An imageomics and multi-network based deep learning model for risk assessment of liver transplantation for hepatocellular cancer
Open accessAbstract
Introduction Liver transplantation (LT) is an effective treatment for hepatocellular carcinoma (HCC), the most common type of primary liver cancer. Patients with small HCC (<5 cm) are given priority over others for transplantation due to clinical allocation policies based on tumor size. Attempting to shift from the prevalent paradigm that successful transplantation and longer disease-free survival can only be achieved in patients with small HCC to expanding the transplantation option to patients with HCC of the highest tumor burden (>5 cm), we developed a convergent artificial intelligence (AI) model that combines transient clinical data with quantitative histologic and radiomic features for more objective risk assessment of liver transplantation for HCC patients. Methods Patients who received a LT for HCC between 2008-2019 were eligible for inclusion in the analysis. All patients with post-LT recurrence were included, and those without recurrence were randomly selected for inclusion in the deep learning model. Pre- and post-transplant magnetic resonance imaging (MRI) scans and reports were compressed using CapsNet networks and natural language processing, respectively, as input for a multiple feature radial basis function network. We applied a histological image analysis algorithm to detect pathologic areas of interest from explant tissue of patients who recurred. The multilayer perceptron was designed as a feed-forward, supervised neural network topology, with the final assessment of recurrence risk. We used area under the curve (AUC) and F-1 score to assess the predictability of different network combinations. Results A total of 109 patients were included (87 in the training group, 22 in the testing group), of which 20 were positive for cancer recurrence. Seven models (AUC; F-1 score) were generated, including clinical features only (0.55; 0.52), magnetic resonance imaging (MRI) only (0.64; 0.61), pathological images only (0.64; 0.61), MRI plus pathology (0.68; 0.65), MRI plus clinical (0.78, 0.75), pathology plus clinical (0.77; 0.73), and a combination of clinical, MRI, and pathology features (0.87; 0.84). The final combined model showed 80 % recall and 89 % precision. The total accuracy of the implemented model was 82 %. Conclusion We validated that the deep learning model combining clinical features and multi-scale histopathologic and radiomic image features can be used to discover risk factors for recurrence beyond tumor size and biomarker analysis. Such a predictive, convergent AI model has the potential to alter the LT allocation system for HCC patients and expand the transplantation treatment option to patients with HCC of the highest tumor burden.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Various combinations of imaging, clinical, and genomic modalities can be fused for tasks such as AD prediction, AD stage detection, and liver transplantation risk assessment.
“For instance, MRI and clinical data can be fused for AD prediction [72] or MRI, clinical and genomic modalities can be fused for AD stage detection [69]. Also, latent representations of multiple imaging modalities and clinical data can be fused for risk assessment of liver transplantation for hepatocellular cancer [71].”
From Multimodal deep learning for biomedical data fusion: a review (2022) · cites this paper as [71]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404 · see citing works
Glove: Global Vectors for Word Representation
Pennington, J., Socher, R., & Manning, C. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1162 · Cited by 34,071 · see citing works
Erdogan, D., Heijnen, B. H. M., Bennink, R. J., Kok, M., Dinant, S., Straatsburg, I. H., Gouma, D. J., & van Gulik, T. M. (2004) · Liver International · doi:10.1111/j.1478-3231.2004.00901.x · Cited by 11,656 · see citing works
