Skip to main content
Top
Published in: Abdominal Radiology 8/2021

01-08-2021 | Artificial Intelligence | Special Section: HCC Treatment

Artificial intelligence in assessment of hepatocellular carcinoma treatment response

Authors: Bradley Spieler, Carl Sabottke, Ahmed W. Moawad, Ahmed M. Gabr, Mustafa R. Bashir, Richard Kinh Gian Do, Vahid Yaghmai, Radu Rozenberg, Marielia Gerena, Joseph Yacoub, Khaled M. Elsayes

Published in: Abdominal Radiology | Issue 8/2021

Login to get access

Abstract

Artificial Intelligence (AI) continues to shape the practice of radiology, with imaging of hepatocellular carcinoma (HCC) being of no exception. This article prepared by members of the LI-RADS Treatment Response (TR LI-RADS) work group and associates, presents recent trends in the utility of AI applications for the volumetric evaluation and assessment of HCC treatment response. Various topics including radiomics, prognostic imaging findings, and locoregional therapy (LRT) specific issues will be discussed in the framework of HCC treatment response classification systems with focus on the Liver Reporting and Data System treatment response algorithm (LI-RADS TRA).
Literature
8.
go back to reference Pérez MJ, Grande RG (2020) Application of artificial intelligence in the diagnosis and treatment of hepatocellular carcinoma: A review. World Journal of Gastroenterology 26(37):5617–5628CrossRef Pérez MJ, Grande RG (2020) Application of artificial intelligence in the diagnosis and treatment of hepatocellular carcinoma: A review. World Journal of Gastroenterology 26(37):5617–5628CrossRef
9.
go back to reference Dong Y, Zhou L, Xia W, Zhao X-Y, Zhang Q, Jian J-M, Gao X, Wang W-P (2020) Preoperative Prediction of Microvascular Invasion in Hepatocellular Carcinoma: Initial Application of a Radiomic Algorithm Based on Grayscale Ultrasound Images. Frontiers in Oncology 10 (353). https://doi.org/10.3389/fonc.2020.00353 Dong Y, Zhou L, Xia W, Zhao X-Y, Zhang Q, Jian J-M, Gao X, Wang W-P (2020) Preoperative Prediction of Microvascular Invasion in Hepatocellular Carcinoma: Initial Application of a Radiomic Algorithm Based on Grayscale Ultrasound Images. Frontiers in Oncology 10 (353). https://​doi.​org/​10.​3389/​fonc.​2020.​00353
18.
go back to reference Morshid A, Elsayes KM, Khalaf AM, Elmohr MM, Yu J, Kaseb AO, Hassan M, Mahvash A, Wang Z, Hazle JD, Fuentes D (2019) A machine learning model to predict hepatocellular carcinoma response to transcatheter arterial chemoembolization. Radiol Artif Intell 1 (5). https://doi.org/10.1148/ryai.2019180021 Morshid A, Elsayes KM, Khalaf AM, Elmohr MM, Yu J, Kaseb AO, Hassan M, Mahvash A, Wang Z, Hazle JD, Fuentes D (2019) A machine learning model to predict hepatocellular carcinoma response to transcatheter arterial chemoembolization. Radiol Artif Intell 1 (5). https://​doi.​org/​10.​1148/​ryai.​2019180021
27.
go back to reference Bilic P, Christ P, Vorontsov E, Chlebus G, Chen H, Dou Q, Fu C-W, Han X, Heng P, Hesser J, Kadoury S, Konopczynski TK, Le M, Li C, Li X, Lipková J, Lowengrub J, Meine H, Moltz J, Pal C, Piraud M, Qi X, Qi J, Rempfler M, Roth K, Schenk A, Sekuboyina A, Zhou P, Hülsemeyer C, Beetz M, Ettlinger F, Grün F, Kaissis G, Lohöfer F, Braren R, Holch J, Hofmann F, Sommer W, Heinemann V, Jacobs C, Mamani G, Ginneken B, Chartrand G, Tang A, Drozdzal M, Ben-Cohen A, Klang E, Amitai M, Konen E, Greenspan H, Moreau J, Hostettler A, Soler L, Vivanti R, Szeskin A, Lev-Cohain N, Sosna J, Joskowicz L, Menze B (2019) The Liver Tumor Segmentation Benchmark (LiTS). ArXiv abs/1901.04056 Bilic P, Christ P, Vorontsov E, Chlebus G, Chen H, Dou Q, Fu C-W, Han X, Heng P, Hesser J, Kadoury S, Konopczynski TK, Le M, Li C, Li X, Lipková J, Lowengrub J, Meine H, Moltz J, Pal C, Piraud M, Qi X, Qi J, Rempfler M, Roth K, Schenk A, Sekuboyina A, Zhou P, Hülsemeyer C, Beetz M, Ettlinger F, Grün F, Kaissis G, Lohöfer F, Braren R, Holch J, Hofmann F, Sommer W, Heinemann V, Jacobs C, Mamani G, Ginneken B, Chartrand G, Tang A, Drozdzal M, Ben-Cohen A, Klang E, Amitai M, Konen E, Greenspan H, Moreau J, Hostettler A, Soler L, Vivanti R, Szeskin A, Lev-Cohain N, Sosna J, Joskowicz L, Menze B (2019) The Liver Tumor Segmentation Benchmark (LiTS). ArXiv abs/1901.04056
28.
go back to reference Grewal M, Srivastava MM, Kumar P, Varadarajan S (2018) RADnet: Radiologist level accuracy using deep learning for hemorrhage detection in CT scans. 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018):281-284 Grewal M, Srivastava MM, Kumar P, Varadarajan S (2018) RADnet: Radiologist level accuracy using deep learning for hemorrhage detection in CT scans. 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018):281-284
30.
go back to reference Çiçek Ö, Abdulkadir A, Lienkamp S, Brox T, Ronneberger O 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation. In: MICCAI, 2016. Çiçek Ö, Abdulkadir A, Lienkamp S, Brox T, Ronneberger O 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation. In: MICCAI, 2016.
31.
go back to reference Lee K, Zung J, Li P, Jain V, Seung HS (2017) Superhuman Accuracy on the SNEMI3D Connectomics Challenge. ArXiv abs/1706.00120 Lee K, Zung J, Li P, Jain V, Seung HS (2017) Superhuman Accuracy on the SNEMI3D Connectomics Challenge. ArXiv abs/1706.00120
