scholarly journals A Clinical-Radiomics Nomogram for Preoperative Prediction of Lymph Node Metastasis in Gallbladder Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Xingyu Liu ◽  
Xiaoyuan Liang ◽  
Lingxiang Ruan ◽  
Sheng Yan
Keyword(s):  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Gallbladder Cancer ◽  
Prediction Models ◽  
Validation Cohort ◽  
External Validation ◽  
Training Cohort ◽  
Clinical Variables ◽  
Node Metastasis ◽  
Preoperative Prediction

ObjectivesThe aim of the current study was to develop and validate a nomogram based on CT radiomics features and clinical variables for predicting lymph node metastasis (LNM) in gallbladder cancer (GBC).MethodsA total of 353 GBC patients from two hospitals were enrolled in this study. A Radscore was developed using least absolute shrinkage and selection operator (LASSO) logistic model based on the radiomics features extracted from the portal venous-phase computed tomography (CT). Four prediction models were constructed based on the training cohort and were validated using internal and external validation cohorts. The most effective model was then selected to build a nomogram.ResultsThe clinical-radiomics nomogram, which comprised Radscore and three clinical variables, showed the best diagnostic efficiency in the training cohort (AUC = 0.851), internal validation cohort (AUC = 0.819), and external validation cohort (AUC = 0.824). Calibration curves showed good discrimination ability of the nomogram using the validation cohorts. Decision curve analysis (DCA) showed that the nomogram had a high clinical utility.ConclusionThe findings showed that the clinical-radiomics nomogram based on radiomics features and clinical parameters is a promising tool for preoperative prediction of LN status in patients with GBC.

Novel perioperative parameters-based nomograms for survival outcomes in upper tract urothelial carcinoma after radical nephroureterectomy.

2019 ◽  
Vol 37 (7_suppl) ◽  
pp. 414-414
Author(s):  
Ping Tan ◽  
Lu Yang ◽  
Hang Xu ◽  
Qiang Wei
Keyword(s):  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Prediction Models ◽  
Validation Cohort ◽  
Fibrinogen Level ◽  
External Validation ◽  
Cox Proportional Hazards ◽  
Radical Nephroureterectomy ◽  
Cancer Specific Survival ◽  
Node Metastasis

414 Background: Recently, several postoperative nomograms for cancer-specific survival (CSS) after radical nephroureterectomy (RNU) were proposed, while they did not incorporate the same variables; meanwhile, many preoperative blood-based parameters, which were recently reported to be related to survival, were not included in their models. In addition, no nomogram for overall survival (OS) was available to date. Methods: The full data of 716 patients were available. The whole cohort was randomly divided into two cohorts: the training cohort for developing the nomograms (n = 508) and the validation cohort for validating the models (n = 208). Univariate and multivariate Cox proportional hazards regression models were used for establishing the prediction models. The discriminative accuracy of nomograms were measured by Harrell’s concordance index (C-index). The clinical usefulness and net benefit of the predictive models were estimated and visualized by using Decision curve analyses (DCA). Results: The median follow-up time was 42.0 months (IQR: 18.0-76.0). For CSS, tumor size, grade and pT stage, lymph node metastasis, NLR, PLR and fibrinogen level were identified as independent risk factors in the final model; while tumor grade and pT stage, lymph node metastasis, PLR, Cys-C and fibrinogen level were identified as independent predictors for OS model. The C-index for CSS prediction was 0.82 (95%CI: 0.79-0.85), and the OS nomogram model had an accuracy of 0.83 (95%CI: 0.80-0.86). The results of bootstrapping showed no deviation from the ideal. The calibration plots for the probability of CSS and OS at 3 or 5-year after RNU showed a favorable agreement between the prediction by the nomograms and actual observation. In the external validation cohort, the C-indexes of the nomograms for predicting CSS and OS were 0.79 (95%CI: 0.74-0.84) and 0.80 (95%CI: 0.75-0.85), respectively. As indicated by calibration plots, optimal agreement was observed between prediction and observation in the external cohort. Conclusions: The nomograms developed and validated based on preoperative blood-based parameters were superior to any single variable for predicting CSS and OS after RNU.

scholarly journals Development and validation of a nomogram for preoperative prediction of lymph node metastasis in early gastric cancer

