Preoperative MR radiomics based on high-resolution T2-weighted images and amide proton transfer-weighted imaging for predicting lymph node metastasis in rectal adenocarcinoma
© 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature..
OBJECTIVES: Lymph node (LN) metastasis is an important prognostic factor in rectal cancer (RC). However, accurate identification of LN metastasis can be challenged for radiologists. The aim of our study was to assess the utility of MRI radiomics based on T2-weighted images (T2WI) and amide proton transfer-weighted (APTw) images for predicting LN metastasis in RC preoperatively.
METHODS: A total of 125 patients with pathologically confirmed rectal adenocarcinoma (RA) from January 2019 to June 2021 who underwent preoperative MR were enrolled in this retrospective study. Radiomics features were extracted from high-resolution T2WI and APTw images of primary tumor. The most relevant radiomics and clinical features were selected using correlation and multivariate logistic analysis. Radiomics models were built using five machine learning algorithms including support vector machine (SVM), logical regression (LR), k- nearest neighbor (KNN), naive bayes (NB), and random forest (RF). The best algorithm was selected for further establish the clinical- radiomics model. The receiver operating characteristic curve (ROC) analysis was used to assess the performance of radiomics and clinical-radiomics model for predicting LN metastasis.
RESULTS: The LR classifier had the best prediction performance, with AUCs of 0.983 (95% CI 0.957-1.000), 0.864 (95% CI 0.729-0.972), 0.851 (95% CI 0.713-0.940) on the training set, validation, and test sets, respectively. In terms of prediction, the clinical-radiomics combined model outperformed the radiomics model. The AUCs of the clinical-radiomics combined model in the validation and test sets were 0.900 (95% CI 0.785-0.986), and 0.929 (95% CI 0.721-0.943), respectively.
CONCLUSION: The radiomics model based on high-resolution T2WI and APTw images can predict LN metastasis accurately in patients with RA.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:48 |
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| Enthalten in: |
Abdominal radiology (New York) - 48(2023), 2 vom: 02. Feb., Seite 458-470 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Wei, Qiurong [VerfasserIn] |
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| Links: |
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| Themen: |
115787-68-3 |
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| Anmerkungen: |
Date Completed 08.02.2023 Date Revised 16.05.2023 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1007/s00261-022-03731-x |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM349733880 |
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| 100 | 1 | |a Wei, Qiurong |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Preoperative MR radiomics based on high-resolution T2-weighted images and amide proton transfer-weighted imaging for predicting lymph node metastasis in rectal adenocarcinoma |
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| 500 | |a Citation Status MEDLINE | ||
| 520 | |a © 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. | ||
| 520 | |a OBJECTIVES: Lymph node (LN) metastasis is an important prognostic factor in rectal cancer (RC). However, accurate identification of LN metastasis can be challenged for radiologists. The aim of our study was to assess the utility of MRI radiomics based on T2-weighted images (T2WI) and amide proton transfer-weighted (APTw) images for predicting LN metastasis in RC preoperatively | ||
| 520 | |a METHODS: A total of 125 patients with pathologically confirmed rectal adenocarcinoma (RA) from January 2019 to June 2021 who underwent preoperative MR were enrolled in this retrospective study. Radiomics features were extracted from high-resolution T2WI and APTw images of primary tumor. The most relevant radiomics and clinical features were selected using correlation and multivariate logistic analysis. Radiomics models were built using five machine learning algorithms including support vector machine (SVM), logical regression (LR), k- nearest neighbor (KNN), naive bayes (NB), and random forest (RF). The best algorithm was selected for further establish the clinical- radiomics model. The receiver operating characteristic curve (ROC) analysis was used to assess the performance of radiomics and clinical-radiomics model for predicting LN metastasis | ||
| 520 | |a RESULTS: The LR classifier had the best prediction performance, with AUCs of 0.983 (95% CI 0.957-1.000), 0.864 (95% CI 0.729-0.972), 0.851 (95% CI 0.713-0.940) on the training set, validation, and test sets, respectively. In terms of prediction, the clinical-radiomics combined model outperformed the radiomics model. The AUCs of the clinical-radiomics combined model in the validation and test sets were 0.900 (95% CI 0.785-0.986), and 0.929 (95% CI 0.721-0.943), respectively | ||
| 520 | |a CONCLUSION: The radiomics model based on high-resolution T2WI and APTw images can predict LN metastasis accurately in patients with RA | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 650 | 4 | |a Amide proton transferweighted imaging | |
| 650 | 4 | |a Lymph node metastasis | |
| 650 | 4 | |a Magnetic resonance imaging | |
| 650 | 4 | |a Radiomic | |
| 650 | 4 | |a Rectal adenocarcinoma | |
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| 700 | 1 | |a Jia, Ziqi |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Jialiang |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Ling |e verfasserin |4 aut | |
| 700 | 1 | |a Yan, Zhaoxian |e verfasserin |4 aut | |
| 700 | 1 | |a Liao, Yuting |e verfasserin |4 aut | |
| 700 | 1 | |a Mao, Liting |e verfasserin |4 aut | |
| 700 | 1 | |a Hu, Shaowei |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Xian |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Weicui |e verfasserin |4 aut | |
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