Endocrine · 2023
Contrast CT radiomic features add value to prediction of prognosis in adrenal cortical carcinoma.
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Liu Jiacheng, Lin Wenhao, Yan Ling, Xie Jialing, Dai Jun, Xu Danfeng, Zhao Juping
Аннотация
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.
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.