CT-reported Lymph Node Status Research Articles

Predictive nomogram for lymph node metastasis and survival in gastric cancer using contrast-enhanced computed tomography-based radiomics: a retrospective study.

Lymph node involvement significantly impacts the survival of gastric cancer patients and is a crucial factor in determining the appropriate treatment. This study aimed to evaluate the potential of enhanced computed tomography (CT)-based radiomics in predicting lymph node metastasis (LNM) and survival in patients with gastric cancer before surgery. Retrospective analysis of clinical data from 192 patients diagnosed with gastric carcinoma was conducted. The patients were randomly divided into a training cohort (n=128) and a validation cohort (n=64). Radiomic features of CT images were extracted using the Pyradiomics software platform, and distinctive features were further selected using a Lasso Cox regression model. Features significantly associated with LNM were identified through univariate and multivariate analyses and combined with radiomic scores to create a nomogram model for predicting lymph node involvement before surgery. The predictive performance of radiomics features, CT-reported lymph node status, and the nomogram model for LNM were compared in the training and validation cohorts by plotting receiver operating characteristic (ROC) curves. High-risk and low-risk groups were identified in both cohorts based on the cut-off value of 0.582 within the radiomics evaluation scheme, and survival rates were compared. Seven radiomic features were identified and selected, and patients were stratified into high-risk and low-risk groups using a 0.582 cut-off radiomics score. Univariate and multivariate analyses revealed that radiomics features, diabetes mellitus, Nutrition Risk Screening (NRS) 2002 score, and CT-reported lymph node status were significant predictors of LNM in patients with gastric cancer. A predictive nomogram model was developed by combining these predictors with the radiomics score, which accurately predicted LNM in gastric cancer patients before surgery and outperformed other models in terms of accuracy and sensitivity. The AUC values for the training and validation cohorts were 0.82 and 0.722, respectively. The high-risk and low-risk groups in both the training and validation cohorts showed significant differences in survival rates. The radiomics nomogram, based on contrast-enhanced computed tomography (CECT ), is a promising non-invasive tool for preoperatively predicting LNM in gastric cancer patients and postoperative survival.

2-[18F]FDG PET-based quantification of lymph node metabolic heterogeneity for predicting lymph node metastasis in patients with colorectal cancer.

The pre-surgical estimation of lymph node (LN) metastasis in colorectal cancer (CRC) poses a significant diagnostic predicament. The associations between LN morphology, density, and metabolic heterogeneity and LN metastasis status in CRCs have been seldomly examined through the lens of radiomics. This research aimed to assess 2-[18F]FDG PET-based quantification of intratumoral metabolic heterogeneity for predicting lymph node metastasis in patients with colorectal cancer. The construction of the model utilized data from 264 CRC patients, all of whom underwent preoperative 2-[18F]FDG PET/CT. Radiomic features were extracted from PET and CT images of LNs. Least absolute shrinkage and selection operator (LASSO) regression was implemented for selecting pertinent imaging features with a tenfold cross-validation. The predictive accuracy for LN metastasis status was juxtaposed against traditional methodologies (comprising CT-reported LN status and PET/CT-reported LN status) by deploying the receiver operating characteristic (ROC) curve analysis. The radiomics signature was evaluated based on discrimination, calibration, and clinical utility parameters. The model was further subjected to validation using an independent cohort of 132 patients from the period of January 2012 to June 2020. The radiomics model was composed of eight significant radiomic features (five from PET and three from CT), encapsulating metabolic and density heterogeneity. The radiomics signature (area under the curve (AUC), 0.908) showcased a significantly superior performance compared to CT-reported LN status (AUC, 0.563, P < 0.001) and PET/CT-reported LN status (AUC, 0.64, P < 0.001) for predicting LN-positive or LN-negative status. The radiomics signature (AUC, 0.885) also showcased a significantly superior performance compared to CT-reported LN status (AUC, 0.587, P < 0.001) and PET/CT-reported LN status (AUC, 0.621, P < 0.001) to identify N1 and N2. This signature maintained its independence from clinical risk factors and exhibited robustness in the validation test set. Decision curve analysis attested to the clinical utility of the radiomics signature. The radiomics signature based on 2-[18F]FDG PET/CT, which derived image features directly from LNs irrespective of clinical risk factors, displayed enhanced diagnostic performance compared to conventional CT or PET/CT-reported LN status. This allows for the identification of pre-surgical LN metastasis status and facilitates a patient-specific prediction of LN metastasis status in CRC patients.

