scholarly journals OC12.06: Validation of objective measurements to predict myometrial or cervical stromal invasion and prediction models to predict high-risk endometrial cancer

2018 ◽  
Vol 52 ◽  
pp. 28-29
Author(s):  
J.Y. Verbakel ◽  
F. Mascilini ◽  
D. Fischerová ◽  
A.C. Testa ◽  
D. Franchi ◽  
...  
Keyword(s):  
Endometrial Cancer ◽  
High Risk ◽  
Prediction Models ◽  
Stromal Invasion ◽  
Objective Measurements ◽  
Cervical Stromal Invasion

scholarly journals Association of Abnormal Pap Smear with Occult Cervical Stromal Invasion in Patients with Endometrial Cancer

2019 ◽  
Vol 20 (9) ◽  
pp. 2847-2850
Author(s):  
Kewalin Khumthong ◽  
Apiwat Aue-Aungkul ◽  
Pilaiwan Kleebkaow ◽  
Bandit Chumworathayi ◽  
Amornrat Temtanakitpaisan ◽  
...  
Keyword(s):  
Endometrial Cancer ◽  
Pap Smear ◽  
Stromal Invasion ◽  
Cervical Stromal Invasion

Cervical Stromal Invasion Predicting Survival in Endometrial Cancer

2010 ◽  
Vol 116 (5) ◽  
pp. 1035-1041 ◽  
Author(s):  
J. Stuart Ferriss ◽  
William Brix ◽  
Rosemary Tambouret ◽  
Christopher P. DeSimone ◽  
Mark Stoler ◽  
...  
Keyword(s):  
Endometrial Cancer ◽  
Stromal Invasion ◽  
Cervical Stromal Invasion

Vaginal brachytherapy alone for patients with Stage II endometrial cancer with inner half cervical stromal invasion

Brachytherapy ◽  
2019 ◽  
Vol 18 (5) ◽  
pp. 606-611 ◽  
Author(s):  
Elizabeth A. Barnes ◽  
Carlos Parra-Herran ◽  
Kevin Martell ◽  
Lisa Barbera ◽  
Amandeep Taggar ◽  
...  
Keyword(s):  
Endometrial Cancer ◽  
Stage Ii ◽  
Stromal Invasion ◽  
Cervical Stromal Invasion

scholarly journals A Prediction Model for Preoperative Risk Assessment in Endometrial Cancer Utilizing Clinical and Molecular Variables

2019 ◽  
Vol 20 (5) ◽  
pp. 1205 ◽  
Author(s):  
Erin Salinas ◽  
Marina Miller ◽  
Andreea Newtson ◽  
Deepti Sharma ◽  
Megan McDonald ◽  
...  
Keyword(s):  
Endometrial Cancer ◽  
High Risk ◽  
Prediction Model ◽  
Somatic Mutation ◽  
Mirna Expression ◽  
Prediction Models ◽  
Gene Copy Number ◽  
The Cancer Genome Atlas ◽  
Gene Copy ◽  
Mutation Data

The utility of comprehensive surgical staging in patients with low risk disease has been questioned. Thus, a reliable means of determining risk would be quite useful. The aim of our study was to create the best performing prediction model to classify endometrioid endometrial cancer (EEC) patients into low or high risk using a combination of molecular and clinical-pathological variables. We then validated these models with publicly available datasets. Analyses between low and high risk EEC were performed using clinical and pathological data, gene and miRNA expression data, gene copy number variation and somatic mutation data. Variables were selected to be included in the prediction model of risk using cross-validation analysis; prediction models were then constructed using these variables. Model performance was assessed by area under the curve (AUC). Prediction models were validated using appropriate datasets in The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. A prediction model with only clinical variables performed at 88%. Integrating clinical and molecular data improved prediction performance up to 97%. The best prediction models included clinical, miRNA expression and/or somatic mutation data, and stratified pre-operative risk in EEC patients. Integrating molecular and clinical data improved the performance of prediction models to over 95%, resulting in potentially useful clinical tests.

scholarly journals A Nomogram Model Involving Immunohistochemical Markers for Predicting the Recurrence of Stage I-II Endometrial Cancer

2021 ◽  
Vol 10 ◽  
Author(s):  
Peng Jiang ◽  
Mingzhu Jia ◽  
Jing Hu ◽  
Zhen Huang ◽  
Ying Deng ◽  
...  
Keyword(s):  
Endometrial Cancer ◽  
Validation Cohort ◽  
Myometrial Invasion ◽  
Stage I ◽  
Stromal Invasion ◽  
Training Cohort ◽  
Immunohistochemical Markers ◽  
Cervical Stromal Invasion ◽  
Classical Parameters ◽  
Medical University

BackgroundThe purpose of this study was to establish a nomogram combining classical parameters and immunohistochemical markers to predict the recurrence of patients with stage I-II endometrial cancer (EC).Methods419 patients with stage I-II endometrial cancer who received primary surgical treatment at the First Affiliated Hospital of Chongqing Medical University were involved in this study as a training cohort. Univariate and multivariate Cox regression analysis of screening prognostic factors were performed in the training cohort to develop a nomogram model, which was further validated in 248 patients (validation cohort) from the Second Affiliated Hospital of Chongqing Medical University. The calibration curve was used for internal and external verification of the model, and the C-index was used for comparison among different models.ResultsThere were 51 recurrent cases in the training cohort while 31 cases in the validation cohort. Univariate analysis showed that age, histological type, histological grade, myometrial invasion, cervical stromal invasion, postoperative adjuvant treatment, and four immunohistochemical makers (Ki67, estrogen receptor, progesterone receptor, P53) were the related factors for recurrence of EC. Multivariate analysis demonstrated that histological type (P = 0.029), myometrial invasion (P = 0.003), cervical stromal invasion (P = 0.001), Ki67 (P < 0.001), ER (P = 0.009) and P53 expression (P = 0.041) were statistically correlated with recurrence of EC. Recurrence-free survival was better predicted by the proposed nomogram with a C-index of 0.832 (95% CI, 0.752–0.912) in the training cohort, and the validation set confirmed the finding with a C-index of 0.861 (95% CI, 0.755–0.967).ConclusionThe nomogram model combining classical parameters and immunohistochemical markers can better predict the recurrence in patients with FIGO stage I-II EC.

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