|
1
|
Makker V, MacKay H, Ray-Coquard I, Levine
DA, Westin SN, Aoki D and Oaknin A: Endometrial cancer. Nat Rev Dis
Primers. 7:882021. View Article : Google Scholar : PubMed/NCBI
|
|
2
|
Medina HN, Penedo FJ, Joachim C,
Deloumeaux J, Koru-Sengul T, Macni J, Bhakkan B, Peruvien J,
Schlumbrecht MP and Pinheiro PS: Endometrial cancer risk and trends
among distinct African descent populations. Cancer. 129:2717–2726.
2023. View Article : Google Scholar : PubMed/NCBI
|
|
3
|
Piechocki M, Koziołek W, Sroka D, Matrejek
A, Miziołek P, Saiuk N, Sledzik M, Jaworska A, Bereza K, Pluta E
and Banas T: Trends in incidence and mortality of gynecological and
breast cancers in Poland (1980–2018). Clin Epidemiol. 14:95–114.
2022. View Article : Google Scholar : PubMed/NCBI
|
|
4
|
Sung H, Ferlay J, Siegel RL, Laversanne M,
Soerjomataram I, Jemal A and Bray F: Global cancer statistics 2020:
GLOBOCAN estimates of incidence and mortality worldwide for 36
cancers in 185 countries. CA Cancer J Clin. 71:209–249. 2021.
View Article : Google Scholar : PubMed/NCBI
|
|
5
|
Miller KD, Siegel RL, Lin CC, Mariotto AB,
Kramer JL, Rowland JH, Stein KD, Alteri R and Jemal A: Cancer
treatment and survivorship statistics, 2016. CA Cancer J Clin.
66:271–289. 2016. View Article : Google Scholar : PubMed/NCBI
|
|
6
|
Mitric C and Bernardini MQ: Endometrial
cancer: Transitioning from histology to genomics. Curr Oncol.
29:741–757. 2022. View Article : Google Scholar : PubMed/NCBI
|
|
7
|
Cancer Genome Atlas Research Network, .
Kandoth C, Schultz N, Cherniack AD, Akbani R, Liu Y, Shen H,
Robertson AG, Pashtan I, Shen R, et al: Integrated genomic
characterization of endometrial carcinoma. Nature. 497:67–73. 2013.
View Article : Google Scholar : PubMed/NCBI
|
|
8
|
Bruno V, Betti M, D'Ambrosio L, Massacci
A, Chiofalo B, Pietropolli A, Piaggio G, Ciliberto G, Nisticò P,
Pallocca M, et al: Machine learning endometrial cancer risk
prediction model: Integrating guidelines of European society for
medical oncology with the tumor immune framework. Int J Gynecol
Cancer. 33:1708–1714. 2023. View Article : Google Scholar : PubMed/NCBI
|
|
9
|
Perrone E, De Felice F, Capasso I,
Distefano E, Lorusso D, Nero C, Arciuolo D, Zannoni GF, Scambia G
and Fanfani F: The immunohistochemical molecular risk
classification in endometrial cancer: A pragmatic and
high-reproducibility method. Gynecol Oncol. 165:585–593. 2022.
View Article : Google Scholar : PubMed/NCBI
|
|
10
|
Rossi EC, Kowalski LD, Scalici J, Cantrell
L, Schuler K, Hanna RK, Method M, Ade M, Ivanova A and Boggess F: A
comparison of sentinel lymph node biopsy to lymphadenectomy for
endometrial cancer staging (FIRES trial): A multicentre,
prospective, cohort study. Lancet Oncol. 18:384–392. 2017.
View Article : Google Scholar : PubMed/NCBI
|
|
11
|
Chen W, Zheng R, Baade PD, Zhang S, Zeng
H, Bray F, Jemal A, Yu XQ and He J: Cancer statistics in China,
2015. CA Cancer J Clin. 66:115–132. 2016. View Article : Google Scholar : PubMed/NCBI
|
|
12
|
Njoku K, Barr CE and Crosbie EJ: Current
and emerging prognostic biomarkers in endometrial cancer. Front
Oncol. 12:8909082022. View Article : Google Scholar : PubMed/NCBI
|
|
13
|
Coll-de la Rubia E, Martinez-Garcia E,
Dittmar G, Gil-Moreno A, Cabrera S and Colas E: Prognostic
biomarkers in endometrial cancer: A systematic review and
meta-analysis. J Clin Med. 9:19002020. View Article : Google Scholar : PubMed/NCBI
|
|
14
|
Vrede SW, van Weelden WJ, Visser NCM,
Bulten J, van der Putten LJM, van de Vijver K, Santacana M, Colas
E, Gil-Moreno A, Moiola CP, et al: Immunohistochemical biomarkers
are prognostic relevant in addition to the ESMO-ESGO-ESTRO risk
classification in endometrial cancer. Gynecol Oncol. 161:787–794.
2021. View Article : Google Scholar : PubMed/NCBI
|
|
15
|
Talhouk A, McConechy MK, Leung S, Yang W,
Lum A, Senz J, Boyd N, Pike J, Anglesio M, Kwon JS, et al:
Confirmation of ProMisE: A simple, genomics-based clinical
classifier for endometrial cancer. Cancer. 123:802–813. 2017.
