Value of clinical features combined with multimodal ultrasound in predicting lymph node metastasis in cervical central area of papillary thyroid carcinoma

Xue, Jie; Li, Siyao; Qu, Ning; Wang, Guoyun; Chen, Huangzhuonan; Zhang, Wu; Cao, Xiaoli

doi:10.1002/jcu.23465

Cited by 4 publications

(6 citation statements)

References 21 publications

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“…All 47 studies are summarized in Supplementary Table 1 [2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48] and were divided based on the tumor of origin: breast cancer, head and neck cancer, and cervical cancer. Most of the papers were published between 2019 and 2022 (n = 19 articles), and the analyses were conducted using MATLAB, R software, SPSS, OriginPro, a papillary thyroid carcinoma (PTC), cervical LN metastasis (LNM) prediction system (custom-built, n = 3), MIPAV, and Mazda.…”

Section: Resultsmentioning

confidence: 99%

“…Feature extraction was mainly performed with PyRadiomics, ITK-SNAP, LifeX, MedCalc, ImageJ, PASW and Prism. The values of the area under the curve (AUC), sensitivity, specificity, and accuracy are summarized in ▶ Table 1 [2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48].…”

Section: Resultsmentioning

confidence: 99%

“…Head and neck cancer studies mainly focused on thyroid cancer, specifically PTC and head and neck squamous cell carcinoma (HNSCC). We identified 20 studies investigating LN status in patients with PTC [13,14,15,16,17,18,19,20,21,22,23,32,35,36,37,38,46,47,48], one study on follicular thyroid carcinoma [25], one on treatment outcome prediction in head and neck malignancies [44], and three studies on HNSCC [29,30,31].…”

Section: Head and Neck Cancermentioning

confidence: 99%

“…Most studies (n = 15 articles) on PTC showed that it is possible to predict lateral or central LNM using radiomics [13], although one study from Kim et al [14] was unsuccessful. Radiomics analysis included models combining shear-wave elastography and B-mode US combined Rad-score [16], strain elastography US and B-mode US [18], radiomics and clinical features [21,36,37,38,48], textural and radiological features [22], grayscale texture analysis [23], and clinical, radiomics, and extrathyroidal extension features [35].…”

Section: Head and Neck Cancermentioning

confidence: 99%

“…The overall performance in the PTC context is heterogeneous, with one failed study, three studies that achieve 0.621, 0.600 and 0.65 AUC values, respectively [13,35,37], nine studies that range between 0.727 and 0.881 [15,16,17,18,19,21,36,44,46] and seven with an AUC over 0.900 [17,20,22,23,31,38,47].…”

Section: Head and Neck Cancermentioning

confidence: 99%

See 4 more Smart Citations

Quantitative ultrasound radiomics analysis to evaluate lymph nodes in patients with cancer: a systematic review

Guerrisi,

Miseo,

Falcone

et al. 2024

Ultraschall Med

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This systematic review aims to evaluate the role of ultrasound (US) radiomics in assessing lymphadenopathy in patients with cancer and the ability of radiomics to predict metastatic lymph node involvement. A systematic literature search was performed in the PubMed (MEDLINE), Cochrane Central Register of Controlled Trials (CENTRAL), and EMBASE (Ovid) databases up to June 13, 2023. 42 articles were included in which the lymph node mass was assessed with a US exam, and the analysis was performed using radiomics methods. From the survey of the selected articles, experimental evidence suggests that radiomics features extracted from US images can be a useful tool for predicting and characterizing lymphadenopathy in patients with breast, head and neck, and cervical cancer. This noninvasive and effective method allows the extraction of important information beyond mere morphological characteristics, extracting features that may be related to lymph node involvement. Future studies are needed to investigate the role of US-radiomics in other types of cancers, such as melanoma.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Head and Neck Cancermentioning

confidence: 99%

Section: Head and Neck Cancermentioning

confidence: 99%

Section: Head and Neck Cancermentioning

confidence: 99%

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Quantitative ultrasound radiomics analysis to evaluate lymph nodes in patients with cancer: a systematic review

