Computerized detection and quantification of microcalcifications in thyroid nodules

Artificial intelligence in screening and diagnosis of surgical diseases: A narrative review

Artificial intelligence (AI) is playing an increasing role in several fields of medicine. It is also gaining popularity among surgeons as a valuable screening and diagnostic tool for many conditions such as benign and malignant colorectal, gastric, thyroid, parathyroid, and breast disorders. In the literature, there is no review that groups together the various application domains of AI when it comes to the screening and diagnosis of main surgical diseases. The aim of this review is to describe the use of AI in these settings. We performed a literature review by searching PubMed, Web of Science, Scopus, and Embase for all studies investigating the role of AI in the surgical setting, published between January 01, 2000, and June 30, 2023. Our focus was on randomized controlled trials (RCTs), meta-analysis, systematic reviews, and observational studies, dealing with large cohorts of patients. We then gathered further relevant studies from the reference list of the selected publications. Based on the studies reviewed, it emerges that AI could strongly enhance the screening efficiency, clinical ability, and diagnostic accuracy for several surgical conditions. Some of the future advantages of this technology include implementing, speeding up, and improving the automaticity with which AI recognizes, differentiates, and classifies the various conditions.

Leveraging deep learning to identify calcification and colloid in thyroid nodules

Background

Both calcification and colloid in thyroid nodules are reflected as echogenic foci in ultrasound images. However, calcification and colloid have significantly different probabilities of malignancy. We explored the performance of a deep learning (DL) model in distinguishing the echogenic foci of thyroid nodules as calcification or colloid.

Methods

We conducted a retrospective study using ultrasound image sets. The DL model was trained and tested on 30,388 images of 1127 nodules. All nodules were pathologically confirmed. The area under the receiver-operator characteristic curve (AUC) was employed as the primary evaluation index.

Results

The YoloV5 (You Only Look Once Version 5) transfer learning model for thyroid nodules based on DL detection showed that the average sensitivity, specificity, and accuracy of distinguishing echogenic foci in the test 1 group (n = 192) was 78.41%, 91.36%, and 77.81%, respectively. The average sensitivity, specificity, and accuracy of the three radiologists were 51.14%, 82.58%, and 61.29%, respectively. The average sensitivity, specificity, and accuracy of distinguishing small echogenic foci in the test 2 group (n = 58) was 70.17%, 77.14%, and 73.33%, respectively. Correspondingly, the average sensitivity, specificity, and accuracy of the radiologists were 57.69%, 63.29%, and 59.38%.

Conclusions

The study demonstrated that DL performed far better than radiologists in distinguishing echogenic foci of thyroid nodules as calcifications or colloid.

Assessing Detection Accuracy of Computerized Sonographic Features and Computer-Assisted Reading Performance in Differentiating Thyroid Cancers

For ultrasound imaging of thyroid nodules, medical guidelines are all based on findings of sonographic features to provide clinicians management recommendations. Due to the recent development of artificial intelligence and machine learning (AI/ML) technologies, there have been computer-assisted detection (CAD) software devices available for clinical use to detect and quantify the sonographic features of thyroid nodules. This study is to validate the accuracy of the computerized sonographic features (CSF) by a CAD software device, namely, AmCAD-UT, and then to assess how the reading performance of clinicians (readers) can be improved providing the computerized features. The feature detection accuracy is tested against the ground truth established by a panel of thyroid specialists and a multiple-reader multiple-case (MRMC) study is performed to assess the sequential reading performance with the assistance of the CSF. Five computerized features, including anechoic area, hyperechoic foci, hypoechoic pattern, heterogeneous texture, and indistinct margin, were tested, with AUCs ranging from 0.888~0.946, 0.825~0.913, 0.812~0.847, 0.627~0.77, and 0.676~0.766, respectively. With the five CSFs, the sequential reading performance of 18 clinicians is found significantly improved, with the AUC increasing from 0.720 without CSF to 0.776 with CSF. Our studies show that the computerized features are consistent with the clinicians’ findings and provide additional value in assisting sonographic diagnosis.

