Can Diffusion-Weighted MR Imaging Be Used as a Tool to Predict Extrathyroidal Extension in Papillary Thyroid Carcinoma?

MRI-based model to predict preoperative extrathyroidal extension in papillary thyroid carcinoma

OBJECTIVE

This study aimed to develop and validate a predictive model for preoperative extrathyroidal extension (ETE) in papillary thyroid carcinoma (PTC) using MRI features.

METHODS

We retrospectively analyzed 140 confirmed PTC cases, divided into training (n = 84) and validation (n = 56) groups. MRI features such as T2-weighted imaging, multiphase contrast-enhanced MRI, and diffusion-weighted imaging were evaluated along with clinical data. Univariate and multivariate logistic regression identified independent predictors of ETE and developed a predictive nomogram. We evaluated the nomogram's discrimination, calibration, and clinical utility, and performed subgroup analyses to explore the relationships between risk factors and baseline data. Predictive performance was assessed using ROC curves and DeLong tests.

RESULTS

Age, protrusion value, and apparent diffusion coefficient_Brightest_rate (ADC_Best_rate) were independent predictors of ETE. The nomogram effectively differentiated ETE from no-ETE, showing strong discrimination, clinical utility, and calibration in both the training (AUC = 0.826, Hosmer-Lemeshow p = 0.882) and validation cohorts (AUC = 0.805, Hosmer-Lemeshow p = 0.585). The model performed consistently across different MRI systems (1.5 T and 3.0 T) and gender subgroups. Notably, ADC_Best_rate (AUC = 0.742) outperformed ADC_mean_rate and ADC_minimum_rate. A significant interaction between ADC_Best_rate and gender (p = 0.02) showed that ADC_Best_rate predicted ETE in PTC more accurately in males (AUC = 0.897) compared to females (AUC = 0.644).

CONCLUSION

Our nomogram model, incorporating age, protrusion value, and ADC_Best_rate, effectively predicted preoperative ETE in PTC patients, aiding surgeons in optimizing therapeutic decision-making. ADC_Best_rate may be a promising potential indicator in MRI functional imaging.

KEY POINTS

Question This study addresses the challenge of accurately predicting extrathyroidal extension (ETE) in papillary thyroid carcinoma (PTC) to improve surgical decision-making. Findings A predictive nomogram incorporating age, protrusion value, and ADC_Best_rate effectively differentiates ETE from no-ETE, showing strong performance in both training and validation cohorts. Clinical relevance This nomogram aids surgeons in identifying patients at risk for ETE, enhancing therapeutic decision-making and potentially improving patient outcomes in PTC management.

Strain Elastography: A Non-Invasive Modality for Extrathyroidal Extension Assessment in Papillary Thyroid Cancer

This study investigated the value of strain elastography (SE) in assessing extrathyroidal extension (ETE) in papillary thyroid cancer (PTC). A total of 277 PTC nodules from 245 patients were included. SE demonstrated higher consistency with pathology compared to US (Kappa value: 0.735 vs. 0.562, p = 0.000, respectively). SE also showed superior specificity (91.4% vs. 74.1%, p = 0.000) and accuracy (87.7% vs. 78.3%, p = 0.003) in diagnosing ETE. The area under the curve for SE was significantly higher than for US (0.865 vs. 0.798, p = 0.006). Age and calcification influenced SE's diagnostic performance. SE is a promising noninvasive tool for ETE assessment in PTC.

The evolving role of MRI in the detection of extrathyroidal extension of papillary thyroid carcinoma: a systematic review and meta-analysis

BACKGROUND

Papillary thyroid carcinoma (PTC) is the predominant form of thyroid cancer, and the presence of extrathyroidal extension (ETE) significantly impacts treatment decisions and prognosis. Accurate preoperative detection of ETE remains challenging, highlighting the need to evaluate advanced imaging techniques.This systematic review and meta-analysis aimed to investigate the diagnostic accuracy of magnetic resonance imaging (MRI) in detecting extrathyroidal extension (ETE) among patients diagnosed with papillary thyroid carcinoma (PTC).

METHOD

We conducted a comprehensive search of global databases including PubMed, Web of Science, EMBASE, and the Cochrane Library, spanning from inception to November 03, 2024. We included studies that utilized preoperative MRI to evaluate the presence of ETE. Quality assessment was carried out using the Joanna Briggs Institute (JBI) standard checklists. Data analysis was performed using Comprehensive Meta-Analysis (CMA) software version 3. The study protocol was registered in PROSPERO (CRD42024499536).

