PDF herunterladen
CC BY-NC-ND 4.0 · Endosc Int Open 2024; 12(11): E1277-E1284
DOI: 10.1055/a-2422-9214
Original article

Enhancing physician support in pancreatic cancer diagnosis: New M-F-RCNN artificial intelligence model using endoscopic ultrasound

Shan-shan Hu
1   Department of Gastroenterology and Hepatology, Sichuan Provincial Peopleʼs Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, Sichuan, China (Ringgold ID: RIN89669)
,
Bowen Duan
2   Endoscopy, Sichuan Cancer Hospital and Institute, Chengdu, China (Ringgold ID: RIN92293)
,
Li Xu
1   Department of Gastroenterology and Hepatology, Sichuan Provincial Peopleʼs Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, Sichuan, China (Ringgold ID: RIN89669)
,
Danping Huang
3   Engineering and Science, Sichuan University of Science and Engineering Artificial Intelligence Key Laboratory of Sichuan Province, Yibin, China (Ringgold ID: RIN602121)
,
Xiaogang Liu
1   Department of Gastroenterology and Hepatology, Sichuan Provincial Peopleʼs Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, Sichuan, China (Ringgold ID: RIN89669)
,
Shihao Gou
3   Engineering and Science, Sichuan University of Science and Engineering Artificial Intelligence Key Laboratory of Sichuan Province, Yibin, China (Ringgold ID: RIN602121)
,
Xiaochen Zhao
4   Hepatobiliary Pancreatic Surgery, Sichuan Academy of Medical Sciences and Sichuan People's Hospital, Chengdu, China (Ringgold ID: RIN89669)
,
Jie Hou
5   Digestive Endoscopy Center of the Dongyuan, Sichuan Academy of Medical Sciences and Sichuan People's Hospital, Chengdu, China (Ringgold ID: RIN89669)
,
Shirong Tan
5   Digestive Endoscopy Center of the Dongyuan, Sichuan Academy of Medical Sciences and Sichuan People's Hospital, Chengdu, China (Ringgold ID: RIN89669)
,
lan ying He
6   Gastroenterology, Chongqing University Cancer Hospital, Chongqing, China (Ringgold ID: RIN605425)
,
Ying Ye
7   Department of Gastroenterology, Chengdu University of Traditional Chinese Medicine Affiliated Fifth People's hospital, Chengdu, China (Ringgold ID: RIN592090)
,
Xiaoli Xie
8   Gastroenterology, The First People's Hospital of Longquanyi District Chengdu, Chengdu, China (Ringgold ID: RIN577890)
,
Hong Shen
1   Department of Gastroenterology and Hepatology, Sichuan Provincial Peopleʼs Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, Sichuan, China (Ringgold ID: RIN89669)
,
Wei-hui Liu
1   Department of Gastroenterology and Hepatology, Sichuan Provincial Peopleʼs Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, Sichuan, China (Ringgold ID: RIN89669)
› InstitutsangabenGefördert durch: Department of Science & Technology of Sichuan Province (Key R&D Projects) No. 2024YFFK0220
› Weitere Informationen
Auch verfügbar auf
   Lizenzen und Reprints

Abstract

Background and study aims Endoscopic ultrasound (EUS) is vital for early pancreatic cancer diagnosis. Advances in artificial intelligence (AI), especially deep learning, have improved medical image analysis. We developed and validated the Modified Faster R-CNN (M-F-RCNN), an AI algorithm using EUS images to assist in diagnosing pancreatic cancer.

Methods We collected EUS images from 155 patients across three endoscopy centers from July 2022 to July 2023. M-F-RCNN development involved enhancing feature information through data preprocessing and utilizing an improved Faster R-CNN model to identify cancerous regions. Its diagnostic capabilities were validated against an external set of 1,000 EUS images. In addition, five EUS doctors participated in a study comparing the M-F-RCNN model's performance with that of human experts, assessing diagnostic skill improvements with AI assistance.