Multitask network for thyroid nodule diagnosis based on TI-RADS

PURPOSE

Assessment of thyroid nodules is usually relied on the experience of the radiologist and is time consuming. Classification model of thyroid nodules can not only reduce the burden on physicians but also provide objective recommendations. However, most classification models based on deep learning simply give a prediction result of the benignity or malignancy of nodules thus physicians have no way of knowing how the deep learning gets the prediction result due to the black-box nature of neural networks. In this work, we integrate the explainability directly into the outputs generated by the model through combining TI-RADS. The inference process of the proposed method is consistent with doctor's clinical diagnosis process, therefore, doctors can better explain the diagnosis results of the model to the patient.

METHODS

A multitask network based on TI-RADS (MTN-TI-RADS) for the classification of thyroid nodules is proposed. In this network, a set of TI-RADS classifications of nodules is first obtained by multitask learning, then the TI-RADS points and the corresponding risk levels are calculated, finally, nodules are classified as benign and malignant. The classification process through the network is consistent with the diagnostic process of physician, thus the results of classification can be easily understood by physicians. In addition, the attention modules are introduced to the spatial and channel domains to let the network focus more on critical features.

RESULTS

To verify the classification performance of our method, we compared the results obtained through our method with the results of the radiologist's evaluation. For the 781 test nodules in the internal dataset and the 886 test nodules in the external dataset, the sensitivity and specificity of MTN-TI-RADS were 0.988, 0.912 in internal dataset, 0.949, 0.930 in external dataset, versus the senior radiologist of 0.925 (P < 0.001), 0.816 (P = 0.005) and 0.910(P = 0.009), 0.836 (P < 0.001), respectively. And the area under the receiver operating characteristic curve (AUC) of MTN-TI-RADS was 0.981 in internal dataset, 0.973 in external dataset, versus the senior radiologist of 0.905, 0.923. For the internal dataset, we also computed the accuracy of the risk level (TR1 to TR5) and the mean absolute error (MAE). The accuracy of the risk level of the proposed method is 78%, and the MAE is 1.30. The MAE of the total points (0 to 14 points) is 1.30.

CONCLUSIONS

An effective and result-interpretable end-to-end thyroid nodule classification network (MTN-TI-RADS) is proposed. MTN-TI-RADS has superior ability to classify malignant and benign thyroid nodules compared to senior radiologists. Based on MTN-TI-RADS, a classification model with strong interpretation and a high degree of physician trust is constructed. The proposed classification network is consistent with the diagnosis process of physicians, thus is more reliable and interpretable, and has great potential for clinical application. This article is protected by copyright. All rights reserved.

Differences in the ultrasonographic appearance of thyroid nodules after radiofrequency ablation

CONTEXT

Radiofrequency ablation (RFA) is a well-tolerated approach to treating benign thyroid nodules (TNs), but no index can predict its success. Other than size decrease, little is known about TN appearance on ultrasonography (US) after RFA.

OBJECTIVE

This study aimed to (a) assess the effectiveness of single-session RFA treatment, (b) determine whether pre-ablation US characteristics correlate with its effectiveness, and (c) demonstrate TN characteristics on baseline and follow-up US.

DESIGN

Retrospective cohort study among the patients who underwent single-session RFA for the treatment of benign TNs at a referral medical center between January 2018 and April 2019.

PATIENTS

A total of 116 patients (137 nodules) were included in the study.

MEASUREMENTS

Characteristics were quantified using commercial software. TNs were classified into 2015 American Thyroid Association (ATA) sonographic patterns and American College of Radiology Thyroid Imaging Reporting and Data System (ACR-TI-RADS) categories.

RESULTS

The average volume reduction ratio (VRR) was 74.51% in 1 year (95% confidence interval, 70.63%-78.39%). The only pre-ablation US feature significantly different between nodules with VRR <50% and VRR >50% was the cyst composition (0.05 vs. 0.02, p-value = .02). The VRR and margin change in the first 3 months after ablation were found to be leading indicators significantly correlated to the VRR in 6 months with correlation coefficients (r) = .72 and -.28 (p-value < .0001 and = .0008) and VRR in 1 year with r = .65 and -.17 (p-value < .0001 and = .046), respectively. After RFA, more TNs became ATA high suspicion (2.9% vs. 19.7%, p < .0001) and more appeared to be the non-ATA patterns (12.4% vs. 23.4%, p < .0001). Also, a greater number of post-RFA TNs were classified as ACR-TI-RADS categories 4 and 5 (40.1% vs. 70.1%, p < .0001).

