Current status of endoscopic ultrasound in the diagnosis of intraductal papillary mucinous neoplasms.

The new Kyoto guidelines for the management of intraductal papillary mucinous neoplasm (IPMN) provide evidence-based recommendations for the diagnosis and treatment of IPMN. Endoscopic ultrasonography (EUS) is a diagnostic modality with a high spatial resolution that allows detailed observation and obtaining cyst fluid or tissue samples via EUS-guided fine needle aspiration (EUS-FNA). Currently, EUS is an indispensable examination method for the diagnosis of pancreatic diseases. On the other hand, there have been concerns that EUS imaging tends to be highly operator-dependent, and may lack objectivity. Previous guidelines have assigned EUS as an option for patients with worrisome features. However, recent reports indicate that the sensitivity of EUS for the diagnosis of mural nodules (MNs) is more than 90%, comparable or superior to that of contrast-enhanced computed tomography or magnetic resonance cholangiopancreatography. The specific advantages of EUS in the diagnosis of IPMN are: (1) high spatial resolution imaging for the diagnosis of MNs, (2) contrast-enhanced EUS for differentiation of intra-cystic MNs from mucous clots, and (3) pathological diagnosis using EUS-FNA and differential diagnosis of a pancreatic cystic tumor by cystic fluid analysis. In order to utilize EUS in the diagnosis of IPMN, endoscopists are required to have the skills to provide sufficiently objective imaging findings.

Deep learning in pulmonary nodule detection and segmentation: a systematic review.

OBJECTIVES: The accurate detection and precise segmentation of lung nodules on computed tomography are key prerequisites for early diagnosis and appropriate treatment of lung cancer. This study was designed to compare detection and segmentation methods for pulmonary nodules using deep-learning techniques to fill methodological gaps and biases in the existing literature. METHODS: This study utilized a systematic review with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, searching PubMed, Embase, Web of Science Core Collection, and the Cochrane Library databases up to May 10, 2023. The Quality Assessment of Diagnostic Accuracy Studies 2 criteria was used to assess the risk of bias and was adjusted with the Checklist for Artificial Intelligence in Medical Imaging. The study analyzed and extracted model performance, data sources, and task-focus information. RESULTS: After screening, we included nine studies meeting our inclusion criteria. These studies were published between 2019 and 2023 and predominantly used public datasets, with the Lung Image Database Consortium Image Collection and Image Database Resource Initiative and Lung Nodule Analysis 2016 being the most common. The studies focused on detection, segmentation, and other tasks, primarily utilizing Convolutional Neural Networks for model development. Performance evaluation covered multiple metrics, including sensitivity and the Dice coefficient. CONCLUSIONS: This study highlights the potential power of deep learning in lung nodule detection and segmentation. It underscores the importance of standardized data processing, code and data sharing, the value of external test datasets, and the need to balance model complexity and efficiency in future research. CLINICAL RELEVANCE STATEMENT: Deep learning demonstrates significant promise in autonomously detecting and segmenting pulmonary nodules. Future research should address methodological shortcomings and variability to enhance its clinical utility. KEY POINTS: Deep learning shows potential in the detection and segmentation of pulmonary nodules. There are methodological gaps and biases present in the existing literature. Factors such as external validation and transparency affect the clinical application.

The diagnostic value of CT-based radiomics nomogram for solitary indeterminate smoothly marginated solid pulmonary nodules.

