Comparison of diagnostic performance of two ultrasound risk stratification systems for thyroid nodules: a systematic review and meta-analysis

A real-world comparison of the diagnostic performances of six different TI-RADS guidelines, including ACR-/Kwak-/K-/EU-/ATA-/C-TIRADS

PURPOSE

To compare the diagnostic performance of six different currently available guidelines including the American College of Radiology Thyroid Imaging and Reporting Data System (ACR-TIRADS), Kwak-TIRADS, Korean TIRADS (K-TIRADS), European TIRADS (EU-TIRADS), American Thyroid Association (ATA) and Chinese TIRADS (C-TIRADS), in differentiating malignant from benign thyroid nodules (TN).

MATERIALS AND METHODS

In this single-center study, between January-2007 and September-2023, ultrasound (US) images of TNs that were pathologically proven either by surgery or by fine needle aspiration biopsy (FNAB), were retrospectively evaluated and categorized according to six different currently available guidelines. Area under curve (AUC), sensitivity, specificity, positive and negative predictive values (PPV and NPV, respectively) and miss rates for malignancy (MRM) were calculated for each guideline.

RESULTS

A total of 829 TNs (n = 234 malignant and n = 595 benign) were included. AUC, sensitivity, specificity, PPV, NPV and accuracy for ACR-TIRADS were 0.786, 99.8 %, 27.1 %, 31.92 %, 99.73 % and 54.6 %, respectively; for Kwak-TIRADS 0.839, 97.8 %, 42.1 %, 36.29 %, 98.11 % and 63.1 %, respectively; for K-TIRADS 0.797, 97.6 %, 41.6 %, 36.01 %, 84.85 % and 62.8, respectively, for EU-TIRADS 0.766, 97.8 %, 35.6 %, 33.89 %, 97.92 % and 59.1 %, respectively, for ATA 0.788, 97.5 %, 49.8 %, 32.86 %, 88.16 % and 64.2 %, respectively and for C-TIRADS 0.842, 0 %, 92.8 %, 54.3 %, 39.53 %, 90.42 %, and 68.8 % respectively. MRM regarding ACR-/Kwak-/K-/EU-/ATA-/C-TIRADS were 2.2 %, 0.5 %, 2.9 %, 2.5 %, 3.3 % and 0.1 %, respectively.

CONCLUSION

Six different currently available TIRADS guidelines can provide effective differentiation of malignant TNs from benign ones with similar diagnostic performances. However; C-TIRADS offered the highest AUC and the lowest MRM than the other guidelines, in this series.

Theranostics of Thyroid Cancer

Molecular imaging is pivotal in evaluating and managing patients with different thyroid cancer histotypes. The existing, pathology-based, risk stratification systems can be usefully refined, by incorporating tumor-specific molecular and molecular imaging biomarkers with theranostic value, allowing patient-specific treatment decisions. Molecular imaging with different radioactive iodine isotopes (ie, I131, I123, I124) is a central component of differentiated carcinoma (DTC)'s risk stratification while [18F]F-fluorodeoxyglucose ([18F]FDG) PET/CT is interrogated about disease aggressiveness and presence of distant metastases. Moreover, it is particularly useful to assess and risk-stratify patients with radioiodine-refractory DTC, poorly differentiated, and anaplastic thyroid cancers. [18F]F-dihydroxyphenylalanine (6-[18F]FDOPA) PET/CT is the most specific and accurate molecular imaging procedure for patients with medullary thyroid cancer (MTC), a neuroendocrine tumor derived from thyroid C-cells. In addition, [18F]FDG PET/CT can be used in patients with more aggressive clinical or biochemical (ie, serum markers levels and kinetics) MTC phenotypes. In addition to conventional radioiodine therapy for DTC, new redifferentiation strategies are now available to restore uptake in radioiodine-refractory DTC. Moreover, peptide receptor theranostics showed promising results in patients with advanced and metastatic radioiodine-refractory DTC and MTC, respectively. The current appropriate role and future perspectives of molecular imaging and theranostics in thyroid cancer are discussed in our present review.