RESULT

Six studies were included in our final quantitative analysis. The included studies were classified into two groups; the first group focused on evaluating the accuracy of MRI in detecting ETE, while the second group assessed the apparent diffusion coefficient (ADC). The accuracy of MRI for overall ETE, minimal ETE (mETE), and gross ETE (gETE) was 81.0% (95% CI: 76.9%-85.6%), 72.9% (95% CI: 66.2%-78.6%), and 83.3% (95% CI: 75.2%-89.1%), respectively. MRI demonstrated a statistically significant difference in detecting gETE compared to mETE (OR = 1.85, 95% CI: 1.01-3.37, P-value = 0.045). Our analysis showed that the ADC of the lesion for b-value 500 is lower in patients with ETE compared to those without ETE (SMD = 0.95, 95% CI: 0.28-1.62, P-value = 0.005).

CONCLUSION

Our findings demonstrate that MRI has substantial accuracy in detecting ETE in PTC, especially for gross ETE. This suggests MRI could be a valuable tool in preoperative planning, helping to guide surgical decision-making by more precisely identifying patients at higher risk. However, the limited number of studies underscores the need for further research to confirm MRI's role in routine clinical practice and to refine imaging protocols for more accurate differentiation between minimal and gross ETE.

Three-dimensional ultrasound-based radiomics nomogram for the prediction of extrathyroidal extension features in papillary thyroid cancer

Purpose

To develop and validate a three-dimensional ultrasound (3D US) radiomics nomogram for the preoperative prediction of extrathyroidal extension (ETE) in papillary thyroid cancer (PTC).

Methods

This retrospective study included 168 patients with surgically proven PTC (non-ETE, n = 90; ETE, n = 78) who were divided into training (n = 117) and validation (n = 51) cohorts by a random stratified sampling strategy. The regions of interest (ROIs) were obtained manually from 3D US images. A larger number of radiomic features were automatically extracted. Finally, a nomogram was built, incorporating the radiomics scores and selected clinical predictors. Receiver operating characteristic (ROC) curves were performed to validate the capability of the nomogram on both the training and validation sets. The nomogram models were compared with conventional US models. The DeLong test was adopted to compare different ROC curves.

Results

The area under the receiver operating characteristic curve (AUC) of the radiologist was 0.67 [95% confidence interval (CI), 0.580-0.757] in the training cohort and 0.62 (95% CI, 0.467-0.746) in the validation cohort. Sixteen features from 3D US images were used to build the radiomics signature. The radiomics nomogram, which incorporated the radiomics signature, tumor location, and tumor size showed good calibration and discrimination in the training cohort (AUC, 0.810; 95% CI, 0.727-0.876) and the validation cohort (AUC, 0.798; 95% CI, 0.662-0.897). The result suggested that the diagnostic efficiency of the 3D US-based radiomics nomogram was better than that of the radiologist and it had a favorable discriminate performance with a higher AUC (DeLong test: p < 0.05).

Conclusions

The 3D US-based radiomics signature nomogram, a noninvasive preoperative prediction method that incorporates tumor location and tumor size, presented more advantages over radiologist-reported ETE statuses for PTC.

Nomogram incorporating preoperative clinical and ultrasound indicators to predict aggressiveness of solitary papillary thyroid carcinoma

Objective

To construct a nomogram based on preoperative clinical and ultrasound indicators to predict aggressiveness of solitary papillary thyroid carcinoma (PTC).

Methods

Preoperative clinical and ultrasound data from 709 patients diagnosed with solitary PTC between January 2017 and December 2020 were analyzed retrospectively. Univariate and multivariate logistic regression analyses were performed to identify the factors associated with PTC aggressiveness, and these factors were used to construct a predictive nomogram. The nomogram's performance was evaluated in the primary and validation cohorts.

Results

The 709 patients were separated into a primary cohort (n = 424) and a validation cohort (n = 285). Univariate analysis in the primary cohort showed 13 variables to be associated with aggressive PTC. In multivariate logistic regression analysis, the independent predictors of aggressive behavior were age (OR, 2.08; 95% CI, 1.30-3.35), tumor size (OR, 4.0; 95% CI, 2.17-7.37), capsule abutment (OR, 2.53; 95% CI, 1.50-4.26), and suspected cervical lymph nodes metastasis (OR, 2.50; 95% CI, 1.20-5.21). The nomogram incorporating these four predictors showed good discrimination and calibration in both the primary cohort (area under the curve, 0.77; 95% CI, 0.72-0.81; Hosmer-Lemeshow test, P = 0.967 and the validation cohort (area under the curve, 0.72; 95% CI, 0.66-0.78; Hosmer-Lemeshow test, P = 0.251).

