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2000
Volume 21, Issue 1
  • ISSN: 1573-4056
  • E-ISSN: 1875-6603

Abstract

Primary liver cancer is the sixth most common cancer and the third leading cause of cancer deaths worldwide, with over 900,000 new cases and more than 800,000 deaths annually. Conventional imaging techniques have improved the diagnosis and assessment of treatment response in patients with Hepatocellular Carcinoma (HCC), but they have many limitations. Introducing Dual-Energy Computed Tomography (DECT) into clinical practice offers an opportunity to address these issues. DECT has unique advantages in diagnosing and evaluating the efficacy of HCC treatment. It can provide quantitative information on various substances and, through multi-parameter and quantitative parameter analysis, can be used for early detection of HCC, identification of benign and malignant lesions, and monitoring of lymph node metastasis and Microvascular Invasion (MVI). Additionally, DECT provides valuable information for evaluating therapeutic efficacy. This review covers the imaging principles of DECT, including its basic principles, scanner design modes, and Image Reconstruction (IR) techniques. It then describes the research progress of DECT in diagnosing HCC and evaluating treatment efficacy. Finally, it briefly discusses some limitations of DECT and its future development directions.

This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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References

  1. SungH. FerlayJ. SiegelR.L. LaversanneM. SoerjomataramI. JemalA. BrayF. Global cancer statistics 2020: GLOBOCAN Estimates of incidence and mortality worldwide for 36 cancers in 185 countries.CA Cancer J. Clin.202171320924910.3322/caac.2166033538338
    [Google Scholar]
  2. SiegelR.L. MillerK.D. FuchsH.E. JemalA. Cancer statistics.CA Cancer J. Clin.202171173310.3322/caac.2165433433946
    [Google Scholar]
  3. VillanuevaA. Hepatocellular carcinoma.N. Engl. J. Med.2019380151450146210.1056/NEJMra171326330970190
    [Google Scholar]
  4. ZhouJ. SunH. WangZ. CongW. ZengM. ZhouW. BieP. LiuL. WenT. KuangM. HanG. YanZ. WangM. LiuR. LuL. RenZ. ZengZ. LiangP. LiangC. ChenM. YanF. WangW. HouJ. JiY. YunJ. BaiX. CaiD. ChenW. ChenY. ChengW. ChengS. DaiC. GuoW. GuoY. HuaB. HuangX. JiaW. LiQ. LiT. LiX. LiY. LiY. LiangJ. LingC. LiuT. LiuX. LuS. LvG. MaoY. MengZ. PengT. RenW. ShiH. ShiG. ShiM. SongT. TaoK. WangJ. WangK. WangL. WangW. WangX. WangZ. XiangB. XingB. XuJ. YangJ. YangJ. YangY. YangY. YeS. YinZ. ZengY. ZhangB. ZhangB. ZhangL. ZhangS. ZhangT. ZhangY. ZhaoM. ZhaoY. ZhengH. ZhouL. ZhuJ. ZhuK. LiuR. ShiY. XiaoY. ZhangL. YangC. WuZ. DaiZ. ChenM. CaiJ. WangW. CaiX. LiQ. ShenF. QinS. TengG. DongJ. FanJ. uidelines for the diagnosis and treatment of primary liver cancer.Liver Cancer202312540544410.1159/00053049537901768
    [Google Scholar]
  5. A Macovski MacovskiA. R E Alvarez Energy-selective reconstructions in X-ray computerised tomography.Phys. Med. Biol.197621573374410.1088/0031‑9155/21/5/002967922
    [Google Scholar]
  6. HounsfieldG.N. Computerized transverse axial scanning (tomography): Part 1. Description of system.Br. J. Radiol.1973465521016102210.1259/0007‑1285‑46‑552‑10164757352
    [Google Scholar]
  7. MarinD. BollD.T. MiletoA. NelsonR.C. State of the art: Dual-energy CT of the abdomen.Radiology2014271232734210.1148/radiol.1413148024761954
    [Google Scholar]
  8. FlohrT.G. McColloughC.H. BruderH. PetersilkaM. GruberK. SüβC. GrasruckM. StierstorferK. KraussB. RaupachR. PrimakA.N. KüttnerA. AchenbachS. BeckerC. KoppA. OhnesorgeB.M. First performance evaluation of a dual-source CT (DSCT) system.Eur. Radiol.200616225626810.1007/s00330‑005‑2919‑216341833
    [Google Scholar]
  9. HamidS. NasirM.U. SoA. AndrewsG. NicolaouS. QamarS.R. Clinical applications of dual-energy CT.Korean J. Radiol.202122697098210.3348/kjr.2020.099633856133
    [Google Scholar]
  10. AgostiniA. BorgheresiA. MariA. FloridiC. BrunoF. CarottiM. SchicchiN. BarileA. MaggiS. GiovagnoniA. Dual-energy CT: Theoretical principles and clinical applications.Radiol. Med. (Torino)2019124121281129510.1007/s11547‑019‑01107‑831792703
    [Google Scholar]
  11. KazaR.K. PlattJ.F. CohanR.H. CaoiliE.M. Al-HawaryM.M. WasnikA. Dual-energy CT with single- and dual-source scanners: Current applications in evaluating the genitourinary tract.Radiographics201232235336910.1148/rg.32211506522411937
    [Google Scholar]
