Malignant Nodules Research Articles

Do as Sonographers Think: Contrast-Enhanced Ultrasound for Thyroid Nodules Diagnosis via Microvascular Infiltrative Awareness.

Dynamic contrast-enhanced ultrasound (CEUS) imaging can reflect the microvascular distribution and blood flow perfusion, thereby holding clinical significance in distinguishing between malignant and benign thyroid nodules. Notably, CEUS offers a meticulous visualization of the microvascular distribution surrounding the nodule, leading to an apparent increase in tumor size compared to gray-scale ultrasound (US). In the dual-image obtained, the lesion size enlarged from gray-scale US to CEUS, as the microvascular appeared to be continuously infiltrating the surrounding tissue. Although the infiltrative dilatation of microvasculature remains ambiguous, sonographers believe it may promote the diagnosis of thyroid nodules. We propose a deep learning model designed to emulate the diagnostic reasoning process employed by sonographers. This model integrates the observation of microvascular infiltration on dynamic CEUS, leveraging the additional insights provided by gray-scale US for enhanced diagnostic support. Specifically, temporal projection attention is implemented on time dimension of dynamic CEUS to represent the microvascular perfusion. Additionally, we employ a group of confidence maps with flexible Sigmoid Alpha Functions to aware and describe the infiltrative dilatation process. Moreover, a self-adaptive integration mechanism is introduced to dynamically integrate the assisted gray-scale US and the confidence maps of CEUS for individual patients, ensuring a trustworthy diagnosis of thyroid nodules. In this retrospective study, we collected a thyroid nodule dataset of 282 CEUS videos. The method achieves a superior diagnostic accuracy and sensitivity of 89.52% and 94.75%, respectively. These results suggest that imitating the diagnostic thinking of sonographers, encompassing dynamic microvascular perfusion and infiltrative expansion, proves beneficial for CEUS-based thyroid nodule diagnosis.

Highly Sensitive and Specific Panels of Plasma Exosomal microRNAs for Identification of Malignant Pulmonary Nodules.

With wide application of computed tomography (CT) in early lung cancer screening, solitary pulmonary nodules (SPNs) are frequently detected. Due to their high etiological diversity and potential for malignancy, rapid and accurate identification and malignant SPNs are crucial in the clinical management. In the present study, plasma exosomal microRNAs were identified and evaluated as sensitive and specific indicators for malignant SPNs. Exosomal miRNAs isolated from the plasmas of pathologically confirmed patients with SPN (four malignant and four benign, designated as the screening set) were subjected for high throughput sequencing and eight candidate miRNAs were selected. The pre-operation plasma levels of the candidate miRNAs in 77 patients with SPN (48 malignant and 29 benign, designated as the identification set) were detected by quantitative PCR, five miRNAs were identified as potential biomarkers for malignant SPNs, and the diagnostic values of the five miRNAs each alone or combined were then analyzed by AUROC analysis. The prediction values of the identified miRNAs were further evaluated in 95 patients with SPN (double blind, 74 malignant and 21 benign, designated as the validation set). High-throughput sequencing identified 45 miRNAs with statistical differences between benign and malignant SPNs. Among the eight candidate miRNAs in the identification set, miR-1-3p alone had the best diagnostic value, with the sensitivities and specificities of 89.6% and 100% for malignant SPNs. Unexpectedly, when miR-1-3p was combined with miR-99a-5p, both the sensitivity and specificity reached 100% for malignant SPNs. miR-1-3p+miR-125b-5p and miR-1-3p+miR-218-5p were also good indicators of malignant SPNs with sensitivities of 95.8% and 97.9%, specificities of 100% and 96.6%. Further analysis of these microRNA combinations in the validation set indicated that the PPV were 91.4%, 97.4%, and 93.5% and the NPV were 100%, 100%, and 88.9% for miR-1-3p+miR-99a-5p, miR-1-3p+miR-218-5p, and miR-1-3p+miR-125b-5p, with the sensitivities were 100%, 100%, and 97.3% and the specificities were 66.7%, 90.5%, and 76.2% for miR-1-3p+miR-99a-5p, miR-1-3p+miR-218-5p, and miR-1-3p+miR-125b-5p, respectively. Through high throughput sequencing, qPCR determination of plasma microRNAs and AUROC analysis, miR-1-3p combined with miR-99a-5p, miR-125b-5p, or miR-218-5p have been found to be sensitive and specific indicators of malignant SPNs in both the identification and validation sets. Our results indicate that the panels of plasma miRNAs can be used as diagnostic biomarkers for malignant SPNs.

