Comparison of 2D and 3D radiomics features with conventional features based on contrast-enhanced CT images for preoperative prediction the risk of thymic epithelial tumors
This work is licensed under the Creative Commons Attribution 4.0 International License.
Engels EA. Epidemiology of thymoma and associated malignancies. J Thorac Oncol 2010; 5(10 Suppl 4): S260-5. doi: 10.1097/JTO.0b013e3181f1f62dEngelsEA.Epidemiology of thymoma and associated malignancies. J Thorac Oncol2010; 5(10 Suppl 4): S260–5. doi: 10.1097/JTO.0b013e3181f1f62dOpen DOISearch in Google Scholar
Travis WD, Brambilla E, Nicholson AG, Yatabe Y, Austin JHM, Beasley MB, et al. The 2015 World Health Organization Classification of Lung Tumors: im-pact of genetic, clinical and radiologic advances since the 2004 classification. J Thorac Oncol 2015; 10: 1243-60. doi: 10.1097/JTO.0000000000000630TravisWDBrambillaENicholsonAGYatabeYAustinJHMBeasleyMBThe 2015 World Health Organization Classification of Lung Tumors: im-pact of genetic, clinical and radiologic advances since the 2004 classification. J Thorac Oncol2015; 10: 1243–60. doi: 10.1097/JTO.0000000000000630Open DOISearch in Google Scholar
Meurgey A, Girard N, Merveilleux du Vignaux C, Maury JM, Tronc F, Thivolet-Bejui F, et al. Assessment of the ITMIG statement on the WHO histological classification and of the eighth TNM staging of thymic epithelial tumors of a series of 188 thymic epithelial tumors. J Thorac Oncol 2017; 12: 1571-81. doi: 10.1016/j.jtho.2017.06.072MeurgeyAGirardNMerveilleux du VignauxCMauryJMTroncFThivolet-BejuiFAssessment of the ITMIG statement on the WHO histological classification and of the eighth TNM staging of thymic epithelial tumors of a series of 188 thymic epithelial tumors. J Thorac Oncol2017; 12: 1571–81. doi: 10.1016/j.jtho.2017.06.072Open DOISearch in Google Scholar
Suster S, Moran CA. Histologic classification of thymoma: the World Health Organization and beyond. Hematol Oncol Clin North Am 2008; 22: 381-92. doi: 10.1016/j.hoc.2008.03.001SusterSMoranCA.Histologic classification of thymoma: the World Health Organization and beyond. Hematol Oncol Clin North Am2008; 22: 381–92. doi: 10.1016/j.hoc.2008.03.001Open DOISearch in Google Scholar
Kondo K, Yoshizawa K, Tsuyuguchi M, Kimura S, Sumitomo M, Morita J, et al. WHO histologic classification is a prognostic indicator in thymoma. Ann Thorac Surg 2004; 77: 1183-8. doi: 10.1016/j.athoracsur.2003.07.042KondoKYoshizawaKTsuyuguchiMKimuraSSumitomoMMoritaJWHO histologic classification is a prognostic indicator in thymoma. Ann Thorac Surg2004; 77: 1183–8. doi: 10.1016/j.athoracsur.2003.07.042Open DOISearch in Google Scholar
Nishino M, Ashiku SK, Kocher ON, Thurer RL, Boiselle PM, Hatabu H. The thymus: a comprehensive review-erratum. Radiographics 2017; 37: 1004. doi: 10.1148/rg.2017174002NishinoMAshikuSKKocherONThurerRLBoisellePMHatabuH.The thymus: a comprehensive review-erratum. Radiographics2017; 37: 1004. doi: 10.1148/rg.2017174002Open DOISearch in Google Scholar
Sadohara J, Fujimoto K, Müller NL, Kato S, Takamori S, Ohkuma K, et al. Thymic epithelial tumors: comparison of CT and MR imaging findings of low-risk thymomas, high-risk thymomas, and thymic carcinomas. Eur J Radiol 2006; 60: 70-9. doi: 10.1016/j.ejrad.2006.05.003SadoharaJFujimotoKMüllerNLKatoSTakamoriSOhkumaKThymic epithelial tumors: comparison of CT and MR imaging findings of low-risk thymomas, high-risk thymomas, and thymic carcinomas. Eur J Radiol2006; 60: 70–9. doi: 10.1016/j.ejrad.2006.05.003Open DOISearch in Google Scholar
Ozawa Y, Hara M, Shimohira M, Sakurai K, Nakagawa M, Shibamoto Y. Associations between computed tomography features of thymomas and their pathological classification. Acta Radiol 2016; 57: 1318-25. doi: 10.1177/0284185115590288OzawaYHaraMShimohiraMSakuraiKNakagawaMShibamotoY.Associations between computed tomography features of thymomas and their pathological classification. Acta Radiol2016; 57: 1318–25. doi: 10.1177/0284185115590288Open DOISearch in Google Scholar
