Malignant Nodules Research Articles

Radiomics Analysis for the Identification of Invasive Pulmonary Subsolid Nodules From Longitudinal Presurgical CT Scans.

Effective identification of malignant part-solid lung nodules is crucial to eliminate risks due to therapeutic intervention or lack thereof. We aimed to develop delta radiomics and volumetric signatures, characterize changes in nodule properties over three presurgical time points, and assess the accuracy of nodule invasiveness identification when combined with immediate presurgical time point radiomics signature and clinical biomarkers. Cohort included 156 part-solid lung nodules with immediate presurgical CT scans and a subset of 122 nodules with scans at 3 presurgical time points. Region of interest segmentation was performed using ITK-SNAP, and feature extraction using CaPTk. Image parameter heterogeneity was mitigated at each time point using nested ComBat harmonization. For 122 nodules, delta radiomics features (ΔRAB= (RB-RA)/RA) and delta volumes (ΔVAB= (VB-VA)/VA) were computed between the time points. Principal Component Analysis was performed to construct immediate presurgical radiomics (Rs1) and delta radiomics signatures (ΔRs31+ ΔRs21+ ΔRs32). Identification of nodule pathology was performed using logistic regression on delta radiomics and immediate presurgical time point signatures, delta volumes (ΔV31+ ΔV21+ ΔV32), and clinical variable (smoking status, BMI) models (train test split (2:1)). In delta radiomics analysis (n= 122 nodules), the best-performing model combined immediate pre-surgical time point and delta radiomics signatures, delta volumes, and clinical factors (classification accuracy [AUC]): (77.5% [0.73]) (train); (71.6% [0.69]) (test). Delta radiomics and volumes can detect changes in nodule properties over time, which are predictive of nodule invasiveness. These tools could improve conventional radiologic assessment, allow for earlier intervention for aggressive nodules, and decrease unnecessary intervention-related morbidity.

Predictive Value of the Nomogram Model Based on Multimodal Ultrasound Features for Benign and Malignant Thyroid Nodules of C-TIRADS Category 4.

To explore the predictive value of the nomogram model based on multimodal ultrasound features for benign and malignant thyroid nodules of C-TIRADS category 4. A retrospective analysis was conducted on the general conditions and ultrasound features of patients who underwent thyroid ultrasound examination and fine needle aspiration biopsy (FNA) or thyroidectomy at the Affiliated Hospital of Zunyi Medical University from April 2020 to April 2023. Predictive signs for benign and malignant nodules of thyroid C-TIRADS category 4 were screened through LASSO regression and multivariate logistic regression analysis to construct a nomogram prediction model. The predictive efficiency and accuracy of the model were assessed through ROC curves and calibration curves. Seven independent risk factors in the predictive model for benign and malignant thyroid nodules of C-TIRADS category 4 were growth pattern, morphology, microcalcifications, SR, arterial phase enhancement intensity, initial perfusion time, and PE [%]. Based on these features, the area under the curve (AUC) of the constructed prediction model was 0.971 (p ＜ .001, 95% CI: 0.952-0.989), with a prediction accuracy of 93.1%. Internal validation showed that the nomogram calibration curve was consistent with reality, and the decision curve analysis indicated that the model has high clinical application value. The nomogram prediction model constructed based on the multimodal ultrasound features of thyroid nodules of C-TIRADS category 4 has high clinical application value.

The dilemma of mediastinal lymphadenopathy between invasive and non-invasive procedures: ROC analysis of T2WI and DWI-MRI advanced parameters correlated with PET-CT and biopsy