Development and Validation of a Computed Tomography-Based Radiomics Nomogram for the Preoperative Prediction of Central Lymph Node Metastasis in Papillary Thyroid Microcarcinoma

This study aims to develop and validate a computed tomography (CT)-based radiomics nomogram for pre-operatively predicting central lymph node metastasis (CLNM) in patients with papillary thyroid microcarcinoma (PTMC) and explore the underlying biological basis by using RNA sequencing data. This study trained 452 PTMC patients across two hospitals from January 2012 to December 2020. The sets were randomly divided into the training (n = 339), internal test (n = 86), external test (n = 27) sets. Radiomics features were extracted from primary lesion's pre-operative CT images for each patient. After screening for features, five algorithms such as K-nearest neighbor, logistics regression, linear-support vector machine (SVM), Gaussian SVM, and polynomial SVM were used to establish the radiomics models. The performance of these five algorithms was evaluated and compared directly to radiologist's interpretation (CT-reported lymph node status). The radiomics signature score (Rad-score) was generated using a linear combination of the selected features. By combining the clinical risk factors and Rad score, a radiomics nomogram was established and compared with Rad-score and clinical model. The performance of the nomogram was evaluated based on the receiver operating characteristic (ROC) curve, calibration curve, and the decision curve analysis (DCA). The potential biological basis of nomogram was revealed by performing genetic analysis based on the RNA sequencing data. A total of 25 radiomic features were ultimately selected to train the machine learning models, and the five machine learning models outperformed the radiologists' interpretation by achieving area under the ROC curves (AUCs) ranging from 0.606 to 0.730 in the internal test set. By incorporating the Rad score and clinical risk factors (sex, age, tumor-diameter, and CT-reported lymph node status), this nomogram achieved AUCs of 0.800 and 0.803 in the internal and external test set, which were higher than that of the Rad-score and clinical model, respectively. Calibration curves and DCA also showed that the nomogram had good performance. As for the biological basis exploration, in patients predicted by nomogram to be PTC patients with CLMN, 109 genes were dysregulated, and some of them were associated with pathways and biological processes such as tumor angiogenesis. This radiomics nomogram successfully identified CLNM on pretreatment imaging across multiple institutions, exceeding the diagnostic ability of radiologists and had the potential to be integrated into clinical decision making as a non-invasive pre-operative tool.

Radiomics Profiling Identifies the Value of CT Features for the Preoperative Evaluation of Lymph Node Metastasis in Papillary Thyroid Carcinoma.

Background: The aim of this study was to identify the increased value of integrating computed tomography (CT) radiomics analysis with the radiologists’ diagnosis and clinical factors to preoperatively diagnose cervical lymph node metastasis (LNM) in papillary thyroid carcinoma (PTC) patients. Methods: A total of 178 PTC patients were randomly divided into a training (n = 125) and a test cohort (n = 53) with a 7:3 ratio. A total of 2553 radiomic features were extracted from noncontrast, arterial contrast-enhanced and venous contrast-enhanced CT images of each patient. Principal component analysis (PCA) and Pearson’s correlation coefficient (PCC) were used for feature selection. Logistic regression was employed to build clinical–radiological, radiomics and combined models. A nomogram was developed by combining the radiomics features, CT-reported lymph node status and clinical factors. Results: The radiomics model showed a predictive performance similar to that of the clinical–radiological model, with similar areas under the curve (AUC) and accuracy (ACC). The combined model showed an optimal predictive performance in both the training (AUC, 0.868; ACC, 86.83%) and test cohorts (AUC, 0.878; ACC, 83.02%). Decision curve analysis demonstrated that the combined model has good clinical application value. Conclusions: Embedding CT radiomics into the clinical diagnostic process improved the diagnostic accuracy. The developed nomogram provides a potential noninvasive tool for LNM evaluation in PTC patients.

