Academic radiology · 2026
Noninvasive Prediction of Tumor Malignancy Grade in Pancreatic Ductal Adenocarcinoma with Dual-Layer Detector CT: A Novel Index Integrating Histopathological Differentiation and Ki-67.
СтатьяПоддержка исследования: вне правительства СШАНазвание 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.
Li Mengru, Wang Bin, Ming Shuai, Cheng Peng, Wang Min, Zhang Wuyang, Li Jingyu, Shi Dan, Wei Wei
Аннотация
RATIONALE AND OBJECTIVES: To preliminarily develop and validate an integrated model based on clinical features and dual-layer detector spectral CT (DLCT) 3D volumetric parameters for the noninvasive prediction of a novel Grade of Malignancy (GOM) in pancreatic ductal adenocarcinoma (PDAC)-a composite index integrating histopathological differentiation and Ki-67 index.
MATERIALS AND METHODS: This retrospective study enrolled 183 patients with pathologically confirmed PDAC. Patients were randomly allocated into training (n=128) and validation (n=55) cohorts. From the portal venous phase scans, three quantitative 3D volumetric parameters were extracted from the tumor volume: iodine concentration (IC), the slope of the spectral attenuation curve, and effective atomic number. Independent predictors for the GOM were identified through univariate and multivariate logistic regression analysis. The discriminatory performance of the developed models was evaluated using receiver operating characteristic curve analysis, and clinical utility was assessed with decision curve analysis.
RESULTS: The integrated model, which combined the DLCT parameter (3D volume of interest-IC) and CA125, demonstrated superior predictive performance compared to models using clinical or DLCT features alone. In the training cohort, the integrated model achieved an area under the curve (AUC) of 0.821 (95% CI: 0.743-0.899), which was robustly validated with an AUC of 0.806 (95% CI: 0.684-0.928) in the validation cohort. Decision curve analysis confirmed that this combined model provided the highest clinical net benefit across a wide range of threshold probabilities.
CONCLUSION: Our findings suggest that an integrated model incorporating the 3D volumetric parameter IC from DLCT and CA125 could be a useful and noninvasive adjunct for preoperative prediction of the comprehensive GOM in PDAC, though further validation is needed.
MATERIALS AND METHODS: This retrospective study enrolled 183 patients with pathologically confirmed PDAC. Patients were randomly allocated into training (n=128) and validation (n=55) cohorts. From the portal venous phase scans, three quantitative 3D volumetric parameters were extracted from the tumor volume: iodine concentration (IC), the slope of the spectral attenuation curve, and effective atomic number. Independent predictors for the GOM were identified through univariate and multivariate logistic regression analysis. The discriminatory performance of the developed models was evaluated using receiver operating characteristic curve analysis, and clinical utility was assessed with decision curve analysis.
RESULTS: The integrated model, which combined the DLCT parameter (3D volume of interest-IC) and CA125, demonstrated superior predictive performance compared to models using clinical or DLCT features alone. In the training cohort, the integrated model achieved an area under the curve (AUC) of 0.821 (95% CI: 0.743-0.899), which was robustly validated with an AUC of 0.806 (95% CI: 0.684-0.928) in the validation cohort. Decision curve analysis confirmed that this combined model provided the highest clinical net benefit across a wide range of threshold probabilities.
CONCLUSION: Our findings suggest that an integrated model incorporating the 3D volumetric parameter IC from DLCT and CA125 could be a useful and noninvasive adjunct for preoperative prediction of the comprehensive GOM in PDAC, though further validation is needed.