BackgroundThe characterization of pathologically enlarged mediastinal lymph nodes is clinically essential for effective disease management and accurate prognosis. Malignancy (metastases and lymphoma) and granulomatous conditions (sarcoidosis and tuberculosis) are the most common causes. Magnetic resonance imaging (MRI) is a good modality to characterize the mediastinal pathologically enlarged lymph nodes based on the excellent soft tissue contrast. It can save high-risk patients from radiation exposure and hazards of intervention such as general anesthesia and biopsy.Aim of the workTo estimate the accuracy of different advanced MRI quantitative parameters in the differentiation between benign and malignant mediastinal lymphadenopathy. This would involve the lesion-to-cord signal intensity ratio (SIR) in the T2-WI and diffusion weighted image (DWI), the lesion-to-chest wall muscle SIR in T2-WI and DWI, and the mean apparent diffusion coefficient (ADC) values. These values would be correlated with the prospective pathological data and the results of the positron emission tomography (PET-CT).ResultsProspectively, the study was conducted during the period between June 2022 and September 2023 on 45 patients with indeterminate or suspicious mediastinal lymphadenopathy identified by CT. MRI examination, PET-CT, and biopsy were applied for all patients. The intra-class correlation coefficient ranged between 0.89 and 0.95. (A) The lesion-to-cord SIR in T2-WI in the malignant group (1.49 ± 0.30) was higher than that in the benign group (0.83 ± 0.24) with P < 0.001. The statistically calculated cutoff value (> 1.2) estimated 90% sensitivity, 100% specificity, with AUC = 0.989. (B) The lesion-to-chest wall muscle SIR in T2-WI in malignant nodes (3.13 ± 0.84) was significantly higher than that in the benign nodes (1.90 ± 0.80) with P < 0.001. The statistically calculated cutoff value (> 2.4) estimated 86.67% sensitivity, 86.67% specificity, with AUC = 0.88. (C) The lesion-to-cord signal SIR in b500-DWI in the malignant node (1.80 ± 0.54) was higher than that in the benign group (0.75 ± 0.29) with P < 0.001. The statistically calculated cutoff value (> 1.2) estimated 100% sensitivity and specificity with AUC = 1.00. (D) The lesion-to-chest wall SIR in b500-DWI in the malignant node (6.43 ± 1.28) was higher than that in the benign node (2.63 ± 0.94) with P < 0.001. The statistically calculated cutoff value (> 4.1) estimated 96.67% sensitivity, 100% specificity, with AUC = 0.997. € The mean ADC values in the malignant nodes (0.83 ± 0.20 × 10–3 mm2/s) were significantly lower than that in the benign nodes (1.85 ± 0.19 × 10−3 mm2/s) with P < 0.001. The statistically calculated cutoff value (≤ 1.2) estimated 100% sensitivity and specificity with AUC = 1.00.ConclusionsBiopsy remains the main diagnostic modality for the characterization of mediastinal lymphadenopathy despite its hazards and limitations. This study proved that MRI could be accepted as an alternative non-invasive imaging tool that can overcome the PET-CT limitations. The lesion signal-to-cord and to-muscle ratios in both T2-WI and DWI-MRI side by side with the mean ADC value showed high statistical accuracy.

Speed and efficiency: evaluating pulmonary nodule detection with AI-enhanced 3D gradient echo imaging.

Evaluating the diagnostic feasibility of accelerated pulmonary MR imaging for detection and characterisation of pulmonary nodules with artificial intelligence-aided compressed sensing. In this prospective trial, patients with benign and malignant lung nodules admitted between December 2021 and December 2022 underwent chest CT and pulmonary MRI. Pulmonary MRI used a respiratory-gated 3D gradient echo sequence, accelerated with a combination of parallel imaging, compressed sensing, and deep learning image reconstruction with three different acceleration factors (CS-AI-7, CS-AI-10, and CS-AI-15). Two readers evaluated image quality (5-point Likert scale), nodule detection and characterisation (size and morphology) of all sequences compared to CT in a blinded setting. Reader agreement was determined using the intraclass correlation coefficient (ICC). Thirty-seven patients with 64 pulmonary nodules (solid n = 57 [3-107 mm] part-solid n = 6 [ground glass/solid 8 mm/4-28 mm/16 mm] ground glass nodule n = 1 [20 mm]) were analysed. Nominal scan times were CS-AI-7 3:53 min; CS-AI-10 2:34 min; CS-AI-15 1:50 min. CS-AI-7 showed higher image quality, while quality remained diagnostic even for CS-AI-15. Detection rates of pulmonary nodules were 100%, 98.4%, and 96.8% for CS-AI factors 7, 10, and 15, respectively. Nodule morphology was best at the lowest acceleration and was inferior to CT in only 5% of cases, compared to 10% for CS-AI-10 and 23% for CS-AI-15. The nodule size was comparable for all sequences and deviated on average < 1 mm from the CT size. The combination of compressed sensing and AI enables a substantial reduction in the scan time of lung MRI while maintaining a high detection rate of pulmonary nodules. Incorporating compressed sensing and AI in pulmonary MRI achieves significant time savings without compromising nodule detection or characteristics. This advancement holds clinical promise, enhancing efficiency in lung cancer screening without sacrificing diagnostic quality. Lung cancer screening by MRI may be possible but would benefit from scan time optimisation. Significant scan time reduction, high detection rates, and preserved nodule characteristics were achieved across different acceleration factors. Integrating compressed sensing and AI in pulmonary MRI offers efficient lung cancer screening without compromising diagnostic quality.

