切换至 "中华医学电子期刊资源库"

中华肝脏外科手术学电子杂志 ›› 2023, Vol. 12 ›› Issue (04) : 361 -366. doi: 10.3877/cma.j.issn.2095-3232.2023.04.001

所属专题: 述评与论坛

述评

计算机视觉技术在肝癌肝移植疗效提升中的研究进展
王晓东, 汪恺, 葛昭, 丁忠祥, 徐骁()   
  1. 310053 杭州,浙江中医药大学第四临床医学院;310006 浙江大学医学院附属杭州市第一人民医院肝胆胰外科;310006 杭州,浙江省肿瘤融合研究与智能医学重点实验室
    310006 浙江大学医学院附属杭州市第一人民医院肝胆胰外科;310006 杭州,浙江省肿瘤融合研究与智能医学重点实验室
    541001 广西壮族自治区桂林医学院附属医院核医学科
    310006 浙江大学医学院附属杭州市第一人民医院放射科
    310006 浙江大学医学院附属杭州市第一人民医院肝胆胰外科;310006 杭州,浙江省肿瘤融合研究与智能医学重点实验室;310003 杭州,浙江大学器官移植研究所;310024 杭州,西湖实验室
  • 收稿日期:2023-03-03 出版日期:2023-08-10
  • 通信作者: 徐骁
  • 基金资助:
    国家自然科学基金重点项目(81930016); 国家自然科学基金重大研究计划(92159202); 国家重点研发计划(2021YFA1100500)

Research progress of computer vision in improvement of therapeutic effect of liver transplantation for liver cancer

Xiaodong Wang, Kai Wang, Zhao Ge   

  • Received:2023-03-03 Published:2023-08-10
引用本文:

王晓东, 汪恺, 葛昭, 丁忠祥, 徐骁. 计算机视觉技术在肝癌肝移植疗效提升中的研究进展[J]. 中华肝脏外科手术学电子杂志, 2023, 12(04): 361-366.

Xiaodong Wang, Kai Wang, Zhao Ge. Research progress of computer vision in improvement of therapeutic effect of liver transplantation for liver cancer[J]. Chinese Journal of Hepatic Surgery(Electronic Edition), 2023, 12(04): 361-366.

计算机视觉(computer vision,CV)技术是人工智能(artificial intelligence,AI)的重要组成部分,其能利用计算机与相机模拟生物视觉,对图像进行精确分析[1]。现代医疗中,CV技术能借助各类AI算法从医学图像中提取出大量视觉特征,辅助疾病诊疗。自1963年Starzl等完成首例人体肝移植手术以来,历经数十年的发展,肝移植技术得到不断改进及完善,目前已成为各类终末期肝病的有效治疗手段[2]。肝移植作为一项涉及供受体双方多种变量因素的复杂手术,其在供受体筛选、移植预后预测等方面将会产生大量的医学图像用作评估,这与CV技术的优势较为契合。有效运用CV技术处理肝移植围手术期医学图像,将为肝癌肝移植疗效提升提供巨大助力。

