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Background: Lymph node metastasis (LNM) is an important risk factor affecting treatment strategy and prognosis for endometrial cancer (EC) patients. A radiomics nomogram was established in assisting lymphadenectomy decisions preoperatively by predicting LNM status in early-stage EC patients. Methods: A total of 707 retrospective clinical early-stage EC patients were enrolled and randomly divided into a training cohort and a test cohort. Radiomics features were extracted from MR imaging. Three models were built, including a guideline-recommended clinical model (grade 1-2 endometrioid tumors by dilatation and curettage and less than 50% myometrial invasion on MRI without cervical infiltration), a radiomics model (selected radiomics features), and a radiomics nomogram model (combing the selected radiomics features, myometrial invasion on MRI, and cancer antigen 125). The predictive performance of the three models was assessed by the area under the receiver operating characteristic (ROC) curves (AUC). The clinical decision curves, net reclassification index (NRI), and total integrated discrimination index (IDI) based on the total included patients to assess the clinical benefit of the clinical model and the radiomics nomogram were calculated. Results: The predictive ability of the clinical model, the radiomics model, and the radiomics nomogram between LNM and non-LNM were 0.66 [95% CI: 0.55-0.77], 0.82 [95% CI: 0.74-0.90], and 0.85 [95% CI: 0.77-0.93] in the training cohort, and 0.67 [95% CI: 0.56-0.78], 0.81 [95% CI: 0.72-0.90], and 0.83 [95% CI: 0.74-0.92] in the test cohort, respectively. The decision curve analysis, NRI (1.06 [95% CI: 0.81-1.32]), and IDI (0.05 [95% CI: 0.03-0.07]) demonstrated the clinical usefulness of the radiomics nomogram. Conclusions: The predictive radiomics nomogram could be conveniently used for individualized prediction of LNM and assisting lymphadenectomy decisions in early-stage EC patients. [ABSTRACT FROM AUTHOR]

Background: Ovarian preservation treatment (OPT) was recommended in young women with early-stage endometrial cancer [superficial myometrial invasion (MI) and grades (G) 1/2-endometrioid adenocarcinoma (EEC)]. A radiomics nomogram was developed to assist radiologists in assessing the depth of MI and in selecting eligible patients for OPT. Methods: From February 2014 to May 2021, 209 G 1/2-EEC patients younger than 45 years (mean 39 ± 4.3 years) were included. Of them, 104 retrospective patients were enrolled in the primary group, and 105 prospective patients were enrolled in the validation group. The radiomics features were extracted based on multi-parametric magnetic resonance imaging, and the least absolute shrinkage and selection operator algorithm was applied to reduce the dimensionality of the data and select the radiomics features that correlated with the depth of MI in G 1/2-EEC patients. A radiomics nomogram for evaluating the depth of MI was developed by combing the selected radiomics features with the cancer antigen 125 and tumor size. Receiver operating characteristic (ROC) curves were used to evaluate the diagnostic performance of the radiomics nomogram and of radiologists without and with the aid of the radiomics nomogram. The net reclassification index (NRI) and total integrated discrimination index (IDI) based on the total included patients to assess the clinical benefit of radiologists with the radiomics nomogram were calculated. Results: In the primary group, for evaluating the depth of MI, the AUCs were 0.96 for the radiomics nomogram; 0.80 and 0.86 for radiologists 1 and 2 without the aid of the nomogram, respectively; and 0.98 and 0.98 for radiologists 1 and 2 with the aid of the nomogram, respectively. In the validation group, the AUCs were 0.88 for the radiomics nomogram; 0.82 and 0.83 for radiologists 1 and 2 without the aid of the nomogram, respectively; and 0.94 and 0.94 for radiologists 1 and 2 with the aid of the nomogram, respectively. The yielded NRI and IDI values were 0.29 and 0.43 for radiologist 1 and 0.23 and 0.37 for radiologist 2, respectively. Conclusions: The radiomics nomogram outperformed radiologists and could help radiologists in assessing the depth of MI and selecting eligible OPTs in G 1/2-EEC patients. [ABSTRACT FROM AUTHOR]