CONCLUSIONS

Radiofrequency ablation therapy is effective for treating TNs. Pre-ablation cyst components, 3-month post-ablation volume reduction and margin change of TNs were related to the 6-month and 1-year response. Clinicians should consider that TNs would appear peculiar on US after RFA, mistakenly suggesting malignant potential.

Artificial Intelligence for Personalized Medicine in Thyroid Cancer: Current Status and Future Perspectives

Thyroid cancers (TC) have increasingly been detected following advances in diagnostic methods. Risk stratification guided by refined information becomes a crucial step toward the goal of personalized medicine. The diagnosis of TC mainly relies on imaging analysis, but visual examination may not reveal much information and not enable comprehensive analysis. Artificial intelligence (AI) is a technology used to extract and quantify key image information by simulating complex human functions. This latent, precise information contributes to stratify TC on the distinct risk and drives tailored management to transit from the surface (population-based) to a point (individual-based). In this review, we started with several challenges regarding personalized care in TC, for example, inconsistent rating ability of ultrasound physicians, uncertainty in cytopathological diagnosis, difficulty in discriminating follicular neoplasms, and inaccurate prognostication. We then analyzed and summarized the advances of AI to extract and analyze morphological, textural, and molecular features to reveal the ground truth of TC. Consequently, their combination with AI technology will make individual medical strategies possible.

Software-Based Analysis of the Taller-Than-Wide Feature of High-Risk Thyroid Nodules

BACKGROUND

Shape is one of the most important features in the diagnosis of malignant thyroid nodules. This characteristic has been described qualitatively, but only shapes that appear markedly different can be easily differentiated at first interpretation. This study sought to clarify whether software-based shape indexes are useful for the detection of thyroid cancers.

METHODS

In the final analysis, 200 participants with 231 pathologically proven nodules participated in the study. Ultrasound features were assessed by clinicians. The tumor contour was auto-defined, and shape indexes were calculated using commercial software.

RESULTS

Of the 231 nodules, 134 were benign and 97 were malignant. The presence of taller-than-wide (TTW) dimensions differed significantly between the benign and malignant thyroid tumors. Designation of TTW assessed by the software had a higher kappa value and proportional agreement than TTW assessed by clinicians. Disagreement between the clinician and software in designating nodules as TTW occurred for 28 nodules. The presence of other ultrasonic characteristics and small differences in the height and width measurements were causes for the incorrect interpretation of the TTW feature.

CONCLUSION

The proposed software-based quantitative analysis of tumor shape seems to be promising as an important advance compared with conventional TTW features evaluated by operators because it allows for a more reliable and consistent distinction and is less influenced by other ultrasonic features.

Risk Stratification in Patients With Follicular Neoplasm on Cytology: Use of Quantitative Characteristics and Sonographic Patterns

OBJECTIVES

Differentiating thyroid nodules with a cytological diagnosis of follicular neoplasm remains an issue. The goal of this study was to determine whether ultrasonographic (US) findings obtained preoperatively from the computer-aided detection (CAD) system are sufficient to further stratify the risk of malignancy for this diagnostic cytological category.

METHODS

From September 2016 to September 2018 in our hospital, patients diagnosed with Bethesda category IV (follicular neoplasm or suspicion of follicular neoplasm) thyroid nodules and underwent surgical excisions were include in the study. Quantification and analysis of tumor features were performed using CAD software. The US findings of the region of interest, including index of composition, margin, echogenicity, texture, echogenic dots indicative of calcifications, tall and wide orientation, and margin were calculated into computerized values. The nodules were further classified into American Thyroid Association (ATA) and American College of Radiology Thyroid Imaging Reporting & Data System (TI-RADS) categories.

RESULTS

92 (10.1%) of 913 patients were diagnosed with Bethesda category IV thyroid nodules. In 65 patients, the histological type of the nodule was identified. The quantitative features between patients with benign and malignant conditions differed significantly. The presence of heterogeneous echotexture, blurred margins, or irregular margins was shown to have the highest diagnostic value. The risks of malignancy for nodules classified as having very low to intermediate suspicion ATA, non-ATA, and high suspicion ATA patterns were 9%, 35.7%, and 51.7%, respectively. Meanwhile, the risks of malignancy were 12.5%, 26.1%, and 53.8% for nodules classified as TIRADS 3, 4, and 5, respectively. When compared to human observers, among whom poor agreement was noticeable, the CAD software has shown a higher average accuracy.

