The clinical value of artificial intelligence in assisting junior radiologists in thyroid ultrasound: a multicenter prospective study from real clinical practice.

BACKGROUND: This study is to propose a clinically applicable 2-echelon (2e) diagnostic criteria for the analysis of thyroid nodules such that low-risk nodules are screened off while only suspicious or indeterminate ones are further examined by histopathology, and to explore whether artificial intelligence (AI) can provide precise assistance for clinical decision-making in the real-world prospective scenario. METHODS: In this prospective study, we enrolled 1036 patients with a total of 2296 thyroid nodules from three medical centers. The diagnostic performance of the AI system, radiologists with different levels of experience, and AI-assisted radiologists with different levels of experience in diagnosing thyroid nodules were evaluated against our proposed 2e diagnostic criteria, with the first being an arbitration committee consisting of 3 senior specialists and the second being cyto- or histopathology. RESULTS: According to the 2e diagnostic criteria, 1543 nodules were classified by the arbitration committee, and the benign and malignant nature of 753 nodules was determined by pathological examinations. Taking pathological results as the evaluation standard, the sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC) of the AI systems were 0.826, 0.815, 0.821, and 0.821. For those cases where diagnosis by the Arbitration Committee were taken as the evaluation standard, the sensitivity, specificity, accuracy, and AUC of the AI system were 0.946, 0.966, 0.964, and 0.956. Taking the global 2e diagnostic criteria as the gold standard, the sensitivity, specificity, accuracy, and AUC of the AI system were 0.868, 0.934, 0.917, and 0.901, respectively. Under different criteria, AI was comparable to the diagnostic performance of senior radiologists and outperformed junior radiologists (all P < 0.05). Furthermore, AI assistance significantly improved the performance of junior radiologists in the diagnosis of thyroid nodules, and their diagnostic performance was comparable to that of senior radiologists when pathological results were taken as the gold standard (all p > 0.05). CONCLUSIONS: The proposed 2e diagnostic criteria are consistent with real-world clinical evaluations and affirm the applicability of the AI system. Under the 2e criteria, the diagnostic performance of the AI system is comparable to that of senior radiologists and significantly improves the diagnostic capabilities of junior radiologists. This has the potential to reduce unnecessary invasive diagnostic procedures in real-world clinical practice.

Deep learning to assist composition classification and thyroid solid nodule diagnosis: a multicenter diagnostic study.

OBJECTIVES: This study aimed to propose a deep learning (DL)-based framework for identifying the composition of thyroid nodules and assessing their malignancy risk. METHODS: We conducted a retrospective multicenter study using ultrasound images from four hospitals. Convolutional neural network (CNN) models were constructed to classify ultrasound images of thyroid nodules into solid and non-solid, as well as benign and malignant. A total of 11,201 images of 6784 nodules were used for training, validation, and testing. The area under the receiver-operating characteristic curve (AUC) was employed as the primary evaluation index. RESULTS: The models had AUCs higher than 0.91 in the benign and malignant grading of solid thyroid nodules, with the Inception-ResNet AUC being the highest at 0.94. In the test set, the best algorithm for identifying benign and malignant thyroid nodules had a sensitivity of 0.88, and a specificity of 0.86. In the human vs. DL test set, the best algorithm had a sensitivity of 0.93, and a specificity of 0.86. The Inception-ResNet model performed better than the senior physicians (p < 0.001). The sensitivity and specificity of the optimal model based on the external test set were 0.90 and 0.75, respectively. CONCLUSIONS: This research demonstrates that CNNs can assist thyroid nodule diagnosis and reduce the rate of unnecessary fine-needle aspiration (FNA). CLINICAL RELEVANCE STATEMENT: High-resolution ultrasound has led to increased detection of thyroid nodules. This results in unnecessary fine-needle aspiration and anxiety for patients whose nodules are benign. Deep learning can solve these problems to some extent. KEY POINTS: • Thyroid solid nodules have a high probability of malignancy. • Our models can improve the differentiation between benign and malignant solid thyroid nodules. • The differential performance of one model was superior to that of senior radiologists. Applying this could reduce the rate of unnecessary fine-needle aspiration of solid thyroid nodules.

