Wang Qiang, Brismar Torkel B, Björk Dennis, Baubeta Erik, Lindell Gert, Björnsson Bergthor, Sparrelid Ernesto
Department of Clinical Science, Intervention and Technology (CLINTEC), Division of Medical Imaging and Technology, Karolinska Institutet, Stockholm, Sweden.
Department of Radiology, Karolinska University Hospital Huddinge, Stockholm, Sweden.
Ann Surg Oncol. 2025 Mar;32(3):1795-1807. doi: 10.1245/s10434-024-16592-z. Epub 2024 Dec 10.
This study aimed to develop and externally validate a model for predicting insufficient future liver remnant (FLR) hypertrophy after portal vein embolization (PVE) based on clinical factors and radiomics of pretreatment computed tomography (CT) PATIENTS AND METHODS: Clinical information and CT scans of 241 consecutive patients from three Swedish centers were retrospectively collected. One center (120 patients) was applied for model development, and the other two (59 and 62 patients) as test cohorts. Logistic regression analysis was adopted for clinical model development. A FLR radiomics signature was constructed from the CT images using the support vector machine. A model combining clinical factors and FLR radiomics signature was developed. Area under the curve (AUC) was adopted for predictive performance evaluation RESULTS: Three independent clinical factors were identified for model construction: pretreatment standardized FLR (odds ratio (OR): 1.12, 95% confidence interval (CI): 1.04-1.20), alanine transaminase (ALT) level (OR: 0.98, 95% CI: 0.97-0.99), and PVE material (OR: 0.27, 95% CI: 0.08-0.87). This clinical model showed an AUC of 0.75, 0.71, and 0.68 in the three cohorts, respectively. A total of 833 radiomics features were extracted, and after feature dimension reduction, 16 features were selected for FLR radiomics signature construction. When adding it to the clinical model, the AUC of the combined model increased to 0.80, 0.76, and 0.72, respectively. However, the increase was not significant.
Pretreatment CT radiomics showed added value to the clinical model for predicting FLR hypertrophy following PVE. Although not reaching statistically significant, the evolving radiomics holds a potential to supplement traditional predictors of FLR hypertrophy.
本研究旨在基于临床因素和预处理计算机断层扫描(CT)的放射组学开发并外部验证一个预测门静脉栓塞(PVE)后未来肝残余量(FLR)肥大不足的模型。
回顾性收集了来自瑞典三个中心的241例连续患者的临床信息和CT扫描数据。一个中心(120例患者)用于模型开发,另外两个中心(分别为59例和62例患者)作为测试队列。采用逻辑回归分析进行临床模型开发。使用支持向量机从CT图像构建FLR放射组学特征。开发了一个结合临床因素和FLR放射组学特征的模型。采用曲线下面积(AUC)进行预测性能评估。
确定了三个用于模型构建的独立临床因素:预处理标准化FLR(比值比(OR):1.12,95%置信区间(CI):1.04 - 1.20)、丙氨酸转氨酶(ALT)水平(OR:0.98,95%CI:0.97 - 0.99)和PVE材料(OR:0.27,95%CI:0.08 - 0.87)。该临床模型在三个队列中的AUC分别为0.75、0.71和0.68。共提取了833个放射组学特征,经过特征降维后,选择了16个特征用于构建FLR放射组学特征。将其添加到临床模型中时,联合模型的AUC分别增加到0.80、0.76和0.72。然而,增加并不显著。
预处理CT放射组学对预测PVE后FLR肥大的临床模型具有附加价值。尽管未达到统计学显著水平,但不断发展的放射组学有潜力补充FLR肥大的传统预测指标。