Development and Validation of an MRI-based Radiomics Nomogram for Assessing Deep Myometrial Invasion in Early Stage Endometrial Adenocarcinoma

Wang, Yaoxin; Bi, Qiu; Deng, Yuchen; Yang, Zhen; Song, Yang; Wu, Yunzhu; Wu, Kunhua

doi:10.1016/j.acra.2022.05.017

Cited by 15 publications

(7 citation statements)

References 35 publications

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“…Two radiologists retrospectively analyzed the MRI images of each patient, and the sensitivity and specificity of the diagnosis of DMI, CSI, and LNM were consistent with those of previous studies ( 19 – 21 ). Many studies ( 5 , 22 – 24 ) have confirmed that patient age, DMI, CSI, and LNM are important prognostic factors in high-risk patients with EC. Our study indicated that advanced age, MRI-reported DMI, CSI, and LNM were independent risk factors for high-grade EC.…”

Section: Discussionmentioning

confidence: 97%

Multiparametric magnetic resonance imaging-based radiomics nomogram for predicting tumor grade in endometrial cancer

Yue

et al. 2023

Front. Oncol.

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BackgroundTumor grade is associated with the treatment and prognosis of endometrial cancer (EC). The accurate preoperative prediction of the tumor grade is essential for EC risk stratification. Herein, we aimed to assess the performance of a multiparametric magnetic resonance imaging (MRI)-based radiomics nomogram for predicting high-grade EC.MethodsOne hundred and forty-three patients with EC who had undergone preoperative pelvic MRI were retrospectively enrolled and divided into a training set (n =100) and a validation set (n =43). Radiomic features were extracted based on T2-weighted, diffusion-weighted, and dynamic contrast-enhanced T1-weighted images. The minimum absolute contraction selection operator (LASSO) was implemented to obtain optimal radiomics features and build the rad-score. Multivariate logistic regression analysis was used to determine the clinical MRI features and build a clinical model. We developed a radiomics nomogram by combining important clinical MRI features and rad-score. A receiver operating characteristic (ROC) curve was used to evaluate the performance of the three models. The clinical net benefit of the nomogram was assessed using decision curve analysis (DCA), net reclassification index (NRI), and integrated discrimination index (IDI).ResultsIn total, 35/143 patients had high-grade EC and 108 had low-grade EC. The areas under the ROC curves of the clinical model, rad-score, and radiomics nomogram were 0.837 (95% confidence interval [CI]: 0.754–0.920), 0.875 (95% CI: 0.797–0.952), and 0.923 (95% CI: 0.869–0.977) for the training set; 0.857 (95% CI: 0.741–0.973), 0.785 (95% CI: 0.592–0.979), and 0.914 (95% CI: 0.827–0.996) for the validation set, respectively. The radiomics nomogram showed a good net benefit according to the DCA. NRIs were 0.637 (0.214–1.061) and 0.657 (0.079–1.394), and IDIs were 0.115 (0.077–0.306) and 0.053 (0.027–0.357) in the training set and validation set, respectively.ConclusionThe radiomics nomogram based on multiparametric MRI can predict the tumor grade of EC before surgery and yield a higher performance than that of dilation and curettage.

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Section: Discussionmentioning

confidence: 97%

Multiparametric magnetic resonance imaging-based radiomics nomogram for predicting tumor grade in endometrial cancer

Yue

et al. 2023

Front. Oncol.

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“…In the present findings, MRS, histological type, LNM and tumor grade were independent factors, and these factors were used to build the model. To the best of our knowledge, there only a few predictive models have been constructed to evaluate MI before surgery, and they are mostly based on MRI radiomics (7,17). The aforementioned studies used parameters such as axial T1-weighted images (T1WIs), T2WIs and diffusion weighted imaging to sketch region of interest, and further least absolute shrinkage and selection operator regression was conducted to narrow the range.…”

Section: Discussionmentioning

confidence: 99%

“…A number of established nomograms are available to screen lymph node metastasis (LNM), recurrence, overall survival and cancer-specific survival rates in patients with EC (5,6). Yet, to the best of our knowledge, risk prediction nomograms that correctly estimate MI are limited, and the most commonly used and validated nomogram is based on magnetic resonance imaging (MRI) (7).…”

Section: Introductionmentioning

confidence: 99%

“…The better predictive value of MRS in comparison with the model based on traditional clinicopathological characteristics has been confirmed in a variety of tumors, such as esophageal cancer (9). MI is currently investigated and nomograms constructed based on radiological features collected with MRI (7), but the exact relationship between the MRS and MI in patients with EC has not yet been reported.…”

Section: Introductionmentioning

confidence: 99%

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Metabolic risk score as a predictor in a nomogram for assessing myometrial invasion for endometrial cancer

2023

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The purpose of the present study was to investigate the predictive value of metabolic syndrome in evaluating myometrial invasion (MI) in patients with endometrial cancer (EC). The study retrospectively included patients with EC who were diagnosed between January 2006 and December 2020 at the Department of Gynecology of Nanjing First Hospital (Nanjing, China). The metabolic risk score (MRS) was calculated using multiple metabolic indicators. Univariate and multivariate logistic regression analyses were performed to determine significant predictive factors for MI. A nomogram was then constructed based on the independent risk factors identified. A calibration curve, a receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were used to evaluate the effectiveness of the nomogram. A total of 549 patients were randomly assigned to a training or validation cohort, with a 2:1 ratio. Data was then gathered on significant predictors of MI in the training cohort, including MRS [odds ratio (OR), 1.06; 95% confidence interval (CI), 1.01-1.11; P=0.023], histological type (OR, 1.98; 95% CI, 1.11-3.53; P=0.023), lymph node metastasis (OR, 3.15; 95% CI, 1.61-6.15; P<0.001) and tumor grade (grade 2: OR, 1.71; 95% CI, 1.23-2.39; P=0.002; Grade 3: OR, 2.10; 95% CI, 1.53-2.88; P<0.001). Multivariate analysis indicated that MRS was an independent risk factor for MI in both cohorts. A nomogram was generated to predict a patient's probability of MI based on the four independent risk factors. ROC curve analysis showed that, compared with the clinical model (model 1), the combined model with MRS (model 2) significantly improved the diagnostic accuracy of MI in patients with EC (area under the curve in model 1 vs. model 2: 0.737 vs. 0.828 in the training cohort and 0.713 vs. 0.759 in the validation cohort). Calibration plots showed that the training and validation cohorts were well calibrated. DCA showed that a net benefit is obtained from the application of the nomogram. Overall, the present study developed and validated a MRS-based nomogram predicting MI in patients with EC preoperatively. The establishment of this model may promote the use of precision medicine and targeted therapy in EC and has the potential to improve the prognosis of patients affected by EC.

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“…Wang Y et al. also established a preoperative model for predicting whether patients with endometrial endometrioid adenocarcinoma have deep myometrial infiltration based on imaging histology ( 31 ). Yan B et al.…”

Section: Discussionmentioning

confidence: 99%

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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Development and Validation of an MRI-based Radiomics Nomogram for Assessing Deep Myometrial Invasion in Early Stage Endometrial Adenocarcinoma

Cited by 15 publications

References 35 publications

Multiparametric magnetic resonance imaging-based radiomics nomogram for predicting tumor grade in endometrial cancer

Multiparametric magnetic resonance imaging-based radiomics nomogram for predicting tumor grade in endometrial cancer

Metabolic risk score as a predictor in a nomogram for assessing myometrial invasion for endometrial cancer

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

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