Ultrasound and clinicopathological characteristics-based model for prediction of pathologic response to neoadjuvant chemotherapy in HER2-positive breast cancer: a case-control study.

BACKGROUND: The objective of this study was to develop a model combining ultrasound (US) and clinicopathological characteristics to predict the pathologic response to neoadjuvant chemotherapy (NACT) in human epidermal growth factor receptor 2 (HER2)-positive breast cancer. MATERIALS AND METHODS: This is a retrospective study that included 248 patients with HER2-positive breast cancer who underwent NACT from March 2018 to March 2022. US and clinicopathological characteristics were collected from all patients in this study, and characteristics obtained using univariate analysis at p < 0.1 were subjected to multivariate analysis and then the conventional US and clinicopathological characteristics independently associated with pathologic complete response (pCR) from the analysis were used to develop US models, clinicopathological models, and their combined models by the area under the receiver operating characteristic (ROC) curve (AUC), accuracy, sensitivity, and specificity to assess their predictive efficacy. RESULTS: The combined model had an AUC of 0.808, a sensitivity of 88.72%, a specificity of 60.87%, and an accuracy of 75.81% in predicting pCR of HER2-positive breast cancer after NACT, which was significantly better than the clinicopathological model (AUC = 0.656) and the US model (AUC = 0.769). In addition, six characteristics were screened as independent predictors, namely the Clinical T stage, Clinical N stage, PR status, posterior acoustic, margin, and calcification. CONCLUSION: The conventional US combined with clinicopathological characteristics to construct a combined model has a good diagnostic effect in predicting pCR in HER2-positive breast cancer and is expected to be a useful tool to assist clinicians in effectively determining the efficacy of NACT in HER2-positive breast cancer patients.

Clinical features and imaging examination assessment of cervical lymph nodes for thyroid carcinoma.

BACKGROUNDS: The purpose of this study is to investigate the relationship between clinical characteristics and cervical lymph node metastasis (LNM) in patients with thyroid carcinoma, as well as estimate the preoperative diagnosis values of ultrasound (US) and contrast enhanced computed tomography (CECT) examinations on the neck for detection of cervical LNM in thyroid carcinoma. METHODS: A retrospective analysis of 3 026 patients with surgically proven thyroid carcinoma was conducted. Patients' clinical characteristics, including gender, age, tumor size, bilateral lesions, multifocality, adenomatous nodules, Hashimoto's thyroiditis (HT), and extrathyroidal extension, were collected to explore their association with cervical LNM in thyroid carcinoma. Preoperative assessments for central lymph node metastasis (CLNM) and lateral lymph node metastasis (LLNM) were conducted through US and CECT. The diagnostic value of US, CECT and US combined with CECT for detection of LNM located in various cervical compartments was estimated based on the pathological results. RESULTS: The risk of cervical LNM was higher in thyroid cancer patients who were male, age < 55 years old, tumor size > 10 mm, bilateral lesions, and extrathyroidal extension, while multifocality, adenomatous nodules and HT had no significant effect on LNM. US, CECT and US combined with CECT all had a higher sensitivity to LLNM (93.1%, 57.8%, 95.4%) than to CLNM (32.3%, 29.0%, 43.4%). US and CECT had a high specificity to both CLNM and LLNM (94.3-97.8%). CONCLUSION: Preoperative clinical characteristics and imaging examinations on patients with thyroid carcinoma are crucial to the evaluation of cervical lymph nodes and conducive to individualizing surgical treatments by clinicians. US combined with CECT are superior to single US or CECT alone in detection of CLNM and LLNM.

Deep learning approaches for differentiating thyroid nodules with calcification: a two-center study.

