Determination of p53abn endometrial cancer : a multitask analysis using radiological-clinical nomogram on MRI
© 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 MRI.
METHODS: We retrospectively recruited 227 patients with pathologically proven EC from our institution. All these patients have undergone molecular pathology diagnosis based on the Cancer Genome Atlas. 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 nonendometrioid, 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 nonendometrioid cancer and high-risk from low- and intermediate-risk groups was apparent diffusion coefficient map (test AUC: 0.665 and 0.690). For all 3 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 vs 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 nonendometrioid and P53abn cancer from other EC molecular subgroups, which might help design a tailed treatment, especially for patients with high-risk EC.
ADVANCES IN KNOWLEDGE: (1) The contrast-enhanced T1WI was the best MRI sequence for differentiation P53abn from the non-P53abn group (test AUC: 0.8). (2) The radiomics-based diagnostic model performed better than the clinical-based model in determining P53abn EC (AUC: 0.834 vs 0.682). (3) The proposed model derived from multi-parametric MRI images achieved a higher accuracy in P53abn EC identification (AUC: 0.845).
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:97 |
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| Enthalten in: |
The British journal of radiology - 97(2024), 1157 vom: 07. Mai, Seite 954-963 |
| 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 Completed 07.05.2024 Date Revised 09.05.2024 published: Print Citation Status MEDLINE |
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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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| 041 | |a eng | ||
| 100 | 1 | |a Ning, Yan |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Determination of p53abn endometrial cancer |b a multitask analysis using radiological-clinical nomogram on MRI |
| 264 | 1 | |c 2024 | |
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| 500 | |a Date Revised 09.05.2024 | ||
| 500 | |a published: Print | ||
| 500 | |a Citation Status MEDLINE | ||
| 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 MRI | ||
| 520 | |a METHODS: We retrospectively recruited 227 patients with pathologically proven EC from our institution. All these patients have undergone molecular pathology diagnosis based on the Cancer Genome Atlas. 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 nonendometrioid, 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 nonendometrioid cancer and high-risk from low- and intermediate-risk groups was apparent diffusion coefficient map (test AUC: 0.665 and 0.690). For all 3 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 vs 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 nonendometrioid and P53abn cancer from other EC molecular subgroups, which might help design a tailed treatment, especially for patients with high-risk EC | ||
| 520 | |a ADVANCES IN KNOWLEDGE: (1) The contrast-enhanced T1WI was the best MRI sequence for differentiation P53abn from the non-P53abn group (test AUC: 0.8). (2) The radiomics-based diagnostic model performed better than the clinical-based model in determining P53abn EC (AUC: 0.834 vs 0.682). (3) The proposed model derived from multi-parametric MRI images achieved a higher accuracy in P53abn EC identification (AUC: 0.845) | ||
| 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 | |
| 650 | 7 | |a Tumor Suppressor Protein p53 |2 NLM | |
| 650 | 7 | |a TP53 protein, human |2 NLM | |
| 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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