Histogram-based analysis of diffusion-weighted imaging for predicting aggressiveness in papillary thyroid carcinoma.

BACKGROUND: To assess the potential of apparent diffusion coefficient (ADC) map in predicting aggressiveness of papillary thyroid carcinoma (PTC) based on whole-tumor histogram-based analysis. METHODS: A total of 88 patients with PTC confirmed by pathology, who underwent neck magnetic resonance imaging, were enrolled in this retrospective study. Whole-lesion histogram features were extracted from ADC maps and compared between the aggressive and non-aggressive groups. Multivariable logistic regression analysis was performed for identifying independent predictive factors. Receiver operating characteristic curve analysis was used to evaluate the performances of significant factors, and an optimal predictive model for aggressiveness of PTC was developed. RESULTS: The aggressive and non-aggressive groups comprised 67 (mean age, 44.03 ± 13.99 years) and 21 (mean age, 43.86 ± 12.16 years) patients, respectively. Five histogram features were included into the final predictive model. ADC_firstorder_TotalEnergy had the best performance (area under the curve [AUC] = 0.77). The final combined model showed an optimal performance, with AUC and accuracy of 0.88 and 0.75, respectively. CONCLUSIONS: Whole-lesion histogram analysis based on ADC maps could be utilized for evaluating aggressiveness in PTC.

Multimodality MRI-based radiomics for aggressiveness prediction in papillary thyroid cancer.

OBJECTIVE: To investigate the ability of a multimodality MRI-based radiomics model in predicting the aggressiveness of papillary thyroid carcinoma (PTC). METHODS: This study included consecutive patients who underwent neck magnetic resonance (MR) scans and subsequent thyroidectomy during the study period. The pathological diagnosis of thyroidectomy specimens was the gold standard to determine the aggressiveness. Thyroid nodules were manually segmented on three modal MR images, and then radiomics features were extracted. A machine learning model was established to evaluate the prediction of PTC aggressiveness. RESULTS: The study cohort included 107 patients with PTC confirmed by pathology (cross-validation cohort: n = 71; test cohort: n = 36). A total of 1584 features were extracted from contrast-enhanced T1-weighted (CE-T1 WI), T2-weighted (T2 WI) and diffusion weighted (DWI) images of each patient. Sparse representation method is used for radiation feature selection and classification model establishment. The accuracy of the independent test set that using only one modality, like CE-T1WI, T2WI or DWI was not particularly satisfactory. In contrast, the result of these three modalities combined achieved 0.917. CONCLUSION: Our study shows that multimodality MR image based on radiomics model can accurately distinguish aggressiveness in PTC from non-aggressiveness PTC before operation. This method may be helpful to inform the treatment strategy and prognosis of patients with aggressiveness PTC.

Radiomics based on multiparametric MRI for extrathyroidal extension feature prediction in papillary thyroid cancer.

BACKGROUND: To determine the predictive capability of MRI-based radiomics for extrathyroidal extension detection in papillary thyroid cancer (PTC) pre-surgically. METHODS: The present retrospective trial assessed individuals with thyroid nodules examined by multiparametric MRI and subsequently administered thyroid surgery. Diagnosis and extrathyroidal extension (ETE) feature of PTC were based on pathological assessment. The thyroid tumors underwent manual segmentation, for radiomic feature extraction. Participants were randomized to the training and testing cohorts, at a ratio of 7:3. The mRMR (maximum correlation minimum redundancy) algorithm and the least absolute shrinkage and selection operator were utilized for radiomics feature selection. Then, a radiomics predictive model was generated via a linear combination of the features. The model's performance in distinguishing the ETE feature of PTC was assessed by analyzing the receiver operating characteristic curve. RESULTS: Totally 132 patients were assessed in this study, including 92 and 40 in the training and test cohorts, respectively). Next, the 16 top-performing features, including 4, 7 and 5 from diffusion weighted (DWI), T2-weighted (T2 WI), and contrast-enhanced T1-weighted (CE-T1WI) images, respectively, were finally retained to construct the radiomics signature. There were 8 RLM, 5 CM, 2 shape, and 1 SZM features. The radiomics prediction model achieved AUCs of 0.96 and 0.87 in the training and testing sets, respectively. CONCLUSIONS: Our study indicated that MRI radiomics approach had the potential to stratify patients based on ETE in PTCs preoperatively.

MRI-based radiomics analysis to predict preoperative lymph node metastasis in papillary thyroid carcinoma.