2020 ◽  
Vol 18 (1) ◽  
Author(s):  
Xiao-Yi Yin ◽  
Tao Pang ◽  
Yu Liu ◽  
Hang-Tian Cui ◽  
Tian-Hang Luo ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Early Gastric Cancer ◽  
Cancer Patients ◽  
Validation Cohort ◽  
External Validation ◽  
Training Cohort ◽  
Node Metastasis ◽  
Gastric Cancer Patients

Abstract Background The status of lymph nodes in early gastric cancer is critical to make further clinical treatment decision, but the prediction of lymph node metastasis remains difficult before operation. This study aimed to develop a nomogram that contained preoperative factors to predict lymph node metastasis in early gastric cancer patients. Methods This study analyzed the clinicopathologic features of 823 early gastric cancer patients who underwent gastrectomy retrospectively, among which 596 patients were recruited in the training cohort and 227 patients in the independent validation cohort. Significant risk factors in univariate analysis were further identified to be independent variables in multivariable logistic regression analysis, which were then incorporated in and presented with a nomogram. And internal and external validation curves were plotted to evaluate the discrimination of the nomogram. Results Totally, six independent predictors, including the tumor size, macroscopic features, histology differentiation, P53, carbohydrate antigen 19-9, and computed tomography-reported lymph node status, were enrolled in the nomogram. Both the internal validation in the training cohort and the external validation in the validation cohort showed the nomogram had good discriminations, with a C-index of 0.82 (95%CI, 0.78 to 0.86) and 0.77 (95%CI, 0.60 to 0.94) respectively. Conclusions Our study developed a new nomogram which contained the most common and significant preoperative risk factors for lymph node metastasis in patients with early gastric cancer. The nomogram can identify early gastric cancer patients with the high probability of lymph node metastasis and help clinicians make more appropriate decisions in clinical practice.

scholarly journals Consolidation Tumor Ratio Combined With Pathological Features Could Predict Status of Lymph Nodes of Early-Stage Lung Adenocarcinoma

2022 ◽  
Vol 11 ◽  
Author(s):  
Liang Zhao ◽  
Guangyu Bai ◽  
Ying Ji ◽  
Yue Peng ◽  
Ruochuan Zang ◽  
...  
Keyword(s):  
Risk Factors ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Lymph Nodes ◽  
Validation Cohort ◽  
Frozen Sections ◽  
Paraffin Sections ◽  
Training Cohort ◽  
Node Metastasis ◽  
The Status

IntroductionStage IA lung adenocarcinoma manifested as part-solid nodules (PSNs), has attracted immense attention owing to its unique characteristics and the definition of its invasiveness remains unclear. We sought to develop a nomogram for predicting the status of lymph nodes of this kind of nodules.MethodsA total of 2,504 patients between September 2018 to October 2020 with part-solid nodules in our center were reviewed. Their histopathological features were extracted from paraffin sections, whereas frozen sections were reviewed to confirm the consistency of frozen sections and paraffin sections. Univariate and multivariate logistic regression analyses and Akaike information criterion (AIC) variable selection were performed to assess the risk factors of lymph node metastasis and construct the nomogram. The nomogram was subjected to bootstrap internal validation and external validation. The concordance index (C-index) was applied to evaluate the predictive accuracy and discriminative ability.ResultsWe enrolled 215 and 161 eligible patients in the training cohort and validation cohort, respectively. The sensitivity between frozen and paraffin sections on the presence of micropapillary/solid subtype was 78.4%. Multivariable analysis demonstrated that MVI, the presence of micropapillary/solid subtype, and CTR >0.61 were independently associated with lymph node metastasis (p < 0.01). Five risk factors were integrated into the nomogram. The nomogram demonstrated good accuracy in estimating the risk of lymph node metastasis, with a C-index of 0.945 (95% CI: 0.916–0.974) in the training cohort and a C-index of 0.975 (95% CI: 0.954–0.995) in the validation cohort. The model’s calibration was excellent in both cohorts.ConclusionThe nomogram established showed excellent discrimination and calibration and could predict the status of lymph nodes for patients with ≤3 cm PSNs. Also, this prediction model has the prediction potential before the end of surgery.

