Li Zhichun, He Qian, Yang Xiao, Zhu Tingting, Li Xinghui, Lei Yan, Tang Wei, Peng Song
Chongqing Health Center for Women and Children, Women and Children's Hospital of Chongqing Medical University, Chongqing, China.
Sichuan Key Laboratory of Medical Imaging, Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, China.
Front Med (Lausanne). 2024 Jul 31;11:1308435. doi: 10.3389/fmed.2024.1308435. eCollection 2024.
To develop a model that integrates radiomics features and clinical factors to predict upper gastrointestinal bleeding (UGIB) in patients with decompensated cirrhosis.
104 decompensated cirrhosis patients with UGIB and 104 decompensated cirrhosis patients without UGIB were randomized according to a 7:3 ratio into a training cohort ( = 145) and a validation cohort ( = 63). Radiomics features of the abdominal skeletal muscle area (SMA) were extracted from the cross-sectional image at the largest level of the third lumbar vertebrae (L3) on the abdominal unenhanced multi-detector computer tomography (MDCT) images. Clinical-radiomics nomogram were constructed by combining a radiomics signature (Rad score) with clinical independent risk factors associated with UGIB. Nomogram performance was evaluated in calibration, discrimination, and clinical utility.
The radiomics signature was built using 11 features. Plasma prothrombin time (PT), sarcopenia, and Rad score were independent predictors of the risk of UGIB in patients with decompensated cirrhosis. The clinical-radiomics nomogram performed well in both the training cohort (AUC, 0.902; 95% CI, 0.850-0.954) and the validation cohort (AUC, 0.858; 95% CI, 0.762-0.953) compared with the clinical factor model and the radiomics model and displayed excellent calibration in the training cohort. Decision curve analysis (DCA) demonstrated that the predictive efficacy of the clinical-radiomics nomogram model was superior to that of the clinical and radiomics model.
Clinical-radiomics nomogram that combines clinical factors and radiomics features has demonstrated favorable predictive effects in predicting the occurrence of UGIB in patients with decompensated cirrhosis. This helps in early diagnosis and treatment of the disease, warranting further exploration and research.
建立一个整合放射组学特征和临床因素的模型，以预测失代偿期肝硬化患者的上消化道出血（UGIB）。
104例伴有UGIB的失代偿期肝硬化患者和104例不伴有UGIB的失代偿期肝硬化患者按7:3的比例随机分为训练队列（n = 145）和验证队列（n = 63）。在腹部非增强多层螺旋计算机断层扫描（MDCT）图像上，从第三腰椎（L3）最大层面的横断面图像中提取腹部骨骼肌区域（SMA）的放射组学特征。通过将放射组学特征（Rad评分）与UGIB相关的临床独立危险因素相结合，构建临床-放射组学列线图。在校准、鉴别和临床实用性方面评估列线图的性能。
使用11个特征构建了放射组学特征。血浆凝血酶原时间（PT）、肌肉减少症和Rad评分是失代偿期肝硬化患者UGIB风险的独立预测因素。与临床因素模型和放射组学模型相比，临床-放射组学列线图在训练队列（AUC，0.902；95%CI，0.850-0.954）和验证队列（AUC，0.858；95%CI，0.762-0.953）中均表现良好，并且在训练队列中显示出良好的校准。决策曲线分析（DCA）表明，临床-放射组学列线图模型的预测效能优于临床模型和放射组学模型。
结合临床因素和放射组学特征的临床-放射组学列线图在预测失代偿期肝硬化患者UGIB的发生方面显示出良好的预测效果。这有助于疾病的早期诊断和治疗，值得进一步探索和研究。