Radiomics nomogram for the preoperative prediction of lymph node metastasis in pancreatic ductal adenocarcinoma

PurposeTo develop and validate a radiomics nomogram for the preoperative prediction of lymph node (LN) metastasis in pancreatic ductal adenocarcinoma (PDAC).Materials and methodsIn this retrospective study, 225 patients with surgically resected, pathologically confirmed PDAC underwent multislice computed tomography (MSCT) between January 2014 and January 2017. Radiomics features were extracted from arterial CT scans. The least absolute shrinkage and selection operator method was used to select the features. Multivariable logistic regression analysis was used to develop the predictive model, and a radiomics nomogram was built and internally validated in 45 consecutive patients with PDAC between February 2017 and December 2017. The performance of the nomogram was assessed in the training and validation cohort. Finally, the clinical usefulness of the nomogram was estimated using decision curve analysis (DCA).ResultsThe radiomics signature, which consisted of 13 selected features of the arterial phase, was significantly associated with LN status (p < 0.05) in both the training and validation cohorts. The multivariable logistic regression model included the radiomics signature and CT-reported LN status. The individualized prediction nomogram showed good discrimination in the training cohort [area under the curve (AUC), 0.75; 95% confidence interval (CI), 0.68–0.82] and in the validation cohort (AUC, 0.81; 95% CI, 0.69–0.94) and good calibration. DCA demonstrated that the radiomics nomogram was clinically useful.ConclusionsThe presented radiomics nomogram that incorporates the radiomics signature and CT-reported LN status is a noninvasive, preoperative prediction tool with favorable predictive accuracy for LN metastasis in patients with PDAC.

Computed Tomography-Based Radiomics Model to Predict Central Cervical Lymph Node Metastases in Papillary Thyroid Carcinoma: A Multicenter Study.

ObjectivesThis study aimed to develop a computed tomography (CT)-based radiomics model to predict central lymph node metastases (CLNM) preoperatively in patients with papillary thyroid carcinoma (PTC).MethodsIn this retrospective study, 678 patients with PTC were enrolled from Yantai Yuhuangding Hot3spital (n=605) and the Affiliated Hospital of Binzhou Medical University (n=73) within August 2010 to December 2020. The patients were randomly divided into a training set (n=423), an internal test set (n=182), and an external test set (n=73). Radiomics features of each patient were extracted from preoperative plain scan and contrast-enhanced CT images (arterial and venous phases). One-way analysis of variance (ANOVA) and least absolute shrinkage and selection operator algorithm were used for feature selection. The K-nearest neighbor, logistics regression, decision tree, linear-support vector machine (linear-SVM), Gaussian-SVM, and polynomial-SVM algorithms were used to establish radiomics models for CLNM prediction. The clinical risk factors were selected by ANOVA and multivariate logistic regression. Incorporated with clinical risk factors, a combined radiomics model was established for the preoperative prediction of CLNM in patients with PTCs. The performance of the combined radiomics model was evaluated using the receiver operating characteristic (ROC) and calibration curves in the training and test sets. The clinical usefulness was evaluated through decision curve analysis (DCA).ResultsA total of 4227 radiomic features were extracted from the CT images of each patient, and 14 non-zero coefficient features associated with CLNM were selected. Four clinical variables (sex, age, tumor diameter, and CT-reported lymph node status) were significantly associated with CLNM. Linear-SVM led to the best prediction model, which incorporated radiomic features and clinical risk factors. Areas under the ROC curves of 0.747 (95% confidence interval [CI] 0.706–0.782), 0.710 (95% CI 0.634–0.786), and 0.764 (95% CI 0.654–0.875) were obtained in the training, internal, and external test sets, respectively. The linear-SVM algorithm also showed better sensitivity (0.702 [95% CI 0.600–0.790] vs. 0.477 [95% CI 0.409–0.545]) and accuracy (0.670 [95% CI 0.600–0.738] vs. 0.642 [95% CI 0.569–0.712]) than an experienced radiologist in the internal test set in the combined radiomics model. The calibration plot reflected a favorable agreement between the actual and estimated probabilities of CLNM. The DCA indicated the clinical usefulness of the combined radiomics model.ConclusionThe combined radiomics model is a non-invasive preoperative tool that incorporates radiomic features and clinical risk factors to predict CLNM in patients with PTC.

A CT-Based Radiomics Nomogram for Preoperative Prediction of Lymph Node Metastasis in Periampullary Carcinomas.