  12. RassouliN. EtesamiM. DhanantwariA. RajiahP. Detector-based spectral CT with a novel dual-layer technology: Principles and applications.Insights Imaging20178658959810.1007/s13244‑017‑0571‑428986761
    [Google Scholar]
  13. GibneyB. RedmondC.E. ByrneD. MathurS. MurrayN. A review of the applications of dual-energy CT in acute neuroimaging.Can. Assoc. Radiol. J.202071325326510.1177/084653712090434732106693
    [Google Scholar]
  14. ZhongH. HuangQ. ZhengX. WangY. QianY. ChenX. WangJ. DuanS. Generation of virtual monoenergetic images at 40 keV of the upper abdomen and image quality evaluation based on generative adversarial networks.BMC Med. Imaging202424115110.1186/s12880‑024‑01331‑338890572
    [Google Scholar]
  15. ToiaG.V. MiletoA. WangC.L. SahaniD.V. Quantitative dual-energy CT techniques in the abdomen.Abdom. Radiol. (N.Y.)20214793003301810.1007/s00261‑021‑03266‑734468796
    [Google Scholar]
  16. LiW. DiaoK. WenY. ShuaiT. YouY. ZhaoJ. LiaoK. LuC. YuJ. HeY. LiZ. High-strength deep learning image reconstruction in coronary CT angiography at 70-kVp tube voltage significantly improves image quality and reduces both radiation and contrast doses.Eur. Radiol.20223252912292010.1007/s00330‑021‑08424‑535059803
    [Google Scholar]
  17. De CeccoC.N. CarusoD. SchoepfU.J. De SantisD. MuscogiuriG. AlbrechtM.H. MeinelF.G. WichmannJ.L. BurchettP.F. Varga-SzemesA. SheaforD.H. HardieA.D. A noise-optimized virtual monoenergetic reconstruction algorithm improves the diagnostic accuracy of late hepatic arterial phase dual-energy CT for the detection of hypervascular liver lesions.Eur. Radiol.20182883393340410.1007/s00330‑018‑5313‑629460075
    [Google Scholar]
  18. VossB.A. KhandelwalA. WellsM.L. InoueA. VenkateshS.K. LeeY.S. JohnsonM.P. FletcherJ.G. Impact of dual-energy 50-keV virtual monoenergetic images on radiologist confidence in detection of key imaging findings of small hepatocellular carcinomas using multiphase liver CT.Acta Radiol.202263111443145210.1177/0284185121105299334723681
    [Google Scholar]
  19. ReimerR.P. Große HokampN. Fehrmann EfferothA. KrauskopfA. ZopfsD. KrögerJ.R. PersigehlT. MaintzD. BunckA.C. Virtual monoenergetic images from spectral detector computed tomography facilitate washout assessment in arterially hyper-enhancing liver lesions.Eur. Radiol.20213153468347710.1007/s00330‑020‑07379‑333180163
    [Google Scholar]
  20. SchwartzF.R. ClarkD.P. RigiroliF. KaliszK. Wildman-TobrinerB. ThomasS. WilsonJ. BadeaC.T. MarinD. Evaluation of the impact of a novel denoising algorithm on image quality in dual-energy abdominal CT of obese patients.Eur. Radiol.202333107056706510.1007/s00330‑023‑09644‑737083742
    [Google Scholar]
  21. MeyerM. NelsonR.C. VernuccioF. GonzálezF. FarjatA.E. PatelB.N. SameiE. HenzlerT. SchoenbergS.O. MarinD. Virtual unenhanced images at dual-energy CT: Influence on renal lesion characterization.Radiology2019291238139010.1148/radiol.201918110030860450
    [Google Scholar]
  22. MingkwansookV. PuwametwongsaK. WatcharakornA. DechasasawatT. Comparative study of true and virtual non-contrast imaging generated from dual-layer spectral CT in patients with upper aerodigestive tract cancer.Pol. J. Radiol.2022878767868710.5114/pjr.2022.12382936643004
    [Google Scholar]
  23. AnzideiM. Di MartinoM. SacconiB. SabaL. BoniF. ZaccagnaF. GeigerD. KirchinM.A. NapoliA. BezziM. CatalanoC. Evaluation of image quality, radiation dose and diagnostic performance of dual-energy CT datasets in patients with hepatocellular carcinoma.Clin. Radiol.201570996697310.1016/j.crad.2015.05.00326095726
    [Google Scholar]
  24. MahmoodU. HorvatN. HorvatJ.V. RyanD. GaoY. CarolloG. DeOcampoR. DoR.K. KatzS. GerstS. SchmidtleinC.R. DauerL. ErdiY. MannelliL. Rapid switching kVp dual energy CT: Value of reconstructed dual energy CT images and organ dose assessment in multiphasic liver CT exams.Eur. J. Radiol.201810210210210810.1016/j.ejrad.2018.02.02229685522
    [Google Scholar]
  25. Guha RoyS. GulatiV. Machado PichardoL. ChakerS. BrodyM. RotenbergS. HayeriR. PootJ. TeytelboymO. Gallstones detection on dual-energy computerized tomography–is it ready for real-world use? A retrospective observational study.J. Comput. Assist. Tomogr.2024481354110.1097/RCT.000000000000153537531641
    [Google Scholar]
  26. Fernández-PérezG.C. Fraga PiñeiroC. Oñate MirandaM. Dual-energy CT: Technical considerations and clinical applications. Radiologia (Engl Ed).202264544510.1016/j.rxeng.2022.06.003