Thyroid nodules: diagnosis and management.

Thyroid nodules, with a prevalence of almost 25% in the general population, are a common occurrence. Their prevalence varies considerably depending on demographics such as age and sex as well as the presence of risk factors. This article provides a comprehensive overview of the prevalence, risk stratification and current management strategies for thyroid nodules, with a particular focus on changes in diagnostic and therapeutic protocols that have occurred over the past 10 years. Several sonography-based stratification systems (such as Thyroid Imaging Reporting and Data Systems (TIRADS)) might help to predict the malignancy risk of nodules, potentially eliminating the need for biopsy in many instances. However, large or suspicious nodules necessitate cytological evaluation following fine-needle aspiration biopsy for accurate classification. In the case of cytology yielding indeterminate results, additional tools, such as molecular testing, can assist in guiding the management plan. Surgery is no longer the only treatment for symptomatic or malignant nodules: active surveillance or local ablative treatments might be beneficial for appropriately selected patients. To enhance clinician-patient interactions and discussions about diagnostic options, shared decision-making tools have been developed. A personalized, risk-based protocol promotes high-quality care while minimizing costs and unnecessary testing.

Surgical management and histopathological patterns of periampullary cancers in Sudanese patients: A single‐center prospective study

AbstractBackgroundHistopathological patterns of periampullary cancers (PAC) provide valuable information for clinicians in the selection of effective postoperative therapies and serve as a prognostic marker for patients. Therefore, this study was conducted to determine the surgical management and histopathological patterns of PAC necessary to achieve excellent surgical outcomes.MethodsThis prospective study was conducted from October 2021 to September 2023 at Ibn Sina Specialized Hospital, Khartoum, Sudan. Eighty patients diagnosed with resectable PAC were enrolled.ResultsPAC was more prevalent in male patients (52.5%), with a male: female ratio of 1.1:1.0. The peak incidence occurred between 46 and 60 years, representing 38.75% of patients. The most significant reported comorbidity was cigarette smoking (36.25% of patients). Scleral icterus was found in 47.5% of patients. Pancreatic head cancer was found in 55% of patients. Histopathological analysis revealed adenocarcinoma in 82.5% of patients. R0 resection (tumor‐free margins) was the most common resection category (87.5% of patients). Lymphovascular invasion was detected in 37.5% of patients. Malignant lymph node involvement (1–3 nodes) was found 47.5% of patients. Perineural invasion was found in 55%. Finally, the most common histopathological differentiation of PAC was well‐differentiated (Grade I) in 67.5% of patients. The mortality rate was 0%.ConclusionsUpdated knowledge regarding the management modalities of PAC is a prerequisite for accurate histopathological assessment and good surgical outcomes. Furthermore, histopathological analysis helps clinicians to select effective postoperative therapies for greater improvement in survival outcomes.

Deep Learning Radiomics Features of Mediastinal Fat and Pulmonary Nodules on Lung CT Images Distinguish Benignancy and Malignancy.

