Purpose The aim of this study was describe a novel sentinel lymph node (SLN) tracer injection procedure featuring dual cervical and fundal indocyanine green (ICG) injection for endometrial cancer and results of SLN biopsy. Methods Between June 26, 2014 and October 31, 2016, 111 patients underwent laparoscopic surgery for endometrial cancer at our institution. In all cases, we performed SLN biopsy with dual cervical and fundal ICG injection. All SLNs were processed with an ultrastaging technique. A total of 69 patients also underwent total pelvic and paraaortic lymphadenectomy. Results The detection rates were as follows: 92.79% (103/111) overall for SLNs; 89.19% (99/111) overall for pelvic SLNs; 61.26% (68/111) for bilateral SLNs; 59.46% (66/111) for paraaortic SLNs, and 4% (4/111) for isolated paraaortic SLNs. We found macroscopic lymph node metastasis in 11 patients (9.9%) and microdisease in lymph nodes in another 10 patients, raising the overall rate of lymph node involvement to 18.92%. There was 1 false-negative (negative SLN biopsy but positive aortic lymphadenectomy) and another positive case in 1 undetected SLN. The sensitivity of detection was 94.44%, specificity 100%, negative predictive value 97.83%, and negative likelihood ratio 0.06 for intermediate and high-risk endometrial cancer groups. Conclusions The SLN biopsy with both cervical and fundal ICG injection offers good overall detection rates and improved mapping of the aortic area. The SLN ultrastaging increases the number of nodes considered positive.

The objective of this study was to evaluate the efficacy of one-step nucleic acid amplification (OSNA) for the detection of sentinel lymph node (SLN) metastasis compared to standard pathological ultrastaging in patients with early-stage endometrial cancer (EC). A total of 526 SLNs from 191 patients with EC were included in the study, and 379 SLNs (147 patients) were evaluated by both methods, OSNA and standard pathological ultrastaging. The central 1 mm portion of each lymph node was subjected to semi-serial sectioning at 200 μm intervals and examined by hematoxylin–eosin and immunohistochemistry with CK19; the remaining tissue was analyzed by OSNA for CK19 mRNA. The OSNA assay detected metastases in 19.7% of patients (14.9% micrometastasis and 4.8% macrometastasis), whereas pathological ultrastaging detected metastasis in 8.8% of patients (3.4% micrometastasis and 5.4% macrometastasis). Using the established cut-off value for detecting SLN metastasis by OSNA in EC (250 copies/μL), the sensitivity of the OSNA assay was 92%, specificity was 82%, diagnostic accuracy was 83%, and the negative predictive value was 99%. Discordant results between both methods were recorded in 20 patients (13.6%). OSNA resulted in an upstaging in 12 patients (8.2%). OSNA could aid in the identification of patients requiring adjuvant treatment at the time of diagnosis.

Perinatal asphyxia represents a major medical disorder and is related to around a fourth of all neonatal deaths worldwide. Specific thresholds for lactate or pH levels define the gold standard for detecting hypoxic-ischemic events as physiological abnormalities. In contrast to current gold standard, we analyze the systemic picture, represented by the whole set of biochemical parameters from blood gas analysis, by multiparametric machine learning algorithms. In a swine model with 22 objects, we investigate the impact of neonatal hypoxic-ischemic encephalopathy on 18 individual physiological parameters. In a first approach, we study the statistical significance of individual parameters by univariate analysis methods. In a second approach, we take the most relevant parameters as input for the development of predictive models by different hybrid and non hybrid classification algorithms. The predictive power of our multiparametric models outperforms by far the limited performance of pH and lactate as reliable indicators, despite strong correlation with hypoxic-ischemic events. We have been able to detect hypoxic-ischemic events even one hour after the episode, with accuracies close to 100% in contrast to pH or lactate-based diagnosis with 62% and 78%, respectively. By all machine learning algorithms, lactate is recognized as the main contributor due to its longer-term evidence of hypoxia-ischemia episodes. However, substantial improvement of the diagnosis is achieved by predictions based on a systemic picture of different physiological parameters. Our results prove the potential applicability of our method as a support tool for decision-making that will allow obstetricians to identify hypoxic ischemic episodes more accurately during labor.