Objectives: This study aimed to explore the value of radiomics nomogram based on computed tomography (CT) on the diagnosis of benign and malignant solitary indeterminate smoothly marginated solid pulmonary nodules (SMSPNs). Methods: This study retrospectively reviewed 205 cases with solitary indeterminate SMSPNs on CT, including 112 cases of benign nodules and 93 cases of malignant nodules. They were divided into training (n=143) and validation (n=62) cohorts based on different CT scanners. Radiomics features of the nodules were extracted from the lung window CT images. The variance threshold method, SelectKBest, and least absolute shrinkage and selection operator were used to select the key radiomics features to construct the rad-score. Through multivariate logistic regression analysis, a nomogram was built by combining rad-score, clinical factors, and CT features. The nomogram performance was evaluated by the area under the receiver operating characteristic curve (AUC). Results: A total of 19 radiomics features were selected to construct the rad-score, and the nomogram was constructed by the rad-score, one clinical factor (history of malignant tumor), and three CT features (including calcification, pleural retraction, and lobulation). The nomogram performed better than the radiomics model, clinical model, and experienced radiologists who specialized in thoracic radiology for nodule diagnosis. The AUC values of the nomogram were 0.942 in the training cohort and 0.933 in the validation cohort. The calibration curve and decision curve showed that the nomogram demonstrated good consistency and clinical applicability. Conclusion: The CT-based radiomics nomogram achieved high efficiency in the preoperative diagnosis of solitary indeterminate SMSPNs, and it is of great significance in guiding clinical decision-making.

Artificial intelligence-based graded training of pulmonary nodules for junior radiology residents and medical imaging students.

BACKGROUND: To evaluate the efficiency of artificial intelligence (AI)-assisted diagnosis system in the pulmonary nodule detection and diagnosis training of junior radiology residents and medical imaging students. METHODS: The participants were divided into three groups. Medical imaging students of Grade 2020 in the Jinzhou Medical University were randomly divided into Groups 1 and 2; Group 3 comprised junior radiology residents. Group 1 used the traditional case-based teaching mode; Groups 2 and 3 used the 'AI intelligent assisted diagnosis system' teaching mode. All participants performed localisation, grading and qualitative diagnosed of 1,057 lung nodules in 420 cases for seven rounds of testing after training. The sensitivity and number of false positive nodules in different densities (solid, pure ground glass, mixed ground glass and calcification), sizes (less than 5 mm, 5-10 mm and over 10 mm) and positions (subpleural, peripheral and central) of the pulmonary nodules in the three groups were detected. The pathological results and diagnostic opinions of radiologists formed the criteria. The detection rate, diagnostic compliance rate, false positive number/case, and kappa scores of the three groups were compared. RESULTS: There was no statistical difference in baseline test scores between Groups 1 and 2, and there were statistical differences with Group 3 (P = 0.036 and 0.011). The detection rate of solid, pure ground glass and calcified nodules; small-, medium-, and large-diameter nodules; and peripheral nodules were significantly different among the three groups (P<0.05). After seven rounds of training, the diagnostic compliance rate increased in all three groups, with the largest increase in Group 2. The average kappa score increased from 0.508 to 0.704. The average kappa score for Rounds 1-4 and 5-7 were 0.595 and 0.714, respectively. The average kappa scores of Groups 1,2 and 3 increased from 0.478 to 0.658, 0.417 to 0.757, and 0.638 to 0.791, respectively. CONCLUSION: The AI assisted diagnosis system is a valuable tool for training junior radiology residents and medical imaging students to perform pulmonary nodules detection and diagnosis.

A Rare Case of Papillary Thyroid Carcinoma in Marine-Lehnhart Syndrome-Indication for Biopsy of Hot Thyroid Nodules?

An uncommon occurrence in which Graves disease (GD) coincides with autonomous functioning thyroid nodules (AFTNs) is termed Marine-Lehnhart syndrome (MLS). While hyperfunctioning nodules in MLS are commonly benign, there exists a rare potential for malignancy. A 41-year-old male patient was initially managed conservatively upon being diagnosed with MLS type 1. However, the emergence of obstructive symptoms prompted a thyroidectomy 4 years after initial presentation. Histological analysis revealed 2 cervical lymph node metastases and papillary thyroid cancer (PTC) within the AFTN.

A Rare Case of Invasive Thyroid Aspergillosis Revealed on 18F-FDG-PET/CT.