BACKGROUND: Calcification is a common phenomenon in both benign and malignant thyroid nodules. However, the clinical significance of calcification remains unclear. Therefore, we explored a more objective method for distinguishing between benign and malignant thyroid calcified nodules. METHODS: This retrospective study, conducted at two centers, involved a total of 631 thyroid nodules, all of which were pathologically confirmed. Ultrasound image sets were employed for analysis. The primary evaluation index was the area under the receiver-operator characteristic curve (AUROC). We compared the diagnostic performance of deep learning (DL) methods with that of radiologists and determined whether DL could enhance the diagnostic capabilities of radiologists. RESULTS: The Xception classification model exhibited the highest performance, achieving an AUROC of up to 0.970, followed by the DenseNet169 model, which attained an AUROC of up to 0.959. Notably, both DL models outperformed radiologists (P < 0.05). The success of the Xception model can be attributed to its incorporation of deep separable convolution, which effectively reduces the model's parameter count. This feature enables the model to capture features more effectively during the feature extraction process, resulting in superior performance, particularly when dealing with limited data. CONCLUSIONS: This study conclusively demonstrated that DL outperformed radiologists in differentiating between benign and malignant calcified thyroid nodules. Additionally, the diagnostic capabilities of radiologists could be enhanced with the aid of DL.

Combined Conventional Ultrasound and Contrast-Enhanced Computed Tomography for Cervical Lymph Node Metastasis Prediction in Papillary Thyroid Carcinoma.

OBJECTIVES: This study aimed to evaluate conventional ultrasound (US) combined with contrast-enhanced computed tomography (CT) of the neck to predict central lymph node metastasis (CLNM) in clinical lymph-negative patients with papillary thyroid carcinoma (PTC), establish a simple preoperative risk-scoring model, and validate its effectiveness in a two-center dataset. METHODS: A total of 423 patients with PTC preoperatively evaluated by US and contrast-enhanced CT were included in the modeling group, and 102 patients from two hospitals were enrolled in the validation group. Independent predictive factors were determined using multivariate logistic regression analysis. Diagnostic performance was evaluated using receiver operating characteristic curve analysis. RESULTS: The independent predictive factors for CLNM were age ≤45 years (odds ratio [OR] = 3.950), nodule presence in the non-upper pole (OR = 2.385), nodule size >12.5 mm (OR = 2.130), Thyroid Imaging Reporting and Data System score ≥9 (OR = 2.857), normalized enhancement CT value ≥0.75 (OR = 3.132), central enhancement (OR = 0.222), and capsular invasion (OR = 3.478). The area under the curve (AUC) of the model was 0.790 (95% confidence interval [CI]: 0.747-0.834), and the sensitivity and specificity were 70.4% and 73.9%, respectively. The AUC in the validation group was 0.827 (95% CI: 0.747-0.907), and the sensitivity and specificity were 88.9% and 63.2%, respectively. CONCLUSIONS: We found conventional US combined with contrast-enhanced CT of the neck to be useful in predicting CLNM preoperatively and established a simple risk-scoring model that might help surgeons with appropriate surgical plans and prognostic evaluation.

Differential diagnosis of non-diffuse primary thyroid lymphoma and papillary thyroid carcinoma by ultrasound combined with computed tomography.

BACKGROUND: Primary thyroid lymphoma (PTL) and papillary thyroid carcinoma (PTC) are both thyroid malignancies, but their therapeutic methods and prognosis are different. This study aims to explore their sonographic and computed tomography(CT)features, and to improve the early diagnosis rate. METHODS: The clinical and imaging data of 50 patients with non-diffuse PTL and 100 patients with PTC confirmed by pathology were retrospectively analysed. RESULTS: Of the 150 patients, from the perspective of clinical data, between non-diffuse PTL and PTC patients existed significant difference in age, maximum diameter of nodule, asymmetric enlargement and Hashimoto's thyroiditis (P < 0.001), but not in gender ratio, echo texture, cystic change and anteroposterior-to-transverse ratio (P > 0.05). With respect to sonographic feature, non-diffuse PTL patients had a higher proportion than PTC patients in markedly hypoechoic, internal linear echogenic strands, posterior echo enhancement, rich vascularity, lack of calcification and homogeneous enhancement, with statistically significant difference (P < 0.05), while PTC patients had a higher proportion than non-diffuse PTL patients in irregular border, circumscribed margin, capsular invasion and significant enhancement, with statistically significant difference (P < 0.001). With respect to CT feature, non-diffuse PTL patients were significantly different from PTC patients in the non-contrast CT value mean, venous phase CT value mean, enhanced intensity and homogeneity of nodules (P < 0.05). Multivariate logistic regression analysis showed that age (OR = 1.226, 95%CI:1.056 ~ 1.423, P = 0.007), posterior echo enhancement (OR = 51.152, 95%CI: 2.934 ~ 891.738, P = 0.007), lack of calcification (OR = 0.013, 95%CI: 0.000 ~ 0.400, P = 0.013) and homogeneous enhancement (OR = 0.020, 95%CI: 0.001 ~ 0.507, P = 0.018) were independent risk factors. CONCLUSIONS: Sonographic and CT features of the presence of posterior echo enhancement, lack of calcification and homogeneous enhancement were valuable to distinguishing non-diffuse PTL from PTC.

