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CT术前预测肝细胞癌微血管侵犯的研究进展
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Abstract:
肝细胞癌(HCC)是世界上最常见的恶性肿瘤之一，预后差、病死率高。微血管侵犯(MVI)是HCC患者无病生存和总体生存的独立因素。因此，术前有效预测MVI状态将可以帮助临床医生制定治疗方案，尤其是手术方式和术后联合治疗的选择，由此来改善患者的预后。本文将对目前基于CT检查术前预测肝细胞癌患者MVI的研究进展进行综述。
Hepatocellular carcinoma (HCC) is one of the most common malignant tumors in the world, with poor prognosis and high mortality. Microvascular invasion (MVI) is an independent factor for dis-ease free survival and overall survival in HCC patients. Therefore, effective preoperative prediction of MVI status will help clinicians formulate treatment plans, especially surgical methods and post-operative combination therapy options, thereby improving the prognosis of patients. This review summarizes the current research progress in preoperative diagnosis of MVI in HCC patients based on computed tomography (CT) examination.
| [1] | Sung, H., Ferlay, J., Siegel, R.L., Laversanne, M., Soerjomataram, I., Jemal, A. and Bray, F. (2021) Global Cancer Sta-tistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA: A Cancer Journal for Clinicians, 71, 209-249. https://doi.org/10.3322/caac.21660 |
| [2] | 中华人民共和国国家卫生健康委员会医政医管局. 原发性肝癌诊疗规范(2019年版) [J]. 临床肝胆病杂志, 2020, 36(2): 277-292. |
| [3] | Heimbach, J.K., Kulik, L.M., Finn, R.S., et al. (2018) AASLD Guidelines for the Treatment of Hepato-cellular Carcinoma. Hepatology (Baltimore, Md.), 67, 358-380. https://doi.org/10.1002/hep.29086 |
| [4] | Lim, K.C., Chow, P.K., Allen, J.C., Siddiqui, F.J., Chan, E.S. and Tan, S.B. (2012) Systematic Review of Outcomes of Liver Re-section for Early Hepatocellular Carcinoma within the Milan Criteria. The British Journal of Surgery, 99, 1622-1629. https://doi.org/10.1002/bjs.8915 |
| [5] | 吴孟超, 汤钊猷, 刘彤华, 等. 原发性肝癌规范化病理诊断指南(2015年版) [J]. 临床肝胆病杂志, 2015, 31(6): 833-839. |
| [6] | Feng, L.H., Dong, H., Lau, W.Y., et al. (2017) Novel Micro-vascular Invasion-Based Prognostic Nomograms to Predict Survival Outcomes in Patients after R0 Resection for Hepa-tocellular Carcinoma. Journal of Cancer Research and Clinical Oncology, 143, 293-303. https://doi.org/10.1007/s00432-016-2286-1 |
| [7] | Roayaie, S., Blume, I.N., Thung, S.N., et al. (2009) A System of Classifying Microvascular Invasion to Predict Outcome after Resection in Patients with Hepatocellular Carcinoma. Gas-troenterology, 137, 850-855.
https://doi.org/10.1053/j.gastro.2009.06.003 |
| [8] | Zhao, H., Chen, C., Fu, X., Yan, X., Jia, W., Mao, L., Jin, H. and Qiu, Y. (2017) Prognostic Value of a Novel Risk Classification of Microvascular Invasion in Patients with Hepatocellu-lar Carcinoma after Resection. Oncotarget, 8, 5474-5486.
https://doi.org/10.18632/oncotarget.12547 |
| [9] | Lei, Z., Li, J., Wu, D., Xia, Y., Wang, Q., Si, A., Wang, K., et al. (2016) Nomogram for Preoperative Estimation of Microvascular Invasion Risk in Hepatitis B Virus-Related Hepatocel-lular Carcinoma within the Milan Criteria. JAMA Surgery, 151, 356-363. https://doi.org/10.1001/jamasurg.2015.4257 |
| [10] | Rodríguez-Perálvarez, M., Luong, T.V., Andreana, L., Meyer, T., Dhillon, A.P. and Burroughs, A.K. (2013) A Systematic Review of Microvascular Invasion in Hepatocellular Carcinoma: Diagnostic and Prognostic Variability. Annals of Surgical Oncology, 20, 325-339. https://doi.org/10.1245/s10434-012-2513-1 |
| [11] | Xu, X.F., Xing, H., Han, J., Li, Z.L., Lau, W.Y., Zhou, Y.H., Gu, W.M., et al. (2019) Risk Factors, Patterns, and Outcomes of Late Recurrence after Liver Resection for Hepatocellular Carcinoma: A Multicenter Study from China. JAMA Surgery, 154, 209-217. https://doi.org/10.1001/jamasurg.2018.4334 |
| [12] | Han, J., Li, Z.L., Xing, H., Wu, H., Zhu, P., Lau, W.Y., et al. (2019) The Impact of Resection Margin and Microvascular Invasion on Long-Term Prognosis after Curative Resection of Hepatocellular Carcinoma: A Multi-Institutional Study. HPB: The Official Journal of the International Hepato Pancreato Biliary Association, 21, 962-971.
