A Texture Analysis–Based Prediction Model for Lymph Node Metastasis in Stage IA Lung Adenocarcinoma

Gu, Yawei; She, Yunlang; Xie, Dong; Dai, Chenyang; Ren, Yijiu; Fan, Ziwen; Zhu, Huanhuan; Sun, Xuejun; Xie, Huikang; Jiang, Gening; Chen, Chang

doi:10.1016/j.athoracsur.2018.02.026

Cited by 47 publications

(53 citation statements)

References 26 publications

(30 reference statements)

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“…Radiomics hypothesizes that intratumor heterogeneity is exhibited on the spatial distribution of voxel intensities (27). Indeed, two dominant features (skewness and zone entropy) in our radiomics signature quantified intratumor heterogeneity, and were reported to be predictors of LN metastasis in other solid tumors (12,14,28). CT-reported tumor size emerged as an independent predictor in the clinical prediction model but did not show sufficient predictive power with the addition of radiomics signature or size-based feature called minor axis alone into a multivariable logistic model (Table E3 [online]).…”

Section: Discussionmentioning

confidence: 87%

“…features from digital medical images that enables mineable high-dimensional data to be applied within clinical decision support to offer improved diagnostic, prognostic, and predictive accuracy (10,11). Previous studies (12)(13)(14) have suggested improvement in preoperative prediction of LN metastasis by using a radiomics-based approach to colorectal cancer, bladder cancer, and lung adenocarcinoma. To our knowledge, there is no published report that has documented whether radiomics signature would facilitate prediction of LN metastasis in BTCs.…”

Section: Implications For Patientmentioning

confidence: 99%

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

et al. 2019

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Section: Discussionmentioning

confidence: 87%

Section: Implications For Patientmentioning

confidence: 99%

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

et al. 2019

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“…To our knowledge, some studies reported radiomics-related methods for detecting metastatic lymph node (41,42). However, these models were built based on radiomic features of one phase, which did not include radiomic dynamic features.…”

Section: Discussionmentioning

confidence: 99%

A Nomogram Combined Radiomics and Kinetic Curve Pattern as Imaging Biomarker for Detecting Metastatic Axillary Lymph Node in Invasive Breast Cancer

Shan

Wang

et al. 2020

Front. Oncol.

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Objective: To construct and validate a nomogram model integrating the magnetic resonance imaging (MRI) radiomic features and the kinetic curve pattern for detecting metastatic axillary lymph node (ALN) in invasive breast cancer preoperatively. Materials and Methods: A total of 145 ALNs from two institutions were classified into negative and positive groups according to the pathologic or surgical results. One hundred one ALNs from institution I were taken as the training cohort, and the other 44 ALNs from institution II were taken as the external validation cohort. The kinetic curve was computed using dynamic contrast-enhanced MRI software. The preprocessed images were used for radiomic feature extraction. The LASSO regression was applied to identify optimal radiomic features and construct the Radscore. A nomogram model was constructed combining the Radscore and the kinetic curve pattern. The discriminative performance was evaluated by receiver operating characteristic analysis and calibration curve. Results: Five optimal features were ultimately selected and contributed to the Radscore construction. The kinetic curve pattern was significantly different between negative and positive lymph nodes. The nomogram model showed a better performance in both training cohort [area under the curve (AUC) = 0.91, 95% CI = 0.83-0.96] and external validation cohort (AUC = 0.86, 95% CI = 0.72-0.94); the calibration curve indicated a better accuracy of the nomogram model for detecting metastatic ALN than either Radscore or kinetic curve pattern alone. Conclusion: A nomogram model integrated the Radscore and the kinetic curve pattern could serve as a biomarker for detecting metastatic ALN in patients with invasive breast cancer.

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“…Several studies have also adopted texture analysis for the differential diagnosis of liver nodules, treatment response evaluation of HCC and prognosis prediction (22,23). In addition, model-based texture analysis has been applied to improve diagnostic efficiency, which could assist clinicians in making treatment decisions (24).…”

Section: Introductionmentioning

confidence: 99%

Model‑based three‑dimensional texture analysis of contrast‑enhanced magnetic resonance imaging as a potential tool for preoperative prediction of microvascular invasion in hepatocellular carcinoma

et al. 2019

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The purpose of the present study was to investigate the value of contrast-enhanced magnetic resonance imaging (CE-MRI) texture analysis for preoperatively predicting microvascular invasion (MVI) in hepatocellular carcinoma (HCC). Accordingly, a retrospective study of 142 patients with pathologically confirmed HCC was performed. The patients were divided into two cohorts: The training cohort (n=99) and the validation cohort (n=43), including the MVI-positive group (n=53) and MVI-negative group (n=89). On the basis of three-dimensional texture analysis, 58 features were extracted from the preoperative CE-MR images of arterial-phase (AP) and portal-venous-phase (PP). The t-test or Kruskal-Wallis test, univariate logistic regression analysis and Pearson correlation were applied for feature reduction. Clinical-radiological features were also analyzed. Multivariate logistic regression analysis was used to build the texture model and combined model with clinical-radiological features. The MVI-predictive performance of the models was evaluated using receiver operating characteristic (ROC) analysis and presented using nomogram. Among the clinical features, a significant difference was found in maximum tumor diameter (P=0.002), tumor differentiation (P=0.026) and α-fetoprotein level (P=0.025) between the two groups in the training cohort. Four MR texture features in AP and five in PP images were identified through feature reduction. On ROC analysis, the AP texture model showed better diagnostic performance than did the PP model in the validation cohort, with an area under the curve (AUC) of 0.773 vs. 0.623, sensitivity of 0.750 vs. 0.500, and specificity of 0.815 vs. 0.926. Together with the clinical features, the combined model of AP improved the AUC, sensitivity and specificity to 0.810, 0.811 and 0.790, respectively, which was demonstrated in nomogram. To conclude, model-based texture analysis of CE-MRI could predict MVI in HCC preoperatively and noninvasively, and the AP image shows better predictive efficiency than PP image. The combined model of AP with clinical-radiological features could improve MVI prediction ability.

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A Texture Analysis–Based Prediction Model for Lymph Node Metastasis in Stage IA Lung Adenocarcinoma

Cited by 47 publications

References 26 publications

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

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

A Nomogram Combined Radiomics and Kinetic Curve Pattern as Imaging Biomarker for Detecting Metastatic Axillary Lymph Node in Invasive Breast Cancer

Model‑based three‑dimensional texture analysis of contrast‑enhanced magnetic resonance imaging as a potential tool for preoperative prediction of microvascular invasion in hepatocellular carcinoma

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