Artificial intelligence in liver cancer - new tools for research and patient management.
Journal
Nature reviews. Gastroenterology & hepatology
ISSN: 1759-5053
Titre abrégé: Nat Rev Gastroenterol Hepatol
Pays: England
ID NLM: 101500079
Informations de publication
Date de publication:
16 Apr 2024
16 Apr 2024
Historique:
accepted:
11
03
2024
medline:
17
4
2024
pubmed:
17
4
2024
entrez:
16
4
2024
Statut:
aheadofprint
Résumé
Liver cancer has high incidence and mortality globally. Artificial intelligence (AI) has advanced rapidly, influencing cancer care. AI systems are already approved for clinical use in some tumour types (for example, colorectal cancer screening). Crucially, research demonstrates that AI can analyse histopathology, radiology and natural language in liver cancer, and can replace manual tasks and access hidden information in routinely available clinical data. However, for liver cancer, few of these applications have translated into large-scale clinical trials or clinically approved products. Here, we advocate for the incorporation of AI in all stages of liver cancer management. We present a taxonomy of AI approaches in liver cancer, highlighting areas with academic and commercial potential, and outline a policy for AI-based liver cancer management, including interdisciplinary training of researchers, clinicians and patients. The potential of AI in liver cancer is immense, but effort is required to ensure that AI can fulfil expectations.
Identifiants
pubmed: 38627537
doi: 10.1038/s41575-024-00919-y
pii: 10.1038/s41575-024-00919-y
doi:
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
© 2024. Springer Nature Limited.
Références
Sung, H. et al. Global Cancer Statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 71, 209–249 (2021).
doi: 10.3322/caac.21660
pubmed: 33538338
Rumgay, H. et al. Global, regional and national burden of primary liver cancer by subtype. Eur. J. Cancer 161, 108–118 (2022).
pubmed: 34942552
doi: 10.1016/j.ejca.2021.11.023
European Association for the Study of the Liver. EASL Clinical Practice Guidelines: management of hepatocellular carcinoma. J. Hepatol. 69, 182–236 (2018).
doi: 10.1016/j.jhep.2018.03.019
Ducreux, M. et al. The management of hepatocellular carcinoma. Current expert opinion and recommendations derived from the 24th ESMO/World Congress on Gastrointestinal Cancer, Barcelona, 2022. ESMO Open. 8, 101567 (2023).
pubmed: 37263081
pmcid: 10245111
doi: 10.1016/j.esmoop.2023.101567
Echle, A. et al. Deep learning in cancer pathology: a new generation of clinical biomarkers. Br. J. Cancer 124, 686–696 (2021).
pubmed: 33204028
doi: 10.1038/s41416-020-01122-x
Friemel, J. et al. Intratumor heterogeneity in hepatocellular carcinoma. Clin. Cancer Res. 21, 1951–1961 (2015).
pubmed: 25248380
doi: 10.1158/1078-0432.CCR-14-0122
Calderaro, J. et al. Histological subtypes of hepatocellular carcinoma are related to gene mutations and molecular tumour classification. J. Hepatol. 67, 727–738 (2017).
pubmed: 28532995
doi: 10.1016/j.jhep.2017.05.014
Solinas, A. & Calvisi, D. F. Lessons from rare tumors: hepatic lymphoepithelioma-like carcinomas. World J. Gastroenterol. 21, 3472–3479 (2015).
pubmed: 25834311
pmcid: 4375568
doi: 10.3748/wjg.v21.i12.3472
Salomao, M., Yu, W. M., Brown, R. S. Jr, Emond, J. C. & Lefkowitch, J. H. Steatohepatitic hepatocellular carcinoma (SH-HCC): a distinctive histological variant of HCC in hepatitis C virus-related cirrhosis with associated NAFLD/NASH. Am. J. Surg. Pathol. 34, 1630–1636 (2010).
pubmed: 20975341
doi: 10.1097/PAS.0b013e3181f31caa
Limousin, W. et al. Molecular-based targeted therapies in patients with hepatocellular carcinoma and hepato-cholangiocarcinoma refractory to atezolizumab/bevacizumab. J. Hepatol. 79, 1450–1458 (2023).
pubmed: 37647991
doi: 10.1016/j.jhep.2023.08.017
Prueksapanich, P. et al. Liver fluke-associated biliary tract cancer. Gut Liver 12, 236–245 (2018).
pubmed: 28783896
doi: 10.5009/gnl17102
European Association for the Study of the Liver. EASL-ILCA clinical practice guidelines on the management of intrahepatic cholangiocarcinoma. J. Hepatol. 79, 181–208 (2023).