Radiomics: Images Are More than Pictures, They Are Data
Gillies, R. J., Kinahan, P. E., & Hricak, H. (2016) · Radiology · doi:10.1148/radiol.2015151169 · Cited by 8,421 · see citing works
Mazzaferro, V., Regalia, E., Doci, R., Andreola, S. A., Pulvirenti, A., Bozzetti, F., Montalto, F., Ammatuna, M., Morabito, A., & Gennari, L. (1996) · New England Journal of Medicine · doi:10.1056/nejm199603143341104 · Cited by 7,173 · see citing works
Computational Radiomics System to Decode the Radiographic Phenotype
(2017) · doi:10.1158/0008-5472.can-17-0339 · Cited by 6,593 · see citing works
Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Gal, Y., & Ghahramani, Z. (2015) · arXiv · doi:10.48550/arxiv.1506.02142 · Cited by 6,471 · see citing works
Radiomics: the bridge between medical imaging and personalized medicine
Lambin, P., Leijenaar, R. T. H., Deist, T. M., Peerlings, J., de Jong, E. E. C., van Timmeren, J., Sanduleanu, S., Larue, R. T. H. M., Even, A. J. G., Jochems, A., et al. (2017) · Nature Reviews Clinical Oncology · doi:10.1038/nrclinonc.2017.141 · Cited by 5,688 · see citing works
Acute Abdomen: A Rare Case of Ruptured Hepatocellular Carcinoma
Gloor, S., Jensen, K. O., Breitenstein, S., Binkert, C. A., Angst, E., & Hetzer, F. H. (2017) · Case Reports in Gastroenterology · doi:10.1159/000463378 · Cited by 5,370 · see citing works
Linguistic Regularities in Continuous Space Word Representations
Mikolov, T., Yih, W.-T., & Zweig, G. (2013) · Cited by 2,913 · see citing works
Model for end-stage liver disease (MELD) and allocation of donor livers
Wiesner, R. H., Edwards, E. B., Freeman, R. B., Harper, A., Kim, W. R., Kamath, P. S., Kremers, W. K., Lake, J. R., Howard, T. K., Merion, R. M., et al. (2003) · Gastroenterology · doi:10.1053/gast.2003.50016 · Cited by 2,524 · see citing works
Yao, F. Y., Ferrell, L. D., Bass, N. M., Watson, J. J., Bacchetti, P., Venook, A. P., Ascher, N. L., & Roberts, J. P. (2001) · Hepatology · doi:10.1053/jhep.2001.24563 · Cited by 2,262 · see citing works
Mazzaferro, V., Llovet, J. M., Miceli, R., Bhoori, S., Schiavo, M., Mariani, L., Camerini, T., Roayaie, S., Schwartz, M., Grazi, G. L., et al. (2008) · The Lancet Oncology · doi:10.1016/s1470-2045(08)70284-5 · Cited by 2,048 · see citing works
Frid-Adar, M., Diamant, I., Klang, E., Amitai, M. M., Goldberger, J., & Greenspan, H. K. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.09.013 · Cited by 1,952 · see citing works
Radiomics: the facts and the challenges of image analysis
Rizzo, S., Botta, F., Raimondi, S., Origgi, D., Fanciullo, C., MORGANTI, A. G., & Bellomi, M. (2018) · European Radiology Experimental · doi:10.1186/s41747-018-0068-z · Cited by 1,048 · see citing works
Explainable Deep Learning Models in Medical Image Analysis
Singh, A., Sengupta, S., & Lakshminarayanan, V. (2020) · Journal of Imaging · doi:10.3390/jimaging6060052 · Cited by 718 · see citing works
Vanderwende, L., Daumé, H., & Kirchhoff, K. (2013) · North American Chapter of the Association for Computational Linguistics · Cited by 685 · see citing works
A deep learning-based radiomics model for prediction of survival in glioblastoma multiforme
Lao, J., Chen, Y., Li, Z.-C., Li, Q., Zhang, J., Liu, J., & Zhai, G. (2017) · Scientific Reports · doi:10.1038/s41598-017-10649-8 · Cited by 630 · see citing works
Chan, A. W. H., Zhong, J., Berhane, S., Toyoda, H., Cucchetti, A., Shi, K., Tada, T., Chong, C. C. N., Xiang, B., Li, L., et al. (2018) · Journal of Hepatology · doi:10.1016/j.jhep.2018.08.027 · Cited by 548 · see citing works
Zhang, B., Tian, J., Dong, D., Gu, D., Dong, Y., Zhang, L., Lian, Z., Liu, J., Luo, X., Pei, S., et al. (2017) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-16-2910 · Cited by 495 · see citing works
The American Cancer Society’s Facts & Figures: 2020 Edition