scholarly journals Development and Validation of a Combined Model for Preoperative Prediction of Lymph Node Metastasis in Peripheral Lung Adenocarcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Qi Li ◽  
Xiao-qun He ◽  
Xiao Fan ◽  
Chao-nan Zhu ◽  
Jun-wei Lv ◽  
...  
Keyword(s):  
Risk Factors ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Tumor Size ◽  
Validation Cohort ◽  
Morphological Features ◽  
Combined Model ◽  
Training Cohort ◽  
Node Metastasis ◽  
Peripheral Lung

BackgroundBased on the “seed and soil” theory proposed by previous studies, we aimed to develop and validate a combined model of machine learning for predicting lymph node metastasis (LNM) in patients with peripheral lung adenocarcinoma (PLADC).MethodsRadiomics models were developed in a primary cohort of 390 patients (training cohort) with pathologically confirmed PLADC from January 2016 to August 2018. The patients were divided into the LNM (−) and LNM (+) groups. Thereafter, the patients were subdivided according to TNM stages N0, N1, N2, and N3. Radiomic features from unenhanced computed tomography (CT) were extracted. Radiomic signatures of the primary tumor (R1) and adjacent pleura (R2) were built as predictors of LNM. CT morphological features and clinical characteristics were compared between both groups. A combined model incorporating R1, R2, and CT morphological features, and clinical risk factors was developed by multivariate analysis. The combined model’s performance was assessed by receiver operating characteristic (ROC) curve. An internal validation cohort containing 166 consecutive patients from September 2018 to November 2019 was also assessed.ResultsThirty-one radiomic features of R1 and R2 were significant predictors of LNM (all P < 0.05). Sex, smoking history, tumor size, density, air bronchogram, spiculation, lobulation, necrosis, pleural effusion, and pleural involvement also differed significantly between the groups (all P < 0.05). R1, R2, tumor size, and spiculation in the combined model were independent risk factors for predicting LNM in patients with PLADC, with area under the ROC curves (AUCs) of 0.897 and 0.883 in the training and validation cohorts, respectively. The combined model identified N0, N1, N2, and N3, with AUCs ranging from 0.691–0.927 in the training cohort and 0.700–0.951 in the validation cohort, respectively, thereby indicating good performance.ConclusionCT phenotypes of the primary tumor and adjacent pleura were significantly associated with LNM. A combined model incorporating radiomic signatures, CT morphological features, and clinical risk factors can assess LNM of patients with PLADC accurately and non-invasively.

scholarly journals Lymphatic Node Metastasis Risk Scoring System: A Novel Instrument for Predicting Lymph Node Metastasis After Thymic Epithelial Tumor Resection

2021 ◽  
Author(s):  
Xinxin Cheng ◽  
Yaxin Lu ◽  
Sai Chen ◽  
Weilin Yang ◽  
Bo Xu ◽  
...  
Keyword(s):  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Validation Cohort ◽  
Predictive Performance ◽  
Roc Curves ◽  
Lasso Regression ◽  
Training Cohort ◽  
Node Metastasis ◽  
Risk Scoring ◽  
Risk Scoring System

Abstract Background The authors aimed to create a novel model to predict lymphatic metastasis in thymic epithelial tumors. Methods Data of 1018 patients were collected from the Surveillance, Epidemiology, and End Results database from 2004 to 2015. To construct a nomogram, the least absolute shrinkage and selection operator (LASSO) regression model was used to select candidate features of the training cohort from 2004 to 2013. A simple model called the Lymphatic Node Metastasis Risk Scoring System (LNMRS) was constructed to predict lymphatic metastasis. Using patients from 2014 to 2015 as the validation cohort, the predictive performance of the model was determined by receiver operating characteristic (ROC) curves. Results The LASSO regression model showed that age, extension, and histology type were significantly associated with lymph node metastasis, which were used to construct the nomogram. Through analysis of the area under the curve (AUC), the nomogram achieved a AUC value of 0.80 (95 % confidence interval [Cl] 0.75–0.85) in the training cohort and 0.82 (95 % Cl 0.70–0.93) in the validation cohort, and had closed calibration curves. Based on the nomogram, the authors constructed the LNMRS model, which had an AUC of 0.80 (95 % Cl 0.75–0.85) in the training cohort and 0.82 (95% Cl 0.70–0.93) in the validation cohort. The ROC curves indicated that the LNMRS had excellent predictive performance for lymph node metastasis. Conclusion This study established a nomogram for predicting lymph node metastasis. The LNMRS model, constructed to predict lymphatic involvement of patients, was more convenient than the nomogram.

