| [1] |
Kim BH, Lee D, Jung KW, et al. Cause of death and cause-specific mortality in primary liver cancer in Korea: a nationwide population-based study in hepatitis B virus endemic area[J]. Clin Mol Hepatol, 2022, 28(2):242-253.
|
| [2] |
Lee YT, Wang JJ, Luu M, et al. The mortality and overall survival trends of primary liver cancer in the United States[J]. J Natl Cancer Inst, 2021, 113(11):1531-1541.
|
| [3] |
宗静静, 卿鑫, 樊哲, 等. 原发性肝癌治疗进展[J]. 东南大学学报(医学版), 2021, 40(4):542-547.
|
| [4] |
Jain A, Nadeem A, Majdi Altoukhi H, et al. Personalized liver cancer risk prediction using big data analytics techniques with image processing segmentation[J]. Comput Intell Neurosci, 2022:8154523.
|
| [5] |
Sun C, Li R, Song Y, et al. Ultrasensitive and reliable organic field-effect transistor-based biosensors in early liver cancer diagnosis[J]. Anal Chem, 2021, 93(15):6188-6194.
|
| [6] |
Makrodimitris S, van Ham RCHJ, Reinders MJT. Automatic gene function prediction in the 2020's[J]. Genes, 2020, 11(11):1264.
|
| [7] |
Mahalingam D, Chelis L, Nizamuddin I, et al. Detection of hepatocellular carcinoma in a high-risk population by a mass spectrometry-based test[J]. Cancers, 2021, 13(13):3109.
|
| [8] |
Hashem S, Elhefnawi M, Habashy S, et al. Machine learning prediction models for diagnosing hepatocellular carcinoma with HCV-related chronic liver disease[J]. Comput Methods Programs Biomed, 2020(196):105551.
|
| [9] |
解添淞, 周正荣. 人工智能及影像组学在腹部肿瘤中的应用进展[J]. 中华放射学杂志, 2020, 54(4):376-377.
|
| [10] |
Zeng X, Chen S. Research on ultrasonic image recognition based on optimization immune algorithm[J]. Comput Math Methods Med, 2021:5868949.
|
| [11] |
Zhang Y, Cui J, Wan W, et al. Multimodal imaging under artificial intelligence algorithm for the diagnosis of liver cancer and its relationship with expressions of EZH2 and p57[J]. Comput Intell Neurosci, 2022:4081654.
|
| [12] |
Sato M, Kobayashi T, Soroida Y, et al. Development of novel deep multimodal representation learning-based model for the differentiation of liver tumors on B-mode ultrasound images[J]. J Gastroenterol Hepatol, 2022, 37(4):678-684.
|
| [13] |
Chou TH, Yeh HJ, Chang CC, et al. Deep learning for abdominal ultrasound: a computer-aided diagnostic system for the severity of fatty liver[J]. J Chin Med Assoc, 2021, 84(9):842-850.
|
| [14] |
Kaga T, Noda Y, Fujimoto K, et al. Deep-learning-based image reconstruction in dynamic contrast-enhanced abdominal CT: image quality and lesion detection among reconstruction strength levels[J]. Clin Radiol, 2021, 76(9):710-715.
|
| [15] |
Kim DW, Lee G, Kim SY, et al. Deep learning-based algorithm to detect primary hepatic malignancy in multiphase CT of patients at high risk for HCC[J]. Eur Radiol, 2021, 31(9):7047-7057.
|
| [16] |
Wang M, Fu F, Zheng B, et al. Development of an AI system for accurately diagnose hepatocellular carcinoma from computed tomography imaging data[J]. Br J Cancer, 2021, 125(8):1111-1121.
|
| [17] |
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.
|
| [18] |
Ichikawa S, Isoda H, Shimizu T, et al. Distinguishing intrahepatic mass-forming biliary carcinomas from hepatocellular carcinoma by computed tomography and magnetic resonance imaging using the Bayesian method: a bi-center study[J]. Eur Radiol, 2020, 30(11): 5992-6002.
|
| [19] |
Wang X, Wang S, Yin X, et al. MRI-based radiomics distinguish different pathological types of hepatocellular carcinoma[J]. Comput Biol Med, 2022(141):105058.
|
| [20] |
Song D, Wang Y, Wang W, et al. Using deep learning to predict microvascular invasion in hepatocellular carcinoma based on dynamic contrast-enhanced MRI combined with clinical parameters[J]. J Cancer Res Clin Oncol, 2021, 147(12):3757-3767.
|
| [21] |
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.
|
| [22] |
郑俣萱. 结合细粒度特征学习的病理图像分类模型研究与实现[D]. 西安: 西安电子科技大学, 2021.
|
| [23] |
Aatresh AA, Alabhya K, Lal S, et al. LiverNet: efficient and robust deep learning model for automatic diagnosis of sub-types of liver hepatocellular carcinoma cancer from H&E stained liver histopathology images[J]. Int J Comput Assist Radiol Surg, 2021, 16(9):1549-1563.