PurposeTo establish and validate a radiomics nomogram for preoperatively predicting lymph node (LN) metastasis in periampullary carcinomas.Materials and MethodsA total of 122 patients with periampullary carcinoma were assigned into a training set (n = 85) and a validation set (n = 37). The preoperative CT radiomics of all patients were retrospectively assessed and the radiomic features were extracted from portal venous-phase images. The one-way analysis of variance test and the least absolute shrinkage and selection operator regression were used for feature selection. A radiomics signature was constructed with logistic regression algorithm, and the radiomics score was calculated. Multivariate logistic regression model integrating independent risk factors was adopted to develop a radiomics nomogram. The performance of the radiomics nomogram was assessed by its calibration, discrimination, and clinical utility with independent validation.ResultsThe radiomics signature, constructed by seven selected features, was closely related to LN metastasis in the training set (p < 0.001) and validation set (p = 0.017). The radiomics nomogram that incorporated radiomics signature and CT-reported LN status demonstrated favorable calibration and discrimination in the training set [area under the curve (AUC), 0.853] and validation set (AUC, 0.853). The decision curve indicated the clinical utility of our nomogram.ConclusionOur CT-based radiomics nomogram, incorporating radiomics signature and CT-reported LN status, could be an individualized and non-invasive tool for preoperative prediction of LN metastasis in periampullary carcinomas, which might assist clinical decision making.

Development and Validation of a Nomogram for Preoperative Prediction of Lymph Node Metastasis in Lung Adenocarcinoma Based on Radiomics Signature and Deep Learning Signature.

Background and PurposeThe preoperative LN (lymph node) status of patients with LUAD (lung adenocarcinoma) is a key factor for determining if systemic nodal dissection is required, which is usually confirmed after surgery. This study aimed to develop and validate a nomogram for preoperative prediction of LN metastasis in LUAD based on a radiomics signature and deep learning signature.Materials and MethodsThis retrospective study included a training cohort of 200 patients, an internal validation cohort of 40 patients, and an external validation cohort of 60 patients. Radiomics features were extracted from conventional CT (computed tomography) images. T-test and Extra-trees were performed for feature selection, and the selected features were combined using logistic regression to build the radiomics signature. The features and weights of the last fully connected layer of a CNN (convolutional neural network) were combined to obtain a deep learning signature. By incorporating clinical risk factors, the prediction model was developed using a multivariable logistic regression analysis, based on which the nomogram was developed. The calibration, discrimination and clinical values of the nomogram were evaluated.ResultsMultivariate logistic regression analysis showed that the radiomics signature, deep learning signature, and CT-reported LN status were independent predictors. The prediction model developed by all the independent predictors showed good discrimination (C-index, 0.820; 95% CI, 0.762 to 0.879) and calibration (Hosmer-Lemeshow test, P=0.193) capabilities for the training cohort. Additionally, the model achieved satisfactory discrimination (C-index, 0.861; 95% CI, 0.769 to 0.954) and calibration (Hosmer-Lemeshow test, P=0.775) when applied to the external validation cohort. An analysis of the decision curve showed that the nomogram had potential for clinical application.ConclusionsThis study presents a prediction model based on radiomics signature, deep learning signature, and CT-reported LN status that can be used to predict preoperative LN metastasis in patients with LUAD.

A Radiomics Nomogram for the Preoperative Prediction of Lymph Node Metastasis in Pancreatic Ductal Adenocarcinoma.

PurposeTo construct and verify a CT-based multidimensional nomogram for the evaluation of lymph node (LN) status in pancreatic ductal adenocarcinoma (PDAC).Materials and MethodsWe retrospectively assessed data from 172 patients with clinicopathologically confirmed PDAC surgically resected between February 2014 and November 2016. Patients were assigned to either a training cohort (n = 121) or a validation cohort (n = 51). We acquired radiomics features from the preoperative venous phase (VP) CT images. The maximum relevance–minimum redundancy (mRMR) algorithm and the least absolute shrinkage and selection operator (LASSO) methods were used to select the optimal features. We used multivariable logistic regression to construct a combined radiomics model for visualization in the form of a nomogram. Performance of the nomogram was evaluated by the receiver operating characteristic (ROC) curve approach, calibration testing, and analysis of clinical usefulness.ResultsA Rad score consisting of 10 LN status-related radiomics features was found to be significantly associated with the actual LN status (P < 0.01). A nomogram that consisted of Rad scores, CT-reported parenchymal atrophy, and CT-reported LN status performed well in terms of predictive power in the training cohort (area under the curve, 0.92), and this was confirmed in the validation cohort (area under the curve, 0.95). The nomogram also performed well in the calibration test and decision curve analysis, demonstrating its potential clinical value.ConclusionA multidimensional radiomics nomogram consisting of Rad scores, CT-reported parenchymal atrophy, and CT-reported LN status may contribute to the non-invasive evaluation of LN status in PDAC patients.