Malignancy Risk Associated with Radioactive Iodine Therapy for Graves’ Disease

BackgroundRadioactive iodine therapy (RAI) is a frequently chosen therapy for Graves’ disease. The aim of this study was to determine whether RAI for Graves’ disease increases the risk of thyroid malignancy. MethodsA retrospective analysis was performed of all Graves’ disease patients who underwent thyroidectomy at a single institution between 2013-2022. Comparative analyses were performed with cohorts based on RAI therapy as the primary grouping variable. Results413 patients were identified, of which 38 received RAI prior to surgery. RAI treated patients were more likely to undergo surgery for known malignancy or indeterminate nodules. RAI patients were also more likely to have malignancies larger than 1cm. Among RAI treated patients, those who developed malignancy were older at the time of Graves’ diagnosis and received early RAI therapy. ConclusionsUse of RAI for treatment of Graves’ disease increases the progression of thyroid carcinoma, but not the prevalence. Older age and early RAI therapy may be risk factors for malignancy in RAI treated patients.

Enhancing the differential diagnosis of small pulmonary nodules: a comprehensive model integrating plasma methylation, protein biomarkers, and LDCT imaging features

BackgroundAccurate differentiation between malignant and benign pulmonary nodules, especially those measuring 5–10 mm in diameter, continues to pose a significant diagnostic challenge. This study introduces a novel, precise approach by integrating circulating cell-free DNA (cfDNA) methylation patterns, protein profiling, and computed tomography (CT) imaging features to enhance the classification of pulmonary nodules.MethodsBlood samples were collected from 419 participants diagnosed with pulmonary nodules ranging from 5 to 30 mm in size, before any disease-altering procedures such as treatment or surgical intervention. High-throughput bisulfite sequencing was used to conduct DNA methylation profiling, while protein profiling was performed utilizing the Olink proximity extension assay. The dataset was divided into a training set and an independent test set. The training set included 162 matched cases of benign and malignant nodules, balanced for sex and age. In contrast, the test set consisted of 46 benign and 49 malignant nodules. By effectively integrating both molecular (DNA methylation and protein profiling) and CT imaging parameters, a sophisticated deep learning-based classifier was developed to accurately distinguish between benign and malignant pulmonary nodules.ResultsOur results demonstrate that the integrated model is both accurate and robust in distinguishing between benign and malignant pulmonary nodules. It achieved an AUC score 0.925 (sensitivity = 83.7%, specificity = 82.6%) in classifying test set. The performance of the integrated model was significantly higher than that of individual methylation (AUC = 0.799, P = 0.004), protein (AUC = 0.846, P = 0.009), and imaging models (AUC = 0.866, P = 0.01). Importantly, the integrated model achieved a higher AUC of 0.951 (sensitivity = 83.9%, specificity = 89.7%) in 5–10 mm small nodules. These results collectively confirm the accuracy and robustness of our model in detecting malignant nodules from benign ones.ConclusionsOur study presents a promising noninvasive approach to distinguish the malignancy of pulmonary nodules using multiple molecular and imaging features, which has the potential to assist in clinical decision-making.Trial registration: This study was registered on ClinicalTrials.gov on 01/01/2020 (NCT05432128). https://classic.clinicaltrials.gov/ct2/show/NCT05432128.

Deep learning model for diagnosis of thyroid nodules with size less than 1 cm: A multicenter, retrospective study

ObjectiveTo develop a ultrasound images based dual-channel deep learning model to achieve accurate early diagnosis of thyroid nodules less than 1 cm. MethodsA dual-channel deep learning model called thyroid nodule transformer network (TNT-Net) was proposed. The model has two input channels for transverse and longitudinal ultrasound images of thyroid nodules, respectively. A total of 9649 nodules from 8455 patients across five hospitals were retrospectively collected. The data were divided into a training set (8453 nodules, 7369 patients), an internal test set (565 nodules, 512 patients), and an external test set (631 nodules, 574 patients). ResultsTNT-Net achieved an area under the curve (AUC) of 0.953 (95 % confidence interval (CI): 0.934, 0.969) on the internal test set and 0.941 (95 % CI: 0.921, 0.957) on the external test set, significantly outperforming traditional deep convolutional neural network models and single-channel swin transformer model, whose AUCs ranged from 0.800 (95 % CI: 0.759, 0.837) to 0.856 (95 % CI: 0.819, 0.881). Furthermore, feature heatmap visualization showed that TNT-Net could extract richer and more energetic malignant nodule patterns. ConclusionThe proposed TNT-Net model significantly improved the recognition capability for thyroid nodules with size less than 1 cm. This model has the potential to reduce overdiagnosis and overtreatment of such nodules, providing essential support for precise management of thyroid nodules while complementing fine-needle aspiration biopsy.

