Application of deep learning to the diagnosis of cervical lymph node metastasis from thyroid cancer with CT

Lee, Jeong Hoon; Ha, Eun Ju; Kim, Ju Han

doi:10.1007/s00330-019-06098-8

Cited by 91 publications

(72 citation statements)

References 30 publications

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“…In a study by Lee et al [32] using US, a CAD system showed accuracy, sensitivity, and specificity for predicting lymph node metastasis of 83.0%, 79.5%, and 87.5%, respectively. A recent development and validation study applied DL to the diagnosis of lymph node metastasis using computed tomography; the AUROCs for the eight tested algorithms were above 0.90, and the best-performing algorithm showed an AUROC of 0.874 in a validation set [33,34]. This approach may serve as a training tool to help resident physicians gain confidence in diagnosing thyroid cancer.…”

Section: Future Developmental Directions Of Ai-based Cad Systemsmentioning

confidence: 96%

Applications of machine learning and deep learning to thyroid imaging: where do we stand?

Baek

2021

Ultrasonography

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Ultrasonography. 2020 Jul 3. Epub ahead of print Ultrasonography (US) is the primary diagnostic tool used to assess the risk of malignancy and to inform decision-making regarding the use of fine-needle aspiration (FNA) and post-FNA management in patients with thyroid nodules. However, since US image interpretation is operator-dependent and interobserver variability is moderate to substantial, unnecessary FNA and/or diagnostic surgery are common in practice. Artificial intelligence (AI)-based computeraided diagnosis (CAD) systems have been introduced to help with the accurate and consistent interpretation of US features, ultimately leading to a decrease in unnecessary FNA. This review provides a developmental overview of the AI-based CAD systems currently used for thyroid nodules and describes the future developmental directions of these systems for the personalized and optimized management of thyroid nodules.

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Section: Future Developmental Directions Of Ai-based Cad Systemsmentioning

confidence: 96%

Applications of machine learning and deep learning to thyroid imaging: where do we stand?

Baek

2021

Ultrasonography

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“…No study has been conducted to investigate the prediction of central cervical LN metastasis. In the actual clinical situation, the sensitivity of imaging examination can reach 0.7 in the diagnosis of lateral cervical LN metastasis, but it is lower than 0.5 in the diagnosis of central cervical LN metastasis (16,38,40,59,66). The accurate determination of central cervical LN metastasis directly affects the use of prophylactic LN dissection.…”

Section: Figure 1 |mentioning

confidence: 99%

“…Thus, a single image may not be representative, which may finally affect the establishment of radiomics model. In a study, deep-learning CT images were used to diagnose the cervical LN metastases in thyroid cancer patients, and the results showed the AUC was as high as 0.953 (66), indicating that multi-sequence CT images may comprehensively reflect the characteristics of tumors and help improve the diagnostic efficiency. This also provides a reference for US radiomics: we can acquire more lesion information from more US images.…”

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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“…It has been applied as the radiological imaging biomarkers to evaluate tumor heterogeneity, and showed promising ability in as tumor diagnosis, presurgical grading, as well as gene mutation prediction (10)(11)(12). Moreover, with quantified analyses of images, it has also been incorporated with various novel computer technologies, such as machine learning (13)(14)(15)(16).…”

Section: Introductionmentioning

confidence: 99%

Comparison of Radiomics-Based Machine-Learning Classifiers in Diagnosis of Glioblastoma From Primary Central Nervous System Lymphoma

Chen

Zheng

et al. 2020

Front. Oncol.

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The purpose of the current study was to evaluate the ability of magnetic resonance (MR) radiomics-based machine-learning algorithms in differentiating glioblastoma (GBM) from primary central nervous system lymphoma (PCNSL). Method: One-hundred and thirty-eight patients were enrolled in this study. Radiomics features were extracted from contrast-enhanced MR images, and the machine-learning models were established using five selection methods (distance correlation, random forest, least absolute shrinkage and selection operator (LASSO), eXtreme gradient boosting (Xgboost), and Gradient Boosting Decision Tree) and three radiomics-based machine-learning classifiers [linear discriminant analysis (LDA), support vector machine (SVM), and logistic regression (LR)]. Sensitivity, specificity, accuracy, and areas under curves (AUC) of models were calculated, with which the performances of classifiers were evaluated and compared with each other. Result: Brilliant discriminative performance would be observed among all classifiers when combined with the suitable selection method. For LDA-based models, the optimal one was Distance Correlation + LDA with AUC of 0.978. For SVM-based models, Distance Correlation + SVM was the one with highest AUC of 0.959, while for LR-based models, the highest AUC was 0.966 established with LASSO + LR. Conclusion: Radiomics-based machine-learning algorithms potentially have promising performances in differentiating GBM from PCNSL.

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Application of deep learning to the diagnosis of cervical lymph node metastasis from thyroid cancer with CT

Cited by 91 publications

References 30 publications

Applications of machine learning and deep learning to thyroid imaging: where do we stand?

Applications of machine learning and deep learning to thyroid imaging: where do we stand?

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

Comparison of Radiomics-Based Machine-Learning Classifiers in Diagnosis of Glioblastoma From Primary Central Nervous System Lymphoma

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