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基于磁共振成像的深度学习放射学诺米克图,用于区分I/II型上皮卵巢癌

Mingxiang Wei¹, Guannan Feng², Xinyi Wang¹

¹Department of Radiology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Gusu School, Nanjing Medical University, Suzhou, Jiangsu, China (M.W., X.W., S.C.).

Academic radiology

|August 29, 2023

概括

使用T2加权MRI的新型深度学习放射学名图 (DLRN) 有效地区分了表皮卵巢癌 (EOC) I型和II型. 这种人工智能工具对改善EOC诊断中的临床决策充满希望.

科学领域:

放射学放射学是一门学科.
在瘤学瘤学.
人工智能的人工智能

背景情况:

卵巢上皮癌 (EOC) 包含不同的亚型,I型和II型,预后和治疗策略不同.
准确区分EOC类型对于有效的患者管理和治疗规划至关重要.

研究的目的:

开发和验证基于T2权重磁共振成像 (MRI) 的深度学习放射学名录 (DLRN).
评估DLRN在区分I型和II型上皮卵巢癌 (EOC) 的能力.

主要方法:

一项涉及437名患者的多中心研究 (培训,内部和外部验证集).
开发一个深度学习 (DL) 模型,一个放射学模型和一个临床模型.
整合DL签名,放射学签名和临床预测因素,形成DLRN.
使用接收机操作特征 (ROC) 分析,Brier分数,校准曲线和决策曲线分析 (DCA) 的性能评估.

主要成果:

DLRN在I型和II型EOC之间实现了高度的区分,AUC为0.888 (内部验证) 和0.866 (外部验证).
在两个验证组中,DLRN的表现明显超过了临床模型的表现.
DLRN 证明了优异的分类准确性和临床实用性,由布里尔分数,校准曲线和 DCA 证实.

结论:

基于T2权重的MRI的DLRN显示了区分I型和II型EOC的巨大潜力.
DLRN可以作为一个有价值的工具,帮助临床医生做出有关EOC诊断和管理的明智决策.

关键词:

深度学习是一种深度学习.卵巢上皮癌是卵巢癌的表皮癌.磁共振成像技术磁共振成像技术这个名字叫做Nomogram.无线电学 (Radiomics) 是一种无线电学.

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