[1]
Esteva A, Robicquet A, Ramsundar B, et al. A guide to deep learning in healthcare[J]. Nat Med, 2019, 25(1):24-29.
[2]
Meirelles Júnior RF, Salvalaggio P, Rezende MB, et al. Liver transplantation: history, outcomes and perspectives[J]. Einstein, 2015, 13(1):149-152.
[3]
Olveres J, González G, Torres F, et al. What is new in computer vision and artificial intelligence in medical image analysis applications[J]. Quant Imaging Med Surg, 2021, 11(8):3830-3853.
[4]
Deo RC. Machine Learning in medicine[J]. Circulation, 2015, 132(20):1920-1930.
[5]
Chan HP, Samala RK, Hadjiiski LM, et al. Deep learning in medical image analysis[J]. Adv Exp Med Biol, 2020(1213):3-21.
[6]
Handelman GS, Kok HK, Chandra RV, et al. eDoctor: machine learning and the future of medicine[J]. J Intern Med, 2018, 284(6): 603-619.
[7]
Shin HC, Roth HR, Gao M, et al. Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning[J]. IEEE Trans Med Imaging, 2016, 35(5):1285-1298.
[8]
Cao B, Zhang KC, Wei B, et al. Status quo and future prospects of artificial neural network from the perspective of gastroenterologists[J]. World J Gastroenterol, 2021, 27(21):2681-2709.
[9]
Kriegeskorte N, Golan T. Neural network models and deep learning[J]. Curr Biol, 2019, 29(7):R231-236.
[10]
Ananda A, Ngan KH, Karabağ C, et al. Classification and visualisation of normal and abnormal radiographs; a comparison between eleven convolutional neural network architectures[J]. Sensors, 2021, 21(16):5381.
[11]
Chu MJ, Dare AJ, Phillips AR, et al. Donor hepatic steatosis and outcome after liver transplantation: a systematic review[J].J Gastrointest Surg, 2015, 19(9):1713-1724.
[12]
杨梦凡, 王睿, 潘斌华, 等. 脂肪变性供肝用于肝癌肝移植的预后及影响因素多中心研究[J]. 中华消化外科杂志, 2022, 21(2): 237-248.
[13]
陈栋, 陈知水. 中国肝移植供肝获取技术规范(2019版)[J]. 临床肝胆病杂志, 2019, 35(12):2700-2702.
[14]
Ding S, Yang W, Sun X, et al. Computed tomography-based radiomic analysis for preoperatively predicting the macrovesicular steatosis grade in cadaveric donor liver transplantation[J]. Biomed Res Int, 2022:2491023.
[15]
Cesaretti M, Brustia R, Goumard C, et al. Use of artificial intelligence as an innovative method for liver graft macrosteatosis assessment[J]. Liver Transpl, 2020, 26(10):1224-1232.
[16]
Cesaretti M, Addeo P, Schiavo L, et al. Assessment of liver graft steatosis: where do we stand?[J]. Liver Transpl, 2019, 25(3):500-509.
[17]
Sun L, Marsh JN, Matlock MK, et al. Deep learning quantification of percent steatosis in donor liver biopsy frozen sections[J]. EBioMedicine, 2020(60):103029.
[18]
Salvi M, Molinaro L, Metovic J, et al. Fully automated quantitative assessment of hepatic steatosis in liver transplants[J]. Comput Biol Med, 2020(123):103836.
[19]
朱志军, 张海明, 叶少军, 等. 活体器官捐献供肝质量控制[J]. 武汉大学学报(医学版), 2021, 42(2):206-210.
[20]
Yin Y, Yakar D, Dierckx RAJO, et al. Liver fibrosis staging by deep learning: a visual-based explanation of diagnostic decisions of the model[J]. Eur Radiol, 2021, 31(12):9620-9627.
[21]
Yasaka K, Akai H, Kunimatsu A, et al. Liver fibrosis: deep convolutional neural network for staging by using Gadoxetic acid-enhanced hepatobiliary phase MR images[J]. Radiology, 2018, 287(1):146-155.
