"One 3D VOI-based deep learning radiomics strategy, clinical model and radiologists for predicting lymph node metastases in pancreatic ductal adenocarcinoma based on multiphasic contrast-enhanced computer tomography"

作者全名："Liao, Hongfan; Yang, Junjun; Li, Yongmei; Liang, Hongwei; Ye, Junyong; Liu, Yanbing"

作者地址："[Liao, Hongfan; Liu, Yanbing] Chongqing Med Univ, Coll Med Informat, Chongqing, Peoples R China; [Yang, Junjun; Ye, Junyong] Chongqing Univ, Minist Educ, Key Lab Optoelect Technol & Syst, Chongqing, Peoples R China; [Li, Yongmei; Liang, Hongwei] Chongqing Med Univ, Affiliated Hosp 1, Dept Radiol, Chongqing, Peoples R China"

通信作者："Liu, YB (通讯作者)，Chongqing Med Univ, Coll Med Informat, Chongqing, Peoples R China."

来源：FRONTIERS IN ONCOLOGY

ESI学科分类：CLINICAL MEDICINE

WOS号：WOS:000872932900001

JCR分区：Q2

影响因子：4.7

年份：2022

卷号：12

期号：　

开始页：　

结束页：　

文献类型：Article

关键词：pancreatic ductal adenocarcinoma; lymph node metastases; deep learning; radiomics; contrast-enhanced computer tomography

摘要："PurposeWe designed to construct one 3D VOI-based deep learning radiomics strategy for identifying lymph node metastases (LNM) in pancreatic ductal adenocarcinoma on the basis of multiphasic contrast-enhanced computer tomography and to assist clinical decision-making. MethodsThis retrospective research enrolled 139 PDAC patients undergoing pre-operative arterial phase and venous phase scanning examination between 2015 and 2021. A primary group (training group and validation group) and an independent test group were divided. The DLR strategy included three sections. (1) Residual network three dimensional-18 (Resnet 3D-18) architecture was constructed for deep learning feature extraction. (2) Least absolute shrinkage and selection operator model was used for feature selection. (3) Fully connected network served as the classifier. The DLR strategy was applied for constructing different 3D CNN models using 5-fold cross-validation. Radiomics scores (Rad score) were calculated for distinguishing the statistical difference between negative and positive lymph nodes. A clinical model was constructed by combining significantly different clinical variables using univariate and multivariable logistic regression. The manifestation of two radiologists was detected for comparing with computer-developed models. Receiver operating characteristic curves, the area under the curve, accuracy, precision, recall, and F1 score were used for evaluating model performance. ResultsA total of 45, 49, and 59 deep learning features were selected via LASSO model. No matter in which 3D CNN model, Rad score demonstrated the deep learning features were significantly different between non-LNM and LNM groups. The AP+VP DLR model yielded the best performance in predicting status of lymph node in PDAC with an AUC of 0.995 (95% CI:0.989-1.000) in training group; an AUC of 0.940 (95% CI:0.910-0.971) in validation group; and an AUC of 0.949 (95% CI:0.914-0.984) in test group. The clinical model enrolled the histological grade, CA19-9 level and CT-reported tumor size. The AP+VP DLR model outperformed AP DLR model, VP DLR model, clinical model, and two radiologists. ConclusionsThe AP+VP DLR model based on Resnet 3D-18 demonstrated excellent ability for identifying LNM in PDAC, which could act as a non-invasive and accurate guide for clinical therapeutic strategies. This 3D CNN model combined with 3D tumor segmentation technology is labor-saving, promising, and effective."

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