Invasive aspergillosis (IA) represents a common form of fungal infection caused by various species of Aspergillus that most frequently affect immunocompromised patients. Typically, this disease occurs preferentially in high-risk groups including patients infected with the human immunodeficiency virus (HIV), patients with leukemia, patients with autoimmune diseases, and organ transplant patients undergoing medical immunosuppression. Considered the second most common cause of opportunistic fungal infection in humans after Candida albicans, this pathogen predominantly affects the lungs, but it may also spread by a hematogenous route to various organs and have a heterogeneous presentation. Owing to its high iodine levels, high perfusion, and enclosed capsule, the thyroid gland is considered to have a lower susceptibility to microbial invasion, and it is fairly uncommon to find associated infectious nodules. In metabolic imaging, 18F-FDG-PET/CT has become increasingly useful for detecting a wide range of infectious and inflammatory diseases and is already the gold standard for certain indications. According to the literature, no studies of hypermetabolic nodular thyroid aspergillosis on 18F-FDG-PET/CT confirmed on histology have yet been reported. Here, we report the first case of a patient with a heterogeneous presentation of IA and the presence of a hypermetabolic nodule in the thyroid with a surprising result.

Detection of pulmonary nodules in chest radiographs: novel cost function for effective network training with purely synthesized datasets.

PURPOSE: Many large radiographic datasets of lung nodules are available, but the small and hard-to-detect nodules are rarely validated by computed tomography. Such difficult nodules are crucial for training nodule detection methods. This lack of difficult nodules for training can be addressed by artificial nodule synthesis algorithms, which can create artificially embedded nodules. This study aimed to develop and evaluate a novel cost function for training networks to detect such lesions. Embedding artificial lesions in healthy medical images is effective when positive cases are insufficient for network training. Although this approach provides both positive (lesion-embedded) images and the corresponding negative (lesion-free) images, no known methods effectively use these pairs for training. This paper presents a novel cost function for segmentation-based detection networks when positive-negative pairs are available. METHODS: Based on the classic U-Net, new terms were added to the original Dice loss for reducing false positives and the contrastive learning of diseased regions in the image pairs. The experimental network was trained and evaluated, respectively, on 131,072 fully synthesized pairs of images simulating lung cancer and real chest X-ray images from the Japanese Society of Radiological Technology dataset. RESULTS: The proposed method outperformed RetinaNet and a single-shot multibox detector. The sensitivities were 0.688 and 0.507 when the number of false positives per image was 0.2, respectively, with and without fine-tuning under the leave-one-case-out setting. CONCLUSION: To our knowledge, this is the first study in which a method for detecting pulmonary nodules in chest X-ray images was evaluated on a real clinical dataset after being trained on fully synthesized images. The synthesized dataset is available at https://zenodo.org/records/10648433 .

Clinicopathological spectrum, management, and outcome of ectopic parathyroid carcinoma: Experience with 24 cases.

PURPOSE: Ectopic parathyroid carcinoma (EPC) is a rare clinical entity with multiple diagnostic pitfalls, making surgical cures challenging. We assessed the clinicopathological spectrum and outcome of EPCs. METHODS: In this retrospective cohort study, 24 EPCs were identified from 133 PC patients treated at a tertiary referral center. The relationship between clinicopathological findings and locations was analyzed. RESULTS: The locations of EPCs were predominantly intrathyroidal (62.5%), followed by 16.7% in the mediastinum, 8.3% in the retropharyngeal space, 8.3% in the carotid sheath, and 4.2% in the upper neck. Intrathyroidal EPC patients experienced higher serum calcium (p = 0.020), a higher rate of vascular invasion (p = 0.040), and a slightly higher incidence of non-R0 initial resection (p = 0.092) than those in other ectopic locations. Intrathyroidal EPC patients also suffered a trend of higher upper aerodigestive tract (UAT) invasion rate (p = 0.070) and higher risks of distant metastasis (p = 0.037) than the other PC patients. The 5-year disease-free survival rate after surgery was slightly compromised at 41.5% in intrathyroidal EPC patients compared with 77.8% among those in other ectopic locations (p = 0.143) and 59.7% among the other PC patients (log-rank = 3.194; p = 0.074), though without statistical significance. CONCLUSION: Intrathyroidal EPC might cause a more biochemically and invasively distinct PC picture compared with other PCs. Special caution should be exercised in the preoperative diagnosis and management of such cases.

