Abdominal radiology (New York) · 2025
CT-based abdominal fat parameters as predictors of recurrence-free survival after radical resection of colorectal cancer: a nomogram approach.
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.
Yin Ke, Ma Li, Ni Ping, Liao Guanyi, Peng Hong, Guo Jinjun
Аннотация
OBJECTIVE: This study aimed to create and validate a nomogram to predict early recurrence (ER) in Colorectal cancer (CRC) patients by combining CT-derived abdominal fat parameters with clinical and pathological characteristics.
METHODS: We conducted a retrospective analysis of 206 CRC patients, dividing them into training (n = 146) and validation (n = 60) cohorts. We quantified abdominal fat parameters, including subcutaneous adipose tissue index (SATI) and visceral adipose tissue index (VATI), using semi-automatic software on CT images at the level of the third lumbar vertebra (L3). We calculated the liver fat fraction (LFF) based on the liver CT value (LFF% = -0.58 × [CT-HU] + 38.2). Finally, we performed Cox regression analysis to identify independent predictors of ER. We constructed a nomogram based on these predictors and evaluated its performance using calibration curves, the concordance index (C-index), and area under the curve (AUC). Internal validation was performed using a 1000-bootstrap resampling method.
RESULTS: LFF, VATI, CEA level, and lymphovascular invasion (LVI) were independent risk factors for ER. The calibration curve showed good concordance, with C-indices of 0.866 (95% CI: 0.808-0.924) and 0.825 (95% CI: 0.736-0.914) in the training and validation cohorts, respectively. Risk stratification effectively distinguished low- and high-risk groups (P < 0.001 for both).
CONCLUSION: A nomogram combines CT-derived abdominal fat parameters with clinical data showed good performance in predicting ER in CRC patients, and provides a tool for personalized monitoring and treatment strategies.
METHODS: We conducted a retrospective analysis of 206 CRC patients, dividing them into training (n = 146) and validation (n = 60) cohorts. We quantified abdominal fat parameters, including subcutaneous adipose tissue index (SATI) and visceral adipose tissue index (VATI), using semi-automatic software on CT images at the level of the third lumbar vertebra (L3). We calculated the liver fat fraction (LFF) based on the liver CT value (LFF% = -0.58 × [CT-HU] + 38.2). Finally, we performed Cox regression analysis to identify independent predictors of ER. We constructed a nomogram based on these predictors and evaluated its performance using calibration curves, the concordance index (C-index), and area under the curve (AUC). Internal validation was performed using a 1000-bootstrap resampling method.
RESULTS: LFF, VATI, CEA level, and lymphovascular invasion (LVI) were independent risk factors for ER. The calibration curve showed good concordance, with C-indices of 0.866 (95% CI: 0.808-0.924) and 0.825 (95% CI: 0.736-0.914) in the training and validation cohorts, respectively. Risk stratification effectively distinguished low- and high-risk groups (P < 0.001 for both).
CONCLUSION: A nomogram combines CT-derived abdominal fat parameters with clinical data showed good performance in predicting ER in CRC patients, and provides a tool for personalized monitoring and treatment strategies.