ABSTRACT
Objective:To compare the clinicopathological features of patients with false negative and true negative pathological biopsy of sentinel lymph nodes in endometrial cancer, and to explore the related factors of missed diagnosis of sentinel lymph nodes.Methods:From January 2020 to January 2022, 31 patients underwent sentinel lymph node biopsy combined with systematic lymph node resection in the First Affiliated Hospital of Shandong First Medical University were retrospectively analyzed, of which 2 were false negative and 29 were true negative. PubMed literature on sentinel lymph node false negative of endometrial cancer was searched from the establishment of the database to December 2022, with the search terms "Sentinel lymph node" "Endometrial neoplasms" and "False negative" . A total of 15 cases of false negative patients with similar methods to this study were extracted. In the false negative group, there were 17 false negative patients with sentinel lymph node negative but systemically excised lymph node positive, including 2 cases in our hospital and 15 cases in the literature. The true negative group included 29 true negative patients with negative sentinel and systemic lymph nodes, all from our hospital. The clinicopathologic features of the two groups were compared.Results:There were statistically significant differences in tumor grade ( χ2=6.09, P=0.014) , lymph vascular space invasion ( P=0.012) and myometrial invasion ( χ2=9.66, P=0.002) between the two groups. However, there was no significant difference in histological type between the two groups ( χ2=0.19, P=0.661) . Conclusion:There is a risk of false negative for sentinel lymph node biopsy in patients with endometrial carcinoma with high-grade tumor, myometrial invasion ≥1/2 and lymph vascular space invasion.
ABSTRACT
Objective: To explore the feasibility of differential diagnosis of nodule or mass pulmonary cryptococcosis (PC), lung adenocarcinoma and lung tuberculosis (TB) based on plain CT scanning radiomics prediction models. Methods: Plain CT data of 28 patients with nodule or mass type PC, 30 with pulmonary adenocarcinoma and 26 with lung TB were retrospectively analyzed. The texture features of lesions on CT images were extracted and selected to establish the optimized texture parameters between PC and lung adenocarcinoma, also between PC and lung TB. Then all samples were divided into training set and testing set according to ratio of 7:3. The random forest method was used to establish prediction model with the optimized texture parameters, and the model was used to evaluate training set data and verified with testing set data. The corresponding ROC curve was drawn, so as to evaluate the model's differential diagnosis efficiency. Results: After screening, 7 optimized feature parameters were obtained between PC and lung adenocarcinoma, while 4 were obtained between PC and lung TB. The AUC, sensitivity, specificity, accuracy of the model for differentiating PC from lung adenocarcinoma was 0.96, 1.00, 0.78 and 0.89,respectively, while for differentiating PC from lung TB was 0.99, 0.88, 0.89 and 0.88, respectively. Conclusion: Radiomics models based plain CT scanning can be used for differentiating and diagnosing nodule or mass PC from lung adenocarcinoma and lung TB.