AUC of 0.946 | ||
| 520 | |a CONCLUSIONS: Two classifiers derived from computed tomography images performed well in predicting LN staging in HC and will be reliable evaluation tools to improve decision-making | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a computed tomography | |
| 650 | 4 | |a deep learning | |
| 650 | 4 | |a hilar cholangiocarcinoma | |
| 650 | 4 | |a lymph node | |
| 650 | 4 | |a radiomics | |
| 700 | 1 | |a Shao, Jiayuan |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Pan |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Lintao |e verfasserin |4 aut | |
| 700 | 1 | |a Ying, Mingliang |e verfasserin |4 aut | |
| 700 | 1 | |a Chai, Siyuan |e verfasserin |4 aut | |
| 700 | 1 | |a Ruan, Shijian |e verfasserin |4 aut | |
| 700 | 1 | |a Tian, Wuwei |e verfasserin |4 aut | |
| 700 | 1 | |a Cheng, Yongna |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Hongbin |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Xiuming |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Xiangming |e verfasserin |4 aut | |
| 700 | 1 | |a Ding, Yong |e verfasserin |4 aut | |
| 700 | 1 | |a Liang, Wenjie |e verfasserin |4 aut | |
| 700 | 1 | |a Wu, Liming |e verfasserin |4 aut | |
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