doi: 10.1016/j.jhep.2023.03.010
Vithayathil, M., Bridegwater, J. & Khan, S. A. Medical therapies for intra-hepatic cholangiocarcinoma. J. Hepatol. 75, 981–983 (2021).
pubmed: 34215442
doi: 10.1016/j.jhep.2021.04.004
Nault, J.-C. & Villanueva, A. Biomarkers for hepatobiliary cancers. Hepatology 73, 115–127 (2021).
pubmed: 32045030
doi: 10.1002/hep.31175
Brunt, E. et al. cHCC-CCA: consensus terminology for primary liver carcinomas with both hepatocytic and cholangiocytic differentation. Hepatology 68, 113–126 (2018).
pubmed: 29360137
doi: 10.1002/hep.29789
Rinella, M. E. et al. A multisociety Delphi consensus statement on new fatty liver disease nomenclature. Ann. Hepatol. 29, 101133 (2024).
pubmed: 37364816
doi: 10.1016/j.aohep.2023.101133
Wong, V. W.-S., Ekstedt, M., Wong, G. L.-H. & Hagström, H. Changing epidemiology, global trends and implications for outcomes of NAFLD. J. Hepatol. 79, 842–852 (2023).
pubmed: 37169151
doi: 10.1016/j.jhep.2023.04.036
Clements, O., Eliahoo, J., Kim, J. U., Taylor-Robinson, S. D. & Khan, S. A. Risk factors for intrahepatic and extrahepatic cholangiocarcinoma: a systematic review and meta-analysis. J. Hepatol. 72, 95–103 (2020).
pubmed: 31536748
doi: 10.1016/j.jhep.2019.09.007
Jing, W. et al. Diabetes mellitus and increased risk of cholangiocarcinoma: a meta-analysis. Eur. J. Cancer Prev. 21, 24–31 (2012).
pubmed: 21857525
doi: 10.1097/CEJ.0b013e3283481d89
Litjens, G. et al. A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60–88 (2017).
pubmed: 28778026
doi: 10.1016/j.media.2017.07.005
Wagner, S. J. et al. Transformer-based biomarker prediction from colorectal cancer histology: a large-scale multicentric study. Cancer Cell 41, 1650–1661.e4 (2023).
pubmed: 37652006
pmcid: 10507381
doi: 10.1016/j.ccell.2023.08.002
Khader, F. et al. Multimodal deep learning for integrating chest radiographs and clinical parameters: a case for transformers. Radiology 309, e230806 (2023).
pubmed: 37787671
doi: 10.1148/radiol.230806
Reis-Filho, J. S. & Kather, J. N. Overcoming the challenges to implementation of artificial intelligence in pathology. J. Natl Cancer Inst. 115, 608–612 (2023).
pubmed: 36929936
pmcid: 10248832
doi: 10.1093/jnci/djad048
Shmatko, A., Ghaffari Laleh, N., Gerstung, M. & Kather, J. N. Artificial intelligence in histopathology: enhancing cancer research and clinical oncology. Nat. Cancer 3, 1026–1038 (2022).
pubmed: 36138135
doi: 10.1038/s43018-022-00436-4
Cheng, N. et al. Deep learning-based classification of hepatocellular nodular lesions on whole-slide histopathologic images. Gastroenterology 162, 1948–1961.e7 (2022).
pubmed: 35202643
doi: 10.1053/j.gastro.2022.02.025
Kiani, A. et al. Impact of a deep learning assistant on the histopathologic classification of liver cancer. NPJ Digit. Med. 3, 23 (2020).
pubmed: 32140566
pmcid: 7044422
doi: 10.1038/s41746-020-0232-8
Calderaro, J. et al. Deep learning-based phenotyping reclassifies combined hepatocellular-cholangiocarcinoma. Nat. Commun. 14, 8290 (2023).
pubmed: 38092727
pmcid: 10719304
doi: 10.1038/s41467-023-43749-3
Chung, T. & Park, Y. N. Up-to-date pathologic classification and molecular characteristics of intrahepatic cholangiocarcinoma. Front. Med. 9, 857140 (2022).
doi: 10.3389/fmed.2022.857140
Albrecht, T. et al. Deep learning-enabled diagnosis of liver adenocarcinoma. Gastroenterology 165, 1262–1275 (2023).
pubmed: 37562657
doi: 10.1053/j.gastro.2023.07.026
Lu, M. Y. et al. AI-based pathology predicts origins for cancers of unknown primary. Nature 594, 106–110 (2021).
