Clinical applications of artificial intelligence in liver imaging

Zech J, Pain M, Titano J et al (2018) Natural language-based machine learning models for the annotation of clinical radiology reports. Radiology 287:570–580. https://doi.org/10.1148/radiol.2018171093

Article  PubMed  Google Scholar 

Wang K, Tan F, Zhu Z, Kong L (2022) Exploring changes in depression and radiology-related publications research focus: a bibliometrics and content analysis based on natural language processing. Front Psychiatry 13:978763. https://doi.org/10.3389/fpsyt.2022.978763

Article  PubMed  PubMed Central  Google Scholar 

Selver MA, Kocaoğlu A, Demir GK et al (2008) Patient oriented and robust automatic liver segmentation for pre-evaluation of liver transplantation. Comput Biol Med 38:765–784. https://doi.org/10.1016/j.compbiomed.2008.04.006

Article  PubMed  Google Scholar 

Lu X, Wu J, Ren X et al (2014) The study and application of the improved region growing algorithm for liver segmentation. Optik 125:2142–2147. https://doi.org/10.1016/j.ijleo.2013.10.049

Article  Google Scholar 

Liao M, Zhao Y-Q, Wang W et al (2016) Efficient liver segmentation in CT images based on graph cuts and bottleneck detection. Phys Med 32:1383–1396. https://doi.org/10.1016/j.ejmp.2016.10.002

Article  PubMed  Google Scholar 

Zhang R, Zhou Z, Wu W et al (2018) An improved fuzzy connectedness method for automatic three-dimensional liver vessel segmentation in CT images. J Healthc Eng 2018:2376317. https://doi.org/10.1155/2018/2376317

Article  PubMed  PubMed Central  Google Scholar 

Mohagheghi S, Foruzan AH (2020) Incorporating prior shape knowledge via data-driven loss model to improve 3D liver segmentation in deep CNNs. Int J Comput Assist Radiol Surg 15:249–257. https://doi.org/10.1007/s11548-019-02085-y

Article  PubMed  Google Scholar 

Khan RA, Luo Y, Wu F-X (2022) RMS-UNet: residual multi-scale UNet for liver and lesion segmentation. Artif Intell Med 124:102231. https://doi.org/10.1016/j.artmed.2021.102231

Article  PubMed  Google Scholar 

Seyama Y, Kokudo N (2009) Assessment of liver function for safe hepatic resection. Hepatol Res 39:107–116. https://doi.org/10.1111/j.1872-034X.2008.00441.x

Article  PubMed  Google Scholar 

Mohagheghi S, Foruzan AH (2021) Developing an explainable deep learning boundary correction method by incorporating cascaded x-Dim models to improve segmentation defects in liver CT images. Comput Biol Med 140:105106. https://doi.org/10.1016/j.compbiomed.2021.105106

Article  PubMed  Google Scholar 

Graffy PM, Sandfort V, Summers RM, Pickhardt PJ (2019) Automated liver fat quantification at nonenhanced abdominal CT for population-based steatosis assessment. Radiology 293:334–342. https://doi.org/10.1148/radiol.2019190512

Article  PubMed  Google Scholar 

Kim DW, Ha J, Lee SS et al (2021) Population-based and personalized reference intervals for liver and spleen volumes in healthy individuals and those with viral hepatitis. Radiology 301:339–347. https://doi.org/10.1148/radiol.2021204183

Article  PubMed  Google Scholar 

Tallam H, Elton DC, Lee S et al (2022) Fully automated abdominal CT biomarkers for type 2 diabetes using deep learning. Radiology 304:85–95. https://doi.org/10.1148/radiol.211914

Article  PubMed  Google Scholar 

Pickhardt PJ, Graffy PM, Zea R et al (2020) Automated CT biomarkers for opportunistic prediction of future cardiovascular events and mortality in an asymptomatic screening population: a retrospective cohort study. Lancet Digit Health 2:e192–e200. https://doi.org/10.1016/S2589-7500(20)30025-X

Article  PubMed  PubMed Central  Google Scholar 

Zhan R, Qi R, Huang S et al (2021) The correlation between hepatic fat fraction evaluated by dual-energy computed tomography and high-risk coronary plaques in patients with non-alcoholic fatty liver disease. Jpn J Radiol 39:763–773. https://doi.org/10.1007/s11604-021-01113-9

Article  CAS  PubMed  Google Scholar 

Martí-Aguado D, Jiménez-Pastor A, Alberich-Bayarri Á et al (2022) Automated whole-liver MRI segmentation to assess steatosis and iron quantification in chronic liver disease. Radiology 302:345–354. https://doi.org/10.1148/radiol.2021211027

Article  PubMed  Google Scholar 

Yoshizawa E, Yamada A (2021) MRI-derived proton density fat fraction. J Med Ultrason 48:497–506. https://doi.org/10.1007/s10396-021-01135-w

Article  Google Scholar 

Cunha GM, Delgado TI, Middleton MS et al (2022) Automated CNN-based analysis versus manual analysis for MR elastography in nonalcoholic fatty liver disease: intermethod agreement and fibrosis stage discriminative performance. AJR Am J Roentgenol 219:224–232. https://doi.org/10.2214/AJR.21.27135

Article  PubMed  Google Scholar 

Wang S, Li B, Li P et al (2021) Feasibility of perfusion and early-uptake 18F-FDG PET/CT in primary hepatocellular carcinoma: a dual-input dual-compartment uptake model. Jpn J Radiol 39:1086–1096. https://doi.org/10.1007/s11604-021-01140-6

Article  PubMed  Google Scholar 

Chlebus G, Schenk A, Moltz JH et al (2018) Automatic liver tumor segmentation in CT with fully convolutional neural networks and object-based postprocessing. Sci Rep 8:15497. https://doi.org/10.1038/s41598-018-33860-7

