Computed Tomography Imaging Features Research Articles

Secondary Epiploic Appendagitis – Report of Three Cases with CT Findings

ABSTRACT Epiploic appendages are the fat-containing peritoneal pouches usually seen in the anti-mesenteric border, adjacent to the large bowel loop. The major components are adipose tissue and vessels. The exact functions are not understood. Epiploic appendagitis (EA) is an inflammatory process, primarily may be due to torsion or vascular occlusion or may be secondarily related to other inflammatory processes. Symptoms vary from mild abdominal pain to severe variety, depending on the underlying cause. Primary EA is a benign self-limiting condition and does not require any surgical intervention, whereas secondary cause of inflammation needs management of underlying pathology. Proper diagnosis plays a vital role in appropriate management. Cross-sectional study like computed tomography (CT) is the ideal choice of imaging. On CT, epiploic appendigitis appears as round to oval fat-attenuating lesions with hyperattenuating peripheral rim, usually abutting the adjacent large bowel. Chronic inflammation shows peripheral calcification, may detach, and appears as intra-abdominal loose body. We describe three cases of secondary EA with CT imaging features, which will guide the clinician in proper management, avoiding unwarranted surgery or hospitalization.

A novel computed tomography scoring system for evaluating the risk of dural defects in anterior surgery for cervical ossification of the posterior longitudinal ligament

ObjectiveTo develop and validate a computed tomography (CT)-based scoring system for evaluating the risk of dural defects (DDs) in anterior surgery for cervical ossification of the posterior longitudinal ligament (OPLL). MethodsWe retrospectively analyzed CT imaging features of 114 OPLL patients in our institute who received anterior decompression surgery. Intraoperative DDs were found in 16 patients. A multivariable logistic regression was used to evaluate the predictors. According to the odd ratio of the included risk factors, we developed a CT scoring system for evaluating the risk of DDs in anterior OPLL surgery. The system was further validated in an independent group of 39 OPLL patients. ResultsWe developed a CT scoring system as follows: hook sign (2 points), K-line (-) (1 point) and broad base (1 point). Thus, the system comprised 4 total points, and patients were at high risks of dural defects when the score ≥3 points. The operating characteristics of a score ≥3 for predicting DDs in the validation group were: sensitivity of 0.83, specificity of 0.94, LR positive of 13.75, LR negative of 0.18 and AUC of 0.886. The discriminatory ability of the proposed score could be demonstrated in the validation cohort. ConclusionsThe relatively simple and easy-to-use scoring system we propose integrates the 3 most reliable spinal CT findings observed in patients with OPLL and a DD. The likelihood to identify the underlying risks of spinal CSF leaks may be useful to triage patients who may benefit from indirect decompression techniques.

Contribution of Red Blood Cells and Platelets to Blood Clot Computed Tomography Imaging and Compressive Mechanical Characteristics.

Thrombus computed tomography (CT) imaging characteristics may correspond with thrombus mechanical properties and thus predict thrombectomy success. The impact of red blood cell (RBC) content on these properties (imaging and mechanics) has been widely studied. However, the additional effect of platelets has not been considered. The objective of the current study was to examine the individual and combined effects of blood clot RBC and platelet content on resultant CT imaging and mechanical characteristics. Human blood clot analogues were prepared from a combination of preselected RBC volumes and platelet concentrations to decouple their contributions. The resulting clot RBC content (%) and platelet content (%) were determined using Martius Scarlet Blue and CD42b staining, respectively. Non-contrast and contrast-enhanced CT (NCCT and CECT) scans were performed to measure the clot densities. CECT density increase was taken as a proxy for clinical perviousness. Unconfined compressive mechanics were analysed by performing 10 cycles of 80% strain. RBC content is the major determinant of clot NCCT density. However, additional consideration of the platelet content improves the association. CECT density increase is influenced by clot platelet and not RBC content. Platelet content is the dominant component driving clot stiffness, especially at high strains. Both RBC and platelet content contribute to the clot's viscoelastic and plastic compressive properties. The current in vitro results suggest that CT density is reflective of RBC content and subsequent clot viscoelasticity and plasticity, and that perviousness reflects the clot's platelet content and subsequent stiffness. However, these indications should be confirmed in a clinical stroke cohort.

Radiogenomics and Texture Analysis to Detect von Hippel-Lindau (VHL) