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Prediction of lymphovascular space invasion in patients with endometrial cancer.

ABSTRACT: Objective: Predict the presence of lymphovascular space invasion (LVSI), using uterine factors such as tumor diameter (TD), grade, and depth of myometrial invasion (MMI). Develop a predictive model that could serve as a marker of LVSI in women with endometrial cancer (EC). Methods: Data from 888 patients with endometrioid EC who were treated between January 2009 and December 2018 were reviewed. The patients' data were retrieved from six institutions. We assessed the differences in the clinicopathological characteristics between patients with and without LVSI. We performed logistic regression analysis to determine which clinicopathological characteristics were the risk factors for positive LVSI status and to estimate the odds ratio (OR) for each covariate. Using the risk factors and OR identified through this process, we created a model that could predict LVSI and analyzed it further using receiver operating characteristic curve analysis. Results: In multivariate logistic regression analysis, tumor size (P = 0.027), percentage of MMI (P < 0.001), and presence of cervical stromal invasion (P = 0.002) were identified as the risk factors for LVSI. Based on the results of multivariate logistic regression analysis, we developed a simplified LVSI prediction model for clinical use. We defined the "LVSI index" as "TD×%MMI×tumor grade×cervical stromal involvement." The area under curve was 0.839 (95% CI= 0.809-0.869; sensitivity, 74.1%; specificity, 80.5%; negative predictive value, 47.3%; positive predictive value, 8.6%; P < 0.001), and the optimal cut-off value was 200. Conclusion: Using the modified risk index of LVSI, it is possible to predict the presence of LVSI in women with endometrioid endometrial cancer. Our prediction model may be an appropriate tool for integration into the clinical decision-making process when assessed either preoperatively or intraoperatively.

SUBMITTER: Kim SI

PROVIDER: S-EPMC8241765 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Prediction of lymphovascular space invasion in patients with endometrial cancer.

Kim Sang Il SI Yoon Joo Hee JH Lee Sung Jong SJ Song Min Jong MJ Kim Jin Hwi JH Lee Hae Nam HN Jung Gyul G Yoo Ji Geun JG

International journal of medical sciences 20210601 13

<b>Objective:</b> Predict the presence of lymphovascular space invasion (LVSI), using uterine factors such as tumor diameter (TD), grade, and depth of myometrial invasion (MMI). Develop a predictive model that could serve as a marker of LVSI in women with endometrial cancer (EC). <b>Methods:</b> Data from 888 patients with endometrioid EC who were treated between January 2009 and December 2018 were reviewed. The patients' data were retrieved from six institutions. We assessed the differences in ...[more]

PMID: 34220310

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Project description:PurposeLymphovascular space invasion (LVSI) predicts for higher rates of recurrence and increased mortality in endometrial cancer. Using 3-tier LVSI scoring, a PORTEC-1 and -2 trials analysis demonstrated that substantial LVSI was associated with worse locoregional (LR-DFS) and distant metastasis disease-free survival (DM-DFS), and these patients possibly benefited from external beam radiation therapy (EBRT). Furthermore, LVSI is a predictor for lymph node (LN) involvement, but the significance of substantial LVSI is unknown in patients with a pathologically negative LN assessment. We aimed to evaluate clinical outcomes of these patients in relation to the 3-tier LVSI scoring system.Methods and materialsWe performed a single-institutional retrospective review of patients with stage I endometrioid-type endometrial cancer who underwent surgical staging with pathologically negative LN evaluation from 2017 to 2019 with 3-tier LVSI scoring (none, focal, or substantial). Clinical outcomes (LR-DFS, DM-DFS, and overall survival) were analyzed using the Kaplan-Meier method.ResultsA total of 335 patients with pathologically LN-negative stage I endometrioid-type endometrial carcinoma were identified. Substantial LVSI was present in 17.6% of patients; 39.7% of patients received adjuvant vaginal brachytherapy and 6.9% of patients received EBRT. Adjuvant radiation treatment varied by LVSI status. In patients with focal LVSI, 81.0% received vaginal brachytherapy. Among patients with substantial LVSI, 57.9% received vaginal brachytherapy alone, and 31.6% of patients received EBRT. The 2-year LR-DFS rates were 92.5%, 98.0%, and 91.4% for no LVSI, focal LVSI, and substantial LVSI, respectively. The 2-year DM-DFS rates were 95.5%, 93.3%, and 93.8% for no LVSI, focal LVSI, and substantial LVSI, respectively.ConclusionsIn our institutional study, patients with pathologically LN-negative stage I endometrial cancer with substantial LVSI had similar rates of LR-DFS and DM-DFS compared with patients with none or focal LVSI. These findings highlight the need for multi-institutional studies to validate the prognostic value of substantial LVSI in this patient population.