Sarcoid Nodule or Lung Cancer? A High-Resolution Computed Tomography-Based Retrospective Study of Pulmonary Nodules in Patients with Sarcoidosis.

Background: The objective of this retrospective study was to compare the characteristics of sarcoid nodules and neoplastic nodules using high-resolution computed tomography (HRCT) in sarcoidosis patients. Methods: This is a single-center retrospective study. From 2010 to 2023, among 685 patients affected by pulmonary sarcoidosis, 23 patients developed pulmonary nodules of a suspicious malignant nature. The HRCT characteristics of biopsy-proven malignant (Group A) vs. inflammatory (Group B) nodules were analyzed and compared. Results: A significant difference was observed between the groups in terms of age (p = 0.012). With regard to HRCT features, statistical distinctions were observed in the appearance of the nodule, more frequently spiculated in the case of lung cancer (p < 0.01), in the diameter of the nodule (Group A: 23.5 mm; Group B: 12.18 mm, p < 0.02), in the median nodule density (Group A: 60.0 HU, Group B: -126.7 HU, p < 0.01), and in the number of pulmonary nodules, as a single parenchymal nodule was more frequently observed in the neoplastic patient group (p = 0.043). In Group A, the 18-PET-CT demonstrated hilar/mediastinal lymphadenopathy in 100% of cases; histology following surgery did not report any cases of malignant lymph node involvement. Conclusions: An accurate clinical evaluation and HRCT investigation are crucial for diagnosing lung cancer in patients with sarcoidosis in order to determine who requires surgical resection. The spiculated morphology of the nodule, greater size, the number of pulmonary nodules, and density using HRCT appear to correlate with the malignant nature of the lesion.

Diagnostic performance of PET-CT and technical efficacy of ultrasound-guided core needle biopsy of small and intermediate size malignant supraclavicular lymph nodes

Aim: We aimed (1) to determine optimal PET-CT SUV and ultrasound (US) features associated for malignant smallintermediate size supraclavicular lymph nodes (SCN) and (2) to evaluate technical efficacy of core needle biopsy (CNB). Materials and methods: Between 2020 and 2023, CNB of SCN with short-axis less than 15 mm were included. PET-CT was evaluated for optimal SUV cut-off value. Multivariate logistic regression was performed for US features. Technical efficacy of CNB (success rate) was calculated, and US features were compared between satisfactory CNB specimen and unsatisfactory CNB specimen.Results: Eighty-four SCN per 80 patients (age 64.0±17.2 years, 45.2% female) were included. The optimal SUV cut-off value was 1.1 with diagnostic accuracy of 89.8%. US features associated with malignancy were anteroposterior dimension ≥9 mm and absence of hilum. The overall CNB success rate was 94%, and unsatisfactory specimen demonstrated shorter transverse diameter (12 mm vs 5 mm, p&lt;0.001), and non-parallel orientation (20.3% vs. 100%, p&lt;0.001).Conclusion: CNB should be considered for small-intermediate size SCN with SUV exceeding 1.1, anteroposterior dimension ≥9 mm, and absence of hilum. SCN with shorter transverse diameter and non-parallel orientation may result in unsatisfactory CNB specimen.

The accuracy of fine needle aspiration cytology and ultrasonography in assessing thyroid nodules in correlation with histopathology: a retrospective study

