Comparisons and Combined Application of Two-Dimensional and Three-Dimensional Real-time Shear Wave Elastography in Diagnosis of Thyroid Nodules

Emerging research trends in artificial intelligence for cancer diagnostic systems: A comprehensive review

This review article offers a comprehensive analysis of current developments in the application of machine learning for cancer diagnostic systems. The effectiveness of machine learning approaches has become evident in improving the accuracy and speed of cancer detection, addressing the complexities of large and intricate medical datasets. This review aims to evaluate modern machine learning techniques employed in cancer diagnostics, covering various algorithms, including supervised and unsupervised learning, as well as deep learning and federated learning methodologies. Data acquisition and preprocessing methods for different types of data, such as imaging, genomics, and clinical records, are discussed. The paper also examines feature extraction and selection techniques specific to cancer diagnosis. Model training, evaluation metrics, and performance comparison methods are explored. Additionally, the review provides insights into the applications of machine learning in various cancer types and discusses challenges related to dataset limitations, model interpretability, multi-omics integration, and ethical considerations. The emerging field of explainable artificial intelligence (XAI) in cancer diagnosis is highlighted, emphasizing specific XAI techniques proposed to improve cancer diagnostics. These techniques include interactive visualization of model decisions and feature importance analysis tailored for enhanced clinical interpretation, aiming to enhance both diagnostic accuracy and transparency in medical decision-making. The paper concludes by outlining future directions, including personalized medicine, federated learning, deep learning advancements, and ethical considerations. This review aims to guide researchers, clinicians, and policymakers in the development of efficient and interpretable machine learning-based cancer diagnostic systems.

A Systematic Review of Real-Time Deep Learning Methods for Image-Based Cancer Diagnostics

Deep Learning (DL) drives academics to create models for cancer diagnosis using medical image processing because of its innate ability to recognize difficult-to-detect patterns in complex, noisy, and massive data. The use of deep learning algorithms for real-time cancer diagnosis is explored in depth in this work. Real-time medical diagnosis determines the illness or condition that accounts for a patient's symptoms and outward physical manifestations within a predetermined time frame. With a waiting period of anywhere between 5 days and 30 days, there are currently several ways, including screening tests, biopsies, and other prospective methods, that can assist in discovering a problem, particularly cancer. This article conducts a thorough literature review to understand how DL affects the length of this waiting period. In addition, the accuracy and turnaround time of different imaging modalities is evaluated with DL-based cancer diagnosis. Convolutional neural networks are critical for real-time cancer diagnosis, with models achieving up to 99.3% accuracy. The effectiveness and cost of the infrastructure required for real-time image-based medical diagnostics are evaluated. According to the report, generalization problems, data variability, and explainable DL are some of the most significant barriers to using DL in clinical trials. Making DL applicable for cancer diagnosis will be made possible by explainable DL.

Impact of Region-of-Interest Size on the Diagnostic Performance of Shear Wave Elastography in Differentiating Thyroid Nodules

This study investigated the impact of different region-of-interest (ROI) sizes (Max, 1 mm, and 2 mm) on shear wave elastography (SWE) in differentiating between malignant and benign thyroid nodules. The study cohort comprised 129 thyroid nodules (50 malignant, 79 benign) and 78 normal subjects. Diagnostic efficacy was assessed through pairwise comparisons of area under the curve (AUC) values in receiver operating characteristic analysis by using DeLong's test. Our results indicated significant differences in all SWE elasticity metrics between the groups, with malignant nodules exhibiting higher values than benign nodules (p < 0.05). Smaller ROIs (1 and 2 mm) were found to outperform the max ROI in terms of diagnostic accuracy, particularly for the Emax and Emin elasticity metrics. Emax(1mm) had the highest diagnostic accuracy, with an AUC of 0.883, sensitivity of 74.0%, and specificity of 86.1%. This study underscores the significant influence of ROI size selection on the diagnostic performance of SWE, offering valuable insights for future research and clinical applications in thyroid nodule assessment.