The value of spectral CT-based radiomics in preoperative prediction of lymph node metastasis of advanced gastric cancer

Objective: To investigate the spectral CT-based radiomics in predicting preoperatively the lymph node metastasis (LNM) of advanced gastric cancer. Methods: From January 2014 to October 2018, the spectral CT imaging and clinical data of 196 gastric adenocarcinoma patients confirmed by pathology in the First Affiliated Hospital of Zhengzhou University were retrospectively enrolled (training set and test set were randomly divided according to the ratio of 1∶1). These 196 patients include143 males and 53 females, aged from 28 to 81 years, with an average age of (59±11) years, and were divided into nodular metastasis group and non-metastasis group according to clinicopathological data. The spectral parameters were measured and calculated, and the CT-reported lymph node (LN) status from CT images were obtained. 273 radiomics features were extracted from the dual-phases CT images in different energy level (40, 65 and 100 keV) to build the radiomics signature respectively. Univariate analysis was used to compare the differences of spectral parameters and radiomics features between two groups, and then the significant indicators were put into multivariable logistic regression analysis to construct combined prediction model and radiomics nomogram. In addition, the performance of prediction model in training and test set were measured using the receiver operating characteristics (ROC) curves and were compared using DeLong test. Results: Both in training set and in test set, the iodine concentration (IC) of tumor in venous phase (VP) in nodular metastasis group were higher than that in non-metastasis group [training set: 22.98 (100 mg/L)>20.31 (100 mg/L), P=0.086; test set: 25.14 (100 mg/L)>21.07 (100 mg/L), P=0.009]. The CT-reported LN status showed significant differences between the two group (P<0.001, P=0.001). The radiomics signatures 40 keV-arterial phase, 65 keV-venous phase, IC-VP of tumor and CT-reported LN status were independent indicators for prediction of preoperative LNM of advanced gastric cancer in combined prediction model (P<0.05). The radiomics nomogram predicated LNM with an area under curve (AUC) and 95% confidence interval (CI) of 0.822 (0.739-0.906) in training set and 0.819(0.732-0.906) in test set, and there were no significant differences in AUC between two sets (P>0.05). Conclusions: The spectral CT-based radiomics can be used to quantitatively predict the LNM of advanced gastric cancer preoperatively.

A radiomics-based model for prediction of lymph node metastasis in gastric cancer

To develop and validate a radiomics-based model for preoperative prediction of lymph node metastasis (LNM) in gastric cancer (GC). A total of 768 GC patients were enrolled in this retrospective study. Radiomics features were extracted from portal venous phase computed tomography (CT) scans. A radiomics signature was built with highly reproducible features using the least absolute shrinkage and selection operator (LASSO) method in the training cohort (n = 486). The signature was further validated in internal validation (n = 240) and external testing cohorts (n = 42). Multivariate logistic regression analysis was conducted to build a model that combined radiomics signature, serum biomarkers, and lymph node status according to CT. Performance of the model was determined by its discrimination, calibration, and clinical usefulness. The predictive value of the model was also evaluated in early stage GC (EGC) subgroup. The radiomics signature comprised 7 robust features showed favorable prediction efficacy in all cohorts. A radiomics-based model that incorporated radiomics signature, serum CA72-4, and CT-reported lymph node status had good calibration and discrimination in training cohort [AUC, 0.92; 95% confidence interval (CI), 0.89-0.95] and validation cohort (AUC 0.86; 95% CI, 0.81-0.91). The model also showed a favorable predictive performance for EGC patients with an AUC of 0.85 (95% CI, 0.76-0.94). Decision curve analysis confirmed the clinical utility of this model. The radiomics-based model showed favorable accuracy for prediction of LNM in GC. The model may also serve as a noninvasive tool for preoperative evaluation of LNM in EGC.

Development and evaluation of a venous computed tomography radiomics model to predict lymph node metastasis from non-small cell lung cancer.

