用于预测门静脉栓塞后肝残余肥厚不足的临床-放射学联合模型的建立和外部验证。

IF 3.4 2区 医学 Q2 ONCOLOGY Annals of Surgical Oncology Pub Date : 2025-03-01 Epub Date: 2024-12-10 DOI:10.1245/s10434-024-16592-z
Qiang Wang, Torkel B Brismar, Dennis Björk, Erik Baubeta, Gert Lindell, Bergthor Björnsson, Ernesto Sparrelid
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引用次数: 0

摘要

目的:本研究旨在建立并外部验证一种基于临床因素和预处理计算机断层扫描(CT)患者放射组学的预测门静脉栓塞(PVE)后未来肝残余(FLR)肥厚不足的模型。方法:回顾性收集来自瑞典三个中心的241例连续患者的临床信息和CT扫描。一个中心(120例患者)用于模型开发,另外两个中心(59例和62例患者)作为测试队列。临床模型的建立采用Logistic回归分析。利用支持向量机从CT图像中构建FLR放射组学特征。建立了一个结合临床因素和FLR放射组学特征的模型。结果:确定了3个独立的临床因素用于模型构建:预处理标准化FLR(优势比(OR): 1.12, 95%可信区间(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833个放射组学特征,经特征降维后,选择16个特征进行FLR放射组学特征构建。加入临床模型后,联合模型的AUC分别增加到0.80、0.76、0.72。然而,增长并不显著。结论:前处理CT放射组学对预测PVE后FLR肥大的临床模型具有附加价值。虽然没有达到统计学意义,但不断发展的放射组学具有补充FLR肥大传统预测因子的潜力。
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Development and External Validation of a Combined Clinical-Radiomic Model for Predicting Insufficient Hypertrophy of the Future Liver Remnant following Portal Vein Embolization.

Objectives: 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.

Conclusions: 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.

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来源期刊
CiteScore
5.90
自引率
10.80%
发文量
1698
审稿时长
2.8 months
期刊介绍: The Annals of Surgical Oncology is the official journal of The Society of Surgical Oncology and is published for the Society by Springer. The Annals publishes original and educational manuscripts about oncology for surgeons from all specialities in academic and community settings.
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