Multimodal Sonographic Appearance and Survival Outcomes of 69 Cases of Primary Thyroid Lymphoma Over 10 Years.

OBJECTIVES: To investigate ultrasound appearance and the survival outcomes for patients with primary thyroid lymphoma (PTL). METHODS: Ultrasonic images and clinical characteristics from pathologically confirmed 69 PTL patients (2008-2019) were retrospectively analyzed. The clinical characteristics, ultrasonic characters, and prognostic factors were analyzed. Survival curves were plotted using the Kaplan-Meier method. Univariate and multivariate analyses were performed. RESULTS: Of the 69 study patients, 23 were indolent PTL and 46 were aggressive PTL. Age (>70 years old) and elevated lactate dehydrogenase levels were statistically different clinical features between aggressive and indolent PTL. From ultrasonic images, 34 cases were nodular, 11 diffuse, and 24 mixed pattern. Mixed types displayed high invasiveness (45.7%) while diffuse types displayed higher inertness (39.1%), with statistically significant differences (P = .000). Invaded thyroid capsule and increased chaotic vascularity also showed significant differences between aggressive and indolent PTL. We also observed statistical difference in overall survival rates between aggressive and indolent PTL (P = .032). Single factor K-M analyses showed that age >70 years, aggressive pathology, and Ki67 >30% were positively correlated with the risk of poor PTL survival (P < .05). CONCLUSIONS: Multimodal ultrasound provides accurate ultrasonographic information and facilitates PTL invasiveness diagnostics for improved clinical treatment. In addition, PTL patients aged >70 years, with aggressive pathology, and Ki67 >30% were more likely to have a poor survival outcome.

AI detection of mild COVID-19 pneumonia from chest CT scans.

OBJECTIVES: An artificial intelligence model was adopted to identify mild COVID-19 pneumonia from computed tomography (CT) volumes, and its diagnostic performance was then evaluated. METHODS: In this retrospective multicenter study, an atrous convolution-based deep learning model was established for the computer-assisted diagnosis of mild COVID-19 pneumonia. The dataset included 2087 chest CT exams collected from four hospitals between 1 January 2019 and 31 May 2020. The true positive rate, true negative rate, receiver operating characteristic curve, area under the curve (AUC) and convolutional feature map were used to evaluate the model. RESULTS: The proposed deep learning model was trained on 1538 patients and tested on an independent testing cohort of 549 patients. The overall sensitivity was 91.5% (195/213; p < 0.001, 95% CI: 89.2-93.9%), the overall specificity was 90.5% (304/336; p < 0.001, 95% CI: 88.0-92.9%) and the general AUC value was 0.955 (p < 0.001). CONCLUSIONS: A deep learning model can accurately detect COVID-19 and serve as an important supplement to the COVID-19 reverse transcription-polymerase chain reaction (RT-PCR) test. KEY POINTS: • The implementation of a deep learning model to identify mild COVID-19 pneumonia was confirmed to be effective and feasible. • The strategy of using a binary code instead of the region of interest label to identify mild COVID-19 pneumonia was verified. • This AI model can assist in the early screening of COVID-19 without interfering with normal clinical examinations.

Implicit kernel sparse shape representation: a sparse-neighbors-based objection segmentation framework.

