Dataset Information

Pilot Study to Establish a Novel Five-Gene Biomarker Panel for Predicting Lymph Node Metastasis in Patients With Early Stage Endometrial Cancer.

ABSTRACT: Introduction: In the United States and Europe, endometrial endometrioid carcinoma (EEC) is the most prevalent gynecologic malignancy. Lymph node metastasis (LNM) is the key determinant of the prognosis and treatment of EEC. A biomarker that predicts LNM in patients with EEC would be beneficial, enabling individualized treatment. Current preoperative assessment of LNM in EEC is not sufficiently accurate to predict LNM and prevent overtreatment. This pilot study established a biomarker signature for the prediction of LNM in early stage EEC. Methods: We performed RNA sequencing in 24 clinically early stage (T1) EEC tumors (lymph nodes positive and negative in 6 and 18, respectively) from Cathay General Hospital and analyzed the RNA sequencing data of 289 patients with EEC from The Cancer Genome Atlas (lymph node positive and negative in 33 and 256, respectively). We analyzed clinical data including tumor grade, depth of tumor invasion, and age to construct a sequencing-based prediction model using machine learning. For validation, we used another independent cohort of early stage EEC samples (n = 72) and performed quantitative real-time polymerase chain reaction (qRT-PCR). Finally, a PCR-based prediction model and risk score formula were established. Results: Eight genes (ASRGL1, ESR1, EYA2, MSX1, RHEX, SCGB2A1, SOX17, and STX18) plus one clinical parameter (depth of myometrial invasion) were identified for use in a sequencing-based prediction model. After qRT-PCR validation, five genes (ASRGL1, RHEX, SCGB2A1, SOX17, and STX18) were identified as predictive biomarkers. Receiver operating characteristic curve analysis revealed that these five genes can predict LNM. Combined use of these five genes resulted in higher diagnostic accuracy than use of any single gene, with an area under the curve of 0.898, sensitivity of 88.9%, and specificity of 84.1%. The accuracy, negative, and positive predictive values were 84.7, 98.1, and 44.4%, respectively. Conclusion: We developed a five-gene biomarker panel associated with LNM in early stage EEC. These five genes may represent novel targets for further mechanistic study. Our results, after corroboration by a prospective study, may have useful clinical implications and prevent unnecessary elective lymph node dissection while not adversely affecting the outcome of treatment for early stage EEC.

SUBMITTER: Huang CY

PROVIDER: S-EPMC6985442 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Pilot Study to Establish a Novel Five-Gene Biomarker Panel for Predicting Lymph Node Metastasis in Patients With Early Stage Endometrial Cancer.

Huang Chia-Yen CY Liao Kuang-Wen KW Chou Chih-Hung CH Shrestha Sirjana S Yang Chi-Dung CD Chiew Men-Yee MY Huang Hsin-Tzu HT Hong Hsiao-Chin HC Huang Shih-Hung SH Chang Tzu-Hao TH Huang Hsien-Da HD

Frontiers in oncology 20200121

<b>Introduction:</b> In the United States and Europe, endometrial endometrioid carcinoma (EEC) is the most prevalent gynecologic malignancy. Lymph node metastasis (LNM) is the key determinant of the prognosis and treatment of EEC. A biomarker that predicts LNM in patients with EEC would be beneficial, enabling individualized treatment. Current preoperative assessment of LNM in EEC is not sufficiently accurate to predict LNM and prevent overtreatment. This pilot study established a biomarker sign ...[more]

PMID: 32039004

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Project description:BackgroundIn recent years, sentinel lymph node excision and ultrastaging have been performed in endometrial carcinomas to obtain information about lymph node status, avoiding unnecessary complete pelvic and paraaortic lymphadenectomy. The purpose of this retrospective study was to provide a comprehensive evaluation of the pathological features of endometrial carcinomas and their significance in association with sentinel lymph node involvement.MethodsPatients with endometrial carcinomas, preceded by sentinel lymph node mapping, were classified into Group-I and Group-II with negative and positive involvement, respectively. The pathological features, associated with sentinel lymph node involvement, were statistically analyzed, including determination of test performance parameters.ResultsAmong 70 patients who had undergone hysterectomy and sentinel lymph node excision, 61 had carcinoma and 9 had atypical hyperplasia. There were 50 patients in Group-I and 10 in Group-II. In Group-II, the significant pathological features were: 1) lower uterine segment involvement (100%), 2) an average tumor size of ≥5 CM, 3) lymphovascular invasion (50%), 4) cervical stromal invasion (40%), and 5) depth of myometrial invasion of ≥50% (50%). The incidences of these pathological features were significantly less in Group-I. Statistical analyses singled out "lower uterine segment involvement" as the most important feature.ConclusionsWe have identified five pathological features which are associated with sentinel lymph node involvement. Since lower uterine segment involvement has occurred in all cases of the Group-II cohort, we recommend FIGO and other organizations that determine staging rules should consider whether tumors that involve the lower uterine segment should be staged as higher than "1a", if the findings in this small series are confirmed by other studies. The results of this study may guide pathologists and oncologists in the diagnostic and therapeutic approaches to management of endometrial carcinomas.

