Correlation between pre-operative diagnosis and final pathological diagnosis of endometrial malignancies; impact on primary surgical treatment

Laban, Mohamed; Nassar, Samir A.; Elsayed, Jilan; Hassanin, Alaa S.

doi:10.1016/j.ejogrb.2021.06.008

Cited by 3 publications

(4 citation statements)

References 20 publications

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“…According to the risk classification of the ESMO guidelines ( 6), the lowest possible risk of nonendometrial endometrioid adenocarcinoma is "intermediate", and the guidelines also state that surgical staging or lymph node biopsy or resection should be considered for intermediate risk or higher, but not for low-risk patients. The type of pathology is confirmed by preoperative D&C, and it is highly consistent with postoperative pathology (35). Therefore, we believe it is necessary to create a separate risk classification model for patients with endometrial endometrioid adenocarcinoma.…”

Section: Discussionmentioning

confidence: 67%

Combined deep-learning MRI-based radiomic models for preoperative risk classification of endometrial endometrioid adenocarcinoma

Yang,

Cao,

Zhou

et al. 2023

Front. Oncol.

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BackgroundDifferences exist between high- and low-risk endometrial cancer (EC) in terms of whether lymph node dissection is performed. Factors such as tumor grade, myometrial invasion (MDI), and lymphovascular space invasion (LVSI) in the European Society for Medical Oncology (ESMO), European SocieTy for Radiotherapy & Oncology (ESTRO) and European Society of Gynaecological Oncology (ESGO) guidelines risk classification can often only be accurately assessed postoperatively. The aim of our study was to estimate the risk classification of patients with endometrial endometrioid adenocarcinoma before surgery and offer individualized treatment plans based on their risk classification.MethodsClinical information and last preoperative pelvic magnetic resonance imaging (MRI) of patients with postoperative pathologically determined endometrial endometrioid adenocarcinoma were collected retrospectively. The region of interest (ROI) was subsequently plotted in T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), and diffusion-weighted imaging (DWI) MRI scans, and the traditional radiomics features and deep-learning image features were extracted. A final radiomics nomogram model integrating traditional radiomics features, deep learning image features, and clinical information was constructed to distinguish between low- and high-risk patients (based on the 2020 ESMO-ESGO-ESTRO guidelines). The efficacy of the model was evaluated in the training and validation sets of the model.ResultsWe finally included 168 patients from January 1, 2020 to July 29, 2021, of which 95 patients in 2021 were classified as the training set and 73 patients in 2020 were classified as the validation set. In the training set, the area under the curve (AUC) of the radiomics nomogram was 0.923 (95%CI: 0.865–0.980) and in the validation set, the AUC of the radiomics nomogram was 0.842 (95%CI: 0.762–0.923). The nomogram had better predictions than both the traditional radiomics model and the deep-learning radiomics model.ConclusionMRI-based radiomics models can be useful for preoperative risk classification of patients with endometrial endometrioid adenocarcinoma.

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Section: Discussionmentioning

confidence: 67%

Combined deep-learning MRI-based radiomic models for preoperative risk classification of endometrial endometrioid adenocarcinoma

Yang,

Cao,

Zhou

et al. 2023

Front. Oncol.

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“…40 Despite these promising results, the limited sample size of their study and the associated costs of SLNB preclude its routine adoption for EH. It is plausible that AEH will constitute the majority of patients in whom SLNB will yield positive lymph nodes as suggested by Laban et al, 5 who found that 89% of AEH was found to harbor EC. Future studies should determine the optimal patient population to benefit from routine addition of SLNB.…”

Section: Discussionmentioning

confidence: 93%

“…3 These alarming rates warrant a search for better diagnostic adjuncts to current endometrial sampling techniques that still have unacceptable miss rates. 4 Laban et al 5 recently published their own center's experience with this diagnostic dilemma; they found that only 61% of their patients had received adequate surgical treatment.…”

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confidence: 99%

Preoperative detection of occult endometrial malignancies in endometrial hyperplasia to improve primary surgical therapy: A scoping review of the literature

