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Updated: Jan 29, 2026

Sentinel Lymph Node Mapping and Biopsy for Endometrial Cancer at Early Stage with Laparoscopy
Published on: August 19, 2021
Intraoperative Ex Vivo Shear-Wave Elastography of Sentinel Lymph Nodes in Endometrial Cancer and Other Gynaecological
Walid Shaalan1, Mohamed Eldesouky1, Theresa Mokry2
1Department of Gynaecology and Obstetrics, Heidelberg University Hospital, Im Neuenheimer Feld 440, 69120 Heidelberg, Germany.
Background:
Accurate intraoperative assessment of sentinel lymph node (SLN) status is critical for staging and guiding surgical management in gynaecological malignancies. Frozen-section histopathology remains the gold standard, but it is time-consuming and resource-intensive. Shear-wave elastography (SWE) quantifies tissue stiffness in real time and may offer a rapid alternative.
Methods:
In this prospective single-centre study, 63 women (median age 62 years) undergoing primary surgery with sentinel lymph node biopsy (SLNB) for endometrial, cervical, vulvar, or early ovarian carcinoma were enrolled. A total of 172 SLNs were excised, submerged in coupling gel, and scanned ex vivo using a 9 MHz linear probe.
Results:
A total of 172 SLNs underwent SWE (mean 2.7 nodes/patient). Endometrial primaries accounted for 58% of nodes, mostly retrieved by robotic-assisted surgery (71.8%). Node dimensions were significantly larger in malignant lesions for sonographic (long-axis: 13.02 ± 3.31 mm vs. 10.80 ± 3.28 mm; p = 0.002) and pathological long-axis measurements (11.45 ± 2.83 mm vs. 9.75 ± 2.61 mm; p = 0.004). Mean SWE velocities were similar between groups (1.381 ± 0.307 vs. 1.343 ± 0.236 m/s; p = 0.541). Histopathology identified metastases in 18% of SLNs, comprising macrometastases (7%), micrometastases (5%), and isolated tumour cells (6%).
Conclusions:
Although ex vivo SWE is rapid, reproducible, and integrates seamlessly into the sterile field, stiffness measurements alone lack sufficient discriminatory power for SLN staging in gynaecological cancers. Future research should focus on three-dimensional SWE, advanced radiomic analyses, and machine-learning algorithms to improve the detection of low-volume metastatic disease.
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