Prevalence of thyroid nodule and relationship with physiological and psychosocial factors among adults in Zhejiang Province, China: a baseline survey of a cohort study.

BACKGROUND: Thyroid nodules have attracted much attention due to their high incidence and potential for malignant transformation. Compared with the clinical assessment and diagnosis of thyroid nodules, there are relatively few studies on the epidemiological risk factors for thyroid nodules. The aim of this study was to investigate the prevalence of thyroid nodule among adults in Zhejiang province and to explore their relationship with physiological and psychosocial factors. METHODS: The data used in this study were obtained from the baseline survey of the Zhejiang Provincial Cohort Study on Environment and Health. From June 2022 to December 2023, a total of 21,712 participants from five representative cities in Zhejiang were recruited for the baseline survey. Based on the inclusion and exclusion criteria, 15,595 adults were included in the analysis. The data were collected via self-report questionnaires and physical examinations. Multivariate logistic regression analysis was subsequently performed. RESULTS: The detection rate of thyroid nodules was 50.98% among adults in Zhejiang province. Age, gender, education level, BMI, tea and alcohol consumption all had a statistically significant association with thyroid nodules (p < 0.05). After adjusting for sociodemographic factors, results of logistic regression analysis showed that good life satisfaction (OR = 0.854, 95% CI: 0.780-0.934) had a lower risk of thyroid nodules, however, poor life satisfaction (OR = 1.406, 95% CI: 1.014-1.951), social isolation (OR = 1.294, 95% CI: 1.089-1.538) and a family history of thyroid nodules (OR = 1.334, 95% CI: 1.064-1.672) had a greater risk of thyroid nodules. CONCLUSION: The detection rate of thyroid nodules in adults of Zhejiang province was an increasing trend compared with that in previous years. In addition to the sensitive thyroid nodule screening technology, influencing factors mentioned in this study might also represent credible candidates for this increase. As variable influence factors, weight management, good interpersonal relationships and life satisfaction should be the focus of health interventions.

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.

Diagnostic value of ultrasound-guided fine-needle aspiration cytology in the pathological natures of thyroid nodules: A retrospective study.