Diagnostic value of ACR TI-RADS combined with three-dimensional shear wave elastography in ACR TI-RADS 4 and 5 thyroid nodules

BACKGROUND

Three-dimensional shear wave elastography (3D-SWE) is a promising method in distinguishing benign and malignant thyroid nodules. By combining with conventional method, it may further improve the diagnostic value. The study aimed to assess the diagnostic value of American College of Radiology (ACR) thyroid imaging reporting and data system (TI-RADS) combined with 3D-SWE in ACR TI-RADS 4 and 5 thyroid nodules.

METHODS

All nodules were examined by conventional ultrasonography, ACR TI-RADS classification, and 3D-SWE examination. Conventional ultrasonography was used to observe the location, size, shape, margin, echogenicity, taller-than-wide sign, microcalcification, and blood flow of thyroid nodules, and then ACR TI-RADS classification was performed. The Young's modulus values (3D-C-Emax, 3D-C-Emean, and elastography standard deviation [3D-C-Esd]) were measured on the reconstructed coronal plane images. According to the receiver operating characteristic (ROC) curve, the best diagnostic efficiency among 3D-C-Emax, 3D-C-Emean, and 3D-C-Esd was selected and the cut-off threshold was calculated. According to the surgical pathology, they were divided into benign group and malignant group. And appropriate statistical methods such as t -test and Mann-Whitney U test were used to compare the difference between the two groups. On this basis, 3D-SWE combined with conventional ACR TI-RADS was reclassified as combined ACR TI-RADS to determine benign or malignant thyroid nodules.

RESULTS

Of the 112 thyroid nodules, 62 were malignant and 50 were benign. The optimal cut-off value of three-dimensional maximum Young's modulus in coronal plane (3D-C-Emax) was 51.5 kPa and the area under the curve (AUC) was 0.798. The AUC, sensitivity, specificity, and accuracy of conventional ACR TI-RADS were 0.828, 83.9%, 66.0%, and 75.9%, respectively. The AUC, sensitivity, specificity, and accuracy of combined ACR TI-RADS were 0.845, 90.3%, 66.0%, and 79.5%, respectively. The difference between the two AUC values was statistically significant.

CONCLUSIONS

Combined ACR TI-RADS has higher diagnostic efficiency than conventional ACR TI-RADS. The sensitivity and accuracy of combined ACR TI-RADS showed significant improvements. It can be used as an effective method in the diagnosis of thyroid nodules.

Combined Shear Wave Elastography and EU TIRADS in Differentiating Malignant and Benign Thyroid Nodules

Although multimodal ultrasound approaches have been suggested to potentially improve the diagnosis of thyroid cancer; the diagnostic utility of the combination of SWE and malignancy-risk stratification systems remains vague due to the lack of standardized criteria. The purpose of the study was to assess the diagnostic value of the combination of grey scale ultrasound assessment using EU TIRADS and shear wave elastography. 121 patients (126 nodules−81 benign; 45 malignant) underwent grey scale ultrasound and SWE imaging of nodules between 0.5 cm and 5 cm prior to biopsy and/or surgery. Nodules were analyzed based on size stratifications: <1 cm (n = 43); 1−2 cm (n = 52) and >2 cm (n = 31) and equivocal cytology status (n = 52), and diagnostic performance assessments were conducted. The combination of EU TIRADS with SWE using the SD parameter; maintained a high sensitivity and significantly improved the specificity of sole EU TIRADS for nodules 1−2 cm (SEN: 72.2% vs. 88.9%, p > 0.05; SPEC: 76.5% vs. 55.9%, p < 0.01) and >2 cm (SEN: 71.4% vs. 85.7%, p > 0.05; SPEC: 95.8% vs. 62.5%, p < 0.01). For cytologically-equivocal nodules; the combination with the SWE minimum parameter resulted in a significant reduction in sensitivity with increased specificity (SEN: 60% vs. 80%; SPEC: 83.4% vs. 37.8%; all p < 0.05). SWE in combination with EU TIRADS is diagnostically efficient in discriminating nodules > 1 cm but is not ideal for discriminating cytologically-equivocal nodules.