pubmed: 33953404
doi: 10.1038/s41586-021-03512-4
Saillard, C. et al. Predicting survival after hepatocellular carcinoma resection using deep-learning on histological slides. Hepatology 72, 2000–2013 (2020).
pubmed: 32108950
doi: 10.1002/hep.31207
Shi, J.-Y. et al. Exploring prognostic indicators in the pathological images of hepatocellular carcinoma based on deep learning. Gut 70, 951–961 (2021).
pubmed: 32998878
doi: 10.1136/gutjnl-2020-320930
Xie, J. et al. Survival prediction on intrahepatic cholangiocarcinoma with histomorphological analysis on the whole slide images. Comput. Biol. Med. 146, 105520 (2022).
pubmed: 35537220
doi: 10.1016/j.compbiomed.2022.105520
Sjöblom, N. et al. Automated image analysis of keratin 7 staining can predict disease outcome in primary sclerosing cholangitis. Hepatol. Res. 53, 322–333 (2023).
pubmed: 36495019
doi: 10.1111/hepr.13867
Cifci, D., Foersch, S. & Kather, J. N. Artificial intelligence to identify genetic alterations in conventional histopathology. J. Pathol. 257, 430–444 (2022).
pubmed: 35342954
doi: 10.1002/path.5898
Campanella, G. et al. H&E-based computational biomarker enables universal EGFR screening for lung adenocarcinoma. Preprint at https://doi.org/10.48550/arXiv.2206.10573 (2022).
Echle, A. et al. Artificial intelligence for detection of microsatellite instability in colorectal cancer – a multicentric analysis of a pre-screening tool for clinical application. ESMO Open. 7, 100400 (2022).
pubmed: 35247870
pmcid: 9058894
doi: 10.1016/j.esmoop.2022.100400
Echle, A. et al. Deep learning for the detection of microsatellite instability from histology images in colorectal cancer: a systematic literature review. ImmunoInformatics 3–4, 100008 (2021).
doi: 10.1016/j.immuno.2021.100008
Farahmand, S. et al. Deep learning trained on hematoxylin and eosin tumor region of interest predicts HER2 status and trastuzumab treatment response in HER2
pubmed: 34493825
doi: 10.1038/s41379-021-00911-w
Fu, Y. et al. Pan-cancer computational histopathology reveals mutations, tumor composition and prognosis. Nat. Cancer 1, 800–810 (2020).
pubmed: 35122049
doi: 10.1038/s43018-020-0085-8
Kather, J. N. et al. Pan-cancer image-based detection of clinically actionable genetic alterations. Nat. Cancer 1, 789–799 (2020).
pubmed: 33763651
pmcid: 7610412
doi: 10.1038/s43018-020-0087-6
Zhang, H. et al. Predicting tumor mutational burden from liver cancer pathological images using convolutional neural network. In 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (eds Yoo, I., Bi, J. & Hu, X) 920–925 (IEEE, 2019).
Zeng, Q. et al. Artificial intelligence predicts immune and inflammatory gene signatures directly from hepatocellular carcinoma histology. J. Hepatol. 1, 116–127 (2022).
doi: 10.1016/j.jhep.2022.01.018
Macias, R. I. R. et al. Clinical relevance of biomarkers in cholangiocarcinoma: critical revision and future directions. Gut 71, 1669–1683 (2022).
pubmed: 35580963
Zeng, Q. et al. Artificial intelligence-based pathology as a biomarker of sensitivity to atezolizumab-bevacizumab in patients with hepatocellular carcinoma: a multicentre retrospective study. Lancet Oncol. 24, 1411–1422 (2023).
pubmed: 37951222
doi: 10.1016/S1470-2045(23)00468-0
Oh, D.-Y. et al. Durvalumab plus gemcitabine and cisplatin in advanced biliary tract cancer. NEJM Evid. https://doi.org/10.1056/EVIDoa2200015 (2022).
Nam, D., Chapiro, J., Paradis, V., Seraphin, T. P. & Kather, J. N. Artificial intelligence in liver diseases: improving diagnostics, prognostics and response prediction. JHEP Rep. 4, 100443 (2022).
pubmed: 35243281
pmcid: 8867112
doi: 10.1016/j.jhepr.2022.100443
Narita, K. et al. Iodine maps derived from sparse-view kV-switching dual-energy CT equipped with a deep learning reconstruction for diagnosis of hepatocellular carcinoma. Sci. Rep. 13, 3603 (2023).
