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基于多参数 MRI 放射组学特征鉴别小(<2cm)胰腺导管腺癌与神经内分泌肿瘤。

Differentiating small (< 2 cm) pancreatic ductal adenocarcinoma from neuroendocrine tumors with multiparametric MRI-based radiomic features.

机构信息

Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310009, China.

Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, 310016, China.

出版信息

Eur Radiol. 2024 Dec;34(12):7553-7563. doi: 10.1007/s00330-024-10837-x. Epub 2024 Jun 13.

Abstract

OBJECTIVES

To assess MR-based radiomic analysis in preoperatively discriminating small (< 2 cm) pancreatic ductal adenocarcinomas (PDACs) from neuroendocrine tumors (PNETs).

METHODS

A total of 197 patients (146 in the training cohort, 51 in the validation cohort) from two centers were retrospectively collected. A total of 7338 radiomics features were extracted from T2-weighted, diffusion-weighted, T1-weighted, arterial phase, portal venous phase and delayed phase imaging. The optimal features were selected by the Mann-Whitney U test, Spearman's rank correlation test and least absolute shrinkage and selection operator method and used to construct the radiomic score (Rad-score). Conventional radiological and clinical features were also assessed. Multivariable logistic regression was used to construct a radiological model, a radiomic model and a fusion model.

RESULTS

Nine optimal features were identified and used to build the Rad-score. The radiomic model based on the Rad-score achieved satisfactory results with AUCs of 0.905 and 0.930, sensitivities of 0.780 and 0.800, specificities of 0.906 and 0.952 and accuracies of 0.836 and 0.863 for the training and validation cohorts, respectively. The fusion model, incorporating CA19-9, tumor margins, pancreatic duct dilatation and the Rad-score, exhibited the best performance with AUCs of 0.977 and 0.941, sensitivities of 0.914 and 0.852, specificities of 0.954 and 0.950, and accuracies of 0.932 and 0.894 for the training and validation cohorts, respectively.

CONCLUSIONS

The MR-based Rad-score is a novel image biomarker for discriminating small PDACs from PNETs. A fusion model combining radiomic, radiological and clinical features performed very well in differentially diagnosing these two tumors.

CLINICAL RELEVANCE STATEMENT

A fusion model combining MR-based radiomic, radiological, and clinical features could help differentiate between small pancreatic ductal adenocarcinomas and pancreatic neuroendocrine tumors.

KEY POINTS

Preoperatively differentiating small pancreatic ductal adenocarcinomas (PDACs) and pancreatic neuroendocrine tumors (PNETs) is challenging. Multiparametric MRI-based Rad-score can be used for discriminating small PDACs from PNETs. A fusion model incorporating radiomic, radiological, and clinical features differentiated small PDACs from PNETs well.

摘要

目的

评估基于磁共振的放射组学分析在术前区分小(<2cm)胰腺导管腺癌(PDAC)和神经内分泌肿瘤(PNET)中的作用。

方法

本研究回顾性收集了来自两个中心的 197 名患者(训练队列 146 名,验证队列 51 名)。从 T2 加权、扩散加权、T1 加权、动脉期、门静脉期和延迟期成像中提取了 7338 个放射组学特征。通过曼-惠特尼 U 检验、斯皮尔曼等级相关检验和最小绝对收缩和选择算子法选择最佳特征,并用于构建放射组学评分(Rad-score)。还评估了常规影像学和临床特征。多变量逻辑回归用于构建放射学模型、放射组学模型和融合模型。

结果

确定了 9 个最佳特征,并用于构建 Rad-score。基于 Rad-score 的放射组学模型在训练队列和验证队列中的 AUC 分别为 0.905 和 0.930,敏感度分别为 0.780 和 0.800,特异度分别为 0.906 和 0.952,准确率分别为 0.836 和 0.863。融合模型,纳入 CA19-9、肿瘤边界、胰管扩张和 Rad-score,在训练队列和验证队列中的 AUC 分别为 0.977 和 0.941,敏感度分别为 0.914 和 0.852,特异度分别为 0.954 和 0.950,准确率分别为 0.932 和 0.894,表现最佳。

结论

基于磁共振的 Rad-score 是一种用于区分小 PDAC 和 PNET 的新型影像生物标志物。融合模型结合放射组学、放射学和临床特征,在鉴别这两种肿瘤方面表现出色。

临床相关性声明

结合基于磁共振的放射组学、放射学和临床特征的融合模型有助于区分小的胰腺导管腺癌和胰腺神经内分泌肿瘤。

关键点

术前区分小的胰腺导管腺癌(PDAC)和胰腺神经内分泌肿瘤(PNET)具有挑战性。基于多参数磁共振的 Rad-score 可用于区分小的 PDAC 和 PNET。融合模型结合放射组学、放射学和临床特征可很好地区分小的 PDAC 和 PNET。

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