BACKGROUND: The aim of the present study was to develop a magnetic resonance imaging (MRI) radiomics model and evaluate its clinical value in predicting preoperative lymph node metastasis (LNM) in patients with papillary thyroid carcinoma (PTC). METHODS: Data of 129 patients with histopathologically confirmed PTC were retrospectively reviewed in our study (90 in training group and 39 in testing group). 395 radiomics features were extracted from T2 weighted imaging (T2WI), diffusion weighted imaging (DWI) and T1 weighted multiphase contrast enhancement imaging (T1C+) respectively. Minimum redundancy maximum relevance (mRMR) was used to eliminate irrelevant and redundant features and least absolute shrinkage and selection operator (LASSO), to additionally select an optimized features' subset to construct the radiomics signature. Predictive performance was validated using receiver operating characteristic curve (ROC) analysis, while decision curve analyses (DCA) were conducted to evaluate the clinical worth of the four models according to different sequences. A radiomics nomogram was built using multivariate logistic regression model. The nomogram's performance was assessed and validated in the training and validation cohorts, respectively. RESULTS: Seven key features were selected from T2WI, five from DWI, ten from T1C+ and seven from the combined images. The scores (Rad-scores) of patients with LNM were significantly higher than patients with non-LNM in both the training cohort and the validation cohort. The combined model performed better than the T2WI, DWI, and T1C+ models alone in both cohorts. In the training cohort, the area under the ROC (AUC) values of T2WI, DWI, T1C+ and combined features were 0.819, 0.826, 0.808, and 0.835, respectively; corresponding values in the validation cohort were 0.798, 0.798, 0.789, and 0.830. The clinical utility of the combined model was confirmed using the radiomics nomogram and DCA. CONCLUSIONS: MRI radiomic model based on anatomical and functional MRI images could be used as a non-invasive biomarker to identify PTC patients at high risk of LNM, which could help to develop individualized treatment strategies in clinical practice.

Machine learning-based multiparametric MRI radiomics for predicting the aggressiveness of papillary thyroid carcinoma.

PURPOSE: To investigate the predictive capability of machine learning-based multiparametric magnetic resonance (MR) imaging radiomics for evaluating the aggressiveness of papillary thyroid carcinoma (PTC) preoperatively. METHODS: This prospective study enrolled consecutive patients who underwent neck MR scans and subsequent thyroidectomy during the study interval. The diagnosis and aggressiveness of PTC were determined by pathological evaluation of thyroidectomy specimens. Thyroid nodules were segmented manually on the MR images, and radiomic features were then extracted. Predictive machine learning modelling was used to evaluate the prediction of PTC aggressiveness. Area under the receiver operating characteristic curve (AUC) values for the model performance were obtained for radiomic features, clinical characteristics, and combinations of radiomic features and clinical characteristics. RESULTS: The study cohort included 120 patients with pathology-confirmed PTC (training cohort: n = 96; testing cohort: n = 24). A total of 1393 features were extracted from T2-weighted, apparent diffusion coefficient (ADC) and contrast-enhanced T1-weighted MR images for each patient. The combination of Least Absolute Shrinkage and Selection Operator for radiomic feature selection and Gradient Boosting Classifier for classifying PTC aggressiveness achieving the AUC of 0.92. In contrast, clinical characteristics alone poorly predicted PTC aggressiveness, with an AUC of 0.56. CONCLUSIONS: Our study showed that machine learning-based multiparametric MR imaging radiomics could accurately distinguish aggressive from non-aggressive PTC preoperatively. This approach may be helpful for informing treatment strategies and prognosis of patients with aggressive PTC.

Magnetic resonance imaging in the prediction of aggressive histological features in papillary thyroid carcinoma.

To identify magnetic resonance imaging (MRI) features in the prediction of tumor aggressiveness in patients with papillary thyroid carcinoma (PTC).In this prospective study, 105 patients with 122 PTCs underwent MRI with T1-weighted, T2-weighted, diffusion-weighted imaging and contrast-enhanced sequences prior to thyroidectomy. Based on exclusion criteria, 62 patients with 62 PTCs were finally suitable for further analysis. Tumor aggressiveness was defined according to the surgical histopathology. Tumor size, apparent diffusion coefficients (ADC) value and MRI features on images were obtained for each patient. Descriptive statistics for tumor aggressiveness, sensitivity, specificity, and accuracy of individual features were determined. A multivariate logistic regression model was developed to identify features that were independently predictive for tumor aggressiveness. Analyses of receiver-operating characteristic (ROC) curve were performed.High aggressive PTC significantly differed from low aggressive PTC in size (P = .016), size classification (P < .001), ADC value (P = .01), angulation on the lateral surface of the lesion (P = .009), signal intensity heterogeneity on ADC maps (P = .003), early enhancement degree (P < .001), tumor margin on delayed contrast-enhanced images (P < .001), and inner lining of delayed ring enhancement (P = .028). The interobserver agreement between the 2 readers was satisfactory with Cohen k ranging from 0.83 to 1.00 (P < .001). Logistic regression model showed lesion size classification and tumor margin on delayed contrast-enhanced images as strongest independent predictors of high aggressive PTC (P = .009 and P = .047), with an accuracy of 83.9%. The area under ROC curve for ADC value and lesion size were 0.68 and 0.81, respectively.These findings suggest that MRI before surgery has the potential to discriminate tumor aggressiveness in patients with PTC.