pubmed: 36869102
pmcid: 9984536
doi: 10.1038/s41598-023-30460-y
Lee, H. J., Kim, J. S., Lee, J. K., Lee, H. A. & Pak, S. Ultra-low-dose hepatic multiphase CT using deep learning-based image reconstruction algorithm focused on arterial phase in chronic liver disease: a non-inferiority study. Eur. J. Radiol. 159, 110659 (2023).
pubmed: 36584563
doi: 10.1016/j.ejrad.2022.110659
Liu, F. et al. Deep learning radiomics based on contrast-enhanced ultrasound might optimize curative treatments for very-early or early-stage hepatocellular carcinoma patients. Liver Cancer 9, 397–413 (2020).
pubmed: 32999867
pmcid: 7506213
doi: 10.1159/000505694
Huang, Z. et al. Deep learning-based radiomics based on contrast-enhanced ultrasound predicts early recurrence and survival outcome in hepatocellular carcinoma. World J. Gastrointest. Oncol. 14, 2380–2392 (2022).
pubmed: 36568943
pmcid: 9782621
doi: 10.4251/wjgo.v14.i12.2380
Müller-Franzes, G. et al. Using machine learning to reduce the need for contrast agents in breast MRI through synthetic images. Radiology 307, e222211 (2023).
pubmed: 36943080
doi: 10.1148/radiol.222211
Ponnoprat, D. et al. Classification of hepatocellular carcinoma and intrahepatic cholangiocarcinoma based on multi-phase CT scans. Med. Biol. Eng. Comput. 58, 2497–2515 (2020).
pubmed: 32794015
doi: 10.1007/s11517-020-02229-2
Ryu, H. et al. Joint segmentation and classification of hepatic lesions in ultrasound images using deep learning. Eur. Radiol. 31, 8733–8742 (2021).
pubmed: 33881566
pmcid: 8523410
doi: 10.1007/s00330-021-07850-9
Laino, M. E. et al. The added value of artificial intelligence to LI-RADS categorization: a systematic review. Eur. J. Radiol. 150, 110251 (2022).
pubmed: 35303556
doi: 10.1016/j.ejrad.2022.110251
Gilbert, S., Harvey, H., Melvin, T., Vollebregt, E. & Wicks, P. Large language model AI chatbots require approval as medical devices. Nat. Med. 29, 2396–2398 (2023).
pubmed: 37391665
doi: 10.1038/s41591-023-02412-6
Perincheri, S. et al. An independent assessment of an artificial intelligence system for prostate cancer detection shows strong diagnostic accuracy. Mod. Pathol. 34, 1588–1595 (2021).
pubmed: 33782551
pmcid: 8295034
doi: 10.1038/s41379-021-00794-x
Saillard, C. et al. Validation of MSIntuit as an AI-based pre-screening tool for MSI detection from colorectal cancer histology slides. Nat. Commun. 14, 6695 (2023).
pubmed: 37932267
pmcid: 10628260
doi: 10.1038/s41467-023-42453-6
Sandbank, J. et al. Validation and real-world clinical application of an artificial intelligence algorithm for breast cancer detection in biopsies. NPJ Breast Cancer 8, 129 (2022).
pubmed: 36473870
pmcid: 9723672
doi: 10.1038/s41523-022-00496-w
Mori, Y., Neumann, H., Misawa, M., Kudo, S.-E. & Bretthauer, M. Artificial intelligence in colonoscopy – now on the market. What’s next? J. Gastroenterol. Hepatol. 36, 7–11 (2021).
pubmed: 33179322
doi: 10.1111/jgh.15339
Lång, K. et al. Artificial intelligence-supported screen reading versus standard double reading in the Mammography Screening with Artificial Intelligence trial (MASAI): a clinical safety analysis of a randomised, controlled, non-inferiority, single-blinded, screening accuracy study. Lancet Oncol. 24, 936–944 (2023).
pubmed: 37541274
doi: 10.1016/S1470-2045(23)00298-X
Oh, J. H. & Jun, D. W. The latest global burden of liver cancer: a past and present threat. Clin. Mol. Hepatol. 29, 355–357 (2023).
pubmed: 36891606
pmcid: 10121295
doi: 10.3350/cmh.2023.0070
Vogel, A. & Saborowski, A. Medical therapy of HCC. J. Hepatol. 76, 208–210 (2022).
pubmed: 34538616
doi: 10.1016/j.jhep.2021.05.017
Bruix, J., Chan, S. L., Galle, P. R., Rimassa, L. & Sangro, B. Systemic treatment of hepatocellular carcinoma: an EASL position paper. J. Hepatol. 75, 960–974 (2021).
