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Automated detection and delineation of hepatocellular carcinoma on multiphasic contrast-enhanced MRI using deep learning

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Abdominal Radiology Aims and scope Submit manuscript

Abstract

Purpose

Liver Imaging Reporting and Data System (LI-RADS) uses multiphasic contrast-enhanced imaging for hepatocellular carcinoma (HCC) diagnosis. The goal of this feasibility study was to establish a proof-of-principle concept towards automating the application of LI-RADS, using a deep learning algorithm trained to segment the liver and delineate HCCs on MRI automatically.

Methods

In this retrospective single-center study, multiphasic contrast-enhanced MRIs using T1-weighted breath-hold sequences acquired from 2010 to 2018 were used to train a deep convolutional neural network (DCNN) with a U-Net architecture. The U-Net was trained (using 70% of all data), validated (15%) and tested (15%) on 174 patients with 231 lesions. Manual 3D segmentations of the liver and HCC were ground truth. The dice similarity coefficient (DSC) was measured between manual and DCNN methods. Postprocessing using a random forest (RF) classifier employing radiomic features and thresholding (TR) of the mean neural activation was used to reduce the average false positive rate (AFPR).

Results

73 and 75% of HCCs were detected on validation and test sets, respectively, using > 0.2 DSC criterion between individual lesions and their corresponding segmentations. Validation set AFPRs were 2.81, 0.77, 0.85 for U-Net, U-Net + RF, and U-Net + TR, respectively. Combining both RF and TR with the U-Net improved the AFPR to 0.62 and 0.75 for the validation and test sets, respectively. Mean DSC between automatically detected lesions using the DCNN + RF + TR and corresponding manual segmentations was 0.64/0.68 (validation/test), and 0.91/0.91 for liver segmentations.

Conclusion

Our DCNN approach can segment the liver and HCCs automatically. This could enable a more workflow efficient and clinically realistic implementation of LI-RADS.

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References

  1. Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal AJCacjfc (2018) Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. 68:394-424

  2. El-Serag HB, Rudolph KL (2007) Hepatocellular carcinoma: epidemiology and molecular carcinogenesis. Gastroenterology 132:2557-2576

    Article  CAS  Google Scholar 

  3. White DL, Thrift AP, Kanwal F, Davila J, El-Serag HB (2017) Incidence of Hepatocellular Carcinoma in All 50 United States, From 2000 Through 2012. Gastroenterology 152:812-820 e815

    Google Scholar 

  4. Eisenhauer EA, Therasse P, Bogaerts J et al (2009) New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). Eur J Cancer 45:228-247

    Article  CAS  Google Scholar 

  5. Ding Y, Rao S-x, Wang W-t, Chen C-z, Li R-c, Zeng M (2018) Comparison of gadoxetic acid versus gadopentetate dimeglumine for the detection of hepatocellular carcinoma at 1.5 T using the liver imaging reporting and data system (LI-RADS v.2017). Cancer Imaging 18:48

  6. Chernyak V, Fowler KJ, Kamaya A et al (2018) Liver Imaging Reporting and Data System (LI-RADS) Version 2018: Imaging of Hepatocellular Carcinoma in At-Risk Patients. Radiology. 10.1148/radiol.2018181494:181494

    Article  PubMed  PubMed Central  Google Scholar 

  7. Han X (2017) Automatic Liver Lesion Segmentation Using A Deep Convolutional Neural Network Method. CoRR abs/1704.07239

  8. Christ P, Ettlinger F, F G, Lipkova JK, G. (2017) LiTS - Liver Tumor Segmentation Challenge. Available via http://www.lits-challenge.com/

  9. Sun C, Guo S, Zhang H et al (2017) Automatic segmentation of liver tumors from multiphase contrast-enhanced CT images based on FCNs. Artif Intell Med 83:58-66

    Article  Google Scholar 

  10. Nayak A, Kayal EB, Arya M et al (2019) Computer-aided diagnosis of cirrhosis and hepatocellular carcinoma using multi-phase abdomen CT. International journal of computer assisted radiology and surgery 14:1341-1352

    Article  Google Scholar 

  11. Roberts LR, Sirlin CB, Zaiem F et al (2018) Imaging for the diagnosis of hepatocellular carcinoma: A systematic review and meta-analysis. Hepatology 67:401-421

    Article  Google Scholar 

  12. Hanna RF, Miloushev VZ, Tang A et al (2016) Comparative 13-year meta-analysis of the sensitivity and positive predictive value of ultrasound, CT, and MRI for detecting hepatocellular carcinoma. Abdom Radiol (NY) 41:71-90

    Article  Google Scholar 

  13. Zhang YD, Zhu FP, Xu X et al (2016) Liver Imaging Reporting and Data System:: Substantial Discordance Between CT and MR for Imaging Classification of Hepatic Nodules. Acad Radiol 23:344-352

    Article  Google Scholar 

  14. Ronneberger O, Fischer P, Brox T (2015) U-Net: Convolutional Networks for Biomedical Image Segmentation, pp 234-241

  15. Christ PF, Ettlinger F, Grün F et al (2017) Automatic Liver and Tumor Segmentation of CT and MRI Volumes using Cascaded Fully Convolutional Neural Networks. CoRR abs/1702.05970

  16. Sahiner B, Pezeshk A, Hadjiiski LM et al (2019) Deep learning in medical imaging and radiation therapy. 46:e1-e36

    Google Scholar 

  17. Milletari F, Navab N, Ahmadi S-A (2016) V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation. CoRR abs/1606.04797

