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Open AccessOpen Access JUSTC Life Science, Info. & Intelligence 18 January 2023

Preoperative diagnosis of hepatocellular carcinoma patients with bile duct tumor thrombus using deep learning method

  • 1.

    School of Management, University of Science and Technology of China, Hefei 230026, China

  • 2.

    Department of Hepatobiliary Pancreatic Surgery, Fujian Provincial Hospital, Fuzhou 350001, China

Cite this:
https://doi.org/10.52396/JUSTC-2022-0057
More Information
  • Author Bio:

    Jinming Liu is a postgraduate student under the tutelage of Prof. Hong Zhang at the University of Science and Technology of China. His research interests focus on pattern recognition and natural language processing

    Hong Zhang is a Professor with the University of Science and Technology of China (USTC). He received his Bachalor’s degree in Mathematics and Ph.D. degree in Statistics from USTC in 1997 and 2003, respectively. His major research interests include statistical genetics, causal inference, and machine learning

  • Corresponding author: E-mail: zhangh@ustc.edu.cn
  • Received Date: 27 March 2022
  • Accepted Date: 31 May 2022
    • Available Online: 18 January 2023
    Abstract

  • Abstract

    Preoperative diagnosis of bile duct tumor thrombus (BDTT) is clinically important as the surgical prognosis of hepatocellular carcinoma (HCC) patients with BDTT is significantly different from that of patients without BDTT. Although dilated bile ducts (DBDs) can act as biomarkers for diagnosing BDTT, it is easy for doctors to ignore DBDs when reporting the imaging scan result, leading to a high missed diagnosis rate in practice. This study aims to develop an artificial intelligence (AI) pipeline for automatically diagnosing HCC patients with BDTT using medical images. The proposed AI pipeline includes two stages. First, the object detection neural network Faster R-CNN was adopted to identify DBDs; then, an HCC patient was diagnosed with BDTT if the proportion of images with at least one identified DBD exceeded some threshold value. Based on 2354 CT images collected from 32 HCC patients (16 with BDTT and 16 without BDTT, 1∶1 matched), the proposed AI pipeline achieves an average true positive rate of 0.92 for identifying DBDs per patient and a patient-level true positive rate of 0.81 for diagnosing BDTT. The AUC value of the patient-level diagnosis of BDTT is 0.94 (95% CI: 0.87, 1.00), compared with 0.71 (95% CI: 0.51, 0.90) achieved by random forest based on preoperative clinical variables. The high accuracies demonstrate that the proposed AI pipeline is successful in the diagnosis and localization of BDTT using CT images.

    Graphical abstract

    Overview of the proposed computer-aided diagnosis process for BDTT.

    Abstract

    Preoperative diagnosis of bile duct tumor thrombus (BDTT) is clinically important as the surgical prognosis of hepatocellular carcinoma (HCC) patients with BDTT is significantly different from that of patients without BDTT. Although dilated bile ducts (DBDs) can act as biomarkers for diagnosing BDTT, it is easy for doctors to ignore DBDs when reporting the imaging scan result, leading to a high missed diagnosis rate in practice. This study aims to develop an artificial intelligence (AI) pipeline for automatically diagnosing HCC patients with BDTT using medical images. The proposed AI pipeline includes two stages. First, the object detection neural network Faster R-CNN was adopted to identify DBDs; then, an HCC patient was diagnosed with BDTT if the proportion of images with at least one identified DBD exceeded some threshold value. Based on 2354 CT images collected from 32 HCC patients (16 with BDTT and 16 without BDTT, 1∶1 matched), the proposed AI pipeline achieves an average true positive rate of 0.92 for identifying DBDs per patient and a patient-level true positive rate of 0.81 for diagnosing BDTT. The AUC value of the patient-level diagnosis of BDTT is 0.94 (95% CI: 0.87, 1.00), compared with 0.71 (95% CI: 0.51, 0.90) achieved by random forest based on preoperative clinical variables. The high accuracies demonstrate that the proposed AI pipeline is successful in the diagnosis and localization of BDTT using CT images.

    • Public Summary

    • We proposed the first AI pipeline for the diagnosis and evaluation of BDTT through indentifying DBDs on CT images.
    • Our AI pipeline achieved a high AUC of 0.94 (95% CI: 0.87, 1.00) in the application to a real dataset.

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    • References(46)
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    [14]
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    Figure  1.  (a) A CT image of an HCC patient with BDTT. (b) A CT image with four labeled bounding boxes for DBDs. (c1)–(c3) Three consecutive CT images of an HCC patient with BDTT. In all images, tumors were marked in ellipses and DBDs caused by BDTT were marked in capsules.