Objective: Thyroid nodules, a clinical disease with high incidence, include benign and malignant types. This study aims to evaluate the diagnostic value of ultrasound-guided fine-needle aspiration cytology (US-FNAC) in determining the pathological nature of thyroid nodules and to conduct an in-depth analysis of its diagnostic efficacy across nodules of varying sizes. Material and Methods: This retrospective study identified 116 patients undergoing thyroidectomy in Zibo Central hospital from January 2022 to March 2023, with 98 meeting the study's inclusion criteria. All patients received ultrasound and US-FNAC examinations before surgery to analyze the ultrasonic features of thyroid nodules. The diagnosis results of pathological natures obtained by US-FNAC were analyzed with the result of post-operative pathological examination as the gold standard. The patients were divided into four groups according to the median and quartile of nodular diameters to explore the diagnostic efficacy of US-FNAC for the pathological natures of thyroid nodules with various diameters and comprehensively evaluate its application value. The evaluation tool of diagnostic efficacy was the receiver operator characteristic (ROC) curve. Results: A total of 98 puncture nodules were evaluated, with a diameter of 0.8-5.2 cm. Post-operative pathological examination showed 10 (10.20%) benign and 88 (89.80%) malignant lesions. The ultrasound examination showed 14 (14.29%) benign and 84 (85.71%) suspected malignant lesions. The US-FNAC results showed 2 cases (2.04%) of type I, 9 cases (9.18%) of type II, 3 cases (3.06%) of type III, 29 cases (29.59%) of type IV, 39 cases (39.80%) of type V, and 16 cases (16.33%) of type VI, including 9 (9.18%) benign and 84 (85.72%) malignant lesions and 5 (5.10%) uncertain pathological natures. According to the median and quartile of nodular diameters, specifically, 1.90 (1.60, 2.30) cm, 93 nodules with decided pathological natures were divided into groups Q1 (n = 24, ≤1.6 cm), Q2 (n = 26, 1.7-1.9 cm), Q3 (n = 24, 2.0-2.3 cm), and Q4 (n = 19, >2.3 cm). The results of ROC analysis showed that the area under the curve (AUC), sensitivity, and specificity of US-FNAC diagnosis were 0.894, 98.80%, and 80.00%, respectively. The AUCs of US-FNAC in groups Q1, Q2, Q3, and Q4 were 0.978, 1.000, 0.977, and 0.971. The AUCs of Q1 group, Q2 group, Q3 group and Q4 group were all > 0.9, and US-FNAC had high diagnostic efficiency for the pathological properties of thyroid nodules with different diameters. Conclusion: US-FNAC has a high diagnostic efficiency for the pathological properties of thyroid nodules. Whether the nodule diameter has an effect on the accuracy of this method requires more clinical evidence.

Preoperative computed tomography-guided localization for pulmonary nodules: a randomized controlled trial of coil and anchored needle localization.

Introduction: In patients with pulmonary nodules (PNs), computed tomography (CT)-guided localization is commonly performed prior to the resection of these nodules through video-assisted thoracic surgery (VATS). Aim: To evaluate the relative clinical efficacy of coil and anchored needle (AN) insertion as approaches to preoperative CT-guided PN localization. Material and methods: This single-center, prospective, open-label, randomized controlled trial (registration number: NCT05183945) enrolled consecutive patients from January 2022 to July 2022, assigning these patients at random to undergo either coil or AN localization prior to VATS. Efficacy and safety outcomes in these two groups were then compared. Results: This study enrolled in total 100 patients with 120 PNs who were assigned at random to the coil (patients = 50; PNs = 60) and AN (patients = 50; PNs = 60) localization groups. The respective technical success rates for coil and AN localization were 98.3% (59/60) and 100% (60/60), with no significant difference between the groups (p = 1.000). The coil group had a significantly longer median duration of localization relative to the AN group (16.0 min vs. 8.0 min, p < 0.001). Similar rates of localization-related pneumothorax (8.3% vs. 5.0%, p = 0.715) and pulmonary hemorrhage (5.0% vs. 13.3%, p = 0.110) were observed in both groups. In addition, the VATS resection procedures achieved 100% technical success rates in both of these localization groups. Conclusions: Both coil- and AN-based localization approaches can be successfully employed to localize PNs prior to VATS resection, with the AN localization procedure requiring less time to complete on average as compared to the coil-based approach.

Diagnostic value of contrast-enhanced ultrasound and shear-wave elastography for small breast nodules.