Viale, P. H. (2020) · Journal of the Advanced Practitioner in Oncology · doi:10.6004/jadpro.2020.11.2.1 · Cited by 490 · see citing works
Radiomics and radiogenomics in lung cancer: A review for the clinician
Thawani, R., McLane, M. J., Beig, N. G., Ghose, S., Prasanna, P., Velcheti, V., & Madabhushi, A. (2017) · Lung Cancer · doi:10.1016/j.lungcan.2017.10.015 · Cited by 451 · see citing works
Zhou, M., Scott, J. G., Chaudhury, B., Hall, L., Goldgof, D. B., Yeom, K. W., Iv, M., Ou, Y., Kalpathy–Cramer, J., Napel, S. A., et al. (2017) · American Journal of Neuroradiology · doi:10.3174/ajnr.a5391 · Cited by 415 · see citing works
Radiomics: from qualitative to quantitative imaging
Rogers, W., Seetha, S. T., Refaee, T., Lieverse, R. I. Y., Granzier, R. W. Y., Ibrahim, A., Keek, S. A., Sanduleanu, S., Primakov, S. P., Beuque, M. P. L., et al. (2020) · British Journal of Radiology · doi:10.1259/bjr.20190948 · Cited by 378 · see citing works
OPTN/SRTR 2017 Annual Data Report: Liver
Kim, W. R., Lake, J. R., Smith, J. M., Schladt, D. P., Skeans, M. A., Noreen, S. M., Robinson, A. M., Miller, E., Snyder, J. J., Israni, A. K., et al. (2019) · American Journal of Transplantation · doi:10.1111/ajt.15276 · Cited by 303 · see citing works
Fish mitigate trophic depletion in marine cave ecosystems
Bussotti, S., FRANCO, A. D., Bianchi, C. N., Chevaldonné, P., Egea, L., Fanelli, E., Lejeusne, C., Musco, L., Navarro‐Barranco, C., Pey, A., et al. (2018) · Scientific Reports · doi:10.1038/s41598-018-27491-1 · Cited by 278 · see citing works
Rapid review: radiomics and breast cancer
Valdora, F., Houssami, N., Rossi, F., Calabrese, M., & Tagliafico, A. S. (2018) · Breast Cancer Research and Treatment · doi:10.1007/s10549-018-4675-4 · Cited by 259 · see citing works
Chlebus, G., Schenk, A., Moltz, J. H., van Ginneken, B., Hahn, H. K., & Meine, H. (2018) · Scientific Reports · doi:10.1038/s41598-018-33860-7 · Cited by 254 · see citing works
Automatic segmentation of liver tumors from multiphase contrast-enhanced CT images based on FCNs
Sun, C., Guo, S., Zhang, H., Li, J., Chen, M., Shu-zhi, M., Jin, L., Liu, X., Li, X., & Qian, X. (2017) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2017.03.008 · Cited by 237 · see citing works
Choi, K. J., Jang, J. K., Lee, S. S., Sung, Y. S., Shim, W. H., Kim, H. S., Yun, J., Choi, J., Lee, Y., Kang, B.-K., et al. (2018) · Radiology · doi:10.1148/radiol.2018180763 · Cited by 219 · see citing works
Responsible Radiomics Research for Faster Clinical Translation
Vallières, M., Zwanenburg, A., Badic, B., Rest, C. C. L., Visvikis, D., & Hatt, M. (2017) · Journal of Nuclear Medicine · doi:10.2967/jnumed.117.200501 · Cited by 196 · see citing works
Miao, R., Luo, H., Zhou, H., Li, G., Bu, D., Yang, X., Zhao, X., Zhang, H., Liu, S., Zhong, Y., et al. (2014) · Journal of Hepatology · doi:10.1016/j.jhep.2014.05.025 · Cited by 161 · see citing works
Automatic Segmentation of High-Throughput RNAi Fluorescent Cellular Images
Yan, P., Zhou, X., Shah, M. A., & Wong, S. T. C. (2008) · IEEE Transactions on Information Technology in Biomedicine · doi:10.1109/titb.2007.898006 · Cited by 160 · see citing works
Self-citationFast CapsNet for Lung Cancer Screening
Mobiny, A., & Nguyen, H. V. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-00934-2_82 · Cited by 156 · see citing works
Stochastic Variational Deep Kernel LearningPreprint
Wilson, A. G., Hu, Z., Salakhutdinov, R., & Xing, E. P. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1611.00336 · Cited by 152 · see citing works
Multiparametric ultrasomics of significant liver fibrosis: A machine learning-based analysis
Li, W., Huang, Y., Zhuang, B., Liu, G., Hu, H.-T., Li, X., Liang, J., Wang, Z., Huang, X.-W., Zhang, C.-Q., et al. (2018) · European Radiology · doi:10.1007/s00330-018-5680-z · Cited by 119 · see citing works
Multimodal Radiomic Features for the Predicting Gleason Score of Prostate Cancer