Diagnostic Value of Different 3-D Shear Wave Elastography Sections in the Diagnosis of Thyroid Nodules

The aim of the study was to explore the value of 3-D shear wave elastography (SWE) in differentiating malignant from benign thyroid nodules. A total of 188 patients with 216 nodules who underwent conventional ultrasound, 2-D SWE and 3-D SWE were included in this study. All patients underwent surgical excision, and the pathological results were the gold standard. Receiver operating characteristic (ROC) curves of the American College of Radiology's Thyroid Imaging Reporting and Data System (ACR TI-RADS), 2-D SWE and 3-D SWE were plotted, and the areas under the curves (AUCs) were compared using a Z-test. There were 62 benign thyroid nodules and 154 malignant thyroid nodules in this study. Young's modulus (E_min, E_mean, E_max, E_sd) values of thyroid malignant nodules in different sections of 2-D SWE and 3-D SWE were significantly higher than those of thyroid benign nodules (p < 0.001). The AUC of E_max in 2-D SWE transverse sections was significantly lower than that in 3-D SWE transverse sections and 3-D SWE sagittal sections (0.768 vs. 0.831 and 0.844, p < 0.05). The AUC of 3-D S-E_max combined with ACR TI-RADS was 0.859; the specificity increased from 54.84% to 85.71%, and the diagnostic accuracy increased from 74.54% to 85.19%, compared with ACR TI-RADS. The difference was statistically significant (p < 0.05). Three-dimensional SWE combined with ACR TI-RADS for the diagnosis of thyroid nodules significantly improved the diagnostic ability of ACR TI-RADS, and was significantly better than 2-D SWE combined with ACR TI-RADS.

Evaluating different combination methods to analyse ultrasound and shear wave elastography images automatically through discriminative convolutional neural network in breast cancer imaging

Purpose

Ultrasound (US) and Shear Wave Elastography (SWE) imaging are non-invasive methods used for breast lesion characterization. While US and SWE images provide both morphological information, SWE visualizes in addition the elasticity of tissue. In this study a Discriminative Convolutional Neural Network (DCNN) model is applied to US and SWE images and their combination to classify the breast lesions into malignant or benign cases. Furthermore, it is identified whether analysing only the region of the elastogram or including the surrounding B-mode image gives a superior performance.

Methods

The dataset used in this study consists of 746 images obtained from 207 patients comprising 486 malignant and 260 benign breast lesions. From each image the US and SWE image was extracted, once including only the region of the elastogram and once including also the surrounding B-mode image. These four datasets were applied individually to a DCNN to determine their predictive capability. Each the best US and SWE dataset were used to examine different combination methods with DCNN. The results were compared to the manual assessment by an expert radiologist.

Results

The combination of US and SWE images with the surrounding B-mode image using two ensembled DCNN models achieved best results with an accuracy of 93.53 %, sensitivity of 94.42 %, specificity of 90.75 % and area under the curve (AUC) of 96.55 %.

Conclusion

This study showed that using the whole US and SWE images through DCNN was superior to methods, in which only the region of elastogram was used. Combining breast cancer US and SWE images with two ensembled DCNN models in parallel improved the results. The accuracy, sensitivity and AUC of the best combination method were significantly superior to the results of using a single dataset through DCNN and to the results of the expert radiologist.

SuperSonic shear imaging for the differentiation between benign and malignant thyroid nodules: a meta-analysis

PURPOSE

To assess the diagnostic value of SuperSonic shear imaging (SSI) for the differentiation between benign and malignant thyroid nodules through meta-analysis.