    [Google Scholar]
  27. ChoiS.H. LeeS.S. ParkS.H. KimK.M. YuE. ParkY. ShinY.M. LeeM.G. LI-RADS classification and prognosis of primary liver cancers at gadoxetic acid–enhanced MRI.Radiology2019290238839710.1148/radiol.201818129030422088
    [Google Scholar]
  28. KaltenbachB. WichmannJ.L. PfeiferS. AlbrechtM.H. BoozC. LengaL. HammerstinglR. D’AngeloT. VoglT.J. MartinS.S. Iodine quantification to distinguish hepatic neuroendocrine tumor metastasis from hepatocellular carcinoma at dual-source dual-energy liver CT.Eur. J. Radiol.2018105105202410.1016/j.ejrad.2018.05.01930017280
    [Google Scholar]
  29. LaroiaS.T. YadavK. KumarS. RastogiA. KumarG. SarinS.K. Material decomposition using iodine quantification on spectral CT for characterising nodules in the cirrhotic liver: A retrospective study.Eur. Radiol. Exp.2021512210.1186/s41747‑021‑00220‑634046753
    [Google Scholar]
  30. UdareA. WalkerD. KrishnaS. ChatelainR. McInnesM.D.F. FloodT.A. SchiedaN. Characterization of clear cell renal cell carcinoma and other renal tumors: Evaluation of dual-energy CT using material-specific iodine and fat imaging.Eur. Radiol.20203042091210210.1007/s00330‑019‑06590‑131858204
    [Google Scholar]
  31. YanW.Q. XinY.K. JingY. LiG.F. WangS.M. RongW.C. XiaoG. LeiX.B. LiB. HuY.C. CuiG.B. Iodine quantification using dual-energy computed tomography for differentiating thymic tumors.J. Comput. Assist. Tomogr.201842687388010.1097/RCT.000000000000080030339550
    [Google Scholar]
  32. MartinS.S. WeidingerS. CzwiklaR. KaltenbachB. AlbrechtM.H. LengaL. VoglT.J. WichmannJ.L. Iodine and fat quantification for differentiation of adrenal gland adenomas from metastases using third-generation dual-source dual-energy computed tomography.Invest. Radiol.201853317317810.1097/RLI.000000000000042528990974
    [Google Scholar]
  33. RizzoS. RadiceD. FemiaM. De MarcoP. OriggiD. PredaL. BarberisM. VigoritoR. MauriG. MauroA. BellomiM. Metastatic and non-metastatic lymph nodes: Quantification and different distribution of iodine uptake assessed by dual-energy CT.Eur. Radiol.201828276076910.1007/s00330‑017‑5015‑528835993
    [Google Scholar]
  34. SchmidtC. BaesslerB. NakhostinD. DasA. EberhardM. AlkadhiH. EulerA. Dual-energy CT-based iodine quantification in liver tumors: Impact of scan-, patient-, and position-related factors.Acad. Radiol.202128678378910.1016/j.acra.2020.04.02132418783
    [Google Scholar]
  35. OhiraS. MochizukiJ. NiwaT. EndoK. MinamitaniM. YamashitaH. KatanoA. ImaeT. NishioT. KoizumiM. NakagawaK. Variation in Hounsfield unit calculated using dual-energy computed tomography: Comparison of dual-layer, dual-source, and fast kilovoltage switching technique.Radiological Phys. Technol.202417245846610.1007/s12194‑024‑00802‑038700638
    [Google Scholar]
  36. BenvenisteA.P. de Castro FariaS. BroeringG. GaneshanD.M. TammE.P. IyerR.B. BhosaleP. Potential Application of dual-energy CT in gynecologic cancer: Initial experience.AJR Am. J. Roentgenol.2017208369570510.2214/AJR.16.1622728075606
    [Google Scholar]
  37. ReginelliA. Del CantoM. ClementeA. GragnanoE. CioceF. UrraroF. MartinelliE. CappabiancaS. The role of dual-energy CT for the assessment of liver metastasis response to treatment: Above the RECIST 1.1 criteria.J. Clin. Med.202312387910.3390/jcm1203087936769527
    [Google Scholar]
  38. BorgesA.P. AntunesC. Caseiro-AlvesF. SpectralC.T. Spectral CT: Current liver applications.Diagnostics20231310167310.3390/diagnostics1310167337238163
    [Google Scholar]
  39. NaritaK. NakamuraY. HigakiT. KondoS. HondaY. KawashitaI. MitaniH. FukumotoW. TaniC. ChosaK. TatsugamiF. AwaiK. Iodine maps derived from sparse-view kV-switching dual-energy CT equipped with a deep learning reconstruction for diagnosis of hepatocellular carcinoma.Sci. Rep.2023131360310.1038/s41598‑023‑30460‑y36869102
    [Google Scholar]
  40. SudarskiS. ApfaltrerP. NanceJ.W.Jr MeyerM. FinkC. HohenbergerP. LeideckerC. SchoenbergS.O. HenzlerT. Objective and subjective image quality of liver parenchyma and hepatic metastases with virtual monoenergetic dual-source dual-energy CT reconstructions: An analysis in patients with gastrointestinal stromal tumor.Acad. Radiol.201421451452210.1016/j.acra.2014.01.00124594421
    [Google Scholar]
  41. MiletoA. NelsonR.C. SameiE. ChoudhuryK.R. JaffeT.A. WilsonJ.M. MarinD. Dual-energy MDCT in hypervascular liver tumors: Effect of body size on selection of the optimal monochromatic energy level.AJR Am. J. Roentgenol.201420361257126410.2214/AJR.13.1222925415703