Conclusion

The proposed nomogram shows good ability to predict PTC aggressiveness and could be useful during treatment decision making.

Advances in knowledge

Our nomogram-based on four indicators-provides comprehensive assessment of aggressive behavior of PTC and could be a useful tool in the clinic.

Diagnostic value of multi-parameter MRI and colour B-ultrasound elastography in benign and malignant thyroid nodules

BACKGROUND

The early diagnosis of thyroid cancer depends on the popularisation and development of diagnostic imaging techniques and the continuous improvement of physician diagnosis.

OBJECTIVE

To investigate the clinical value of multi-parameter magnetic resonance imaging (MRI) and colour B-ultrasound elastography in thyroid nodules.

METHODS

The clinical and imaging data of 252 patients with thyroid nodules who were admitted to our hospital were collected. All patients underwent preoperative colour B-ultrasound elastography and MRI. The postoperative pathological results were the gold standard for diagnosing benign and malignant thyroid nodules. The accuracy, sensitivity and specificity of MRI, colour B-ultrasound elastography and their combination for diagnosing benign and malignant thyroid nodules were compared.

RESULTS

This study included 252 patients with 388 nodules. There were 169 patients with solitary nodules and 83 patients with multiple nodules. The maximum diameter of the thyroid nodules was 0.32-1.00 (0.75 ± 0.20) cm. The accuracy of MRI diagnosis (348/388) was 89.69%, the sensitivity was 92.98%, and the specificity was 65.22%. The diagnostic accuracy, sensitivity and specificity of colour B-ultrasound elastography (332/388) were 85.57%, 88.30% and 65.22%, respectively. The accuracy rate of combined diagnosis (376/388) was 96.91%, the sensitivity was 98.25%, and the specificity was 86.96%, which was significantly higher than MRI and colour B-ultrasound elastography alone. The area under the curve (AUC) of MRI, colour B-ultrasound elastography and combined diagnosis were 0.768, 0.791 and 0.926, respectively. The AUC of the three diagnostic methods was > 0.7, indicating that the three diagnostic methods had good diagnostic value. The AUC for combined diagnosis was significantly higher than that of MRI and colour B-mode ultrasound elastography alone.

CONCLUSION

Combined ultrasound and MRI have high diagnostic accuracy and specificity for benign and malignant thyroid nodules. This diagnostic method can be applied in clinical practice.

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.

Prediction of Central Lymph Node Metastasis in cN0 Papillary Thyroid Carcinoma by CT Radiomics

RATIONALE AND OBJECTIVES

To explore the feasibility of the preoperative prediction of pathological central lymph node metastasis (CLNM) status in patients with negative clinical lymph node (cN0) papillary thyroid carcinoma (PTC) using a computed tomography (CT) radiomics signature.

MATERIALS AND METHODS

A total of 97 PTC cN0 nodules with CLNM pathology data (pN0, with CLNM, n = 59; pN1, without CLNM, n = 38) in 85 patients were divided into a training set (n = 69) and a validation set (n = 28). For each lesion, 321 radiomic features were extracted from nonenhanced, arterial and venous phase CT images. Minimum redundancy and maximum relevance and the least absolute shrinkage and selection operator were used to find the most important features with which to develop a radiomics signature in the training set. The performance of the radiomics signature was evaluated by receiver operating characteristic curves, calibration curves and decision curve analysis .

RESULTS

Three nonzero the least absolute shrinkage and selection operator coefficient features were selected for radiomics signature construction. The radiomics signature for distinguishing the pN0 and pN1 groups achieved areas under the curve of 0.79 (95% CI 0.67, 0.91) in the training set and 0.77 (95% CI 0.55, 0.99) in the validation set. The calibration curves demonstrated good agreement between the radiomics score-predicted probability and the pathological results in the two sets (p= 0.399, p = 0.191). The decision curve analysis curves showed that the model was clinically useful.

CONCLUSION

This radiomic signature could be helpful to predict CLNM status in cN0 PTC patients.

Optimising diffusion-weighted imaging of the thyroid gland using dedicated surface coil

AIM

To assess the feasibility of applying field-of-view (FOV) optimised and constrained undistorted single-shot (FOCUS) diffusion-weighted imaging (DWI) in the thyroid gland by comparing its image quality with conventional DWI (C-DWI) qualitatively and quantitatively using a dedicated surface coil exclusively designed for the thyroid gland at 3 T magnetic resonance imaging (MRI).