Background: Accurately diagnosing thyroid nodules is vital for preventing unnecessary surgeries and providing prompt therapy. Although fine needle aspiration cytology (FNAC) and ultrasonography (US) are widely used diagnostic methods, their reliability is questioned. This study investigates the effectiveness of US and FNAC in thyroid nodule diagnosis and differentiates benign from malignant nodules in relation to final histopathological diagnosis. Method: A retrospective study including 307 adult patients with thyroid diseases who underwent neck US and FNAC before surgery was conducted between April 2019 and May 2023. The diagnostic efficacy of US, FNAC, and their combination usage was compared to histopathological results. Result: Histopathological findings revealed that 187(61%) cases were benign, while 120 (39%) were malignant. The US features of “taller-than-wider” forms and hypoechoic appearance had the highest diagnostic accuracy in characterizing malignant thyroid nodules, with 83% and 73% accuracy, respectively. The combination of US parameters demonstrated high sensitivity, specificity, positive predictive value, and negative predictive value (NPV) of 88.33%, 63.10%, 60.6%, and 89.4%, with a statistically significant area under the ROC curve (AUC: 0.828, P&lt;0.001) than individual parameters. FNAC’s sensitivity, specificity, PPV NPV, and accuracy in detecting malignant lesions were 50%, 95%, 86%, 75%, and 77%, respectively, with acceptable discrimination and statistical significance (AUC: 0.723, P&lt;0.0001). The combination of US parameters and FNAC significantly improved the AUC value (AUC: 0.878, P&lt;0.0001), sensitivity (83.33%), and specificity (79.14%). Univariate analysis showed that hypoechoic appearance, heterogenicity, large mass size (&gt;4 cm), “taller-than-wider,” infiltrative margins, and microcalcifications were risk factors for malignancy in thyroid nodules and were statistically significant (all P values &lt;0.05). Conclusion: Combining US characteristics with FNAC results can afford the maximum analytical accuracy in distinguishing benign from malignant thyroid nodules. This strategy is practical due to its simplicity, minimal invasiveness, and cost-effectiveness, enabling robust management regimens and avoiding additional surgical procedures.

Volatile Organic Compounds in Exhaled Breath, Blood, and Urine Detected in Patients with Thyroid Carcinoma Using Gas Chromatography-Ion Mobility Spectrometry- A Pilot Study.

Background&#xD;Distinguishing between malignant and benign thyroid nodules remains a significant challenge for clinicians and researchers globally. The use of volatile organic compounds (VOCs) has emerged as a novel approach in cancer diagnosis. This prospective pilot study aims to identify VOCs in exhaled breath, blood, and urine that can differentiate benign from malignant thyroid nodules using gas chromatography-ion mobility spectrometry (GC-IMS).&#xD;Methods&#xD;Patients with thyroid nodules scheduled for surgery were enrolled at the Maastricht University Medical Center (MUMC+). Breath samples were analyzed using a BreathSpec GC-IMS machine (G.A.S. Dortmund, Germany), specifically designed for breath analysis. All blood and urine samples were analyzed with a separate GC-IMS device, the FlavourSpec® (G.A.S., Dortmund, Germany).&#xD;Results &#xD;In this proof-of-concept study, 70 patients undergoing thyroid surgery at MUMC+ were consecutively included. Of these patients, 29 were confirmed to have thyroid cancer after surgical resection. The overall analysis did not reveal statistically significant differences in VOCs in breath, urine and blood, between patients with benign and malignant thyroid cancer. &#xD;Conclusion&#xD;This proof-of-concept study demonstrated that GC-IMS was unable to adequately distinguish between the VOC profiles of malignant and benign thyroid nodules. However, this study had a small sample size and future larger studies are needed to investigate the potential of using VOCs to distinguish between benign and malignant thyroid nodules. Furthermore, future research should focus on investigating potential confounders that affect patient VOC profiles.&#xD;(NCT04883294)&#xD.

Ring-Enhancement on CEUS: Is it Useful in the Differential Diagnosis of Solid Thyroid Nodules?

To investigate the efficiency of contrast-enhanced ultrasound (CEUS) features, particularly ring-enhancement patterns, in the differential diagnosis of thyroid nodules. 302 nodules with CEUS ring-enhancement were retrospectively enrolled, including 135 benign and 167 malignant ones. The ring-enhancement patterns were classified into regular and irregular hyper- or hypo-ring enhancement. Comparative analyses of ultrasound (US) and CEUS features between benign and malignant nodules were performed. The diagnostic performances of the ring-enhancement patterns and Chinese Thyroid Imaging Reporting and Data System (C-TIRADS) were compared in nodules with different sizes. Irregular hypo-ring enhancement was much more common in malignancies than that in benign ones, and it was an independent predictor for thyroid malignant nodules. With irregular hypo-ring enhancement as the diagnostic criteria for malignant nodules, the specificity was higher than that of C-TIRADS (85.2% vs. 75.6%, p = .037) while the AUC was comparable (0.845 vs. 0.803, p = .136) in all nodules. When the nodule size was taken into account, the specificity and AUC were both significantly higher than those of C-TIRADS (92.8% vs. 81.1%, p = .021; 0.907 vs. 0.823, p = .026) in nodules ≥10 mm, which can decrease the unnecessary FNA rate. Irregular hypo-ring enhancement was a valuable CEUS feature for the differential diagnosis of thyroid nodules, especially in nodules ≥10 mm.

