Journal of cardiovascular computed tomography · 2026
Radiomics-based characterization of valvular calcium improves risk stratification for paravalvular regurgitation after TAVI.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
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.
Morais Thamara Carvalho, Assunção Júnior Antonildes Nascimento, Câmara Sérgio Figueiredo, Grego da Silva Carla Franco, Liberato Gabriela, Borges Leal Assunção Bruna Morhy, Kanhouche Gabriel, Serra Vinícius Cardoso, Vaz André, Gelain Marco Antonio, Sandoli de Brito Fabio, Abizaid Alexandre, Ribeiro Henrique Barbosa, Nomura Cesar Higa
Аннотация
BACKGROUND: Significant paravalvular regurgitation (sPVR) remains a clinically relevant complication after transcatheter aortic valve implantation (TAVI) and is influenced by valvular calcium characteristics that are incompletely captured by conventional CT metrics.
PURPOSE: To evaluate whether CT-based radiomics of aortic valvular calcium can predict sPVR after TAVI and to assess the incremental value of radiomic features beyond standard anatomic and procedural parameters.
MATERIALS AND METHODS: This retrospective multicenter study included 393 patients undergoing TAVI with preprocedural contrast-enhanced, ECG-gated CT. Predictive models were developed in a development cohort (n = 195) and tested in an external validation cohort (n = 198). Aortic valvular calcium was manually segmented on CT images. A radiomics-derived score (RDS) composed of five features was generated using least absolute shrinkage and selection operator logistic regression. Model performance for predicting postprocedural sPVR (moderate or severe), assessed by transthoracic echocardiography according to VARC-3 criteria, was compared between a traditional model based on procedural and CT anatomic variables and an enhanced model incorporating the RDS.
RESULTS: Among 393 patients undergoing TAVI (development cohort, n = 195; external validation cohort, n = 198), the RDS showed consistent discrimination for sPVR in the development (AUC, 0.76) and validation (AUC, 0.74) cohorts. Incorporation of the RDS significantly improved model discrimination (AUC, 0.76 to 0.84; p = 0.004), risk reclassification, and model calibration. In multivariable analysis, the RDS remained independently associated with sPVR (odds ratio, 2.31 per 0.1-unit increase; p = 0.018).
CONCLUSION: CT-based radiomics of valvular calcium provides incremental information beyond conventional CT metrics for identifying patients at risk of incomplete prosthesis sealing after TAVI. These findings support the integration of radiomics into routine preprocedural CT assessment to enhance individualized risk stratification in TAVI candidates.
PURPOSE: To evaluate whether CT-based radiomics of aortic valvular calcium can predict sPVR after TAVI and to assess the incremental value of radiomic features beyond standard anatomic and procedural parameters.
MATERIALS AND METHODS: This retrospective multicenter study included 393 patients undergoing TAVI with preprocedural contrast-enhanced, ECG-gated CT. Predictive models were developed in a development cohort (n = 195) and tested in an external validation cohort (n = 198). Aortic valvular calcium was manually segmented on CT images. A radiomics-derived score (RDS) composed of five features was generated using least absolute shrinkage and selection operator logistic regression. Model performance for predicting postprocedural sPVR (moderate or severe), assessed by transthoracic echocardiography according to VARC-3 criteria, was compared between a traditional model based on procedural and CT anatomic variables and an enhanced model incorporating the RDS.
RESULTS: Among 393 patients undergoing TAVI (development cohort, n = 195; external validation cohort, n = 198), the RDS showed consistent discrimination for sPVR in the development (AUC, 0.76) and validation (AUC, 0.74) cohorts. Incorporation of the RDS significantly improved model discrimination (AUC, 0.76 to 0.84; p = 0.004), risk reclassification, and model calibration. In multivariable analysis, the RDS remained independently associated with sPVR (odds ratio, 2.31 per 0.1-unit increase; p = 0.018).
CONCLUSION: CT-based radiomics of valvular calcium provides incremental information beyond conventional CT metrics for identifying patients at risk of incomplete prosthesis sealing after TAVI. These findings support the integration of radiomics into routine preprocedural CT assessment to enhance individualized risk stratification in TAVI candidates.