Sentinel lymph node biopsy in endometrial cancer: state of the art

Corte, Luigi Della; Giampaolino, Pierluigi; Mercorio, Antonio; Riemma, Gaetano; Schiattarella, Antonio; Franciscis, Pasquale De; Bifulco, Giuseppe

doi:10.21037/tcr.2020.04.21

Cited by 13 publications

(10 citation statements)

References 61 publications

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“…Fertility retention technology can effectively improve the quality of life of gynecological cancer patients, and has become the goal and hope for cancer survivors to live a better life [ 19 ]. Studies based on systems biology proteomics have highlighted the exact potential molecular mechanisms associated with SLN and EC grades [ 20 , 21 ]. The aim of these investigations is to examine the endometrial cells, determine the disease extent, and detect the presence/absence of metastasis.…”

Section: Discussionmentioning

confidence: 99%

Diagnosis and Prediction of Endometrial Carcinoma Using Machine Learning and Artificial Neural Networks Based on Public Databases

Zhao

Zhang

Wang

et al. 2022

Genes

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Endometrial carcinoma (EC), a common female reproductive system malignant tumor, affects thousands of people with high morbidity and mortality worldwide. This study was aimed at developing a prediction model for the diagnosis of EC in the general population. First, we obtained datasets GSE63678, GSE106191, and GSE115810 from the Gene Expression Omnibus (GEO) database, dataset GSE17025 from the GEO database, and the RNA sequence of EC from The Cancer Genome Atlas (TCGA) database to constitute the training, test, and validation groups, respectively. Subsequently, the 96 most significantly differentially expressed genes (DEGs) were identified and analyzed for function and pathway enrichment in the training group. Next, we acquired the disease-specific genes by random forest and established an artificial neural network for the diagnosis. Receiver operating characteristic (ROC) curves were utilized to identify the signature across the three groups. Finally, immune infiltration was analyzed to reveal tumor-immune microenvironment (TIME) alterations in EC. The top 96 DEGs (77 down-regulated and 19 up-regulated genes) were primarily enriched in the interleukin-17 signaling pathway, protein digestion and absorption, and transcriptional misregulation in cancer. Subsequently, 14 characterizing genes of EC were identified by random forest. In the training, test, and validation groups, the artificial neural network was constructed with high diagnostic accuracies of 0.882, 0.864, and 0.839, respectively, and areas under the ROC curve (AUCs) of 0.928, 0.921, and 0.782, respectively. Finally, resting and activated mast cells were found to have increased in TIME. We constructed an artificial diagnostic model with excellent reliability for EC and uncovered variations in the immunological ecosystem of EC through integrated bioinformatics approaches, which might be potential diagnostic targets for EC.

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Section: Discussionmentioning

confidence: 99%

Diagnosis and Prediction of Endometrial Carcinoma Using Machine Learning and Artificial Neural Networks Based on Public Databases

Zhao

Zhang

Wang

et al. 2022

Genes

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“…Pelvic and para-aortic lymphadenectomy are essential parts of surgical staging for early-stage patients and cytoreduction for advanced-stage patients in gynecological malignancies. The safety of lymphadenectomy should be raised by effective treatment towards complications, as well as further strategies of prophylaxis, such as individualized lymph nodes dissection 21 and sentinel lymph node mapping 22 to avoid unnecessary dissections, and treatment of PA-MSHA before conservative management. 23 …”

Section: Discussionmentioning

confidence: 99%

Topical Application of Pseudomonas aeruginosa-Mannose Sensitive Hemagglutinin (PA-MSHA) for Refractory Lymphatic Leakage Following Lymphadenectomy in Patients with Gynecological Malignancies

