Application of Carbon Nanoparticles in Tracing Sentinel Lymph Node in Endometrial Cancer

Abstract

Carbon nanoparticle (CNP) tracer technology is a new type of tracing method adopted in the medical field. It can black stain the pelvic lymph node of endometrial cancer and has many advantages such as non-invasiveness and easy tolerance. To analyze the indicative role of CNP in the diagnosis of sentinel lymph node (SLN) metastasis in endometrial cancer (EC) patients, 102 patients who were diagnosed as EC by hysteroscopy were deemed as the research objects. The two-point method was adopted to inject CNP suspension into the cervix of the patients. Hematoxylin-eosin staining (HE) was adopted for lymph node metastasis detection. The cytokeratin pathological micro metastasis detection method was employed to detect whether the lymph node was metastasized, and its distribution area was recorded. Moreover, the detection rate, diagnostic accuracy, sensitivity, and specificity of SLN was recorded. The results showed that the proportion of stage I patients was 74.37%, the proportion of stage II patients was 15.68%, and the proportion of stage III patients was 9.95%. The proportion of well-differentiated patients was 51.47%, the proportion of moderately differentiated patients was 38.94%, and the proportion of poorly differentiated patients was 9.59%. The number of lymph node dissections was 2305, of which 590 was SLN and 1715 was NSLN. The SLN distribution areas were common iliac area (45.17%), obturator area (34.44%), external iliac area (11.15%), and internal iliac area (9.34%). 18 patients had SLN pelvic cavity metastasis, and no signs of NSLN metastasis were found. The detection rate of SLN was 95.26%. The diagnosis accuracy was 98.55%, the sensitivity was 95.27%, and the specificity was 87.28%. In short, CNP played an imperative role in the diagnosis of SLN metastasis in EC patients. The main distribution areas of patients with SLN were common iliac area and obturator area. The lymph node detection rate and accuracy were high.

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