Institutional Repository of Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
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 |
ISSN | 2305-5839 |
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 |
DOI | 10.21037/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 | |
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. |
条目包含的文件 | 下载所有文件 | |||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
atm-09-04-295.pdf(743KB) | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论