文章：

基于肾周脂肪CT影像组学的上尿路尿路上皮癌生存模型：整合纹理特征与临床预测因子

Perirenal Fat CT Radiomics-Based Survival Model for Upper Tract Urothelial Carcinoma: Integrating Texture Features with Clinical Predictors

原文发布日期：8 November 2024

DOI: 10.3390/cancers16223772

类型: Article

开放获取: 是

英文摘要：

Background:Upper tract urothelial carcinoma (UTUC) presents significant challenges in prognostication due to its rarity and complex anatomy. This study introduces a novel approach integrating perirenal fat (PRF) radiomics with clinical factors to enhance prognostic accuracy in UTUC.Methods:The study retrospectively analyzed 103 UTUC patients who underwent radical nephroureterectomy. PRF radiomics features were extracted from preoperative CT scans using a semi-automated segmentation method. Three prognostic models were developed: clinical, radiomics, and combined. Model performance was assessed using concordance index (C-index), time-dependent Area Under the Curve (AUC), and integrated Brier score.Results: The combined model demonstrated superior performance (C-index: 0.784, 95% CI: 0.707–0.861) compared to the radiomics (0.759, 95% CI: 0.678–0.840) and clinical (0.653, 95% CI: 0.547–0.759) models. Time-dependent AUC analysis revealed the radiomics model’s particular strength in short-term prognosis (12-month AUC: 0.9281), while the combined model excelled in long-term predictions (60-month AUC: 0.8403). Key PRF radiomics features showed stronger prognostic value than traditional clinical factors.Conclusions:Integration of PRF radiomics with clinical data significantly improves prognostic accuracy in UTUC. This approach offers a more nuanced analysis of the tumor microenvironment, potentially capturing early signs of tumor invasion not visible through conventional imaging. The semi-automated PRF segmentation method presents advantages in reproducibility and ease of use, facilitating potential clinical implementation.

摘要翻译： 

背景：上尿路尿路上皮癌（UTUC）因其罕见性和解剖结构复杂性，在预后评估方面存在显著挑战。本研究提出一种整合肾周脂肪（PRF）影像组学特征与临床因素的新方法，旨在提升UTUC预后评估的准确性。 方法：本研究回顾性分析了103例接受根治性肾输尿管切除术的UTUC患者。通过半自动分割方法从术前CT图像中提取PRF影像组学特征。构建了三种预后模型：临床模型、影像组学模型及联合模型。采用一致性指数（C-index）、时间依赖性曲线下面积（AUC）和综合Brier评分评估模型性能。 结果：联合模型（C-index：0.784，95% CI：0.707–0.861）表现优于影像组学模型（0.759，95% CI：0.678–0.840）和临床模型（0.653，95% CI：0.547–0.759）。时间依赖性AUC分析显示，影像组学模型在短期预后方面表现突出（12个月AUC：0.9281），而联合模型在长期预测中更具优势（60个月AUC：0.8403）。关键PRF影像组学特征显示出比传统临床因素更强的预后价值。 结论：PRF影像组学与临床数据的整合显著提高了UTUC预后评估的准确性。该方法能更精细地分析肿瘤微环境，可能捕捉到传统影像学无法显示的早期肿瘤侵袭迹象。半自动PRF分割方法在可重复性和易用性方面具有优势，有助于潜在的临床推广应用。

原文链接：

Perirenal Fat CT Radiomics-Based Survival Model for Upper Tract Urothelial Carcinoma: Integrating Texture Features with Clinical Predictors