Tomiyama N, Johkoh T, Mihara N, Honda O, Kozuka T, Koyama M, et al. Using the World Health Organization Classification of thymic epithelial neoplasms to describe CT findings. AJR Am J Roentgenol 2002; 179: 881-6. doi: 10.2214/ajr.179.4.1790881TomiyamaNJohkohTMiharaNHondaOKozukaTKoyamaMUsing the World Health Organization Classification of thymic epithelial neoplasms to describe CT findings. AJR Am J Roentgenol2002; 179: 881–6. doi: 10.2214/ajr.179.4.1790881Open DOISearch in Google Scholar
Lambin P, Leijenaar RTH, Deist TM, Peerlings J, de Jong EEC, van Timmeren J, et al. Radiomics: the bridge between medical imaging and personal-ized medicine. Nat Rev Clin Oncol 2017; 14: 749-62. doi: 10.1038/nr-clinonc.2017.141LambinPLeijenaarRTHDeistTMPeerlingsJde JongEECvan TimmerenJRadiomics: the bridge between medical imaging and personal-ized medicine. Nat Rev Clin Oncol2017; 14: 749–62. doi: 10.1038/nr-clinonc.2017.141Open DOISearch in Google Scholar
Aerts HJ. The potential of radiomic-based phenotyping in precision medi-cine: a review. JAMA Oncol 2016; 2: 1636-42. doi: 10.1001/jamaon-col.2016.2631AertsHJ.The potential of radiomic-based phenotyping in precision medi-cine: a review. JAMA Oncol2016; 2: 1636–42. doi: 10.1001/jamaon-col.2016.2631Open DOISearch in Google Scholar
Song J, Yin Y, Wang H, Chang Z, Liu Z, Cui L. A review of original articles pub-lished in the emerging field of radiomics. Eur J Radiol 2020; 127: 108991. doi: 10.1016/j.ejrad.2020.108991SongJYinYWangHChangZLiuZCuiL.A review of original articles pub-lished in the emerging field of radiomics. Eur J Radiol2020; 127: 108991. doi: 10.1016/j.ejrad.2020.108991Open DOISearch in Google Scholar
Iannarelli A, Sacconi B, Tomei F, Anile M, Longo F, Bezzi M, et al. Analysis of CT features and quantitative texture analysis in patients with thymic tu-mors: correlation with grading and staging. Radiol Med 2018; 123: 345-50. doi: 10.1007/s11547-017-0845-4IannarelliASacconiBTomeiFAnileMLongoFBezziMAnalysis of CT features and quantitative texture analysis in patients with thymic tu-mors: correlation with grading and staging. Radiol Med2018; 123: 345–50. doi: 10.1007/s11547-017-0845-4Open DOISearch in Google Scholar
Yasaka K, Akai H, Nojima M, Shinozaki-Ushiku A, Fukayama M, Nakajima J, et al. Quantitative computed tomography texture analysis for estimating histological subtypes of thymic epithelial tumors. Eur J Radiol 2017; 92: 84-92. doi: 10.1016/j.ejrad.2017.04.017YasakaKAkaiHNojimaMShinozaki-UshikuAFukayamaMNakajimaJQuantitative computed tomography texture analysis for estimating histological subtypes of thymic epithelial tumors. Eur J Radiol2017; 92: 8492. doi: 10.1016/j.ejrad.2017.04.017Open DOISearch in Google Scholar
Wang X, Sun W, Liang H, Mao X, Lu Z. Radiomics signatures of computed to-mography imaging for predicting risk categorization and clinical stage of thy-momas. Biomed Res Int 2019; 2019: 3616852. doi: 10.1155/2019/3616852WangXSunWLiangHMaoXLuZ.Radiomics signatures of computed to-mography imaging for predicting risk categorization and clinical stage of thy-momas. Biomed Res Int2019; 2019: 3616852. doi: 10.1155/2019/3616852Open DOISearch in Google Scholar
Lee GD, Kim HR, Choi SH, Kim YH, Kim DK, Park SI. Prognostic stratifica-tion of thymic epithelial tumors based on both Masaoka-Koga stage and WHO classification systems. J Thorac Dis 2016; 8: 901-10. doi: 10.21037/jtd.2016.03.53LeeGDKimHRChoiSHKimYHKimDKParkSI.Prognostic stratifica-tion of thymic epithelial tumors based on both Masaoka-Koga stage and WHO classification systems. J Thorac Dis2016; 8: 901–10. doi: 10.21037/jtd.2016.03.53Open DOISearch in Google Scholar
