Malignant Nodules Research Articles

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.

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<0.001), and non-parallel orientation (20.3% vs. 100%, p<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<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<0.0001). The combination of US parameters and FNAC significantly improved the AUC value (AUC: 0.878, P<0.0001), sensitivity (83.33%), and specificity (79.14%). Univariate analysis showed that hypoechoic appearance, heterogenicity, large mass size (>4 cm), “taller-than-wider,” infiltrative margins, and microcalcifications were risk factors for malignancy in thyroid nodules and were statistically significant (all P values <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
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).
Methods
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).
Results 
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. 
Conclusion
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.
(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.

The Clinical Application of Superb Microvascular Imagingin Evaluating Thyroid Related Diseases: ASystematic Review.

This article reviews all published articles on the application of superb microvascular imaging (SMI) technique in thyroid related diseases before December 31, 2023. SMI, a recently developed ultrasound imaging modality, could display microvascular information by eliminating clutter and preserving low flow signals. Most studies used SMI to help diagnose malignant thyroid nodules and concluded that SMI performed better than color Doppler imaging and power Doppler imaging. At present, there is no consensus when depicting the morphology of vascularity of thyroid nodules (TNs). This problem may be solved by quantitative SMI which makes it possible to quantitatively evaluate the vascularity of TNs. SMI is also applied to evaluate cervical lymph node or thyroid inflammatory diseases. Although, SMI has some limitations, such as no standard for the normal perfusion patterns, and it has a broad application prospect in the diagnosis and management of thyroid related diseases.

Evaluation of ACR TI-RADS for Predicting Malignancy in Thyroid Nodules: Insights from Fine-Needle Aspiration Cytology and Histopathology Results

Evaluation of ACR TI-RADS for Predicting Malignancy in Thyroid Nodules: Insights from Fine-Needle Aspiration Cytology and Histopathology Results

Prediction of benign and malignant ground glass pulmonary nodules based on multi-feature fusion of attention mechanism.

The purpose of this study was to develop and validate a new feature fusion algorithm to improve the classification performance of benign and malignant ground-glass nodules (GGNs) based on deep learning. We retrospectively collected 385 cases of GGNs confirmed by surgical pathology from three hospitals. We utilized 239 GGNs from Hospital 1 as the training and internal validation set, and 115 and 31 GGNs from Hospital 2 and Hospital 3, respectively, as external test sets 1 and 2. Among these GGNs, 172 were benign and 203 were malignant. First, we evaluated clinical and morphological features of GGNs at baseline chest CT and simultaneously extracted whole-lung radiomics features. Then, deep convolutional neural networks (CNNs) and backpropagation neural networks (BPNNs) were applied to extract deep features from whole-lung CT images, clinical, morphological features, and whole-lung radiomics features separately. Finally, we integrated these four types of deep features using an attention mechanism. Multiple metrics were employed to evaluate the predictive performance of the model. The deep learning model integrating clinical, morphological, radiomics and whole lung CT image features with attention mechanism (CMRI-AM) achieved the best performance, with area under the curve (AUC) values of 0.941 (95% CI: 0.898-0.972), 0.861 (95% CI: 0.823-0.882), and 0.906 (95% CI: 0.878-0.932) on the internal validation set, external test set 1, and external test set 2, respectively. The AUC differences between the CMRI-AM model and other feature combination models were statistically significant in all three groups (all p<0.05). Our experimental results demonstrated that (1) applying attention mechanism to fuse whole-lung CT images, radiomics features, clinical, and morphological features is feasible, (2) clinical, morphological, and radiomics features provide supplementary information for the classification of benign and malignant GGNs based on CT images, and (3) utilizing baseline whole-lung CT features to predict the benign and malignant of GGNs is an effective method. Therefore, optimizing the fusion of baseline whole-lung CT features can effectively improve the classification performance of GGNs.

Association of pre-op TSH levels with Thyroid Carcinoma in a Tertiary Care Setup in Karachi, Pakistan

Objectives: This study aimed to explore the relationship between pre-operative TSH levels and the presence of thyroid cancer in patients with nodular thyroid disease in the Pakistani population, using data gathered from patients treated at Dr. Ruth K. M. Pfau Civil Hospital Karachi. Study Design &amp; Setting: A cross-sectional study was conducted at the Dow University of Health Sciences and Dr. Ruth K. M. Pfau Civil Hospital Karachi from January 2022 to December 2022. Methodology: Patients with thyroid swellings (presenting with either solitary nodules or with multinodular goiter), both benign as well as suspected/confirmed malignancy based on FNAC results were recruited in the study. Preoperative TSH levels, along with other clinical data, were collected. Thyroidectomy was carried out in patients fulfilling the criteria for surgery, with specimens sent for histopathology. An independent t-test was used to compare TSH levels between malignant and benign nodules. Results: A total of 82 patients were enrolled. Malignancy was confirmed in 41.5% (25 papillary carcinoma, 9 follicular carcinoma). Significantly higher mean TSH levels were observed in patients with malignant nodules (4.76 IU/mL) compared to those with benign nodules (2.48 IU/mL) (p &lt; 0.001). Conclusion: This study suggests a potential association between elevated pre-operative TSH levels and thyroid cancer in the Pakistani population. These findings warrant further investigation to explore causality and potential underlying mechanisms. The study highlights the value of TSH monitoring, particularly in resource-constrained settings.