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.