Software using artificial intelligence for nodule and cancer detection in CT lung cancer screening: systematic review of test accuracy studies

ObjectivesTo examine the accuracy and impact of artificial intelligence (AI) software assistance in lung cancer screening using CT.MethodsA systematic review of CE-marked, AI-based software for automated detection and analysis of...

Risk of Malignancy in Indeterminate Liver Nodules Among Patients with Cirrhosis: A Retrospective Cohort Study

BackgroundSeveral studies have shown a higher risk of liver cancer from indeterminate liver nodules, but the exact occurrence and predictors of liver cancer in this group are still unclear. Our aim is to study the development of liver cancer in this population and identify any potential risk factors.MethodsThis retrospective study evaluated cirrhotic patients with indeterminate liver nodules from 2013 to 2023.Data from electronic patient records was analyzed to assess the association between HCC and baseline factors. Subgroup exploratory analysis compared characteristics of patients with de novo HCC and those with nodule transformation HCC.ResultsOut of 116 patients with liver nodules, 19 (16%) developed HCC in up to 7.5-year follow-up. Univariate Cox regression analysis showed a significant association between HCC incidence and smoking [hazard ratio (HR) 2.60, 95% Confidence Interval [CI] 1.01–6.74), nodule diameter exceeding 2 cm (HR 5.41, 95% CI 1.45–20.18), and baseline LI-RADS score 3 (HR 3.78, 95% CI 1.36–19.52). Multivariate Cox regression analysis revealed significant independent associations with nodule diameters 1 cm to < 2 cm (adjusted HR 3.35, 95% CI 1.06–10.60) and greater than 2 cm (adjusted HR 5.85, 95% CI 1.10–31.16), as well as with LI-RADS 3 lesions (adjusted HR 3.75, 95% CI 1.16–12.11) with adjusting other potential predictors and covariates.ConclusionOur findings show a higher incidence of HCC in patients with indeterminate liver nodules, increasing over time and reaching 30% at seven years. Nodules larger than 1–2 cm or LI-RADS 3 lesions pose increased risk for HCC. Enhanced surveillance is necessary given the lack of clear management guidelines.

A Transfer Learning-Based Framework for Classifying Lymph Node Metastasis in Prostate Cancer Patients.

Background: Prostate cancer is the second most common new cancer diagnosis in the United States. It is usually slow-growing, and when it is low-grade and confined to the prostate gland, it can be treated either conservatively (through active surveillance) or with surgery. However, if the cancer has spread beyond the prostate, such as to the lymph nodes, then that indicates a more aggressive cancer, and surgery may not be adequate. Methods: The challenge is that it is often difficult for radiologists reading prostate-specific imaging such as magnetic resonance images (MRIs) to differentiate malignant lymph nodes from non-malignant ones. An emerging field is the development of artificial intelligence (AI) models, including machine learning and deep learning, for medical imaging to assist in diagnostic tasks. Earlier research focused on implementing texture algorithms to extract imaging features used in classification models. More recently, researchers began studying the use of deep learning for both stand-alone feature extraction and end-to-end classification tasks. In order to tackle the challenges inherent in small datasets, this study was designed as a scalable hybrid framework utilizing pre-trained ResNet-18, a deep learning model, to extract features that were subsequently fed into a machine learning classifier to automatically identify malignant lymph nodes in patients with prostate cancer. For comparison, two texture algorithms were implemented, namely the gray-level co-occurrence matrix (GLCM) and Gabor. Results: Using an institutional prostate lymph node dataset (42 positives, 84 negatives), the proposed framework achieved an accuracy of 76.19%, a sensitivity of 79.76%, and a specificity of 69.05%. Using GLCM features, the classification achieved an accuracy of 61.90%, a sensitivity of 74.07%, and a specificity of 42.86%. Using Gabor features, the classification achieved an accuracy of 65.08%, a sensitivity of 73.47%, and a specificity of 52.50%. Conclusions: Our results demonstrate that a hybrid approach, i.e., using a pre-trainined deep learning model for feature extraction, followed by a machine learning classifier, is a viable solution. This hybrid approach is especially useful in medical-imaging-based applications with small datasets.

