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Volume 20, Issue 1
  • ISSN: 1573-4056
  • E-ISSN: 1875-6603
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Abstract

Background

The Glypican 3 (GPC3)-positive expression in Hepatocellular Carcinoma (HCC) is associated with a worse prognosis. Moreover, GPC3 has emerged as an immunotherapeutic target in advanced unresectable HCC systemic therapy. It is significant to diagnose GPC3-positive HCCs before therapy. Regarding imaging diagnosis of HCC, dynamic contrast-enhanced CT is more common than MRI in many regions.

Objective

The aim of this study was to construct and validate a radiomics model based on contrast-enhanced CT to predict the GPC3 expression in HCC.

Methods

This retrospective study included 141 (training cohort: n = 100; validation cohort: n = 41) pathologically confirmed HCC patients. Radiomics features were extracted from the Artery Phase (AP) images of contrast-enhanced CT. Logistic regression with the Least Absolute Shrinkage and Selection Operator (LASSO) regularization was used to select features to construct radiomics score (Rad-score). A final combined model, including the Rad-score of the selected features and clinical risk factors, was established. Receiver Operating Characteristic (ROC) curve analysis, Delong test, and Decision Curve Analysis (DCA) were used to assess the predictive performance of the clinical and radiomics models.

Results

5 features were selected to construct the AP radiomics model of contrast-enhanced CT. The radiomics model of AP from contrast-enhanced CT was superior to the clinical model of AFP in training cohorts (P < 0.001), but not superior to the clinical model in validation cohorts (P = 0.151). The combined model (AUC = 0.867 . 0.895), including AP Rad-score and serum Alpha-Fetoprotein (AFP) levels, improved the predictive performance more than the AFP model (AUC = 0.651 . 0.718) in the training and validation cohorts. The combined model, with a higher decision curve indicating more net benefit, exhibited a better predictive performance than the AP radiomics model. DCA revealed that at a range threshold probability approximately above 60%, the combined model added more net benefit compared to the AP radiomics model of contrast-enhanced CT.

Conclusion

A combined model including AP Rad-score and serum AFP levels based on contrast-enhanced CT could preoperatively predict GPC3-positive expression in HCC.

© 2024 The Author(s). Published by Bentham Science Publisher.
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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2024-01-01
2025-07-05