Results Internally, the M-F-RCNN model surpassed traditional algorithms with an average precision of 97.35%, accuracy of 96.49%, and recall rate of 5.44%. In external validation, its sensitivity, specificity, and accuracy were 91.7%, 91.5%, and 91.6%, respectively, outperforming non-expert physicians. The model also significantly enhanced the diagnostic skills of doctors.

Conclusions: The M-F-RCNN model shows exceptional performance in diagnosing pancreatic cancer via EUS images, greatly improving diagnostic accuracy and efficiency, thus enhancing physician proficiency and reducing diagnostic errors.

Keywords

Endoscopic ultrasonography - Pancreas - Endoscopy Upper GI Tract - Precancerous conditions & cancerous lesions (displasia and cancer) stomach - Diagnosis and imaging (inc chromoendoscopy, NBI, iSCAN, FICE, CLE)


Publikationsverlauf

Eingereicht: 27. Juni 2024

Angenommen nach Revision: 26. September 2024

Accepted Manuscript online:
30. September 2024

Artikel online veröffentlicht:
07. November 2024

© 2024. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial-License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/).

Georg Thieme Verlag KG
Oswald-Hesse-Straße 50, 70469 Stuttgart, Germany

 

  • References

  • 1 Sung H, Ferlay J, Siegel RL. et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin 2021; 71: 209-249
    CrossrefPubMedSuche in Google Scholar
  • 2 Khalaf N, El-Serag HB, Abrams HR. et al. Burden of pancreatic cancer: from epidemiology to practice. Clin Gastroenterol Hepatol Off Clin Pract J Am Gastroenterol Assoc 2021; 19: 876-884
    PubMedSuche in Google Scholar
  • 3 King D, Kamran U, Dosanjh A. et al. Rate of pancreatic cancer following a negative endoscopic ultrasound and associated factors. Endoscopy 2022; 54: 1053-1061
    Thieme ConnectPubMedSuche in Google Scholar
  • 4 Guo T, Xu T, Zhang S. et al. The role of EUS in diagnosing focal autoimmune pancreatitis and differentiating it from pancreatic cancer. Endosc Ultrasound 2021; 10: 280
    CrossrefPubMedSuche in Google Scholar
  • 5 Rana SS. Evaluating the role of endoscopic ultrasound in pancreatitis. Expert Rev Gastroenterol Hepatol 2022; 16: 953-965
    CrossrefPubMedSuche in Google Scholar
  • 6 Bhinder B, Gilvary C, Madhukar NS. et al. Artificial intelligence in cancer research and precision medicine. Cancer Discov 2021; 11: 900-915
    CrossrefPubMedSuche in Google Scholar
  • 7 Greener JG, Kandathil SM, Moffat L. et al. A guide to machine learning for biologists. Nat Rev Mol Cell Biol 2022; 23: 40-55
    CrossrefPubMedSuche in Google Scholar
  • 8 Gao Q, Yang L, Lu M. et al. The artificial intelligence and machine learning in lung cancer immunotherapy. J Hematol OncolJ Hematol Oncol 2023; 16: 55
    CrossrefPubMedSuche in Google Scholar
  • 9 Kim M, Chen C, Wang P. et al. Detection of ovarian cancer via the spectral fingerprinting of quantum-defect-modified carbon nanotubes in serum by machine learning. Nat Biomed Eng 2022; 6: 267-275
    CrossrefPubMedSuche in Google Scholar
  • 10 Kuwahara T, Hara K, Mizuno N. et al. Artificial intelligence using deep learning analysis of endoscopic ultrasonography images for the differential diagnosis of pancreatic masses. Endoscopy 2023; 55: 140-149
    Thieme ConnectPubMedSuche in Google Scholar
  • 11 Zhu M, Xu C, Yu J. et al. Differentiation of pancreatic cancer and chronic pancreatitis using computer-aided diagnosis of endoscopic ultrasound (EUS) Images: A diagnostic test. PLoS ONE 2013; 8: e63820
    CrossrefPubMedSuche in Google Scholar