Heterotopic High-Grade Salivary duct Carcinoma Likely Arising within Cervical Lymph Nodes

Abstract Introduction/Objective Salivary duct carcinoma arising in heterotopic salivary gland tissue is an extremely rare occurrence, with less than five cases reported in the literature. We present a unique case of heterotopic high-grade salivary duct carcinoma likely arising within cervical lymph nodes. Methods/Case Report A 56-year-old male presented with enlarged cervical lymph nodes for few months. Physical examination was unremarkable. Computerized tomography (CT) scan of head and neck showed two enlarged level 1B lymph nodes, measuring 1.0 and 1.4 cm, with unremarkable salivary glands. Patient had left cervical level II lymph node biopsy that showed metastatic poorly differentiated carcinoma. Immunohistochemically, the tumor cells were positive for GATA3, cytokeratin 903, androgen receptor, p16 (variable nonspecific), and were negative for TTF-1, p40, p63, ER, PR and NKX3.1, favoring salivary gland ductal carcinoma. Positron emission tomography (PET) CT scan showed a hypermetabolic left submandibular gland and level 2A lymph nodes. Subsequently, the patient underwent left modified radical neck dissection. Sectioning of the submandibular gland revealed no discrete lesions. Multiple, firm to rubbery lymph nodes ranging from 0.4 cm to 2.3 cm, with tan-pink cut surfaces were identified. Microscopic examination showed normal submandibular salivary gland parenchyma. However, eight out of nineteen lymph nodes were positive for metastatic high-grade salivary duct carcinoma comprising tumor cells forming nests and cords with ample eosinophilic cytoplasm, pleomorphic round to oval nuclei and numerous mitotic figures. Several benign and malignant lymph nodes in levels I, II and IV showed heterotopic salivary gland tissue. Immunohistochemical stains for androgen receptor and p40 were positive and negative respectively, supported the final diagnosis of high-grade salivary duct carcinoma. Results (if a Case Study enter NA) NA Conclusion Given the absence of carcinoma in the completely sampled submandibular gland, the origin of the intra- nodal tumor deposits was most likely to be heterotopic salivary gland tissue. This case emphasizes the importance of meticulous pathological sampling required for definitive diagnosis.

Assessment of the Diagnostic Performance of a Commercially Available Artificial Intelligence Algorithm for Risk Stratification of Thyroid Nodules on Ultrasound.

Background: Thyroid nodules are challenging to accurately characterize on ultrasound (US), though the emergence of risk stratification systems and more recently artificial intelligence (AI) algorithms has improved nodule classification. The purpose of this study was to evaluate the performance of a recent Food and Drug Administration (FDA)-cleared AI tool for detection of malignancy in thyroid nodules on US. Methods: One year of consecutive thyroid US with ≥1 nodule from Duke University Hospital and its affiliate community hospital (649 nodules from 347 patients) were retrospectively evaluated. Included nodules had ground truth diagnoses by surgical pathology, fine needle aspiration (FNA), or three-year follow-up US showing stability. An FDA-cleared AI tool (Koios DS Thyroid) analyzed each nodule to generate (i) American College of Radiology Thyroid Imaging Reporting and Data System (ACR TI-RADS) descriptors, scores, and follow-up recommendations and (ii) an AI-adapter score to further adjust risk assessments and recommendations. Four groups were then compared: (i) Koios with AI-adapter, (ii) Koios without AI-adapter, (iii) clinical radiology report, and (iv) radiology report combined with AI-adapter. Performance of the final recommendations (FNA or no FNA) was determined based on ground truth, and comparison between the four groups was made using sensitivity, specificity, and receiver-operating-curve analysis. Results: Of 649 nodules, 32 were malignant and 617 were benign. Performance of Koios with AI-adapter enabled was similar to radiologists (area under the curve [AUC] 0.70 for both, [CI 0.60-0.81] and [0.60-0.79], respectively). Koios with AI-adapter had improved specificity compared to radiologists (0.63 [CI: 0.59-0.67] versus 0.43 [CI: 0.38-0.48]) but decreased sensitivity (0.69 [CI: 0.50-0.83) versus 0.81 [CI: 0.61, 0.92]). Highest performance was seen when the radiology interpretation was combined with the AI-adapter (AUC 0.76, [CI: 0.67-0.85]). Combined with the AI-adapter, radiologist specificity improved from 0.43 ([CI: 0.38-0.48]) to 0.53 ([CI: 0.49-0.58]) (McNemar's test p < 0.001), resulting in 17% fewer FNA recommendations, with unchanged sensitivity (0.81, p = 1). Conclusion: Koios DS demonstrated standalone performance similar to radiologists, though with lower sensitivity and higher specificity. Performance was best when radiologist interpretations were combined with the AI-adapter component, with improved specificity and reduced unnecessary FNA recommendations.