CONCLUSIONS

For patients with nodules diagnosed as Bethesda category IV, the software-based characterizations of US features, along with the associated ATA patterns and TIRADS system, were shown helpful in the risk stratification of malignancy.

Thyroid nodules risk stratification through deep learning based on ultrasound images

PURPOSE

Clinically, the risk stratification of thyroid nodules is usually used to formulate the next treatment plan. The American College of Radiology (ACR) thyroid imaging reporting and data system (TI-RADS) is a type of medical standard widely used in classification diagnosis. It divides the nodule's ACR TI-RADS level into five levels by quantitative scoring, from benign to high suspicion of malignancy. However, such assessment often relies on the radiologists' experience and is time consuming. So computer-aided diagnosis is necessary. But many deep learning (DL) models are difficult for doctors to understand, limiting their applicability in clinical practice. In this work, we mainly focus on how to achieve automatic thyroid nodules risk stratification based on deep integration of deep learning and clinical experience.

METHODS

An automatic hierarchical method of thyroid nodules risk based on deep learning is proposed, called risk stratification network (RS-Net). It incorporates medical experience based on ACR TI-RADS. The convolutional neural network (CNN) is used to classify the five categories in ACR TI-RADS and assign their points respectively. According to the point totals, the level of risk can be obtained. In addition, a dataset involving 13 984 thyroid ultrasound images is established to develop and evaluate the proposed method.

RESULTS

We have extensively compared the results of this paper with the evaluation results of sonographers. The accuracy of the risk stratification (TR1 to TR5) of the proposed method is 65%, and the mean absolute error (MAE) is 0.54. The MAE of the point totals (0 to 13 points) is 1.67. The Pearson's correlation between our method evaluation and doctor evaluation reached 0.84. For the benign and malignant classification, the performance indices accuracy, sensitivity, specificity, PPV, and NPV were 88.0%, 98.1%, 79.1%, 80.5%, and 97.9%, respectively. Our method's level of thyroid nodules risk stratification is comparable to that of a senior doctor.

CONCLUSIONS

This work provides a way to automate the risk stratification of thyroid nodules. Our method can effectively avoid missed diagnosis and misdiagnosis caused by the difference of observers so as to assist doctors to improve efficiency and diagnosis rate. Compared with the previous benign and malignant classification, the proposed method incorporates clinical experience. So it can greatly increase the clinicians' trust in the DL model, thereby improving the applicability of the model in clinical practice.

Multi-Reader Multi-Case Study for Performance Evaluation of High-Risk Thyroid Ultrasound with Computer-Aided Detection

Physicians use sonographic characteristics as a reference for the possible diagnosis of thyroid cancers. The purpose of this study was to investigate whether physicians were more effective in their tentative diagnosis based on the information provided by a computer-aided detection (CAD) system. A computer compared software-defined and physician-adjusted tumor loci. A multicenter, multireader, and multicase (MRMC) study was designed to compare clinician performance without and with the use of CAD. Interobserver variability was also analyzed. Excellent, satisfactory, and poor segmentations were observed in 25.3%, 58.9%, and 15.8% of nodules, respectively. There were 200 patients with 265 nodules in the study set. Nineteen physicians scored the malignancy potential of the nodules. The average area under the curve (AUC) of all readers was 0.728 without CAD and significantly increased to 0.792 with CAD. The average standard deviation of the malignant potential score significantly decreased from 18.97 to 16.29. The mean malignant potential score significantly decreased from 35.01 to 31.24 for benign cases. With the CAD system, an additional 7.6% of malignant nodules would be suggested for further evaluation, and biopsy would not be recommended for an additional 10.8% of benign nodules. The results demonstrated that applying a CAD system would improve clinicians’ interpretations and lessen the variability in diagnosis. However, more studies are needed to explore the use of the CAD system in an actual ultrasound diagnostic situation where much more benign thyroid nodules would be seen.