  18. He K, Zhang X, Ren S, Sun J (2016) Identity Mappings in Deep Residual Networks. CoRR abs/1603.05027

  19. Bilic P, Christ PF, Vorontsov E et al (2019) The Liver Tumor Segmentation Benchmark (LiTS).

  20. Isensee F, Kickingereder P, Wick W, Bendszus M, Maier-Hein KH (2018) No New-Net. CoRR abs/1809.10483

  21. Ulyanov D, Vedaldi A, Lempitsky V Instance normalization: the missing ingredient for fast stylization. CoRR abs/1607.0 (2016),

  22. Abadi M, Barham P, Chen J et al (2016) Tensorflow: a system for large-scale machine learningOSDI, pp 265-283

  23. Avants BB, Tustison N, Song G (2009) Advanced normalization tools (ANTS). Insight j 2:1-35

    Google Scholar 

  24. Simard PY, Steinkraus D, Platt JC (2003) Best practices for convolutional neural networks applied to visual document analysisSeventh International Conference on Document Analysis and Recognition, 2003 Proceedings, pp 958-963

  25. Dice LR (1945) Measures of the amount of ecologic association between species. Ecology 26:297-302

    Article  Google Scholar 

  26. Janowczyk A, Madabhushi A (2016) Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases. Journal of pathology informatics 7

  27. van Griethuysen JJM, Fedorov A, Parmar C et al (2017) Computational Radiomics System to Decode the Radiographic Phenotype. 77:e104-e107

    Google Scholar 

  28. Bandos AI, Rockette HE, Song T, Gur D (2009) Area under the free-response ROC curve (FROC) and a related summary index. Biometrics 65:247-256

    Article  Google Scholar 

  29. Lin LI (1989) A concordance correlation coefficient to evaluate reproducibility. Biometrics 45:255-268

    Article  CAS  Google Scholar 

  30. Vorontsov E, Tang A, Pal C, Kadoury S (2018) Liver lesion segmentation informed by joint liver segmentation2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), pp 1332-1335

  31. Chlebus G, Schenk A, Moltz JH, van Ginneken B, Hahn HK, Meine H (2018) Automatic liver tumor segmentation in CT with fully convolutional neural networks and object-based postprocessing. Sci Rep 8:15497

    Article  Google Scholar 

  32. Bousabarah K, Ruge M, Brand J-S et al (2020) Deep convolutional neural networks for automated segmentation of brain metastases trained on clinical data. Radiation Oncology 15:1-9

    Article  Google Scholar 

  33. Kickingereder P, Isensee F, Tursunova I et al (2019) Automated quantitative tumour response assessment of MRI in neuro-oncology with artificial neural networks: a multicentre, retrospective study. The Lancet Oncology 20:728-740

    Article  Google Scholar 

  34. Azer SA (2019) Deep learning with convolutional neural networks for identification of liver masses and hepatocellular carcinoma: A systematic review. World Journal of Gastrointestinal Oncology 11:1218

    Article  Google Scholar 

  35. Hamm CA, Wang CJ, Savic LJ et al (2019) Deep learning for liver tumor diagnosis part I: development of a convolutional neural network classifier for multi-phasic MRI. European Radiology. 10.1007/s00330-019-06205-9

    Article  PubMed  PubMed Central  Google Scholar 

  36. Wang CJ, Hamm CA, Savic LJ et al (2019) Deep learning for liver tumor diagnosis part II: convolutional neural network interpretation using radiologic imaging features. European Radiology. 10.1007/s00330-019-06214-8

    Article  PubMed  PubMed Central  Google Scholar 

  37. Shi W, Kuang S, Cao S et al (2020) Deep learning assisted differentiation of hepatocellular carcinoma from focal liver lesions: choice of four-phase and three-phase CT imaging protocol. Abdominal Radiology (New York)

Download references

Funding

The funding was provided by National Cancer Institute (Grant No. NIH/NCI R01 CA206180).

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Authors and Affiliations

  1. Department of Radiology and Biomedical Imaging, Yale University School of Medicine, 333 Cedar Street, New Haven, CT, 06520, USA

    Khaled Bousabarah, Brian Letzen, Jonathan Tefera, Lynn Savic, Isabel Schobert, Todd Schlachter, Lawrence H. Staib, Julius Chapiro & MingDe Lin

  2. Department of Stereotactic and Functional Neurosurgery, University Hospital of Cologne, Cologne, Germany

    Khaled Bousabarah & Martin Kocher

  3. Visage Imaging GmbH, Lepsiusstraße 70, Berlin, 12163, Germany

    Khaled Bousabarah

  4. Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität, and Berlin Institute of Health, Institute of Radiology, 10117, Berlin, Germany

    Jonathan Tefera, Lynn Savic & Isabel Schobert

  5. Department of Biomedical Engineering, Yale School of Engineering and Applied Science, New Haven, CT, 06520, USA

    Lawrence H. Staib

  6. Department of Electrical Engineering, Yale School of Engineering and Applied Science, New Haven, CT, 06520, USA

    Lawrence H. Staib

  7. Visage Imaging, Inc, 12625 High Bluff Dr., Suite 205, San Diego, CA, 92130, USA

    MingDe Lin

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  1. Khaled Bousabarah
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Correspondence to Julius Chapiro.

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Bousabarah, K., Letzen, B., Tefera, J. et al. Automated detection and delineation of hepatocellular carcinoma on multiphasic contrast-enhanced MRI using deep learning. Abdom Radiol 46, 216–225 (2021). https://doi.org/10.1007/s00261-020-02604-5

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  • DOI: https://doi.org/10.1007/s00261-020-02604-5

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