影响因子：

5.9

DOI码：

10.1007/s00330-022-09355-5

所属单位：

SPRINGER

发表刊物：

EUROPEAN RADIOLOGY

刊物所在地：

ONE NEW YORK PLAZA, SUITE 4600 , NEW YORK, NY 10004, UNITED STATES

关键字：

Mouth neoplasms;Lymphatic metastasis;Tomography;X-ray computed;Deep learning;Diagnosis;computer-assisted

摘要：

Objectives Lymph node (LN) metastasis is a common cause of recurrence in oral cancer; however, the accuracy of distinguishing positive and negative LNs is not ideal. Here, we aimed to develop a deep learning model that can identify, locate, and distinguish LNs in contrast-enhanced CT (CECT) images with a higher accuracy. Methods The preoperative CECT images and corresponding postoperative pathological diagnoses of 1466 patients with oral cancer from our hospital were retrospectively collected. In stage I, full-layer images (five common anatomical structures) were labeled; in stage II, negative and positive LNs were separately labeled. The stage I model was innovatively employed for stage II training to improve accuracy with the idea of transfer learning (TL). The Mask R-CNN instance segmentation framework was selected for model construction and training. The accuracy of the model was compared with that of human observers. Results A total of 5412 images and 5601 images were labeled in stage I and II, respectively. The stage I model achieved an excellent segmentation effect in the test set (AP(50)-0.7249). The positive LN accuracy of the stage II TL model was similar to that of the radiologist and much higher than that of the surgeons and students (0.7042 vs. 0.7647 (p = 0.243), 0.4216 (p < 0.001), and 0.3629 (p < 0.001)). The clinical accuracy of the model was highest (0.8509 vs. 0.8000, 0.5500, 0.4500, and 0.6658 of the Radiology Department). Conclusions The model was constructed using a deep neural network and had high accuracy in LN localization and metastasis discrimination, which could contribute to accurate diagnosis and customized treatment planning.

合写作者：

Xi, Linlin,Wei, Lili,Wu, Luping,Xu, Yuming,Liu, Bailve,Li, Bo,Liu, Ke,Hou, Gaigai,Lin, Hao,Shao, Zhe,Shang, Zhengjun

第一作者：

Xu, Xiaoshuai

论文类型：

文章

通讯作者：

Su, Kehua

文献类型：

J

卷号：

33

页面范围：

4304-4312

ISSN号：

0938-7994

是否译文：

否

发表时间：

2023-01-23

收录刊物：

SCI