Project description:BackgroundCervical cancer (CC) remains the second leading cause of cancer-related death in women, and the ability to accurately anticipate the presence or absence of lymphovascular space invasion (LVSI) is critical to maintaining optimal patient outcomes. The objective of this study was to establish and verify an MRI radiomics-based model to predict the status of LVSI in patients with operable CC.MethodsThe current study performed a retrospective analysis, with 86 patients in the training cohort and 38 patients in the testing group, specifically focusing on patients with CC. The radiomics feature extraction process included ADC, T2WI-SPAIR, and T2WI sequences. The training group data were used for the initial radionics-based model building, and the model predictive performance was subsequently validated using data from patients recruited in the experimental group.ResultsThe development of the radiomics scoring model has been completed with 17 selected features. The study found several risk factors associated with LVSI. These risk factors included moderate tumor differentiation (P = 0.005), poor tumor differentiation (P = 0.001), and elevated combined sequence-based radiomics scores (P = 0.001). Radiomics scores based on predictive model, combined sequences, ADC, T2WI-SPAIR, and T2WI exhibited AUCs of 0.897, 0.839, 0.815, 0.698, and 0.739 in the training cohort, respectively, with corresponding testing cohort values of 0.833, 0.833, 0.683, 0.692, and 0.725. Excellent consistency was shown by the calibration curve analysis, which showed a higher degree of agreement between the actual and anticipated LVSI status. Moreover, the decision curve analysis outcomes demonstrated the medical application of this prediction model.ConclusionThis investigation indicated that the MRI radiomics model was successfully developed and validated to predict operable CC patient LVSI status, attaining high overall diagnostic accuracy. However, further external validation and more deeper analysis on a larger sample size are still needed.

Project description:BackgroundDetection of lymphovascular space invasion (LVSI) in early cervical cancer (CC) is challenging. To date, no standard clinical markers or screening tests have been used to detect LVSI preoperatively. Therefore, non-invasive risk stratification tools are highly desirable.ObjectiveTo train and validate a multi-parametric magnetic resonance imaging (mpMRI)-based radiomics model to detect LVSI in patients with CC and investigate its potential as a complementary tool to enhance the efficiency of risk assessment strategies.Materials and methodsThe model was developed from the tumor volume of interest (VOI) of 125 patients with CC. A total of 1037 radiomics features obtained from conventional magnetic resonance imaging (MRI), including a small field-of-view (sFOV) high-resolution (HR)-T2-weighted MRI (T2WI), apparent diffusion coefficient (ADC), T2WI, fat-suppressed (FS)-T2WI, as well as axial and sagittal contrast-enhanced T1-weighted MRI (T1c). We conducted a radiomics-based characterization of each tumor region using pretreatment image data. Feature selection was performed using the least absolute shrinkage and selection operator method on the training set. The predictive performance was compared with single variates (clinical data and single-layer radiomics signatures) analyzed using a receiver operating characteristic (ROC) curve. Three-fold cross-validation performed 20 times was used to evaluate the accuracy of the trained classifiers and the stability of the selected features. The models were validated by using a validation set.ResultsFeature selection extracted the six most important features (3 from sFOV HR-T2WI, 1 T2WI, 1 FS-T2WI, and 1 T1c) for model construction. The mpMRI-combined radiomics model (area under the curve [AUC]: 0.940) reached a significantly higher performance (better than the clinical parameters [AUC: 0.730]), including any single-layer model using sFOV HR-T2WI (AUC: 0.840), T2WI (AUC: 0.770), FS-T2WI (AUC: 0.710), ADC maps (AUC: 0.650), sagittal, and axial T1c values (AUC: 0.710, 0.680) in the validation set.ConclusionBiomarkers using multi-parametric radiomics features derived from preoperative MR images could predict LVSI in patients with CC.

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Prediction of lymphovascular space invasion in patients with endometrial cancer.

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Prediction of lymphovascular space invasion in patients with endometrial cancer.

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