This paper introduces a new implicit-kernel-sparse-shape-representation-based object segmentation framework. Given an input object whose shape is similar to some of the elements in the training set, the proposed model can automatically find a cluster of implicit kernel sparse neighbors to approximately represent the input shape and guide the segmentation. A distance-constrained probabilistic definition together with a dualization energy term is developed to connect high-level shape representation and low-level image information. We theoretically prove that our model not only derives from two projected convex sets but is also equivalent to a sparse-reconstruction-error-based representation in the Hilbert space. Finally, a "wake-sleep"-based segmentation framework is applied to drive the evolutionary curve to recover the original shape of the object. We test our model on two public datasets. Numerical experiments on both synthetic images and real applications show the superior capabilities of the proposed framework.

Machine learning-based model constructed from ultrasound radiomics and clinical features for predicting HER2 status in breast cancer patients with indeterminate (2+) immunohistochemical results.

BACKGROUND: We aimed to predict human epidermal growth factor receptor 2 (HER2) 2+ status in patients with breast cancer by constructing and validating machine learning models utilizing ultrasound (US) radiomics and clinical features. METHODS: We analyzed 203 breast cancer cases immunohistochemically determined as HER2 2+ and used fluorescence in situ hybridization (FISH) as the confirmation method. From each case, the study analyzed 840 extracted radiomics features and 11 clinicopathologic features. Cases were randomly split into training (n = 141) and validation sets (n = 62) at a 7:3 ratio. Univariate logistic regression analysis was first performed on the 11 clinicopathologic characteristics. The least absolute shrinkage and selection operator (LASSO) and decision tree (DT) techniques were employed for post-feature selection. Finally, 19 radiomics features were utilized in logistic regression (LR) and Naive Bayesian (NB) classifiers. Model performance was gauged using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. RESULTS: Our models exhibited notable diagnostic efficacy in differentiating HER2-positive from negative breast cancer cases. In the validation sets, the LR model outperformed the NB model with an AUC of 0.860 and accuracy of 83.8% compared to NB's AUC of 0.684 and accuracy of 79.0%. The LR model demonstrated higher sensitivity (92.3% vs. 46.2%) while the NB model had a better specificity (91.8% vs. 63.3%) in the validation set. CONCLUSIONS: Machine learning models grounded on radiomics efficiently predicted IHC HER2 2+ status in breast cancer patients, suggesting potential enhancements in clinical decision-making for treatment and management.

Predicting hormone receptor status in invasive breast cancer through radiomics analysis of long-axis and short-axis ultrasound planes.

The hormone receptor (HR) status plays a significant role in breast cancer, serving as the primary guide for treatment decisions and closely correlating with prognosis. This study aims to investigate the predictive value of radiomics analysis in long-axis and short-axis ultrasound planes for distinguishing between HR-positive and HR-negative breast cancers. A cohort of 505 patients from two hospitals was stratified into discovery (Institute 1, 416 patients) and validation (Institute 2, 89 patients) cohorts. A comprehensive set of 788 ultrasound radiomics features was extracted from both long-axis and short-axis ultrasound planes, respectively. Utilizing least absolute shrinkage and selection operator (LASSO) regression analysis, distinct models were constructed for the long-axis and short-axis data. Subsequently, radiomics scores (Rad-scores) were computed for each patient. Additionally, a combined model was formulated by integrating data from long-axis and short-axis Rad-scores along with clinical factors. The diagnostic efficacy of all models was evaluated by the area under the receiver operating characteristic (ROC) curve (AUC). The long-axis and short-axis models, consisting of 11 features and 15 features, respectively, were established, yielding AUCs of 0.743 and 0.751 in the discovery cohort, and 0.795 and 0.744 in the validation cohort. The calculated long-axis and short-axis Rad-scores exhibited significant differences between HR-positive and HR-negative groups across all cohorts (all p < 0.001). Univariate analysis identified ultrasound-reported tumor size as an independent predictor. The combined model, incorporating long-axis and short-axis Rad-scores along with tumor size, achieved superior AUCs of 0.788 and 0.822 in the discovery and validation cohorts, respectively. The combined model effectively distinguishes between HR-positive and HR-negative breast cancers based on ultrasound radiomics features and tumor size, which may offer a valuable tool to facilitate treatment decision making and prognostic assessment.

Prognosis and influencing factors of follicular thyroid cancer.