https://doi.org/10.1016/j.hpb.2018.11.005 |
| [13] | Wang, H., Du, P.C., Wu, M.C. and Cong, W.M. (2018) Postopera-tive Adjuvant Transarterial Chemoembolization for Multinodular Hepatocellular Carcinoma within the Barcelona Clinic Liver Cancer Early Stage and Microvascular Invasion. Hepatobiliary Surgery and Nutrition, 7, 418-428. https://doi.org/10.21037/hbsn.2018.09.05 |
| [14] | Wang, L., Chen, B., Li, Z., Yao, X., Liu, M., Rong, W., Wu, F., et al. (2019) Optimal Postoperative Adjuvant Treatment Strategy for HBV-Related Hepatocellular Carcinoma with Micro-vascular Invasion: A Propensity Score Analysis. OncoTargets and Therapy, 12, 1237-1247. https://doi.org/10.2147/OTT.S179247 |
| [15] | Song, L., Li, J.W. and Luo, Y. (2021) The Importance of a Nonsmooth Tumor Margin and Incomplete Tumor Capsule in Predicting HCC Microvascular Invasion on Preoperative Imaging Ex-amination: A Systematic Review and Meta-Analysis. Clinical Imaging, 76, 77-82. https://doi.org/10.1016/j.clinimag.2020.11.057 |
| [16] | Banerjee, S., Wang, D.S., Kim, H.J., Sirlin, C.B., Chan, M.G., Korn, R.L., Rutman, A.M., et al. (2015) A Computed Tomography Radiogenomic Biomarker Predicts Microvascular In-vasion and Clinical Outcomes in Hepatocellular Carcinoma. Hepatology (Baltimore, Md.), 62, 792-800. https://doi.org/10.1002/hep.27877 |
| [17] | Zhang, T., Pandey, G., Xu, L., Chen, W., Gu, L., Wu, Y. and Chen, X. (2020) The Value of TTPVI in Prediction of Microvascular Invasion in Hepatocellular Carcinoma. Cancer Management and Research, 12, 4097-4105.
https://doi.org/10.2147/CMAR.S245475 |
| [18] | Lambin, P., Rios-Velazquez, E., Leijenaar, R., Carvalho, S., van Stiphout, R.G., Granton, P., Zegers, C.M., et al. (2012) Radiomics: Extracting More Information from Medical Images Using Advanced Feature Analysis. European Journal of Cancer (Oxford, England: 1990), 48, 441-446. https://doi.org/10.1016/j.ejca.2011.11.036 |
| [19] | Gillies, R., Kinahan, P. and Hricak, H. (2016) Radiomics: Images Are More than Pictures, They Are Data. Radiology, 278, 563-577. https://doi.org/10.1148/radiol.2015151169 |
| [20] | Lambin, P., Leijenaar, R.T.H., Deist, T.M., Peerlings, J., de Jong, E.E.C., van Timmeren, J., Sanduleanu, S., et al. (2017) Radiomics: The Bridge between Medical Imaging and Personal-ized Medicine. Nature Reviews. Clinical Oncology, 14, 749-762. https://doi.org/10.1038/nrclinonc.2017.141 |
| [21] | Ji, G.W., Zhu, F.P., Xu, Q., Wang, K., Wu, M.Y., Tang, W.W., Li, X.C. and Wang, X.H. (2019) Machine-Learning Analy-sis of Contrast-Enhanced CT Radiomics Predicts Recurrence of Hepatocellular Carcinoma after Resection: A Mul-ti-Institutional Study. EBioMedicine, 50, 156-165. https://doi.org/10.1016/j.ebiom.2019.10.057 |
| [22] | Shan, Q.Y., Hu, H.T., Feng, S.T., Peng, Z.P., Chen, S.L., Zhou, Q., Li, X., et al. (2019) CT-Based Peritumoral Radiomics Signatures to Predict Early Recurrence in Hepatocellular Carcinoma after Curative Tumor Resection or Ablation. Cancer Imaging, 19, Article No. 11. https://doi.org/10.1186/s40644-019-0197-5 |
