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

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

doi:10.1016/j.eclinm.2024.102775

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

EClinicalMedicine. 2024 Aug 16:75:102775. doi: 10.1016/j.eclinm.2024.102775. eCollection 2024 Sep.

Authors

Pingyi Zhu^{1

2}, Chenchen Dai^{1

2}, Ying Xiong³, Jianyi Qu^{1

2}, Ruiting Wang^{1

2}, Linpeng Yao⁴, Feng Zhang⁵, Jun Hou⁶, Mengsu Zeng^{1

2}, Jianming Guo³, Shuo Wang^{7

8}, Feng Chen⁴, Jianjun Zhou^{1

9

10

11

12}

Affiliations

¹ Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China.
² Shanghai Institute of Medical Imaging, Shanghai, China.
³ Department of Urology, Zhongshan Hospital, Fudan University, Shanghai, China.
⁴ Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
⁵ Department of Gastroenterology and Hepatology, Zhongshan Hospital, Fudan University, Shanghai, China.
⁶ Department of Pathology, Zhongshan Hospital, Fudan University, Shanghai, China.
⁷ Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, China.
⁸ Shanghai Key Laboratory of MICCAI, Shanghai, China.
⁹ Department of Radiology, Zhongshan Hospital (Xiamen), Fudan University, Xiamen 361015, China.
¹⁰ Xiamen Municipal Clinical Research Center for Medical Imaging, Xiamen 361015, China.
¹¹ Fujian Province Key Clinical Specialty for Medical Imaging, Xiamen 361015, China.
¹² Xiamen Key Laboratory of Clinical Transformation of Imaging Big Data and Artificial Intelligence, Xiamen 361015, China.

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.

Keywords: Prognosis; Renal cell carcinoma; SSIGN.