Computed Tomography Radiomic Nomogram for Preoperative Prediction of Extrathyroidal Extension in Papillary Thyroid Carcinoma

Chen, Bin; Zhong, Lianzhen; Dong, Di; Zheng, Jianjun; Fang, Mengjie; Yu, Chunyao; Dai, Qi; Zhang, Liwen; Tian, Jie; Lu, Wei; Jin, Yinhua

doi:10.3389/fonc.2019.00829

Cited by 19 publications

(15 citation statements)

References 40 publications

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“…In the validation cohort, the prediction of central neck LN metastasis in PTC patients with US radiomics nomogram displayed significantly higher accuracy (0.812 vs. 0.653; P < 0.01), sensitivity (0.816 vs. 0.134; P < 0.01), and AUC (0.858 vs. 0.529; P < 0.01) than those of conventional US which was conducted by several US clinicians with more than 10 years' experience in the thyroid ultrasonography. This suggests that the machinelearning based radiomics is superior to experienced clinicians once enough clinical risk information has been provided, which was consistent with previous findings from the artificial intelligence studies on thyroid tumors (62,63). The predictive efficacy of this model in the validation cohort was compared with previously reported, and results showed the advantage of this model: the sensitivity of this model was 0.816, but that of combined use of CT and US was 0.33-0.66 (15)(16)(17)(18); the AUC of this model was 0.858, but the AUC of models established based on different clinical parameters was 0.706-0.764 (41)(42)(43)(44).…”

Section: Figure 1 |supporting

confidence: 89%

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 |supporting

confidence: 89%

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

et al. 2020

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“…To evaluate the incremental utility of the constructed classifiers, the decision curve of the radiomics model was plotted for both datasets ( 28 ). The net benefit was computed by subtracting the proportion of false-positive (FP) patients from the proportion of true-positive patients (TP), weighted by the relative harm of false-negative (FN) and false-positive results.…”

Section: Methodsmentioning

confidence: 99%

“…If the net benefit values of the model are greater than those of the two reference schemes, the model will show a clinical benefit ( 23 ). Through a threshold probability, the decision curve indicates which of the given models is the best for a patient or clinician ( 28 ).…”

Section: Methodsmentioning

confidence: 99%

Multi-Parametric Magnetic Resonance Imaging-Based Radiomics Analysis of Cervical Cancer for Preoperative Prediction of Lymphovascular Space Invasion

et al. 2022

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BackgroundDetection of lymphovascular space invasion (LVSI) in early cervical cancer (CC) is challenging. To date, no standard clinical markers or screening tests have been used to detect LVSI preoperatively. Therefore, non-invasive risk stratification tools are highly desirable.ObjectiveTo train and validate a multi-parametric magnetic resonance imaging (mpMRI)-based radiomics model to detect LVSI in patients with CC and investigate its potential as a complementary tool to enhance the efficiency of risk assessment strategies.Materials and MethodsThe model was developed from the tumor volume of interest (VOI) of 125 patients with CC. A total of 1037 radiomics features obtained from conventional magnetic resonance imaging (MRI), including a small field-of-view (sFOV) high-resolution (HR)-T2-weighted MRI (T2WI), apparent diffusion coefficient (ADC), T2WI, fat-suppressed (FS)-T2WI, as well as axial and sagittal contrast-enhanced T1-weighted MRI (T1c). We conducted a radiomics-based characterization of each tumor region using pretreatment image data. Feature selection was performed using the least absolute shrinkage and selection operator method on the training set. The predictive performance was compared with single variates (clinical data and single-layer radiomics signatures) analyzed using a receiver operating characteristic (ROC) curve. Three-fold cross-validation performed 20 times was used to evaluate the accuracy of the trained classifiers and the stability of the selected features. The models were validated by using a validation set.ResultsFeature selection extracted the six most important features (3 from sFOV HR-T2WI, 1 T2WI, 1 FS-T2WI, and 1 T1c) for model construction. The mpMRI-combined radiomics model (area under the curve [AUC]: 0.940) reached a significantly higher performance (better than the clinical parameters [AUC: 0.730]), including any single-layer model using sFOV HR-T2WI (AUC: 0.840), T2WI (AUC: 0.770), FS-T2WI (AUC: 0.710), ADC maps (AUC: 0.650), sagittal, and axial T1c values (AUC: 0.710, 0.680) in the validation set.ConclusionBiomarkers using multi-parametric radiomics features derived from preoperative MR images could predict LVSI in patients with CC.

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“…Chen et al designed a CT radiomic model to predict ETE preoperatively in patients with PTC. The result had an adaptive AUC of 0.837 [ 41 ].…”

Section: Radiomics In Endocrine Neoplasmsmentioning

confidence: 99%

Machine Learning: Applications and Advanced Progresses of Radiomics in Endocrine Neoplasms

Wang

Zhang

et al. 2021

Journal of Oncology

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Endocrine neoplasms remain a great threat to human health. It is extremely important to make a clear diagnosis and timely treatment of endocrine tumors. Machine learning includes radiomics, which has long been utilized in clinical cancer research. Radiomics refers to the extraction of valuable information by analyzing a large amount of standard data with high-throughput medical images mainly including computed tomography, positron emission tomography, magnetic resonance imaging, and ultrasound. With the quantitative imaging analysis and model building, radiomics can reflect specific underlying characteristics of a disease that otherwise could not be evaluated visually. More and more promising results of radiomics in oncological practice have been seen in recent years. Radiomics may have the potential to supplement traditional imaging analysis and assist in providing precision medicine for patients. Radiomics had developed rapidly in endocrine neoplasms practice in the past decade. In this review, we would introduce the general workflow of radiomics and summarize the applications and developments of radiomics in endocrine neoplasms in recent years. The limitations of current radiomic research studies and future development directions would also be discussed.

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Computed Tomography Radiomic Nomogram for Preoperative Prediction of Extrathyroidal Extension in Papillary Thyroid Carcinoma

Cited by 19 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

Multi-Parametric Magnetic Resonance Imaging-Based Radiomics Analysis of Cervical Cancer for Preoperative Prediction of Lymphovascular Space Invasion

Machine Learning: Applications and Advanced Progresses of Radiomics in Endocrine Neoplasms

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