MRI-based radiomics analysis to evaluate the clinicopathological characteristics of cervical carcinoma: a multicenter study

1. Bray, F, Ferlay, J, Soerjomataram, I, et al. Global cancer statistics 2018: gLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 2018;6:394–424.
Google Scholar | Crossref2. Xie, L, Chu, R, Wang, K, et al. Prognostic assessment of cervical cancer patients by clinical staging and surgical-pathological factor: a support vector machine-based approach. Front Oncol 2020;10:1353.
Google Scholar | Crossref | Medline3. Lee, SI, Atri, M. 2018 FIGO staging system for uterine cervical cancer: enter cross-sectional imaging. Radiology 2019;292:15–24.
Google Scholar | Crossref | Medline4. Zhang, W, Chen, C, Liu, P, et al. Impact of pelvic MRI in routine clinical practice on staging of IB1-IIA2 cervical cancer. Cancer Manag Res 2019;11:3603–3609.
Google Scholar | Crossref | Medline5. Otero-García, MM, Mesa-Álvarez, A, Nikolic, O, et al. Role of MRI in staging and follow-up of endometrial and cervical cancer: pitfalls and mimickers. Insights Imaging 2019;10:19.
Google Scholar | Crossref | Medline6. Salib, MY, Russell, JHB, Stewart, VR, et al. 2018 FIGO staging classification for cervical cancer: added benefits of imaging. Radiographics 2020;40:1807–1822.
Google Scholar | Crossref | Medline7. Degregorio, A, Widschwendter, P, Ebner, F, et al. Influence of the new FIGO classification for cervical cancer on patient survival: a retrospective analysis of 265 histologically confirmed cases with FIGO stages IA to IIB. Oncology 2020;98:91–97.
Google Scholar | Crossref | Medline8. Choi, HJ, Ju, W, Myung, SK, et al. Diagnostic performance of computer tomography, magnetic resonance imaging, and positron emission tomography or positron emission tomography/computer tomography for detection of metastatic lymph nodes in patients with cervical cancer: meta-analysis. Cancer Sci 2010;101:1471–1479.
Google Scholar | Crossref | Medline9. Takeuchi, S . Biology and treatment of cervical adenocarcinoma. Chin J Cancer Res 2016;28:254–262.
Google Scholar | Crossref | Medline10. Vinh-Hung, V, Bourgain, C, Vlastos, G, et al. Prognostic value of histopathology and trends in cervical cancer: a SEER population study. BMC Cancer 2007;71:64.
Google Scholar11. Fujiwara, H, Yokota, H, Monk, B, et al. Gynecologic cancer InterGroup (GCIG) consensus review for cervical adenocarcinoma. Int J Radiat Oncol Biol Phys 2014;24:S96–S101.
Google Scholar12. Gien, LT, Beauchemin, MC, Thomas, G, et al. Adenocarcinoma: a unique cervical cancer. Gynecol Oncol 2010;116:140–146.
Google Scholar | Crossref | Medline13. Spaans, VM, Trietsch, MD, Peters, AA, et al. Precise classification of cervical carcinomas combined with somatic mutation profiling contributes to predicting disease outcome. PLoS One 2015;10:e0133670.
Google Scholar | Crossref | Medline14. Peters, WA, Liu, PY, Barrett, RJ, et al. Concurrent chemotherapy and pelvic radiation therapy compared with pelvic radiation therapy alone as adjuvant therapy after radical surgery in high-risk early-stage cancer of the cervix. J Clin Oncol 2000;18:1606–1613.
Google Scholar | Crossref | Medline | ISI15. Rotman, M, Sedlis, A, Piedmonte, MR, et al. A phase III randomized trial of postoperative pelvic irradiation in stage IB cervical carcinoma with poor prognostic features: follow-up of a gynecologic oncology group study. Int J Radiat Oncol Biol Phys 2006;65:169–176.
Google Scholar | Crossref | Medline16. Limkin, EJ, Sun, R, Dercle, L, et al. Promises and challenges for the implementation of computational medical imaging (radiomics) in oncology. Ann Oncol 2017;28:1191–1206.