Background: The study aims to evaluate the diagnostic efficacy of contrast-enhanced ultrasound (CEUS) and shear-wave elastography (SWE) in detecting small malignant breast nodules in an effort to inform further refinements of the Breast Imaging Reporting and Data System (BI-RADS) classification system. Methods: This study retrospectively analyzed patients with breast nodules who underwent conventional ultrasound, CEUS, and SWE at Gongli Hospital from November 2015 to December 2019. The inclusion criteria were nodules ≤ 2 cm in diameter with pathological outcomes determined by biopsy, no prior treatments, and solid or predominantly solid nodules. The exclusion criteria included pregnancy or lactation and low-quality images. Imaging features were detailed and classified per BI-RADS. Diagnostic accuracy was assessed using receiver operating characteristic curves. Results: The study included 302 patients with 305 breast nodules, 113 of which were malignant. The diagnostic accuracy was significantly improved by combining the BI-RADS classification with CEUS and SWE. The combined approach yielded a sensitivity of 88.5%, specificity of 87.0%, positive predictive value of 80.0%, negative predictive value of 92.8%, and accuracy of 87.5% with an area under the curve of 0.877. Notably, 55.8% of BI-RADS 4A nodules were downgraded to BI-RADS 3 and confirmed as benign after pathological examination, suggesting the potential to avoid unnecessary biopsies. Conclusion: The integrated use of the BI-RADS classification, CEUS, and SWE enhances the accuracy of differentiating benign and malignant small breast nodule, potentially reducing the need for unnecessary biopsies.

Preoperative computed tomography-guided localization for pulmonary nodules: comparison between hook-wire and anchored needle localization.

Introduction: Both hook-wire (HW) and anchored needle (AN) techniques can be used for preoperative computed tomography (CT)-guided localization for pulmonary nodules (PNs). But the outcomes associated with these two materials remain unclear. Aim: To assess the relative safety and efficacy of preoperative CT-guided HW and AN localization for PNs. Material and methods: This was a retrospective analysis of data collected from two institutions. Consecutive patients with PNs between January 2020 and December 2021 who underwent preoperative CT-guided HW or AN localization followed by video-assisted thoracoscopic surgery (VATS) procedures were included in these analyses, which compared the safety and clinical efficiency of these two localization strategies. Results: In total, 98 patients (105 PNs) and 93 patients (107 PNs) underwent CT-guided HW and AN localization procedures, respectively. The HW and AN groups exhibited similar rates of successful PN localization (95.2% vs. 99.1%, p = 0.117), but the dislodgement rate in the HW group was significantly higher than that for the AN group (4.8% vs. 0.0%, p = 0.029). The mean pain score of patients in the HW group was significantly higher than that for the AN group (p = 0.001). HW and AN localization strategies were associated with comparable pneumothorax (21.4% vs. 16.1%, p = 0.349) and pulmonary hemorrhage (29.6% vs. 23.7%, p = 0.354) rates. All patients other than 1 individual in the HW group successfully underwent VATS-guided limited resection. Conclusions: These data suggest that AN represents a safe, well-tolerated, feasible preoperative localization strategy for PNs that may offer value as a replacement for HW localization.

Timing of the repeat thyroid fine-needle aspiration biopsy: does early repeat biopsy change the rate of nondiagnostic or atypia of undetermined significance cytology result?

PURPOSE: To determine whether early repeat fine needle aspiration biopsy (FNA) has an effect on adequate or atypia of undetermined significance (AUS) cytology rates in thyroid nodules with inadequate or AUS result in the first FNA. METHODS: Nodules of patients who underwent repeat biopsy due to insufficient or AUS cytology between 2019-2022 were included. Data of the patients and ultrasonographic, cytological and histopathological results of the nodules were recorded. Additionally, the time between the two biopsies was noted. The first was called "initial" and the second was called "rebiopsy". Five different paired groups were formed according to the time between two consecutive biopsies; before and after 1 month, 45 days, 2 months, 3 months, and 6 months. The groups were compared in terms of adequate and AUS cytological results. RESULTS: We evaluated 1129 patients with 2187 nodules undergoing FNAB. After excluding nodules with one FNA result and/or missing data, 966 nodules of 628 patients who underwent FNA at least twice were included. Initial cytology was nondiagnostic (ND) in 665 (30.4%) and AUS in 301 (13.8%) nodules. The mean age of the patients was 52.0 ± 11.9 years, and the female sex ratio was 78.8% (n = 495). There were no differences in adequate or AUS rebiopsy results according to the different time interval groups (p > 0.05 for all). AUS result was statistically insignificantly more frequent in nodules with initially AUS nodules when rebiopsy was performed before 1 month in comparison to after 1 month (53.8%, 27.1%; p = 0.054). Accuracy of rebiopsy was also similar in the time intervals groups (p > 0.05 for all). CONCLUSION: In patients with inadequate or AUS initial biopsy, the rate of adequate or AUS cytology results at rebiopsy did not vary with the timing of repeat biopsy indicating that there may be no need to wait 1 month for a repeat biopsy. In patients with suspicious nodules, biopsy might be repeated before 1 month.