    Figure  2.  The proposed AI pipeline for diagnosing BDTT. First, CT images in the portal venous phase with tumors were selected, then the selected images were center-cropped and resized to a unified size. After filtering and preprocessing, the trained DBD detector was applied to identify DBDs on the resulting images. Finally, a patient was diagnosed as HCC with BDTT if the image-level positive proportion (I-PP) was greater than the optimal threshold.

    Figure  3.  CT images of an HCC patient with BDTT at the same location from (a) plain scan phase, (b) arterial phase, (c) portal venous phase, and (d) delayed phase. DBDs caused by BDTT were marked in capsules.

    Figure  4.  Flow chart of Faster R-CNN. The pre-processed CT images were first transformed via DAM, then CNN backbone extracted multiscale feature maps from the transformed image. Based on the extracted features, at the first stage, RPN output proposal regions which tend to contain DBDs, then at the second stage, R-CNN further classified the proposal regions and refined their coordinates as the final outputs.

    Figure  5.  Several examples of successfully detected DBDs: (a)–(d) were the ground truth annotations of CT images of four different HCC patients with BDTT (tumors were marked in ellipses and DBDs caused by BDTT were marked in rectangles); (a')–(d') were the corresponding bounding boxes output by Faster R-CNN.

    Figure  6.  Box plots of image-level positive proportions (I-PPs) for the case group and the matched control group. I-PPs were calculated from the output results of the three different detectors, respectively, and the P-values for difference comparison were calculated by the Wilcoxon Mann-Whitney test.

    Figure  7.  ROC curves of the proposed AI pipeline with Faster R-CNN (solid line), Reppoints (longdash line), Foveabox (dotdash line), and random forest (dotted line). The corresponding AUCs were 0.94, 0.92, 0.89, and 0.71, respectively. The corresponding sensitivities corresponding to the optimal threshold values were 0.81, 0.75, 0.88, and 0.69, respectively. The corresponding specificities corresponding to the optimal threshold values were 1.00, 0.94, 0.88, and 0.75, respectively.

    Figure  8.  The first row shows two samples of undetected DBDs: (al) tiny DBD and (a2) inconspicuous DBD. The second row shows three types of dominant false positive bounding boxes: (b1) DBD-like liver tumor region and (b2) gap between liver and other tissues, (b3) irrelevant structures outside the liver region