OBJECTIVES: Follicular thyroid cancer (FTC) is prone to distant metastasis, and patients with distant metastasis often have poor prognosis. In this study, the impact of metastasis and other relevant factors on the prognosis of follicular thyroid carcinoma was examined. METHODS: This was a retrospective study. Data were obtained from Zhejiang Cancer Hospital, Sun Yat-sen University Cancer Center and Hangzhou First People's Hospital affiliated with Zhejiang University School of Medicine, from January 2009 to June 2021 for 153 FTC patients. The patients were assigned into three groups according to their distant metastasis: distant metastasis at initial diagnosis (M1), distant metastasis during follow-up (M2), and no evidence of distant metastasis over the course of the study (M0). Data were collected and summarized on clinical data, laboratory parameters, imaging features, postoperative pathologic subtypes, and metastases. The Cox proportional hazard model was used to perform the univariate and multivariate analysis. Kaplan-Meier curves were used to evaluate cancer-specific survival (CSS). RESULTS: Based on metastasis, the patients were assigned into three groups, including 31 in the M1 group, 15 in the M2 group, and 107 in the M0 group. These individuals were followed up for an average of 5.9 years, and the group included 46 patients with distant metastasis (31 confirmed at diagnosis and 15 found during follow-up). Univariate Cox regression analysis showed that age, Hashimoto's thyroiditis (HT), surgery method, postoperative adjuvant therapy, histologic subtype, nodule size, calcification, TSH, and distant metastasis all impacted prognosis. Multivariate Cox regression analysis suggested that histologic subtype (widely invasive; HR: 7.440; 95% CI: 3.083, 17.954; p < 0.001), nodule size (≥40 mm; HR: 8.622; 95% CI: 3.181, 23.369; p < 0.001) and distant metastasis (positive; HR: 6.727; 95% CI: 2.488, 18.186; p < 0.001) were independent risk factors affecting the prognosis of follicular thyroid cancer. CONCLUSIONS: Histologic subtype, nodule size, and distant metastasis are important risk factors for the prognosis of follicular thyroid cancer. Patients with metastatic follicular thyroid cancer have a poor prognosis, especially with metastasis at the time of initial diagnosis. As a result, this group of patients requires individualized treatment and closer follow-up.

Deep learning-assisted distinguishing breast phyllodes tumors from fibroadenomas based on ultrasound images: a diagnostic study.

OBJECTIVES: To evaluate the performance of ultrasound-based deep learning (DL) models in distinguishing breast phyllodes tumors (PTs) from fibroadenomas (FAs) and their clinical utility in assisting radiologists with varying diagnostic experiences. METHODS: We retrospectively collected 1180 ultrasound images from 539 patients (247 PTs and 292 FAs). Five DL network models with different structures were trained and validated using nodule regions annotated by radiologists on breast ultrasound images. DL models were trained using the methods of transfer learning and 3-fold cross-validation. The model demonstrated the best evaluation index in the 3-fold cross-validation was selected for comparison with radiologists' diagnostic decisions. Two-round reader studies were conducted to investigate the value of DL model in assisting six radiologists with different levels of experience. RESULTS: Upon testing, Xception model demonstrated the best diagnostic performance (AUC: 0.87, 95%CI: 0.81-0.92), outperforming all radiologists (all p < 0.05). Additionally, the DL model enhanced the diagnostic performance of radiologists. Accuracy demonstrated improvements of 4%, 4%, and 3% for senior, intermediate, and junior radiologists, respectively. CONCLUSIONS: The DL models showed superior predictive abilities compared to experienced radiologists in distinguishing breast PTs from FAs. Utilizing the model led to improved efficiency and diagnostic performance for radiologists with different levels of experience (6-25 years of work). ADVANCES IN KNOWLEDGE: We developed and validated a DL model based on the largest available dataset to assist in diagnosing PTs. This model has the potential to allow radiologists to discriminate two types of breast tumors which are challenging to identify with precision and accuracy, and subsequently to make more informed decisions about surgical plans.

Factors influencing extrathyroidal extension of papillary thyroid cancer and evaluation of ultrasonography for its diagnosis: a retrospective analysis.