| [23] | Mokrane, F.Z., Lu, L., Vavasseur, A., Otal, P., Peron, J.M., Luk, L., et al. (2020) Radiomics Machine-Learning Signature for Diagnosis of Hepatocellular Carcinoma in Cirrhotic Patients with Indeterminate Liver Nodules. European Radiology, 30, 558-570. https://doi.org/10.1007/s00330-019-06347-w |
| [24] | Chong, H.H., Yang, L., Sheng, R.F., Yu, Y.L., Wu, D.J., Rao, S.X., Yang, C. and Zeng, M.S. (2021) Multi-Scale and Multi-Parametric Radiomics of Gadoxetate Disodium-Enhanced MRI Predicts Microvascular Invasion and Outcome in Patients with Solitary Hepatocellular Carcinoma ≤ 5 cm. European Radiology, 31, 4824-4838.
https://doi.org/10.1007/s00330-020-07601-2 |
| [25] | Hu, M.J., Yu, Y.X., Fan, Y.F. and Hu, C.H. (2021) CT-Based Radiomics Model to Distinguish Necrotic Hepatocellular Carcinoma from Pyogenic Liver Abscess. Clinical Radiology, 76, 161.e11-161.e17.
https://doi.org/10.1016/j.crad.2020.11.002 |
| [26] | Xu, X., Zhang, H.L., Liu, Q.P., Sun, S.W., Zhang, J., Zhu, F.P., Yang, G., et al. (2019) Radiomic Analysis of Contrast-Enhanced CT Predicts Microvascular Invasion and Outcome in Hepatocellular Carcinoma. Journal of Hepatology, 70, 1133-1144. https://doi.org/10.1016/j.jhep.2019.02.023 |
| [27] | Zhang, X., Ruan, S., Xiao, W., Shao, J., Tian, W., Liu, W., Zhang, Z., et al. (2020) Contrast-Enhanced CT Radiomics for Preoperative Evaluation of Microvascular Invasion in Hepatocel-lular Carcinoma: A Two-Center Study. Clinical and Translational Medicine, 10, e111. https://doi.org/10.1002/ctm2.111 |
| [28] | Greenspan, H., Ginneken, B.V. and Summers, R.M. (2016) Guest Editorial Deep Learning in Medical Imaging: Overview and Future Promise of an Exciting New Technique. IEEE Transactions on Medical Imaging, 35, 1153-1159.
https://doi.org/10.1109/TMI.2016.2553401 |
| [29] | Huang, S., Yang, J., Fong, S. and Zhao, Q. (2020) Artificial Intel-ligence in Cancer Diagnosis and Prognosis: Opportunities and Challenges. Cancer Letters, 471, 61-71. https://doi.org/10.1016/j.canlet.2019.12.007 |
| [30] | Zhu, Y., Wang, M., Yin, X., Zhang, J., Meijering, E. and Hu, J. (2022) Deep Learning in Diverse Intelligent Sensor Based Systems. Sensors (Basel, Switzerland), 23, Article No. 62. https://doi.org/10.3390/s23010062 |
| [31] | Ioannou, G.N., Tang, W., Beste, L.A., Tincopa, M.A., Su, G.L., Van, T., Tapper, E.B., et al. (2020) Assessment of a Deep Learning Model to Predict Hepatocellular Carcinoma in Patients with Hepatitis C Cirrhosis. JAMA Network Open, 3, e2015626. https://doi.org/10.1001/jamanetworkopen.2020.15626 |
| [32] | Wu, Y., White, G.M., Cornelius, T., Gowdar, I., Ansari, M.H., Supanich, M.P. and Deng, J. (2020) Deep Learning LI-RADS Grading System Based on Contrast Enhanced Mul-tiphase MRI for Differentiation between LR-3 and LR-4/LR-5 Liver Tumors. Annals of Translational Medicine, 8, 701. https://doi.org/10.21037/atm.2019.12.151 |
| [33] | Liu, Q.P., Xu, X., Zhu, F.P., Zhang, Y.D. and Liu, X.S. (2020) Prediction of Prognostic Risk Factors in Hepatocellular Carcinoma with Transarterial Chemoembolization Using Mul-ti-Modal Multi-Task Deep Learning. EClinicalMedicine, 23, Article ID: 100379. https://doi.org/10.1016/j.eclinm.2020.100379 |