32.
go back to reference Ronneberger O, Fischer P, Brox T U-Net: Convolutional Networks for Biomedical Image Segmentation. In, Cham, 2015. Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015. Springer International Publishing, pp 234-241 Ronneberger O, Fischer P, Brox T U-Net: Convolutional Networks for Biomedical Image Segmentation. In, Cham, 2015. Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015. Springer International Publishing, pp 234-241
33.
go back to reference Angermann C, Haltmeier M Random 2.5D U-net for Fully 3D Segmentation. In, Cham, 2019. Machine Learning and Medical Engineering for Cardiovascular Health and Intravascular Imaging and Computer Assisted Stenting. Springer International Publishing, pp 158-166 Angermann C, Haltmeier M Random 2.5D U-net for Fully 3D Segmentation. In, Cham, 2019. Machine Learning and Medical Engineering for Cardiovascular Health and Intravascular Imaging and Computer Assisted Stenting. Springer International Publishing, pp 158-166
34.
go back to reference Meine H, Chlebus G, Ghafoorian M, Endo I, Schenk A (2018) Comparison of U-net-based Convolutional Neural Networks for Liver Segmentation in CT. ArXiv abs/1810.04017 Meine H, Chlebus G, Ghafoorian M, Endo I, Schenk A (2018) Comparison of U-net-based Convolutional Neural Networks for Liver Segmentation in CT. ArXiv abs/1810.04017
37.
go back to reference Lu F, Wu F, Hu P, Peng Z, Kong D (2016) Automatic 3D liver location and segmentation via convolutional neural network and graph cut. International Journal of Computer Assisted Radiology and Surgery 12:171–182CrossRefPubMed Lu F, Wu F, Hu P, Peng Z, Kong D (2016) Automatic 3D liver location and segmentation via convolutional neural network and graph cut. International Journal of Computer Assisted Radiology and Surgery 12:171–182CrossRefPubMed
41.
go back to reference Christ PF, Elshaer MEA, Ettlinger F, et al. (2016) Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional Neural Networks and 3D Conditional Random Fields. In: Ourselin S, Joskowicz L, Sabuncu MR, Unal G, Wells W (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016. Cham: Springer International Publishing, pp 415–423CrossRef Christ PF, Elshaer MEA, Ettlinger F, et al. (2016) Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional Neural Networks and 3D Conditional Random Fields. In: Ourselin S, Joskowicz L, Sabuncu MR, Unal G, Wells W (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016. Cham: Springer International Publishing, pp 415–423CrossRef
44.
go back to reference Ben-Cohen A, Diamant I, Klang E, Amitai M, Greenspan H Fully Convolutional Network for Liver Segmentation and Lesions Detection. In, Cham, 2016. Deep Learning and Data Labeling for Medical Applications. Springer International Publishing, pp 77-85 Ben-Cohen A, Diamant I, Klang E, Amitai M, Greenspan H Fully Convolutional Network for Liver Segmentation and Lesions Detection. In, Cham, 2016. Deep Learning and Data Labeling for Medical Applications. Springer International Publishing, pp 77-85
45.
go back to reference Vivanti R, Szeskin A, Lev-Cohain N, Sosna J, Joskowicz L (2017) Automatic detection of new tumors and tumor burden evaluation in longitudinal liver CT scan studies. International Journal of Computer Assisted Radiology and Surgery 12:1945–1957CrossRefPubMed Vivanti R, Szeskin A, Lev-Cohain N, Sosna J, Joskowicz L (2017) Automatic detection of new tumors and tumor burden evaluation in longitudinal liver CT scan studies. International Journal of Computer Assisted Radiology and Surgery 12:1945–1957CrossRefPubMed
46.
go back to reference Vivanti R, Joskowicz L, Lev-Cohain N, Ephrat A, Sosna J (2018) Patient-specific and global convolutional neural networks for robust automatic liver tumor delineation in follow-up CT studies. Medical & Biological Engineering & Computing 56:1699–1713CrossRef Vivanti R, Joskowicz L, Lev-Cohain N, Ephrat A, Sosna J (2018) Patient-specific and global convolutional neural networks for robust automatic liver tumor delineation in follow-up CT studies. Medical & Biological Engineering & Computing 56:1699–1713CrossRef
80.
go back to reference Ma J, Li Y, Wu Y, Zhang M, He J, Qiu Y, Yang X Automatic Liver Tumor Segmentation Based on Random Forest and Fuzzy Clustering. In, Cham, 2019. The Proceedings of the International Conference on Sensing and Imaging. Springer International Publishing, pp 403-410 Ma J, Li Y, Wu Y, Zhang M, He J, Qiu Y, Yang X Automatic Liver Tumor Segmentation Based on Random Forest and Fuzzy Clustering. In, Cham, 2019. The Proceedings of the International Conference on Sensing and Imaging. Springer International Publishing, pp 403-410
Metadata
Title
Artificial intelligence in assessment of hepatocellular carcinoma treatment response
Authors
Bradley Spieler
Carl Sabottke
Ahmed W. Moawad
Ahmed M. Gabr
Mustafa R. Bashir
Richard Kinh Gian Do
Vahid Yaghmai
Radu Rozenberg
Marielia Gerena
Joseph Yacoub
Khaled M. Elsayes
Publication date
01-08-2021
Publisher
Springer US
Published in
Abdominal Radiology / Issue 8/2021
Print ISSN: 2366-004X
Electronic ISSN: 2366-0058
DOI
https://doi.org/10.1007/s00261-021-03056-1