Pathologists usually explore extrathyroidal extensions (ETEs) in thyroid cancer; however, sonographers are often not concerned with ETEs. We investigated factors influencing ETEs and the efficacy of ultrasound evaluation of thyroid capsule invasion. We retrospectively analysed 1933 papillary thyroid carcinoma patients who underwent thyroidectomy during 2018-2021. Patients were divided into three groups: no ETE, minor ETE (mETE), and gross ETE. Clinical characteristic differences were assessed using binary logistic regression analysis to identify ETE predictors, and the kappa test was performed to analyse consistency between ultrasonographic and pathological diagnoses of ETE. The mETE group was more likely to have larger tumour diameters and more extensive lymph node metastasis (LNM) than the no ETE group and more likely to be diagnosed in the isthmus. In the multivariate logistic regression analysis, longest tumour diameter, lesion site, LNM extent, and thyroglobulin concentration were significant mETE predictors. Minimal consistency existed between pathological and ultrasonographic examinations for neighbouring tissue invasion. Many clinical differences were observed between the no ETE and mETE groups, suggesting the importance of considering mETE. Therefore, sonographers should pay more attention to relationships between nodules and capsule and indicate these on ultrasound reports to provide more accurate preoperative ETE information for surgeons.

Association between papillary thyroid carcinoma and cervical lymph node metastasis based on ultrasonic radio frequency signals.

OBJECTIVE: Papillary thyroid carcinoma (PTC) has a high propensity for cervical lymph node metastasis (CLNM). We evaluated the association between PTC radio frequency (RF) signals and CLNM. METHODS: Patients with PTC (n = 170) confirmed by pathology after thyroidectomy between July 2019 and May 2022 were enrolled in this retrospective cohort study. Patients were divided into positive and negative groups according to CLNM. Univariate analysis was performed to predict CLNM and a receiver operating characteristic (ROC) curve was generated to evaluate the diagnostic performance of RF signals and the Thyroid imaging Reporting and Data System. RESULTS: Of 170 patients with 182 nodules included in the study, 11 had multiple nodules. Univariate analysis showed that age, maximum tumor diameter, cross-sectional and longitudinal aspect ratio, RF quantitative parameters (cross-sectional intercept, mid-band, S1, and S4, and longitudinal Higuchi, slope, intercept, mid-band, S1), and echogenic foci were independently associated with CLNM (p < 0.05). The area under the curve (AUC) values of the maximum tumor diameter, longitudinal slope, and echogenic foci were 0.68, 0.61, and 0.62, respectively. Linear regression analysis of maximum tumor diameter, longitudinal slope, and echogenic foci showed that the correlations between longitudinal slope and CLNM were greater than that of echogenic foci (ß = 0.203 vs. ß = 0.154). CONCLUSION: Longitudinal slope and echogenic foci have similar diagnostic efficacy for predicting the risk of CLNM in PTC, although longitudinal slope has a greater correlation with CLNM.

Long Noncoding RNA LINC01614 is a Diagnostic and Prognostic Marker for Breast Cancer.

BACKGROUND: The long intergenic non-coding RNA 01614 (LINC01614) is aberrantly expressed in various malignancies, suggesting its role in oncogenesis. However, it has not been well studied in breast cancer. METHODS: The cancer genome atlas databases (TCGA) and public database of breast cancer gene-expression miner (bc-GenExMiner) were utilized to analyze the prognostic role of LINC01614 in breast cancer. Kaplan-Meier, and Cox regression analyses were conducted for survival analysis. Nomograms were built to predict survival. We used deconvolution-based methods, such as TIMER (Tumor Immune Estimation Resource) and CIBERSORT (cell-type identification by estimating relative subsets of RNA transcripts), to explore the relationship between LINC01614 and immune cell characteristics. RESULTS: The very abnormal expression of LINC01614 was found in 14 types of malignancy, including breast cancer. The LINC01614 was significantly overexpressed in human epidermal growth factor receptor 2 (HER2)+, estrogen receptor (ER)+, progesterone receptor (PR)+, and non-triple negative breast cancer (non-TNBC). According to survival analysis, the higher expression of LINC01614 was related with poor survival. The co-expressed genes analysis exhibited that LINC01614 was closely associated with the collagen-associated process and phosphoinositide 3-kinases-protein kinase B (PI3K-Akt) signaling pathway. Moreover, this study has explored the association among LINC01614 expression, tumor-infiltrating immune cells, and the efficacy of chemotherapeutics. CONCLUSIONS: Our data reveal the expression pattern of LINC01614 in breast carcinoma with different molecular subtypes. The results also indicated that the LINC01614 could be a novel diagnostic and prognostic marker for breast carcinoma.

