Predicting Malignancy in Thyroid Nodules: Radiomics Score Versus 2017 American College of Radiology Thyroid Imaging, Reporting and Data System

Liang, Jin-Yu; Huang, Xiaowen; Hu, Hang-Tong; Liu, Yihao; Zhou, Qian; Cao, Qinghua; Wang, Wei; Liu, Baoxian; Zheng, Yanling; Li, Xin; Xie, Xian-Jin; Lü, Ming-De; Peng, Sui; Liu, Longzhong; Xiao, Hang

doi:10.1089/thy.2017.0525

Cited by 77 publications

(64 citation statements)

References 20 publications

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“…This method was first demonstrated to be useful in analyzing CT or MRI images on clinical oncology 23,24 . Recently, radiomics based on analysis of US images showed better performances than other routine methods 25 . However, analyzing US images by radiomics has some limitations including object segmentation and extraction of hard-coded features 22 .…”

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confidence: 99%

Deep learning radiomics can predict axillary lymph node status in early-stage breast cancer

et al. 2020

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Accurate identification of axillary lymph node (ALN) involvement in patients with early-stage breast cancer is important for determining appropriate axillary treatment options and therefore avoiding unnecessary axillary surgery and complications. Here, we report deep learning radiomics (DLR) of conventional ultrasound and shear wave elastography of breast cancer for predicting ALN status preoperatively in patients with early-stage breast cancer. Clinical parameter combined DLR yields the best diagnostic performance in predicting ALN status between disease-free axilla and any axillary metastasis with areas under the receiver operating characteristic curve (AUC) of 0.902 (95% confidence interval [CI]: 0.843, 0.961) in the test cohort. This clinical parameter combined DLR can also discriminate between low and heavy metastatic burden of axillary disease with AUC of 0.905 (95% CI: 0.814, 0.996) in the test cohort. Our study offers a noninvasive imaging biomarker to predict the metastatic extent of ALN for patients with early-stage breast cancer.

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confidence: 99%

Deep learning radiomics can predict axillary lymph node status in early-stage breast cancer

et al. 2020

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“…Although several previous studies have applied histogram and texture analysis in thyroid nodules, most have investigated only a small number of imaging parameters and were not based on high-dimensional data [23][24][25][26][27][28]. Furthermore, the majority of studies applying histogram or texture analysis have focused on differentiating malignant and benign thyroid nodules [23][24][25][26][27]29]. Therefore, in this study, we attempted to develop a radiomics signature for the prediction of lateral LNM in patients with cPTC based solely on the radiomic features of the primary thyroid tumor.…”

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confidence: 99%

Radiomics signature for prediction of lateral lymph node metastasis in conventional papillary thyroid carcinoma

et al. 2020

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Preoperative neck ultrasound (US) for lateral cervical lymph nodes is recommended for all patients undergoing thyroidectomy for thyroid malignancy, but it is operator dependent. We aimed to develop a radiomics signature using US images of the primary tumor to preoperatively predict lateral lymph node metastasis (LNM) in patients with conventional papillary thyroid carcinoma (cPTC). Methods Four hundred consecutive cPTC patients from January 2004 to February 2006 were enrolled as the training cohort, and 368 consecutive cPTC patients from March 2006 to February 2007 served as the validation cohort. A radiomics signature, which consisted of 14 selected features, was generated by the least absolute shrinkage and selection operator (LASSO) regression model in the training cohort. The discriminating performance of the radiomics signature was assessed in the validation cohort with the area under the receiver operating characteristic curve (AUC). Results The radiomics signature was significantly associated with lateral cervical lymph node status (p < 0.001). The AUC of its performance in discriminating metastatic and non-metastatic lateral cervical lymph nodes was 0.710 (95% CI: 0.649-0.770) in the training cohort and was 0.621 (95% CI: 0.560-0.682) in the validation cohort. Conclusions The present study showed that US radiomic features of the primary tumor were associated with lateral cervical lymph node status. Although their discriminatory performance was

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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).…”

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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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Predicting Malignancy in Thyroid Nodules: Radiomics Score Versus 2017 American College of Radiology Thyroid Imaging, Reporting and Data System

Abstract: Compared with ACR TI-RADS evaluation by junior radiologists, the radiomics score showed good performance in predicting malignancy of thyroid nodules in our set of histologically verified thyroid nodules from two tertiary hospitals.

Cited by 77 publications

References 20 publications

Deep learning radiomics can predict axillary lymph node status in early-stage breast cancer

Deep learning radiomics can predict axillary lymph node status in early-stage breast cancer

Radiomics signature for prediction of lateral lymph node metastasis in conventional papillary thyroid carcinoma

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

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