Chaddad, A., Kucharczyk, M. J., & Niazi, T. M. (2018) · Cancers · doi:10.3390/cancers10080249 · Cited by 110 · see citing works
Exploration of PET and MRI radiomic features for decoding breast cancer phenotypes and prognosis
Huang, S.-Y., Franc, B. L., Harnish, R. J., Liu, G., Mitra, D., Copeland, T. P., Arasu, V. A., Kornak, J., Jones, E. F., Behr, S. C., et al. (2018) · npj Breast Cancer · doi:10.1038/s41523-018-0078-2 · Cited by 108 · see citing works
Chen, Y., Luo, Y., Huang, W., Hu, D., Zheng, R., Cong, S., Meng, F.-K., Yang, H., Lin, H.-J., Sun, Y., et al. (2017) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2017.07.012 · Cited by 105 · see citing works
Radiomics and radiogenomics for precision radiotherapy
Wu, J., Tha, K. K., Xing, L., & Li, R. (2018) · Journal of Radiation Research · doi:10.1093/jrr/rrx102 · Cited by 95 · see citing works
Yeruva, S. L. H., Zhao, F., Miller, K. D., Tevaarwerk, A. J., Wagner, L. I., Gray, R. J., Sparano, J. A., & Connolly, R. M. (2018) · npj Breast Cancer · doi:10.1038/s41523-017-0053-3 · Cited by 93 · see citing works
Patel, T. A., Puppala, M., Ogunti, R., Ensor, J., He, T., Shewale, J. B., Ankerst, D. P., Kaklamani, V., Rodriguez, A. A., Wong, S. T. C., et al. (2016) · Cancer · doi:10.1002/cncr.30245 · Cited by 79 · see citing works
Self-citationRadiomics and radiogenomics of primary liver cancers
Jeong, W. K., Jamshidi, N., Felker, E. R., Raman, S. S., & Lu, D. (2018) · Clinical and Molecular Hepatology · doi:10.3350/cmh.2018.1007 · Cited by 72 · see citing works
Machine Learning Prediction of Liver Stiffness Using Clinical and T2-Weighted MRI Radiomic Data
He, L., Li, H., Dudley, J. A., Maloney, T., Brady, S. L., Somasundaram, E., Trout, A. T., & Dillman, J. R. (2019) · American Journal of Roentgenology · doi:10.2214/ajr.19.21082 · Cited by 58 · see citing works
Stocker, M. R., Zhao, L.-J., Nesbitt, S. J., Wu, X.-C., & Chun, L. (2017) · Scientific Reports · doi:10.1038/srep46885 · Cited by 52 · see citing works
A Deep Learning–Based Decision Support Tool for Precision Risk Assessment of Breast Cancer
He, T., Puppala, M., Ezeana, C. F., Huang, Y.-S., Chou, P.-H., Yu, X., Chen, S., Wang, L., Yin, Z., Danforth, R. L., et al. (2019) · JCO Clinical Cancer Informatics · doi:10.1200/cci.18.00121 · Cited by 51 · see citing works
Self-citationMETEOR: An Enterprise Health Informatics Environment to Support Evidence-Based Medicine
Puppala, M., He, T., Chen, S., Ogunti, R., Yu, X., Fu-hai, L., Jackson, R. E., & Wong, S. T. C. (2015) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2015.2450181 · Cited by 49 · see citing works
Self-citationHigh-quality spectral-spatial reconstruction using saliency detection and deep feature enhancement
Xie, W., Shi, Y., Li, Y., Jia, X., & Lei, J. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2018.11.004 · Cited by 47 · see citing works
Ladju, R. B., Pascut, D., Massi, M. N., Tiribelli, C., & Sukowati, C. H. C. (2017) · Oncotarget · doi:10.18632/oncotarget.23359 · Cited by 44 · see citing works
Victor, D. W., Monsour, H., Boktour, M. R., Lunsford, K. E., Balogh, J., Graviss, E. A., Nguyen, D. T., McFadden, R., Divatia, M. K., Heyne, K., et al. (2019) · Transplantation · doi:10.1097/tp.0000000000002835 · Cited by 36 · see citing works
Self-citationDeep Learners and Deep Learner Descriptors for Medical Applications
(2020) · Intelligent systems reference library · doi:10.1007/978-3-030-42750-4 · Cited by 24 · see citing works
Deep learning analytics for diagnostic support of breast cancer disease management
He, T., Puppala, M., Ogunti, R., Mancuso, J. J., Yu, X., Chen, S., Chang, J. C., Patel, T. A., & Wong, S. T. C. (2017) · IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI ...) · doi:10.1109/bhi.2017.7897281 · Cited by 17 · see citing works
Self-citationTransplantation for Hepatocellular Carcinoma: Is There a Tumor Size Limit?