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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. TabrizianP. JibaraG. ShragerB. SchwartzM. RoayaieS. Recurrence of hepatocellular cancer after resection: Patterns, treatments, and prognosis.Ann. Surg.2015261594795510.1097/SLA.000000000000071025010665
     [Google Scholar]
  3. RoayaieS. ObeidatK. SpositoC. MarianiL. BhooriS. PellegrinelliA. LabowD. LlovetJ.M. SchwartzM. MazzaferroV. Resection of hepatocellular cancer ≤2 cm: Results from two Western centers.Hepatology20135741426143510.1002/hep.2583222576353
     [Google Scholar]
  4. KurebayashiY. MatsudaK. UenoA. TsujikawaH. YamazakiK. MasugiY. KwaW.T. EffendiK. HasegawaY. YagiH. AbeY. KitagoM. OjimaH. SakamotoM. Immunovascular classification of HCC reflects reciprocal interaction between immune and angiogenic tumor microenvironments.Hepatology20227551139115310.1002/hep.3220134657298
     [Google Scholar]
  5. LlovetJ.M. MontalR. SiaD. FinnR.S. Molecular therapies and precision medicine for hepatocellular carcinoma.Nat. Rev. Clin. Oncol.2018151059961610.1038/s41571‑018‑0073‑430061739
     [Google Scholar]
  6. De StefanoF. ChaconE. TurciosL. MartiF. GedalyR. Novel biomarkers in hepatocellular carcinoma.Dig. Liver Dis.201850111115112310.1016/j.dld.2018.08.01930217732
     [Google Scholar]
  7. WangY. DengB. Hepatocellular carcinoma: Molecular mechanism, targeted therapy, and biomarkers.Cancer Metastasis Rev.202342362965210.1007/s10555‑023‑10084‑436729264
     [Google Scholar]
  8. CalderaroJ. CouchyG. ImbeaudS. AmaddeoG. LetouzéE. BlancJ.F. LaurentC. HajjiY. AzoulayD. Bioulac-SageP. NaultJ.C. Zucman-RossiJ. Histological subtypes of hepatocellular carcinoma are related to gene mutations and molecular tumour classification.J. Hepatol.201767472773810.1016/j.jhep.2017.05.01428532995
     [Google Scholar]
  9. ZhouF. ShangW. YuX. TianJ. Glypican‐3: A promising biomarker for hepatocellular carcinoma diagnosis and treatment.Med. Res. Rev.201838274176710.1002/med.2145528621802
     [Google Scholar]
  10. DargelC. Bassani-SternbergM. HasreiterJ. ZaniF. BockmannJ.H. ThieleF. BohneF. WisskirchenK. WildeS. SprinzlM.F. SchendelD.J. KrackhardtA.M. UckertW. WohlleberD. SchiemannM. StemmerK. HeikenwälderM. BuschD.H. RichterG. MannM. ProtzerU. T cells engineered to express a t-cell receptor specific for glypican-3 to recognize and kill hepatoma cells in vitro and in mice.Gastroenterology201514941042105210.1053/j.gastro.2015.05.05526052074
     [Google Scholar]
  11. ShimizuY. SuzukiT. YoshikawaT. TsuchiyaN. SawadaY. EndoI. NakatsuraT. Cancer immunotherapy‐targeted glypican‐3 or neoantigens.Cancer Sci.2018109353154110.1111/cas.1348529285841
     [Google Scholar]
  12. HaruyamaY. KataokaH. Glypican-3 is a prognostic factor and an immunotherapeutic target in hepatocellular carcinoma.World J. Gastroenterol.201622127528310.3748/wjg.v22.i1.27526755876
     [Google Scholar]
  13. NingS. BinC. NaH. PengS. YiD. Xiang-huaY. Fang-yinZ. Da-yongZ. Rong-chengL. Glypican-3, a novel prognostic marker of hepatocellular cancer, is related with postoperative metastasis and recurrence in hepatocellular cancer patients.Mol. Biol. Rep.201239135135710.1007/s11033‑011‑0745‑y21655958
     [Google Scholar]
  14. GuoM. ZhangH. ZhengJ. LiuY. Glypican-3: A new target for diagnosis and treatment of hepatocellular carcinoma.J. Cancer20201182008202110.7150/jca.3997232127929
     [Google Scholar]
  15. MakkoukA. YangX.C. BarcaT. LucasA. TurkozM. WongJ.T.S. NishimotoK.P. BrodeyM.M. TabrizizadM. GunduraoS.R.Y. BaiL. BhatA. AnZ. AbbotS. SatpayevD. AftabB.T. HerrmanM. Off-the-shelf Vδ1 gamma delta T cells engineered with glypican-3 (GPC-3)-specific chimeric antigen receptor (CAR) and soluble IL-15 display robust antitumor efficacy against hepatocellular carcinoma.J. Immunother. Cancer2021912e00344110.1136/jitc‑2021‑00344134916256
     [Google Scholar]
  16. Caraballo GalvaL.D. JiangX. HusseinM.S. ZhangH. MaoR. BrodyP. PengY. HeA.R. Kehinde-IgeM. SadekR. QiuX. ShiH. HeY. Novel low‐avidity glypican‐3 specific CARTs resist exhaustion and mediate durable antitumor effects against HCC.Hepatology202276233034410.1002/hep.3227934897774
     [Google Scholar]