This study investigated the relationship between mediastinal fat and pulmonary nodule status, aiming to develop a deep learning-based radiomics model for diagnosing benign and malignant pulmonary nodules. We proposed a combined model using CT images of both pulmonary nodules and the fat around the chest (mediastinal fat). Patients from three centers were divided into training, validation, internal testing, and external testing sets. Quantitative radiomics and deep learning features from CT images served as predictive factors. A logistic regression model was used to combine data from both pulmonary nodules and mediastinal adipose regions, and personalized nomograms were created to evaluate the predictive performance. The model incorporating mediastinal fat outperformed the nodule-only model, with C-indexes of 0.917 (training), 0.903 (internal testing), 0.942 (external testing set 1), and 0.880 (external testing set 2). The inclusion of mediastinal fat significantly improved predictive performance (NRI = 0.243, p < 0.05). A decision curve analysis indicated that incorporating mediastinal fat features provided greater patient benefits. Mediastinal fat offered complementary information for distinguishing benign from malignant nodules, enhancing the diagnostic capability of this deep learning-based radiomics model. This model demonstrated strong diagnostic ability for benign and malignant pulmonary nodules, providing a more accurate and beneficial approach for patient care.

The added value of shear wave elastography when combined to conventional ultrasonography and fine-needle aspiration cytology in detection of malignant thyroid nodule

BackgroundThis study aims to evaluate the effectiveness of ultrasound criteria, TI-RADS score, and elastography in distinguishing benign thyroid nodules from malignant ones, as a substitute for invasive diagnostic procedures. Although the minority of thyroid nodules is malignant, invasive diagnostic procedures are often necessary.MethodsIn this prospective study, a total of 104 thyroid nodules were assessed using fine-needle aspiration cytology. The euthyroid state of the patients was ensured. Conventional ultrasound and shear wave elastography (SWE) were performed by a single experienced operator. Final postoperative histopathologic findings were compared to conventional ultrasound and elastography findings.ResultsSWE elasticity indices (EIs) were found to be significantly higher in malignant nodules than benign ones (p < 0.05). Combined results of both TI-RADS and SWE increased the sensitivity of detection of malignant nodules.ConclusionCombined SWE and TI-RADS could enhance the diagnostic accuracy in differentiating thyroid nodules by leveraging their complementary strengths.

The diagnosis and management of thyroid nodules: Consensus statement of the Indian Thyroid Society

ABSTRACT Thyroid nodules are a common occurrence in the Indian population. The current management involving an individualized approach is increasingly becoming relevant instead of a broad diagnostic and management algorithm. The consensus statements derived in this article aim to provide a summary of the current medical evidence for the diagnosis and management of thyroid nodules, which assists in optimizing recommendations in the Indian setting. The task force of experts has provided inputs to address specific clinical questions in this consensus. The statements are formulated after a thorough analysis of several published studies and guidelines to address the screening, diagnosis, and management of thyroid nodules. A well-defined grading system is used to appraise the evidence and grade the strength of recommendations. This guideline covers risk stratification of thyroid nodules (differentiating benign from malignant lesions) and a guide to the use of fine-needle aspiration cytology to improve definitive management. The guideline covers evidence-based recommendations for the management of benign, cytologically indeterminate, and malignant thyroid nodules. The panel has also touched upon the aspects of nondiagnostic thyroid nodule management and intraoperative neuromonitoring. These evidence-based expert consensus statements can provide useful and practical insights to aid the practicing clinician.

Integrating cardiovascular risk assessment into mobile low-dose CT lung screenings in rural Appalachia: A comprehensive analysis of the relationship between lung cancer risk, coronary artery calcium burden, and cardiovascular risk reduction strategies

