基于磁共振成像的深度学习放射组学列线图用于鉴别Ⅰ/Ⅱ型上皮性卵巢癌。

Deep Learning Radiomics Nomogram Based on Magnetic Resonance Imaging for Differentiating Type I/II Epithelial Ovarian Cancer.

机构信息

Department 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.).

Department of Gynecology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Gusu School, Nanjing Medical University, Suzhou, Jiangsu, China (G.F.).

出版信息

Acad Radiol. 2024 Jun;31(6):2391-2401. doi: 10.1016/j.acra.2023.08.002. Epub 2023 Aug 27.

DOI:10.1016/j.acra.2023.08.002

PMID:37643927

Abstract

RATIONALE AND OBJECTIVES

To develop and validate a T2-weighted magnetic resonance imaging (MRI)-based deep learning radiomics nomogram (DLRN) to differentiate between type I and type II epithelial ovarian cancer (EOC).

MATERIALS AND METHODS

This multicenter study incorporated 437 patients from ﬁve centers, divided into training (n = 271), internal validation (n = 68), and external validation (n = 98) sets. The deep learning (DL) model was constructed using the largest orthogonal slices of the tumor area. The extracted radiomics features were employed in building the radiomics model. The clinical model was developed based on clinical characteristics. A DLRN was built by integrating the DL signature, radiomics signature, and independent clinical predictors. Model performances were evaluated through receiver operating characteristic (ROC) analysis, Brier score, calibration curve, and decision curve analysis (DCA). The areas under the ROC curve (AUCs) were compared using the DeLong test. A two-tailed P < 0.05 was considered signiﬁcantly different.

RESULTS

The DLRN exhibited satisfactory discrimination between type I and type II EOC with the AUC of 0.888 (95% confidence interval [CI] 0.810, 0.966) and 0.866 (95% CI 0.786, 0.946) in the internal and external validation sets, respectively. These AUCs significantly exceeded those of the clinical model (P = 0.013 and 0.043, in the internal and external validation sets, respectively). The DLRN demonstrated optimal classification accuracy and clinical application value, according to Brier scores, calibration curves, and DCA.

CONCLUSION

A T2-weighted MRI-based DLRN showed promising potential in differentiating between type I and type II EOC, which could offer assistance in clinical decision-making.

摘要

背景与目的

开发并验证一种基于 T2 加权磁共振成像（MRI）的深度学习放射组学列线图（DLRN），以区分 I 型和 II 型上皮性卵巢癌（EOC）。

材料与方法

这项多中心研究纳入了来自五个中心的 437 名患者，分为训练集（n=271）、内部验证集（n=68）和外部验证集（n=98）。使用肿瘤区域的最大正交切片构建深度学习（DL）模型。提取的放射组学特征用于构建放射组学模型。基于临床特征建立临床模型。通过整合 DL 特征、放射组学特征和独立的临床预测因子，构建 DLRN。通过接受者操作特征（ROC）分析、Brier 评分、校准曲线和决策曲线分析（DCA）评估模型性能。使用 DeLong 检验比较 ROC 曲线下面积（AUC）。双侧 P<0.05 为差异有统计学意义。

结果

DLRN 在区分 I 型和 II 型 EOC 方面表现出令人满意的性能，其内部验证集和外部验证集的 AUC 分别为 0.888（95%置信区间 [CI] 0.810，0.966）和 0.866（95% CI 0.786，0.946）。这些 AUC 显著高于临床模型（内部验证集：P=0.013；外部验证集：P=0.043）。根据 Brier 评分、校准曲线和 DCA，DLRN 表现出最佳的分类准确性和临床应用价值。