Zhu H, Luo H, Li Y, Zhang Y, Wu Z, Yang Y. The superior value of radiomics to sonographic assessment for ultrasound-based evaluation of extrathyroidal extension in papillary thyroid carcinoma: a retrospective study. Radiol Oncol 2024; 58: 386-96. doi: 10.2478/raon-2024-0040ZhuHLuoHLiYZhangYWuZYangY.The superior value of radiomics to sonographic assessment for ultrasound-based evaluation of extrathyroidal extension in papillary thyroid carcinoma: a retrospective study. Radiol Oncol2024; 58: 386–96. doi: 10.2478/raon-2024-0040Open DOISearch in Google Scholar
Bo Z, Song J, He Q, Chen B, Chen Z, Xie X, et al. Application of artificial intelligence radiomics in the diagnosis, treatment, and prognosis of hepa-tocellular carcinoma. Comput Biol Med 2024; 173: 108337. doi: 10.1016/j.compbiomed.2024.108337BoZSongJHeQChenBChenZXieXApplication of artificial intelligence radiomics in the diagnosis, treatment, and prognosis of hepa-tocellular carcinoma. Comput Biol Med2024; 173: 108337. doi: 10.1016/j.compbiomed.2024.108337Open DOISearch in Google Scholar
Warkentin MT, Al-Sawaihey H, Lam S, Liu G, Diergaarde B, Yuan JM, et al. Radiomics analysis to predict pulmonary nodule malignancy using machine learning approaches. Thorax 2024; 79: 307-15. doi: 10.1136/thorax-2023-220226WarkentinMTAl-SawaiheyHLamSLiuGDiergaardeBYuanJMRadiomics analysis to predict pulmonary nodule malignancy using machine learning approaches. Thorax2024; 79: 307–15. doi: 10.1136/thorax-2023-220226Open DOISearch in Google Scholar
Sui H, Liu L, Li X, Zuo P, Cui J, Mo Z. CT-based radiomics features analysis for predicting the risk of anterior mediastinal lesions. J Thorac Dis 2019; 11: 1809-18. doi: 10.21037/jtd.2019.05.32SuiHLiuLLiXZuoPCuiJMoZ.CT-based radiomics features analysis for predicting the risk of anterior mediastinal lesions. J Thorac Dis2019; 11: 1809–18. doi: 10.21037/jtd.2019.05.32Open DOISearch in Google Scholar
Marom EM, Milito MA, Moran CA, Liu P, Correa AM, Kim ES, et al. Computed tomography findings predicting invasiveness of thymoma. J Thorac Oncol 2011; 6: 1274-81. doi: 10.1097/JTO.0b013e31821c4203MaromEMMilitoMAMoranCALiuPCorreaAMKimESComputed tomography findings predicting invasiveness of thymoma. J Thorac Oncol2011; 6: 1274–81. doi: 10.1097/JTO.0b013e31821c4203Open DOISearch in Google Scholar
Abdel Razek AAK, Khairy M, Nada N. Diffusion-weighted MR imaging in thymic epithelial tumors: correlation with World Health Organization clas-sification and clinical staging. Radiology 2014; 273: 268-75. doi: 10.1148/radiol.14131643Abdel RazekAAKKhairyMNadaN.Diffusion-weighted MR imaging in thymic epithelial tumors: correlation with World Health Organization clas-sification and clinical staging. Radiology2014; 273: 268–75. doi: 10.1148/radiol.14131643Open DOISearch in Google Scholar
Xiao G, Rong WC, Hu YC, Shi ZQ, Yang Y, Ren JL, et al. MRI radiomics analysis for predicting the pathologic classification and TNM Staging of thymic epi-thelial tumors: a pilot study. AJR Am J Roentgenol 2020; 214: 328-40. doi: 10.2214/AJR.19.21696XiaoGRongWCHuYCShiZQYangYRenJLMRI radiomics analysis for predicting the pathologic classification and TNM Staging of thymic epi-thelial tumors: a pilot study. AJR Am J Roentgenol2020; 214: 328–40. doi: 10.2214/AJR.19.21696Open DOISearch in Google Scholar
Kostic Peric J, Cirkovic A, Srzentic Drazilov S, Samardzic N, Skodric Trifunovic V, Jovanovic D, et al. Molecular profiling of rare thymoma using next-generation sequencing: meta-analysis. Radiol Oncol 2023; 57: 12-19. doi: 10.2478/raon-2023-0013Kostic PericJCirkovicASrzentic DrazilovSSamardzicNSkodric TrifunovicVJovanovicDMolecular profiling of rare thymoma using next-generation sequencing: meta-analysis. Radiol Oncol2023; 57: 12–19. doi: 10.2478/raon-2023-0013Open DOISearch in Google Scholar