|
1
|
Siegel RL, Miller KD, Fuchs HE and Jemal
A: Cancer statistics, 2021. CA Cancer J Clin. 71:7–33.
2021.PubMed/NCBI
|
|
2
|
Siegel RL, Giaquinto AN and Jemal A:
Cancer statistics, 2024. CA Cancer J Clin. 74:12–49.
2024.PubMed/NCBI
|
|
3
|
Clarke MA, Devesa SS, Harvey SV and
Wentzensen N: Hysterectomy-corrected uterine corpus cancer
incidence trends and differences in relative survival reveal racial
disparities and rising rates of nonendometrioid cancers. J Clin
Oncol. 37:1895–1908. 2019. View Article : Google Scholar : PubMed/NCBI
|
|
4
|
Concin N, Matias-Guiu X, Vergote I, Cibula
D, Mirza MR, Marnitz S, Ledermann J, Bosse T, Chargari C, Fagotti
A, et al: ESGO/ESTRO/ESP guidelines for the management of patients
with endometrial carcinoma. Int J Gynecol Cancer. 31:12–39. 2021.
View Article : Google Scholar : PubMed/NCBI
|
|
5
|
National Comprehensive Cancer Network, .
NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines):
Uterine Neoplasms (Version1.2020) [EB/QL]. [2020-03-06]. Available
from:. https://www.nccn.org/pro-fessionals/physician_gls/pdf/uterine.pdf
|
|
6
|
Satta S, Dolciami M, Celli V, Di Stadio F,
Perniola G, Palaia I, Pernazza A, Della Rocca C, Rizzo S, Catalano
C, et al: Quantitative diffusion and perfusion MRI in the
evaluation of endometrial cancer: Validation with histopathological
parameters. Br J Radiol. 94:202100542021. View Article : Google Scholar : PubMed/NCBI
|
|
7
|
Lin G, Huang YT, Chao A, Ng KK, Yang LY,
Ng SH and Lai CH: Influence of menopausal status on diagnostic
accuracy of myometrial invasion in endometrial cancer:
Diffusion-weighted and dynamic Contrast-enhanced MRI at 3 T. Clin
Radiol. 70:1260–1268. 2015. View Article : Google Scholar : PubMed/NCBI
|
|
8
|
Zheng L, Zheng S, Yuan X, Wang X, Zhang Z
and Zhang G: Comparison of dynamic contrast-enhanced magnetic
resonance imaging with T2-weighted imaging for preoperative staging
of early endometrial carcinoma. Onco Targets Ther. 8:1743–1751.
2015. View Article : Google Scholar : PubMed/NCBI
|
|
9
|
Keles DK, Evrimler S, Merd N and Erdemoglu
E: Endometrial cancer: The role of MRI quantitative assessment in
preoperative staging and risk stratification. Acta Radiol.
63:1126–1133. 2022. View Article : Google Scholar : PubMed/NCBI
|
|
10
|
Wang T, She Y, Yang Y, Liu X, Chen S,
Zhong Y, Deng J, Zhao M, Sun X, Xie D and Chen C: Radiomics for
survival risk stratification of clinical and pathologic stage IA
Pure-solid Non-small cell lung cancer. Radiology. 302:425–434.
2022. View Article : Google Scholar : PubMed/NCBI
|
|
11
|
Hectors SJ, Lewis S, Besa C, King MJ, Said
D, Putra J, Ward S, Higashi T, Thung S, Yao S, et al: MRI radiomics
features predict immuno-oncological characteristics of
hepatocellular carcinoma. Eur Radiol. 30:3759–3769. 2020.
View Article : Google Scholar : PubMed/NCBI
|
|
12
|
Yang H, Yan S, Li J, Zheng X, Yao Q, Duan
S, Zhu J, Li C and Qin J: Prediction of acute versus chronic
osteoporotic vertebral fracture using radiomics-clinical model on
CT. Eur J Radiol. 149:1101972022. View Article : Google Scholar : PubMed/NCBI
|
|
13
|
Dou G, Shan D, Wang K, Wang X, Liu Z,
Zhang W, Li D, He B, Jing J, Wang S, et al: Integrating coronary
plaque information from CCTA by ML Predicts MACE in patients with
suspected CAD. J Pers Med. 12:5962022. View Article : Google Scholar : PubMed/NCBI
|
|
14
|
Pei Q, Yi X, Chen C, Pang P, Fu Y, Lei G,
Chen C, Tan F, Gong G, Li Q, et al: Pre-treatment CT-based
radiomics nomogram for predicting microsatellite instability status
in colorectal cancer. Eur Radiol. 32:714–724. 2022. View Article : Google Scholar : PubMed/NCBI
|
|
15
|
Wan S, Zhou T, Che R, Li Y, Peng J, Wu Y,
Gu S, Cheng J and Hua X: CT-based machine learning radiomics
predicts CCR5 expression level and survival in ovarian cancer. J
Ovarian Res. 16:12023. View Article : Google Scholar : PubMed/NCBI
|
|
16
|
Kasius JC, Pijnenborg JMA, Lindemann K,
Forsse D, van Zwol J, Kristensen GB, Krakstad C, Werner HMJ and
Amant F: Risk stratification of endometrial cancer patients: FIGO
stage, biomarkers and molecular classification. Cancers (Basel).