ObjectiveMobile low-dose computed tomography (LDCT) lung screenings are part of an outreach program in rural Appalachia to detect early lung cancer. Coronary artery calcium (CAC) scoring on LDCT can identify calcium deposits in coronary arteries and can prompt consideration of risk modification for prevention of cardiovascular disease (CVD) events. It is not known if Lung CT Screening Reporting & Data System (Lung-RADS) scoring correlates with CAC scores. There is no clear guidance for patients undergoing LDCT screenings to receive follow-up regarding CAC or prevention of associated CVD risk. MethodsThis was a retrospective review of mobile LDCT LCS in adults with no known history of CVD. CT images were obtained at 100 kVp with a slice thickness of 3 mm. Agatston CAC scoring was performed retroactively. Lung-RADS scores were categorized as: Negative (1), Benign (2), Probably Benign (3), and Suspicious (4). CAC scoring was grouped as 0, 1–100, 101–399, and ≥400. Descriptive statistics and chi-square analyses were utilized. ResultsA total of 526 LDCT screenings were included. Over 54 % of patients had coronary calcification on LDCT LCS. 161 patients (30.6 %) had a CAC score of ≥100 and 75 patients (14.3 %) had a CAC score ≥400. Of patients with a CAC score ≥100, 7.5 % received referrals for follow-up after the LDCT screen and 9.3 % had additional cardiac testing. Of those with a CAC score ≥100 not already on a statin (45.3 %) and not already on aspirin (63.3 %), few were started within 3 months of LDCT for prevention (8.2 % and 5.9 % respectively). Among patients with a Lung-RADS score of 4, 17 % had a CAC score >400, whereas only 12 % with a Lung-RADS score of 1 fell into the same CAC category. Higher Lung-RADS scores correlated with fewer patients with CAC of 0. A significant correlation was observed between higher Lung-RADS scores and elevated CAC scores (p = 0.02). ConclusionIn patients with no CVD history, coronary artery calcification was frequently identified on mobile LDCT lung screenings in rural communities. Patients with higher probabilities of malignant lung nodules may also be at increased risk for significant coronary artery disease. Calcium scoring from LDCT screenings allowed for simultaneous assessment of lung cancer and CVD risk. Unfortunately, few referrals or CVD prevention medications were initiated. Awareness of CAC score utility, follow-up for identified coronary calcifications, and consideration of primary prevention medications when indicated, would be beneficial in patients undergoing LDCT lung screenings, especially in rural areas with limited healthcare access.

Is it Possible to Use Diffusion weighted MRI to Distinguish and Discriminate between Pathological Subtypes of Benign and Malignant Thyroid Nodules ?

Is it Possible to Use Diffusion weighted MRI to Distinguish and Discriminate between Pathological Subtypes of Benign and Malignant Thyroid Nodules ?

Shear-wave elastography as a supplementary tool for axillary staging in patients undergoing breast cancer diagnosis

BackgroundPreoperative evaluation of axillary lymph node status is crucial for the selection of both systemic and surgical treatment in early breast cancer. This study assessed the particular role of additional shear wave elastography (SWE) in axillary staging in patients undergoing initial breast cancer diagnostics.MethodsOne hundred patients undergoing axillary lymph node biopsy due to a sonographically suspicious axillary lymph node were prospectively evaluated with SWE using virtual touch tissue imaging quantification (VTIQ). Mean values of tissue stiffness for axillary tissue and lymph node tissue were measured prior to core-cut biopsy of the lymph node. All lymph nodes were clip-marked during the biopsy. Cut-off values to differentiate between malignant and benign lymph nodes were defined using Youden’s index.ResultsLymph nodes with evidence of malignant tumor cells in the final pathological examination showed a significantly higher velocity as measured by SWE, with a mean velocity of 3.48 ± 1.58 m/s compared to 2.33 ± 0.62 m/s of benign lymph nodes (p < 0.0001). The statistically optimal cutoff to differentiate between malignant and benign lymph nodes was 2.66 m/s with a sensitivity of 69.8% and a specificity of 87.5%.ConclusionsLymph node metastases assessed with SWE showed significantly higher elasticity values compared to benign lymph nodes. Thus, SWE provides an additional useful and quantifiable parameter for the sonographic assessment of suspicious axillary lymph nodes in the context of pre-therapeutic axillary staging in order to differentiate between benign and metastatic processes and support the guidance of definitive biopsy work-up.Critical relevance statementShear-wave elastography provides an additional useful and quantifiable parameter for the assessment of suspicious axillary lymph nodes in the context of pre-therapeutic axillary staging in order to differentiate between benign and metastatic processes and support guiding the definitive biopsy work-up.Key PointsSWE is a quantifiable ultrasound parameter in breast cancer diagnosis.SWE shows a significantly higher velocity in malignant lymph nodes.SWE is useful in improving the sensitivity and specificity of axillary staging.Graphical

Evaluation of axillary lymph nodes in breast cancer patients with clinically negative axilla using contrast enhanced ultrasonography

Contrast enhanced ultrasonography enables dynamic evaluation of the microvasculature down to the capillaries when using high resolution ultrasound probes. It’s application in the evaluation of axillary lymph nodes in breast cancer patients with clinically negative axilla has been studied in 42 patients. The results of pre operative CEUS evaluation was correlated with histopathology status of axillary nodes after the harvesting of nodes during modified radical mastectomy or sentinel node biopsy. Heterogeneous enhancement with micro bubbles of the axillary nodes was found to be the most distinguishing criteria for malignant nodes.