Guerrisi,

Miseo,

Falcone

et al. 2024

Ultraschall Med

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Nomogram for predicting cervical lymph node metastasis of papillary thyroid carcinoma using deep learning-based super-resolution ultrasound image

Li,

Zhao,

Chen

et al. 2024

Discov Onc

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Objectives To investigate the feasibility and effectiveness of a deep learning (DL) super-resolution (SR) ultrasound image reconstruction model for predicting cervical lymph node status in patients with papillary thyroid carcinoma(PTC). Methods In this retrospective study, researchers recruited 544 patients with PTC and randomly assigned them to training and test sets. SR ultrasound images were acquired using SR technology to improve image resolution, and artificial features and DL features were extracted from the original (OR) and SR images, respectively, to construct a ML, DL model. The best model was selected and aggregated with clinical parameters to construct the nomogram. The performance of the model is evaluated by ROC curves, calibration curves and decision curves. Results In distinguishing the presence or absence of metastatic lymph nodes, the predictive performance of the SR_ResNet 101 and SR_SVM models based on SR outperformed those based on OR. In the test set, SR_SVM AUC was 0.878 (95% CI 0.8203–0.9358), accuracy 0.854, while OR_SVM AUC was 0.822 (95% CI 0.7500–0.8937), accuracy 0.665. SR_ResNet 101 AUC was 0.799 (95% CI 0.7175–0.8806), accuracy 0.793, and OR_ResNet101 AUC was 0.751 (95% CI 0.6620–0.8401), accuracy 0.713. Subsequently, Nomogram_A and Nomogram_B were constructed by integrating the SR_SVM model and SR_ResNet 101 model, respectively, with clinical parameters, while Nomogram_C was constructed solely based on clinical indicators. In the test set, Nomogram_A demonstrated the best performance with an AUC of 0.930 (95% CI 0.8913–0.9682) and accuracy was 0.829. Nomogram_B AUC 0.868 (95% CI 0.8102–0.9261) and accuracy was 0.829, while Nomogram_C AUC 0.880 (95% CI 0.8257–0.9349) and accuracy was 0.787. The DeLong test revealed that the diagnostic performance of Nomogram_A based on SR_SVM was significantly higher than that of Nomogram_B, Nomogram_C, and the level of Radiologist (P < 0.05). The calibration curves and Hosmer–Lemeshow tests confirmed a high degree of fit, and the decision curve analysis demonstrated clinical value and potential patient benefit. Conclusions The predictive model constructed using SR reconstructed ultrasound images demonstrated superior performance in predicting preoperative cervical lymph node metastasis in PTC compared to OR images. The nomogram prediction model based on SR images has the potential to enhance the accuracy of predictive models and aid in clinical decision-making. Supplementary Information The online version contains supplementary material available at 10.1007/s12672-024-01601-0.

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Ultrasound-Base Radiomics for Discerning Lymph Node Metastasis in Thyroid Cancer: A Systematic Review and Meta-analysis

Zhang,

Liu,

Wang

et al. 2024

Academic Radiology

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Value of clinical features combined with multimodal ultrasound in predicting lymph node metastasis in cervical central area of papillary thyroid carcinoma

Cited by 4 publications

References 21 publications

Quantitative ultrasound radiomics analysis to evaluate lymph nodes in patients with cancer: a systematic review

Quantitative ultrasound radiomics analysis to evaluate lymph nodes in patients with cancer: a systematic review

Nomogram for predicting cervical lymph node metastasis of papillary thyroid carcinoma using deep learning-based super-resolution ultrasound image

Ultrasound-Base Radiomics for Discerning Lymph Node Metastasis in Thyroid Cancer: A Systematic Review and Meta-analysis

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