The Value of Radiomics Features Based on HRCT in Predicting whether the Lung Sub-Centimeter Pure Ground Glass Nodule is Benign or Malignant.

OBJECTIVE: In this study, a radiomics model was created based on High-Resolution Computed Tomography (HRCT) images to noninvasively predict whether the sub-centimeter pure Ground Glass Nodule (pGGN) is benign or malignant. METHODS: A total of 235 patients (251 sub-centimeter pGGNs) who underwent preoperative HRCT scans and had postoperative pathology results were retrospectively evaluated. The nodules were randomized in a 7:3 ratio to the training (n=175) and the validation cohort (n=76). The volume of interest was delineated in the thin-slice lung window, from which 1316 radiomics features were extracted. The Least Absolute Shrinkage and Selection Operator (LASSO) was used to select the radiomics features. Univariate and multivariable logistic regression were used to evaluate the independent risk variables. The performance was assessed by obtaining Receiver Operating Characteristic (ROC) curves for the clinical, radiomics, and combined models, and then the Decision Curve Analysis (DCA) assessed the clinical applicability of each model. RESULTS: Sex, volume, shape, and intensity mean were chosen by univariate analysis to establish the clinical model. Two radiomics features were retained by LASSO regression to build the radiomics model. In the training cohort, the Area Under the Curve (AUC) of the radiomics (AUC=0.844) and combined model (AUC=0.871) was higher than the clinical model (AUC=0.773). In evaluating whether or not the sub-centimeter pGGN is benign, the DCA demonstrated that the radiomics and combined model had a greater overall net benefit than the clinical model. CONCLUSION: The radiomics model may be useful in predicting the benign and malignant sub-centimeter pGGN before surgery.

Assessment of the American College of Radiology Thyroid Imaging Reporting and Data System (ACR TI-RADS™) with modification of the management recommendations for pediatric thyroid nodules.

BACKGROUND: Thyroid nodules are unusual in children, but when present, they carry a higher risk for malignancy, as compared to adults. Several guidelines have been created to address the risk stratification for malignancy of thyroid nodules in adults, but none has been completely validated in children. A few authors have proposed lowering the size threshold to the American College of Radiology Thyroid Imaging, Reporting and Data System (ACR TI-RADS™) management guidelines to decrease missed carcinomas at presentation in children; however, little information is known regarding their accuracy. OBJECTIVE: To assess the performance of proposed modifications of the ACR TI-RADS™ size criteria to guide management decisions in pediatric thyroid nodules and to assess the associated increase in number of fine needle aspiration (FNA) and follow-up exams. MATERIALS AND METHODS: This is a retrospective study of children under 18 years old who underwent ultrasound assessment of a thyroid nodule at a tertiary care pediatric institution between January 2006 and August 2021. The largest dimension, maximum ACR TI-RADS™ score, and final thyroid nodules' diagnoses were documented. The course of action based on the adult ACR TI-RADS™ and after modifying the size threshold for management recommendations was documented and compared. Statistics included descriptive analysis, weighted Kappa statistics, sensitivity, specificity, accuracy, and positive/negative predictive values of the ACR TI-RADS™ presented with 95% confidence intervals (CI) using either Clopper-Pearson or standard logit methods. RESULTS: Of 116 nodules, 18 (15.5%) were malignant. Most malignant nodules (94.4%, n = 17) were ACR TI-RADS™ 4 and ACR TI-RADS™ 5 categories. Based on the adult ACR TI-RADS™ criteria, 24 (24.5%) benign and 15 (83.3%) malignant nodules would have undergone FNA; 14 (14.3%) benign and 3 (16.7%) malignant nodules would have been followed up; and 60 (61.2%) benign and none of malignant nodules would have been dismissed. Three (16.7%) malignant nodules would not have been recommended FNA at presentation, delaying their diagnoses. By lowering the size-threshold criteria of the ACR TI-RADS™ guidelines, no malignancy would have been missed at presentation, but this also resulted in a higher number of FNA from 24 (24.5%) to 36 (36.7%) and follow-up ultrasound exams from 14 (14.3%) to 62 (63.3%). CONCLUSION: Applying potential modifications to the ACR TI-RADS™ guideline lowering the size threshold criteria of the thyroid nodule to guide management decisions for pediatric thyroid nodules can lead to early detection of malignant nodules in children, but at the cost of a significantly increased number of biopsies or ultrasound exams. Further tailoring of the guideline with larger multicentric studies is needed, before warranting its acceptance and general use in the pediatric population.

