References
1. Arbyn, M., E. Weiderpass, L. Bruni, et al., Estimates of incidence and mortality of cervical cancer in 2018: a worldwide analysis. The Lancet. Global health, 2020. 8 (2): p. e191-e203.
2. Marquina, G., A. Manzano, and A. Casado, Targeted Agents in Cervical Cancer: Beyond Bevacizumab. Current oncology reports, 2018.20 (5): p. 40.
3. Cohen, P.A., A. Jhingran, A. Oaknin, and L. Denny, Cervical cancer. Lancet, 2019. 393 (10167): p. 169-182.
4. Quinn, M.A., J.L. Benedet, F. Odicino, et al., Carcinoma of the Cervix Uteri. International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics, 2006. 95 Suppl 1 .
5. Pötter, R., K. Tanderup, M.P. Schmid, et al., MRI-guided adaptive brachytherapy in locally advanced cervical cancer (EMBRACE-I): a multicentre prospective cohort study. The Lancet. Oncology, 2021.22 (4): p. 538-547.
6. Kong, S.-Y., K. Huang, C. Zeng, X. Ma, and S. Wang, The association between short-term response and long-term survival for cervical cancer patients undergoing neoadjuvant chemotherapy: a system review and meta-analysis. Scientific reports, 2018. 8 (1): p. 1545.
7. Kirwan, J.M., P. Symonds, J.A. Green, et al., A systematic review of acute and late toxicity of concomitant chemoradiation for cervical cancer. Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology, 2003. 68 (3): p. 217-226.
8. Gadducci, A., M.E. Guerrieri, and S. Cosio, Adenocarcinoma of the uterine cervix: Pathologic features, treatment options, clinical outcome and prognostic variables. Crit Rev Oncol Hematol, 2019.135 : p. 103-114.
9. Rose, P.G., J. Java, C.W. Whitney, et al., Nomograms Predicting Progression-Free Survival, Overall Survival, and Pelvic Recurrence in Locally Advanced Cervical Cancer Developed From an Analysis of Identifiable Prognostic Factors in Patients From NRG Oncology/Gynecologic Oncology Group Randomized Trials of Chemoradiotherapy. Journal of clinical oncology : official journal of the American Society of Clinical Oncology, 2015. 33 (19): p. 2136-2142.
10. Park, H., K.A. Kim, J.-H. Jung, J. Rhie, and S.Y. Choi, MRI features and texture analysis for the early prediction of therapeutic response to neoadjuvant chemoradiotherapy and tumor recurrence of locally advanced rectal cancer. European radiology, 2020.30 (8): p. 4201-4211.
11. Zhenyu, Liu, Shuo, et al., The Applications of Radiomics in Precision Diagnosis and Treatment of Oncology: Opportunities and Challenges. Theranostics, 2019.
12. Yip, S.S.F. and H.J.W.L. Aerts, Applications and limitations of radiomics. Physics in medicine and biology, 2016. 61 (13): p. R150-R166.
13. Verma, V., C.B. Simone, S. Krishnan, et al., The Rise of Radiomics and Implications for Oncologic Management. Journal of the National Cancer Institute, 2017. 109 (7).
14. Wu, Q., S. Wang, X. Chen, et al., Radiomics analysis of magnetic resonance imaging improves diagnostic performance of lymph node metastasis in patients with cervical cancer. Radiother Oncol, 2019.138 : p. 141-148.
15. Wang, T., T. Gao, J. Yang, 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 : p. 128-135.
16. Gao, S., S. Du, Z. Lu, et al., Multiparametric PET/MR (PET and MR-IVIM) for the evaluation of early treatment response and prediction of tumor recurrence in patients with locally advanced cervical cancer.Eur Radiol, 2020. 30 (2): p. 1191-1201.
17. Fang, M., Y. Kan, D. Dong, et al., Multi-Habitat Based Radiomics for the Prediction of Treatment Response to Concurrent Chemotherapy and Radiation Therapy in Locally Advanced Cervical Cancer.Front Oncol, 2020. 10 : p. 563.
18. Sun, C., X. Tian, Z. Liu, et al., Radiomic analysis for pretreatment prediction of response to neoadjuvant chemotherapy in locally advanced cervical cancer: A multicentre study. EBioMedicine, 2019. 46 : p. 160-169.
19. Fang, J., B. Zhang, S. Wang, et al., Association of MRI-derived radiomic biomarker with disease-free survival in patients with early-stage cervical cancer. Theranostics, 2020. 10 (5): p. 2284-2292.
20. Lucia, F., D. Visvikis, M. Vallieres, et al., External validation of a combined PET and MRI radiomics model for prediction of recurrence in cervical cancer patients treated with chemoradiotherapy.Eur J Nucl Med Mol Imaging, 2019. 46 (4): p. 864-877.
21. Tian, X., C. Sun, Z. Liu, et al., Prediction of Response to Preoperative Neoadjuvant Chemotherapy in Locally Advanced Cervical Cancer Using Multicenter CT-Based Radiomic Analysis. Front Oncol, 2020.10 : p. 77.
22. Wang, W., X. Hou, J. Yan, et al., Outcome and toxicity of radical radiotherapy or concurrent Chemoradiotherapy for elderly cervical cancer women. BMC cancer, 2017. 17 (1): p. 510.