METHODS

Online database searches were performed on PubMed, EMBASE, the Cochrane Library, and the Web of Science until 31 July 2021. The Quality Assessment of Diagnostic Accuracy Studies-2 tool was used to assess the quality of the included studies. Three measures of diagnostic test performance were used to examine the value of SSI, including the summary area under the receiver operating characteristic curve (AUROC), the summary diagnostic odds ratio (DOR), and the summary sensitivity and specificity. Heterogeneity was explored using meta-regression and subgroup analyses.

RESULTS

Finally, 21 studies with 3376 patients were included in this study. There were a total of 4296 thyroid nodules, in which 1806 malignant nodules and 2490 benign ones were involved. Thyroid nodules exhibited a malignancy rate of 42.0% (range 5.6-79.8%), 95.1% of which were of papillary variant. SSI showed a summary sensitivity of 74% [95% confidence interval (CI) 67-79%], specificity of 82% (95% CI 77-87%) and AUROC of 0.85 (95% CI 0.82-0.88) for the differentiation between benign and malignant thyroid nodules. The summary positive likelihood ratio (LR), negative LR, and DOR were 4.2 (95% CI 3.3-5.3), 0.32 (95% CI 0.26-0.40), and 13 (95% CI 9-18), respectively.

CONCLUSIONS

SSI showed high accuracy in the diagnostic differentiation between benign and malignant thyroid nodules and can be served as a noninvasive and important adjunct for thyroid nodule evaluation.

Application of real-time shear wave elastography in the assessment of male infertility

BACKGROUND

Shear wave elastography (SWE) is recognized as a suitable imaging modality for identifying and characterizing testicular diseases. Recent exploration of SWE has focused on its feasibility in evaluating histopathological changes in the testicular parenchyma, with researchers increasingly focusing on the relationship between testicular stiffness and male fertility. In this study, we aimed to investigate the diagnostic value of SWE for distinguishing the relationship between spermatogenic defects and testicular stiffness in males of reproductive age.

METHODS

This was a single center, cross-sectional study conducted from July 2017 to December 2019. A total of 1,116 consecutive patients who were voluntarily participating in in-vitro fertilization (IVF)-assisted conception at our hospital were recruited to the study. The cohort included 497 normozoospermia patients (Group I), 335 with normozoospermia and decreased motility and agglutination (Group II), 138 with oligozoospermia (Group III), 105 with non-obstructive azoospermia (Group-NOA), and 41 with obstructive azoospermia (Group-OA). We conducted SWE of each participant's testes and the testicular elastic modulus was calculated. The differences of testicular elastic modulus were compared among groups. Linear regression analysis was conducted to determine the correlation between sperm concentration and either testicular volume or testicular elastic modulus. Receiver operating characteristic (ROC) curves were drawn to evaluate the diagnostic efficiency of the maximum elastic modulus (Emax), mean elastic modulus (Emean), and maximum minus the minimum elastic modulus {E[max-min]}.

RESULTS

The Emax, Emean, and E[max-min] increased gradually in groups I, II, III, and Group-NOA, with statistical differences between groups (P<0.01). Testicular volume was shown to be positively correlated with sperm concentration (r=0.476; P<0.01), while the Emax, Emean, and E[max-min] were negatively correlated with sperm concentration (r=-0.511, -0.357, and -0.524, respectively; P<0.01). The ROC curves were established based on the Emax, Emean, and E[max-min] and were used to distinguish Group-OA from Group-NOA. The areas under the ROC curve (AUCs) were 0.910, 0.863, and 0.900, respectively. We also used ROC curves to distinguish the severe oligozoospermia subgroup and Group-NOA from other groups, for which the AUCs were 0.877, 0.791, and 0.878, respectively.

CONCLUSIONS

The SWE is an effective supplement to routine ultrasound examination and can be used to diagnose and differentiate spermatogenetic dysfunction.

