Comparison of three different risk-stratification models for predicting lymph node involvement in endometrioid endometrial cancer clinically confined to the uterus

Objectives: The objectives of this study is to investigate the correlation of magnetic resonance imaging (MRI) in predicting the depth of myometrial invasion, cervical involvement and lymph node involvement and actual histopathological findings in the women with endometrial cancer. Methods: This is a reterospective study of the patients of endometrial cancer from Nov 2011 to Jan 2016 who underwent Surgery (Total abdominal Hystrectomy with B/l salpingoophorectomy with peritoneal washings with b/l pelvic lymphadenectomy with or without para aortic lymphadenectomy) at our centre Max Superspeciality Hospital. CE MRI Pelvis has been done pre operatively in every patient. After the surgery Histopathological reports of the specimen checked and compared with MRI findings of that case. The purpose of the study is to evaluate the validity of MRI findings of endometrial cancer in comparison to final histopathological findings. Results: For the detection of myometrial invasion, overall sensitivity of MRI is 93.9%, specificity is 66.6%, for cervical involvement Senstivity is 60% and specificity 1s 93.75% and for detection of lymph node involvement sensitivity is 66.6% and specificity is 93.5%. Most common Finding on MRI is thickened endometrium with disruption of Junction jone. Conclusions: Preoperative pelvic MRI is a sensitive method of identifying invasion to the myometrium in endometrial cancer. MRI Is a sensitive noninvasive modality in predicting locoregional spread in ca endometrium. Senstivity in detecting Myometrial invasion is high but sensitivity is less in detecting cervical involvement and lymph node involvement is less.

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Lymph Node Metastasis in Patients With Endometrioid Endometrial Cancer: Overtreatment Is the Main Issue

International Journal of Gynecological Cancer ◽

10.1097/igc.0000000000000937 ◽

2017 ◽

Vol 27 (4) ◽

pp. 748-753 ◽

Cited By ~ 8

Author(s):

Alper Karalok ◽

Taner Turan ◽

Derman Basaran ◽

Osman Turkmen ◽

Gunsu Comert Kimyon ◽

...

Keyword(s):

Endometrial Cancer ◽

Lymph Node ◽

Lymph Node Metastasis ◽

Histological Grade ◽

Myometrial Invasion ◽

Lymph Node Involvement ◽

Node Metastasis ◽

Deep Myometrial Invasion ◽

Endometrioid Endometrial Cancer ◽

Grade 3

ObjectiveThe aim of this study was to evaluate the effectiveness of histological grade, depth of myometrial invasion, and tumor size to identify lymph node metastasis (LNM) in patients with endometrioid endometrial cancer (EC).MethodsA retrospective computerized database search was performed to identify patients who underwent comprehensive surgical staging for EC between January 1993 and December 2015. The inclusion criterion was endometrioid type EC limited to the uterine corpus. The associations between LNM and surgicopathological factors were evaluated by univariate and multivariate analyses.ResultsIn total, 368 patients were included. Fifty-five patients (14.9%) had LNM. Median tumor sizes were 4.5 cm (range, 0.7–13 cm) and 3.5 cm (range, 0.4–33.5 cm) in patients with and without LNM, respectively (P = 0.005). No LMN was detected in patients without myometrial invasion, whereas nodal spread was observed in 7.7% of patients with superficial myometrial invasion and in 22.6% of patients with deep myometrial invasion (P < 0.0001). Lymph node metastasis tended to be more frequent in patients with grade 3 disease compared with those with grade 1 or 2 disease (P = 0.131).ConclusionsThe risk of lymph node involvement was 30%, even in patients with the highest-risk uterine factors, that is, those who had tumors of greater than 2 cm, deep myometrial invasion, and grade 3 disease, indicating that 70% of these patients underwent unnecessary lymphatic dissection. A precise balance must be achieved between the desire to prevent unnecessary lymphadenectomy and the ability to diagnose LNM.

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Detection of Lymphovascular Invasion by D2-40 (Podoplanin) Immunoexpression in Endometrial Cancer

International Journal of Gynecological Cancer ◽

10.1097/igc.0b013e318269139b ◽

2012 ◽

Vol 22 (8) ◽

pp. 1442-1448 ◽

Cited By ~ 25

Author(s):

Sarah K. Weber ◽

Axel Sauerwald ◽

Martin Pölcher ◽

Michael Braun ◽

Manuel Debald ◽

...