23. Mell, L.K., J.D. Kochanski, J.C. Roeske, et al., Dosimetric predictors of acute hematologic toxicity in cervical cancer patients treated with concurrent cisplatin and intensity-modulated pelvic radiotherapy. International journal of radiation oncology, biology, physics, 2006. 66 (5): p. 1356-1365.
24. Rastegar, S., M. Vaziri, Y. Qasempour, et al., Radiomics for classification of bone mineral loss: A machine learning study. Diagn Interv Imaging, 2020. 101 (9): p. 599-610.
25. Budan, F., K. Szigeti, M. Weszl, et al., Novel radiomics evaluation of bone formation utilizing multimodal (SPECT/X-ray CT) in vivo imaging. PLoS One, 2018. 13 (9): p. e0204423.
26. Pecorelli, S., L. Zigliani, and F. Odicino, Revised FIGO staging for carcinoma of the cervix. International Journal of Gynecology & Obstetrics, 2009. 105 (2): p. 107-108.
27. Choi, S.H., S.H. Kim, H.J. Choi, B.K. Park, and H.J. Lee,Preoperative magnetic resonance imaging staging of uterine cervical carcinoma: results of prospective study. Journal of computer assisted tomography, 2004. 28 (5): p. 620-627.
28. Choi, H.J., S.H. Kim, S.-S. Seo, et al., MRI for pretreatment lymph node staging in uterine cervical cancer. AJR. American journal of roentgenology, 2006. 187 (5): p. W538-W543.
29. Eisenhauer, E.A., P. Therasse, J. Bogaerts, et al., New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). Eur J Cancer, 2009. 45 (2): p. 228-47.
30. Zhang, L., D.V. Fried, X.J. Fave, et al., IBEX: an open infrastructure software platform to facilitate collaborative work in radiomics. Medical physics, 2015. 42 (3): p. 1341-1353.
31. Koutsouleris, N., L. Kambeitz-Ilankovic, S. Ruhrmann, et al.,Prediction Models of Functional Outcomes for Individuals in the Clinical High-Risk State for Psychosis or With Recent-Onset Depression: A Multimodal, Multisite Machine Learning Analysis. JAMA Psychiatry, 2018. 75 (11): p. 1156-1172.
32. Saeys, Y., I. Inza, and P. Larrañaga, A review of feature selection techniques in bioinformatics. Bioinformatics, 2007.23 (19): p. 2507-2517.
33. Jiang, W., Y. Song, Z. Sun, J. Qiu, and L. Shi, Dosimetric Factors and Radiomics Features Within Different Regions of Interest in Planning CT Images for Improving the Prediction of Radiation Pneumonitis. Int J Radiat Oncol Biol Phys, 2021. 110 (4): p. 1161-1170.
34. Jordan H. Creed, T.A.G., Anders E. Berglund, MatSurv: Survival analysis and visualization in MATLAB. Journal of Open Source Software, 2020. 5 (46): p. 1830.
35. Cohen, P.A., A. Jhingran, A. Oaknin, and L. Denny, Cervical cancer. Lancet (London, England), 2019. 393 (10167): p. 169-182.
36. Lucia, F., D. Visvikis, M.-C. Desseroit, et al., Prediction of outcome using pretreatment F-FDG PET/CT and MRI radiomics in locally advanced cervical cancer treated with chemoradiotherapy. European journal of nuclear medicine and molecular imaging, 2018. 45 (5): p. 768-786.
37. Fang, M., Y. Kan, D. Dong, et al., Multi-Habitat Based Radiomics for the Prediction of Treatment Response to Concurrent Chemotherapy and Radiation Therapy in Locally Advanced Cervical Cancer.Frontiers in oncology, 2020. 10 : p. 563.
38. Ho, J.C., P.K. Allen, P.R. Bhosale, et al., Diffusion-Weighted Magnetic Resonance Imaging as a Predictor of Outcome in Cervical Cancer After Chemoradiation. International journal of radiation oncology, biology, physics, 2017. 97 (3): p. 546-553.
39. Kuncman, Ł., K. Stawiski, M. Masłowski, J. Kucharz, and J. Fijuth,Dose-volume parameters of MRI-based active bone marrow predict hematologic toxicity of chemoradiotherapy for rectal cancer.Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft … [et al], 2020. 196 (11).
40. Rose, B.S., Y. Liang, S.K. Lau, et al., Correlation between radiation dose to ¹⁸F-FDG-PET defined active bone marrow subregions and acute hematologic toxicity in cervical cancer patients treated with chemoradiotherapy. International journal of radiation oncology, biology, physics, 2012. 83 (4): p. 1185-1191.
41. Grigiene, R., K.P. Valuckas, E. Aleknavicius, J. Kurtinaitis, and S.R. Letautiene,
The value of prognostic factors for uterine cervical cancer patients treated with irradiation alone. BMC cancer, 2007. 7: p. 234.
42. Bishop, A.J., P.K. Allen, A.H. Klopp, L.A. Meyer, and P.J. Eifel,Relationship between low hemoglobin levels and outcomes after treatment with radiation or chemoradiation in patients with cervical cancer: has the impact of anemia been overstated? International journal of radiation oncology, biology, physics, 2015. 91 (1): p. 196-205.