  • 12 Karako K, Mihara Y, Arita J. et al. Automated liver tumor detection in abdominal ultrasonography with a modified faster region-based convolutional neural networks (Faster R-CNN) architecture. Hepatobiliary Surg Nutr 2022; 11: 675-683
    CrossrefPubMedSuche in Google Scholar
  • 13 Dollar P, Appel R, Belongie S. et al. Fast Feature pyramids for object detection. IEEE Trans Pattern Anal Mach Intell 2014; 36: 1532-1545
    CrossrefPubMedSuche in Google Scholar
  • 14 Pan J, Sun H, Song Z. et al. Dual-resolution dual-path convolutional neural networks for fast object detection. Sensors 2019; 19: 3111
    CrossrefPubMedSuche in Google Scholar
  • 15 Ren S, He K, Girshick R. et al. Faster R-CNN: Towards real-time object detection with region proposal networks. IEEE Trans Pattern Anal Mach Intell 2017; 39: 1137-1149
    CrossrefPubMedSuche in Google Scholar
  • 16 Zhang X, Jiang L, Yang D. et al. Urine sediment recognition method based on multi-view deep residual learning in microscopic image. J Med Syst 2019; 43: 325
    CrossrefPubMedSuche in Google Scholar
  • 17 Li X, Lv C, Wang W. et al. Generalized focal loss: Towards efficient representation learning for dense object detection. IEEE Trans Pattern Anal Mach Intell 2023; 45: 3139-3153
    PubMedSuche in Google Scholar
  • 18 Lin T-Y, Goyal P, Girshick R. et al. Focal loss for dense object detection. IEEE Trans Pattern Anal Mach Intell 2020; 42: 318-327
    CrossrefPubMedSuche in Google Scholar
  • 19 Cao K, Xia Y, Yao J. et al. Large-scale pancreatic cancer detection via non-contrast CT and deep learning. Nat Med 2023; 29: 3033-3043
    CrossrefPubMedSuche in Google Scholar
  • 20 Drukker L. Real-time identification of fetal anomalies on ultrasound using artificial intelligence: what’s next?. Ultrasound Obstet Gynecol 2022; 59: 285-287
    CrossrefPubMedSuche in Google Scholar
  • 21 Bera K, Braman N, Gupta A. et al. Predicting cancer outcomes with radiomics and artificial intelligence in radiology. Nat Rev Clin Oncol 2022; 19: 132-146
    CrossrefPubMedSuche in Google Scholar
  • 22 Marya NB, Powers PD, Chari ST. et al. Utilisation of artificial intelligence for the development of an EUS-convolutional neural network model trained to enhance the diagnosis of autoimmune pancreatitis. Gut 2021; 70: 1335-1344
    CrossrefPubMedSuche in Google Scholar
  • 23 Yoon H, Zhu YI, Yarmoska SK. et al. Design and demonstration of a configurable imaging platform for combined laser, ultrasound, and elasticity imaging. IEEE Trans Med Imaging 2019; 38: 1622-1632
    CrossrefPubMedSuche in Google Scholar
  • 24 Lu X, Liu X, Xiao Z. et al. Self-supervised dual-head attentional bootstrap learning network for prostate cancer screening in transrectal ultrasound images. Comput Biol Med 2023; 165: 107337
    CrossrefPubMedSuche in Google Scholar
  • 25 Oh S, Kim Y-J, Park Y-T. et al. Automatic pancreatic cyst lesion segmentation on EUS images using a deep-learning approach. Sensors 2021; 22: 245
    CrossrefPubMedSuche in Google Scholar
  • 26 Yin H, Yang X, Sun L. et al. The value of artificial intelligence techniques in predicting pancreatic ductal adenocarcinoma with EUS images: A meta-analysis and systematic review. Endosc Ultrasound 2023; 12: 50-58
    CrossrefPubMedSuche in Google Scholar
  • 27 Tong T, Gu J, Xu D. et al. Deep learning radiomics based on contrast-enhanced ultrasound images for assisted diagnosis of pancreatic ductal adenocarcinoma and chronic pancreatitis. BMC Med 2022; 20: 74
    CrossrefPubMedSuche in Google Scholar