Quantitative Framework for Risk Stratification of Thyroid Nodules With Ultrasound: A Step Toward Automated Triage of Thyroid Cancer

OBJECTIVE. The purpose of this study was to explore whether a quantitative framework can be used to sonographically differentiate benign and malignant thyroid nodules at a level comparable to that of experts. MATERIALS AND METHODS. A dataset of ultrasound images of 92 biopsy-confirmed nodules was collected retrospectively. The nodules were delineated and annotated by two expert radiologists using the standardized Thyroid Imaging Reporting and Data System lexicon of the American College of Radiology. In the framework studied, quantitative features of echogenicity, texture, edge sharpness, and margin curvature properties of thyroid nodules were analyzed in a regularized logistic regression model to predict malignancy of a nodule. The framework was validated by leave-one-out cross-validation technique, and ROC AUC, sensitivity, and specificity were used to compare with those obtained with six expert annotation-based classifiers. RESULTS. The AUC of the proposed method was 0.828 (95% CI, 0.715-0.942), which was greater than or comparable to that of the expert classifiers, for which the AUC values ranged from 0.299 to 0.829 (p = 0.99). Use of the proposed framework could have avoided biopsy of 20 of 46 benign nodules in a curative strategy (at sensitivity of 1, statistically significantly higher than three expert classifiers) or helped identify 10 of 46 malignancies in a conservative strategy (at specificity of 1, statistically significantly higher than five expert classifiers). CONCLUSION. When the proposed quantitative framework was used, thyroid nodule malignancy was predicted at the level of expert classifiers. Such a framework may ultimately prove useful as the basis for a fully automated system of thyroid nodule triage.

Recognition of calcifications in thyroid nodules based on attention-gated collaborative supervision network of ultrasound images

BACKGROUND

Calcification is an important criterion for classification between benign and malignant thyroid nodules. Deep learning provides an important means for automatic calcification recognition, but it is tedious to annotate pixel-level labels for calcifications with various morphologies.

OBJECTIVE

This study aims to improve accuracy of calcification recognition and prediction of its location, as well as to reduce the number of pixel-level labels in model training.

METHODS

We proposed a collaborative supervision network based on attention gating (CS-AGnet), which was composed of two branches: a segmentation network and a classification network. The reorganized two-stage collaborative semi-supervised model was trained under the supervision of all image-level labels and few pixel-level labels.

RESULTS

The results show that although our semi-supervised network used only 30% (289 cases) of pixel-level labels for training, the accuracy of calcification recognition reaches 92.1%, which is very close to 92.9% of deep supervision with 100% (966 cases) pixel-level labels. The CS-AGnet enables to focus the model's attention on calcification objects. Thus, it achieves higher accuracy than other deep learning methods.

CONCLUSIONS

Our collaborative semi-supervised model has a preferable performance in calcification recognition, and it reduces the number of manual annotations of pixel-level labels. Moreover, it may be of great reference for the object recognition of medical dataset with few labels.

The Diagnostic Efficiency of Ultrasound Computer-Aided Diagnosis in Differentiating Thyroid Nodules: A Systematic Review and Narrative Synthesis

Computer-aided diagnosis (CAD) techniques have emerged to complement qualitative assessment in the diagnosis of benign and malignant thyroid nodules. The aim of this review was to summarize the current evidence on the diagnostic performance of various ultrasound CAD in characterizing thyroid nodules. PUBMED, EMBASE and Cochrane databases were searched for studies published until August 2019. The Quality Assessment of Studies of Diagnostic Accuracy included in Systematic Review 2 (QUADAS-2) tool was used to assess the methodological quality of the studies. Reported diagnostic performance data were analyzed and discussed. Fourteen studies with 2232 patients and 2675 thyroid nodules met the inclusion criteria. The study quality based on QUADAS-2 assessment was moderate. At best performance, grey scale CAD had a sensitivity of 96.7% while Doppler CAD was 90%. Combined techniques of qualitative grey scale features and Doppler CAD assessment resulted in overall increased sensitivity (92%) and optimal specificity (85.1%). The experience of the CAD user, nodule size and the thyroid malignancy risk stratification system used for interpretation were the main potential factors affecting diagnostic performance outcomes. The diagnostic performance of CAD of thyroid ultrasound is comparable to that of qualitative visual assessment; however, combined techniques have the potential for better optimized diagnostic accuracy.

Clinical Value of a Computer-Aided Diagnosis System in Thyroid Nodules: Analysis of a Reading Map Competition

We evaluated the accuracy of human and computer-aided diagnosis (CAD) in a reading map diagnosis competition for detection of thyroid cancers via ultrasonography (US). The competition comprised 33 thyroid nodule images randomly chosen between 2015 and 2017. One hundred seventy-seven contestants including one operator using CAD participated in the competition. The competition was separated into an online part and a live part. We compared the average accuracy of contestants and CAD in the detection of thyroid cancers. The accuracy of contestants and the CAD system was 60.3% and 84.8%, respectively. The accuracy of the CAD system was higher than that of the contestants with different technical titles. The areas under the curve for CAD and contestants were 0.985 (0.881-1.00) and 0.659 (0.645-0.673) (Z = 7.55, p < 0.01). The CAD system had high accuracy in this thyroid nodule reading map competition, and may be an adjuvant tool for radiologists.

