A multidomain fusion model of radiomics and deep learning to discriminate between PDAC and AIP based on (18)F-FDG PET/CT images.
A multidomain fusion model of radiomics and deep learning to discriminate between PDAC and AIP based on (18)F-FDG PET/CT images.
复制标题
基于 18F-FDG PET/CT 图像区分 PDAC 和 AIP 的放射组学和深度学习的多域融合模型
DOI：
10.1007/s11604-022-01363-1
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发表时间：
2023-04
影响因子：
2.1
通讯作者：
Zuo, Changjing
中科院分区：
文献类型：
--
作者：
Wei, Wenting;Jia, Guorong;Wu, Zhongyi;Wang, Tao;Wang, Heng;Wei, Kezhen;Cheng, Chao;Liu, Zhaobang;Zuo, Changjing
关键词：
基于18F - 氟脱氧葡萄糖正电子发射断层扫描/计算机断层扫描（18F - FDG PET/CT）图像探索影像组学和深度学习特征的多域融合模型，以区分胰腺导管腺癌（PDAC）和自身免疫性胰腺炎（AIP），这可有效提高疾病诊断的准确性。
这项回顾性研究纳入了48例AIP患者（平均年龄65 ± 12.0岁；范围37 - 90岁）和64例PDAC患者（平均年龄66 ± 11.3岁；范围32 - 88岁）。讨论了基于18F - FDG PET/CT图像识别PDAC和AIP的三种不同方法，包括影像组学模型（RAD_model）、深度学习模型（DL_model）和多域融合模型（MF_model）。我们还比较了这三种模型中PET/CT、PET和CT图像的分类结果。此外，通过分析影像组学和深度学习特征之间的相关性，我们探索了深度学习抽象特征的属性。采用五折交叉验证计算受试者工作特征（ROC）曲线下面积（AUC）、准确率（Acc）、敏感度（Sen）和特异度（Spe），以定量评估不同分类模型的性能。
实验结果表明，与影像组学和深度学习模型相比，多域融合模型具有最佳的综合性能，其AUC、准确率、敏感度、特异度分别为96.4%（95%置信区间95.4 - 97.3%）、90.1%（95%置信区间88.7 - 91.5%）、87.5%（95%置信区间84.3 - 90.6%）和93.0%（95%置信区间90.3 - 95.6%）。并且我们的研究证明，PET/CT的多模态特征优于单独使用PET或CT特征。影像组学的一阶特征为深度学习模型提供了有价值的补充信息。
本文的初步结果表明，我们提出的多域融合模型充分利用了基于18F - FDG PET/CT图像的影像组学和深度学习特征的价值，为PDAC和AIP的鉴别提供了有竞争力的准确性。
Purpose To explore a multidomain fusion model of radiomics and deep learning features based on ^18F-fluorodeoxyglucose positron emission tomography/computed tomography (^18F-FDG PET/CT) images to distinguish pancreatic ductal adenocarcinoma (PDAC) and autoimmune pancreatitis (AIP), which could effectively improve the accuracy of diseases diagnosis. Materials and methods This retrospective study included 48 patients with AIP (mean age, 65 ± 12.0 years; range, 37–90 years) and 64 patients with PDAC patients (mean age, 66 ± 11.3 years; range, 32–88 years). Three different methods were discussed to identify PDAC and AIP based on ^18F-FDG PET/CT images, including the radiomics model (RAD_model), the deep learning model (DL_model), and the multidomain fusion model (MF_model). We also compared the classification results of PET/CT, PET, and CT images in these three models. In addition, we explored the attributes of deep learning abstract features by analyzing the correlation between radiomics and deep learning features. Five-fold cross-validation was used to calculate receiver operating characteristic (ROC), area under the roc curve (AUC), accuracy (Acc), sensitivity (Sen), and specificity (Spe) to quantitatively evaluate the performance of different classification models. Results The experimental results showed that the multidomain fusion model had the best comprehensive performance compared with radiomics and deep learning models, and the AUC, accuracy, sensitivity, specificity were 96.4% (95% CI 95.4–97.3%), 90.1% (95% CI 88.7–91.5%), 87.5% (95% CI 84.3–90.6%), and 93.0% (95% CI 90.3–95.6%), respectively. And our study proved that the multimodal features of PET/CT were superior to using either PET or CT features alone. First-order features of radiomics provided valuable complementary information for the deep learning model. Conclusion The preliminary results of this paper demonstrated that our proposed multidomain fusion model fully exploits the value of radiomics and deep learning features based on ^18F-FDG PET/CT images, which provided competitive accuracy for the discrimination of PDAC and AIP.
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DOI：
10.1016/j.hpb.2017.09.001
发表时间：
2018-03
期刊：
HPB : the official journal of the International Hepato Pancreato Biliary Association
影响因子：
--
作者：
Pak LM;Schattner MA;Balachandran V;D'Angelica MI;DeMatteo RP;Kingham TP;Jarnagin WR;Allen PJ
通讯作者：
Allen PJ
影响因子：
5.2
作者：
Anderson EM;Thomassian S;Gong J;Hendifar A;Osipov A
通讯作者：
Osipov A
影响因子：
2.6
作者：
Dickerson, L. D.;Farooq, A.;Yip, V. S.
通讯作者：
Yip, V. S.
DOI：
10.1007/s11432-020-2849-3
发表时间：
2020-04-15
期刊：
Science China Information Sciences
影响因子：
--
作者：
Fang M;He B;Li L;Dong D;Yang X;Li C;Meng L;Zhong L;Li H;Li H;Tian J
通讯作者：
Tian J
影响因子：
5.9
作者：
Liu, Zhaobang;Li, Ming;Yang, Xiaodong
通讯作者：
Yang, Xiaodong