Predictive value of the single most suspicious ultrasound feature in subcentimeter thyroid nodules: a retrospective observational cohort study

PurposeProper management of subcentimeter thyroid nodules remains challenging for both clinicians and patients. Conducting extensive sonographic research using a safe and inexpensive tool for identifying thyroid nodules is necessary. The aim of this study was to identify whether having the highest-risk ultrasound (US) characteristic suggests that US-guided fine-needle aspiration (FNA) biopsy of subcentimeter nodules is more appropriate for the identification of malignancy than active surveillance (AS) or surgery.MethodsThe data of patients with highly suspicious subcentimeter thyroid nodules and US characteristic data who underwent surgery were retrospectively examined.ResultsAmong a total of 556 subcentimeter nodules, 223 (40.1%) were benign, and 333 (59.9%) were malignant, with a mean maximal nodule size of 8.1 mm. In addition to age younger than 45 years, several US features were significantly associated with malignancy: irregular margins, the presence of microcalcifications, and taller-than-wide shapes (P < 0.001). Multivariate analysis also revealed that a taller-than-wide shape (OR = 8.988, P = 0.0015) was an independent factor associated with malignancy in subcentimeter thyroid nodules. The diagnostic performance of preoperative FNA was classified as a malignancy, with a sensitivity of 98.4%, specificity of 100%, positive predictive value of 100%, and negative predictive value of 76.9%.ConclusionsThis is one of the few reports based on actual data of the most suspicious US features in subcentimeter thyroid nodules. A taller-than-wide shape US feature is most significantly associated with malignancy. FNA is a simple, accurate, and reliable preoperative method for diagnosing malignant subcentimeter thyroid nodules with highly suspicious US characteristics. AS was less appropriate than FNA for subcentimeter nodules with a taller-than-wide shape, especially in patients ≤ 45 years of age.

Insights into Ultrasound Features and Risk Stratification Systems in Pediatric Patients with Thyroid Nodules.

Thyroid nodules in pediatric patients are less common than in adults but show a higher malignancy rate. Accordingly, the management of thyroid nodules in pediatric patients is more complex the younger the patient is, needing careful evaluation by physicians. In adult patients, specific ultrasound (US) features have been associated with an increased risk of malignancy (ROM) in thyroid nodules. Moreover, several US risk stratification systems (RSSs) combining the US features of the nodule were built to define the ROM. RSSs are developed for the adult population and their use has not been fully validated in pediatric patients. This study aimed to evaluate the available data about US features of thyroid nodules in pediatric patients and to provide a summary of the evidence regarding the performance of RSS in predicting malignancy. Moreover, insights into the management of thyroid nodules in pediatric patients will be provided.

Weakly Supervised Lymph Nodes Segmentation Based on Partial Instance Annotations with Pre-trained Dual-branch Network and Pseudo Label Learning

Assessing the presence of potentially malignant lymph nodes aids in estimating cancer progression, and identifying surrounding benign lymph nodes can assist in determining potential metastatic pathways for cancer. For quantitative analysis, automatic segmentation of lymph nodes is crucial. However, due to the labor-intensive and time-consuming manual annotation process required for a large number of lymph nodes, it is more practical to annotate only a subset of the lymph node instances to reduce annotation costs. In this study, we propose a pre-trained Dual-Branch network with Dynamically Mixed Pseudo label (DBDMP) to learn from partial instance annotations for lymph nodes segmentation. To obtain reliable pseudo labels for lymph nodes that are not annotated, we employ a dual-decoder network to generate different outputs that are then dynamically mixed. We integrate the original weak partial annotations with the mixed pseudo labels to supervise the network. To further leverage the extensive amount of unannotated voxels, we apply a self-supervised pre-training strategy to enhance the model’s feature extraction capability. Experiments on the mediastinal Lymph Node Quantification (LNQ) dataset demonstrate that our method, compared to directly learning from partial instance annotations, significantly improves the Dice Similarity Coefficient (DSC) from 11.04% to 54.10% and reduces the Average Symmetric Surface Distance (ASSD) from 20.83 mm to 8.72 mm. The code is available at &lt;a href='https://github.com/WltyBY/LNQ2023_training_code'&gt;https://github.com/WltyBY/LNQ2023_training_code&lt;/a&gt;