Evaluation of thyroid nodules by shear wave elastography: a review of current knowledge

PURPOSE

Shear wave elastography (SWE), as a tool for diagnosing thyroid malignancy, has gathered considerable attention during the past decade. Diverging results exist regarding the diagnostic performance of thyroid SWE.

METHODS

A comprehensive literature review of thyroid SWE was conducted using the terms "Thyroid" and "shear wave elastography" in PubMed.

RESULTS

The majority of studies found SWE promising for differentiating malignant and benign thyroid nodules on a group level, whereas results are less convincing on the individual level due to huge overlap in elasticity indices. Further, there is lack of consensus on the optimum outcome reflecting nodule elasticity and the cut-off point predicting thyroid malignancy. While heterogeneity between studies hinders a clinically meaningful meta-analysis, the results are discussed in a clinical perspective with regard to applicability in clinical practice as well as methodological advantages and pitfalls of this technology.

CONCLUSION

Technological as well as biological hindrances seem to exist for SWE to be clinically reliable in assessing benign and malignant thyroid nodules. Structural heterogeneity of thyroid nodules in combination with operator-dependent factors such as pre-compression and selection of scanning plane are likely explanations for these findings. Standardization and consensus on the SWE acquisition process applied in future studies are needed for SWE to be considered a clinically reliable diagnostic tool for detection of thyroid cancer.

Comparison of ACR TI-RADS, Kwak TI-RADS, ATA guidelines and KTA/KSThR guidelines in combination with SWE in the diagnosis of thyroid nodules

OBJECTIVE

To compare the diagnostic efficacy of ACR TI-RADS, Kwak TI-RADS, ATA guidelines and KTA/KSThR guidelines in combination with shear wave elastography (SWE) for thyroid nodules.

METHODS

The retrospective study included 566 thyroid nodules with maximum diameter≥5 mm which confirmed by FNA cytology or/and surgical pathology. The sensitivity, specificity, accuracy, Youden index of diagnosis of thyroid nodules by ACR TI-RADS, Kwak TI-RADS, ATA guidelines, KTA/KSThR guidelines and SWE were calculated. The ROC curve was drawn to determine the cut-off values of the four ultrasound classification systems and SWE Emax. The diagnostic efficacy of the four ultrasound classification systems in combination with SWE were calculated and compared with those of pre-combination.

RESULTS

The ROC curves indicated that the cut-off value of ACR TI-RADS, Kwak TI-RADS, ATA guidelines, KTA/KSThR guidelines and Emax of SWE was TR5, 4c, high-suspicion, high-suspicion, and 41.7 kPa, respectively, and the area under the ROC curve (AUC) was 0.907(0.879-0.934), 0904(0.876-0.932), 0.894(0.863-0.924), 0.888(0.856-0.919), 0.886(0.859-0.913), respectively. After combination with SWE, the the sensitivities of the four ultrasound classification systems for the diagnosis of nodules were improved, and the differences were statistically significant (all P≤0.001); the specificities were decreased, but the differences were not statistically significant (all P > 0.05); the accuracies were improved, but only the difference of ACR TI-RADS was statistically significant (x2 = 4.45, P = 0.035); the differences in the AUCs were not significant (all P > 0.05).

CONCLUSIONS

The four ultrasound classification systems and SWE all had high performance in the diagnosis of thyroid nodules. The four classification systems in combination with SWE were all beneficial to the differential diagnosis of nodules, and ACR TI-RADS in combination with SWE was more effective, especially for TR3 and TR4 nodules.

Diagnostic performance of combination of ultrasound elastography and BRAF gene detection in malignant thyroid nodule: a retrospective study

OBJECTIVES

The aims of our study were to explore the preoperative diagnostic value of ultrasound elastography combined with BRAF gene detection in malignant thyroid nodule, and find whether shear wave elastography (SWE) combined with BRAF gene detection can improve the diagnostic sensitivity and specificity.

