Magnetic Resonance Imaging Characteristics of Papillary Thyroid Carcinoma for the Prediction of Cervical Central Compartment Lymph Node Metastasis

Exploration of the Evaluation Value of Combined Magnetic Resonance Imaging and Ultrasound Blood Flow Parameters in Cervical Lymph Node Metastasis of Thyroid Cancer

Background

Thyroid cancer exhibits the highest cervical lymph node metastasis rate (20-50%) among head and neck malignancies, with occult metastasis occurring in 30-80% of papillary carcinoma cases. However, conventional single-modality imaging faces certain challenges: MRI has limited sensitivity for detecting micro-metastases (<2mm), while Doppler ultrasound may overlook metastases in isoechoic lymph nodes. Therefore, it is crucial to evaluate the diagnostic value of combining MRI and CDUS. This study aims to retrospectively analyze the diagnostic value of combining MRI and CDUS blood flow parameters in detecting cervical lymph node metastasis in thyroid cancer and to compare the diagnostic performance with MRI or CDUS alone.

Objective

To analyze the evaluation value of combining MRI and color Doppler ultrasound (CDUS) blood flow parameters in detecting cervical lymph node metastasis of thyroid cancer, particularly for occult metastases.

Methods

A retrospective analysis was conducted on 263 thyroid cancer patients (June 2022-June 2024). Diagnostic consistency between MRI, CDUS parameters (resistive index, pulsatility index, vascular patterns) and pathology were compared. Multimodal evaluation criteria were established: (1) MRI positive signs (lymph node diameter >8mm, cystic change, enhancement heterogeneity) (2) CDUS thresholds (RI≥0.75, PI≥1.25 with chaotic vascularity).

Results

Among 263 patients, 98 had pathologically confirmed metastases. CDUS showed higher consistency with pathology (Kappa=0.783) than MRI (Kappa=0.645). Combined modality achieved 94.9% sensitivity vs 86.7% (CDUS) and 78.6% (MRI), with accuracy improving from 82.1%/75.3% to 89.4% (P<0.05). Notably, 12/22 occult metastases (≤3mm) were only detected by combined approach.

Conclusion

The synergistic combination leverages MRI's structural characterization and CDUS's hemodynamic sensitivity, effectively overcoming single-modality limitations in detecting micro-metastases. This dual-assessment protocol addresses thyroid cancer's propensity for early lymphatic spread, providing critical preoperative staging guidance.

MRI-Based Texture Analysis for Preoperative Prediction of BRAF V600E Mutation in Papillary Thyroid Carcinoma

Purpose

BRAF V600E mutation can compensate for the low detection rate by fine-needle aspiration (FNA) and is related to aggressiveness and lymph node metastasis. This study aimed to investigate the relationship between texture analysis features based on magnetic resonance imaging (MRI) and mutations.

Methods

Retrospective analysis was performed on patients with postoperative pathology confirmed papillary thyroid carcinoma (PTC) from 2017 to 2021. One thousand one hundred and thirty-two texture features were extracted from T2-weighted imaging (T2WI) and contrast-enhanced T1-weighted imaging (CE-T1WI) separately by outlining the tumor volume of interest (VOI). Univariate, minimum redundancy maximum relevance (mRMR), and multivariate analyses were used for feature selection to construct 3 models (T2WI, CE-T1WI, and combined model) to predict mutation. The reproducibility between observers was evaluated by intraclass correlation coefficient (ICC). Receiver operating characteristic (ROC) analysis was used to assess the performance of models. The diagnostic performance of the optimal cut-off value of models were calculated and validated by 10-fold cross-validation.