Google Scholar | Crossref | Medline17. Wu, Q, Shi, D, Dou, S, et al. Radiomics analysis of multiparametric MRI evaluates the pathological features of cervical squamous cell carcinoma. J Magn Reson Imaging 2019;49:1141–1148.
Google Scholar | Crossref | Medline18. Wang, W, Cao, K, Jin, S, et al. Differentiation of renal cell carcinoma subtypes through MRI-based radiomics analysis. Eur Radiol 2020;10:5738–5747.
Google Scholar | Crossref19. Huang, YQ, Liang, CH, He, L, et al. Development and validation of a radiomics nomogram for preoperative prediction of lymph node metastasis in development and validation of a radiomics nomogram for preoperative prediction of lymph node metastasis in colorectal cancer. J Clin Oncol 2016;34:1–9.
Google Scholar | Crossref | Medline20. Wu, S, Zheng, J, Li, Y, et al. A radiomics nomogram for the preoperative prediction of lymph node metastasis in bladder cancer. Clin Cancer Res 2017;23:6904–6911.
Google Scholar | Crossref | Medline21. Hou, L, Zhou, W, Ren, J, et al. Radiomics analysis of multiparametric MRI for the preoperative prediction of lymph node metastasis in cervical cancer. Front Oncol 2020;10:1393.
Google Scholar | Crossref | Medline22. Fang, J, Zhang, B, Wang, S. Association of MRI-derived radiomic biomarker with disease-free survival in patients with early-stage cervical cancer. Theranostics 2020;10:2284–2292.
Google Scholar | Crossref | Medline23. Zhang, J, Qin, L, Chen, HM, et al. Overall survival, locoregional recurrence, and distant metastasis of definitive concurrent chemoradiotherapy for cervical squamous cell carcinoma and adenocarcinoma: before and after propensity score matching analysis of a cohort study. Am J Cancer Res 2020;10:1808–1820.
Google Scholar | Medline24. Cui, Y, Yang, X, Shi, Z, et al. Radiomics analysis of multiparametric MRI for prediction of pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer. Eur Radiol 2019;29:1211–1220.
Google Scholar | Crossref | Medline25. Liu, Y, Zhang, Y, Cheng, R, et al. Radiomics analysis of apparent diffusion coefficient in cervical cancer: a preliminary study on histological grade evaluation. J Magn Reson Imaging 2019;49:280–290.
Google Scholar | Crossref | Medline26. Sauerbrei, W, Royston, P, Binder, H, et al. Selection of important variables and determination of functional form for continuous predictors in multivariable model building. Stat Med 2007;26:5512–5528.
Google Scholar | Crossref | Medline | ISI27. Koh, WJ, Abu-Rustum, NR, Bean, S, et al. Cervical cancer, version 3.2019. J Natl Compr Canc Netw 2019;1:64–84.
Google Scholar | Crossref28. Galic, V, Herzog, TJ, Lewin, SN, et al. Prognostic significance of adenocarcinoma histology in women with cervical cancer. Gynecol Oncol 2012;125:287–291.
Google Scholar | Crossref | Medline29. Zhang, J, Qin, L, Chen, HM, et al. Outcome patterns of cervical adenocarcinoma and squamous cell carcinoma following curative surgery: before and after propensity score matching analysis of a cohort study. Am J Cancer Res 2020;10: 1793–1807.
Google Scholar | Medline30. Ren, S, Zhao, R, Cui, W, et al. Computed tomography-based radiomics signature for the preoperative differentiation of pancreatic adenosquamous carcinoma from pancreatic ductal adenocarcinoma. Front Oncol 2020;10:1–10.
Google Scholar | Crossref | Medline31. Wang, W, Cao, K, Jin, S, et al. Differentiation of renal cell carcinoma subtypes through MRI-based radiomics analysis. Eur Radiol 2020;30:5738–5747.
Google Scholar | Crossref | Medline32. Liu, S, Liu, S, Zhang, C, et al. Exploratory study of a CT radiomics model for the classification of small cell lung cancer and non-small-cell lung cancer. Front Oncol 2020;10:1–10.