Deep learning nomogram for predicting lymph node metastasis using computed tomography image in cervical cancer

2021 ◽  
pp. 028418512110589
Author(s):  
Peijun Li ◽  
Bao Feng ◽  
Yu Liu ◽  
Yehang Chen ◽  
Haoyang Zhou ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Cervical Cancer ◽  
Deep Learning ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Validation Cohort ◽  
External Validation ◽  
Lymph Node Status ◽  
Internal Validation ◽  
Node Metastasis

Background Deep learning (DL) has been used on medical images to grade, differentiate, and predict prognosis in many tumors. Purpose To explore the effect of computed tomography (CT)-based deep learning nomogram (DLN) for predicting cervical cancer lymph node metastasis (LNM) before surgery. Material and Methods In total, 418 patients with stage IB-IIB cervical cancer were retrospectively enrolled for model exploration (n = 296) and internal validation (n = 122); 62 patients from another independent institution were enrolled for external validation. A convolutional neural network (CNN) was used for DL features extracting from all lesions. The least absolute shrinkage and selection operator (Lasso) logistic regression was used to develop a deep learning signature (DLS). A DLN incorporating the DLS and clinical risk factors was proposed to predict LNM individually. The performance of the DLN was evaluated on internal and external validation cohorts. Results Stage, CT-reported pelvic lymph node status, and DLS were found to be independent predictors and could be used to construct the DLN. The combination showed a better performance than the clinical model and DLS. The proposed DLN had an area under the curve (AUC) of 0.925 in the training cohort, 0.771 in the internal validation cohort, and 0.790 in the external validation cohort. Decision curve analysis and stratification analysis suggested that the DLN has potential ability to generate a personalized probability of LNM in cervical cancer. Conclusion The proposed CT-based DLN could be used as a personalized non-invasive tool for preoperative prediction of LNM in cervical cancer, which could facilitate the choice of clinical treatment methods.

scholarly journals A nomogram prediction model for lymph node metastasis in endometrial cancer patients

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Zhiling Wang ◽  
Shuo Zhang ◽  
Yifei Ma ◽  
Wenhui Li ◽  
Jiguang Tian ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Endometrial Cancer ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
External Validation ◽  
Clinicopathological Parameters ◽  
Bootstrap Sample ◽  
Multivariate Logistic Regression ◽  
Node Metastasis ◽  
Increased Risk

Abstract Background This study aimed to explore the risk factors for lymph node metastasis (LNM) in patients with endometrial cancer (EC) and develop a clinically useful nomogram based on clinicopathological parameters to predict it. Methods Clinical information of patients who underwent staging surgery for EC was abstracted from Qilu Hospital of Shandong University from January 1st, 2005 to June 31st, 2019. Parameters including patient-related, tumor-related, and preoperative hematologic examination-related were analyzed by univariate and multivariate logistic regression to determine the correlation with LNM. A nomogram based on the multivariate results was constructed and underwent internal and external validation to predict the probability of LNM. Results The overall data from the 1517 patients who met the inclusion criteria were analyzed. 105(6.29%) patients had LNM. According the univariate analysis and multivariate logistic regression analysis, LVSI is the most predictive factor for LNM, patients with positive LVSI had 13.156-fold increased risk for LNM (95%CI:6.834–25.324; P < 0.001). The nomogram was constructed and incorporated valuable parameters including histological type, histological grade, depth of myometrial invasion, LVSI, cervical involvement, parametrial involvement, and HGB levels from training set. The nomogram was cross-validated internally by the 1000 bootstrap sample and showed good discrimination accuracy. The c-index for internal and external validation of the nomogram are 0.916(95%CI:0.849–0.982) and 0.873(95%CI:0.776–0.970), respectively. Conclusions We developed and validated a 7-variable nomogram with a high concordance probability to predict the risk of LNM in patients with EC.

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