View Article : Google Scholar : PubMed/NCBI
|
|
16
|
Singh N, Piskorz AM, Bosse T,
Jimenez-Linan M, Rous B, Brenton JD, Gilks CB and Köbel M: p53
immunohistochemistry is an accurate surrogate for TP53 mutational
analysis in endometrial carcinoma biopsies. J Pathol. 250:336–345.
2020. View Article : Google Scholar : PubMed/NCBI
|
|
17
|
Thiel KW, Devor EJ, Filiaci VL, Mutch D,
Moxley K, Secord AA, Tewari KS, McDonald ME, Mathews C, Cosgrove C,
et al: TP53 sequencing and p53 immunohistochemistry predict
outcomes when bevacizumab is added to frontline chemotherapy in
endometrial cancer: An NRG Oncology/Gynecologic oncology group
study. J Clin Oncol. 40:3289–3300. 2022. View Article : Google Scholar : PubMed/NCBI
|
|
18
|
Perrone E, Capasso I, De Felice F,
Giannarelli D, Dinoi G, Petrecca A, Palmieri L, Foresta A, Nero C,
Arciuolo D, et al: Back to the future: The impact of oestrogen
receptor profile in the era of molecular endometrial cancer
classification. Eur J Cancer. 186:98–112. 2023. View Article : Google Scholar : PubMed/NCBI
|
|
19
|
Guan J, Xie L, Luo X, Yang B, Zhang H, Zhu
Q and Chen X: The prognostic significance of estrogen and
progesterone receptors in grade I and II endometrioid endometrial
adenocarcinoma: Hormone receptors in risk stratification. J Gynecol
Oncol. 30:e132019. View Article : Google Scholar : PubMed/NCBI
|
|
20
|
Huvila J, Talve L, Carpén O, Edqvist PH,
Pontén F, Grénman S and Auranen A: Progesterone receptor negativity
is an independent risk factor for relapse in patients with early
stage endometrioid endometrial adenocarcinoma. Gynecol Oncol.
130:463–469. 2013. View Article : Google Scholar : PubMed/NCBI
|
|
21
|
Jia M, Pi J, Zou J, Feng M, Chen H, Lin C,
Yang S and Xiao X: The potential value of ki-67 in prognostic
classification in early low-risk endometrial cancer. Cancer
Control. 30:107327482312069292023. View Article : Google Scholar : PubMed/NCBI
|
|
22
|
Wang Z, Zhang S, Ma Y, Li W, Tian J and
Liu T: A nomogram prediction model for lymph node metastasis in
endometrial cancer patients. BMC Cancer. 21:7482021. View Article : Google Scholar : PubMed/NCBI
|
|
23
|
Feng X, Li XC, Yang X, Cheng Y, Dong YY,
Wang JY, Zhou JY and Wang JL: Metabolic syndrome score as an
indicator in a predictive nomogram for lymph node metastasis in
endometrial cancer. BMC Cancer. 23:6222023. View Article : Google Scholar : PubMed/NCBI
|
|
24
|
Yang XL, Huang H, Kou LN, Lai H, Chen XP
and Wu DJ: Construction and validation of a prognostic model for
stage IIIC endometrial cancer patients after surgery. Eur J Surg
Oncol. 48:1173–1180. 2022. View Article : Google Scholar : PubMed/NCBI
|
|
25
|
van der Putten LJM, Visser NCM, van de
Vijver K, Santacana M, Bronsert P, Bulten J, Hirschfeld M, Colas E,
Gil-Moreno A, Garcia A, et al: Added value of estrogen receptor,
progesterone receptor, and L1 cell adhesion molecule expression to
histology-based endometrial carcinoma recurrence prediction models:
An ENITEC collaboration study. Int J Gynecol Cancer. 28:514–523.
2018. View Article : Google Scholar : PubMed/NCBI
|
|
26
|
Liu L, Yuan S, Yao S, Cao W and Wang L:
EPPK1 as a prognostic biomarker in type I endometrial cancer and
its correlation with immune infiltration. Int J Gen Med.
17:1677–1694. 2024. View Article : Google Scholar : PubMed/NCBI
|
|
27
|
Chen J, Yang P, Li S and Feng Y: Increased
FOXM1 expression was associated with the prognosis and the
recruitment of neutrophils in endometrial cancer. J Immunol Res.
2023:54375262023. View Article : Google Scholar : PubMed/NCBI
|
|
28
|
Ma H, Feng PH, Yu SN, Lu ZH, Yu Q and Chen
J: Identification and validation of TNFRSF4 as a high-profile
biomarker for prognosis and immunomodulation in endometrial
carcinoma. BMC Cancer. 22:5432022. View Article : Google Scholar : PubMed/NCBI
|
|
29
|
Piedimonte S, Feigenberg T, Drysdale E,
Kwon J, Gotlieb WH, Cormier B, Plante M, Lau S, Helpman L, Renaud
MC, et al: Predicting recurrence and recurrence-free survival in
high-grade endometrial cancer using machine learning. J Surg Oncol.
126:1096–1103. 2022. View Article : Google Scholar : PubMed/NCBI
|
|
30
|
Feng Y, Wang Z, Xiao M, Li J, Su Y,
Delvoux B, Zhang Z, Dekker A, Xanthoulea S, Zhang Z, et al: An
applicable machine learning model based on preoperative
examinations predicts histology, stage, and grade for endometrial
cancer. Front Oncol. 12:9045972022. View Article : Google Scholar : PubMed/NCBI
|
