Contrast CT radiomic features add value to prediction of prognosis in adrenal cortical carcinoma
© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature..
OBJECTIVE: Adrenocortical carcinoma (ACC) is a rare and aggressive malignancy with poor prognosis due to high postoperative recurrence rates. The aim of this study is to develop a contrast CT radiomic feature-based prognosis prediction model for ACC and evaluate its performance by comparison with ENSAT staging system and S-GRAS score.
METHODS: Included in this study were 39 ACC patients, from which we extracted 1411 radiomic features. Using cross-validated least absolute shrinkage and selection operator regression (cv-LASSO regression), we generated a radiomic index. Additionally, we further validated the radiomic index using both univariate and multivariate Cox regression analyses. We constructed a radiomic nomogram that incorporated the radiomic signature and compared it with ENSAT stage and S-GRAS score in terms of calibration, discrimination and clinical usefulnes.
RESULTS: In this study, the average progression free survival (PFS) of 39 patients was 20.4 (IQR 9.1-60.1) months and the average overall survival (OS) was 57.8 (IQR 32.4-NA). The generated radiomic features were significantly associated with PFS, OS, independent of clinical-pathologic risk factors (HR 0.16, 95%CI 0.02-0.99, p = 0.05; HR 0.20, 95%CI 0.04-1.07, p = 0.06, respectively). The radiomic index, ENSAT stage, resection status, and Ki67% index incorporated nomogram exhibited better performance for both PFS and OS prediction as compared with the S-GRAS and ENSAT nomogram (C-index: 0.75 vs. C-index: 0.68, p = 0.030 and 0.67, p = 0.025; C-index: 0.78 vs. C-index: 0.72, p = 0.003 and 0.73, p = 0.006). Calibration curve analysis showed that the radiomics-based model performs best in predicting the two-year PFS and the three-year OS. Decision curve analysis demonstrated that the radiomic index nomogram outperformed the S-GRAS and ENSAT nomogram in predicting the two-year PFS and the three-year OS.
CONCLUSION: The contrast CT radiomic-based nomogram performed better than S-GRAS or ENSAT in predicting PFS and OS in ACC patients.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:83 |
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| Enthalten in: |
Endocrine - 83(2024), 3 vom: 15. März, Seite 763-774 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Liu, Jiacheng [VerfasserIn] |
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| Links: |
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| Themen: |
Adrenal cortical carcinoma |
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| Anmerkungen: |
Date Completed 01.03.2024 Date Revised 15.03.2024 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1007/s12020-023-03568-4 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM364601175 |
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| 100 | 1 | |a Liu, Jiacheng |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Contrast CT radiomic features add value to prediction of prognosis in adrenal cortical carcinoma |
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| 500 | |a Citation Status MEDLINE | ||
| 520 | |a © 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. | ||
| 520 | |a OBJECTIVE: Adrenocortical carcinoma (ACC) is a rare and aggressive malignancy with poor prognosis due to high postoperative recurrence rates. The aim of this study is to develop a contrast CT radiomic feature-based prognosis prediction model for ACC and evaluate its performance by comparison with ENSAT staging system and S-GRAS score | ||
| 520 | |a METHODS: Included in this study were 39 ACC patients, from which we extracted 1411 radiomic features. Using cross-validated least absolute shrinkage and selection operator regression (cv-LASSO regression), we generated a radiomic index. Additionally, we further validated the radiomic index using both univariate and multivariate Cox regression analyses. We constructed a radiomic nomogram that incorporated the radiomic signature and compared it with ENSAT stage and S-GRAS score in terms of calibration, discrimination and clinical usefulnes | ||
| 520 | |a RESULTS: In this study, the average progression free survival (PFS) of 39 patients was 20.4 (IQR 9.1-60.1) months and the average overall survival (OS) was 57.8 (IQR 32.4-NA). The generated radiomic features were significantly associated with PFS, OS, independent of clinical-pathologic risk factors (HR 0.16, 95%CI 0.02-0.99, p = 0.05; HR 0.20, 95%CI 0.04-1.07, p = 0.06, respectively). The radiomic index, ENSAT stage, resection status, and Ki67% index incorporated nomogram exhibited better performance for both PFS and OS prediction as compared with the S-GRAS and ENSAT nomogram (C-index: 0.75 vs. C-index: 0.68, p = 0.030 and 0.67, p = 0.025; C-index: 0.78 vs. C-index: 0.72, p = 0.003 and 0.73, p = 0.006). Calibration curve analysis showed that the radiomics-based model performs best in predicting the two-year PFS and the three-year OS. Decision curve analysis demonstrated that the radiomic index nomogram outperformed the S-GRAS and ENSAT nomogram in predicting the two-year PFS and the three-year OS | ||
| 520 | |a CONCLUSION: The contrast CT radiomic-based nomogram performed better than S-GRAS or ENSAT in predicting PFS and OS in ACC patients | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Adrenal cortical carcinoma | |
| 650 | 4 | |a Prediction | |
| 650 | 4 | |a Progression-free survival | |
| 650 | 4 | |a Radiomic features | |
| 700 | 1 | |a Lin, Wenhao |e verfasserin |4 aut | |
| 700 | 1 | |a Yan, Ling |e verfasserin |4 aut | |
| 700 | 1 | |a Xie, Jialing |e verfasserin |4 aut | |
| 700 | 1 | |a Dai, Jun |e verfasserin |4 aut | |
| 700 | 1 | |a Xu, Danfeng |e verfasserin |4 aut | |
| 700 | 1 | |a Zhao, Juping |e verfasserin |4 aut | |
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