    [Google Scholar]
  42. SatoM. IchikawaY. DomaeK. YoshikawaK. KaniiY. YamazakiA. NagasawaN. NagataM. IshidaM. SakumaH. Deep learning image reconstruction for improving image quality of contrast-enhanced dual-energy CT in abdomen.Eur. Radiol.20223285499550710.1007/s00330‑022‑08647‑035238970
    [Google Scholar]
  43. LiM. FanY. YouH. LiC. LuoM. ZhouJ. LiA. ZhangL. YuX. DengW. ZhouJ. ZhangD. ZhangZ. ChenH. XiaoY. HuangB. WangJ. Dual-energy CT deep learning radiomics to predict macrotrabecular-massive hepatocellular carcinoma.Radiology20233082e23025510.1148/radiol.23025537606573
    [Google Scholar]
  44. ThorD. TitternesR. PoludniowskiG. Spatial resolution, noise properties, and detectability index of a deep learning reconstruction algorithm for dual‐energy CT of the abdomen.Med. Phys.20235052775278610.1002/mp.1630036774193
    [Google Scholar]
  45. SeoJ.Y. JooI. YoonJ.H. KangH.J. KimS. KimJ.H. AhnC. LeeJ.M. Deep learning-based reconstruction of virtual monoenergetic images of kVp-switching dual energy CT for evaluation of hypervascular liver lesions: Comparison with standard reconstruction technique.Eur. J. Radiol.202215415411039010.1016/j.ejrad.2022.11039035724579
    [Google Scholar]
  46. SemaanS. Vietti VioliN. LewisS. ChatterjiM. SongC. BesaC. BabbJ.S. FielM.I. SchwartzM. ThungS. SirlinC.B. TaouliB. Hepatocellular carcinoma detection in liver cirrhosis: diagnostic performance of contrast-enhanced CT vs. MRI with extracellular contrast vs. gadoxetic acid.Eur. Radiol.20203021020103010.1007/s00330‑019‑06458‑431673837
    [Google Scholar]
  47. FanP.L. ChuJ. WangQ. WangC. The clinical value of dual‐energy computed tomography and diffusion‐weighted imaging in the context of liver cancer: A narrative review.J. Clin. Ultrasound202250686286810.1002/jcu.2319735338779
    [Google Scholar]
  48. YoonJ.H. ChangW. LeeE.S. LeeS.M. LeeJ.M. Double low-dose dual-energy liver CT in patients at high-risk of HCC.Invest. Radiol.202055634034810.1097/RLI.000000000000064331917765
    [Google Scholar]
  49. AscentiG SofiaC MazziottiS Dual-energy CT with iodine quantification in distinguishing between bland and neoplastic portal vein thrombosis in patients with hepatocellular carcinoma.Clin Radiol.201671993810.1016/j.crad.2016.05.002
    [Google Scholar]
  50. SofiaC. CattafiA. SilipigniS. PitroneP. CarerjM.L. MarinoM.A. PitroneA. AscentiG. Portal vein thrombosis in patients with chronic liver diseases: From conventional to quantitative imaging.Eur. J. Radiol.202114214210985910.1016/j.ejrad.2021.10985934284232
    [Google Scholar]
  51. ChenX LuY ShiX Development and validation of a novel model to predict regional lymph node metastasis in patients with hepatocellular carcinoma.Front Oncol.20221283595710.3389/fonc.2022.835957
    [Google Scholar]
  52. YangA. XiaoW. JuW. LiaoY. ChenM. ZhuX. WuC. HeX. Prevalence and clinical significance of regional lymphadenectomy in patients with hepatocellular carcinoma.ANZ J. Surg.201989439339810.1111/ans.1509630856685
    [Google Scholar]
  53. OrcuttS.T. AnayaD.A. Liver resection and surgical strategies for management of primary liver cancer.Cancer Contr.2018251107327481774462110.1177/107327481774462129327594
    [Google Scholar]
  54. LeeJ. ParkH.Y. KimW.W. ParkC.S. LeeR.K. KimH.J. KimW.H. LeeS.W. JeongS.Y. ChaeY.S. LeeS.J. ParkJ.Y. ParkJ.Y. JungJ.H. Value of accurate diagnosis for metastatic supraclavicular lymph nodes in breast cancer: Assessment with neck US, CT, and 18F-FDG PET/CT.Diagn. Interv. Radiol.202127332332810.5152/dir.2021.2019034003120
    [Google Scholar]
  55. MoH. HuangR. WeiX. HuangL. HuangJ. ChenJ. QinM. LuW. YuX. LiuM. DingK. Diagnosis of metastatic lymph nodes in patients with hepatocellular carcinoma using dual-energy computed tomography.J. Comput. Assist. Tomogr.202347335536010.1097/RCT.000000000000142937184996
    [Google Scholar]
  56. ZengY.R. YangQ.H. LiuQ.Y. MinJ. LiH.G. LiuZ.F. LiJ.X. Dual energy computed tomography for detection of metastatic lymph nodes in patients with hepatocellular carcinoma.World J. Gastroenterol.201925161986199610.3748/wjg.v25.i16.198631086466
    [Google Scholar]
  57. YangC. ZhangS. JiaY. YuY. DuanH. ZhangX. MaG. RenC. YuN. Dual energy spectral CT imaging for the evaluation of small hepatocellular carcinoma microvascular invasion.Eur. J. Radiol.2017959522222710.1016/j.ejrad.2017.08.02228987671
    [Google Scholar]