pubmed: 34256065
doi: 10.1016/j.jhep.2021.07.004
Rebouissou, S. & Nault, J.-C. Advances in molecular classification and precision oncology in hepatocellular carcinoma. J. Hepatol. 72, 215–229 (2020).
pubmed: 31954487
doi: 10.1016/j.jhep.2019.08.017
Thirunavukarasu, A. J. et al. Large language models in medicine. Nat. Med. 29, 1930–1940 (2023).
pubmed: 37460753
doi: 10.1038/s41591-023-02448-8
Singhal, K. et al. Large language models encode clinical knowledge. Nature 620, 172–180 (2023).
pubmed: 37438534
pmcid: 10396962
doi: 10.1038/s41586-023-06291-2
Jiang, L. Y. et al. Health system-scale language models are all-purpose prediction engines. Nature 619, 357–362 (2023).
pubmed: 37286606
pmcid: 10338337
doi: 10.1038/s41586-023-06160-y
Truhn, D., Reis-Filho, J. S. & Kather, J. N. Large language models should be used as scientific reasoning engines, not knowledge databases. Nat. Med. 29, 2983–2984 (2023).
pubmed: 37853138
doi: 10.1038/s41591-023-02594-z
Clusmann, J. et al. The future landscape of large language models in medicine. Commun. Med. 3, 141 (2023).
pubmed: 37816837
pmcid: 10564921
doi: 10.1038/s43856-023-00370-1
Cheng, K. et al. Artificial intelligence in sports medicine: could GPT-4 make human doctors obsolete? Ann. Biomed. Eng. 51, 1658–1662 (2023).
pubmed: 37097528
doi: 10.1007/s10439-023-03213-1
Yang, X. et al. A large language model for electronic health records. NPJ Digit. Med. 5, 194 (2022).
pubmed: 36572766
pmcid: 9792464
doi: 10.1038/s41746-022-00742-2
Adams, L. C. et al. Leveraging GPT-4 for post hoc transformation of free-text radiology reports into structured reporting: a multilingual feasibility study. Radiology 307, e230725 (2023).
pubmed: 37014240
doi: 10.1148/radiol.230725
Lipkova, J. et al. Artificial intelligence for multimodal data integration in oncology. Cancer Cell 40, 1095–1110 (2022).
pubmed: 36220072
pmcid: 10655164
doi: 10.1016/j.ccell.2022.09.012
Unger, M. & Kather, J. N. A systematic analysis of deep learning in genomics and histopathology for precision oncology. BMC Med. Genomics 17, 48 (2024).
pubmed: 38317154
pmcid: 10845449
doi: 10.1186/s12920-024-01796-9
Chen, R. J. et al. Pan-cancer integrative histology-genomic analysis via multimodal deep learning. Cancer Cell 40, 865–878.e6 (2022).
pubmed: 35944502
pmcid: 10397370
doi: 10.1016/j.ccell.2022.07.004
Chaudhary, K., Poirion, O. B., Lu, L. & Garmire, L. X. Deep learning-based multi-omics integration robustly predicts survival in liver cancer. Clin. Cancer Res. 24, 1248–1259 (2018).
pubmed: 28982688
doi: 10.1158/1078-0432.CCR-17-0853
Tu, T. et al. Towards generalist biomedical AI. NEJM AI 1, 3 (2023).
Schneider, L. et al. Integration of deep learning-based image analysis and genomic data in cancer pathology: a systematic review. Eur. J. Cancer 160, 80–91 (2022).
pubmed: 34810047
doi: 10.1016/j.ejca.2021.10.007
Hou, J., Jia, X., Xie, Y. & Qin, W. Integrative histology-genomic analysis predicts hepatocellular carcinoma prognosis using deep learning. Genes 13, 1770 (2022).
pubmed: 36292654
pmcid: 9601633
doi: 10.3390/genes13101770
Boehm, K. M. et al. Multimodal data integration using machine learning improves risk stratification of high-grade serous ovarian cancer. Nat. Cancer 3, 723–733 (2022).
pubmed: 35764743
pmcid: 9239907
doi: 10.1038/s43018-022-00388-9
Foersch, S. et al. Multistain deep learning for prediction of prognosis and therapy response in colorectal cancer. Nat. Med. 29, 430–439 (2023).
pubmed: 36624314
doi: 10.1038/s41591-022-02134-1
Bubeck, S. et al. Sparks of artificial general intelligence: early experiments with GPT-4. Preprint at https://doi.org/10.48550/arXiv.2303.12712 (2023).
