Tumor contour irregularity on preoperative CT predicts prognosis in renal cell carcinoma: a multi-institutional study.

IF 9.6 1区 医学 Q1 MEDICINE, GENERAL & INTERNAL EClinicalMedicine Pub Date : 2024-08-16 eCollection Date: 2024-09-01 DOI:10.1016/j.eclinm.2024.102775
Pingyi Zhu, Chenchen Dai, Ying Xiong, Jianyi Qu, Ruiting Wang, Linpeng Yao, Feng Zhang, Jun Hou, Mengsu Zeng, Jianming Guo, Shuo Wang, Feng Chen, Jianjun Zhou
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Abstract

Background: Radiology-based prognostic biomarkers play a crucial role in patient counseling, enhancing surveillance, and designing clinical trials effectively. This study aims to assess the predictive significance of preoperative CT-based tumor contour irregularity in determining clinical outcomes among patients with renal cell carcinoma (RCC).

Methods: We conducted a retrospective multi-institutional review involving 2218 patients pathologically diagnosed with RCC. The training and internal validation sets included patients at Zhongshan Hospital between January 2009 and August 2019. The external test set comprised patients from the First Affiliated Hospital, Zhejiang University School of Medicine (January 2016 to January 2018), the Xiamen Branch of Zhongshan Hospital (November 2017 to June 2023), and the Cancer Imaging Archive. The contour irregularity degree (CID), quantified as the ratio of irregular cross-sections to the total tumor cross-sections, was analyzed for its prognostic relevance across different subgroups of RCC patients. A novel CID-based scoring system was developed, and its predictive efficacy was evaluated and compared with existing prognostic models.

Findings: The CID exhibited significant discriminatory power in predicting overall survival (OS), recurrence-free survival (RFS), and disease-specific survival (DSS) among patients with RCC tumors measuring 3 cm or larger (all p < 0.001). Multivariate analyses confirmed the CID as an independent prognostic indicator. Notably, the CID augmented prognostic stratification among RCC patients within distinct risk subgroups delineated by SSIGN models and ISUP grades. The CID-based nomogram (C-Model) demonstrated robust predictive performance, with C-index values of 0.88 (95%CI: 0.84-0.92) in the training set, 0.92 (95%CI: 0.88-0.98) in the internal validation set, and 0.86 (95%CI: 0.81-0.90) in the external test set, surpassing existing prognostic models.

Interpretation: Routine imaging-based assessment of the CID serves as an independent prognostic factor, offering incremental prognostic value to existing models in RCC patients with tumors measuring 3 cm or larger.

Funding: This study was funded by grants from National Natural Science Foundation of China; Shanghai Municipal Health Commission; China National Key R&D Program and Science and Technology Commission of Shanghai Municipality.

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术前 CT 显示肿瘤轮廓不规则可预测肾细胞癌的预后:一项多机构研究。
背景:基于放射学的预后生物标志物在患者咨询、加强监测和有效设计临床试验方面发挥着至关重要的作用。本研究旨在评估基于术前 CT 的肿瘤轮廓不规则性对肾细胞癌(RCC)患者临床预后的预测意义:我们对2218例病理诊断为RCC的患者进行了多机构回顾性研究。训练集和内部验证集包括 2009 年 1 月至 2019 年 8 月期间中山医院的患者。外部测试集包括浙江大学医学院附属第一医院(2016年1月至2018年1月)、中山医院厦门分院(2017年11月至2023年6月)和癌症影像档案馆的患者。轮廓不规则度(CID)被量化为不规则横截面与肿瘤总横截面之比,研究人员分析了轮廓不规则度在不同亚组RCC患者中的预后相关性。研究开发了一种基于CID的新型评分系统,并对其预测效果进行了评估,同时将其与现有的预后模型进行了比较:CID在预测3厘米或更大RCC肿瘤患者的总生存期(OS)、无复发生存期(RFS)和疾病特异性生存期(DSS)方面表现出明显的鉴别力(所有数据均为负值):基于成像的CID常规评估是一个独立的预后因素,为肿瘤大于或等于3厘米的RCC患者的现有模型提供了增量预后价值:本研究得到了国家自然科学基金、上海市卫生委员会、国家重点研发计划和上海市科学技术委员会的资助。
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来源期刊
EClinicalMedicine
EClinicalMedicine Medicine-Medicine (all)
CiteScore
18.90
自引率
1.30%
发文量
506
审稿时长
22 days
期刊介绍: eClinicalMedicine is a gold open-access clinical journal designed to support frontline health professionals in addressing the complex and rapid health transitions affecting societies globally. The journal aims to assist practitioners in overcoming healthcare challenges across diverse communities, spanning diagnosis, treatment, prevention, and health promotion. Integrating disciplines from various specialties and life stages, it seeks to enhance health systems as fundamental institutions within societies. With a forward-thinking approach, eClinicalMedicine aims to redefine the future of healthcare.
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