Clinicopathologic association and prognostic impact of microcystic, elongated and fragmented pattern invasion, combined with tumor budding in endometrioid endometrial cancer

Enhancing prognostic insights: myometrial invasion patterns in endometrial carcinoma, with emphasis on MELF pattern-a comprehensive review and meta-analysis

PURPOSE

The microcystic, elongated, and fragmented (MELF) pattern, characterized by myxoid and inflamed stroma, is readily identifiable as a form of myometrial infiltration. This meta-analysis endeavors to assess the prognostic significance of MELF infiltration patterns in patients diagnosed with endometrial cancer.

METHODS

A comprehensive literature search, spanning until 11 October 2023, across PubMed, Embase, Cochrane, and Web of Science databases, identified 23 relevant studies involving 5199 patients. Data analysis was performed using Stata 16.0.

RESULTS

Analysis indicates that MELF infiltration predicts a higher risk of lymph node metastasis in endometrial cancer patients [hazard ratios (HR) = 5.05; 95% confidence interval (CI), 3.62-7.05; P < 0.05]. Notably, this association remains consistent across various patient demographics, analytical approaches, study designs, and treatment modalities. However, MELF infiltration does not significantly correlate with recurrence (HR = 1.05; 95% CI, 0.73-1.52; P > 0.05), overall survival (HR = 1.24; 95% CI, 0.91-1.68; P > 0.05), or disease-free survival (HR = 1.40; 95% CI, 0.85-2.28; P > 0.05).

CONCLUSION

While MELF infiltration heightens the risk of lymph node metastasis in endometrial cancer, its impact on recurrence, overall survival, and disease-free survival remains statistically insignificant.

Independent Tissue-Based Biomarkers in Endometrioid Endometrial Cancer: Tumor Budding in Microsatellite Instability and WHO Grading in Copy-Number-Low Patients

The molecular characterization of endometrial endometrioid adenocarcinomas has provided major advances in its prognostic stratification. However, risk assessment of microsatellite instability (MSI) and copy-number (CN)-low cases remains a challenge. Thus, we aimed to identify tissue-based morphologic biomarkers that might help in the prognostic stratification of these cases. Histomorphologic parameters (WHO grading, tumor budding (TB), tumor-stroma ratio (as a quantitative description of stromal desmoplasia), tumor-infiltrating lymphocytes (TIL), "microcystic, elongated, fragmented" (MELF) pattern) were analyzed in resection specimens of the TCGA-UCEC cohort (n = 228). For each quantitative parameter, a two-tiered system was developed utilizing systematically determined cutoffs. Associations with survival outcomes were calculated in univariate and multivariate analysis and validated in two independent cohorts. In MSI tumors, only TB remained an independent prognostic factor. TB (≥3 buds/high-power field) was associated with inferior outcomes and with lymph node metastases. The prognostic significance of TB was confirmed in two validation cohorts. For CN-low tumors, established grading defined by the WHO was independently prognostic with inferior outcomes for high-grade tumors. The evaluation of TB might help in identifying MSI-patients with unfavorable prognosis who, e.g., could benefit from lymphadenectomy. WHO-based grading facilitates independent prognostic stratification of CN-low endometrioid adenocarcinomas. Therefore, we propose the utilization of TB and WHO-based grading, two tissue-based and easy-to-assess biomarkers, in MSI/CN-low endometrial carcinomas for improved clinical management.

Clinical-grade endometrial cancer detection system via whole-slide images using deep learning

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.