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自动化 CT 体成分分析能否预测行部分肾切除术和根治性肾切除术的 RCC 患者的高级别 Clavien-Dindo 并发症?

Can automated CT body composition analysis predict high-grade Clavien-Dindo complications in patients with RCC undergoing partial and radical nephrectomy?

机构信息

Department of Radiology, Emirdag City of Hospital, Afyonkarahisar, Turkey.

Department of Radiology, University of Health Sciences, Adana City Training and Research Hospital, Adana, Turkey.

出版信息

Scott Med J. 2023 May;68(2):63-67. doi: 10.1177/00369330231166122. Epub 2023 Mar 22.

Abstract

INTRODUCTION

This study investigated the relationship between body tissue composition analysis and complications according to the Clavien-Dindo classification in patients with renal cell carcinoma (RCC) who underwent partial (PN) or radical nephrectomies (RN).

METHODS

We obtained all data of 210 patients with RCC from the 2019 Kidney and Kidney Tumor Segmentation Challenge (C4KC-KiTS) dataset and obtained radiological images from the cancer image archive. Body composition was assessed with automated artificial intelligence software using the convolutional network segmentation technique from abdominal computed tomography images. We included 125 PN and 63 RN in the study. The relationship between body fat and muscle tissue distribution and complications according to the Clavien-Dindo classification was evaluated between these two groups.

RESULTS

Clavien-Dindo 3A and higher (high grade) complications were developed in 9 of 125 patients who underwent PN and 7 of 63 patients who underwent RN. There was no significant difference between all body composition values between patients with and without high-grade complications.

CONCLUSION

This study showed that body muscle-fat tissue distribution did not affect patients with 3A and above complications according to the Clavien-Dindo classification in patients who underwent nephrectomy due to RCC.

摘要

介绍

本研究调查了接受部分(PN)或根治性肾切除术(RN)的肾细胞癌(RCC)患者根据 Clavien-Dindo 分类的身体组织成分分析与并发症之间的关系。

方法

我们从 2019 年肾脏和肾脏肿瘤分割挑战赛(C4KC-KiTS)数据集获得了 210 例 RCC 患者的所有数据,并从癌症图像档案中获得了放射学图像。使用来自腹部 CT 图像的卷积网络分割技术,通过自动化人工智能软件评估身体成分。我们将 125 例 PN 和 63 例 RN 纳入研究。评估了这两组患者中体脂肪和肌肉组织分布与 Clavien-Dindo 分类并发症之间的关系。

结果

9 例接受 PN 的患者和 7 例接受 RN 的患者发生 Clavien-Dindo 3A 及以上(高级别)并发症。有高级别并发症的患者与无高级别并发症的患者之间的所有身体成分值均无显著差异。

结论

本研究表明,在因 RCC 接受肾切除术的患者中,根据 Clavien-Dindo 分类,肌肉脂肪组织分布不会影响 3A 及以上并发症患者。

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