Diagnostic Performance Evaluation of a Computer-Assisted Imaging Analysis System for Ultrasound Risk Stratification of Thyroid Nodules

OBJECTIVE. Ultrasound-based stratification of the malignancy risk of thyroid nodules has potential variability. The purpose of this study is to evaluate the diagnostic effectiveness of the first commercially available system for computer-aided diagnosis (CADx) imaging analysis. MATERIALS AND METHODS. Ultrasound images of 300 thyroid nodules (135 of which were malignant) acquired before surgical treatment were retrospectively reviewed by a thyroid expert, and his classification of each image was then compared with the classification rendered by an image analysis program (AmCAD-UT, AmCAD Biomed). The American Thyroid Association (ATA) classification system, the European Thyroid Imaging Reporting and Data System (EU-TIRADS), and the classification system jointly proposed by American and Italian associations of clinical endocrinologists (the American Association of Clinical Endocrinologists [AACE], the American College of Endocrinology [ACE], and Associazione Medici Endocrinologi [AME]) were used for risk stratification. RESULTS. The diagnostic performance of the thyroid expert when the ATA system was used was as follows: sensitivity, 87.0%; specificity, 91.2%; positive predictive value, 90.5%; and negative predictive value, 90.9%. Compared with the expert, the CADx program, when used with the three classification systems, had a similar sensitivity but a lower specificity and positive predictive value. Regarding the negative predictive value, the results of the expert did not differ from those of the CADx program when it applied the ATA classification system (90.9% vs 86.3%; p = 0.07). The ROC AUC value was 0.88 for the expert clinician and 0.72 for the CADx program when the ATA classification system was used. CONCLUSION. The CADx ultrasound image analysis program described in the present study is useful for risk stratification of thyroid nodules, but it does not perform better than a sonography expert.

Computer-Aided Diagnostic Technique in 2-Deoxy-2-[18F]fluoro-D-glucose-Positive Thyroid Nodule: Clinical Experience of 74 Non-thyroid Cancer Patients

This study verified the value of a computer-aided diagnosis (CAD) technique assisting in ultrasonography (US) diagnosis of 2-deoxy-2-[¹⁸F]fluoro-D-glucose (¹⁸FDG)-avid thyroid incidentalomas on positron emission tomography. A total of 82 ¹⁸FDG-avid thyroid incidentalomas from 74 non-thyroid cancer patients were retrospectively analyzed with respect to US and CAD parameters (anechoic area, hyper-echoic foci, hypo-echogenicity, heterogeneity, margin, taller-than-wide shape, eccentric area) and were compared with 38 other non-¹⁸FDG-avid nodules found in the same patient group. Fine-needle aspiration cytology or surgical intervention pathology was performed for diagnosis. No significant differences in nodule size or CAD parameters were found in ¹⁸FDG-avid nodules reported as benign, indeterminate or malignant. Significantly more taller-than-wide nodules were thyroid originating than metastatic (0.30 vs. 0.16, p < 0.05). Nevertheless, combined CAD and positron emission tomography/computed tomography scores and a discrimination point of 4 resulted in a sensitivity of 75% and a specificity of 80% in prediction of incidentaloma benignity.

Quantitative analysis of echogenicity for patients with thyroid nodules

Hypoechogenicity has been described qualitatively and is potentially subject to intra- and inter-observer variability. The aim of this study was to clarify whether quantitative echoic indexes (EIs) are useful for the detection of malignant thyroid nodules. Overall, 333 participants with 411 nodules were included in the final analysis. Quantification of echogenicity was performed using commercial software (AmCAD-UT; AmCad BioMed, Taiwan). The coordinates of three defined regions, the nodule, thyroid parenchyma, and strap muscle regions, were recorded in the database separately for subsequent analysis. And the results showed that ultrasound echogenicity (US-E), as assessed by clinicians, defined hypoechogenicity as an independent factor for malignancy. The EI, adjusted EI (EI_N-T; EI_N-M) and automatic EI_(N-R)/R values between benign and malignant nodules were all significantly different, with lower values for malignant nodules. All of the EIs showed similar percentages of sensitivity and specificity and had better accuracies than US-E. In conclusion, the proposed quantitative EI seems more promising to constitute an important advancement than the conventional qualitative US-E in allowing for a more reliable distinction between benign and malignant thyroid nodules.