Laban

El-Swaify

Ali

et al. 2022

Intl J Gynecology & Obste

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The risk of undertreating occult endometrial cancer is a problem faced by gynecologists when treating endometrial hyperplasia. The objective of this study is to highlight diagnostic adjuncts to endometrial sampling techniques to improve preoperative detection of co‐existing cancer. A systematic search of databases till July 2021: PubMed, ISI‐Clarivate Web of Science, Scopus, and CENTRAL. A search of the related literature was also carried out. Two authors screened potential studies. Studies were included if they examined the diagnostic performance of any predictors of concurrent cancer in patients diagnosed with endometrial hyperplasia. Authors charted variables related to literature characteristics (e.g., authors, year of publication), population characteristics (e.g., preoperative diagnoses), and variables related to our research questions (e.g., postoperative diagnoses, risk predictors). After screening 591 potential studies, 28 studies were included. Studies included the data of 7409 endometrial hyperplasia patients with 2377 concurrent endometrial cancer cases (32.1%). Forty potential predictors of concurrent cancer were investigated. We examined three categories of potential predictors: clinical (22 studies), histopathologic/imaging (16 studies), and molecular (six studies) predictors. The proposed predictors, age, menopausal status, diabetes, WHO and endometrial intraepithelial neoplasia histopathologic criteria, pelvic magnetic resonance imaging, and molecular profiling are promising diagnostic adjuncts.

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“…Currently, EC is mainly diagnosed based on uterine curettage or biopsy findings. Some data suggest that the susceptibility of endometrial biopsy for EC is 52–94% [ 42 , 43 , 44 , 45 , 46 ]. The accuracy of differentiation of EC in other studies was slightly lower than our model ( Table 2 ) [ 21 , 47 , 48 ].…”

Section: Discussionmentioning

confidence: 99%

Diagnosis and Prediction of Endometrial Carcinoma Using Machine Learning and Artificial Neural Networks Based on Public Databases

Zhao

Zhang

Wang

et al. 2022

Genes

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Endometrial carcinoma (EC), a common female reproductive system malignant tumor, affects thousands of people with high morbidity and mortality worldwide. This study was aimed at developing a prediction model for the diagnosis of EC in the general population. First, we obtained datasets GSE63678, GSE106191, and GSE115810 from the Gene Expression Omnibus (GEO) database, dataset GSE17025 from the GEO database, and the RNA sequence of EC from The Cancer Genome Atlas (TCGA) database to constitute the training, test, and validation groups, respectively. Subsequently, the 96 most significantly differentially expressed genes (DEGs) were identified and analyzed for function and pathway enrichment in the training group. Next, we acquired the disease-specific genes by random forest and established an artificial neural network for the diagnosis. Receiver operating characteristic (ROC) curves were utilized to identify the signature across the three groups. Finally, immune infiltration was analyzed to reveal tumor-immune microenvironment (TIME) alterations in EC. The top 96 DEGs (77 down-regulated and 19 up-regulated genes) were primarily enriched in the interleukin-17 signaling pathway, protein digestion and absorption, and transcriptional misregulation in cancer. Subsequently, 14 characterizing genes of EC were identified by random forest. In the training, test, and validation groups, the artificial neural network was constructed with high diagnostic accuracies of 0.882, 0.864, and 0.839, respectively, and areas under the ROC curve (AUCs) of 0.928, 0.921, and 0.782, respectively. Finally, resting and activated mast cells were found to have increased in TIME. We constructed an artificial diagnostic model with excellent reliability for EC and uncovered variations in the immunological ecosystem of EC through integrated bioinformatics approaches, which might be potential diagnostic targets for EC.

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Correlation between pre-operative diagnosis and final pathological diagnosis of endometrial malignancies; impact on primary surgical treatment

Cited by 3 publications

References 20 publications

Combined deep-learning MRI-based radiomic models for preoperative risk classification of endometrial endometrioid adenocarcinoma

Combined deep-learning MRI-based radiomic models for preoperative risk classification of endometrial endometrioid adenocarcinoma

Preoperative detection of occult endometrial malignancies in endometrial hyperplasia to improve primary surgical therapy: A scoping review of the literature

Diagnosis and Prediction of Endometrial Carcinoma Using Machine Learning and Artificial Neural Networks Based on Public Databases

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