Differential regional importance mapping for thyroid nodule malignancy prediction with potential to improve needle aspiration biopsy sampling reliability.

Objective: Existing guidelines for ultrasound-guided fine-needle aspiration biopsy lack specifications on sampling sites, but the number of biopsies improves diagnostic reliability. We propose the use of class activation maps (CAMs) and our modified malignancy-specific heat maps that locate important deep representations of thyroid nodules for class predictions. Methods: We applied adversarial noise perturbations to the segmented concentric "hot" nodular regions of equal sizes to differentiate regional importance for the malignancy diagnostic performances of an accurate ultrasound-based artificial intelligence computer-aided diagnosis (AI-CADx) system using 2,602 retrospectively collected thyroid nodules with known histopathological diagnosis. Results: The AI system demonstrated high diagnostic performance with an area under the curve (AUC) value of 0.9302 and good nodule identification capability with a median dice coefficient >0.9 when compared to radiologists' segmentations. Experiments confirmed that the CAM-based heat maps reflect the differentiable importance of different nodular regions for an AI-CADx system to make its predictions. No less importantly, the hot regions in malignancy heat maps of ultrasound images in comparison with the inactivated regions of the same 100 malignant nodules randomly selected from the dataset had higher summed frequency-weighted feature scores of 6.04 versus 4.96 rated by radiologists with more than 15 years of ultrasound examination experience according to widely used ultrasound-based risk stratification American College of Radiology (ACR) Thyroid Imaging Reporting and Data System (TI-RADS) in terms of nodule composition, echogenicity, and echogenic foci, excluding shape and margin attributes, which could only be evaluated on the whole rather than on the sub-nodular component levels. In addition, we show examples demonstrating good spatial correspondence of highlighted regions of malignancy heat map to malignant tumor cell-rich regions in hematoxylin and eosin-stained histopathological images. Conclusion: Our proposed CAM-based ultrasonographic malignancy heat map provides quantitative visualization of malignancy heterogeneity within a tumor, and it is of clinical interest to investigate in the future its usefulness to improve fine-needle aspiration biopsy (FNAB) sampling reliability by targeting potentially more suspicious sub-nodular regions.

Value of Artificial Intelligence in Improving the Accuracy of Diagnosing TI-RADS Category 4 Nodules.

OBJECTIVE: Considerable heterogeneity is observed in the malignancy rates of thyroid nodules classified as category 4 according to the Thyroid Imaging Reporting and Data System (TI-RADS). This study was aimed at comparing the diagnostic performance of artificial intelligence algorithms and radiologists with different experience levels in distinguishing benign and malignant TI-RADS 4 (TR4) nodules. METHODS: Between January 2019 and September 2022, 1117 TR4 nodules with well-defined pathological findings were collected for this retrospective study. An independent external data set of 125 TR4 nodules was incorporated for testing purposes. Traditional feature-based machine learning (ML) models, deep convolutional neural networks (DCNN) models and a fusion model that integrated the prediction outcomes from all models were used to classify benign and malignant TR4 nodules. A fivefold cross-validation approach was employed, and the diagnostic performance of each model and radiologists was compared. RESULTS: In the external test data set, the area under the receiver operating characteristic curve (AUROC) of the three DCNN-based secondary transfer learning models-InceptionV3, DenseNet121 and ResNet50-were 0.852, 0.837 and 0.856, respectively. These values were higher than those of the three traditional ML models-logistic regression, multilayer perceptron and random forest-at 0.782, 0.790, and 0.767, respectively, and higher than that of an experienced radiologist (0.815). The fusion diagnostic model we developed, with an AUROC of 0.880, was found to outperform the experienced radiologist in diagnosing TR4 nodules. CONCLUSION: The integration of artificial intelligence algorithms into medical imaging studies could improve the accuracy of identifying high-risk TR4 nodules pre-operatively and have significant clinical application potential.