scholarly journals A nomogram prediction model for lymph node metastasis in endometrial cancer patients

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Zhiling Wang ◽  
Shuo Zhang ◽  
Yifei Ma ◽  
Wenhui Li ◽  
Jiguang Tian ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Endometrial Cancer ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
External Validation ◽  
Clinicopathological Parameters ◽  
Bootstrap Sample ◽  
Multivariate Logistic Regression ◽  
Node Metastasis ◽  
Increased Risk

Abstract Background This study aimed to explore the risk factors for lymph node metastasis (LNM) in patients with endometrial cancer (EC) and develop a clinically useful nomogram based on clinicopathological parameters to predict it. Methods Clinical information of patients who underwent staging surgery for EC was abstracted from Qilu Hospital of Shandong University from January 1st, 2005 to June 31st, 2019. Parameters including patient-related, tumor-related, and preoperative hematologic examination-related were analyzed by univariate and multivariate logistic regression to determine the correlation with LNM. A nomogram based on the multivariate results was constructed and underwent internal and external validation to predict the probability of LNM. Results The overall data from the 1517 patients who met the inclusion criteria were analyzed. 105(6.29%) patients had LNM. According the univariate analysis and multivariate logistic regression analysis, LVSI is the most predictive factor for LNM, patients with positive LVSI had 13.156-fold increased risk for LNM (95%CI:6.834–25.324; P < 0.001). The nomogram was constructed and incorporated valuable parameters including histological type, histological grade, depth of myometrial invasion, LVSI, cervical involvement, parametrial involvement, and HGB levels from training set. The nomogram was cross-validated internally by the 1000 bootstrap sample and showed good discrimination accuracy. The c-index for internal and external validation of the nomogram are 0.916(95%CI:0.849–0.982) and 0.873(95%CI:0.776–0.970), respectively. Conclusions We developed and validated a 7-variable nomogram with a high concordance probability to predict the risk of LNM in patients with EC.

scholarly journals A nomogram prediction model for lymph node metastasis in endometrial cancer patients

2020 ◽  
Author(s):  
Zhiling Wang ◽  
Shuo Zhang ◽  
Yifei Ma ◽  
Wenhui Li ◽  
Jiguang Tian ◽  
...  
Keyword(s):  
Endometrial Cancer ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Histological Grade ◽  
Myometrial Invasion ◽  
Clinicopathological Parameters ◽  
Bootstrap Sample ◽  
Systematic Lymphadenectomy ◽  
Node Metastasis ◽  
Hematologic Examination

Abstract Background: The determination of lymph node(LN) status is critical for evaluating prognosis and identifying the necessity of adjuvant therapy of endometrial cancer(EC) patients. However, the significance of systematic lymphadenectomy remains controversial. This study aimed to explore the risk factors for lymph node metastasis(LNM) in patients with EC and develop a clinically useful nomogram based on clinicopathological parameters to predict it.Methods: A total of 1517 consecutive patients who underwent staging surgery for EC were abstracted from Qilu Hospital of Shandong University. Parameters including patient-related, tumor-related, and preoperative hematologic examination-related were analyzed by univariate and multivariate logistic regression to determine the correlation with LNM. Based on the multivariate results, a nomogram was constructed and underwent further validation to predict the probability of LNM.Results: The nomogram was constructed and incorporated valuable parameters from the final multivariate model including histological type, histological grade, depth of myometrial invasion, LVSI, cervical involvement, parametrial involvement, and HGB levels. The nomogram was cross-validated internally by the 200 bootstrap sample and showed good discrimination accuracy with an AUC of 0.899.Conclusions: We developed and validated a 7-variable nomogram with a high concordance probability to predict the risk of LNM in patients with EC.