Other articles of this Issue 8/2021

Abdominal Radiology 8/2021 Go to the issue
Live Webinar | 27-06-2024 | 18:00 (CEST)

Keynote webinar | Spotlight on medication adherence

Live: Thursday 27th June 2024, 18:00-19:30 (CEST)

WHO estimates that half of all patients worldwide are non-adherent to their prescribed medication. The consequences of poor adherence can be catastrophic, on both the individual and population level.

Join our expert panel to discover why you need to understand the drivers of non-adherence in your patients, and how you can optimize medication adherence in your clinics to drastically improve patient outcomes.

Prof. Kevin Dolgin
Prof. Florian Limbourg
Prof. Anoop Chauhan
Developed by: Springer Medicine
Obesity Clinical Trial Summary

At a glance: The STEP trials

A round-up of the STEP phase 3 clinical trials evaluating semaglutide for weight loss in people with overweight or obesity.

Developed by: Springer Medicine

Highlights from the ACC 2024 Congress

Year in Review: Pediatric cardiology

Watch Dr. Anne Marie Valente present the last year's highlights in pediatric and congenital heart disease in the official ACC.24 Year in Review session.

Year in Review: Pulmonary vascular disease

The last year's highlights in pulmonary vascular disease are presented by Dr. Jane Leopold in this official video from ACC.24.

Year in Review: Valvular heart disease

Watch Prof. William Zoghbi present the last year's highlights in valvular heart disease from the official ACC.24 Year in Review session.

Year in Review: Heart failure and cardiomyopathies

Watch this official video from ACC.24. Dr. Biykem Bozkurt discusses last year's major advances in heart failure and cardiomyopathies.