Objective: To construct a MRI radiomics model and help radiologists to improve the assessments of pelvic lymph node metastasis (PLNM) in endometrial cancer (EC) preoperatively.Methods: During January 2014 and May 2019, 622 EC patients (age 56.6 ± 8.8 years; range 27-85 years) from five different centers (A to E) were divided into training set, validation set 1 (351 cases from center A), and validation set 2 (271 cases from centers B-E). The radiomics features were extracted basing on T2WI, DWI, ADC, and CE-T1WI images, and most related radiomics features were selected using the random forest classifier to build a radiomics model. The ROC curve was used to evaluate the performance of training set and validation sets, radiologists based on MRI findings alone, and with the aid of the radiomics model. The clinical decisive curve (CDC), net reclassification index (NRI), and total integrated discrimination index (IDI) were used to assess the clinical benefit of using the radiomics model.Results: The AUC values were 0.935 for the training set, 0.909 and 0.885 for validation sets 1 and 2, 0.623 and 0.643 for the radiologists 1 and 2 alone, and 0.814 and 0.842 for the radiomics-aided radiologists 1 and 2, respectively. The AUC, CDC, NRI, and IDI showed higher diagnostic performance and clinical net benefits for the radiomics-aided radiologists than for the radiologists alone.Conclusions: The MRI-based radiomics model could be used to assess the status of pelvic lymph node and help radiologists improve their performance in predicting PLNM in EC.Key Points: • A total of 358 radiomics features were extracted. The 37 most important features were selected using the random forest classifier. • The reclassification measures of discrimination confirmed that the radiomics-aided radiologists performed better than the radiologists alone, with an NRI of 1.26 and an IDI of 0.21 for radiologist 1 and an NRI of 1.37 and an IDI of 0.24 for radiologist 2. [ABSTRACT FROM AUTHOR]

Background: High‐ and low‐risk endometrial cancer (EC) differ in whether lymphadenectomy is performed. Assessment of high‐risk EC is essential for planning surgery appropriately. Purpose: To develop a radiomics nomogram for high‐risk EC prediction preoperatively. Study Type: Retrospective. Population: In all, 717 histopathologically confirmed EC patients (mean age, 56 years ± 9) divided into a primary group (394 patients from Center A), validation groups 1 and 2 (146 patients from Center B and 177 patients from Centers C–E). Field Strength/Sequence: 1.5/3T scanners; T2‐weighted imaging, diffusion‐weighted imaging, apparent diffusion coefficient, and contrast enhancement sequences. Assessment: A radiomics nomogram was generated by combining the selected radiomics features and clinical parameters (metabolic syndrome, cancer antigen 125, age, tumor grade following curettage, and tumor size). The area under the curve (AUC) of the receiver operator characteristic was used to evaluate the predictive performance of the radiomics nomogram for high‐risk EC. The surgical procedure suggested by the nomogram was compared with the actual procedure performed for the patients. Net benefit of the radiomics nomogram was evaluated by a clinical decision curve (CDC), net reclassification index (NRI), and integrated discrimination improvement (IDI). Statistical Tests: Binary least absolute shrinkage and selection operator (LASSO) logistic regression, linear regression, and multivariate binary logistic regression were used to select radiomics features and clinical parameters. Results: The AUC for prediction of high‐risk EC for the radiomics nomogram in the primary group, validation groups 1 and 2 were 0.896 (95% confidence interval [CI]: 0.866–0.926), 0.877 (95% CI: 0.825–0.930), and 0.919 (95% CI: 0.879–0.960), respectively. The nomogram achieved good net benefit by CDC analysis for high‐risk EC. NRIs were 1.17, 1.28, and 1.51, and IDIs were 0.41, 0.60, and 0.61 in the primary group, validation groups 1 and 2, respectively. Data Conclusion: The radiomics nomogram exhibited good performance in the individual prediction of high‐risk EC, and might be used for surgical management of EC. Level of Evidence: 4 Technical Efficacy Stage: 2 J. MAGN. RESON. IMAGING 2020;52:1872–1882. [ABSTRACT FROM AUTHOR]

Background: Disputes exist regarding whether the apparent diffusion coefficient (ADC) can differentiate the tumor grade, deep myometrial invasion and lymphovascular space invasion (LVSI) in endometrial cancer. The aim of this review was to assess the diagnostic performance of the ADC value in endometrial cancer. Material and Methods: The PubMed, Web of Science, Embase and Cochrane Library databases were searched for studies that used the ADC value to assess tumor grade, deep myometrial invasion and LVSI in endometrial cancer. We used forest plots to analyze the heterogeneity and generate the pooled sensitivity (SEN) and specificity (SPE). We used summary receiver operating characteristic (SROC) curves to work out the area under the SROC curve (AUC). Likelihood ratios (LRs) were also obtained. Results: Of the 460 identified studies, 11 studies met our inclusion criteria and were included. Overall, nine studies (491 patients) aimed at differentiating high tumor grade had a pooled SEN, SPE and AUC of 77%, 73% and 81%, respectively; three studies (181 patients) for differentiating deep myometrial invasion had a pooled SEN, SPE and AUC of 71%, 67% and 77%, respectively; and two studies (106 patients) for differentiating LVSI had a pooled SEN and SPE of 66% and 74%, respectively. The positive and negative LRs were 2.77 and 0.35 for the tumor grade, 2.08 and 0.45 for deep myometrial invasion, and 2.48 and 0.45 for LVSI. Conclusion: This meta-analysis showed that the ADC value had a moderate diagnostic performance for the tumor grade, deep myometrial invasion and LVSI in endometrial cancer. [ABSTRACT FROM AUTHOR]