The objective of this study was to develop a venous computed tomography (CT)-based radiomics model to predict the lymph node metastasis (LNM) in patients with non-small cell lung cancer (NSCLC). A total of 411 consecutive patients with NSCLC underwent tumor resection and lymph node (LN) dissection from January 2018 to September 2018 in our hospital. A radiologist with 20 years of diagnostic experience retrospectively reviewed all CT scans and classified all visible LNs into LNM and non-LNM groups without the knowledge of pathological diagnosis. A logistic regression model (radiomics model) in classification of pathology-confirmed NSCLC patients with and without LNM was developed on radiomics features for NSCLC patients. A morphology model was also developed on qualitative morphology features in venous CT scans. A training group included 288 patients (99 with and 189 without LNM) and a validation group included 123 patients (42 and 81, respectively). The receiver operating characteristic curve was performed to discriminate LNM (+) from LNM (−) for CT-reported status, the morphology model and the radiomics model. The area under the curve value in LNM classification on the training group was significantly greater at 0.79 (95% confidence interval [CI]: 0.77–0.81) by use of the radiomics model (build by best 10 features in predicting LNM) compared with 0.51 by CT-reported LN status (P < .001) or 0.66 (95% CI: 0.64–0.68) by morphology model (build by tumor size and spiculation) (P < .001). Similarly, the area under the curve value on the validation group was 0.73 (95% CI: 0.70–0.76) by the radiomics model, compared with 0.52 or 0.63 (95% CI: 0.60–0.66) by the other 2 (both P < .001). A radiomics model shows excellent performance for predicting LNM in NSCLC patients. This predictive radiomics model may benefit patients to get better treatments such as an appropriate surgery.

Association of the tumour stroma percentage in the preoperative biopsies with lymph node metastasis in colorectal cancer

BackgroundPreoperative prediction of lymph node (LN) status is integral to determining the most appropriate treatment strategy for colorectal cancer (CRC). This study aimed to develop and validate a nomogram to predict LN metastasis in CRC preoperatively.MethodsA total of 530 patients were enrolled and divided into training and validation cohorts. The tumour stroma percentage (TSP) of the preoperative biopsies was assessed. The risk factors for LN metastasis were selected, and a nomogram was constructed subsequently. The performance of the nomogram was assessed by using the AUROC and the calibration curve, and then validated in the validation cohort.ResultsHigh TSP was significantly associated with LN metastasis in both the training and validation cohorts. Computed tomography (CT)-reported T stage, CT-reported LN status, preoperative tumour differentiation, carcinoembryonic antigen, carbohydrate antigen 19-9 and TSP were independent predictors of LN metastasis in CRC. A nomogram incorporating the six predictors was constructed. The nomogram yielded good discrimination and calibration, with an AUROC of 0.846 (95% CI: 0.807−0.886) and 0.809 (95% CI: 0.745−0.872) in the training and validation cohorts, respectively.ConclusionsAssessment of TSP in the preoperative biopsies provided additional information about the LN status. The nomogram was useful for tailored therapy in CRC preoperatively.

CT radiomics nomogram for the preoperative prediction of lymph node metastasis in gastric cancer.

To investigate the role of computed tomography (CT) radiomics for the preoperative prediction of lymph node (LN) metastasis in gastric cancer. This retrospective study included 247 consecutive patients (training cohort, 197 patients; test cohort, 50 patients) with surgically proven gastric cancer. Dedicated radiomics prototype software was used to segment lesions on preoperative arterial phase (AP) CT images and extract features. A radiomics model was constructed to predict the LN metastasis by using a random forest (RF) algorithm. Finally, a nomogram was built incorporating the radiomics scores and selected clinical predictors. Receiver operating characteristic (ROC) curves were used to validate the capability of the radiomics model and nomogram on both the training and test cohorts. The radiomics model showed a favorable discriminatory ability in the training cohort with an area under the curve (AUC) of 0.844 (95% CI, 0.759 to 0.909), which was confirmed in the test cohort with an AUC of 0.837 (95% CI, 0.705 to 0.926). The nomogram consisted of radiomics scores and the CT-reported LN status showed excellent discrimination in the training and test cohorts with AUCs of 0.886 (95% CI, 0.808 to 0.941) and 0.881 (95% CI, 0.759 to 0.956), respectively. The CT-based radiomics nomogram holds promise for use as a noninvasive tool in the individual prediction of LN metastasis in gastric cancer. • CT radiomics showed a favorable performance for the prediction of LN metastasis in gastric cancer. • Radiomics model outperformed the routine CT in predicting LN metastasis in gastric cancer. • The radiomics nomogram holds potential in the individualized prediction of LN metastasis in gastric cancer.

Biliary Tract Cancer at CT: A Radiomics-based Model to Predict Lymph Node Metastasis and Survival Outcomes.