Results

A total of 80 PTCs (22 BRAF V600E wild-type and 58 BRAF V600E mutant) were included in our study. Good interobserver agreement was found on texture features we selected (all ICCs >0.75). The area under the ROC curves (AUCs) for the T2WI model, CE-T1WI model, and combined model were 0.83 (95% CI: 0.75-0.91), 0.83 (95% CI: 0.73-0.90), and 0.88 (95% CI: 0.81-0.94), respectively. The accuracy, sensitivity, specificity, PPV, and NPV were 0.776, 0.679, 0.905, 0.905, and 0.679 for the T2WI model at a cut-off value of 0.674; 0.755, 0.750, 0.762, 0.808, and 0.696 for the CE-T1WI model at a cut-off value of 0.573; 0.816, 0.893, 0.714, 0.806, and 0.833 for the combined model at a cut-off value of 0.420.

Conclusion

MRI-based texture analysis could be a potential method for predicting BRAF V600E mutation in PTC preoperatively.

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.

Efficacy of radiofrequency ablation for metastatic papillary thyroid cancer with and without initial biochemical complete status

OBJECTIVE

The application of radiofrequency ablation (RFA) for recurrent thyroid cancer has been demonstrated to effectively manage lesions at critical locations, such as abutting the trachea, with limited complications. Comprehensive investigation of both biochemical (B) and structural (S) change after RFA remains limited. We herein present the first single-center experience of RFA for the treatment of locoregional recurrent thyroid cancer in Taiwan.

DESIGN

23 patients were enrolled, and the treatment responses after RFA were divided into four groups (E, S(+), B(+), and SB(+)), and then compared. The RFA technique, follow-up strategy, changes in pre-and post-operative status, and complications are presented. The volume reduction rate at 1, 3, and 6 months, and the differing responses between lesions abutting/not abutting the trachea are also discussed.

RESULTS

In patients with pre-RFA structural and biochemical incomplete (SB(+)) status, presenting with lesion with an initial maximum diameter of >3.2cm, a higher rate of structural incomplete status at the 6-month follow-up was noted in ROC analysis, with a sensitivity of 57% and specificity of 91%. Favorable structural remission after RFA was noted, and 60.9% of patients achieved biochemical complete status. No significant correlation was noted between the trachea-abutted lesion number and complete remission (p= 0.474). No significant difference in RFA efficacy was noted between the lesions abutting/not abutting the trachea.

CONCLUSIONS

This retrospective study reveals that RFA can achieve both structural and biochemical improvements for locoregionally recurrent thyroid cancer, with a low complication rate. Nearly half of the patients achieved an excellent response after RFA, while a favorable treatment response can be achieved despite the lesion abutting the trachea, with a mean VRR of 84.74%.

Magnetic resonance imaging (MRI) radiomics of papillary thyroid cancer (PTC): a comparison of predictive performance of multiple classifiers modeling to identify cervical lymph node metastases before surgery

PURPOSE

To compare predictive efficiency of multiple classifiers modeling and establish a combined magnetic resonance imaging (MRI) radiomics model for identifying lymph node (LN) metastases of papillary thyroid cancer (PTC) preoperatively.

MATERIALS AND METHODS

A retrospective analysis based on the preoperative MRI scans of 109 PTC patients including 77 patients with LN metastases and 32 patients without metastases was conducted, and we divided enroll cases into trained group and validation group. Radiomics signatures were selected from fat-suppressed T2-weighted MRI images, and the optimal characteristics were confirmed by spearman correlation test, hypothesis testing and random forest methods, and then, eight predictive models were constructed by eight classifiers. The receiver operating characteristic (ROC) curves analysis were performed to demonstrate the effectiveness of the models.

RESULTS

The area under the curve (AUC) of ROC based on MRI texture diagnosed LN status by naked eye was 0.739 (sensitivity = 0.571, specificity = 0.906). Based on the 5 optimal signatures, the best AUC of MRI radiomics model by logistics regression classifier had a considerable prediction performance with AUCs 0.805 in trained group and 0.760 in validation group, respectively, and a combination of best radiomics model with visual diagnosis of MRI texture had a high AUC as 0.969 (sensitivity = 0.938, specificity = 1.000), suggesting combined model had a preferable diagnostic efficiency in evaluating LN metastases of PTC.

CONCLUSION

Our combined radiomics model with visual diagnosis could be a potentially effective strategy to preoperatively predict LN metastases in PTC patients before clinical intervention.

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.