|
| [24] |
Wang R, He Y, Yao C, et al. Classification and segmentation of hyperspectral data of hepatocellular carcinoma samples using 1-D convolutional neural network[J]. Cytometry A, 2020, 97(1):31-38.
|
| [25] |
王炯亮, 李文轩, 陈敏山, 等. 人工智能在肝细胞癌研究的应用现状与前景[J]. 中华医学杂志, 2021, 101(6):435-441.
|
| [26] |
刘红枝, 刘景丰. 人工智能在原发性肝癌外科治疗中的应用现状与展望[J]. 临床肝胆病杂志, 2022, 38(1):10-14.
|
| [27] |
Wang S, Ye Z, Pan Z, et al. "Shared decision making assistant": a smartphone application to meet the decision-making needs of patients with primary liver cancer[J]. Comput Inform Nurs, 2021, 39(12):984-991.
|
| [28] |
Mojtahed A, Núñez L, Connell J, et al. Repeatability and reproducibility of deep-learning-based liver volume and Couinaud segment volume measurement tool[J]. Abdom Radiol, 2022, 47(1):143-151.
|
| [29] |
Mai RY, Lu HZ, Bai T, et al. Artificial neural network model for preoperative prediction of severe liver failure after hemihepatectomy in patients with hepatocellular carcinoma[J]. Surgery, 2020, 168(4):643-652.
|
| [30] |
Zhang T, Wei Y, He X, et al. Prediction of remnant liver regeneration after right hepatectomy in patients with hepatocellular carcinoma using preoperative CT texture analysis and clinical features[J]. Contrast Media Mol Imaging, 2021:5572470.
|
| [31] |
Madani A, Namazi B, Altieri MS, et al. Artificial intelligence for intraoperative guidance: using semantic segmentation to identify surgical anatomy during laparoscopic cholecystectomy[J]. Ann Surg, 2022, 276(2):363-369.
|
| [32] |
Tokuyasu T, Iwashita Y, Matsunobu Y, et al. Development of an artificial intelligence system using deep learning to indicate anatomical landmarks during laparoscopic cholecystectomy[J]. Surg Endosc, 2021, 35(4):1651-1658.
|
| [33] |
Felli E, Al-Taher M, Collins T, et al. Automatic liver viability scoring with deep learning and hyperspectral imaging[J]. Diagnostics, 2021, 11(9):1527.
|
| [34] |
Giannone F, Felli E, Cherkaoui Z, et al. Augmented reality and image-guided robotic liver surgery[J]. Cancers, 2021, 13(24):6268.
|
| [35] |
Liu X, Plishker W, Kane TD, et al. Preclinical evaluation of ultrasound-augmented needle navigation for laparoscopic liver ablation[J]. Int J Comput Assist Radiol Surg, 2020, 15(5):803-810.
|
| [36] |
Solbiati M, Ierace T, Muglia R, et al. Thermal ablation of liver tumors guided by augmented reality: an initial clinical experience[J]. Cancers, 2022, 14(5):1312.
|
| [37] |
Boldanova T, Fucile G, Vosshenrich J, et al. Supervised learning based on tumor imaging and biopsy transcriptomics predicts response of hepatocellular carcinoma to transarterial chemoembolization[J]. Cell Rep Med, 2021, 2(11):100444.
|
| [38] |
Abuhelwa AY, Badaoui S, Yuen HY, et al. A clinical scoring tool validated with machine learning for predicting severe hand-foot syndrome from sorafenib in hepatocellular carcinoma[J]. Cancer Chemother Pharmacol, 2022, 89(4):479-485.
|
| [39] |
Zeng J, Zeng J, Lin K, et al. Development of a machine learning model to predict early recurrence for hepatocellular carcinoma after curative resection[J]. Hepatobiliary Surg Nutr, 2022, 11(2):176-187.
|
| [40] |
Ji GW, Fan Y, Sun DW, et al. Machine learning to improve prognosis prediction of early hepatocellular carcinoma after surgical resection[J]. J Hepatocell Carcinoma, 2021(8):913-923.
|
| [41] |
Qian X, Zheng H, Xue K, et al. Recurrence risk of liver cancer post-hepatectomy using machine learning and study of correlation with immune infiltration[J]. Front Genet, 2021(12):733654.
|
| [42] |
Tohme S, Yazdani HO, Rahman A, et al. The use of machine learning to create a risk score to predict survival in patients with hepatocellular carcinoma: a TCGA cohort analysis[J]. Can J Gastroenterol Hepatol, 2021:5212953.
|
| [43] |
Lee IC, Huang JY, Chen TC, et al. Evolutionary learning-derived clinical-radiomic models for predicting early recurrence of hepatocellular carcinoma after resection[J]. Liver Cancer, 2021, 10(6):572-582.
|