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image of Deep Learning-assisted Diagnosis of Extrahepatic Common Bile Duct Obstruction Using MRCP Imaging and Clinical Parameters

Abstract

Background:

Extrahepatic Common Bile Duct Obstruction (EHBDO) is a serious condition that requires accurate diagnosis for effective treatment. Magnetic Resonance Cholangiopancreatography (MRCP) is a widely used noninvasive imaging technique for visualizing bile ducts, but its interpretation can be complex.

Objective:

This study aimed to develop a deep learning-based classification model that integrates MRCP images and clinical parameters to assist radiologists in diagnosing EHBDO more accurately.

Methods:

A total of 465 patients with clinical data were included, of whom 143 also had MRCP images. Missing clinical values were addressed through data imputation. Object detection techniques were used to isolate the common bile duct region in the MRCP images. A multimodal deep learning fusion model was developed by combining the extracted imaging features with selected clinical parameters. To account for the varying significance of different features, a weighted loss function was applied. The performance of the fusion model was compared to that of single-modality approaches (using only MRCP images or clinical data), specifically the accuracy, sensitivity, specificity, and Area Under The Curve (AUC).

Results:

The performance of the proposed deep learning fusion model was superior to that of models using only MRCP images or clinical parameters. The fusion model achieved an accuracy of 89.8%, AUC of 90.4%, sensitivity of 81.8%, and specificity of 95.7% in diagnosing EHBDO. By integrating MRCP imaging data and clinical parameters, the proposed deep learning model significantly enhanced the accuracy of EHBDO diagnosis.

Conclusion:

This proposed multimodal approach outperformed traditional single-modality methods, presenting a valuable tool for improving the diagnostic accuracy of bile duct obstruction.

This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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2025-01-20
2025-02-19
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References