    [1]
    Navadgi S, Chang C C, Bartlett A, et al. Systematic review and meta-analysis of outcomes after liver resection in patients with hepatocellular carcinoma (HCC) with and without bile duct thrombus. HPB, 2016, 18 (4): 312–316. doi: 10.1016/j.hpb.2015.12.003
    [2]
    Lu W, Tang H, Yang Z, et al. A proposed modification for the Barcelona clinic liver cancer staging system: Adding bile duct tumor thrombus status in patients with hepatocellular carcinoma. The American Journal of Surgery, 2020, 220 (4): 965–971. doi: 10.1016/j.amjsurg.2020.04.003
    [3]
    Meng K W, Dong M, Zhang W G, et al. Clinical characteristics and surgical prognosis of hepatocellular carcinoma with bile duct invasion. Gastroenterology Research and Practice, 2014, 2014: 604971. doi: 10.1155/2014/604971
    [4]
    Wang D D, Wu L Q, Wang Z S. Prognosis of hepatocellular carcinoma with bile duct tumor thrombus after R0 resection: A matched study. Hepatobiliary & Pancreatic Diseases International, 2016, 15 (6): 626–632. doi: 10.1016/S1499-3872(16)60143-1
    [5]
    Wang C, Yang Y, Sun D, et al. Prognosis of hepatocellular carcinoma patients with bile duct tumor thrombus after hepatic resection or liver transplantation in Asian populations: A meta-analysis. PLoS One, 2017, 12 (5): e0176827. doi: 10.1371/journal.pone.0176827
    [6]
    Shao W, Sui C, Liu Z, et al. Surgical outcome of hepatocellular carcinoma patients with biliary tumor thrombi. World Journal of Surgical Oncology, 2011, 9: 2. doi: 10.1186/1477-7819-9-2
    [7]
    Rammohan A, Sathyanesan J, Rajendran K, et al. Bile duct thrombi in hepatocellular carcinoma: Is aggressive surgery worthwhile? HPB, 2015, 17 (6): 508–513. doi: 10.1111/hpb.12383
    [8]
    Shiomi M, Kamiya J, Nagino M, et al. Hepatocellular carcinoma with biliary tumor thrombi: Aggressive operative approach after appropriate preoperative management. Surgery, 2001, 129 (6): 692–698. doi: 10.1067/msy.2001.113889
    [9]
    Zhou X, Wang J, Tang M, et al. Hepatocellular carcinoma with hilar bile duct tumor thrombus versus hilar Cholangiocarcinoma on enhanced computed tomography: A diagnostic challenge. BMC Cancer, 2020, 20 (1): 54. doi: 10.1186/s12885-020-6539-7
    [10]
    Liu Q Y, Zhang W D, Chen J Y, et al. Hepatocellular carcinoma with bile duct tumor thrombus: Dynamic computed tomography findings and histopathologic correlation. Journal of Computer Assisted Tomography, 2011, 35: 187–194. doi: 10.1097/RCT.0b013e3182067f2e
    [11]
    Zeng H, Xu L, Wen J, et al. Hepatocellular carcinoma with bile duct tumor thrombus: a clinicopathological analysis of factors predictive of recurrence and outcome after surgery. Medicine, 2015, 94 (1): e364. doi: 10.1097/MD.0000000000000364
    [12]
    Liu Q, Chen J, Li H, et al. Hepatocellular carcinoma with bile duct tumor thrombi: Correlation of magnetic resonance imaging features to histopathologic manifestations. European Journal of Radiology, 2010, 76 (1): 103–109. doi: 10.1016/j.ejrad.2009.05.020
    [13]
    Liu Q Y, Huang S Q, Chen J Y, et al. Small hepatocellular carcinoma with bile duct tumor thrombi: CT and MRI findings. Abdominal Imaging, 2010, 35 (5): 537–542. doi: 10.1007/s00261-009-9571-2
    [14]
    Wu J Y, Huang L M, Bai Y N, et al. Imaging features of hepatocellular car­cinoma with bile duct tumor thrombus: A multicenter study. Frontiers in Oncology, 2021, 11: 723455. doi: 10.3389/fonc.2021.723455
    [15]
    Yue-Hei Ng J, Hausknecht M, Vijayanarasimhan S, et al. Beyond short snippets: Deep networks for video classification. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Boston, USA: IEEE, 2015: 4694–4702.
    [16]
    Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Boston, USA: IEEE, 2015: 3431–3440.
    [17]
    Goodfellow I, Pouget-Abadie J, Mirza M, et al. Generative adversarial networks. Communications of the ACM, 2020, 63 (11): 139–144. doi: 10.1145/3422622
    [18]
    Kingma D P, Welling M. Auto-encoding variational Bayes. 2013. https://arxiv.org/abs/1312.6114 . Accessed February 1, 2022
    [19]
    Pranata Y D, Wang K C, Wang J C, et al. Deep learning and SURF for automated classification and detection of calcaneus fractures in CT images. Computer Methods and Programs in Biomedicine, 2019, 171: 27–37. doi: 10.1016/j.cmpb.2019.02.006
    [20]
    Li W, Jia F, Hu Q. Automatic segmentation of liver tumor in CT images with deep convolutional neural networks. Journal of Computer and Communications, 2015, 3 (11): 146–151. doi: 10.4236/jcc.2015.311023
    [21]
    Basu S, Wagstyl K, Zandifar A, et al. Early prediction of alzheimer’s disease progression using variational autoencoders. In: Medical Image Computing and Computer Assisted Intervention—MICCAI 2019. Cham: Springer, 2019: 205–213.
    [22]