METHODS

From 1480 patients with thyroid nodule examined between January 2015 and December 2017, a retrospective analysis was performed on 161 patients who underwent thyroidectomy. Diagnosis was confirmed by postoperative pathology, including 139 malignant thyroid nodules and 22 benign thyroid nodules. All the patients underwent SWE, BRAF gene detection, and the combination for their preoperative evaluation. The sensitivities, specificities, and accuracies of SWE, BRAF gene detection, and the combination for detection of malignant thyroid nodules were calculated and then compared using Fisher's exact probability test, based on the original preoperative reports and postoperative pathology. A receiver-operating characteristic curve analysis was performed to compare the diagnostic performance of SWE, BRAF gene detection, and combination for detecting malignant thyroid nodules.

RESULTS

Based on the original preoperative reports and postoperative pathology, SWE, BRAF gene detection, and the combination showed sensitivities of 88.67%, 78.41%, 92.8%, and specificities of 72.77%, 77.27%, 95.45%. A correct diagnosis was obtained in 85.82%, 78.26%, 93.16% and missed diagnosis rates were 12.23%, 21.58%, and 7.19%. The sensitivities, specificities, and correct diagnosis rate in the combination group were significantly higher than any single detection method (P<0.05). The missed diagnosis rate in the combination group was significantly lower than any single detection method (P<0.05). The receptor operating characteristics curve analysis showed a significantly higher diagnostic performance for the combination than for SWE and BRAF gene detection (P<0.05). The interobserver agreement for detecting malignant thyroid nodule was better for the combination than for SWE or BRAF gene detection alone.

CONCLUSION

For the detection of a malignant thyroid nodule, SWE combined with BRAF gene detection was more sensitive and showed a higher diagnostic performance than SWE or BRAF gene detection alone.

Diagnostic performances of the KWAK-TIRADS classification, elasticity score, and Bethesda System for Reporting Thyroid Cytopathology of TI-RADS category 4 thyroid nodules

OBJECTIVE

To explore the value of the KWAK Thyroid Imaging Reporting and Data System (KWAK-TIRADS), elasticity score (ES), and Bethesda System for Reporting Thyroid Cytopathology (BSRTC) in the diagnosis of suspicious thyroid nodules.

MATERIALS AND METHODS

The study included 392 cases of TI-RADS category 4 thyroid nodules that underwent thyroidectomy between January 2017 and October 2019. All patients underwent ultrasonography, ultrasound elastography, and fine-needle aspiration cytology (FNAC) before surgery. The nodules were classified into different categories based on the KWAK-TIRADS, ES, and BSRTC. Patients were divided into two groups based on postoperative pathological characteristics. The sensitivity (Se), specificity (Sp), and area under the receiver operating characteristic (ROC) curve were calculated. Student's t-test and Pearson chi-square test were used to compare diagnostic performance.

RESULTS

There were 159 patients in the benign group and 233 in the malignant group. The percentage of malignant nodules in KWAK-TIRADS categories 4a, 4b, and 4c were 44.3%, 64.8%, and 92.9%, respectively. The percentages of malignant nodules in ES 2, 3, 4, and 5 were 0%, 37.1%, 93.8%, and 100%, respectively. The percentage of malignant nodules in BSRTC levels I, II, III, IV, V and VI were 57.1%, 2.8%, 9.9%, 76.6%, 99.1%, and 100%, respectively. Among those methods, the BSRTC had better diagnostic efficiency than the KWAK-TIRADS and ES (Sp 81.1%, Se 93.6%, and AUC 0.918, P<0.01). Among the combined methods, KWAK-TIRADS+ES+BSRTC was more effective than KWAK-TIRADS+ES, KWAK-TIRADS+BSRTC, and ES+BSRTC (Sp 93.7%, Se 91.4%, and AUC 0.967, P<0.01).

CONCLUSION

The combination of KWAK-TIRADS, ES, and BSRTC can improve the accuracy of identifying category 4 thyroid nodules.