[22]
Lan Q, Li Y, Robertson J, et al. Modeling of pre-transplantation liver viability with spatial-temporal smooth variable selection[J]. Comput Methods Programs Biomed, 2021(208):106264.
[23]
Xi IL, Wu J, Guan J, et al. Deep learning for differentiation of benign and malignant solid liver lesions on ultrasonography[J]. Abdom Radiol, 2021, 46(2):534-543.
[24]
Yasaka K, Akai H, Abe O, et al. Deep learning with convolutional neural network for differentiation of liver masses at dynamic contrast-enhanced CT: a preliminary study[J]. Radiology, 2018, 286(3):887-896.
[25]
Gao R, Zhao S, Aishanjiang K, et al. Deep learning for differential diagnosis of malignant hepatic tumors based on multi-phase contrast-enhanced CT and clinical data[J]. J Hematol Oncol, 2021, 14(1):154.
[26]
Erstad DJ, Tanabe KK. Prognostic and therapeutic implications of microvascular invasion in hepatocellular carcinoma[J]. Ann Surg Oncol, 2019, 26(5):1474-1493.
[27]
Jiang YQ, Cao SE, Cao S, et al. Preoperative identification of microvascular invasion in hepatocellular carcinoma by XGBoost and deep learning[J]. J Cancer Res Clin Oncol, 2021, 147(3):821-833.
[28]
Zhou W, Jian W, Cen X, et al. Prediction of microvascular invasion of hepatocellular carcinoma based on contrast-enhanced MR and 3D convolutional neural networks[J]. Front Oncol, 2021(11):588010.
[29]
Mao B, Zhang L, Ning P, et al. Preoperative prediction for pathological grade of hepatocellular carcinoma via machine learning-based radiomics[J]. Eur Radiol, 2020, 30(12):6924-6932.
[30]
Wu M, Tan H, Gao F, et al. Predicting the grade of hepatocellular carcinoma based on non-contrast-enhanced MRI radiomics signature[J]. Eur Radiol, 2019, 29(6):2802-2811.
[31]
Yang F, Wan Y, Xu L, et al. MRI-radiomics prediction for cytokeratin 19-positive hepatocellular carcinoma: a multicenter study[J]. Front Oncol, 2021(11):672126.
[32]
Gotra A, Sivakumaran L, Chartrand G, et al. Liver segmentation: indications, techniques and future directions[J]. Insights Imaging, 2017, 8(4):377-392.
[33]
Hu P, Wu F, Peng J, et al. Automatic 3D liver segmentation based on deep learning and globally optimized surface evolution[J]. Phys Med Biol, 2016, 61(24):8676-8698.
[34]
Lu F, Wu F, Hu P, et al. Automatic 3D liver location and segmentation via convolutional neural network and graph cut[J]. Int J Comput Assist Radiol Surg, 2017, 12(2):171-182.
[35]
李云峰, 尹新民, 朱斯维, 等. 3D打印技术辅助腹腔镜解剖性肝Ⅷ段切除术的应用价值[J]. 中华消化外科杂志, 2021, 20(5):548-554.
[36]
Zein NN, Hanouneh IA, Bishop PD, et al. Three-dimensional print of a liver for preoperative planning in living donor liver transplantation[J]. Liver Transpl, 2013, 19(12):1304-1310.
[37]
Wang P, Que W, Zhang M, et al. Application of 3-dimensional printing in pediatric living donor liver transplantation: a single-center experience[J]. Liver Transpl, 2019, 25(6):831-840.
[38]
Soejima Y, Taguchi T, Sugimoto M, et al. Three-dimensional printing and biotexture modeling for preoperative simulation in living donor liver transplantation for small infants[J]. Liver Transpl, 2016, 22(11): 1610-1614.
[39]