| [34] | Wang, W., Chen, Q., Iwamoto, Y., Han, X., Zhang, Q., Hu, H., Lin, L. and Chen, Y.W. (2019) Deep Learning-Based Radiomics Models for Early Recurrence Prediction of Hepatocel-lular Carcinoma with Multi-Phase CT Images and Clinical Data. Annual International Conference of the IEEE Engineer-ing in Medicine and Biology Society, Berlin, 23-27 July 2019, 4881-4884. https://doi.org/10.1109/EMBC.2019.8856356 |
| [35] | Zhou, Q., Zhou, Z., Chen, C., Fan, G., Chen, G., Heng, H., Ji, J. and Dai, Y. (2019) Grading of Hepatocellular Carcinoma Using 3D SE-DenseNet in Dynamic Enhanced MR Images. Computers in Biology and Medicine, 107, 47-57.
https://doi.org/10.1016/j.compbiomed.2019.01.026 |
| [36] | Yasaka, K., Akai, H., Abe, O. and Kiryu, S. (2018) Deep Learning with Convolutional Neural Network for Differentiation of Liver Masses at Dynamic Contrast-Enhanced CT: A Preliminary Study. Radiology, 286, 887-896.
https://doi.org/10.1148/radiol.2017170706 |
| [37] | Jiang, Y.Q., Cao, S.E., Cao, S., Chen, J.N., Wang, G.Y., Shi, W.Q., Deng, Y.N., et al. (2021) Preoperative Identification of Microvascular Invasion in Hepatocellular Carcinoma by XGBoost and Deep Learning. Journal of Cancer Research and Clinical Oncology, 147, 821-833. https://doi.org/10.1007/s00432-020-03366-9 |
| [38] | Liu, S.C., Lai, J., Huang, J.Y., Cho, C.F., Lee, P.H., Lu, M.H., Yeh, C.C., Yu, J. and Lin, W.C. (2021) Predicting Microvascular Invasion in Hepatocellular Carcinoma: A Deep Learn-ing Model Validated across Hospitals. Cancer Imaging: The Official Publication of the International Cancer Imaging Society, 21, Article No. 56.
https://doi.org/10.1186/s40644-021-00425-3 |
| [39] | Vorontsov, E., Molchanov, P., Gazda, M., Beckham, C., et al. (2022) Towards Annotation-Efficient Segmentation via Image-to-Image Translation. Medical Image Analysis, 82, Article ID: 102624.
https://doi.org/10.1016/j.media.2022.102624 |
| [40] | Yang, Z. and Li, S. (2022) Dual-Path Network for Liver and Tumor Segmentation in CT Images Using Swin Transformer Encoding Approach. Current Medical Imaging. |
| [41] | Pettit, R.W., Marlatt, B.B., Corr, S.J., Havelka, J. and Rana, A. (2022) nnU-Net Deep Learning Method for Segmenting Pa-renchyma and Determining Liver Volume from Computed Tomography Images. Annals of Surgery Open, 3, e155. https://doi.org/10.1097/AS9.0000000000000155 |
| [42] | Guo, X., Schwartz, L.H. and Zhao, B. (2019) Automatic Liver Segmentation by Integrating Fully Convolutional Networks into Active Contour Models. Medical Physics, 46, 4455-4469. https://doi.org/10.1002/mp.13735 |
| [43] | Wang, L., Wu, M., Li, R., Xu, X., Zhu, C. and Feng, X. (2022) MVI-Mind: A Novel Deep-Learning Strategy Using Computed Tomography (CT)-Based Radiomics for End-to-End High Efficiency Prediction of Microvascular Invasion in Hepatocellular Carcinoma. Cancers (Basel), 14, Article No. 2956. https://doi.org/10.3390/cancers14122956 |