Keyword(s):

Overall Survival ◽

Endometrial Cancer ◽

Lymph Node ◽

Lymph Node Metastasis ◽

Lymphovascular Invasion ◽

Lymph Node Involvement ◽

Lymph Vessel ◽

Node Metastasis ◽

Lymph Vessel Invasion ◽

Node Involvement

BackgroundLymph node involvement is a major feature in tumor spread of endometrial cancer and predicts prognosis. Therefore, evaluation of lymph vessel invasion (LVI) in tumor tissue as a predictor for lymph node metastasis is of great importance. Immunostaining of D2-40 (podoplanin), a specific marker for lymphatic endothelial cells, might be able to increase the detection rate of LVI compared with conventional hematoxylin-eosin (H-E) staining. The aim of this retrospective study was to analyze the eligibility of D2-40–based LVI evaluation for the prediction of lymph node metastases and patients’ outcome.Patients and MethodsImmunohistochemical staining with D2-40 monoclonal antibodies was performed on paraffin-embedded tissue sections of 182 patients with primary endometrioid adenocarcinoma treated in 1 gynecologic cancer center. Tumors were screened for the presence of LVI. Correlations with clinicopathological features and clinical outcome were assessed.ResultsImmunostaining of D2-40 significantly increased the frequency LVI detection compared with conventional H-E staining. Lymph vessel invasion was identified by D2-40 in 53 (29.1%) of 182 tumors compared with 34 (18.3%) of 182 carcinomas by routine H-E staining (P = 0.001). D2-40 LVI was detectable in 81.0% (17/21) of nodal-positive tumors and significantly predicted lymph node metastasis (P = 0.001). Furthermore, D2-40 LVI was an independent prognostic factor for patients overall survival considering tumor stage, lymph node involvement, and tumor differentiation (P < 0.01). D2-40–negative tumors confined to the inner half of the myometrium showed an excellent outcome (5-year overall survival, 97.8%).ConclusionsD2-40–based LVI assessment improves the histopathological detection of lymphovascular invasion in endometrial cancer. Furthermore, LVI is of prognostic value and predicts lymph node metastasis. D2-40 LVI detection might help to select endometrial cancer patients who will benefit from a lymphadenectomy.

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Race Associated With Variable Risk of Lymph Node Involvement in Endometrial Cancer

International Journal of Radiation Oncology*Biology*Physics ◽

10.1016/j.ijrobp.2020.07.2598 ◽

2020 ◽

Vol 108 (3) ◽

pp. e468-e469

Author(s):

E. Anderson ◽

M. Luu ◽

M.P. Sittig ◽

D.J. Lu ◽

B. Rimel ◽

...

Keyword(s):

Endometrial Cancer ◽

Lymph Node ◽

Lymph Node Involvement ◽

Node Involvement

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EP468 Tumor size as prognostic factor of lymph node involvement in endometrial cancer

10.1136/ijgc-2019-esgo.527 ◽

2019 ◽

Author(s):

D Al-Dali ◽

M Pérez de Puig ◽

C López ◽

M Fernández ◽

G Salinas ◽

...

Keyword(s):

Endometrial Cancer ◽

Lymph Node ◽

Prognostic Factor ◽

Tumor Size ◽

Lymph Node Involvement ◽

Node Involvement

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A novel prediction method for lymph node involvement in endometrial cancer: machine learning

International Journal of Gynecological Cancer ◽

10.1136/ijgc-2018-000033 ◽

2018 ◽

Vol 29 (2) ◽

pp. 320-324 ◽

Cited By ~ 4

Author(s):

Emre Günakan ◽

Suat Atan ◽

Asuman Nihan Haberal ◽

İrem Alyazıcı Küçükyıldız ◽

Ehad Gökçe ◽

...

Keyword(s):

Machine Learning ◽

Endometrial Cancer ◽

Lymph Node ◽

Prediction Models ◽

Lymph Node Involvement ◽

Machine Learning Algorithms ◽

Lymph Node Status ◽

Tumor Diameter ◽

Imaging Results ◽

Node Involvement

ObjectiveThe necessity of lymphadenectomy and the prediction of lymph node involvement (LNI) in endometrial cancer (EC) have been hotly-debated questions in recent years. Machine learning is a broad field that can produce results and estimations. In this study we constructed prediction models for EC patients using the Naïve Bayes machine learning algorithm for LNI prediction.MethodsThe study assessed 762 patients with EC. Algorithm models were based on the following histopathological factors: V1: final histology; V2: presence of lymphovascular space invasion (LVSI); V3: grade; V4: tumor diameter; V5: depth of myometrial invasion (MI); V6: cervical glandular stromal invasion (CGSI); V7: tubal or ovarian involvement; and V8: pelvic LNI. Logistic regression analysis was also used to evaluate the independent factors affecting LNI.ResultsThe mean age of patients was 59.1 years. LNI was detected in 102 (13.4%) patients. Para-aortic LNI (PaLNI) was detected in 54 (7.1%) patients, of which four patients had isolated PaLNI. The accuracy rate of the algorithm models was found to be between 84.2% and 88.9% and 85.0% and 97.6% for LNI and PaLNI, respectively. In multivariate analysis, the histologic type, LVSI, depth of MI, and CGSI were independently and significantly associated with LNI (p<0.001 for all).ConclusionsMachine learning may have a place in the decision tree for the management of EC. This is a preliminary report about the use of a new statistical technique. Larger studies with the addition of sentinel lymph node status, laboratory findings, or imaging results with machine learning algorithms may herald a new era in the management of EC.

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