Computed tomography radiomics study of invasion and instability of lung adenocarcinoma manifesting as ground glass nodule.

Background: Ground-glass nodule (GGN) is the most common manifestation of lung adenocarcinoma on computed tomography (CT). Clinically, the success rate of preoperative diagnosis of GGN by puncture biopsy and other means is still low. The aim of this study is to investigate the clinical and radiomics characteristics of lung adenocarcinoma presenting as GGN on CT images using radiomics analysis methods, establish a radiomics model, and predict the classification of pathological tissue and instability of GGN type lung adenocarcinoma. Methods: This study retrospectively collected 249 patients with 298 GGN lesions who were pathologically confirmed of having lung adenocarcinoma. The images were imported into the Siemens scientific research prototype software to outline the region of interest and extract the radiomics features. Logistic model A (a radiomics model to identify the infiltration of lung adenocarcinoma manifesting as GGNs) was established using features after the dimensionality reduction process. The receiver operating characteristic (ROC) curve of the model on training set and the verification set was drawn, and the area under the curve (AUC) was calculated. Second, a total of 112 lesions were selected from 298 lesions originating from CT images of at least two occasions, and the time between the first CT and the preoperative CT was defined as not less than 90 days. The mass doubling time (MDT) of all lesions was calculated. According to the different MDT diagnostic thresholds instability was predicted. Finally, their AUCs were calculated and compared. Results: There were statistically significant differences in age and lesion location distribution between the "noninvasive" lesion group and the invasive lesion group (P<0.05), but there were no statistically significant differences in sex (P>0.05). Model A had an AUC of 0.89, sensitivity of 0.75, and specificity of 0.86 in the training set and an AUC of 0.87, sensitivity of 0.63, and specificity of 0.90 in the validation set. There was no significant difference statistically in MDT between "noninvasive" lesions and invasive lesions (P>0.05). The AUCs of radiomics models B1, B2 and B3 were 0.89, 0.80, and 0.81, respectively; the sensitivities were 0.71, 0.54, and 0.76, respectively; the specificities were 0.83, 0.77, and 0.60, respectively; and the accuracies were 0.78, 0.65, and 0.69, respectively. Conclusions: There were statistically significant differences in age and location of lesions between the "noninvasive" lesion group and the invasive lesion group. The radiomics model can predict the invasiveness of lung adenocarcinoma manifesting as GGNs. There was no significant difference in MDT between "noninvasive" lesions and invasive lesions. The radiomics model can predict the instability of lung adenocarcinoma manifesting as GGN. When the threshold of MDT was set at 813 days, the model had higher specificity, accuracy, and diagnostic efficiency.