Preoperative prediction of perineural invasion and lymphovascular invasion with CT radiomics in gastric cancer
© 2024 The Authors..
Objectives: To determine whether contrast-enhanced CT radiomics features can preoperatively predict lymphovascular invasion (LVI) and perineural invasion (PNI) in gastric cancer (GC).
Methods: A total of 148 patients were included in the LVI group, and 143 patients were included in the PNI group. Three predictive models were constructed, including clinical, radiomics, and combined models. A nomogram was developed with clinical risk factors to predict LVI and PNI status. The predictive performance of the three models was mainly evaluated using the mean area under the curve (AUC). The performance of three predictive models was assessed concerning calibration and clinical usefulness.
Results: In the LVI group, the predictive power of the combined model (AUC=0.871, 0.822) outperformed the clinical model (AUC=0.792, 0.728) and the radiomics model (AUC=0.792, 0.728) in both the training and testing cohorts. In the PNI group, the combined model (AUC=0.834, 0.828) also had better predictive power than the clinical model (AUC=0.764, 0.632) and the radiomics model (AUC=0.764, 0.632) in both the training and testing cohorts. The combined models also showed good calibration and clinical usefulness for LVI and PNI prediction.
Conclusion: CECT-based radiomics analysis might serve as a non-invasive method to predict LVI and PNI status in GC.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:12 |
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| Enthalten in: |
European journal of radiology open - 12(2024) vom: 01. Feb., Seite 100550 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
He, Yaoyao [VerfasserIn] |
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| Links: |
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| Themen: |
Contrast-enhanced CT |
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| Anmerkungen: |
Date Revised 06.02.2024 published: Electronic-eCollection Citation Status PubMed-not-MEDLINE |
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| doi: |
10.1016/j.ejro.2024.100550 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM368033945 |
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| 245 | 1 | 0 | |a Preoperative prediction of perineural invasion and lymphovascular invasion with CT radiomics in gastric cancer |
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| 500 | |a Date Revised 06.02.2024 | ||
| 500 | |a published: Electronic-eCollection | ||
| 500 | |a Citation Status PubMed-not-MEDLINE | ||
| 520 | |a © 2024 The Authors. | ||
| 520 | |a Objectives: To determine whether contrast-enhanced CT radiomics features can preoperatively predict lymphovascular invasion (LVI) and perineural invasion (PNI) in gastric cancer (GC) | ||
| 520 | |a Methods: A total of 148 patients were included in the LVI group, and 143 patients were included in the PNI group. Three predictive models were constructed, including clinical, radiomics, and combined models. A nomogram was developed with clinical risk factors to predict LVI and PNI status. The predictive performance of the three models was mainly evaluated using the mean area under the curve (AUC). The performance of three predictive models was assessed concerning calibration and clinical usefulness | ||
| 520 | |a Results: In the LVI group, the predictive power of the combined model (AUC=0.871, 0.822) outperformed the clinical model (AUC=0.792, 0.728) and the radiomics model (AUC=0.792, 0.728) in both the training and testing cohorts. In the PNI group, the combined model (AUC=0.834, 0.828) also had better predictive power than the clinical model (AUC=0.764, 0.632) and the radiomics model (AUC=0.764, 0.632) in both the training and testing cohorts. The combined models also showed good calibration and clinical usefulness for LVI and PNI prediction | ||
| 520 | |a Conclusion: CECT-based radiomics analysis might serve as a non-invasive method to predict LVI and PNI status in GC | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Contrast-enhanced CT | |
| 650 | 4 | |a Gastric cancer | |
| 650 | 4 | |a Lymphovascular invasion | |
| 650 | 4 | |a Perineural invasion | |
| 650 | 4 | |a Radiomics | |
| 700 | 1 | |a Yang, Miao |e verfasserin |4 aut | |
| 700 | 1 | |a Hou, Rong |e verfasserin |4 aut | |
| 700 | 1 | |a Ai, Shuangquan |e verfasserin |4 aut | |
| 700 | 1 | |a Nie, Tingting |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Jun |e verfasserin |4 aut | |
| 700 | 1 | |a Hu, Huaifei |e verfasserin |4 aut | |
| 700 | 1 | |a Guo, Xiaofang |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Yulin |e verfasserin |4 aut | |
| 700 | 1 | |a Yuan, Zilong |e verfasserin |4 aut | |
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