Rare Presentation of Anaplastic Carcinoma as a Mural Nodule in Mucinous Cystadenoma with Concurrent Benign Brenner Tumor

Abstract Introduction/Objective Mural nodules in ovarian mucinous tumors, particularly which are anaplastic carcinomas, represent an exceedingly rare and complex diagnostic category. The literature documents only a few such tumors, predominantly sarcoma-like, with favorable prognoses. In contrast, malignant mural nodules, including anaplastic carcinoma and sarcomatous or carcinosarcomatous types, are notably rare, with fewer than ten cases reported. The co-occurrence of an anaplastic carcinoma mural nodule with a benign Brenner tumor presents distinct diagnostic and treatment complexities stemming from mixed tumor pathology and associated genetic mutations like KRAS and p53, critical in the development of these unusual nodules. This report deepens our insight into rare ovarian tumors. Methods/Case Report A 60-year-old woman with persistent lower abdominal pain underwent surgery for a suspected benign ovarian cyst based on initial imaging. The removed ovary contained a 10.5 x 10 x 8 cm unilocular cyst lined with benign, mucin-containing columnar epithelium. However, a 2 cm anaplastic carcinoma mural nodule was identified within the cyst, exhibiting cellular pleomorphism, nuclear atypia, and high mitotic activity. Immunohistochemical staining confirmed the carcinoma with diffuse cytokeratin and EMA positivity. The fallopian tube was free of tumor involvement, and no capsular or lymphovascular invasion was seen. The cancer was staged as PT1a, pNx, pMx. Results (if a Case Study enter NA) NA Conclusion Anaplastic carcinoma, when present as a mural nodule within a benign-looking mucinous cystadenoma, demands an aggressive treatment strategy due to its poor prognosis and aggressive nature. Such cases require a deviation from standard benign treatment protocols to include extensive surgery, possibly, depending on disease progression, chemotherapy or radiation.

The Current Progress of Artificial Intelligence in Approach to Thyroid Nodules: A Narrative Review

Background: Artificial intelligence (AI) can play a significant role in the future of thyroidology. Thyroid nodules are common conditions that may benefit from AI through more accurate and efficient diagnosis, risk stratification, and medical or surgical management. Objective: This paper aims to review the latest developments in AI applications for diagnosing and managing thyroid nodules and cancers. Methods: English full-text articles published in the PubMed and Google Scholar databases from January 2014 to March 2024 were collected and reviewed to provide a comprehensive understanding of the topic. A total of 45 studies were selected based on relevance, robust methodology, statistical significance, and broader topic coverage. Results: Artificial intelligence has emerged as a powerful tool for managing thyroid nodules. First, several studies have demonstrated how AI-powered ultrasound interpretation enhances the diagnosis and classification of nodules while reducing the need for invasive fine-needle aspiration (FNA) biopsies. Second, AI significantly improves the cytopathological differentiation between benign and malignant thyroid nodules by minimizing reliance on pathologists' expertise and implementing standardized diagnostic criteria. When cytopathology is inconclusive, AI also aids in identifying molecular markers from omics data, distinguishing between normal and cancerous cells. Moreover, AI tools have been developed for prognosis assessment, predicting distant metastasis, recurrence, and surveillance by integrating medical imaging features with molecular and clinical factors. Additionally, some AI tools are designed for intraoperative evaluation, improving surgical techniques and reducing complications during thyroidectomy. In non-surgical treatments, several models have been developed to optimize therapeutic doses of radioactive iodine (RAI) and predict the outcomes of new drug formulations. Conclusions: Artificial intelligence has the potential to assist physicians in accurate thyroid nodule diagnosis, classification, decision-making, optimizing treatment strategies, and improving patient outcomes. However, there are still limitations to this technology. Artificial intelligence-driven tools require further advancements before they can be fully integrated into clinical practice and replace specialists.