Scientific Status Quo of Small Renal Lesions: Diagnostic Assessment and Radiomics

Background: Small renal masses (SRMs) are defined as contrast-enhanced renal lesions less than or equal to 4 cm in maximal diameter, which can be compatible with stage T1a renal cell carcinomas (RCCs). Currently, 50-61% of all renal tumors are found incidentally. Methods: The characteristics of the lesion influence the choice of the type of management, which include several methods SRM of management, including nephrectomy, partial nephrectomy, ablation, observation, and also stereotactic body radiotherapy. Typical imaging methods available for differentiating benign from malignant renal lesions include ultrasound (US), contrast-enhanced ultrasound (CEUS), computed tomography (CT), and magnetic resonance imaging (MRI). Results: Although ultrasound is the first imaging technique used to detect small renal lesions, it has several limitations. CT is the main and most widely used imaging technique for SRM characterization. The main advantages of MRI compared to CT are the better contrast resolution and tissue characterization, the use of functional imaging sequences, the possibility of performing the examination in patients allergic to iodine-containing contrast medium, and the absence of exposure to ionizing radiation. For a correct evaluation during imaging follow-up, it is necessary to use a reliable method for the assessment of renal lesions, represented by the Bosniak classification system. This classification was initially developed based on contrast-enhanced CT imaging findings, and the 2019 revision proposed the inclusion of MRI features; however, the latest classification has not yet received widespread validation. Conclusions: The use of radiomics in the evaluation of renal masses is an emerging and increasingly central field with several applications such as characterizing renal masses, distinguishing RCC subtypes, monitoring response to targeted therapeutic agents, and prognosis in a metastatic context.

Machine Learning and Radiomics Analysis for Tumor Budding Prediction in Colorectal Liver Metastases Magnetic Resonance Imaging Assessment

PURPOSE

We aimed to assess the efficacy of machine learning and radiomics analysis using magnetic resonance imaging (MRI) with a hepatospecific contrast agent, in a pre-surgical setting, to predict tumor budding in liver metastases.

METHODS

Patients with MRI in a pre-surgical setting were retrospectively enrolled. Manual segmentation was made by means 3D Slicer image computing, and 851 radiomics features were extracted as median values using the PyRadiomics Python package. Balancing was performed and inter- and intraclass correlation coefficients were calculated to assess the between observer and within observer reproducibility of all radiomics extracted features. A Wilcoxon-Mann-Whitney nonparametric test and receiver operating characteristics (ROC) analysis were carried out. Balancing and feature selection procedures were performed. Linear and non-logistic regression models (LRM and NLRM) and different machine learning-based classifiers including decision tree (DT), k-nearest neighbor (KNN) and support vector machine (SVM) were considered.

RESULTS

The internal training set included 49 patients and 119 liver metastases. The validation cohort consisted of a total of 28 single lesion patients. The best single predictor to classify tumor budding was original_glcm_Idn obtained in the T1-W VIBE sequence arterial phase with an accuracy of 84%; wavelet_LLH_firstorder_10Percentile was obtained in the T1-W VIBE sequence portal phase with an accuracy of 92%; wavelet_HHL_glcm_MaximumProbability was obtained in the T1-W VIBE sequence hepatobiliary excretion phase with an accuracy of 88%; and wavelet_LLH_glcm_Imc1 was obtained in T2-W SPACE sequences with an accuracy of 88%. Considering the linear regression analysis, a statistically significant increase in accuracy to 96% was obtained using a linear weighted combination of 13 radiomic features extracted from the T1-W VIBE sequence arterial phase. Moreover, the best classifier was a KNN trained with the 13 radiomic features extracted from the arterial phase of the T1-W VIBE sequence, obtaining an accuracy of 95% and an AUC of 0.96. The validation set reached an accuracy of 94%, a sensitivity of 86% and a specificity of 95%.

CONCLUSIONS

Machine learning and radiomics analysis are promising tools in predicting tumor budding. Considering the linear regression analysis, there was a statistically significant increase in accuracy to 96% using a weighted linear combination of 13 radiomics features extracted from the arterial phase compared to a single radiomics feature.