Daoud, A., Teeter, L. D., Ghobrial, R. M., Graviss, E. A., Mogawer, S., Sholkamy, A. A., Elshazli, M., & Gaber, A. O. (2018) · Transplantation Proceedings · doi:10.1016/j.transproceed.2018.04.038 · Cited by 8 · see citing works
Self-citationExamining the Benefits of Capsule Neural NetworksPreprint
Punjabi, A., Schmid, J. M., & Katsaggelos, A. K. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2001.10964 · Cited by 7 · see citing works
Yu, W., Cao, L., Dey, N., Ashour, A. S., & Shi, F. (2017) · Frontiers in artificial intelligence and applications · doi:10.3233/978-1-61499-785-6-509 · Cited by 4 · see citing works
Azer, S. A. (2020) · Intelligent systems reference library · doi:10.1007/978-3-030-42750-4_7 · Cited by 3 · see citing works
Flagged first, then by citations
Research works printed but not linked: 2
- [1]American Cancer Society. Facts & Figures 2020. American Cancer Society. Atlanta, Ga. 2020. PMC7848816 33532112
- [47]Mazzaferro Vincenzo, Llovet Josep M., Miceli Rosalba, Bhoori Sherrie, Schiavo Marcello, Mariani Luigi, Camerini Tiziana " Predicting survival after liver transplantation in patients with hepatocellular carcinoma beyond the Milan criteria: a retrospective, exploratory analysis." The lancet oncology 10, no. 1 (2009): 35– 43. 19058754
Cited by
Sorted by citations
Multimodal deep learning for biomedical data fusion: a review
Stahlschmidt, S. R., Ulfenborg, B., & Synnergren, J. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbab569 · Cited by 766 · see citing works
“Also, latent representations of multiple imaging modalities and clinical data can be fused for risk assessment of liver transplantation for hepatocellular cancer [71].”
BackgroundArtificial intelligence, machine learning, and deep learning in liver transplantation
Bhat, M., Rabindranath, M., Chara, B. S., & Simonetto, D. A. (2023) · Journal of Hepatology · doi:10.1016/j.jhep.2023.01.006 · Cited by 235 · see citing works
Nam, D., Chapiro, J., Paradis, V., Seraphin, T. P., & Kather, J. N. (2022) · JHEP Reports · doi:10.1016/j.jhepr.2022.100443 · Cited by 232 · see citing works
Xia, T., Zhao, B., Li, B., Lei, Y., Song, Y. S., Wang, Y., Tang, T., & Ju, S. (2023) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.28982 · Cited by 111 · see citing works
Guo, Z., Shen, Y., Wan, S., Shang, W.-L., & Yu, K. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2021.3139541 · Cited by 102 · see citing works
Sharma, P., Nayak, D. R., Balabantaray, B. K., Tanveer, M., & Nayak, R. (2023) · Neural Networks · doi:10.1016/j.neunet.2023.11.006 · Cited by 99 · see citing works
Artificial Intelligence in Surgery: A Systematic Review of Use and Validation
Kenig, N., Echeverría, J. M., & Vives, A. M. (2024) · Journal of Clinical Medicine · doi:10.3390/jcm13237108 · Cited by 103 · see citing works
Artificial Intelligence with Great Potential in Medical Informatics: A Brief Review
Lin, H. (2024) · Medinformatics · doi:10.47852/bonviewmedin42022204 · Cited by 75 · see citing works
Applications of natural language processing in radiology: A systematic review
Linna, N. B., & Kahn, C. E. (2022) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2022.104779 · Cited by 39 · see citing works
Application of machine learning in liver transplantation: a review
Tran, J., Sharma, D., Gotlieb, N., Xu, W., & Bhat, M. (2022) · Hepatology International · doi:10.1007/s12072-021-10291-7 · Cited by 33 · see citing works
