Efficacy of apparent diffusion coefficient in predicting aggressive histological features of papillary thyroid carcinoma

Role of advanced MRI sequences for thyroid lesions assessment. A narrative review

Despite not being the first imaging modality for thyroid gland assessment, Magnetic Resonance Imaging (MRI), thanks to its optimal tissue contrast and spatial resolution, has provided some advancements in detecting and characterizing thyroid abnormalities. Recent research has been focused on improving MRI sequences and employing advanced techniques for a more comprehensive understanding of thyroid pathology. Although not yet standard practice, advanced MRI sequences have shown high accuracy in preliminary studies, correlating well with histopathological results. They particularly show promise in determining malignancy risk in thyroid lesions, which may reduce the need for invasive procedures like biopsies. In this line, functional MRI sequences like Diffusion Weighted Imaging (DWI), Dynamic Contrast-Enhanced MRI (DCE-MRI), and Arterial Spin Labeling (ASL) have demonstrated their potential usefulness in evaluating both diffuse thyroid conditions and focal lesions. Multicompartmental DWI models, such as Intravoxel Incoherent Motion (IVIM) and Diffusion Kurtosis Imaging (DKI), and novel methods like Amide Proton Transfer (APT) imaging or artificial intelligence (AI)-based analyses are being explored for their potential valuable insights into thyroid diseases. This manuscript reviews the critical physical principles and technical requirements for optimal functional MRI sequences of the thyroid and assesses the clinical utility of each technique. It also considers future prospects in the context of advanced MR thyroid imaging and analyzes the current role of advanced MRI sequences in routine 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.

Texture and shape analysis of diffusion-weighted imaging for thyroid nodules classification using machine learning

PURPOSE

To assess whether the integration between (a) functional imaging features that will be extracted from diffusion-weighted imaging (DWI); and (b) shape and texture imaging features as well as volumetric features that will be extracted from T2-weighted magnetic resonance imaging (MRI) can noninvasively improve the diagnostic accuracy of thyroid nodules classification.

PATIENTS AND METHODS

In a retrospective study of 55 patients with pathologically proven thyroid nodules, T2-weighted and diffusion-weighted MRI scans of the thyroid gland were acquired. Spatial maps of the apparent diffusion coefficient (ADC) were reconstructed in all cases. To quantify the nodules' morphology, we used spherical harmonics as a new parametric shape descriptor to describe the complexity of the thyroid nodules in addition to traditional volumetric descriptors (e.g., tumor volume and cuboidal volume). To capture the inhomogeneity of the texture of the thyroid nodules, we used the histogram-based statistics (e.g., kurtosis, entropy, skewness, etc.) of the T2-weighted signal. To achieve the main goal of this paper, a fusion system using an artificial neural network (NN) is proposed to integrate both the functional imaging features (ADC) with the structural morphology and texture features. This framework has been tested on 55 patients (20 patients with malignant nodules and 35 patients with benign nodules), using leave-one-subject-out (LOSO) for training/testing validation tests.

RESULTS

The functionality, morphology, and texture imaging features were estimated for 55 patients. The accuracy of the computer-aided diagnosis (CAD) system steadily improved as we integrate the proposed imaging features. The fusion system combining all biomarkers achieved a sensitivity, specificity, positive predictive value, negative predictive value, F1-score, and accuracy of 92.9 % (confidence interval [CI]: 78.9 % -- 99.5 % ), 95.8 % (CI: 87.4 % -- 99.7 % ), 93 % (CI: 80.7 % -- 99.5 % ), 96 % (CI: 88.8 % -- 99.7 % ), 92.8 % (CI: 83.5 % -- 98.5 % ), and 95.5 % (CI: 88.8 % -- 99.2 % ), respectively, using the LOSO cross-validation approach.

CONCLUSION

The results demonstrated in this paper show the promise that integrating the functional features with morphology as well as texture features by using the current state-of-the-art machine learning approaches will be extremely useful for identifying thyroid nodules as well as diagnosing their malignancy.