Computerized analysis of calcification of thyroid nodules as visualized by ultrasonography

OBJECTIVE

The purpose of this study is to quantify computerized calcification features from ultrasonography (US) images of thyroid nodules in order to determine the ability to differentiate between malignant and benign thyroid nodules.

METHODS

We designed and implemented a computerized analysis scheme to quantitatively analyze the US features of the calcified thyroid nodules from 99 pathologically determined calcified thyroid nodules. Univariate analysis was used to identify features that were significantly associated with tumor malignancy, and neural-network analysis was performed to classify tumors as benign or malignant. The diagnostic performance of the neural network was evaluated using receiver operating characteristic (ROC) analysis, where in the area under the ROC curve (Az) summarized the diagnostic performance of specific calcification features.

RESULTS

The performance values for each calcification feature were as follows: ratio of calcification distance=0.80, number of calcifications=0.68, skewness=0.82, and maximum intensity=0.75. The combined value of the four features was 0.84.With a threshold of 0.64, the Az value of calcification features was 0.83 with a sensitivity of 83.0%, specificity of 82.4%, and accuracy of 82.8%.

CONCLUSIONS

These results support the clinical feasibility of using computerized analysis of calcification features from thyroid US for differentiating between malignant and benign nodules.

Computerized quantification of ultrasonic heterogeneity in thyroid nodules

To test whether computerized quantification of ultrasonic heterogeneity can be of help in the diagnosis of thyroid malignancy, we evaluated ultrasonic heterogeneity with an objective and quantitative computerized method in a prospective setting. A total of 400 nodules including 271 benign thyroid nodules and 129 malignant thyroid nodules were evaluated. Patient clinical data were collected, and the grading of heterogeneity on conventional gray-scale ultrasound images was retrospectively reviewed by a thyroid specialist. Quantification of ultrasonic heterogeneity (heterogeneity index, HI) was performed by a proprietary program implemented with methods proposed in this article. HI values differed significantly between benign and malignant nodules, diagnosed by a combination of fine-needle aspiration and surgical pathology results (p < 0.001, area under the curve = 0.714). The ultrasonic heterogeneity of these samples, as assessed by an experienced clinician, could not significantly differentiate between benign and malignant thyroid nodules. However, nodules with marked ultrasonic heterogeneity had higher HI values than nodules with homogeneous nodules. These results indicate that the new computer-aided diagnosis method for evaluation of the ultrasonic heterogeneity of thyroid nodules is an objective and quantitative method that is correlated with conventional ultrasonic heterogeneity assessment, but can better aid in the diagnosis of thyroid malignancy.

Quantitative analysis of dynamic power Doppler sonograms for patients with thyroid nodules

To clarify and determine whether power Doppler sonograms are useful for the detection of malignant thyroid nodules, a computerized quantification method was used to evaluate the vascular density of a thyroid nodule in a prospective setting. Sonographic power Doppler images were collected in consecutive frames (45 frames of images), and a proprietary program (AmCAD-UV) was implemented using methods proposed in this article automatically calculated a quantified power Doppler vascular index (PDVI). The minimum PDVI value (PDVImin) was suggested as a measure of the vascular density of the nodule. The vascular densities of the peripheral and central areas of the nodule, referred to as central PDVImin and Ring PDVImin, respectively, were also evaluated. For 238 tumors (79 malignant and 159 benign) from 208 patients, all of the proposed indices of benign lesions were significantly higher than those of the malignant lesions. The area under the receiver operating characteristic curve (AUC) reaches 71% with the PDVImin. When the vascular patterns were further classified into intra-nodular and peripheral vascularity types, no vascularity type was observed significantly more frequently in malignant nodules than in benign nodules. These proposed computerized vascular indices provide a quantification method to objectively evaluate thyroid nodules and have potential as predictors of thyroid malignancy. The conventional vascular characterizations of malign nodules, that is, more vessels are observed in malignant nodules than in benign nodules, are shown to be unreliable in our study. Instead, a higher value of the quantified power Doppler vascular density was observed in benign nodules.