Use of machine learning models for the prognostication of liver transplantation: A systematic review
Chongo, G., & Soldera, J. (2024) · World Journal of Transplantation · doi:10.5500/wjt.v14.i1.88891 · Cited by 32 · see citing works
Deep learning based radiomics for gastrointestinal cancer diagnosis and treatment: A minireview
Wong, P. K., Chan, I. N., Yan, H.-M., Gao, S., Wong, C. H., Yan, T., Yao, L., Hu, Y., Wang, Z., & Yu, H. H. (2022) · World Journal of Gastroenterology · doi:10.3748/wjg.v28.i45.6363 · Cited by 29 · see citing works
Iseke, S., Zeevi, T., Kucukkaya, A. S., Raju, R., Gross, M., Haider, S. P., Petukhova-Greenstein, A., Kuhn, T. N., Lin, M., Nowak, M., et al. (2022) · American Journal of Roentgenology · doi:10.2214/ajr.22.28077 · Cited by 26 · see citing works
Artificial Intelligence in Liver Diseases: Recent Advances
Lu, F., Meng, Y., Song, X., Li, X., Liu, Z., Gu, C., Zheng, X., Yi, J., Cai, W., Pinyopornpanish, K., et al. (2024) · Advances in Therapy · doi:10.1007/s12325-024-02781-5 · Cited by 25 · see citing works
Wei, J., Jiang, H., Zhou, Y., Tian, J., Furtado, F., & Catalano, O. A. (2023) · Digestive and Liver Disease · doi:10.1016/j.dld.2022.12.015 · Cited by 25 · see citing works
A Survey on Deep Learning for Precision Oncology
Wang, C.-W., Khalil, M.-A., & Firdi, N. (2022) · Diagnostics · doi:10.3390/diagnostics12061489 · Cited by 23 · see citing works
Bazoge, A., Morin, E., Daille, B., & Gourraud, P. (2023) · JMIR Medical Informatics · doi:10.2196/42477 · Cited by 29 · see citing works
Artificial intelligence in liver imaging: methods and applications
Zhang, P., Gao, C., Huang, Y., Chen, X., Pan, Z., Wang, L., Dong, D., Li, S., & Qi, X. (2024) · Hepatology International · doi:10.1007/s12072-023-10630-w · Cited by 23 · see citing works
Artificial intelligence techniques in liver cancer
Wang, L., Fatemi, M., & Alizad, A. (2024) · Frontiers in Oncology · doi:10.3389/fonc.2024.1415859 · Cited by 22 · see citing works
Nag, P. K., Bhagat, A., & Priya, R. (2025) · IEEE Access · doi:10.1109/access.2025.3562131 · Cited by 24 · see citing works
A deep-learning-based model for assessment of autoimmune hepatitis from histology: AI(H)
Ercan, C., Kordy, K., Knuuttila, A., Zhou, X., Kumar, D., Koponen, V., Mesenbrink, P. G., Eppenberger‐Castori, S., Amini, P., Pedrosa, M. C., et al. (2024) · Archiv für Pathologische Anatomie und Physiologie und für Klinische Medicin · doi:10.1007/s00428-024-03841-5 · Cited by 19 · see citing works
Nie, P., Zhang, J., Miao, W., Duan, S., Wang, T., Zhang, J., Gu, J., Wang, N., Zhang, R., Wang, X., et al. (2023) · European Radiology · doi:10.1007/s00330-023-09591-3 · Cited by 15 · see citing works
Deep Segmentation Feature-Based Radiomics Improves Recurrence Prediction of Hepatocellular Carcinoma
Wang, J., Wu, D., Sun, M., Peng, Z., Lin, Y. Y., Lin, H., Chen, J., Long, T., Li, Z., Xie, C., et al. (2022) · BME Frontiers · doi:10.34133/2022/9793716 · Cited by 13 · see citing works
Multi-Tier Ensemble Learning Model With Neighborhood Component Analysis to Predict Health Diseases
Dhar, J., & Ayele, N. A. (2021) · IEEE Access · doi:10.1109/access.2021.3117963 · Cited by 11 · see citing works
Liu, Z., Xu, J., Que, S., Geng, L., Zhou, L., Mardinoğlu, A., & Zheng, S. (2022) · Journal of Clinical and Translational Hepatology · doi:10.14218/jcth.2021.00219 · Cited by 10 · see citing works
Clinical Deployment of Machine Learning Tools in Transplant Medicine: What Does the Future Hold?
