This paper is in the following e-collection/theme issue:

Cancer Prognosis Models and Machine Learning (27) Clinical Information and Decision Making (1683) Machine Learning (1765) Innovations and Technology in Cancer Care (468) Cancer Epidemiology, Cancer Surveillance and Infodemiology (95) Artificial Intelligence (1769)

Published on in Vol 11 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/66269, first published .
Interpretable Machine Learning to Predict the Malignancy Risk of Follicular Thyroid Neoplasms in Extremely Unbalanced Data: Retrospective Cohort Study and Literature Review

Interpretable Machine Learning to Predict the Malignancy Risk of Follicular Thyroid Neoplasms in Extremely Unbalanced Data: Retrospective Cohort Study and Literature Review

Interpretable Machine Learning to Predict the Malignancy Risk of Follicular Thyroid Neoplasms in Extremely Unbalanced Data: Retrospective Cohort Study and Literature Review

Authors of this article:

Rui Shan1 Author Orcid Image ;   Xin Li2 Author Orcid Image ;   Jing Chen1 Author Orcid Image ;   Zheng Chen3 Author Orcid Image ;   Yuan-Jia Cheng4 Author Orcid Image ;   Bo Han5, 6 Author Orcid Image ;   Run-Ze Hu1 Author Orcid Image ;   Jiu-Ping Huang7 Author Orcid Image ;   Gui-Lan Kong8, 9 Author Orcid Image ;   Hui Liu10 Author Orcid Image ;   Fang Mei11, 12 Author Orcid Image ;   Shi-Bing Song2 Author Orcid Image ;   Bang-Kai Sun13 Author Orcid Image ;   Hui Tian3 Author Orcid Image ;   Yang Wang14 Author Orcid Image ;   Wu-Cai Xiao1 Author Orcid Image ;   Xiang-Yun Yao7 Author Orcid Image ;   Jing-Ming Ye4 Author Orcid Image ;   Bo Yu7 Author Orcid Image ;   Chun-Hui Yuan2 Author Orcid Image ;   Fan Zhang7 Author Orcid Image ;   Zheng Liu1 Author Orcid Image
Article Authors Cited by (1) Tweetations Metrics

Rui Shan   1 , MM ;   Xin Li   2 * , MD ;   Jing Chen   1 , MM ;   Zheng Chen   3 , MD ;   Yuan-Jia Cheng   4 , MD ;   Bo Han   5, 6 , PhD ;   Run-Ze Hu   1 , BMed ;   Jiu-Ping Huang   7 , MM ;   Gui-Lan Kong   8, 9 , PhD ;   Hui Liu   10 , PhD ;   Fang Mei   11, 12 , PhD ;   Shi-Bing Song   2 , MD ;   Bang-Kai Sun   13 , MS ;   Hui Tian   3 , MM ;   Yang Wang   14 , MD, PhD ;   Wu-Cai Xiao   1 , MM ;   Xiang-Yun Yao   7 , MD ;   Jing-Ming Ye   4 , MD ;   Bo Yu   7 , BMed ;   Chun-Hui Yuan   2 , MD ;   Fan Zhang   7 , MD ;   Zheng Liu   1 * , PhD

1 Department of Maternal and Child Health, School of Public Health, Peking University, Beijing, China

2 Department of General Surgery, Peking University Third Hospital, Beijing, China

3 Department of Ultrasound, Peking University People's Hospital, Beijing, China

4 Department of Thyroid and Breast Surgery, Peking University First Hospital, Beijing, China

5 Department of Pathology, Peking University People's Hospital, Beijing, China

6 The Key Laboratory of Experimental Teratology, Ministry of Education and Department of Pathology, School of Basic Medical Sciences, Jinan, China

7 Department of Ultrasound, Peking University Third Hospital, Beijing, China

8 National Institute of Health Data Science, Peking University, Beijing, China

9 Advanced Institute of Information Technology, Peking University, Beijing, China

10 Institute of Advanced Clinical Medicine, Peking University, Beijing, China

11 Department of Pathology, Peking University Third Hospital, Beijing, China

12 School of Basic Medical Sciences, Peking University Health Science Center, Beijing, China

13 Information Management and Big Data Center, Peking University Third Hospital, Beijing, China

14 Department of Cardiovascular Medicine, First Affiliated Hospital of Xi’an Jiaotong University, Xi'an, China

*these authors contributed equally

Corresponding Author:

  • Zheng Liu, PhD
  • Department of Maternal and Child Health
  • School of Public Health
  • Peking University
  • Haidian No 38, Xueyuan Road, Haidian District
  • Beijing 100091
  • China
  • Phone: 86 82801222
  • Email: liuzheng@bjmu.edu.cn