Moor, M. et al. Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023).
pubmed: 37045921
doi: 10.1038/s41586-023-05881-4
Wu, C. et al. Can GPT-4V(ision) serve medical applications? Case studies on GPT-4V for multimodal medical diagnosis. Preprint at https://doi.org/10.48550/arXiv.2310.09909 (2023).
Li, L. & Wang, H. Heterogeneity of liver cancer and personalized therapy. Cancer Lett. 379, 191–197 (2016).
pubmed: 26213370
doi: 10.1016/j.canlet.2015.07.018
Rinaldi, L. et al. Risk of hepatocellular carcinoma after HCV clearance by direct-acting antivirals treatment predictive factors and role of epigenetics. Cancers 12, 1351 (2020).
pubmed: 32466400
pmcid: 7352473
doi: 10.3390/cancers12061351
Degasperi, E. et al. Factors associated with increased risk of de novo or recurrent hepatocellular carcinoma in patients with cirrhosis treated with direct-acting antivirals for HCV infection. Clin. Gastroenterol. Hepatol. 17, 1183–1191.e7 (2019).
pubmed: 30613002
doi: 10.1016/j.cgh.2018.10.038
Yang, Z. et al. On-treatment risks of cirrhosis and hepatocellular carcinoma among a large cohort of predominantly non-Asian patients with non-cirrhotic chronic hepatitis B. JHEP Rep. 5, 100852 (2023).
pubmed: 37701335
pmcid: 10494462
doi: 10.1016/j.jhepr.2023.100852
Cotter, T. G. & Rinella, M. Nonalcoholic fatty liver disease 2020: the state of the disease. Gastroenterology 158, 1851–1864 (2020).
pubmed: 32061595
doi: 10.1053/j.gastro.2020.01.052
Huang, D. Q., El-Serag, H. B. & Loomba, R. Global epidemiology of NAFLD-related HCC: trends, predictions, risk factors and prevention. Nat. Rev. Gastroenterol. Hepatol. 18, 223–238 (2021).
pubmed: 33349658
doi: 10.1038/s41575-020-00381-6
Lee, S. et al. CT and MRI liver imaging reporting and data system version 2018 for hepatocellular carcinoma: a systematic review with meta-analysis. J. Am. Coll. Radiol. 17, 1199–1206 (2020).
pubmed: 32640250
doi: 10.1016/j.jacr.2020.06.005
Singal, A. G. et al. AASLD practice guidance on prevention, diagnosis, and treatment of hepatocellular carcinoma. Hepatology 78, 1922–1965 (2023).
pubmed: 37199193
doi: 10.1097/HEP.0000000000000466
Chen, J. et al. Biomarker discovery and application – an opportunity to resolve the challenge of liver cancer diagnosis and treatment. Pharmacol. Res. 189, 106674 (2023).
pubmed: 36702425
doi: 10.1016/j.phrs.2023.106674
Reig, M. et al. BCLC strategy for prognosis prediction and treatment recommendation: the 2022 update. J. Hepatol. 76, 681–693 (2022).
pubmed: 34801630
doi: 10.1016/j.jhep.2021.11.018
Belghiti, J. & Kianmanesh, R. Surgical treatment of hepatocellular carcinoma. HPB 7, 42–49 (2005).
pubmed: 18333160
pmcid: 2023921
doi: 10.1080/13651820410024067
Hyun, M. H. et al. Hepatic resection compared to chemoembolization in intermediate- to advanced-stage hepatocellular carcinoma: a meta-analysis of high-quality studies. Hepatology 68, 977–993 (2018).
pubmed: 29543988
doi: 10.1002/hep.29883
Kokudo, T. et al. Survival benefit of liver resection for hepatocellular carcinoma associated with portal vein invasion. J. Hepatol. 65, 938–943 (2016).
pubmed: 27266618
doi: 10.1016/j.jhep.2016.05.044
van Lienden, K. P. et al. Portal vein embolization before liver resection: a systematic review. Cardiovasc. Interv. Radiol. 36, 25–34 (2013).
doi: 10.1007/s00270-012-0440-y
Golfieri, R., Bargellini, I., Spreafico, C. & Trevisani, F. Patients with Barcelona clinic liver cancer stages B and C hepatocellular carcinoma: time for a subclassification. Liver Cancer 8, 78–91 (2019).
pubmed: 31019899
doi: 10.1159/000489791
Kim, J. H. et al. New intermediate-stage subclassification for patients with hepatocellular carcinoma treated with transarterial chemoembolization. Liver Int. 37, 1861–1868 (2017).
pubmed: 28581250
doi: 10.1111/liv.13487
Johnson, P. J. et al. Assessment of liver function in patients with hepatocellular carcinoma: a new evidence-based approach – the ALBI grade. J. Clin. Oncol. 33, 550–558 (2015).