Rabindranath, M., Naghibzadeh, M., Zhao, X., Holdsworth, S., Brudno, M., Sidhu, A., & Bhat, M. (2023) · Transplantation · doi:10.1097/tp.0000000000004876 · Cited by 9 · see citing works
Amador, K., Gutierrez, A., Winder, A. J., Fiehler, J., Wilms, M., & Forkert, N. D. (2023) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2023.104567 · Cited by 9 · see citing works
Machine Learning Based Diagnostic Paradigm in Viral and Non-Viral Hepatocellular Carcinoma
Asif, A., Ahmed, F., Zeeshan, Khan, J. A., Allogmani, E., El-Rashidy, N. M., Manzoor, S., & Anwar, M. S. (2024) · IEEE Access · doi:10.1109/access.2024.3369491 · Cited by 8 · see citing works
Kaur, J., & Kaur, P. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.106442 · Cited by 8 · see citing works
Lu, M., Wu, J. C., Lu, H. H.-S., Eslam, M., & Yu, M. (2025) · Gut and Liver · doi:10.5009/gnl250268 · Cited by 8 · see citing works
AI Innovations in Liver Transplantation: From Big Data to Better Outcomes
Avramidou, E., Todorov, D., Katsanos, G., Antoniadis, N., Kofinas, A., Vasileiadou, S., Karakasi, K.-E., & Tsoulfas, G. (2025) · Livers · doi:10.3390/livers5010014 · Cited by 8 · see citing works
Sabitha, P., & Meeragandhi, G. (2022) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2022.103833 · Cited by 8 · see citing works
Patel, K., Connor, A. A., Kodali, S., Mobley, C. M., Victor, D., Hobeika, M. J., Dib, Y. E., Saharia, A., Cheah, Y. L., Simon, C. J., et al. (2025) · Artificial Intelligence Surgery · doi:10.20517/ais.2024.103 · Cited by 7 · see citing works
Self-citationTurning the Tide—Artificial Intelligence in the Evolving Landscape of Liver Cancer
Grapa, C. M., Mocan, T., Mocan, L. P., Motofelea, A., Stănciulescu, R., Crăciun, R., Vârciu, A., Sparchez, Z. A., & Mocan, T. (2025) · Cancers · doi:10.3390/cancers17183003 · Cited by 7 · see citing works
Moroney, J., Trivella, J. P., George, B., & White, S. B. (2023) · Cancers · doi:10.3390/cancers15102791 · Cited by 6 · see citing works
Wu, Z., Liu, D., Ouyang, S., Hu, J., Ding, J., Qiu, G., Gao, J., Luo, J., & Ren, K. (2025) · Insights into Imaging · doi:10.1186/s13244-025-02082-7 · Cited by 5 · see citing works
Che, F., Zhu, J., Li, Q., Jiang, H., Wei, Y., & Song, B. (2025) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.70048 · Cited by 5 · see citing works
Segmentation model of soft tissue sarcoma based on self-supervised learning
Zheng, M., Guo, C., Zhu, Y., Gang, X., Fu, C., & Wang, S. (2024) · Frontiers in Oncology · doi:10.3389/fonc.2024.1247396 · Cited by 3 · see citing works
Current state of artificial intelligence in liver transplantation
Montgomery, A., & Rana, A. A. (2025) · Transplantation Reports · doi:10.1016/j.tpr.2025.100173 · Cited by 3 · see citing works
Current status and prospects of artificial intelligence in liver cancer management
Wang, M., Yu, F., & Zhang, Y. (2025) · Intelligent Oncology · doi:10.1016/j.intonc.2025.06.003 · Cited by 3 · see citing works
Akbulut, S., & Çolak, C. (2026) · World Journal of Gastrointestinal Oncology · doi:10.4251/wjgo.v18.i2.113870 · Cited by 2 · see citing works
Artificial intelligence in the prediction of progression and outcomes in viral hepatitis
Yang, H. (2023) · Elsevier eBooks · doi:10.1016/b978-0-323-99136-0.00013-1 · Cited by 2 · see citing works
Xu, Y., Zhao, J., Chen, Q., Wang, F., Lin, L., Hu, H., Tong, R., Li, J., & Chen, Y. (2023) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc40787.2023.10340893 · Cited by 2 · see citing works
From Pixels to Predictions: Exploring the Role of Artificial Intelligence in Radiology
Aiyappa, M. J. A., & Shetty, B. S. K. (2024) · Biological and medical physics, biomedical engineering · doi:10.1007/978-981-97-5345-1_2 · Cited by 1 · see citing works
Akbulut, S., Çolak, C., & Güldoğan, E. (2026) · Bioengineering · doi:10.3390/bioengineering13080951 · Cited by 1 · see citing works
Tang, N., Zhu, J. F., Zeng, Y., Zhang, X., & Zhou, J. (2022) · Contrast Media & Molecular Imaging · doi:10.1155/2022/5292200
Artificial intelligence in liver transplantation
Rabindranath, M., Azhie, A., & Bhat, M. (2023) · Elsevier eBooks · doi:10.1016/b978-0-323-99136-0.00017-9
Deep Learning in the Diagnosis and Treatment of Liver Cancer: Review and Pro-spects
李, 冰. (2023) · Advances in Clinical Medicine · doi:10.12677/acm.2023.1391973