Differentiating Giant Cell Glioblastoma from Classic Glioblastoma With Diffusion-Weighted Imaging

BACKGROUND

Differential diagnosis of giant cell glioblastoma (GC) and classic glioblastoma (GBM) using conventional radiological modalities is difficult. This study aimed to use diffusion-weighted imaging (DWI) to distinguish GC from GBM and thereby improve the accuracy of preoperative assessment of patients with GB.

METHODS

The clinical, magnetic resonance imaging, and pathologic data of 12 patients with GC and 21 patients with GBM were retrospectively analyzed. Independent sample t tests were used to compare the minimum apparent diffusion coefficient (ADC_min) and the normalized apparent diffusion coefficients (nADC) of the 2 tumor types. Receiver operating curve (ROC) analysis was used to assess the diagnostic efficacy of ADC_min and nADC values.

RESULTS

Compared with that of the classic GBM group, the ADC_min (0.98 ± 0.14 vs. 0.80 ± 0.19×10^-3 mm²/second, P = 0.007) and nADC (1.42 ± 0.25 vs. 1.17 ± 0.25, P = 0.011) of the GC group were significantly higher. ROC curve analysis showed that the maximum area under the curve of ADC_min and nADC were 0.800 ± 0.080 and 0.778 ± 0.082, respectively. The sensitivity, specificity, and accuracy distinguishing GC and classic GBM was best (83.33%, 76.19%, and 78.79%, respectively) when ADC_min = 0.84×10^-3 mm²/second (maximum area under the ROC, 0.800). Its positive and negative predictive values under this condition were 88.89% and 66.67%, respectively.

CONCLUSIONS

By distinguishing GC from classic GBM, the ADC_min parameter of DWI can improve the accuracy of the preoperative differential diagnosis of the 2 tumor types.

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.

First ever case report of co-occurrence of hobnail variant of papillary thyroid carcinoma and intrathyroid parathyroid adenoma in the same thyroid lobe

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First reported case of hobnail variant of papillary thyroid cancer and Intrathyroid parathyroid adenoma occurring within same thyroid lobe.

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Next-generation sequencing of the mutation spectrum of hobnail variant of papillary thyroid cancer showed BRAFV600E mutation.

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Studies that define other molecular abnormalities may be useful as therapeutic targets.

Introduction

The hobnail variant of papillary thyroid cancer (PTC) is rare. Intrathyroid parathyroid adenoma (ITPA) is also rare. Co-ocurrence of PTC and ITPA in the same thyroid lobe is extremely rare. Likewise, primary hyperparathyroidism with such non-medullary thyroid carcinoma is rare. The specific molecular profile of hobnail PTC (HPTC) is different from the classic, poorly differentiated and anaplastic variants and may contribute to its aggressive behavior. HPTC’s genetic profile remains unclear.

Presentation of case

A 61-year-old woman presented to our endocrine clinic with generalized aches, bone pain, polyuria, and right neck swelling of a few months’ duration. Laboratory findings revealed hypercalcemia and hyperparathyroidism. Ultrasound of the neck showed 4.6 cm complex nodule within the right thyroid lobe. Sestamibi scan suggested parathyroid adenoma in the right thyroid lobe. Fine-needle aspiration (FNA) revealed atypical follicular lesion of undetermined significance. She underwent right lobectomy, which normalized the intraoperative intact parathyroid hormone levels. Final pathology with immunohistochemical stains demonstrated HPTC and IPTA (2 cm each). Next-generation sequencing investigated the mutation spectrum of HPTC and detected BRAFV600E mutation.

Conclusions

A parathyroid adenoma should not exclude the diagnosis of thyroid carcinoma. Thyroid evaluation is needed for patients with primary hyperparathyroidism to prevent missing concurrent thyroid cancers. Cytomorphologic features to distinguish thyroid from parathyroid cells on FNA cytology must be considered. Immunohistochemical stains are important. BRAFV600E is the most common mutation in HPTC. This is possibly the first reported case of HPTC and ITPA co-occurring within the same thyroid lobe. Studies that define other molecular abnormalities may be useful as therapeutic targets.