CASIA OpenIR  > 中国科学院分子影像重点实验室
Application of deep learning to predict underestimation in ductal carcinoma in situ of the breast with ultrasound
Qian, Lang1,2; Lv, Zhikun3,4; Zhang, Kai1,2; Wang, Kun3,4; Zhu, Qian1,2; Zhou, Shichong1,2; Chang, Cai1,2; Tian, Jie3,4,5
发表期刊ANNALS OF TRANSLATIONAL MEDICINE
ISSN2305-5839
2021-02-01
卷号9期号:4页码:9
摘要

Background: To develop an ultrasound-based deep learning model to predict postoperative upgrading of pure ductal carcinoma in situ (DCIS) diagnosed by core needle biopsy (CNB) before surgery. Methods: Of the 360 patients with DCIS diagnosed by CNB and identified retrospectively, 180 had lesions upstaged to ductal carcinoma in situ with microinvasion (DCISM) or invasive ductal carcinoma (IDC) postoperatively. Ultrasound images obtained from the hospital database were divided into a training set (n=240) and validation set (n=120), with a ratio of 2:1 in chronological order. Four deep learning models, based on the ResNet and VggNet structures, were established to classify the ultrasound images into postoperative upgrade and pure DCIS. We obtained the area under the receiver operating characteristic curve (AUROC), specificity, sensitivity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) to estimate the performance of the predictive models. The robustness of the models was evaluated by a 3-fold cross-validation. Results: Clinical features were not significantly different between the training set and the test set (P value >0.05). The area under the receiver operating characteristic curve of our models ranged from 0.724 to 0.804. The sensitivity, specificity, and accuracy of the optimal model were 0.733, 0.750, and 0.742, respectively. The three-fold cross-validation results showed that the model was very robust. Conclusions: The ultrasound-based deep learning prediction model is effective in predicting DCIS that will be upgraded postoperatively.

关键词Artificial intelligence (AI) ductal carcinoma in situ (DCIS) core needle biopsy (CNB) prediction of upstaging
学科领域计算机图象处理
DOI10.21037/atm-20-3981
关键词[WOS]CORE-NEEDLE-BIOPSY ; INVASION ; DIAGNOSIS
收录类别SCI
语种英语
资助项目Ministry of Science and Technology of China[2017YFA0205200] ; National Natural Science Foundation of China[61671449] ; National Natural Science Foundation of China[81227901] ; National Natural Science Foundation of China[81527805] ; National Natural Science Foundation of China[81830058] ; Science and Technology Commission of Shanghai Municipality[18411967400] ; Shanghai Municipal Commission of Health and Family Planning[20174Y0011]
项目资助者Ministry of Science and Technology of China ; National Natural Science Foundation of China ; Science and Technology Commission of Shanghai Municipality ; Shanghai Municipal Commission of Health and Family Planning
WOS研究方向Oncology ; Research & Experimental Medicine
WOS类目Oncology ; Medicine, Research & Experimental
WOS记录号WOS:000624902700025
出版者AME PUBL CO
七大方向——子方向分类医学影像处理与分析
引用统计
被引频次:8[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/43998
专题中国科学院分子影像重点实验室
通讯作者Zhou, Shichong; Tian, Jie
作者单位1.Fudan Univ, Dept Ultrasonog, Shanghai Canc Ctr, Shanghai 200032, Peoples R China
2.Fudan Univ, Shanghai Med Coll, Dept Oncol, Shanghai, Peoples R China
3.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
4.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China
5.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Beijing, Peoples R China
通讯作者单位中国科学院分子影像重点实验室
推荐引用方式
GB/T 7714
Qian, Lang,Lv, Zhikun,Zhang, Kai,et al. Application of deep learning to predict underestimation in ductal carcinoma in situ of the breast with ultrasound[J]. ANNALS OF TRANSLATIONAL MEDICINE,2021,9(4):9.
APA Qian, Lang.,Lv, Zhikun.,Zhang, Kai.,Wang, Kun.,Zhu, Qian.,...&Tian, Jie.(2021).Application of deep learning to predict underestimation in ductal carcinoma in situ of the breast with ultrasound.ANNALS OF TRANSLATIONAL MEDICINE,9(4),9.
MLA Qian, Lang,et al."Application of deep learning to predict underestimation in ductal carcinoma in situ of the breast with ultrasound".ANNALS OF TRANSLATIONAL MEDICINE 9.4(2021):9.
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