Zhou¹,

Xu²,

He³

et al. 2021

CMAR

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Purpose To observe the feasibility and efficacy of Pseudomonas aeruginosa -mannose sensitive hemagglutinin (PA-MSHA) in refractory lymphatic leakage following lymphadenectomy among patients with gynecological cancers. Patients and Methods Ten cases with post-operative massive lymphatic leakage were collected, in which patients failed to respond to conservative treatment. Topical PA-MSHA injection of a single dose (2mL) was performed through drainage tube or transvaginal catheter into pelvic or peritoneal cavity. Drainage volumes and side effects were recorded. Results The incidence of refractory lymphatic leakage following pelvic and para-aortic lymphadenectomy was 2.44% (10/409). All ten patients (100%) had quick recovery and were discharged within 72 hours. Among them, one patient (10%) experienced fever and six patients (60%) experienced abdominal pain, one of which was moderate and relieved by routine analgesic treatment. During 11 (6–38) months of follow-up time, no long-term side effect was observed. Conclusion Topical injection of PA-MSHA of a single dose appears a feasible and effective treatment for refractory post-operative lymphatic leakage.

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“…Actually, the adequacy of the EST is a fundamental step for the tailoring of surgical extension, in particularly regarding the type of lymph nodal staging (39). In fact, while for ovarian cancer the open debate regards the different therapeutic surgical strategies to predict and to achieve a complete resection (40)(41)(42)(43)(44), instead, for endometrial, and similarly for vulvar (45) and cervical cancer (46), the current research topic is the validity of sentinel lymph node technique (47)(48)(49).…”

Section: Discussionmentioning

confidence: 99%

Impact of endometrial sampling technique and biopsy volume on the diagnostic accuracy of endometrial cancer

Ferrari¹,

Forte²,

Arrigoni³

et al. 2020

Transl Cancer Res TCR

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Background: Histotype and tumor grading of endometrial cancer are the most important factors that have to be assessed by preoperative endometrial sampling, and their concordance with the final surgical and definitive histological findings is of paramount importance. We aim to compare histotype and tumor grading concordance of various endometrial sampling techniques (ESTs) and to investigate the role of endometrial volume biopsy.Methods: We performed a retrospective analysis of patients with apparent early stage endometrial cancer collecting demographic, clinical data, type of EST, pathological characteristics of endometrial biopsies and final specimens. We classified ESTs as dilation and curettage (D&C), diagnostic hysteroscopy with D&C, outpatient hysteroscopy and operative hysteroscopy with or without D&C. Diagnostic and operative hysteroscopy were performed with Bettocchi's 5 mm hysteroscope. We evaluated concordance for histotype, and tumor grading, and we performed subgroup analysis based on the technique and final tumor grading. Concordance was classified from good, moderate, sufficient, fair, poor and none using Cohen k-statistic. Finally, we investigated the existence of independent risk factors for discordant tumor grading using multivariate binary logistic regression.Results: We collected 148 patients and of these 131 (88.5%) were diagnosed with endometrioid histotype and 65 (44%), 46 (31%) and 37 (25%) respectively with well, moderate and poor differentiated tumors. Atypical hyperplasia (AH) was detected preoperatively in 28 patients (19%). Histotype concordance was fair (k=0.35) and tumor grading concordance was moderate (k=0.45); particularly, concordance was fair in welldifferentiated cases (k=0.38); concordance was moderate in moderate-and poor-differentiated cases (k=0.52) and good (k=0.71). Operative hysteroscopy showed moderate concordance for histotype (k=0.41), while grading concordance was fair for G1 (k=0.41), moderate for G2 (k=0.58) and good for G3 (k=0.72), regardless the use of D&C. Preoperative volume biopsy did not impact the concordance of tumor grading, while the adoption of operative hysteroscopy (with or without D&C) decreased the risk of grading discordance in G3 tumors (HR 0.17; 95%

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Sentinel lymph node biopsy in endometrial cancer: state of the art

Cited by 13 publications

References 61 publications

Diagnosis and Prediction of Endometrial Carcinoma Using Machine Learning and Artificial Neural Networks Based on Public Databases

Diagnosis and Prediction of Endometrial Carcinoma Using Machine Learning and Artificial Neural Networks Based on Public Databases

Topical Application of Pseudomonas aeruginosa-Mannose Sensitive Hemagglutinin (PA-MSHA) for Refractory Lymphatic Leakage Following Lymphadenectomy in Patients with Gynecological Malignancies

Impact of endometrial sampling technique and biopsy volume on the diagnostic accuracy of endometrial cancer

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