Dataset Information

Prediction of lymphovascular space invasion in endometrial cancer using the 55-gene signature selected by DNA microarray analysis.

ABSTRACT: Lymphovascular space invasion (LVSI) is considered to be the beginning of lymphogenous and hematogenous metastases. It is strongly related to dissemination, and therefore could be a valuable predictive sign of lymph node metastases and distant spread. Recently, the presence of LVSI in endometrial cancer (EC) has been shown to be an independent prognostic factor. The preoperative diagnosis of LVSI by pathological examination is difficult and LVSI is detected after surgery. The aim of the current study was to explore candidate genes as potential diagnostic biomarkers and determine whether they are predictors of LVSI in patients with EC. A total of 88 surgical specimens obtained from EC patients who had undergone surgical resection at Fukushima Medical University Hospital between 2010 and 2015 were analyzed using DNA microarray. LVSI was significantly associated with poor prognostic factors in EC such as higher tumor grade, lymph node metastasis, deep myometrium invasion, advanced stage and recurrence. Fifty-five candidate genes were significantly differentially expressed between 26 LVSI-positive and 62 LVSI-negative samples. All 88 samples were divided into two groups according to hierarchical clustering of 55 genes. Regarding diagnostic accuracy, sensitivity and negative predictive value were both high (92% and 95%, respectively); further, specificity and positive predictive value were both moderate (63% and 71%, respectively). Our data suggests that the 55-gene signature could contribute to predicting LVSI in EC, and provide clinically important information for better management. The molecular signatures of 55 genes may be also useful for understanding the underlying mechanism of LVSI.

SUBMITTER: Watanabe T

PROVIDER: S-EPMC6762169 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Prediction of lymphovascular space invasion in endometrial cancer using the 55-gene signature selected by DNA microarray analysis.

Watanabe Takafumi T Honma Reiko R Kojima Manabu M Nomura Shinji S Furukawa Shigenori S Soeda Shu S Watanabe Shinya S Fujimori Keiya K

PloS one 20190926 9

Lymphovascular space invasion (LVSI) is considered to be the beginning of lymphogenous and hematogenous metastases. It is strongly related to dissemination, and therefore could be a valuable predictive sign of lymph node metastases and distant spread. Recently, the presence of LVSI in endometrial cancer (EC) has been shown to be an independent prognostic factor. The preoperative diagnosis of LVSI by pathological examination is difficult and LVSI is detected after surgery. The aim of the current ...[more]

PMID: 31557240

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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.

Dataset Information

Prediction of lymphovascular space invasion in endometrial cancer using the 55-gene signature selected by DNA microarray analysis.

Publications

Prediction of lymphovascular space invasion in endometrial cancer using the 55-gene signature selected by DNA microarray analysis.

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