一个基于CT放射学的脏清细胞癌的WHO/ISUP病理分级的预测模型:一个多中心研究
Chunying Wu1, Yuzhen Xi2, Juanjuan Hu3
1Department of Radiology, Affiliated Hangzhou First People's Hospital, Westlake University School of Medicine, Hangzhou, China.
BMC nephrology
|July 2, 2025
概括
使用CT放射学和临床成像特征的组合模型准确地预测了清细胞细胞癌 (ccRCC) 的病理级别. 这种方法提高了ccRCC患者的预后评估.
科学领域:
- 放射学 放射学是一门学科.
- 在瘤学瘤学.
- 医疗成像医学成像
背景情况:
- 清细胞细胞癌 (ccRCC) 的分级对于预后和治疗至关重要.
- 准确的ccRCC的非侵入性分级仍然是一个挑战.
研究的目的:
- 评估CT放射学与临床成像特征相结合的预测价值,用于世卫组织/ISUP对ccRCC的病理分类.
- 开发和验证ccRCC分级的预测模型.
主要方法:
- 一项多中心回顾性研究包括169名ccRCC患者.
- CT皮质相图像用于3D瘤细分和放射性特征提取.
- 使用后勤回归和随机森林分类来开发和评估一个结合的预测模型.
主要成果:
- 瘤大小,出血和瘤血栓被确定为独立的预测因素.
- 组合模型在训练队列中获得了优异的分辨性性能,AUC为0.895,超过了仅使用放射学 (AUC=0.873) 和仅使用临床成像 (AUC=0.712) 的模型.
- 组合模型在验证 (AUC=0.885) 和外部 (AUC=0.860) 队列中表现出强大的预测能力.
结论:
- CT放射学与临床成像特征相结合,为预测ccRCC病理等级提供了一种有效的方法.
- 这种综合模型为改善ccRCC患者的预后评估提供了有价值的工具.
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