Scholarlink

<p>Accurate preoperative discrimination of renal cell carcinoma (RCC) subtypes is critical for treatment stratification. We aimed to develop and validate an automated deep learning system for simultaneous tumor segmentation and histopathological subtyping using multicenter contrast-enhanced CT (CECT) imaging. To this end, we constructed a two-stage system comprising separate segmentation and classification models. The segmentation model was trained on 245 scans from Nanfang Hospital and 210 from the KiTS19 public dataset. The classification model was developed and validated on a total of 750 patients, comprising an internal cohort from Nanfang Hospital (553 patients; 328 training, 112 validation, 113 testing) and two external validation cohorts: one from Beijing Tongren Hospital (<i>n</i> = 111) and another combined from two other centers (<i>n</i> = 86). The model demonstrated strong generalizability for discriminating clear cell RCC, with AUCs of 0.878 (internal validation), 0.892 (internal testing), 0.911 (external set Ⅰ), and 0.892 (external set Ⅱ). The model’s computational efficiency reached 0.24 s per file and reduced FLOPs by four times compared to conventional 3D CNNs. This study validates the efficiency and clinical applicability of the YOLOv11 framework for RCC subtyping. Future efforts should integrate prospective data and multimodal imaging to enhance sensitivity for small lesions.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Deep learning-based segmentation and subtype prediction of renal cell carcinoma on contrast-enhanced CT

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月14日

查看全文
  • Xiaoliang Chen,
  • Yingwei Xie,
  • Zehai Huang,
  • Bihong Xu,
  • Fengjin Zhao,
  • Wenbin Guo,
  • Gan Mo,
  • Haoxu Wu,
  • Peidan Peng,
  • Zhiyong Zhang,
  • Xiangbo Zeng,
  • Yuanchao Zhu,
  • Jiawei Zhou,
  • Yishan Zhang,
  • Wanlong Tan,
  • Hao Ping,
  • Zaosong Zheng

摘要

Accurate preoperative discrimination of renal cell carcinoma (RCC) subtypes is critical for treatment stratification. We aimed to develop and validate an automated deep learning system for simultaneous tumor segmentation and histopathological subtyping using multicenter contrast-enhanced CT (CECT) imaging. To this end, we constructed a two-stage system comprising separate segmentation and classification models. The segmentation model was trained on 245 scans from Nanfang Hospital and 210 from the KiTS19 public dataset. The classification model was developed and validated on a total of 750 patients, comprising an internal cohort from Nanfang Hospital (553 patients; 328 training, 112 validation, 113 testing) and two external validation cohorts: one from Beijing Tongren Hospital (n = 111) and another combined from two other centers (n = 86). The model demonstrated strong generalizability for discriminating clear cell RCC, with AUCs of 0.878 (internal validation), 0.892 (internal testing), 0.911 (external set Ⅰ), and 0.892 (external set Ⅱ). The model’s computational efficiency reached 0.24 s per file and reduced FLOPs by four times compared to conventional 3D CNNs. This study validates the efficiency and clinical applicability of the YOLOv11 framework for RCC subtyping. Future efforts should integrate prospective data and multimodal imaging to enhance sensitivity for small lesions.