pubmed: 25512453
doi: 10.1200/JCO.2014.57.9151
Finn, R. S. et al. Atezolizumab plus bevacizumab in unresectable hepatocellular carcinoma. N. Engl. J. Med. 382, 1894–1905 (2020).
pubmed: 32402160
doi: 10.1056/NEJMoa1915745
Abou-Alfa Ghassan, K. et al. Tremelimumab plus durvalumab in unresectable hepatocellular carcinoma. NEJM Evid. 1, EVIDoa2100070 (2022).
pubmed: 38319892
Costa, F., Wiedenmann, B., Roderburg, C., Mohr, R. & Abou-Alfa, G. K. Systemic treatment in patients with Child-Pugh B liver dysfunction and advanced hepatocellular carcinoma. Cancer Med. 12, 13978–13990 (2023).
pubmed: 37162288
pmcid: 10358256
doi: 10.1002/cam4.6033
Montironi, C. et al. Inflamed and non-inflamed classes of HCC: a revised immunogenomic classification. Gut 72, 129–140 (2023).
pubmed: 35197323
doi: 10.1136/gutjnl-2021-325918
Llovet, J. M. et al. Hepatocellular carcinoma. Nat. Rev. Dis. Prim. 7, 6 (2021).
pubmed: 33479224
doi: 10.1038/s41572-020-00240-3
Altman, D. G., Simera, I., Hoey, J., Moher, D. & Schulz, K. EQUATOR: reporting guidelines for health research. Open. Med. 2, e49–e50 (2008).
pubmed: 21602941
pmcid: 3090180
Collins, G. S. & Moons, K. G. M. Reporting of artificial intelligence prediction models. Lancet 393, 1577–1579 (2019).
pubmed: 31007185
doi: 10.1016/S0140-6736(19)30037-6
Liu, X. et al. Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension. Lancet Digit. Health 2, e537–e548 (2020).
pubmed: 33328048
pmcid: 8183333
doi: 10.1016/S2589-7500(20)30218-1
Rivera, S. C. et al. Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI Extension. BMJ 370, m3210 (2020).
pubmed: 32907797
pmcid: 7490785
doi: 10.1136/bmj.m3210
Hernandez-Boussard, T., Bozkurt, S., Ioannidis, J. P. A. & Shah, N. H. MINIMAR (MINimum Information for Medical AI Reporting): developing reporting standards for artificial intelligence in health care. J. Am. Med. Inform. Assoc. 27, 2011–2015 (2020).
pubmed: 32594179
pmcid: 7727333
doi: 10.1093/jamia/ocaa088
Vasey, B. et al. Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI. Nat. Med. 28, 924–933 (2022).
pubmed: 35585198
doi: 10.1038/s41591-022-01772-9
Schömig-Markiefka, B. et al. Quality control stress test for deep learning-based diagnostic model in digital pathology. Mod. Pathol. 34, 2098–2108 (2021).
pubmed: 34168282
pmcid: 8592835
doi: 10.1038/s41379-021-00859-x
Castelo-Branco, L. et al. ESMO guidance for reporting oncology real-world evidence (GROW). Ann. Oncol. 34, 1097–1112 (2023).
pubmed: 37848160
doi: 10.1016/j.annonc.2023.10.001
Ng, A. Y. et al. Prospective implementation of AI-assisted screen reading to improve early detection of breast cancer. Nat. Med. 29, 3044–3049 (2023).
pubmed: 37973948
pmcid: 10719086
doi: 10.1038/s41591-023-02625-9
Dembrower, K. et al. Artificial intelligence for breast cancer detection in screening mammography in Sweden: a prospective, population-based, paired-reader, non-inferiority study. Lancet Digit. Health 5, e703–e711 (2023).
pubmed: 37690911
doi: 10.1016/S2589-7500(23)00153-X
Houssami, N. & Marinovich, M. L. AI for mammography screening: enter evidence from prospective trials. Lancet Digital health 5, e641–e642 (2023).
pubmed: 37690910
doi: 10.1016/S2589-7500(23)00176-0
Qin, S. et al. Camrelizumab plus rivoceranib versus sorafenib as first-line therapy for unresectable hepatocellular carcinoma (CARES-310): a randomised, open-label, international phase 3 study. Lancet 402, 1133–1146 (2023).
pubmed: 37499670
doi: 10.1016/S0140-6736(23)00961-3
Piha-Paul, S. A. et al. Efficacy and safety of pembrolizumab for the treatment of advanced biliary cancer: results from the KEYNOTE-158 and KEYNOTE-028 studies. Int. J. Cancer 147, 2190–2198 (2020).