MATERIALS AND METHODS

In this prospective study, 32 healthy volunteers who had undergone 3 T the thyroid gland MRI with FOCUS-DWI and C-DWI were enrolled. Two independent reviewers assessed the overall image quality, artefacts, sharpness, and geometric distortion based on a five-point Likert scale. The signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and apparent diffusion coefficient (ADC) were quantified for both sequences. Interobserver agreement, qualitative scores, and quantitative parameters were compared between two sequences.

RESULTS

Agreement between the two readers was good for FOCUS-DWI (κ = 0.714-0.778) and moderate to good for C-DWI (κ = 0.525-0.672) in qualitative image quality assessment. Qualitatively, image quality (overall image quality, artefacts, sharpness, and geometric distortion) was significantly better in FOCUS-DWI than that in the C-DWI (all p<0.05); however, quantitatively, FOCUS-DWI had significantly lower SNRs (p<0.001) and CNRs (p=0.012) compared with C-DWI. The ADC value on FOCUS-DWI was significantly higher than that on C-DWI (p<0.001).

CONCLUSION

FOCUS-DWI depicted the thyroid gland with significantly better image quality qualitatively and less ghost artefacts, but had significantly lower SNR and CNR quantitatively, compared with C-DWI, suggesting that both DWI sequences have advantages and could be chosen for different purposes.

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.

Discrimination between malignant and benign thyroid tumors by diffusion-weighted imaging - A systematic review and meta analysis

PURPOSE

Magnetic resonance imaging is used to stage thyroid tumors. Diffusion weighted imaging (DWI) and apparent diffusion coefficient (ADC) can be used to reflect tumor microstructure. Our aim was to compare ADC values of malignant and benign thyroid lesions based on a large sample.

METHODS

MEDLINE library, EMBASE and SCOPUS databases were screened for the associations between ADC values and thyroid lesions up to August 2021. The primary endpoint of the systematic review were ADC values of benign and malignant thyroid lesions. In total, 29 studies were suitable for the analysis and were included into the present study.

RESULTS

The included studies comprised a total of 2137 lesions, 1118 (52.3%) benign and 1019 (47.7%) malignant lesions. The pooled mean ADC value of the benign thyroid lesions was 1.88 × 10^-3 mm²/s [95% CI 1.77-2.0] and the pooled mean ADC value of malignant thyroid lesions was 1.15 × 10^-3 mm²/s [95% CI 1.04-1.25].

CONCLUSIONS

ADC can well discriminate benign and malignant thyroid tumors. Therefore, DWI should be implemented into the presurgical diagnostic work-up in clinical routine.

Diffusion-weighted MRI of ischemic stroke at 3T: Value of synthetic b-values

OBJECTIVES

Diffusion-weighted imaging (DWI) plays a crucial role in the diagnosis of ischemic stroke. We assessed the value of computed and acquired high b-value DWI in comparison with conventional b = 1000 s mm^-2 DWI for ischemic stroke at 3T.

METHODS

We included 36 patients with acute ischemic stroke who presented with diffusion abnormalities on DWI performed within 24 h of symptom onset. B-values of 0, 500, 1000 and 2000 s mm^-2 were acquired. Synthetic images with b-values of 1000, 1500, 2000 and 2500 s mm^-2 were computed. Two readers compared synthetic (syn) and acquired (acq) b = 2000 s mm^-2 images with acquired b = 1000 s mm^-2 images in terms of lesion detection rate, image quality, presence of uncertain hyperintensities and lesion conspicuity. Readers also selected their preferred b-value. Contrast ratio (CR) measurements were performed. Non-parametrical statistical tests and weighted Cohens' κ tests were computed.

RESULTS

Syn1000 and syn1500 matched acq1000 images in terms of lesion detection rate, image quality and presence of uncertain hyperintensities but presented with significantly improved lesion conspicuity (p < 0.01) and were frequently selected as preferred b-values. Acq2000 images exhibited a similar lesion detection rate and improved lesion conspicuity (p < 0.01) but worse image quality (p < 0.01) than acq1000 images. Syn2000 and syn2500 images performed significantly worse (p < 0.01) than acq1000 images in most or all categories. CR significantly increased with increasing b-values.

CONCLUSION

Synthetic images at b = 1000 and 1500 s mm^-2 and acquired DWI images at b = 2000 s mm^-2 may be of clinical value due to improved lesion conspicuity.

ADVANCES IN KNOWLEDGE

Synthetic b-values enable improved lesion conspicuity for DWI of ischemic stroke.

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.