Purpose To evaluate a radiomics model for predicting lymph node (LN) metastasis in biliary tract cancers (BTCs) and to determine its prognostic value for disease-specific and recurrence-free survival. Materials and Methods For this retrospective study, a radiomics model was developed on the basis of a primary cohort of 177 patients with BTC who underwent resection and LN dissection between June 2010 and December 2016. Radiomic features were extracted from portal venous CT scans. A radiomics signature was built on the basis of reproducible features by using the least absolute shrinkage and selection operator method. Multivariable logistic regression model was adopted to establish a radiomics nomogram. Nomogram performance was determined by its discrimination, calibration, and clinical usefulness. The model was internally validated in 70 consecutive patients with BTC between January 2017 and February 2018. Results The radiomics signature, composed of three LN-status-related features, was associated with LN metastasis in primary and validation cohorts (P < .001). The radiomics nomogram that incorporated radiomics signature and CT-reported LN status showed good calibration and discrimination in primary cohort (area under the curve, 0.81) and validation cohort (area under the curve, 0.80). Patients at high risk of LN metastasis portended lower disease-specific and recurrence-free survival than did those at low risk after surgery (both P < .001). High-risk LN metastasis was an independent preoperative predictor of disease-specific survival (hazard ratio, 3.37; P < .001) and recurrence-free survival (hazard ratio, 1.98; P = .003). Conclusion A radiomics model derived from portal phase CT of the liver has good performance for predicting lymph node metastasis in biliary tract cancer and may help to improve clinical decision making. © RSNA, 2018 Online supplemental material is available for this article. See also the editorial by Laghi and Voena in this issue.

Preoperative Prediction Nomogram Based on a Novel Serum miRNA Signature and CT Scans for Lymph Node Metastasis in Colorectal Cancer

Background: Preoperative prediction of lymph node (LN) status is of crucial importance for appropriate treatment planning in patients with colorectal cancer (CRC). Here, we sought to develop and validate a non-invasive nomogram model to preoperatively predict LN metastasis in CRC. Methods: In the discovery set (n=20), LN-status-related miRNAs were screened from high-throughput sequencing data of human CRC serum samples. In the training set (n=218), a miRNA panel-clinicopathologic nomogram was developed by logistic regression analysis for preoperative prediction of LN metastasis. In the validation set (n=198), we validated the above nomogram with respect to its discrimination, calibration and clinical application. Findings: We identified four differently expressed miRNAs (miR-122-5p, miR-146b-5p, miR-186-5p and miR-193a-5p), which, when combined in the miRNA-based panel, could provide higher LN prediction capability compared with computed tomography (CT) scans (P<0.05 in both training and validation sets). Furthermore, a nomogram integrating the miRNA-based panel and CT-reported LN status was constructed in the training set, which performed well in both training and validation sets (AUC: 0.913 and 0.883, respectively). Decision curve analysis demonstrated the clinical usefulness of the nomogram. Interpretation: Our nomogram is a reliable prediction model that can be conveniently and efficiently used to improve the accuracy of preoperative prediction of LN metastasis in patients with CRC. Funding: This project was supported by National Natural Science Foundation of China (Nos. 81472025, 81772271, 81601846, 81702084 and 81301506), Shandong Technological Development Project (2016CYJS01A02), Taishan Scholar Program of Shandong Province, Natural Science of Basic Scientific Research Foundation of Shandong University (2017BTS01), Science Foundation of Qilu Hospital of Shandong University (2015QLMS51) and Fundamental Research Funds of Shandong University (2014QLKY03). Declaration of Interest: The authors declare no potential conflicts of interest. Ethical Approval: Ethical approval was obtained for this retrospective study, and the informed consent requirement was waved.

Radiomics nomogram outperforms size criteria in discriminating lymph node metastasis in resectable esophageal squamous cell carcinoma.

To determine the value of radiomics in predicting lymph node (LN) metastasis in resectable esophageal squamous cell carcinoma (ESCC) patients. Data of 230 consecutive patients were retrospectively analyzed (154 in the training set and 76 in the test set). A total of 1576 radiomics features were extracted from arterial-phase CT images of the whole primary tumor. LASSO logistic regression was performed to choose the key features and construct a radiomics signature. A radiomics nomogram incorporating this signature was developed on the basis of multivariable analysis in the training set. Nomogram performance was determined and validated with respect to its discrimination, calibration and reclassification. Clinical usefulness was estimated by decision curve analysis. The radiomics signature including five features was significantly associated with LN metastasis. The radiomics nomogram, which incorporated the signature and CT-reported LN status (i.e. size criteria), distinguished LN metastasis with an area under curve (AUC) of 0.758 in the training set, and performance was similar in the test set (AUC 0.773). Discrimination of the radiomics nomogram exceeded that of size criteria alone in both the training set (p <0.001) and the test set (p=0.005). Integrated discrimination improvement (IDI) and categorical net reclassification improvement (NRI) showed significant improvement in prognostic value when the radiomics signature was added to size criteria in the test set (IDI 17.3%; p<0.001; categorical NRI 52.3%; p<0.001). Decision curve analysis supported that the radiomics nomogram is superior to size criteria. The radiomics nomogram provides individualized risk estimation of LN metastasis in ESCC patients and outperforms size criteria. • A radiomics nomogram was built and validated to predict LN metastasis in resectable ESCC. • The radiomics nomogram outperformed size criteria. • Radiomics helps to unravel intratumor heterogeneity and can serve as a novel biomarker for determination of LN status in resectable ESCC.