scholarly journals Preoperative Predictors of Lymph Node Metastasis in Colon Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Yansong Xu ◽  
Yi Chen ◽  
Chenyan Long ◽  
Huage Zhong ◽  
Fangfang Liang ◽  
...  
Keyword(s):  
Colon Cancer ◽  
Logistic Regression ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Multivariate Logistic Regression Analysis ◽  
Multivariate Logistic Regression ◽  
Node Metastasis ◽  
Logistic Analysis ◽  
Primary Colon Cancer ◽  
Primary Colon

BackgroundLymph node metastasis (LNM) is a well-established prognostic factor for colon cancer. Preoperative LNM evaluation is relevant for planning colon cancer treatment. The aim of this study was to construct and evaluate a nomogram for predicting LNM in primary colon cancer according to pathological features.Patients and MethodsSix-hundred patients with clinicopathologically confirmed colon cancer (481 cases in the training set and 119 cases in the validation set) were enrolled in the Affiliated Cancer Hospital of Guangxi Medical University from January 2010 to December 2019. The expression of molecular markers (p53 and β-catenin) was determined by immunohistochemistry. Multivariate logistic regression was used to screen out independent risk factors, and a nomogram was established. The accuracy and discriminability of the nomogram were evaluated by consistency index and calibration curve.ResultsUnivariate logistic analysis revealed that LNM in colon cancer is significantly correlated (P &lt;0.05) with tumor size, grading, stage, preoperative carcinoembryonic antigen (CEA) level, and peripheral nerve infiltration (PNI). Multivariate logistic regression analysis confirmed that CEA, grading, and PNI were independent prognostic factors of LNM (P &lt;0.05). The nomogram for predicting LNM risk showed acceptable consistency and calibration capability in the training and validation sets.ConclusionsPreoperative CEA level, grading, and PNI were independent risk factor for LNM. Based on the present parameters, the constructed prediction model of LNM has potential application value.

scholarly journals Identification of a MicroRNA Signature Associated With Lymph Node Metastasis in Endometrial Endometrioid Cancer

2021 ◽  
Vol 12 ◽  
Author(s):  
Kaiyou Fu ◽  
Yanrui Li ◽  
Jianyuan Song ◽  
Wangyu Cai ◽  
Wei Wu ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Endometrial Cancer ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Protein Modification ◽  
Mirna Gene ◽  
Pathway Enrichment Analysis ◽  
Mirna Signature ◽  
Cell Functions ◽  
Node Metastasis

BackgroundLymph node metastasis (LNM) is an important prognostic factor in endometrial cancer. Anomalous microRNAs (miRNAs) are associated with cell functions and are becoming a powerful tool to characterize malignant transformation and metastasis. The aim of this study was to construct a miRNA signature to predict LNM in endometrial endometrioid carcinoma (EEC).MethodCandidate target miRNAs related to LNM in EEC were screened by three methods including differentially expressed miRNAs (DEmiRs), weighted gene co-expression network analysis (WGCNA), and decision tree algorithms. Samples were randomly divided into the training and validation cohorts. A miRNA signature was built using a logistic regression model and was evaluated by the area under the curve (AUC) of receiver operating characteristic curve (ROC) and decision curve analysis (DCA). We also conducted pathway enrichment analysis and miRNA–gene regulatory network to look for potential genes and pathways engaged in LNM progression. Survival analysis was performed, and the miRNAs were tested whether they expressed differently in another independent GEO database.ResultThirty-one candidate miRNAs were screened and a final 15-miRNA signature was constructed by logistic regression. The model showed good calibration in the training and validation cohorts, with AUC of 0.824 (95% CI, 0.739–0.912) and 0.821 (95% CI, 0.691–0.925), respectively. The DCA demonstrated the miRNA signature was clinically useful. Hub miRNAs in signature seemed to contribute to EEC progression via mitotic cell cycle, cellular protein modification process, and molecular function. MiR-34c was statistically significant in survival that a higher expression of miR-34c indicated a higher survival time. MiR-34c-3p, miR-34c-5p, and miR-34b-5p were expressed differentially in GSE75968.ConclusionThe miRNA signature could work as a noninvasive method to detect LNM in EEC with a high prediction accuracy. In addition, miR-34c cluster may be a key biomarker referring LNM in endometrial cancer.

