La Radiologia medica · 2025
Deep learning model integrating contrast-enhanced ultrasound spatiotemporal imaging with clinical data for the differential diagnosis between hepatocellular carcinoma and intrahepatic cholangiocarcinoma.
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.
Li Wei, Liu Zhong, Cheng Meiqing, Huang Bin, Hou Chao, Luo Yudi, Yang Kai, Lu Minhua, Chen Xin, Wang Wei
Аннотация
PURPOSE: This study aimed to develop a deep learning model, capable of extracting both spatial and temporal features from contrast-enhanced ultrasound (CEUS) data and integrating with patient clinical parameters, for the differential diagnosis between hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC).
MATERIALS AND METHODS: We retrospectively analyzed the CEUS data (ultrasound contrast agent: SonoVue®-sulfur hexafluoride microbubbles) from 165 ICC patients and 140 date-matched HCC patients. A deep learning model, namely CEUS-CD-Net, was developed to extract spatial-temporal features from dynamic CEUS data and integrate them with patient clinical parameters for the differential diagnosis between HCC and ICC. The performance of CEUS-CD-Net was evaluated using the area under the receiver operating characteristic curve (AUC), with comparisons against other methods including the single-source data-based models (CEUS-Net and CD-Net, based merely on dynamic CEUS or patient clinical data), CEUS static image-based model (sCEUS-Net), time-intensity curve-based model (TIC-Model), and the assessment by radiologists.
RESULTS: CEUS-CD-Net achieved an AUC of 0.884 (95% CI, 0.794-0.938) on the test cohort, significantly outperforming the single-source data-based models of CEUS-Net (0.827 [0.730-0.896]) and CD-Net (0.812 [0.718-0.887]), as well as sCEUS-Net (0.772 [0.669-0.851]) and TIC-Model (0.731 [0.633-0.823]). In the subset of determinate cases, CEUS-CD-Net achieved an AUC of 0.893 [0.806-0.950], which was better than the one obtained by radiologists' assessment (0.790 [0.683-0.868]). Model visualization results revealed that CEUS-CD-Net surpassed radiologists in discerning subtle patterns reflected by CEUS.
CONCLUSION: The integration of spatial and temporal features of dynamic CEUS data, coupled with clinical parameters of patients in CEUS-CD-Net, significantly improved the differential diagnosis between HCC and ICC.
MATERIALS AND METHODS: We retrospectively analyzed the CEUS data (ultrasound contrast agent: SonoVue®-sulfur hexafluoride microbubbles) from 165 ICC patients and 140 date-matched HCC patients. A deep learning model, namely CEUS-CD-Net, was developed to extract spatial-temporal features from dynamic CEUS data and integrate them with patient clinical parameters for the differential diagnosis between HCC and ICC. The performance of CEUS-CD-Net was evaluated using the area under the receiver operating characteristic curve (AUC), with comparisons against other methods including the single-source data-based models (CEUS-Net and CD-Net, based merely on dynamic CEUS or patient clinical data), CEUS static image-based model (sCEUS-Net), time-intensity curve-based model (TIC-Model), and the assessment by radiologists.
RESULTS: CEUS-CD-Net achieved an AUC of 0.884 (95% CI, 0.794-0.938) on the test cohort, significantly outperforming the single-source data-based models of CEUS-Net (0.827 [0.730-0.896]) and CD-Net (0.812 [0.718-0.887]), as well as sCEUS-Net (0.772 [0.669-0.851]) and TIC-Model (0.731 [0.633-0.823]). In the subset of determinate cases, CEUS-CD-Net achieved an AUC of 0.893 [0.806-0.950], which was better than the one obtained by radiologists' assessment (0.790 [0.683-0.868]). Model visualization results revealed that CEUS-CD-Net surpassed radiologists in discerning subtle patterns reflected by CEUS.
CONCLUSION: The integration of spatial and temporal features of dynamic CEUS data, coupled with clinical parameters of patients in CEUS-CD-Net, significantly improved the differential diagnosis between HCC and ICC.