  58. ZhuY. FengB. CaiW. WangB. MengX. WangS. MaX. ZhaoX. Prediction of microvascular invasion in solitary AFP-negative hepatocellular carcinoma ≤ 5 cm using a combination of imaging features and quantitative dual-layer spectral-detector CT parameters.Acad. Radiol.20233030Suppl. 1S104S11610.1016/j.acra.2023.02.01536958989
    [Google Scholar]
  59. KimT.M. LeeJ.M. YoonJ.H. JooI. ParkS.J. JeonS.K. SchmidtB. MartinS. Prediction of microvascular invasion of hepatocellular carcinoma: Value of volumetric iodine quantification using preoperative dual-energy computed tomography.Cancer Imaging20202016010.1186/s40644‑020‑00338‑732811570
    [Google Scholar]
  60. ChouC.T. ChenR.C. LinW.C. KoC.J. ChenC.B. ChenY.L. Prediction of microvascular invasion of hepatocellular carcinoma: Preoperative CT and histopathologic correlation.AJR Am. J. Roentgenol.20142033W253W25910.2214/AJR.13.1059525148181
    [Google Scholar]
  61. ToiaG.V. KimS. DigheM.K. MiletoA. Dual-energy computed tomography in body imaging.Semin. Roentgenol.201853213214610.1053/j.ro.2018.02.00429861005
    [Google Scholar]
  62. DunningC.A.S. RajendranK. InoueA. RajiahP. WeberN. FletcherJ.G. McColloughC.H. LengS. Optimal virtual monoenergetic photon energy (keV) for photon-counting-detector computed tomography angiography.J. Comput. Assist. Tomogr.202347456957510.1097/RCT.000000000000145036790898
    [Google Scholar]
  63. AhnfeltA. DahlmanP. SegelsjöM. MagnussonM.O. MagnussonA. Accuracy of iodine quantification using dual-energy computed tomography with focus on low concentrations.Acta Radiol.202263562363110.1177/0284185121100946233887965
    [Google Scholar]
  64. XuR. WangJ. HuangX. ZhangQ. XieY. PangL. BaiL. ZhouJ. Clinical value of spectral CT imaging combined with AFP in identifying liver cancer and hepatic focal nodular hyperplasia.J. BUON20192441429143431646787
    [Google Scholar]
  65. YuY. LinX. ChenK. ChaiW. HuS. TangR. ZhangJ. CaoL. YanF. Hepatocellular carcinoma and focal nodular hyperplasia of the liver: Differentiation with CT spectral imaging.Eur. Radiol.20132361660166810.1007/s00330‑012‑2747‑023306709
    [Google Scholar]
  66. WangN. JuY. WuJ. LiuA. ChenA. LiuJ. LiuY. LiJ. Differentiation of liver abscess from liver metastasis using dual-energy spectral CT quantitative parameters.Eur. J. Radiol.201911311320420810.1016/j.ejrad.2019.02.02430927948
    [Google Scholar]
  67. OtaT. OnishiH. FukuiH. TsuboyamaT. NakamotoA. HondaT. MatsumotoS. TatsumiM. TomiyamaN. Prediction models for differentiating benign from malignant liver lesions based on multiparametric dual-energy non-contrast CT.Eur. Radiol.Epub ahead of print. 202410.1007/s00330‑024‑11024‑839186105
    [Google Scholar]
  68. ZhongJ. WangL. YanC. XingY. HuY. DingD. GeX. LiJ. LuW. ShiX. YuanF. YaoW. ZhangH. Deep learning image reconstruction generates thinner slice iodine maps with improved image quality to increase diagnostic acceptance and lesion conspicuity: A prospective study on abdominal dual-energy CT.BMC Med. Imaging202424115910.1186/s12880‑024‑01334‑038926711
    [Google Scholar]
  69. TerziogluF. SidkyE.Y. PhillipsJ.P. ReiserI.S. BalG. PanX. Optimizing dual‐energy CT technique for iodine‐based contrast‐to‐noise ratio, a theoretical study.Med. Phys.20245142871288110.1002/mp.1701038436473
    [Google Scholar]
  70. NagayamaY. IyamaA. OdaS. TaguchiN. NakauraT. UtsunomiyaD. KikuchiY. YamashitaY. Dual-layer dual-energy computed tomography for the assessment of hypovascular hepatic metastases: Impact of closing k-edge on image quality and lesion detectability.Eur. Radiol.20192962837284710.1007/s00330‑018‑5789‑030377793
    [Google Scholar]
  71. MarinD. Ramirez-GiraldoJ.C. GuptaS. FuW. StinnettS.S. MiletoA. BelliniD. PatelB. SameiE. NelsonR.C. Effect of a noise-optimized second-generation monoenergetic algorithm on image noise and conspicuity of hypervascular liver tumors: An in vitro and in vivo study.AJR Am. J. Roentgenol.201620661222123210.2214/AJR.15.1551227058192
    [Google Scholar]
  72. LiS. YuanL. LuT. YangX. RenW. WangL. ZhaoJ. DengJ. LiuX. XueC. SunQ. ZhangW. ZhouJ. Deep learning imaging reconstruction of reduced-dose 40 keV virtual monoenergetic imaging for early detection of colorectal cancer liver metastases.Eur. J. Radiol.202316816811112810.1016/j.ejrad.2023.11112837816301
    [Google Scholar]
  73. FengF. ZhaoY. Hepatocellular carcinoma: Prevention, diagnosis, and treatment.Med. Princ. Pract.202433541442310.1159/00053934938772352
    [Google Scholar]