pubmed: 32359091
doi: 10.1002/ijc.33013
Havel, J. J., Chowell, D. & Chan, T. A. The evolving landscape of biomarkers for checkpoint inhibitor immunotherapy. Nat. Rev. Cancer 19, 133–150 (2019).
pubmed: 30755690
pmcid: 6705396
doi: 10.1038/s41568-019-0116-x
Slater, S. & Cunningham, D. Pembrolizumab plus chemotherapy as first-line treatment for advanced biliary tract cancer. Lancet 401, 1826–1827 (2023).
pubmed: 37075782
doi: 10.1016/S0140-6736(23)00767-5
World Health Organization. Ethics and governance of artificial intelligence for health: WHO guidance. WHO https://www.who.int/publications/i/item/9789240029200 (2021).
Yang, F. et al. Global trajectories of liver cancer burden from 1990 to 2019 and projection to 2035. Chin. Med. J. 136, 1413–1421 (2023).
pubmed: 37114647
pmcid: 10278715
doi: 10.1097/CM9.0000000000002703
European Association for the Study of the Liver, American Association for the Study of Liver Diseases, Latin American Association for the Study of the Liver, Asian Pacific Association for the Study of the Liver Ending stigmatizing language in alcohol and liver disease: a liver societies’ statement. J. Hepatol. 79, 1347–1348 (2023).
doi: 10.1016/j.jhep.2023.07.016
Truhn, D., Müller-Franzes, G. & Kather, J. N. The ecological footprint of medical AI. Eur. Radiol. 34, 1176–1178 (2023).
pubmed: 37580599
pmcid: 10853292
doi: 10.1007/s00330-023-10123-2
Varoquaux, G. & Cheplygina, V. Machine learning for medical imaging: methodological failures and recommendations for the future. NPJ Digit. Med. 5, 48 (2022).
pubmed: 35413988
pmcid: 9005663
doi: 10.1038/s41746-022-00592-y
Seyyed-Kalantari, L., Zhang, H., McDermott, M. B. A., Chen, I. Y. & Ghassemi, M. Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations. Nat. Med. 27, 2176–2182 (2021).
pubmed: 34893776
pmcid: 8674135
doi: 10.1038/s41591-021-01595-0
World Health Organization. Regulatory Considerations on Artificial Intelligence for Health (WHO, 2023).
Xia, T.-Y. et al. Predicting microvascular invasion in hepatocellular carcinoma using CT-based radiomics model. Radiology 307, e222729 (2023).
pubmed: 37097141
doi: 10.1148/radiol.222729
Jensen, C. T. et al. Reduced-dose deep learning reconstruction for abdominal CT of liver metastases. Radiology 303, 90–98 (2022).
pubmed: 35014900
doi: 10.1148/radiol.211838
Yamashita, R., Long, J., Saleem, A., Rubin, D. L. & Shen, J. Deep learning predicts postsurgical recurrence of hepatocellular carcinoma from digital histopathologic images. Sci. Rep. 11, 2047 (2021).
pubmed: 33479370
pmcid: 7820423
doi: 10.1038/s41598-021-81506-y
Peng, J. et al. Residual convolutional neural network for predicting response of transarterial chemoembolization in hepatocellular carcinoma from CT imaging. Eur. Radiol. 30, 413–424 (2020).
pubmed: 31332558
doi: 10.1007/s00330-019-06318-1
Zhen, S.-H. et al. Deep learning for accurate diagnosis of liver tumor based on magnetic resonance imaging and clinical data. Front. Oncol. 10, 680 (2020).
pubmed: 32547939
pmcid: 7271965
doi: 10.3389/fonc.2020.00680
Hamm, C. A. et al. Deep learning for liver tumor diagnosis part I: development of a convolutional neural network classifier for multi-phasic MRI. Eur. Radiol. 29, 3338–3347 (2019).
pubmed: 31016442
pmcid: 7251621
doi: 10.1007/s00330-019-06205-9
Kather, J. N. Artificial intelligence in oncology: chances and pitfalls. J. Cancer Res. Clin. Oncol. 149, 7995–7996 (2023).
pubmed: 36920564
pmcid: 10374782
doi: 10.1007/s00432-023-04666-6
Derraz, B. et al. New regulatory thinking is needed for AI-based personalised drug and cell therapies in precision oncology. NPJ Precis. Oncol. 8, 23 (2024).
pubmed: 38291217
pmcid: 10828509
doi: 10.1038/s41698-024-00517-w