A Multi-Modal Machine Learning Methodology for Predicting Solitary Pulmonary Nodule Malignancy in Patients Undergoing PET/CT Examination

This study explores a multi-modal machine-learning-based approach to classify solitary pulmonary nodules (SPNs). Non-small cell lung cancer (NSCLC), presenting primarily as SPNs, is the leading cause of cancer-related deaths worldwide. Early detection and appropriate management of SPNs are critical to improving patient outcomes, necessitating efficient diagnostic methodologies. While CT and PET scans are pivotal in the diagnostic process, their interpretation remains prone to human error and delays in treatment implementation. This study proposes a machine-learning-based network to mitigate these concerns, integrating CT, PET, and manually extracted features in a multi-modal manner by integrating multiple image modalities and tabular features). CT and PET images are classified by a VGG19 network, while additional SPN features in combination with the outputs of VGG19 are processed by an XGBoost model to perform the ultimate diagnosis. The proposed methodology is evaluated using patient data from the Department of Nuclear Medicine of the University Hospital of Patras in Greece. We used 402 patient cases with human annotations to internally validate the model and 96 histopathological-confirmed cases for external evaluation. The model exhibited 97% agreement with the human readers and 85% diagnostic performance in the external set. It also identified the VGG19 predictions from CT and PET images, SUVmax, and diameter as key malignancy predictors. The study suggests that combining all available image modalities and SPN characteristics improves the agreement of the model with the human readers and the diagnostic efficiency.

Diagnostic efficacy of the contrast-enhanced ultrasound thyroid imaging reporting and data system classification for benign and malignant thyroid nodules.

The contrasted-enhanced ultrasound thyroid imaging reporting and data system (CEUS TI-RADS) is the first international risk stratification system for thyroid nodules based on conventional ultrasound (US) and CEUS. This study aimed to evaluate the diagnostic efficacy of CEUS TI-RADS for benign and malignant thyroid nodules and to assess the related interobserver agreement. The study recruited 433 patients who underwent thyroid US and CEUS between January 2019 and June 2023 at the Affiliated Hospital of Guangdong Medical University. A retrospective analysis of 467 thyroid nodules confirmed by fine-needle aspiration (FNA) and/or surgery was performed. Further, a CEUS TI-RADS classification was assigned to each thyroid nodule based on the CEUS TI-RADS scoring criteria for the US and CEUS features of the nodule. The nodules were grouped based on their sizes as follows: size ≤1 cm, group A; size >1 and ≤4 cm, group B; and size >4 cm, group C. Multivariate logistic regression was used to analyze independent risk factors for malignant thyroid nodules. Pathological assessment was the reference standard for establishing the sensitivity (SEN), specificity (SPE), accuracy (ACC), positive predictive value (PPV), and negative predictive value (NPV) of CEUS TI-RADS in diagnosing malignant thyroid nodules. The area under the curve (AUC) in the receiver operating characteristic (ROC) curve analysis was used to compare the diagnostic efficacy of the scoring system in predicting malignancy in three groups of nodules. The intragroup correlation coefficient (ICC) was adopted to assess the interobserver agreement of the CEUS TI-RADS score. Out of the 467 thyroid nodules, 262 were malignant and 205 were benign. Logistic regression analysis revealed that the independent risk factors for malignant thyroid nodules included punctate echogenic foci (P<0.001), taller-than-wide shape (P=0.015), extrathyroidal invasion (P=0.020), irregular margins/lobulation (P=0.036), hypoechoicity on US (P=0.038), and hypoenhancement on CEUS (P<0.001). The AUC for the CEUS TI-RADS in diagnosing malignant thyroid nodules was 0.898 for all nodules, 0.795 for group A, 0.949 for group B, and 0.801 for group C, with the optimal cutoff values of the CEUS TI-RADS being 5 points, 6 points, 5 points, and 5 points, respectively. Among these groups of nodules, group B had the highest AUC, with the SEN, SPE, ACC, PPV, and NPV for diagnosing malignant nodules being 95.9%, 88.1%, 92.8%, 92.6%, and 93.2%, respectively. The ICC of the CEUS TI-RADS classification between senior and junior physicians was 0.862 (P<0.001). In summary, CEUS TI-RADS demonstrated significant efficacy in distinguishing thyroid nodules. Nonetheless, there were variations in its capacity to detect malignant nodules across diverse sizes, and it demonstrate optimal performance in 1- to 4-cm nodules. These findings may serve as important insights for clinical diagnoses.