  74. LópezC.L. CalvoM. CámaraJ.C. García-ParedesB. Gómez-MartinC. LópezA.M. Pazo-CidR. SastreJ. YayaR. FeliuJ. SEOM-GEMCAD-TTD clinical guidelines for the management of hepatocarcinoma patients (2023).Clin. Transl. Oncol.202426112800281110.1007/s12094‑024‑03568‑438914756
    [Google Scholar]
  75. TengW. WuT.C. LinS.M. Hepatocellular carcinoma systemic treatment 2024 update: From early to advanced stage.Biomed. J.202410081510081510.1016/j.bj.2024.10081539561966
    [Google Scholar]
  76. VasuriF. RenzulliM. FittipaldiS. BrocchiS. ClementeA. CappabiancaS. BolondiL. GolfieriR. D’ErricoA. Pathobiological and radiological approach for hepatocellular carcinoma subclassification.Sci. Rep.2019911474910.1038/s41598‑019‑51303‑931611584
    [Google Scholar]
  77. LiH. ZhangD. PeiJ. HuJ. LiX. LiuB. WangL. Dual-energy computed tomography iodine quantification combined with laboratory data for predicting microvascular invasion in hepatocellular carcinoma: A two-centre study.Br. J. Radiol.20249711601467147510.1093/bjr/tqae11638870535
    [Google Scholar]
  78. KimY. LeeJ.S. LeeH.W. KimB.K. ParkJ.Y. KimD.Y. AhnS.H. GohM.J. KangW. KimS.U. Sorafenib versus nivolumab after lenvatinib treatment failure in patients with advanced hepatocellular carcinoma.Eur. J. Gastroenterol. Hepatol.202335219119710.1097/MEG.000000000000246636574310
    [Google Scholar]
  79. MiletoA. SameiE. Hallway conversations in physics.AJR Am. J. Roentgenol.20172081W24W2710.2214/AJR.16.1646227786544
    [Google Scholar]
  80. UneN. Takano-KasuyaM. KitamuraN. OhtaM. InoseT. KatoC. NishimuraR. TadaH. MiyagiS. IshidaT. UnnoM. KameiT. GondaK. The anti-angiogenic agent lenvatinib induces tumor vessel normalization and enhances radiosensitivity in hepatocellular tumors.Med. Oncol.20213866010.1007/s12032‑021‑01503‑z33881631
    [Google Scholar]
  81. KudoM. IkedaM. UeshimaK. SakamotoM. ShiinaS. TateishiR. NousoK. HasegawaK. FuruseJ. MiyayamaS. MurakamiT. YamashitaT. KokudoN. Response evaluation criteria in cancer of the liver version 6 (Response evaluation criteria in cancer of the liver 2021 revised version).Hepatol. Res.202252432933610.1111/hepr.1374635077590
    [Google Scholar]
  82. HaoL. LiS. YeF. WangH. ZhongY. ZhangX. HuX. HuangX. The current status and future of targeted-immune combination for hepatocellular carcinoma.Front. Immunol.20241515141896510.3389/fimmu.2024.141896539161764
    [Google Scholar]
  83. LvP. LiuJ. YanX. ChaiY. ChenY. GaoJ. PanY. LiS. GuoH. ZhouY. CT spectral imaging for monitoring the therapeutic efficacy of VEGF receptor kinase inhibitor AG-013736 in rabbit VX2 liver tumours.Eur. Radiol.201727391892610.1007/s00330‑016‑4458‑427287476
    [Google Scholar]
  84. FiteEL MakaryMS Transarterial chemoembolization treatment paradigms for hepatocellular carcinoma.Cancers2024163243010.3390/cancers16132430
    [Google Scholar]
  85. FengL.H. ZhuY.Y. ZhouJ.M. WangM. XuW.Q. ZhangT. MaoA.R. CongW.M. DongH. WangL. Adjuvant TACE may not improve recurrence-free or overall survival in HCC patients with low risk of recurrence after hepatectomy.Front. Oncol.20231313110449210.3389/fonc.2023.110449237293583
    [Google Scholar]
  86. BaiT. LuS. ChenJ. YeJ. WangX. TangZ. FuH. LiL. WuF. Value of hepatic artery digital subtraction angiography in the detection of small hepatocellular carcinoma lesions.Environ. Toxicol.202439104754476210.1002/tox.2431639162414
    [Google Scholar]
  87. NakamuraY. HigakiT. HondaY. TatsugamiF. TaniC. FukumotoW. NaritaK. KondoS. AkagiM. AwaiK. Advanced CT techniques for assessing hepatocellular carcinoma.Radiol. Med. (Torino)2021126792593510.1007/s11547‑021‑01366‑433954894
    [Google Scholar]
  88. HauboldJ. LudwigJ.M. LiY. BuechterM. WetterA. UmutluL. TheysohnJ.M. Measuring the density of iodine depositions: Detecting an invisible residual tumor after conventional transarterial chemoembolization.PLoS One2020151e022797210.1371/journal.pone.022797231995589
    [Google Scholar]
  89. ChoiW.S. ChangW. LeeM. HurS. KimH.C. JaeH.J. ChungJ.W. ChoiJ.W. Spectral CT-based iodized oil quantification to predict tumor response following chemoembolization of hepatocellular carcinoma.J. Vasc. Interv. Radiol.2021321162210.1016/j.jvir.2020.09.02033162309
    [Google Scholar]
  90. YueX. JiangQ. HuX. CenC. SongS. QianK. LuY. YangM. LiQ. HanP. Quantitative dual-energy CT for evaluating hepatocellular carcinoma after transarterial chemoembolization.Sci. Rep.20211111112710.1038/s41598‑021‑90508‑934045528
