Determination of p53abn endometrial cancer : a multitask analysis using Radiological-Clinical nomogram on MR imaging
© The Author(s) 2024. Published by Oxford University Press on behalf of the British Institute of Radiology. All rights reserved. For permissions, please email: journals.permissionsoup.com..
OBJECTIVES: We aimed to differentiate endometrial cancer (EC) between TP53mutation (P53abn) and Non-P53abn subtypes using radiological-clinical nomogram on EC body volume MR imaging.
MATERIALS AND METHODS: We retrospectively recruited two hundred twenty-seven patients with pathologically proven EC from our institution. All these patients have undergone molecular pathology diagnosis based on the cancer genome atlas (TCGA). Clinical characteristics and histological diagnosis were recorded from the hospital information system. Radiomics features were extracted from online Pyradiomics processors. The diagnostic performance across different acquisition protocols was calculated and compared. The radiological-clinical nomogram was established to determine the non-endometrioid, high-risk, and P53abn EC group.
RESULTS: The best MRI sequence for differentiation P53abn from the non-P53abn group was contrast-enhanced T1WI (test AUC: 0.8). The best MRI sequence both for differentiation endometrioid cancer from non-endometrioid cancer and high risk from low-and intermediate-risk groups was apparent diffusion coefficient map (test AUC: 0.665 and 0.690). For all three tasks, the combined model incorporating all the best discriminative features from each sequence yielded the best performance. The combined model achieved an AUC of 0.845 in the testing cohorts for P53abn cancer identification. The MR-based radiomics diagnostic model performed better than the clinical-based model in determining P53abn EC (AUC : 0.834 versus 0.682).
CONCLUSION: In the present study, the diagnostic model based on the combination of both radiomics and clinical features yielded a higher performance in differentiating non-endometrioid and P53abn cancer from other EC molecular subgroups, which might help design a tailed treatment, especially for patients with high-risk EC.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - year:2024 |
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| Enthalten in: |
The British journal of radiology - (2024) vom: 27. März |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Ning, Yan [VerfasserIn] |
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| Links: |
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| Themen: |
Endometrial Neoplasms |
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| Anmerkungen: |
Date Revised 28.03.2024 published: Print-Electronic Citation Status Publisher |
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| doi: |
10.1093/bjr/tqae066 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM370284135 |
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| 245 | 1 | 0 | |a Determination of p53abn endometrial cancer |b a multitask analysis using Radiological-Clinical nomogram on MR imaging |
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| 520 | |a © The Author(s) 2024. Published by Oxford University Press on behalf of the British Institute of Radiology. All rights reserved. For permissions, please email: journals.permissionsoup.com. | ||
| 520 | |a OBJECTIVES: We aimed to differentiate endometrial cancer (EC) between TP53mutation (P53abn) and Non-P53abn subtypes using radiological-clinical nomogram on EC body volume MR imaging | ||
| 520 | |a MATERIALS AND METHODS: We retrospectively recruited two hundred twenty-seven patients with pathologically proven EC from our institution. All these patients have undergone molecular pathology diagnosis based on the cancer genome atlas (TCGA). Clinical characteristics and histological diagnosis were recorded from the hospital information system. Radiomics features were extracted from online Pyradiomics processors. The diagnostic performance across different acquisition protocols was calculated and compared. The radiological-clinical nomogram was established to determine the non-endometrioid, high-risk, and P53abn EC group | ||
| 520 | |a RESULTS: The best MRI sequence for differentiation P53abn from the non-P53abn group was contrast-enhanced T1WI (test AUC: 0.8). The best MRI sequence both for differentiation endometrioid cancer from non-endometrioid cancer and high risk from low-and intermediate-risk groups was apparent diffusion coefficient map (test AUC: 0.665 and 0.690). For all three tasks, the combined model incorporating all the best discriminative features from each sequence yielded the best performance. The combined model achieved an AUC of 0.845 in the testing cohorts for P53abn cancer identification. The MR-based radiomics diagnostic model performed better than the clinical-based model in determining P53abn EC (AUC : 0.834 versus 0.682) | ||
| 520 | |a CONCLUSION: In the present study, the diagnostic model based on the combination of both radiomics and clinical features yielded a higher performance in differentiating non-endometrioid and P53abn cancer from other EC molecular subgroups, which might help design a tailed treatment, especially for patients with high-risk EC | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Endometrial Neoplasms | |
| 650 | 4 | |a Genes | |
| 650 | 4 | |a Machine Learning | |
| 650 | 4 | |a Magnetic resonance imaging | |
| 650 | 4 | |a Nomograms | |
| 650 | 4 | |a p53 | |
| 700 | 1 | |a Liu, Wei |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Haijie |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Feiran |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Xiaojun |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Yida |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Tianping |e verfasserin |4 aut | |
| 700 | 1 | |a Yang, Guang |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, He |e verfasserin |4 aut | |
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