  1. Smith H.J. Extrahepatic bile duct obstruction in primary carcinoma of the lung: Incidence, diagnosis, and non-operative treatment. J. National Med. Assoc. 1980 72 3 215 20
    [Google Scholar]
  2. Qin X.L. Wang Z.R. Shi J.S. Lu M. Wang L. He Q.R. Utility of serum CA19-9 in diagnosis of cholangiocarcinoma: In comparison with CEA. World J. Gastroenterol. 2004 10 3 427 432 10.3748/wjg.v10.i3.427 14760772
    [Google Scholar]
  3. Sugimoto Y. Diagnosing malignant distal bile duct obstruction using artificial intelligence based on clinical biomarkers. Sci. Rep. 2023 13 1 3262 10.1038/s41598‑023‑28058‑5
    [Google Scholar]
  4. Goldfinger M.H. Ridgway G.R. Ferreira C. Langford C.R. Cheng L. Kazimianec A. Borghetto A. Wright T.G. Woodward G. Hassanali N. Nicholls R.C. Simpson H. Waddell T. Vikal S. Mavar M. Rymell S. Wigley I. Jacobs J. Kelly M. Banerjee R. Brady J.M. Quantitative MRCP imaging: Accuracy, repeatability, reproducibility, and cohort‐derived normative ranges. J. Magn. Reson. Imaging 2020 52 3 807 820 10.1002/jmri.27113 32147892
    [Google Scholar]
  5. Griffin N. Edwards C.G. Grant L.A. Magnetic resonance cholangiopancreatography: The ABC of MRCP. Insights Imaging 2012 3 1 11 21 10.1007/s13244‑011‑0129‑9 22695995
    [Google Scholar]
  6. Gao W. Wang W. Song D. Wang K. Lian D. Yang C. Zhu K. Zheng J. Zeng M. Rao S. Wang M. A multiparametric fusion deep learning model based on DCE‐MRI for preoperative prediction of microvascular invasion in intrahepatic cholangiocarcinoma. J. Magn. Reson. Imaging 2022 56 4 1029 1039 10.1002/jmri.28126 35191550
    [Google Scholar]
  7. Hou J.U. Park S.W. Park S.M. Park D.H. Park C.H. Min S. Efficacy of an artificial neural network algorithm based on thick-slab magnetic resonance cholangiopancreatography images for the automated diagnosis of common bile duct stones J. Gastroenterol. Hepatol. 2021 36 12 3532 3540 10.1111/jgh.15569
    [Google Scholar]
  8. Njei B. Kanmounye U.S. Seto N. McCarty T.R. Mohan B.P. Fozo L. Navaneethan U. Artificial intelligence in medical imaging for cholangiocarcinoma diagnosis: A systematic review with scientometric analysis. J. Gastroenterol. Hepatol. 2023 38 6 874 882 10.1111/jgh.16180 36919223
    [Google Scholar]
  9. Park M-S. Differentiation of extrahepatic bile duct cholangiocarcinoma from benign stricture: Findings at MRCP versus ERCP. Radiology 2004 233 1 234 240
    [Google Scholar]
  10. Zhang X. Jia N. Wang Y. Multi-input dense convolutional network for classification of hepatocellular carcinoma and intrahepatic cholangiocarcinoma. Biomed. Signal Process. Control 2023 80 104226 10.1016/j.bspc.2022.104226
    [Google Scholar]
  11. Jama W.M.A.J. World medical association declaration of helsinki: Ethical principles for medical research involving human subjects. JAMA 2013 310 20 2191 2194 10.1001/jama.2013.281053 24141714
    [Google Scholar]
  12. Aljiffry M. Walsh M.J. Molinari M. Advances in diagnosis, treatment and palliation of cholangiocarcinoma: 1990-2009. World J. Gastroenterol. 2009 15 34 4240 4262 10.3748/wjg.15.4240 19750567
    [Google Scholar]
  13. Wang X. Qi X. Li H. Shao X. Guo X. An extremely increased CA19-9 level due to common bile duct stone: A case report. AME Med. J. 2017 2 2 1 5 10.21037/amj.2017.02.02
    [Google Scholar]
  14. Sombattheera S. Proungvitaya T. Limpaiboon T. Wongkham S. Wongkham C. Luvira V. Proungvitaya S. Total serum bile acid as a potential marker for the diagnosis of cholangiocarcinoma without jaundice. Asian Pac. J. Cancer Prev. 2015 16 4 1367 1370 10.7314/APJCP.2015.16.4.1367 25743800
    [Google Scholar]
  15. Azur M.J. Stuart E.A. Frangakis C. Leaf P.J. Multiple imputation by chained equations: What is it and how does it work? Int. J. Methods Psychiatr. Res. 2011 20 1 40 49 10.1002/mpr.329 21499542
    [Google Scholar]
  16. Zhang Z. Multiple imputation with multivariate imputation by chained equation (MICE) package. Ann. Transl. Med. 2016 4 2 30 26889483
    [Google Scholar]
  17. Wang C-Y. Bochkovskiy A. Liao H-Y.M. “YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors,” arXiv 2022 2207 02696 10.1109/CVPR52729.2023.00721
    [Google Scholar]
  18. He K. Zhang X. Ren S. Sun J. Deep residual learning for image recognition Proceedings of the IEEE conference on computer vision and pattern recognition Las Vegas, NV, USA, 27-30, June, 2016, pp. 770-778.
    [Google Scholar]
  19. Thung K.H. Wee C.Y. A brief review on multi-task learning. Mult. Tools Appl. 2018 77 22 29705 29725 10.1007/s11042‑018‑6463‑x
    [Google Scholar]
  20. Kendall A. Gal Y. Cipolla R. Multi-task learning using uncertainty to weigh losses for scene geometry and semantics. arXiv 1705 07115
    [Google Scholar]
  21. Rumelhart D.E. Hinton G.E. Williams R.J. Learning representations by back-propagating errors. Nature 1986 323 6088 533 536 10.1038/323533a0
    [Google Scholar]
  22. Ke G. Lightgbm: A highly efficient gradient boosting decision tree. Proceedings of the 31st International Conference on Neural Information Processing Systems 2017 3149 3157
    [Google Scholar]
  23. Tan M. Le Q. Efficientnet: Rethinking model scaling for convolutional neural networks. arXiv 2019 1905 11946
    [Google Scholar]
  24. Szegedy C. Vanhoucke V. Ioffe S. Shlens J. Wojna Z. Rethinking the inception architecture for computer vision. arXiv 2015 1512 00567 10.1109/CVPR.2016.308
    [Google Scholar]
  25. Loshchilov I. Hutter F. Sgdr: Stochastic gradient descent with warm restarts arXiv 2016 03983
    [Google Scholar]
  26. Selvaraju R.R. Cogswell M. Das A. Vedantam R. Parikh D. Batra D. Grad-cam: Visual explanations from deep networks via gradient-based localization. Proceedings of the IEEE international conference on computer vision Venice, Italy, 22-29, October, 2017, pp. 618-626. 10.1109/ICCV.2017.74
    [Google Scholar]
  27. Logeswaran R. A computer-aided multidisease diagnostic system using MRCP. J. Digit. Imaging 2008 21 2 235 242 10.1007/s10278‑007‑9029‑4 17345003
    [Google Scholar]
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