European radiology · 2024
Deep learning radiomic nomogram outperforms the clinical model in distinguishing intracranial solitary fibrous tumors from angiomatous meningiomas and can predict patient prognosis.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Liang Xiaohong, Ke Xiaoai, Hu Wanjun, Jiang Jian, Li Shenglin, Xue Caiqiang, Liu Xianwang, Dend Juan, Yan Cheng, Gao Mingzi, Zhao Liqin, Zhou Junlin
Аннотация
OBJECTIVES: To evaluate the value of a magnetic resonance imaging (MRI)-based deep learning radiomic nomogram (DLRN) for distinguishing intracranial solitary fibrous tumors (ISFTs) from angiomatous meningioma (AMs) and predicting overall survival (OS) for ISFT patients.
METHODS: In total, 1090 patients from Beijing Tiantan Hospital, Capital Medical University, and 131 from Lanzhou University Second Hospital were categorized as primary cohort (PC) and external validation cohort (EVC), respectively. An MRI-based DLRN was developed in PC to distinguish ISFTs from AMs. We validated the DLRN and compared it with a clinical model (CM) in EVC. In total, 149 ISFT patients were followed up. We carried out Cox regression analysis on DLRN score, clinical characteristics, and histological stratification. Besides, we evaluated the association between independent risk factors and OS in the follow-up patients using Kaplan-Meier curves.
RESULTS: The DLRN outperformed CM in distinguishing ISFTs from AMs (area under the curve [95% confidence interval (CI)]: 0.86 [0.84-0.88] for DLRN and 0.70 [0.67-0.72] for CM, p < 0.001) in EVC. Patients with high DLRN score [per 1 increase; hazard ratio (HR) 1.079, 95% CI: 1.009-1.147, p = 0.019] and subtotal resection (STR) [per 1 increase; HR 2.573, 95% CI: 1.337-4.932, p = 0.004] were associated with a shorter OS. A statistically significant difference in OS existed between the high and low DLRN score groups with a cutoff value of 12.19 (p < 0.001). There is also a difference in OS between total excision (GTR) and STR groups (p < 0.001).
CONCLUSION: The proposed DLRN outperforms the CM in distinguishing ISFTs from AMs and can predict OS for ISFT patients.
CLINICAL RELEVANCE STATEMENT: The proposed MRI-based deep learning radiomic nomogram outperforms the clinical model in distinguishing ISFTs from AMs and can predict OS of ISFT patients, which could guide the surgical strategy and predict prognosis for patients.
KEY POINTS: Distinguishing ISFTs from AMs based on conventional radiological signs is challenging. The DLRN outperformed the CM in our study. The DLRN can predict OS for ISFT patients.
METHODS: In total, 1090 patients from Beijing Tiantan Hospital, Capital Medical University, and 131 from Lanzhou University Second Hospital were categorized as primary cohort (PC) and external validation cohort (EVC), respectively. An MRI-based DLRN was developed in PC to distinguish ISFTs from AMs. We validated the DLRN and compared it with a clinical model (CM) in EVC. In total, 149 ISFT patients were followed up. We carried out Cox regression analysis on DLRN score, clinical characteristics, and histological stratification. Besides, we evaluated the association between independent risk factors and OS in the follow-up patients using Kaplan-Meier curves.
RESULTS: The DLRN outperformed CM in distinguishing ISFTs from AMs (area under the curve [95% confidence interval (CI)]: 0.86 [0.84-0.88] for DLRN and 0.70 [0.67-0.72] for CM, p < 0.001) in EVC. Patients with high DLRN score [per 1 increase; hazard ratio (HR) 1.079, 95% CI: 1.009-1.147, p = 0.019] and subtotal resection (STR) [per 1 increase; HR 2.573, 95% CI: 1.337-4.932, p = 0.004] were associated with a shorter OS. A statistically significant difference in OS existed between the high and low DLRN score groups with a cutoff value of 12.19 (p < 0.001). There is also a difference in OS between total excision (GTR) and STR groups (p < 0.001).
CONCLUSION: The proposed DLRN outperforms the CM in distinguishing ISFTs from AMs and can predict OS for ISFT patients.
CLINICAL RELEVANCE STATEMENT: The proposed MRI-based deep learning radiomic nomogram outperforms the clinical model in distinguishing ISFTs from AMs and can predict OS of ISFT patients, which could guide the surgical strategy and predict prognosis for patients.
KEY POINTS: Distinguishing ISFTs from AMs based on conventional radiological signs is challenging. The DLRN outperformed the CM in our study. The DLRN can predict OS for ISFT patients.