Abstract Background Inadequate surface matching, variation in the guide design, and soft tissue on the skeletal surface may make it difficult to accurately place the 3D-printed patient-specific instrument (PSI) exactly to the designated site, leading to decreased accuracy, or even errors. Consequently, we developed a novel 3D-printed PSI with fluoroscopy-guided positioning markers to enhance the accuracy of osteotomies in joint-preserving surgery. The current study was to compare whether the fluoroscopically calibrated PSI (FCPSI) can achieve better accuracy compared with freehand resection and conventional PSI (CPSI) resection. Methods Simulated joint-preserving surgery was conducted using nine synthetic left knee bone models. Osteotomies adjacent to the knee joint were designed to evaluate the accuracy at the epiphysis side. The experiment was divided into three groups: free-hand, conventional PSI (CPSI), and fluoroscopically Calibrated PSI (FCPSI). Post-resection CT scans were quantitatively analyzed. Analysis of variance (ANOVA) was used. Result FCPSI improved the resection accuracy significantly. The mean location accuracy is 2.66 mm for FCPSI compared to 6.36 mm (P < 0.001) for freehand resection and 4.58 mm (P = 0.012) for CPSI. The mean average distance is 1.27 mm compared to 2.99 mm (p < 0.001) and 2.11 mm (p = 0.049). The mean absolute angle is 2.16° compared to 8.50° (p < 0.001) and 5.54° (p = 0.021). The mean depth angle is 1.41° compared to 8.10° (p < 0.001) and 5.32° (p = 0.012). However, there were no significant differences in the front angle compared to the freehand resection group (P = 0.055) and CPSI (P = 0.599) group. The location accuracy observed with FCPSI was maintained at 4 mm, while CPSI and freehand resection exhibited a maximum deviation of 8 mm. Conclusion The fluoroscopically calibrated 3D-printed patient-specific instruments improve the accuracy of osteotomy during bone tumor resection adjacent to joint joints compared to conventional PSI and freehand resection. In conclusion, this novel 3D-printed PSI offers significant accuracy improvement in joint preserving surgery with a minimal increase in time and design costs.

The incidence of endometrial endometrioid carcinoma (EEC) has been gradually increasing over the past decade. Fertility-sparing therapy with progestin is a treatment option for EEC or endometrial atypical hyperplasia (AH). The present study evaluated the role of numerous prognostic factors following fertility-sparing therapy for EEC or AH. Furthermore, the present study assessed the strength of various clinicopathological indicators for the prediction of treatment efficacy. A retrospective analysis was performed of patients with EEC and AH who received fertility-sparing therapy between August 2013 and September 2021 at Peking University People's Hospital (Beijing, China). Endometrial specimens were obtained from each patient after 3 months of treatment and at the end of the fertility-sparing therapy, before treatment efficacy and prognosis were evaluated using the χ2 test. Furthermore, the protein expression levels of EEC biomarkers, such as estrogen receptor (ER), progesterone receptor (PR), paired box 2 (PAX2), PTEN and p53 were assessed using immunohistochemistry. The overall complete response (CR) rate of fertility-sparing treatment in the EEC group was 67.39% (31/46), whereas that in the AH group was 86.49% (32/37). The difference between the CR rates in the EEC and AH groups was statistically significant (P<0.05). There was no association between prognosis after treatment and ER, PAX2, PTEN or Ki-67 expression in the initially untreated AH or EEC groups. However, tissues with >50% positive PR expression were demonstrated to have a higher CR rate compared with those with ≤50% positive PR expression in both the EEC and AH groups. Furthermore, the PAX2-positive group tended to demonstrate higher CR rates compared with the PAX2-negative group in the patients with EEC. In conclusion, these data suggested that fertility-sparing therapy is effective for patients with EEC and AH who wish to remain fertile after treatment. Specifically, in the AH group, a higher proportion of patients achieved a CR whilst also achieving this more rapidly. Furthermore, PR was demonstrated to be a useful marker for the evaluation of EEC and AH.

Background: The aim of this study was to evaluate whether molecular classification was associated with treatment response in women with endometrial endometrioid carcinoma (EEC) or Endometrial atypical hyperplasia/ endometrial intraepithelial neoplasia (EAH/EIN) treated with progestin.Methods: A retrospective analysis of 59 patients with EEC or EAH/EIN who received fertility-sparing therapy between 2013 and 2021 was performed. For each patient, medical records and pathological reports were reviewed. The treatment efficacy and tumor prognosis were evaluated. Immunohistochemistry analysis for p53 and MSH2, MSH6, PSM2, MLH1 were performed. Molecular classification was analyzed using a 11-gene panel based on next generation sequencing technology.Results: 23 of 39 patients with EEC received complete response (CR) after fertility-sparing treatment which was significantly lower than the EAH/EIN group (58.97 % vs 80.0 %, P < 0.05). Molecular classification via the Cancer Genome Atlas (TCGA) algorithm was successfully applied to 59 cases. The distribution of specimens into the four molecular classes was as follows: 83.05 % (49/59) CNL(copy number-low),6.78 % (4/59) MSI-H (microsatellite instability -high), 5.08 %(3/59) POLE-mutated and 5.08 % (3/59) CNH(copy number-high). MSI and TP53 sequencing results were concordant with immunohistochemistry analyses of MMR and p53 protein. The patients with CNH and MSI-H subtypes showed worse prognosis than those with POLE-mutated and CNL subtypes.Conclusions: Molecular classification of EAH/EIN prior to management with progestin treatment was feasible and may predict patients at risk of progression.

The accurate pathological diagnosis of endometrial cancer (EC) improves the curative effect and reduces the mortality rate. Deep learning has demonstrated expert-level performance in pathological diagnosis of a variety of organ systems using whole-slide images (WSIs). It is urgent to build the deep learning system for endometrial cancer detection using WSIs. The deep learning model was trained and validated using a dataset of 601 WSIs from PUPH. The model performance was tested on three independent datasets containing a total of 1,190 WSIs. For the retrospective test, we evaluated the model performance on 581 WSIs from PUPH. In the prospective study, 317 consecutive WSIs from PUPH were collected from April 2022 to May 2022. To further evaluate the generalizability of the model, 292 WSIs were gathered from PLAHG as part of the external test set. The predictions were thoroughly analyzed by expert pathologists. The model achieved an area under the receiver operating characteristic curve (AUC), sensitivity, and specificity of 0.928, 0.924, and 0.801, respectively, on 1,190 WSIs in classifying EC and non-EC. On the retrospective dataset from PUPH/PLAGH, the model achieved an AUC, sensitivity, and specificity of 0.948/0.971, 0.928/0.947, and 0.80/0.938, respectively. On the prospective dataset, the AUC, sensitivity, and specificity were, in order, 0.933, 0.934, and 0.837. Falsely predicted results were analyzed to further improve the pathologists' confidence in the model. The deep learning model achieved a high degree of accuracy in identifying EC using WSIs. By pre-screening the suspicious EC regions, it would serve as an assisted diagnostic tool to improve working efficiency for pathologists.