Ivanics T, Salinas-Miranda E, Abreu P, et al. A pre-TACE radiomics model to predict HCC progression and recurrence in liver transplantation: a pilot study on a novel biomarker[J]. Transplantation, 2021, 105(11):2435-2444.
[40]
Ivanics T, Nelson W, Patel MS, et al. The toronto postliver transplantation hepatocellular carcinoma recurrence calculator: a machine learning approach[J]. Liver Transpl, 2022, 28(4):593-602.
[41]
Yamashita R, Long J, Saleem A, et al. Deep learning predicts postsurgical recurrence of hepatocellular carcinoma from digital histopathologic images[J]. Sci Rep, 2021, 11(1):2047.
[42]
Daoud A, Teeter L, Ghobrial RM, et al. Transplantation for hepatocellular carcinoma: is there a tumor size limit?[J]. Transplant Proc, 2018, 50(10):3577-3581.
[43]
He T, Fong JN, Moore LW, et al. An imageomics and multi-network based deep learning model for risk assessment of liver transplantation for hepatocellular cancer[J]. Comput Med Imaging Graph, 2021(89): 101894.
[44]
Krenzien F, Keshi E, Splith K, et al. Diagnostic biomarkers to diagnose acute allograft rejection after liver transplantation: systematic review and meta-analysis of diagnostic accuracy studies[J]. Front Immunol, 2019(10):758.
[45]
Yen LH, Sabatino JC. Imaging complications of liver transplantation: a multimodality pictorial review[J]. Abdom Radiol, 2021, 46(6): 2444-2457.
[46]
Kochhar G, Parungao JM, Hanouneh IA, et al. Biliary complications following liver transplantation[J]. World J Gastroenterol, 2013, 19(19):2841-2846.
[47]
Lin CC, Ou HY, Chuang YH, et al. Diffusion-weighted magnetic resonance imaging in liver graft rejection[J]. Transplant Proc, 2018, 50(9):2675-2678.
[1] 李淼, 朱连华, 韩鹏, 姜波, 费翔. 高帧频超声造影评价肝细胞癌血管形态与风险因素的研究[J]. 中华医学超声杂志(电子版), 2023, 20(09): 911-915.
[2] 张梅芳, 谭莹, 朱巧珍, 温昕, 袁鹰, 秦越, 郭洪波, 侯伶秀, 黄文兰, 彭桂艳, 李胜利. 早孕期胎儿头臀长正中矢状切面超声图像的人工智能质控研究[J]. 中华医学超声杂志(电子版), 2023, 20(09): 945-950.
[3] 唐玮, 何融泉, 黄素宁. 深度学习在乳腺癌影像诊疗和预后预测中的应用[J]. 中华乳腺病杂志(电子版), 2023, 17(06): 323-328.
[4] 邢晓伟, 刘雨辰, 赵冰, 王明刚. 基于术前腹部CT的卷积神经网络对腹壁切口疝术后复发预测价值[J]. 中华疝和腹壁外科杂志(电子版), 2023, 17(06): 677-681.
[5] 杜锡林, 谭凯, 贺小军, 白亮亮, 赵瑶瑶. 肝细胞癌转化治疗方式[J]. 中华肝脏外科手术学电子杂志, 2023, 12(06): 597-601.
[6] 魏小勇. 原发性肝癌转化治疗焦点问题探讨[J]. 中华肝脏外科手术学电子杂志, 2023, 12(06): 602-607.
[7] 张其坤, 商福超, 李琪, 栗光明, 王孟龙. 联合脾切除对肝癌合并门静脉高压症患者根治性切除术后的生存获益分析[J]. 中华肝脏外科手术学电子杂志, 2023, 12(06): 613-618.
[8] 严庆, 刘颖, 邓斐文, 陈焕伟. 微血管侵犯对肝癌肝移植患者生存预后的影响[J]. 中华肝脏外科手术学电子杂志, 2023, 12(06): 624-629.
[9] 廖梅, 张红君, 金洁玚, 吕艳, 任杰. 床旁超声造影对肝移植术后早期肝动脉血栓的诊断价值[J]. 中华肝脏外科手术学电子杂志, 2023, 12(06): 630-634.
[10] 张文华, 陶焠, 胡添松. 不同部位外生型肝癌临床病理特点及其对术后肝内复发和预后影响[J]. 中华肝脏外科手术学电子杂志, 2023, 12(06): 651-655.
[11] 李秉林, 吕少诚, 潘飞, 姜涛, 樊华, 寇建涛, 贺强, 郎韧. 供肝灌注液病原菌与肝移植术后早期感染的相关性分析[J]. 中华肝脏外科手术学电子杂志, 2023, 12(06): 656-660.
[12] 吕垒, 冯啸, 何凯明, 曾凯宁, 杨卿, 吕海金, 易慧敏, 易述红, 杨扬, 傅斌生. 改良金氏评分在儿童肝豆状核变性急性肝衰竭肝移植手术时机评估中价值并文献复习[J]. 中华肝脏外科手术学电子杂志, 2023, 12(06): 661-668.
[13] 韩宇, 张武, 李安琪, 陈文颖, 谢斯栋. MRI肝脏影像报告和数据系统对非肝硬化乙肝患者肝细胞癌的诊断价值[J]. 中华肝脏外科手术学电子杂志, 2023, 12(06): 669-673.
[14] 张维志, 刘连新. 基于生物信息学分析IPO7在肝癌中的表达及意义[J]. 中华肝脏外科手术学电子杂志, 2023, 12(06): 694-701.
[15] 陈安, 冯娟, 杨振宇, 杜锡林, 柏强善, 阴继凯, 臧莉, 鲁建国. 基于生物信息学分析CCN4在肝细胞癌中表达及其临床意义[J]. 中华肝脏外科手术学电子杂志, 2023, 12(06): 702-707.
阅读次数
全文


摘要