基于CT的放射学和机器学习用于区分良性,边界和早期恶性卵巢瘤
Jia Chen1,2,3,4, Lei Liu5, Ziying He6
1Department of Radiology, Guangxi Medical University Cancer Hospital, 71 Hedi Road, Nanning, Guangxi, People's Republic of China.
Journal of imaging informatics in medicine
|February 12, 2024
概括
基于CT的放射学模型有效地区分良性,边缘和早期恶性卵巢瘤. 这种机器学习方法显示出强大的诊断性能,有助于术前诊断.
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:
- 卵巢瘤需要精确的手术前分化.
- 区分良性,边缘性和早期恶性卵巢瘤在临床上具有意义.
- 基于计算机断层扫描 (CT) 的放射学为非侵入性瘤表征提供了潜在的潜力.
研究的目的:
- 评估基于CT的放射学模型的诊断价值,以区分良性卵巢瘤 (BeOT),边缘性卵巢瘤 (BOT) 和早期恶性卵巢瘤 (eMOT).
主要方法:
- 对258名病理确诊的卵巢瘤患者的回顾性分析.
- 从CT图像中提取4238个放射性特征.
- 使用Wilcoxon-Mann-Whitney测试,LASSO和SVM进行特征选择.
- 开发和评估五个机器学习 (ML) 诊断模型,使用交叉验证和外部测试队列.
- 通过接收器运行特征 (ROC) 曲线和曲线下的面积 (AUC) 进行性能评估.
主要成果:
- 随机森林 (RF) 放射学模型在训练队列中实现了最高的诊断性能 (微/宏平均AUC:0.98/0.99).
- 射频模型通过LOOCV (微/宏平均AUC:0.89/0.88) 和外部验证 (微/宏平均AUC:0.81/0.79) 证明了良好的概括性.
结论:
- 基于CT的放射学模型显示了卵巢瘤手术前差异诊断的巨大潜力.
- 开发的放射学方法可以帮助临床医生区分BeOT,BOT和eMOT,从而有可能改善患者管理.
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