Google Scholar | Medline33. Ren, C, Zhang, J, Qi, M, et al. Machine learning based on clinico-biological features integrated 18F-FDG PET/CT radiomics for distinguishing squamous cell carcinoma from adenocarcinoma of lung. Eur J Nucl Med Mol Imaging 2020;45:1538–1549.
Google Scholar34. Saida, T, Sakata, A, Tanaka, YO, et al. Clinical and MRI characteristics of uterine cervical adenocarcinoma: its variants and mimics. Korean J Radiol 2019;20:364–377.
Google Scholar | Crossref | Medline35. Wang, M, Perucho, JAU, Tse, KY, et al. MRI Texture features differentiate clinicopathological characteristics of cervical carcinoma. Eur Radiol 2020;30:5384–5391.
Google Scholar | Crossref | Medline36. Singh, AK, Grigsby, PW, Dehdashti, F, et al. FDG-PET lymph node staging and survival of patients with FIGO stage IIIb cervical carcinoma. Int J Radiat Oncol Biol Phys 2003;56:489–493.
Google Scholar | Crossref | Medline37. Wright, JD, Matsuo, K, Huang, Y, et al. Prognostic performance of the 2018 International Federation of Gynecology and Obstetrics cervical cancer staging guidelines. Obstet Gynecol 2019;134:49–57.
Google Scholar | Crossref | Medline38. Delgado, G, Bundy, B, Zaino, R, et al. Prospective surgical-pathological study of disease-free interval in patients with stage IB squamous cell carcinoma of the cervix: a gynecologic oncology group study. Gynecol Oncol 1990;38:352–327.
Google Scholar | Crossref39. Balleyguier, C, Sala, E, Da Cunha, T, et al. Staging of uterine cervical cancer with MRI: guidelines of the European Society of Urogenital Radiology. Eur Radiol 2011;21:1102–1110.
Google Scholar | Crossref | Medline | ISI40. Wu, Q, Zheng, D, Shi, L, et al. Differentiating metastatic from nonmetastatic lymph nodes in cervical cancer patients using monoexponential, biexponential, and stretched exponential diffusion-weighted MR imaging. Eur Radiol 2017;27:5272–5279.
Google Scholar | Crossref | Medline41. Cibula, D, Zi kan, M, Slama, J, et al. Risk of micrometastases in non-sentinel pelvic lymph nodes in cervical cancer. Gynecol Oncol 2016;143:83–86.
Google Scholar | Crossref | Medline42. Wang, T, Gao, T, Yang, J, et al. Preoperative prediction of pelvic lymph nodes metastasis in early-stage cervical cancer using radiomics nomogram developed based on T2-weighted MRI and diffusion-weighted imaging. Eur J Radiol 2019;114:128–135.
Google Scholar | Crossref | Medline43. Wu, Q, Wang, S, Chen, X, et al. Radiomics analysis of magnetic resonance imaging improves diagnostic performance of lymph node metastasis in patients with cervical cancer. Radiother Oncol 2019;138:141–148.
Google Scholar | Crossref | Medline44. Wu, S, Zheng, J, Li, Y, et al. A radiomics nomogram for the preoperative prediction of lymph node metastasis in bladder Cancer. Clin Cancer Res 2017;23:6904–6911.
Google Scholar | Crossref | Medline45. Huang, Y, Liang, C, He, L, et al. Development and validation of a radiomics nomogram for preoperative prediction of lymph node metastasis in development and validation of a radiomics nomogram for preoperative prediction of lymph node metastasis in colorectal cancer. J Clin Oncol 2016;34:2157–2164.
Google Scholar | Crossref | Medline | ISI46. Kan, Y, Dong, D, Zhang, Y, et al. Radiomic signature as a predictive factor for lymph node metastasis in early-stage cervical cancer. J Magn Reson Imaging 2019;49:304–310.
Google Scholar | Crossref | Medline

留言 (0)

沒有登入
gif