Lightweight Deep Learning Model to Monitor the Diabetes Patient for Continuous Risk Assessment
Seth, A., Vasudevan, K., Nirmala, K., Jamberi, K., Dalai, C., & Gaikwad, A. S. (2023) · International Conference on Recent Trends in Data Science and its Applications · doi:10.13052/rp-9788770040723.061
Multimodal Deep Learning Analysis for Biomedical Data Fusion
Sinha, D., Rao, B. J., Basha, D. K., Kumar, P. P., Shilpa, N., & Sharma, S. (2024) · doi:10.1002/9781394197705.ch4
Empowering Clinical Decision Making
Rajeev, D., Remya, S., & Nayyar, A. (2024) · doi:10.1002/9781394234196.ch16
Planning and Preparing for the Operation: The Role of Artificial Intelligence in Modern Surgery
Foianini, J. E., & Beattie, G. (2024) · Springer eBooks · doi:10.1007/978-3-031-67391-7_7
Streamlined Deep Learning Approach for Real-Time Diabetes Risk AssessmentPreprint
Seth, A., Vasudevan, K., Nirmala, K., Jamberi, K., Dalai, C., & Gaikwad, A. S. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5110922
Liava, C., & Shah, V. H. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-15728-8.00002-1
Artificial intelligence and radiological diagnostics in hepatology
Kolotilov, M. M., Solodushchenko, V. V., Tarasyuk, B. A., & Березенко, В. С. (2025) · The Ukrainian Journal of Clinical Surgery · doi:10.26779/2786-832x.2025.3.62
Hierarchical attention fusion of EUS-doppler features for GISTs risk assessment
Wei, Q., Gao, Y., Liang, S., Chen, K., Wang, Y., & Guo, Y. (2025) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2025.102584
Artificial Neural Networks and Deep Learning in Solid Organ Transplantation
Smith, B. H., & Đenić, A. (2025) · Transplantation · doi:10.1097/tp.0000000000005500
Raju, J., & Ranjana, P. (2025) · Biomedical Engineering Applications Basis and Communications · doi:10.4015/s1016237225500656
Deep Learning as a Diagnostic Discovery Engine in Solid Organ Transplant Pathology
Wu, J., Jiang, C., Fang, Z., Shang, G., Yang, C., & Zhu, D. (2025) · Organ medicine. · doi:10.1002/orm2.70024
Pan, L., Li, X., Chen, Z., Nie, C., Chu, L., & Peng, S. (2025) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm66473.2025.11356372
Joshi, R. C., Srivastava, P., Mishra, R., Číka, P., Bürget, R., & Dutta, M. K. (2026) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2026.109225
Esmailzadeh, A., Norouzkhani, N., Heidari, A. E., & Habibi, M. R. M. (2026) · Canadian Liver Journal · doi:10.3138/canlivj-2025-0041
Coimbra, F. J. F., de Godoy, A. L., Fernandes, P. H. D. S., Neto, H. F. E. C., Pinheiro, R. N., Boin, I. D. F. S. F., Andraus, W., Tustumi, F., AMARAL, P. C. G., Brandão‐Mello, C. E., et al. (2026) · ABCD Arquivos Brasileiros de Cirurgia Digestiva (São Paulo) · doi:10.1590/0102-672020260000035e1964
- Authors
- He, T., Fong, J. N., Moore, L. W., Ezeana, C. F., Victor, D., Divatia, M., Vasquez, M., Ghobrial, R. M., & Wong, S. T. C.
- Affiliations
- Houston Methodist (US)
- Corresponding author
- Ghobrial, R. Mark
- Wong, Stephen T.C.
- ORCID
- Venue
- Computerized Medical Imaging and Graphics
- Publisher
- Elsevier BV
- Published
- 2021-04
- Volume
- 89
- Pages
- 101894
- ISSN
- 0730-4862 (ISSN-L)
- 0895-6111
- PMC
- PMC8054468
- OpenAlex
- W3135798895
- Cited by
- 63
- References
- 57
- Citations by year
- 2026: 5 · 2025: 17 · 2024: 14 · 2023: 14 · 2022: 10 · 2021: 3
- FWCI
- 5.42
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- Locations
- Computerized Medical Imaging and Graphics (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- John S Dunn Foundation
- National Institutes of Health
- Ting Tsung and Wei Fong Chao Family Foundation
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Health Sciences › Medicine › Hepatology › Hepatocellular Carcinoma Treatment and Prognosis
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Keywords
- liver transplantation, hepatocellular carcinoma, recurrence risk prediction, radiomics, histopathology, magnetic resonance imaging, capsule networks, natural language processing, imageomics, tumor size, organ allocation, post-transplant relapse, radial basis function neural network, multilayer perceptron, clinical risk assessment
- MeSH terms
- Deep Learning, Artificial Intelligence, Carcinoma, Hepatocellular, Humans, Liver Neoplasms, Neoplasm Recurrence, Local, Prognosis, Retrospective Studies, Liver Transplantation, Risk Assessment
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