    [Google Scholar]
  91. ThaissW.M. HaberlandU. KaufmannS. HeppT. SchulzeM. BlumA.C. KetelsenD. NikolaouK. HorgerM. SauterA.W. Dose optimization of perfusion-derived response assessment in hepatocellular carcinoma treated with transarterial chemoembolization: Comparison of volume perfusion CT and iodine concentration.Acad. Radiol.20192691154116310.1016/j.acra.2018.09.02630482626
    [Google Scholar]
  92. WangJ. ShenJ. Spectral CT in evaluating the therapeutic effect of transarterial chemoembolization for hepatocellular carcinoma.Medicine (Baltimore)20179652e923610.1097/MD.000000000000923629384909
    [Google Scholar]
  93. HurJ. LeeE.S. ParkH.J. ChoiW. ParkS.B. Diagnostic performance of dual-energy computed tomography for HCC after transarterial chemoembolization: Utility of virtual unenhanced and low keV virtual monochromatic images.Medicine (Baltimore)202210142e3117110.1097/MD.000000000003117136281184
    [Google Scholar]
  94. ReigM. FornerA. RimolaJ. Ferrer-FàbregaJ. BurrelM. Garcia-CriadoÁ. KelleyR.K. GalleP.R. MazzaferroV. SalemR. SangroB. SingalA.G. VogelA. FusterJ. AyusoC. BruixJ. BCLC strategy for prognosis prediction and treatment recommendation: The 2022 update.J. Hepatol.202276368169310.1016/j.jhep.2021.11.01834801630
    [Google Scholar]
  95. LiJ ZhaoS LingZ Dual-energy computed tomography imaging in early-stage hepatocellular carcinoma: A preliminary study.Contrast. Media. Mol. Imaging20222022214634310.1155/2022/2146343
    [Google Scholar]
  96. LiJ.P. ZhaoS. JiangH.J. JiangH. ZhangL.H. ShiZ.X. FanT.T. WangS. Quantitative dual-energy computed tomography texture analysis predicts the response of primary small hepatocellular carcinoma to radiofrequency ablation.Hepatobiliary Pancreat. Dis. Int.202221656957610.1016/j.hbpd.2022.06.00335729000
    [Google Scholar]
  97. LeeS.H. LeeJ.M. KimK.W. KlotzE. KimS.H. LeeJ.Y. HanJ.K. ChoiB.I. Dual-energy computed tomography to assess tumor response to hepatic radiofrequency ablation: potential diagnostic value of virtual noncontrast images and iodine maps.Invest. Radiol.2011462778410.1097/RLI.0b013e3181f23fcd20856125
    [Google Scholar]
  98. ZhangL. WangN. MaoJ. LiuX. GaoZ. DaiX. FengB. Dual-energy CT–derived volumetric iodine concentration for the assessment of therapeutic response after microwave ablation in a rabbit model with intrahepatic VX2 tumor.J. Vasc. Interv. Radiol.201829101455146110.1016/j.jvir.2018.04.01930217747
    [Google Scholar]
  99. GassertF.G. SchackyC.E. Müller-LeisseC. GassertF.T. PahnG. LaugwitzK.L. MakowskiM.R. NadjiriJ. Calcium scoring using virtual non-contrast images from a dual-layer spectral detector CT: Comparison to true non-contrast data and evaluation of proportionality factor in a large patient collective.Eur. Radiol.20213186193619910.1007/s00330‑020‑07677‑w33474570
    [Google Scholar]
  100. ReimerR.P. HokampN.G. NiehoffJ. ZopfsD. LennartzS. HeidarM. WahbaR. StippelD. MaintzD. dos SantosD.P. WybranskiC. Value of spectral detector computed tomography for the early assessment of technique efficacy after microwave ablation of hepatocellular carcinoma.PLoS One2021166e025267810.1371/journal.pone.025267834129650
    [Google Scholar]
  101. BäumlerW. BeyerL.P. LürkenL. WiggermannP. StroszczynskiC. DollingerM. SchichoA. Detection of incomplete irreversible electroporation (IRE) and microwave ablation (MWA) of hepatocellular carcinoma (HCC) using iodine quantification in dual energy computed tomography (DECT).Diagnostics202212498610.3390/diagnostics1204098635454034
    [Google Scholar]
  102. MuléS. PigneurF. QueleverR. TenenhausA. BaranesL. RichardP. TacherV. HerinE. PasquierH. RonotM. RahmouniA. VilgrainV. LucianiA. Can dual-energy CT replace perfusion CT for the functional evaluation of advanced hepatocellular carcinoma?Eur. Radiol.20182851977198510.1007/s00330‑017‑5151‑y29168007
    [Google Scholar]
  103. ChenY. ShiK. LiZ. WangH. LiuN. ZhanP. LiuX. ShangB. HouP. GaoJ. LyuP. Survival prediction of hepatocellular carcinoma by measuring the extracellular volume fraction with single-phase contrast-enhanced dual-energy CT imaging.Front. Oncol.20231313119942610.3389/fonc.2023.119942637538109
    [Google Scholar]
  104. LewinM. Laurent-BellueA. DesterkeC. RaduA. FeghaliJ.A. FarahJ. AgostiniH. NaultJ.C. VibertE. GuettierC. Evaluation of perfusion CT and dual-energy CT for predicting microvascular invasion of hepatocellular carcinoma.Abdom. Radiol. (N.Y.)20224762115212710.1007/s00261‑022‑03511‑735419748