  17. ShiD. ShiY. KasebA.O. QiX. ZhangY. ChiJ. LuQ. GaoH. JiangH. WangH. YuanD. MaH. WangH. LiZ. ZhaiB. Chimeric antigen receptor-glypican-3 t-cell therapy for advanced hepatocellular carcinoma: Results of phase I trials.Clin. Cancer Res.202026153979398910.1158/1078‑0432.CCR‑19‑325932371538
     [Google Scholar]
  18. FuY. UrbanD.J. NaniR.R. ZhangY.F. LiN. FuH. ShahH. GorkaA.P. GuhaR. ChenL. HallM.D. SchnermannM.J. HoM. Glypican‐3‐specific antibody drug conjugates targeting hepatocellular carcinoma.Hepatology201970256357610.1002/hep.3032630353932
     [Google Scholar]
  19. GuD. XieY. WeiJ. LiW. YeZ. ZhuZ. TianJ. LiX. MRI‐based radiomics signature: A potential biomarker for identifying glypican 3‐positive hepatocellular carcinoma.J. Magn. Reson. Imaging20205261679168710.1002/jmri.2719932491239
     [Google Scholar]
  20. ChongH. GongY. ZhangY. DaiY. ShengR. ZengM. Radiomics on gadoxetate disodium-enhanced MRI: Non-invasively identifying glypican 3-positive hepatocellular carcinoma and postoperative recurrence.Acad. Radiol.2023301496310.1016/j.acra.2022.04.00635562264
     [Google Scholar]
  21. YuY. FanY. WangX. ZhuM. HuM. ShiC. HuC. Gd-EOB-DTPA-enhanced MRI radiomics to predict vessels encapsulating tumor clusters (VETC) and patient prognosis in hepatocellular carcinoma.Eur. Radiol.202232295997010.1007/s00330‑021‑08250‑934480625
     [Google Scholar]
  22. WangW. GuD. WeiJ. DingY. YangL. ZhuK. LuoR. RaoS.X. TianJ. ZengM. A radiomics-based biomarker for cytokeratin 19 status of hepatocellular carcinoma with gadoxetic acid–enhanced MRI.Eur. Radiol.20203053004301410.1007/s00330‑019‑06585‑y32002645
     [Google Scholar]
  23. LewisS. HectorsS. TaouliB. Radiomics of hepatocellular carcinoma.Abdom. Radiol.202146111112310.1007/s00261‑019‑02378‑531925492
     [Google Scholar]
  24. ZhaoJ. GaoS. SunW. GrimmR. FuC. HanJ. ShengR. ZengM. Magnetic resonance imaging and diffusion-weighted imaging-based histogram analyses in predicting glypican 3-positive hepatocellular carcinoma.Eur. J. Radiol.202113910973210.1016/j.ejrad.2021.10973233905978
     [Google Scholar]
  25. FanY. YuY. WangX. HuM. HuC. Radiomic analysis of Gd-EOB-DTPA-enhanced MRI predicts Ki-67 expression in hepatocellular carcinoma.BMC Med. Imaging202121110010.1186/s12880‑021‑00633‑034130644
     [Google Scholar]
  26. MorfordL.A. DavisC. JinL. DobierzewskaA. PetersonM.L. SpearB.T. The oncofetal gene glypican 3 is regulated in the postnatal liver by zinc fingers and homeoboxes 2 and in the regenerating liver by alpha-fetoprotein regulator 2.Hepatology20074651541154710.1002/hep.2182517668883
     [Google Scholar]
  27. LiN. WeiL. LiuX. BaiH. YeY. LiD. LiN. BaxaU. WangQ. LvL. ChenY. FengM. LeeB. GaoW. HoM. A frizzled‐like cysteine‐rich domain in glypican‐3 mediates wnt binding and regulates hepatocellular carcinoma tumor growth in mice.Hepatology20197041231124510.1002/hep.3064630963603
     [Google Scholar]
  28. CapurroM. MartinT. ShiW. FilmusJ. Glypican-3 binds to frizzled and plays a direct role in the stimulation of canonical Wnt signaling.J. Cell Sci.2014127Pt 7jcs.14087110.1242/jcs.14087124496449
     [Google Scholar]
  29. FanY. YuY. WangX. HuM. DuM. GuoL. SunS. HuC. Texture analysis based on Gd-EOB-DTPA-Enhanced MRI for identifying vessels encapsulating tumor clusters (VETC)-positive hepatocellular carcinoma.J. Hepatocell. Carcinoma2021834935910.2147/JHC.S29375533981636
     [Google Scholar]
  30. BodardS. Cancer imaging and prevention of renal failure.Bull Cancer2022455122392397
     [Google Scholar]
/content/journals/cmir/10.2174/0115734056277475240215115629
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Radiomic Analysis of Contrast-Enhanced CT Predicts Glypican 3-Positive Hepatocellular Carcinoma
Curr. Med. Imaging 20, e15734056277475 (2024); https://doi.org/10.2174/0115734056277475240215115629
/content/journals/cmir/10.2174/0115734056277475240215115629
/content/journals/cmir/10.2174/0115734056277475240215115629
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  • Article Type:     Research Article
Keyword(s): AFP; Contrast-enhanced CT; DCA; GPC3-positve; Hepatocellular carcinoma; Radiomics

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