| Objective:Carbon nanoparticle(CNP)was used as a tracer to identify sentinel lymph node(SLN)of endometrial carcinoma(EC),and its detection rate and influential factors,sensitivity,negative predictive value(NPV)and SLN distribution characteristics were evaluated under the SLN algorithm.to explore the value and feasibility of CNP identification of SLN in endometrial cancer.Method:This study included 50 patients with endometrial cancer who underwent surgical treatment in Dalian Maternal and Child Health Hospital from June 2018 to July2019.All patients were diagnosed with curative curettage or hysteroscopy before operation.The CNP was injected at 3,6,9,and 12 o'clock in the cervix during surgery.The first black-stained lymph node was SLN.Any suspicious lymph nodes were removed during the operation and endometrial cancer staging was completed.All lymph nodes were examined by hematoxylin-eosin(HE)staining.Results:50 patients were included,aged 32-59 years,with an average of 56.18±8.03years old,and a body mass index(BMI)of 25.98±3.89kg/m~2.All patients underwent laparoscopic surgery without conversion to open surgery.Postoperative staging was performed according to the 2014 International Obstetrics and Gynaecology Union(FIGO)standard,which included 39 cases(78%)in IA,5 cases(10%)in IB,2(4%)in stage II,and 2 in IIIC1(4%),1 case in stage IIIC2(2%),1 case in stage IVB(2%).Histological classification:42 cases(84%)of endometrioid carcinoma,including 12cases of G1(24%),21 cases of G2(2%)and 9 cases of G3(18%).There were 8 cases of serous carcinoma(16%),1 case of carcinosarcoma(2%),1 case of clear cell carcinoma(2%),and 1 case of neuroendocrine carcinoma(2%).In the 50 patients,39 had at least 1SLN,and the detection rate was 78%(39/50).There were 22 cases(44%)of unilateral imaging,of which 1 case was para-aortic lymph nodes,17 cases(34%)of bilateral imaging,2 cases were para-aortic lymph nodes,and 1 case was anterior iliac lymph nodes.Only SLN biopsy sensitivity was 75%(3/4),false negative rate was 25%(1/4),and negative predictive value was 97.2%(35/36).If the SLN algorithm is followed,the false negative rate is reduced to 20%(1/5),the sensitivity is increased to 80%,and the negative predictive value is 97.8%(44/45).Comparing SLN with pelvic and abdominal lymph node status,kappa Value was 0.878,p<0.05.The development time is 12-20minutes,and the median development time is 15 minutes.A total of 183 SLNs were resected,and the most common external iliac lymph nodes were 83(45.4%).The rest were intra-condylar(21.3%),obturator(13.7%),paraabdominal aorta(11.5%),and total condyle(4.915),Parauterine(2.73%)and presacral lymph nodes(0.54%).There was no significant correlation between the detection rate and histological type,histological grade of endometrioid carcinoma,LVSI or not,and depth of muscular invasion,p>0.05.Among high-risk endometrial cancer patients,the detection rate was 76.2%(16/21).Under SLN algorithm,the sensitivity was 80%(4/5)and the NPV was 94.1%(16/17).The kappa value was 0.859,P<0.05.Conclusion:Carbon nanoparticles as a tracer can predict the pelvic and abdominal lymph node metastasis in SLN mapping of endometrial cancer more reliably,and has clinical application value.Following SLN algorithm can improve the sensitivity of SLN mapping and reduce the false negative predictive value.Carbon nanoparticles still have good diagnostic accuracy in high-risk endometrial cancer,and large sample studies are still needed for further verification. |
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