Quantitative Assessment of Thyroid Nodules Using Dual-Energy Computed Tomography: Iodine Concentration Measurement and Multiparametric Texture Analysis for Differentiating between Malignant and Benign Lesions

Diagnosis of benign and malignant thyroid nodules by a dual-layer spectral detector CT-based nomogram

Introduction

Preoperative diagnosis of benign and malignant thyroid nodules is crucial for appropriate clinical treatment and individual patient management. In this study, a double-layer spectral detector computed tomography (DLCT)-based nomogram for the preoperative classification of benign and malignant thyroid nodules was developed and tested.

Methods

A total of 405 patients with pathological findings of thyroid nodules who underwent DLCT preoperatively were retrospectively recruited. They were randomized into a training cohort (n=283) and a test cohort (n=122). Information on clinical features, qualitative imaging features and quantitative DLCT parameters was collected. Univariate and multifactorial logistic regression analyses were used to screen independent predictors of benign and malignant nodules. A nomogram model based on the independent predictors was developed to make individualized predictions of benign and malignant thyroid nodules. Model performance was evaluated by calculating the area under the receiver operating characteristic curve (AUC), calibration curve and decision curve analysis(DCA).

Results

Standardized iodine concentration in the arterial phase, the slope of the spectral hounsfield unit(HU) curves in the arterial phase, and cystic degeneration were identified as independent predictors of benign and malignant thyroid nodules. After combining these three metrics, the proposed nomogram was diagnostically effective, with AUC values of 0.880 for the training cohort and 0.884 for the test cohort. The nomogram showed a better fit (all p > 0.05 by Hosmer−Lemeshow test) and provided a greater net benefit than the simple standard strategy within a large range of threshold probabilities in both cohorts.

Discussion

The DLCT-based nomogram has great potential for the preoperative prediction of benign and malignant thyroid nodules. This nomogram can be used as a simple, noninvasive, and effective tool for the individualized risk assessment of benign and malignant thyroid nodules, helping clinicians make appropriate treatment decisions.

Diagnostic Effectiveness of Dual Source Dual Energy Computed Tomography for Benign and Malignant Thyroid Nodules

Objective

To evaluate the diagnostic effectiveness of dual source dual energy computed tomography (DS-DECT) for benign and malignant thyroid nodules.

Methods

Between January 2019 and December 2021, 60 patients with surgically and pathologically verified thyroid nodules treated at our institution were recruited. DS-DECT was administered to all patients. The iodine content of lesioned and normal tissues, the normalized iodine concentration (NIC) and standardized CT values of benign and malignant nodules, the consistency of examination results and pathological findings, and diagnostic effectiveness were all investigated.

Results

The diagnosis accuracy was the same as that of surgical pathology, producing a 100% accuracy for the 60 patients with thyroid nodules (42 were benign and 18 were malignant). The iodine content of lesioned solid tissue differed significantly from that of normal tissue, as did the iodine content of malignant and benign nodules (P < 0.05). In the arterial phase, no significant difference was found in NIC and standardized CT values between benign and malignant nodules (P > 0.05). The optimal critical NIC for differentiating benign and malignant nodules in the venous phase was 0.74 and the standardized CT value was 0.79 HU according to the receiver operating characteristics (ROC) curve. Malignant nodules were diagnosed when the NIC was <0.74 and the standardized CT value was <0.79 HU, with AUC values of 0.89 and 0.93, respectively, where the sensitivity and specificity of the differential diagnosis of NIC were 90.48% (38/42) and 88.89% (16/18), respectively, and those of the differential diagnosis of standardized CT value were 92.86% (39/ 42) and 94.44% (17/18), respectively. The diagnosis accuracy of DS-DECT was 100%, and the diagnostic results of morphological characteristics and pathological testing were consistent. The sensitivity and specificity of the NIC values and standardized CT values in the venous phase differential diagnosis of benign and malignant nodules were compatible with the morphological differential diagnosis.

Conclusion

DS-DECT is highly accurate in determining the benignity and malignancy of thyroid nodules and has a strong potential for clinical promotion to allow for prompt treatment.

Radiomics analysis for differentiating of cervical lymphadenopathy between cancer of unknown primary and malignant lymphoma on unenhanced computed tomography

To investigate the usefulness of texture analysis to discriminate between cervical lymph node (LN) metastasis from cancer of unknown primary (CUP) and cervical LN involvement of malignant lymphoma (ML) on unenhanced computed tomography (CT). Cervical LN metastases in 17 patients with CUP and cervical LN involvement in 17 patients with ML were assessed by ¹⁸F-FDG PET/CT. The texture features were obtained in the total cross-sectional area (CSA) of the targeted LN, following the contour of the largest cervical LN on unenhanced CT. Values for the max standardized uptake value (SUVmax) and the mean SUV value (SUVmean), and 34 texture features were compared using a Mann-Whitney U test. The diagnostic accuracy and area under the curve (AUC) of the combination of the texture features were evaluated by support vector machine (SVM) with nested cross-validation. The SUVmax and SUVmean did not differ significantly between cervical LN metastases from CUP and cervical LN involvement from ML. However, significant differences of 9 texture features of the total CSA were observed (p = 0.001 - 0.05). The best AUC value of 0.851 for the texture feature of the total CSA were obtained from the correlation in the gray-level co-occurrence matrix features. SVM had the best AUC and diagnostic accuracy of 0.930 and 84.8%. Radiomics analysis appears to be useful for differentiating cervical LN metastasis from CUP and cervical LN involvement of ML on unenhanced CT.

