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Guo Y, Li L, Yang K, Song Y, Xu L, Zhao F, Li W. Enhancing prognostic insights: myometrial invasion patterns in endometrial carcinoma, with emphasis on MELF pattern-a comprehensive review and meta-analysis. Eur J Cancer Prev 2024:00008469-990000000-00153. [PMID: 38870068 DOI: 10.1097/cej.0000000000000903] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2024]
Abstract
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.
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Affiliation(s)
| | - Luying Li
- School of Medicine, Shihezi University
| | | | | | - Lu Xu
- School of Medicine, Shihezi University
| | | | - Wenting Li
- School of Medicine, Shihezi University
- Department of Gynaecology, The First Affiliated Hospital of Shihezi University, Shihezi, China
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Stögbauer F, Geß B, Brambs C, Lautizi M, Kacprowski T, Ourailidis I, Bronger H, Kiechle M, Noske A, Keller G, Jesinghaus M, Poremba C, Weichert W, Boxberg M. Independent Tissue-Based Biomarkers in Endometrioid Endometrial Cancer: Tumor Budding in Microsatellite Instability and WHO Grading in Copy-Number-Low Patients. Cancers (Basel) 2023; 15:3832. [PMID: 37568650 PMCID: PMC10416921 DOI: 10.3390/cancers15153832] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 07/20/2023] [Accepted: 07/21/2023] [Indexed: 08/13/2023] Open
Abstract
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.
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Affiliation(s)
- Fabian Stögbauer
- Institute of Pathology, School of Medicine, Technical University of Munich (TUM), 81675 Munich, Germany; (F.S.); (M.L.); (A.N.); (G.K.); (W.W.)
| | - Barbara Geß
- Department of Obstetrics and Gynecology, Technical University of Munich, 81675 Munich, Germany; (B.G.); (H.B.)
| | - Christine Brambs
- Lucerne Cantonal Hospital, Department of Obstetrics & Gynecology, Division of Gynecologic Oncology, 6000 Lucerne, Switzerland;
| | - Manuela Lautizi
- Institute of Pathology, School of Medicine, Technical University of Munich (TUM), 81675 Munich, Germany; (F.S.); (M.L.); (A.N.); (G.K.); (W.W.)
- Chair of Experimental Bioinformatics, TUM School of Life Sciences Weihenstephan, Technical University of Munich, 85354 Freising, Germany
| | - Tim Kacprowski
- Division Data Science in Biomedicine, Peter L. Reichertz Institute for Medical Informatics, TU Braunschweig and Hannover Medical School, 38106 Brunswick, Germany;
- Braunschweig Integrated Centre of Systems Biology (BRICS), 38106 Brunswick, Germany
| | - Iordanis Ourailidis
- Institute of Pathology, Heidelberg University Hospital, 69120 Heidelberg, Germany;
| | - Holger Bronger
- Department of Obstetrics and Gynecology, Technical University of Munich, 81675 Munich, Germany; (B.G.); (H.B.)
- German Cancer Consortium (DKTK) Partner Site Munich, 81675 Munich, Germany
| | - Marion Kiechle
- Department of Obstetrics and Gynecology, Technical University of Munich, 81675 Munich, Germany; (B.G.); (H.B.)
| | - Aurelia Noske
- Institute of Pathology, School of Medicine, Technical University of Munich (TUM), 81675 Munich, Germany; (F.S.); (M.L.); (A.N.); (G.K.); (W.W.)
| | - Gisela Keller
- Institute of Pathology, School of Medicine, Technical University of Munich (TUM), 81675 Munich, Germany; (F.S.); (M.L.); (A.N.); (G.K.); (W.W.)
| | - Moritz Jesinghaus
- Institute of Pathology, University Hospital Marburg, Baldingerstraße, 35043 Marburg, Germany;
| | | | - Wilko Weichert
- Institute of Pathology, School of Medicine, Technical University of Munich (TUM), 81675 Munich, Germany; (F.S.); (M.L.); (A.N.); (G.K.); (W.W.)
- German Cancer Consortium (DKTK) Partner Site Munich, 81675 Munich, Germany
| | - Melanie Boxberg
- Institute of Pathology, School of Medicine, Technical University of Munich (TUM), 81675 Munich, Germany; (F.S.); (M.L.); (A.N.); (G.K.); (W.W.)
- German Cancer Consortium (DKTK) Partner Site Munich, 81675 Munich, Germany
- Pathologie München-Nord, 80992 Munich, Germany;
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IAVAZZO C, FOTIOU A, GKEGKES ID, VRACHNIS N. Microcystic Elongated and Fragmented Pattern Invasion in Endometrial Cancer: Possible Prognostic Value to Precise and Individualized Therapeutic Strategies. Medeni Med J 2022; 37:352-353. [PMID: 36578170 PMCID: PMC9808849 DOI: 10.4274/mmj.galenos.2022.39024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Affiliation(s)
- Christos IAVAZZO
- Metaxa Cancer Hospital, Clinic of Gynaecological Oncology, Piraeus, Greece
| | - Alexandros FOTIOU
- Metaxa Cancer Hospital, Clinic of Gynaecological Oncology, Piraeus, Greece
| | - Ioannis D. GKEGKES
- Royal Devon and Exeter NHS Foundation Trust, Department of Colorectal Surgery, Exeter, United Kingdom,* Address for Correspondence: Royal Devon and Exeter NHS Foundation Trust, Department of Colorectal Surgery, Exeter, United Kingdom E-mail:
| | - Nikolaos VRACHNIS
- National and Kapodistrian University of Athens Medical School, Attikon Hospital, Third Department of Obstetrics and Gynecology, Athens, Greece
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Zhang X, Ba W, Zhao X, Wang C, Li Q, Zhang Y, Lu S, Wang L, Wang S, Song Z, Shen D. Clinical-grade endometrial cancer detection system via whole-slide images using deep learning. Front Oncol 2022; 12:1040238. [PMID: 36408137 PMCID: PMC9668742 DOI: 10.3389/fonc.2022.1040238] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Accepted: 10/19/2022] [Indexed: 11/05/2022] Open
Abstract
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.
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Affiliation(s)
- Xiaobo Zhang
- Department of Pathology, Peking University People’s Hospital, Beijing, China
| | - Wei Ba
- Department of Pathology, Chinese PLA General Hospital, Beijing, China
| | - Xiaoya Zhao
- Department of Pathology, Peking University People’s Hospital, Beijing, China
| | - Chen Wang
- Department of Pathology, Peking University People’s Hospital, Beijing, China
| | - Qiting Li
- R&D Department, China Academy of Launch Vehicle Technology, Beijing, China
| | - Yinli Zhang
- Department of Pathology, Peking University People’s Hospital, Beijing, China
| | - Shanshan Lu
- Department of Pathology, Peking University People’s Hospital, Beijing, China
| | - Lang Wang
- Thorough Lab, Thorough Future, Beijing, China
| | - Shuhao Wang
- Thorough Lab, Thorough Future, Beijing, China
- *Correspondence: Danhua Shen, ; Zhigang Song, ; Shuhao Wang,
| | - Zhigang Song
- Department of Pathology, Chinese PLA General Hospital, Beijing, China
- *Correspondence: Danhua Shen, ; Zhigang Song, ; Shuhao Wang,
| | - Danhua Shen
- Department of Pathology, Peking University People’s Hospital, Beijing, China
- *Correspondence: Danhua Shen, ; Zhigang Song, ; Shuhao Wang,
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