Deep-learning radiomics based on ultrasound can objectively evaluate thyroid nodules and assist in improving the diagnostic level of ultrasound physicians.

The incidence rate of thyroid nodules has reached 65%, but only 5-15% of these modules are malignant. Therefore, accurately determining the benign and malignant nature of thyroid nodules can prevent unnecessary treatment. We aimed to develop a deep-learning (DL) radiomics model based on ultrasound (US), explore its diagnostic efficacy for benign and malignant thyroid nodules, and verify whether it improved the diagnostic level of physicians. We retrospectively included 1,076 thyroid nodules from 817 patients at three institutions. The radiomics and DL features of the US images were extracted and used to construct radiomics signature (Rad_sig) and deep-learning signature (DL_sig). A Pearson correlation analysis and least absolute shrinkage and selection operator (LASSO) regression analysis were used for feature selection. Clinical US semantic signature (C_US_sig) was constructed based on clinical information and US semantic features. Next, a combined model was constructed based on the above three signatures in the form of a nomogram. The model was constructed using a development set (institution 1: 719 nodules), and the model was evaluated using two external validation sets (institution 2: 74 nodules, and institution 3: 283 nodules). The performance of the model was assessed using decision curve analysis (DCA) and calibration curves. Furthermore, the C_US_sigs of junior physicians, senior physicians, and expers were constructed. The DL radiomics model was used to assist the physicians with different levels of experience in the interpretation of thyroid nodules. In the development and validation sets, the combined model showed the highest performance, with areas under the curve (AUCs) of 0.947, 0.917, and 0.929, respectively. The DCA results showed that the comprehensive nomogram had the best clinical utility. The calibration curves indicated good calibration for all models. The AUCs for distinguishing between benign and malignant thyroid nodules by junior physicians, senior physicians, and experts were 0.714-0.752, 0.740-0.824, and 0.891-0.908, respectively; however, with the assistance of DL radiomics, the AUCs reached 0.858-0.923, 0.888-0.944, and 0.912-0.919, respectively. The nomogram based on DL radiomics had high diagnostic efficacy for thyroid nodules, and DL radiomics could assist physicians with different levels of experience to improve their diagnostic level.

Stages of benign thyroid nodules: principles and ultrasound signs.

Benign thyroid nodules are significantly common and occur in 50-60% of the population. Therefore, differentiation from malignant nodes and the choice of treatment tactics in some cases of benign pathology remain relevant. Despite advances in the clinical evaluation of thyroid nodules, methodological challenges exist due to empirically simplistic understandings of the nodular process. Different opinions on the pathogenesis of thyroid nodules and the history of the formation of the idea of ​​the stages of nodules are considered. For the first time, based on natural principles and many years of ultrasound analysis of changes in benign thyroid nodules, three stages of the nodular process were identified: Development, Wasting and Scarring. The stage of exhaustion has three substages: Initial, Moderate and Significant Wasting. The principles of stage-by-stage changes in nodules are explained and their ultrasound signs are shown. The key principle of the stages of nodules is the ratio of the magnitudes of the processes of regeneration (proliferation) and destruction in the nodule. Separate stage changes may occur in node segments. In such cases, part of the segments may show signs of the Development stage, another part-Wasting, and the third part-Scarring. The different variants of thyroid nodules are explained in terms of stages. Practical recommendations for differentiating ultrasound signs of nodules associated with stages are proposed. Knowledge about the staged changes in thyroid nodules helps reduce the likelihood of diagnostic errors, better navigate the prognosis and choice of treatment tactics, and recommend preventive ultrasound examination of the thyroid.