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基于人工智能的新型慢性胰腺炎病理等级的开发和验证
Chengwei Chen1, Jian Zhou1, Shaojia Mo1
1Department of Radiology, Changhai Hospital, Shanghai, China.
概括
使用深度学习开发了一种针对慢性胰腺炎 (CP) 的新组织病理学分级系统. 这种新型的分类 - - 基因病理学衍生CPpG - - 准确地反映了疾病的严重程度,并且可以通过CT扫描进行非侵入性预测.
科学领域:
- 胃肠病学 胃肠病学
- 病理学 病理学 病理学
- 医疗成像医学成像
背景情况:
- 准确的慢性胰腺炎 (CP) 严重程度评估对于有效治疗至关重要.
- 目前的治疗缺乏标准化的组织病理学分级系统.
- 这项研究引入了CP的新病理等级 (由组织病理学衍生的CPpG).
研究的目的:
- 开发和验证一种新的CP病理等级 (组织病理学衍生CPpG).
- 通过深度学习利用量化的病理和放射性特征.
- 用CT扫描来评估CPpG的非侵入性预测.
主要方法:
- 确认CP或复发性急性胰腺炎 (2011-2023) 的患者的回顾性招募.
- 使用DeeplabV3+进行全幻灯片CP图像的自动细分和量化.
- 隐性类分析,以开发出基因病理学衍生CPpG和深度学习放射学得分 (DLRS) 以基于CT的预测.
主要成果:
- 与CPpG II相比,由组织病理学衍生的CPpG I显示出不同比例的acini,stroma,小岛和淋巴细胞.
- 在预测CPpG方面,DLRS取得了很高的性能 (验证AUC:0.84,测试AUC:0.76).
- CT预测的等级与内分泌/外分泌功能和患者预后有显著的相关性.
结论:
- 一个新的病理分类,由组织病理学衍生的CPpG,准确地反映了CP的严重程度.
- 非侵入性DLRS显示了动态CP严重性监测的潜力.
- CT预测的CPpG有助于评估胰腺功能和预后.
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