Project description:ObjectivesTo develop and validate the imbalanced data correction based PET/CT radiomics model for predicting lymph node metastasis (LNM) in clinical stage T1 lung adenocarcinoma (LUAD).MethodsA total of 183 patients (148/35 non-metastasis/LNM) with pathologically confirmed LUAD were retrospectively included. The cohorts were divided into training vs. validation cohort in a ratio of 7:3. A total of 487 radiomics features were extracted from PET and CT components separately for radiomics model construction. Four clinical features and seven PET/CT radiological features were extracted for traditional model construction. To balance the distribution of majority (non-metastasis) class and minority (LNM) class, the imbalance-adjustment strategies using ten data re-sampling methods were adopted. Three multivariate models (denoted as Traditional, Radiomics, and Combined) were constructed using multivariable logistic regression analysis, where the combined model incorporated all of the significant clinical, radiological, and radiomics features. One hundred times repeated Monte Carlo cross-validation was used to assess the application order of feature selection and imbalance-adjustment strategies in the machine learning pipeline. Prediction performance of each model was evaluated using the area under the receiver operating characteristic curve (AUC) and Geometric mean score (G-mean).ResultsA total of 2 clinical parameters, 2 radiological features, 3 PET, and 5 CT radiomics features were significantly associated with LNM. The combined model with Edited Nearest Neighbors (ENN) re-sampling methods showed strong prediction performance than traditional model or radiomics model with the AUC of 0.94 (95%CI = 0.86-0.97) vs. 0.89 (95%CI = 0.79-0.93), 0.92 (95%CI = 0.85-0.97), and G-mean of 0.88 vs. 0.82, 0.80 in the training cohort, and the AUC of 0.75 (95%CI = 0.57-0.91) vs. 0.68 (95%CI = 0.36-0.83), 0.71 (95%CI = 0.48-0.83) and G-mean of 0.76 vs. 0.64, 0.51 in the validation cohort. The combination of performing feature selection before data re-sampling obtains a better result than the reverse combination (AUC 0.76 ± 0.06 vs. 0.70 ± 0.07, p<0.001).ConclusionsThe combined model (consisting of age, histological type, C/T ratio, MATV, and radiomics signature) integrated with ENN re-sampling methods had strong lymph node metastasis prediction performance for imbalance cohorts in clinical stage T1 LUAD. Radiomics signatures extracted from PET/CT images could provide complementary prediction information compared with traditional model.

Project description:BackgroundHepatic alveolar echinococcosis (AE) is a severe zoonotic parasitic disease, and accurate preoperative prediction of lymph node (LN) metastasis in AE patients is crucial for disease management, but it remains an unresolved challenge. The aim of this study was to establish a radiomics model for the preoperative prediction of LN metastasis in hepatic AE patients.MethodsA total of 100 hepatic AE patients who underwent hepatectomy and hepatoduodenal ligament LN dissection at Qinghai Provincial People's Hospital between January 2016 and August 2023 were included in the study. The patients were randomly divided into a training set and a validation set at an 8:2 ratio. Radiomic features were extracted from three-dimensional images of the hepatoduodenal ligament LNs delineated on arterial phase computed tomography (CT) scans of hepatic AE patients. Least absolute shrinkage and selection operator (LASSO) regression was applied for data dimensionality reduction and feature selection. Multivariate logistic regression analysis was performed to develop a prediction model, and the predictive performance of the model was evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA).ResultsA total of 7 radiomics features associated with LN status were selected using LASSO regression. The classification performances of the training set and validation set were consistent, with area under the operating characteristic curve (AUC) values of 0.928 and 0.890, respectively. The model also demonstrated good stability in subsequent validation.ConclusionIn this study, we established and evaluated a radiomics-based prediction model for LN metastasis in patients with hepatic AE using CT imaging. Our findings may provide a valuable reference for clinicians to determine the occurrence of LN metastasis in hepatic AE patients preoperatively, and help guide the implementation of individualized surgical plans to improve patient prognosis.

Dataset Information

Pilot Study to Establish a Novel Five-Gene Biomarker Panel for Predicting Lymph Node Metastasis in Patients With Early Stage Endometrial Cancer.

Publications

Pilot Study to Establish a Novel Five-Gene Biomarker Panel for Predicting Lymph Node Metastasis in Patients With Early Stage Endometrial Cancer.

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