Article  CAS  PubMed  PubMed Central  Google Scholar 

Meng L, Tian Y, Bu S (2020) Liver tumor segmentation based on 3D convolutional neural network with dual scale. J Appl Clin Med Phys 21:144–157. https://doi.org/10.1002/acm2.12784

Article  PubMed  Google Scholar 

Liu T, Liu J, Ma Y et al (2021) Spatial feature fusion convolutional network for liver and liver tumor segmentation from CT images. Med Phys 48:264–272. https://doi.org/10.1002/mp.14585

Article  PubMed  Google Scholar 

Barat M, Chassagnon G, Dohan A et al (2021) Artificial intelligence: a critical review of current applications in pancreatic imaging. Jpn J Radiol 39:514–523. https://doi.org/10.1007/s11604-021-01098-5

Article  PubMed  Google Scholar 

Cay N, Mendi BAR, Batur H, Erdogan F (2022) Discrimination of lipoma from atypical lipomatous tumor/well-differentiated liposarcoma using magnetic resonance imaging radiomics combined with machine learning. Jpn J Radiol 40:951–960. https://doi.org/10.1007/s11604-022-01278-x

Article  CAS  PubMed  Google Scholar 

Du G, Zeng Y, Chen D et al (2023) Application of radiomics in precision prediction of diagnosis and treatment of gastric cancer. Jpn J Radiol 41:245–257. https://doi.org/10.1007/s11604-022-01352-4

Article  PubMed  Google Scholar 

Tsuneta S, Oyama-Manabe N, Hirata K et al (2021) Texture analysis of delayed contrast-enhanced computed tomography to diagnose cardiac sarcoidosis. Jpn J Radiol 39:442–450. https://doi.org/10.1007/s11604-020-01086-1

Article  PubMed  Google Scholar 

Nomura Y, Hanaoka S, Nakao T et al (2021) Performance changes due to differences in training data for cerebral aneurysm detection in head MR angiography images. Jpn J Radiol 39:1039–1048. https://doi.org/10.1007/s11604-021-01153-1

Article  PubMed  Google Scholar 

Wang X, Dai S, Wang Q et al (2021) Investigation of MRI-based radiomics model in differentiation between sinonasal primary lymphomas and squamous cell carcinomas. Jpn J Radiol 39:755–762. https://doi.org/10.1007/s11604-021-01116-6

Article  PubMed  Google Scholar 

Fusco R, Granata V, Grazzini G et al (2022) Radiomics in medical imaging: pitfalls and challenges in clinical management. Jpn J Radiol 40:919–929. https://doi.org/10.1007/s11604-022-01271-4

Article  PubMed  Google Scholar 

Li X, Chai W, Sun K et al (2022) The value of whole-tumor histogram and texture analysis based on apparent diffusion coefficient (ADC) maps for the discrimination of breast fibroepithelial lesions: corresponds to clinical management decisions. Jpn J Radiol 40:1263–1271. https://doi.org/10.1007/s11604-022-01304-y

Article  PubMed  Google Scholar 

Yuan G, Qu W, Li S et al (2023) Noninvasive assessment of renal function and fibrosis in CKD patients using histogram analysis based on diffusion kurtosis imaging. Jpn J Radiol 41:180–193. https://doi.org/10.1007/s11604-022-01346-2

Article  PubMed  Google Scholar 

Anai K, Hayashida Y, Ueda I et al (2022) The effect of CT texture-based analysis using machine learning approaches on radiologists’ performance in differentiating focal-type autoimmune pancreatitis and pancreatic duct carcinoma. Jpn J Radiol 40:1156–1165. https://doi.org/10.1007/s11604-022-01298-7

Article  PubMed  PubMed Central  Google Scholar 

Shimizu H, Mori N, Mugikura S et al (2023) Application of texture and volume model analysis to dedicated axillary high-resolution 3D T2-weighted MR imaging: a novel method for diagnosing lymph node metastasis in patients with clinically node-negative breast cancer. Magn Reson Med Sci. https://doi.org/10.2463/mrms.mp.2022-0091

Article  PubMed  Google Scholar 

Murata S, Hagiwara A, Kaga H et al (2022) Comparison of brain volume measurements made with 0.3- and 3-T MR imaging. Magn Reson Med Sci 21:517–524. https://doi.org/10.2463/mrms.tn.2020-0034

Article  PubMed  Google Scholar 

Ohyu S, Tozaki M, Sasaki M et al (2022) Combined use of texture features and morphological classification based on dynamic contrast-enhanced MR imaging: differentiating benign and malignant breast masses with high negative predictive value. Magn Reson Med Sci 21:485–498. https://doi.org/10.2463/mrms.mp.2020-0160

Article  CAS  PubMed  Google Scholar 

Tsujita Y, Sofue K, Ueshima E et al (2022) Clinical application of quantitative MR imaging in nonalcoholic fatty liver disease. Magn Reson Med Sci. https://doi.org/10.2463/mrms.rev.2021-0152

Article  PubMed  Google Scholar 

Kunimatsu A, Yasaka K, Akai H et al (2022) Texture analysis in brain tumor MR imaging. Magn Reson Med Sci 21:95–109. https://doi.org/10.2463/mrms.rev.2020-0159

Article  PubMed  Google Scholar 

Hu H-T, Shan Q-Y, Chen S-L et al (2020) CT-based radiomics for preoperative prediction of early recurrent hepatocellular carcinoma: technical reproducibility of acquisition and scanners. Radiol Med 125:697–705. https://doi.org/10.1007/s11547-020-01174-2

Article  PubMed  Google Scholar 

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