Deep learning approach of diffusion-weighted imaging as an outcome predictor in laryngeal and hypopharyngeal cancer patients with radiotherapy-related curative treatment: a preliminary study

OBJECTIVES

This preliminary study aimed to develop a deep learning (DL) model using diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) maps to predict local recurrence and 2-year progression-free survival (PFS) in laryngeal and hypopharyngeal cancer patients treated with various forms of radiotherapy-related curative therapy.

METHODS

Seventy patients with laryngeal and hypopharyngeal cancers treated by radiotherapy, chemoradiotherapy, or induction-(chemo)radiotherapy were enrolled and divided into training (N = 49) and test (N = 21) groups based on presentation timeline. All patients underwent MR before and 4 weeks after the start of radiotherapy. The DL models that extracted imaging features on pre- and intra-treatment DWI and ADC maps were trained to predict the local recurrence within a 2-year follow-up. In the test group, each DL model was analyzed for recurrence prediction. Additionally, the Kaplan-Meier and multivariable Cox regression analyses were performed to evaluate the prognostic significance of the DL models and clinical variables.

RESULTS

The highest area under the receiver operating characteristics curve and accuracy for predicting the local recurrence in the DL model were 0.767 and 81.0%, respectively, using intra-treatment DWI (DWI_intra). The log-rank test showed that DWI_intra was significantly associated with PFS (p = 0.013). DWI_intra was an independent prognostic factor for PFS in multivariate analysis (p = 0.023).

CONCLUSION

DL models using DWI_intra may have prognostic value in patients with laryngeal and hypopharyngeal cancers treated by curative radiotherapy. The model-related findings may contribute to determining the therapeutic strategy in the early stage of the treatment.

KEY POINTS

• Deep learning models using intra-treatment diffusion-weighted imaging have prognostic value in patients with laryngeal and hypopharyngeal cancers treated by curative radiotherapy. • The findings from these models may contribute to determining the therapeutic strategy at the early stage of the treatment.

Computed tomography in rheumatology - From DECT to high-resolution peripheral quantitative CT

In this chapter, we discuss current updates and applications of Dual Energy Computed Tomography (DECT), iodine-DECT mapping, and high-resolution peripheral quantitative CT (HR-pQCT) in rheumatology. DECT provides a noninvasive diagnosis of gout and can help to differentiate gout from CPPD. Accuracy of DECT varies in various stages of gout. DECT needs specialized hardware, software, and skilled post-processing and interpretation. Sensitivity reduces significantly with deeper tissues such as hip and shoulder. Iodine map enables to delineate inflammatory lesions such as capsulitis and tenosynovitis by improving iodine contrast. Iodine quantification with an iodine map is a promising objective method to evaluate therapeutic effect of inflammatory arthritis. HR-pQCT allows for highly sensitive and specific measures of bone erosions and osteophytes in inflammatory joint diseases, documenting change over time, e.g. in cohorts undergoing immunosuppressive treatments. However, assessing the images requires trained readers, and (semi)-automated scripts to detect bone damage are still undergoing validation and further development.

Can contrast-enhancement computed tomography texture and histogram analyses help to differentiate malignant from benign thyroid nodules?

PURPOSE

We aimed to determine the ability of contrast-enhanced computed tomography (CECT) texture and histogram analyses to differentiate between benign and malignant thyroid nodules.

MATERIALS AND METHODS

The clinical data from 49 patients with 60 thyroid nodules were retrospectively analyzed. Nodules were classified as malignant or benign based on their histological results. Five texture and histogram parameters of thyroid nodules from CECT images, including entropy, mean, standard deviation, skewness, and kurtosis, were compared and analyzed between the two groups. Regions of interest in axial CECT images were delineated manually by two radiologists. Interobserver agreement in texture and histogram parameters between the two radiologists was assessed using the intraclass correlation coefficient (ICC). The Mann-Whitney U test and receiver operating characteristic curve analysis were conducted to estimate the diagnostic capability of texture parameters.

RESULTS

Interobserver reproducibility (ICC = 0.919-0.969) was excellent. Among the 60 nodules, 36 were malignant and 24 were benign. Entropy of malignant thyroid nodules was significantly higher compared with benign thyroid nodules (P = 0.005). A trend toward a higher kurtosis value was observed in malignant thyroid nodules (P = 0.062). When an entropy value of 6.55 was used as a cutoff for differentiating benign from malignant thyroid nodules, the optimal area under the curve, sensitivity, and specificity were 0.716 (0.585-0.847, 95% confidence interval, P = 0.005), 75.0%, and 62.5%, respectively.

CONCLUSIONS

CECT texture and histogram analyses can be used to differentiate benign from malignant thyroid nodules.