Tumefactive Pulmonary amyloidosis in an elderly male: A Diagnostic Dilemma

Abstract Introduction/Objective Tumefactive pulmonary amyloidosis is a rare variant of pulmonary amyloidosis characterized by the extracellular deposition of abnormal protein fibrils in the lung parenchyma, forming tumor-like masses called amyloidomas. Although most cases are clinically silent, it may present with findings that mimic a wide range of respiratory conditions (eg. primary or metastatic malignant nodules, granulomatous disease, multiple pulmonary hamartomas etc), thereby necessitating a comprehensive and broad-based approach to differential diagnosis. Methods/Case Report We present a case of an 88-year-old male with a history of smoking and multiple comorbidities. He presented with numerous bilateral pulmonary nodules identified on a CT scan, concerning for malignancy. A biopsy from a left upper lobe lesion revealed necrotic tissue and was non-diagnostic. A PET scan to assess for metastatic disease was unremarkable, and demonstrated mild hypermetabolic activity, confined to the lungs, with standardized uptake values (SUV) ranging from 4.3 to a maximum SUV of 8.9. A subsequent right lung biopsy was positive for amyloid, confirmed by Congo red staining. A Mass spectrometry analysis further identified the amyloid as AL type. Serum, and urine protein electrophoresis with immunofixation were negative for M protein and the serum free light chain assay was normal as well. Significantly, the bone marrow biopsy also yielded normal results, and the patient was diagnosed with a localized form of AL amyloidosis. Results (if a Case Study enter NA) NA Conclusion This case highlights how tumefactive pulmonary amyloidosis poses a diagnostic challenge due to its rarity and resemblance to malignancy on imaging. Careful histopathological examination, including immunohistochemical stains and amyloid typing, was pivotal in establishing the correct diagnosis and underscores the essential role of pathology and laboratory medicine in differentiating rare pulmonary conditions, along with the need for a multidisciplinary approach in complex diagnostic scenarios.

Serum exosomal small nucleolar RNA (snoRNA) signatures as a predictive biomarker for benign and malignant pulmonary nodules

Low-dose CT (LDCT) is increasingly recognized as the preferred method for detecting pulmonary nodules. However, distinguishing whether a nodule is benign or malignant often necessitates repeated scans or invasive tissue sampling procedures. Therefore, there is a pressing need for non-invasive techniques to minimize unnecessary interventions. This study aim to investigate the expression profile of exosomal snoRNA in the serum of patients with benign and malignant pulmonary nodules. We identified a total of 278 snoRNAs in serum exosomes, revealing significant differences in snoRNA levels between patients with malignant and benign nodules. Specifically, the upregulated snoRNAs U78 and U37 were validated through qRT-PCR and were found significantly elevated in the serum of patients with malignant pulmonary nodules, positioning them as promising biomarkers for the early detection of lung cancer. This study underscores the potential of serum exosomal U78 and U37 as critical tools for assessing the risk of pulmonary nodules identified through CT screening.

Assessment of solitary pulmonary nodules using dual-layer spectral detector computed tomography.

We aim to quantitatively investigate the difference between benign and malignant solid pulmonary nodules that appeared on dual-energy spectral computed tomography, and assess the diagnostic accuracy of several parameters derived from computed tomography in differentiating malignant from benign pulmonary nodules. Between September 2021 and December 2022, spectral images of 71 patients (male:female = 44:27, mean age = 71.0 years) confirmed by pathology were retrospectively analyzed in the venous phase. Patients were classified into the malignant group and the benign group. The iodine concentration values of the nodules, normalized iodine concentration of the nodules to the neighboring vessels, virtual monochromatic images of 40 and 80 keV, and slope of the spectral curve were calculated and compared between the benign and malignant groups. Receiver operating characteristic curves and the area under the curve were performed to assess the diagnostic performance of the above parameters. Both virtual monochromatic images and iodine concentration maps prove to be highly useful in differentiating benign and malignant pulmonary nodules. The malignant pulmonary nodules have higher iodine density and slope of the spectral curve than the benign lesions. The combined model of iodine density and curve slope with an optimal cutoff of 0.39 (area under the curve = 0.82) yielded a sensitivity of 95% and a specificity of 63%. Contrast-enhanced dual-energy spectral computed tomography allows promising capability of distinguishing malignant from benign lesions, potential for avoiding unnecessary invasive procedure or surgery.