A Radiomics Nomogram for the Preoperative Prediction of Lymph Node Metastasis in Bladder Cancer.

Purpose: To develop and validate a radiomics nomogram for the preoperative prediction of lymph node (LN) metastasis in bladder cancer.Experimental Design: A total of 118 eligible bladder cancer patients were divided into a training set (n = 80) and a validation set (n = 38). Radiomics features were extracted from arterial-phase CT images of each patient. A radiomics signature was then constructed with the least absolute shrinkage and selection operator algorithm in the training set. Combined with independent risk factors, a radiomics nomogram was built with a multivariate logistic regression model. Nomogram performance was assessed in the training set and validated in the validation set. Finally, decision curve analysis was performed with the combined training and validation set to estimate the clinical usefulness of the nomogram.Results: The radiomics signature, consisting of nine LN status-related features, achieved favorable prediction efficacy. The radiomics nomogram, which incorporated the radiomics signature and CT-reported LN status, also showed good calibration and discrimination in the training set [AUC, 0.9262; 95% confidence interval (CI), 0.8657-0.9868] and the validation set (AUC, 0.8986; 95% CI, 0.7613-0.9901). The decision curve indicated the clinical usefulness of our nomogram. Encouragingly, the nomogram also showed favorable discriminatory ability in the CT-reported LN-negative (cN0) subgroup (AUC, 0.8810; 95% CI, 0.8021-0.9598).Conclusions: The presented radiomics nomogram, a noninvasive preoperative prediction tool that incorporates the radiomics signature and CT-reported LN status, shows favorable predictive accuracy for LN metastasis in patients with bladder cancer. Multicenter validation is needed to acquire high-level evidence for its clinical application. Clin Cancer Res; 23(22); 6904-11. ©2017 AACR.

Development and Validation of a Radiomics Nomogram for Preoperative Prediction of Lymph Node Metastasis in Colorectal Cancer.

To develop and validate a radiomics nomogram for preoperative prediction of lymph node (LN) metastasis in patients with colorectal cancer (CRC). The prediction model was developed in a primary cohort that consisted of 326 patients with clinicopathologically confirmed CRC, and data was gathered from January 2007 to April 2010. Radiomic features were extracted from portal venous-phase computed tomography (CT) of CRC. Lasso regression model was used for data dimension reduction, feature selection, and radiomics signature building. Multivariable logistic regression analysis was used to develop the predicting model, we incorporated the radiomics signature, CT-reported LN status, and independent clinicopathologic risk factors, and this was presented with a radiomics nomogram. The performance of the nomogram was assessed with respect to its calibration, discrimination, and clinical usefulness. Internal validation was assessed. An independent validation cohort contained 200 consecutive patients from May 2010 to December 2011. The radiomics signature, which consisted of 24 selected features, was significantly associated with LN status (P < .001 for both primary and validation cohorts). Predictors contained in the individualized prediction nomogram included the radiomics signature, CT-reported LN status, and carcinoembryonic antigen level. Addition of histologic grade to the nomogram failed to show incremental prognostic value. The model showed good discrimination, with a C-index of 0.736 (C-index, 0.759 and 0.766 through internal validation), and good calibration. Application of the nomogram in the validation cohort still gave good discrimination (C-index, 0.778 [95% CI, 0.769 to 0.787]) and good calibration. Decision curve analysis demonstrated that the radiomics nomogram was clinically useful. This study presents a radiomics nomogram that incorporates the radiomics signature, CT-reported LN status, and clinical risk factors, which can be conveniently used to facilitate the preoperative individualized prediction of LN metastasis in patients with CRC.