Artificial intelligence system of faster region-based convolutional neural network surpassing senior radiologists in evaluation of metastatic lymph nodes of rectal cancer.
10.1097/CM9.0000000000000095
- Author:
Lei DING
1
;
Guang-Wei LIU
1
;
Bao-Chun ZHAO
2
;
Yun-Peng ZHOU
3
;
Shuai LI
4
;
Zheng-Dong ZHANG
4
;
Yu-Ting GUO
4
;
Ai-Qin LI
5
;
Yun LU
1
;
Hong-Wei YAO
6
;
Wei-Tang YUAN
7
;
Gui-Ying WANG
8
;
Dian-Liang ZHANG
9
;
Lei WANG
10
Author Information
1. Shandong Key Laboratory of Digital Medicine & Computer Assisted Surgery, Qingdao University, Qingdao, Shandong 266003, China.
2. Department of Follow-up, Affiliated Hospital of Qingdao University, Qingdao University, Qingdao, Shandong 266003, China.
3. Department of Gastroenterology Surgery, Affiliated Hospital of Qingdao University, Qingdao University, Qingdao, Shandong 266003, China.
4. State Key Laboratory of Virtual Reality Technology & Systems, Beihang University, Beijing 100191, China.
5. Department of Outpatient Administration, Affiliated Hospital of Qingdao University, Qingdao University, Qingdao, Shandong 266003, China.
6. Department of General Surgery, Beijing Friendship Hospital, Capital Medical University & National Clinical Research Center for Digestive Diseases, Beijing 100050, China.
7. Department of General Surgery, First Affiliated Hospital of Zhengzhou University, Zhenzhou, Henan 450052, China.
8. Department of General Surgery, Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei 050011, China.
9. Department of General Surgery, Qingdao Municipal Hospital, Qingdao, Shandong 266011, China.
10. Department of General Surgery, The Sixth Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong 510655, China.
- Publication Type:Journal Article
- MeSH:
Adult;
Aged;
Aged, 80 and over;
Artificial Intelligence;
Female;
Humans;
Lymphatic Metastasis;
Male;
Middle Aged;
Neoplasm Recurrence, Local;
Neoplasm Staging;
Neural Networks (Computer);
Pathologists;
Radiologists;
Rectal Neoplasms;
diagnostic imaging;
mortality;
pathology
- From:
Chinese Medical Journal
2019;132(4):379-387
- CountryChina
- Language:English
-
Abstract:
BACKGROUND:An artificial intelligence system of Faster Region-based Convolutional Neural Network (Faster R-CNN) is newly developed for the diagnosis of metastatic lymph node (LN) in rectal cancer patients. The primary objective of this study was to comprehensively verify its accuracy in clinical use.
METHODS:Four hundred fourteen patients with rectal cancer discharged between January 2013 and March 2015 were collected from 6 clinical centers, and the magnetic resonance imaging data for pelvic metastatic LNs of each patient was identified by Faster R-CNN. Faster R-CNN based diagnoses were compared with radiologist based diagnoses and pathologist based diagnoses for methodological verification, using correlation analyses and consistency check. For clinical verification, the patients were retrospectively followed up by telephone for 36 months, with post-operative recurrence of rectal cancer as a clinical outcome; recurrence-free survivals of the patients were compared among different diagnostic groups, by methods of Kaplan-Meier and Cox hazards regression model.
RESULTS:Significant correlations were observed between any 2 factors among the numbers of metastatic LNs separately diagnosed by radiologists, Faster R-CNN and pathologists, as evidenced by rradiologist-Faster R-CNN of 0.912, rPathologist-radiologist of 0.134, and rPathologist-Faster R-CNN of 0.448 respectively. The value of kappa coefficient in N staging between Faster R-CNN and pathologists was 0.573, and this value between radiologists and pathologists was 0.473. The 3 groups of Faster R-CNN, radiologists and pathologists showed no significant differences in the recurrence-free survival time for stage N0 and N1 patients, but significant differences were found for stage N2 patients.
CONCLUSION:Faster R-CNN surpasses radiologists in the evaluation of pelvic metastatic LNs of rectal cancer, but is not on par with pathologists.
TRIAL REGISTRATION:www.chictr.org.cn (No. ChiCTR-DDD-17013842).
