Radiomic analysis for preoperative prediction of cervical lymph node metastasis in patients with papillary thyroid carcinoma

Lu, Wei; Zhong, Lianzhen; Dong, Di; Fang, Mengjie; Dai, Qi; Leng, Shaoyi; Zhang, Liwen; Sun, Wei; Tian, Jie; Zheng, Jianjun; Jin, Yinhua

doi:10.1016/j.ejrad.2019.07.018

Cited by 64 publications

(59 citation statements)

References 40 publications

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“…The radiomics based on traditional machine learning requires manual extraction of image features, but the entire process is interpretable, and its features are relatively stable. Currently, this is mainly used the differentiation of malignant tumor from benign tumor in patients with thyroid diseases (47,62,64,65), and little is known about its application in the prediction of LN metastasis in patients with malignant thyroid tumors. Two studies investigated the prediction of whole cervical LN metastasis with radiomics (44,47), and one investigated the prediction of lateral cervical LN metastasis (25).…”

Section: Figure 1 |mentioning

confidence: 99%

An Ultrasound Radiomics Nomogram for Preoperative Prediction of Central Neck Lymph Node Metastasis in Papillary Thyroid Carcinoma

et al. 2020

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This study aimed to establish and validate an ultrasound radiomics nomogram for the preoperative prediction of central lymph node (LN) metastasis in patients with papillary thyroid carcinoma (PTC). Patients and Methods: The prediction model was developed in 609 patients with clinicopathologically confirmed unifocal PTC who received ultrasonography between Jan 2018 and June 2018. Radiomic features were extracted after the ultrasonography of PTC. Lasso regression model was used for data dimensionality reduction, feature selection, and radiomics signature building. The predicting model was established based on the multivariable logistic regression analysis in which the radiomics signature, ultrasonography-reported LN status, and independent clinicopathologic risk factors were incorporated, and finally a radiomics nomogram was established. The performance of the nomogram was assessed with respect to the discrimination and consistence. An independent validation was performed in 326 consecutive patients from July 2018 to Sep 2018. Results: The radiomics signature consisted of 23 selected features and was significantly associated with LN status in both primary and validation cohorts. The independent predictors in the radiomics nomogram included the radiomics signature, age, TG level, TPOAB level, and ultrasonography-reported LN status. The model showed good discrimination and consistence in both cohorts: C-index of 0.816 (95% CI, 0.808-0.824) in the primary cohort and 0.858 (95% CI, 0.849-0.867) in the validation cohort. The area under receiver operating curve was 0.858. In the validation cohort, the accuracy, sensitivity, specificity and AUC of this model were 0.812, 0.816, 0.810, and 0.858 (95% CI, 0.785-0.930), respectively. Decision curve analysis indicated the radiomics nomogram was clinically useful. Conclusion: This study presents a convenient, clinically useful ultrasound radiomics nomogram that can be used for the pre-operative individualized prediction of central LN metastasis in patients with PTC.

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Section: Figure 1 |mentioning

confidence: 99%

An Ultrasound Radiomics Nomogram for Preoperative Prediction of Central Neck Lymph Node Metastasis in Papillary Thyroid Carcinoma

et al. 2020

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“…As compared with images of T1-weighted, T2-weighted, and diffusion-weighted, contrast-enhancement images had relatively small image noise and distortion, which can more clearly visualize the delineation of ALNs (11). The first postcontrast phase images were chosen for feature extraction because the average peak enhancement at the early postcontrast stage was significantly different between negative and positive ALNs (18,33), and the distribution of the contrast agent in lesions was more homogeneous in this phase (34). ITK-SNAP software (version 3.6, http://www.itksnap.org) was utilized to segment volumes of interest (VOIs) of ALNs on contrast-enhanced images.…”

Section: Image Processingmentioning

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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“…There are fewer studies on CT-based radiomic assessment of cervical LN metastasis in PTC. Lu et al [21] analyzed the performance of CT radiomics for predicting cervical LN metastasis from a PTC primary lesion and found that the clinical nomogram yielded an AUC of 0.867 when incorporating the radiomic signature. Lee et al [33] predicted cervical LN metastasis in thyroid cancer using CT deep learning.…”

Section: Discussionmentioning

confidence: 99%

“…The prediction model can assist clinical diagnosis and prognostic analysis and facilitate more accurate diagnoses and effective treatments [19,20]. The predictive value of PTC primary lesion on cervical LN metastasis by CT radiomic model has been reported [21]. However, to the best of our knowledge, there is rare reliable report of CT radiomics to predict cervical LN metastasis in PTC by analyzing LN itself.…”

Section: Introductionmentioning

confidence: 99%

Computed Tomography Radiomics for the Preoperative Prediction of Cervical Lymph Node Metastasis in Papillary Thyroid Carcinoma: Development and External Validation

Han¹,

Wei²,

Ding³

et al. 2020

Preprint

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Background Cervical lymph node (LN) status is a critical factor related to the treatment and prognosis of papillary thyroid carcinoma (PTC). The aim of this study was to investigate the preoperative predictions of cervical LN metastasis in PTC using computed tomography (CT) radiomics.Methods A total of 134 PTC patients who underwent CT examinations were enrolled in the study at two institutes between January 2018 and January 2020. Of these patients, 289 cervical LNs (institute 1: 206 LNs from 88 patients; institute 2: 83 LNs from 46 patents) were selected. All the cases had been confirmed by surgery and pathology. Each LN was segmented and 1408 radiomic features were calculated radiomic features in noncontrast and contrast-enhanced CT images. Features were selected using the Boruta algorithm followed by an iterative culling-out algorithm. We compared four machine learning classifiers, including random forest (RF), support vector machine (SVM), neural network (NN), and naïve bayes (NB) for the classification of LN metastasis. The models were first trained and validated by 10-fold cross-validation using data from institute 1 and then tested using independent data from institute 2. The performance of the models was compared using the area under the receiver operating characteristic curves (AUC).Results Seven radiomic features were selected for building the models − 3 histogram statistical textures, 1 gray level co-occurrence matrix texture, and 3 gray level zone size matrix textures. The AUCs of the radiomic models with 10-fold cross-validation were 0.941 (95% confidence interval [CI]: 0.93–0.95), 0.943 (95% CI: 0.93–0.95), 0.914 (95% CI: 0.90–0.95), and 0.905 (95% CI: 0.88–0.91) for RF, SVM, NN, and NB, respectively. The AUCs for the testing data were 0.926 (95% CI: 0.86–0.98), 0.932 (95% CI: 0.88–0.98), 0.925 (95% CI: 0.86–0.97), and 0.912 (95% CI: 0.83–0.98) for RF, SVM, NN, and NB, respectively.Conclusions CT radiomic model demonstrated robustness in preoperative classification of LN metastases for patients with PTC, which may provide significant support for clinical decision making and prognosis evaluation.

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Radiomic analysis for preoperative prediction of cervical lymph node metastasis in patients with papillary thyroid carcinoma

Cited by 64 publications

References 40 publications

An Ultrasound Radiomics Nomogram for Preoperative Prediction of Central Neck Lymph Node Metastasis in Papillary Thyroid Carcinoma

An Ultrasound Radiomics Nomogram for Preoperative Prediction of Central Neck Lymph Node Metastasis in Papillary Thyroid Carcinoma

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

Computed Tomography Radiomics for the Preoperative Prediction of Cervical Lymph Node Metastasis in Papillary Thyroid Carcinoma: Development and External Validation

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