    Zhang R, Tan S, Wang R, et al. Biomarker localization by combining CNN classifier and generative adversarial network. In: Medical Image Computing and Computer Assisted Intervention—MICCAI 2019. Switzerland: Springer, Cham, 2019: 209–217.
    [23]
    Ronneberger O, Fischer P, Brox T. U-net: Convolutional networks for biomedical image segmentation. In: Navab N, Hornegger J, Wells W, editors. Medical Image Computing and Computer-Assisted Intervention—MICCAI 2015. Switzerland: Springer, Cham, 2015: 234–241.
    [24]
    Vorontsov E, Tang A, Pal C, et al. Liver lesion segmentation informed by joint liver segmentation. In: 15th International Symposium on Biomedical Imaging (ISBI 2018). Washington, USA: IEEE, 2018: 1332–1335.
    [25]
    Christ P F, Ettlinger F, Grün F, et al. Automatic liver and tumor segmentation of CT and MRI volumes using cascaded fully convolutional neural networks. 2017. https://arxiv.org/abs/1702.05970. Accessed January 12, 2022.
    [26]
    Alirr O I. Deep learning and level set approach for liver and tumor segmentation from CT scans. Journal of Applied Clinical Medical Physics, 2020, 21 (10): 200–209. doi: 10.1002/acm2.13003
    [27]
    Ren S, He K, Girshick R, et al. Faster R-CNN: Towards real-time object detection with region proposal networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39 (6): 1137–1149. doi: 10.1109/TPAMI.2016.2577031
    [28]
    Yang Z, Liu S, Hu H, et al. RepPoints: Point set representation for object detection. In: 2019 IEEE/CVF International Conference on Computer Vision (ICCV). Seoul, South Korea: IEEE, 2020: 9656–9665.
    [29]
    Kong T, Sun F, Liu H, et al. FoveaBox: Beyound anchor-based object detection. IEEE Transactions on Image Processing, 2020, 29: 7389–7398. doi: 10.1109/TIP.2020.3002345
    [30]
    Thian Y L, Li Y, Jagmohan P, et al. Convolutional neural networks for automated fracture detection and localization on wrist radiographs. Radiology: Artificial Intelligence, 2019, 1: e180001. doi: 10.1148/ryai.2019180001
    [31]
    Olczak J, Fahlberg N, Maki A, et al. Artificial intelligence for analyzing or­thopedic trauma radiographs: deep learning algorithms—Are they on par with humans for diagnosing fractures? Acta Orthopaedica, 2017, 88 (6): 581–586. doi: 10.1080/17453674.2017.1344459
    [32]
    Kim D H, MacKinnon T. Artificial intelligence in fracture detection: transfer learning from deep convolutional neural networks. Clinical Radiology, 2018, 73 (5): 439–445. doi: 10.1016/j.crad.2017.11.015
    [33]
    Boot T, Irshad H. Diagnostic assessment of deep learning algorithms for detection and segmentation of lesion in mammographic images. In: Medical Image Computing and Computer Assisted Intervention—MICCAI 2020. Cham: Springer, 2020: 56–65.
    [34]
    Ho D, Imai K, King G, et al. Matchit: Nonparametric preprocessing for parametric causal inference. Journal of Statistical Software, 2011, 42 (8): 1–28. doi: 10.18637/jss.v042.i08
    [35]
    Robin X, Turck N, Hainard A, et al. pROC: An open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics, 2011, 12 (1): 77. doi: 10.1186/1471-2105-12-77
    [36]
    Development CoreR Team. R: A Language and Environment for Statistical Computing. Vienna, Austria, 2013.
    [37]
    Tzutalin. Labelimg. 2015. https://github.com/tzutalin/labelImg. Accessed March 20, 2022.
    [38]
    Redmon J, Divvala S, Girshick R, et al. You only look once: Unified, real-time object detection. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas, USA: IEEE, 2016: 779–788.
    [39]
    Liu W, Anguelov D, Erhan D, et al. SSD: Single shot multibox detector. In: Leibe B, Matas J, Sebe N, editors. Computer Vision–ECCV 2016. Cham: Springer, 2016: 21–37.
    [40]
    Zhang S, Chi C, Yao Y, et al. Bridging the gap between anchor-based and anchor-free detection via adaptive training sample selection. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Seattle, USA: IEEE, 2020: 9756–9765.
    [41]
    Lin T Y, Maire M, Belongie S, et al. Microsoft COCO: Common objects in context. In: Fleet D, Pajdla T, Schiele B, editors. Computer Vision–ECCV 2014. Cham: Springer, 2014: 740–755.
    [42]
    He K, Zhang X, Ren S, et al. Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas, USA: IEEE, 2016: 770–778.
    [43]
    Lin T Y, Dollár P, Girshick R, et al. Feature pyramid networks for object detection. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu, USA: IEEE, 2017: 936–944.
    [44]
    Pang J, Chen K, Shi J, et al. Libra R-CNN: Towards balanced learning for object detection. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Long Beach, USA: IEEE, 2019: 821–830.
    [45]
    Liu S, Qi L, Qin H, et al. Path aggregation network for instance segmentation. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, USA: IEEE, 2018: 8759–8768.
    [46]
    Breiman L. Random forests. Machine Learning, 2001, 45: 5–32. doi: 10.1023/A:1010933404324
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