13:58482021. View Article : Google Scholar : PubMed/NCBI
|
|
17
|
Wang Y, Bi Q, Deng Y, Yang Z, Song Y, Wu Y
and Wu K: Development and validation of an MRI-based radiomics
nomogram for assessing deep myometrial invasion in early stage
endometrial adenocarcinoma. Acad Radiol. 30:668–679. 2023.
View Article : Google Scholar : PubMed/NCBI
|
|
18
|
Chen J, Wang X, Lv H, Zhang W, Tian Y,
Song L and Wang Z: Development and external validation of a
clinical-radiomics nomogram for preoperative prediction of LVSI
status in patients with endometrial carcinoma. J Cancer Res Clin
Oncol. 149:13943–13953. 2023. View Article : Google Scholar : PubMed/NCBI
|
|
19
|
Wang JJ, Zhang XH, Guo XH, Ying Y, Wang X,
Luan ZH, Lv WQ and Wang PF: Prediction of lymphovascular space
invision in endometrial cancer based on Multi-parameter MRI
radiomics model. Curr Med Imaging. 20:e157340562663662024.
View Article : Google Scholar : PubMed/NCBI
|
|
20
|
Bonatti M, Pedrinolla B, Cybulski AJ,
Lombardo F, Negri G, Messini S, Tagliaferri T, Manfredi R and
Bonatti G: Prediction of histological grade of endometrial cancer
by means of MRI. Eur J Radiol. 103:44–50. 2018. View Article : Google Scholar : PubMed/NCBI
|
|
21
|
Nougaret S, Reinhold C, Alsharif SS,
Addley H, Arceneau J, Molinari N, Guiu B and Sala E: Endometrial
cancer: Combined MR volumetry and diffusion-weighted imaging for
assessment of myometrial and lymphovascular invasion and tumor
grade. Radiology. 276:797–808. 2015. View Article : Google Scholar : PubMed/NCBI
|
|
22
|
Yue X, He X, He S, Wu J, Fan W, Zhang H
and Wang C: Multiparametric magnetic resonance imaging-based
radiomics nomogram for predicting tumor grade in endometrial
cancer. Front Oncol. 13:10811342023. View Article : Google Scholar : PubMed/NCBI
|
|
23
|
Zheng T, Yang L, Du J, Dong Y, Wu S, Shi
Q, Wang X and Liu L: Combination analysis of a Radiomics-based
predictive model with clinical indicators for the preoperative
assessment of histological grade in endometrial carcinoma. Front
Oncol. 11:5824952021. View Article : Google Scholar : PubMed/NCBI
|
|
24
|
Soslow RA, Tornos C, Park KJ, Malpica A,
Matias-Guiu X, Oliva E, Parkash V, Carlson J, McCluggage WG and
Gilks CB: Endometrial carcinoma diagnosis: Use of FIGO grading and
genomic subcategories in clinical practice: Recommendations of the
international society of gynecological pathologists. Int J Gynecol
Pathol. 38 (Suppl 1):S64–S74. 2019. View Article : Google Scholar : PubMed/NCBI
|
|
25
|
Cui T, Shi F, Gu B, Jin Y, Guo J, Zhang C,
Ren J and Yue Y: Peritumoral enhancement for the evaluation of
myometrial invasion in Low-risk endometrial carcinoma on dynamic
Contrast-enhanced MRI. Front Oncol. 11:7937092022. View Article : Google Scholar : PubMed/NCBI
|
|
26
|
Kurman RJ, Lora HE and Ronnett BM:
Blaustein's Pathology of the Female Genital Tract. 6th edition.
Springer; London: 2011, View Article : Google Scholar
|
|
27
|
Batista TP, Cavalcanti CL, Tejo AA and
Bezerra AL: Accuracy of preoperative endometrial sampling diagnosis
for predicting the final pathology grading in uterine endometrioid
carcinoma. Eur J Surg Oncol. 42:1367–1371. 2016. View Article : Google Scholar : PubMed/NCBI
|
|
28
|
van Hanegem N, Prins MM, Bongers MY,
Opmeer BC, Sahota DS, Mol BW and Timmermans A: The accuracy of
endometrial sampling in women with postmenopausal bleeding: A
systematic review and meta-analysis. Eur J Obstet Gynecol Reprod
Biol. 197:147–155. 2016. View Article : Google Scholar : PubMed/NCBI
|