    [Google Scholar]
  105. PanC. DaiF. ShengL. Clinical application of spectral CT perfusion scanning in evaluating the blood supply source of portal vein tumor thrombus in hepatocellular carcinoma.Front. Oncol.202313134867910.3389/fonc.2023.134867938304029
    [Google Scholar]
  106. NagayamaY. NakauraT. OdaS. UtsunomiyaD. FunamaY. IyamaY. TaguchiN. NamimotoT. YukiH. KidohM. HirataK. NakagawaM. YamashitaY. Dual-layer DECT for multiphasic hepatic CT with 50 percent iodine load: A matched-pair comparison with a 120 kVp protocol.Eur. Radiol.20182841719173010.1007/s00330‑017‑5114‑329063254
    [Google Scholar]
  107. LvP. ZhouZ. LiuJ. ChaiY. ZhaoH. GuoH. MarinD. GaoJ. Can virtual monochromatic images from dual-energy CT replace low-kVp images for abdominal contrast-enhanced CT in small- and medium-sized patients?Eur. Radiol.20192962878288910.1007/s00330‑018‑5850‑z30506223
    [Google Scholar]
  108. LyuP. LiZ. ChenY. WangH. LiuN. LiuJ. ZhanP. LiuX. ShangB. WangL. GaoJ. Deep learning reconstruction CT for liver metastases: Low-dose dual-energy vs standard-dose single-energy.Eur. Radiol.2023341283810.1007/s00330‑023‑10033‑337532899
    [Google Scholar]
  109. AsmundoL. RizzettoF. Srinivas RaoS. SgrazzuttiC. VicentinI. KambadakoneA. CatalanoO.A. VanzulliA. Dual-energy CT applications on liver imaging: What radiologists and radiographers should know? A systematic review.Abdom. Radiol. (N.Y.)202449113811382310.1007/s00261‑024‑04380‑y38811447
    [Google Scholar]
  110. Srinivas-RaoS. CaoJ. MarinD. KambadakoneA. Dual-energy computed tomography to photon counting computed tomography: Emerging technological innovations.Radiol. Clin. North Am.202361693394410.1016/j.rcl.2023.06.01537758361
    [Google Scholar]
  111. ParakhA. LennartzS. AnC. RajiahP. YehB.M. SimeoneF.J. SahaniD.V. KambadakoneA.R. Dual-energy CT images: Pearls and pitfalls.Radiographics20214119811910.1148/rg.202120010233411614
    [Google Scholar]
  112. MeyerM. NanceJ.W.Jr SchoepfU.J. MoscarielloA. WeiningerM. RoweG.W. RuzsicsB. KangD.K. ChiaramidaS.A. SchoenbergS.O. FinkC. HenzlerT. Cost-effectiveness of substituting dual-energy CT for SPECT in the assessment of myocardial perfusion for the workup of coronary artery disease.Eur. J. Radiol.201281123719372510.1016/j.ejrad.2010.12.05521277132
    [Google Scholar]
  113. LowYL FinkelsteinE Cost-effective analysis of dual-energy computed tomography for the diagnosis of occult hip fractures among older adults.Value Health2021241754176210.1016/j.jval.2021.06.005
    [Google Scholar]
  114. JacobsenM.C. ThrowerS.L. GerR.B. LengS. CourtL.E. BrockK.K. TammE.P. CressmanE.N.K. CodyD.D. LaymanR.R. Multi‐energy computed tomography and material quantification: Current barriers and opportunities for advancement.Med. Phys.20204783752377110.1002/mp.1424132453879
    [Google Scholar]
  115. Ghazi SherbafF. SairH.I. ShakoorD. FritzJ. SchwaigerB.J. JohnsonM.H. DemehriS. DECT in detection of vertebral fracture–associated bone marrow edema: A systematic review and meta-analysis with emphasis on technical and imaging interpretation parameters.Radiology2021300111011910.1148/radiol.202120362433876973
    [Google Scholar]
  116. KokuboR. SaitoK. YamadaT. TanakaT. TajimaY. SuzukiK. Comparison of liver fibrosis and function indices with extracellular volume using dual-energy CT: A retrospective study.Curr. Med. Imaging Rev.202218111180118510.2174/157340561866622040710023735392787
    [Google Scholar]
  117. McColloughC.H. LengS. Use of artificial intelligence in computed tomography dose optimisation.Ann. ICRP2020491_suppl11312510.1177/014664532094082732870019
    [Google Scholar]
  118. LellM.M. KachelrießM. Recent and upcoming technological developments in computed tomography.Invest. Radiol.202055181910.1097/RLI.000000000000060131567618
    [Google Scholar]
  119. ChuB. GanL. ShenY. SongJ. LiuL. LiJ. LiuB. A deep learning image reconstruction algorithm for improving image quality and hepatic lesion detectability in abdominal dual-energy computed tomography: Preliminary results.J. Digit. Imaging20233662347235510.1007/s10278‑023‑00893‑y37580484
    [Google Scholar]
  120. BerbísM.A. Paulano GodinoF. Royuela del ValJ. Alcalá MataL. LunaA. Clinical impact of artificial intelligence-based solutions on imaging of the pancreas and liver.World J. Gastroenterol.20232991427144510.3748/wjg.v29.i9.142736998424
    [Google Scholar]
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