Direct Comparison of Logistic Regression and Recursive Partitioning to Predict Lymph Node Metastasis in Endometrial Cancer

2015 ◽  
Vol 25 (6) ◽  
pp. 1037-1043 ◽  
Author(s):  
Martin Koskas ◽  
Dominique Luton ◽  
Olivier Graesslin ◽  
Emmanuel Barranger ◽  
Françoise Clavel-Chapelon ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Endometrial Cancer ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Early Stage ◽  
Recursive Partitioning ◽  
Operating Characteristics ◽  
Node Metastasis ◽  
Validation Set ◽  
Early Stage Endometrial Cancer

ObjectiveThe purpose was to compare logistic regression model (LRM) and recursive partitioning (RP) to predict lymph node metastasis in early-stage endometrial cancer.Methods/MaterialsThree models (1 LRM and 2 RP, a simple and a complex) were built in a same training set extracted from the Surveillance, Epidemiology, and End Results database for 18,294 patients who underwent hysterectomy and lymphadenectomy for stage I or II endometrial cancer. The 3 models were validated in a same validation set of 499 patients. Model performance was quantified with respect to discrimination (evaluated by the areas under the receiver operating characteristics curves) and calibration.ResultsIn the training set, the areas under the receiver operating characteristics curves were similar for LRM (0.80 [95% confidence interval [CI], 0.79–0.81]) and the complex RP model (0.79 [95% CI, 0.78–0.80]) and higher when compared with the simple RP model (0.75 [95% CI, 0.74–0.76]). In the validation set, LRM (0.77 [95% CI, 0.75–0.79]) outperformed the simple RP model (0.72 [95% CI, 0.70–0.74]). The complex RP model had good discriminative performances (0.75 [95% CI, 0.73–0.77]). Logistic regression model also outperformed the simple RP model in terms of calibration.ConclusionsIn these real data sets, LRM outperformed the simple RP model to predict lymph node metastasis in early-stage endometrial cancer. It is therefore more suitable for clinical use considering the complexity of an RP complex model with similar performances.

Prospective evaluation of sentinel node navigation surgery in Japanese patients with low-risk endometrial cancer—safety and occurrence of lymphedema

2021 ◽  
Author(s):  
Hitoshi Niikura ◽  
Asami Toki ◽  
Tomoyuki Nagai ◽  
Satoshi Okamoto ◽  
Shogo Shigeta ◽  
...  
Keyword(s):  
Endometrial Cancer ◽  
Lymph Node ◽  
Sentinel Lymph Node ◽  
Sentinel Lymph Node Biopsy ◽  
Lymph Node Metastasis ◽  
Lower Extremity ◽  
Lymph Node Biopsy ◽  
Occurrence Rate ◽  
Node Metastasis ◽  
Node Biopsy

Abstract Objective The present study aimed to clarify the occurrence rate of lymphedema and prognosis in patients with endometrial cancer according to sentinel lymph node biopsy alone with intraoperative histopathological examination. Methods The study included 45 consecutive patients with endometrial cancer treated at Tohoku University Hospital between October 2014 and August 2017. All patients had endometrial carcinoma with endometrioid histology Grade 1 or Grade 2 confirmed by biopsy and stage I on magnetic resonance imaging and/or computed tomography at their preoperative evaluation. Sentinel lymph node detection was performed by radioisotope and dye. Patients who were diagnosed intraoperatively as negative for sentinel lymph node metastasis did not undergo further systematic pelvic lymphadenectomy. The occurrence rate of lymphedema and prognosis was evaluated. Results Bilateral sentinel lymph nodes were detected in 44 of 45 patients (97%). Forty-three patients underwent sentinel lymph node biopsy alone, and only two patients underwent systematic lymphadenectomy. Sentinel lymph node metastases were detected in one patient intraoperatively and two patients postoperatively as ITCs. No patients experienced recurrence. New symptomatic lower-extremity lymphedema was identified in one of 43 patients (2.3%) who underwent sentinel lymph node biopsy alone. Conclusion Sentinel lymph node biopsy alone with intraoperative histopathological diagnosis appears to be a safe and effective strategy to detect lymph node metastasis and to reduce the number of patients with lower-extremity lymphedema among patients with endometrial cancer.

The incidence of pelvic lymph node metastasis by FIGO staging for patients with adequately surgically staged endometrial adenocarcinoma of endometrioid histology

2008 ◽  
Vol 18 (2) ◽  
pp. 269-273 ◽  
Author(s):  
D. S. Chi ◽  
R. R. Barakat ◽  
M. J. Palayekar ◽  
D. A. Levine ◽  
Y. Sonoda ◽  
...  
Keyword(s):  
Endometrial Cancer ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Myometrial Invasion ◽  
Tumor Grade ◽  
Pelvic Lymph Node ◽  
Study Cohort ◽  
Node Metastasis ◽  
Figo Staging ◽  
Pelvic Lymph Node Metastasis

The seminal Gynecologic Oncology Group study on surgical pathologic spread patterns of endometrial cancer demonstrated the risk of pelvic lymph node metastasis for clinical stage I endometrial cancer based on tumor grade and thirds of myometrial invasion. However, the FIGO staging system assigns surgical stage by categorizing depth of myometrial invasion in halves. The objective of this study was to determine the incidence of pelvic lymph node metastasis in endometrial cancer based on tumor grade and myometrial invasion as per the current FIGO staging system. We reviewed the records of all patients who underwent primary surgical staging for clinical stage I endometrial cancer at our institution between May 1993 and November 2005. To make the study cohort as homogeneous as possible, we included only cases of endometrioid histology. We also included only patients who had adequate staging, which was defined as a total hysterectomy with removal of at least eight pelvic lymph nodes. During the study period, 1036 patients underwent primary surgery for endometrial cancer. The study cohort was composed of the 349 patients who met study inclusion criteria. Distribution of tumor grade was as follows: grade 1, 80 (23%); grade 2, 182 (52%); and grade 3, 87 (25%). Overall, 30 patients (9%) had pelvic lymph node metastasis. The incidence of pelvic lymph node metastasis in relation to tumor grade and depth of myometrial invasion (none, inner half, and outer half) was as follows: grade 1–0%, 0%, and 0%, respectively; grade 2–4%, 10%, and 17%, respectively; and grade 3–0%, 7%, and 28%, respectively. We determined the incidence of pelvic nodal metastasis in a large cohort of endometrial cancer patients of uniform histologic subtype in relation to tumor grade and a one-half myometrial invasion cutoff. These data are more applicable to current surgical practice than the previously described one-third myometrial invasion cutoff results.

scholarly journals Vas deferens invasion: A neglected issue in the sampling of radical prostatectomy materials

2014 ◽  
Vol 8 (7-8) ◽  
pp. 554 ◽  
Author(s):  
Nuri Yigit ◽  
Yildirim Karslioglu ◽  
Bulent Kurt
Keyword(s):  
Lymph Node ◽  
Radical Prostatectomy ◽  
Lymph Node Metastasis ◽  
Vas Deferens ◽  
Prognostic Significance ◽  
Seminal Vesicles ◽  
Node Metastasis ◽  
Increased Risk ◽  
Staging Systems ◽  
Independent Prognostic Significance

A radical prostatectomy affects the prostate, bilateral seminal vesicles (SV), and the distal parts of the bilateral vasa deferentia (VD). SV invasion (SVI) is associated with an increased risk of lymph node metastasis and recurrence. However, the significance of VD invasion (VDI), either with or without the involvement of their surgical margins, has not been fully appreciated. We think VDI might have an independent prognostic significance, as does SVI, and should be incorporated into the pathology guidelines and the staging systems of prostatic adenocarcinoma. Our case illustrates this.

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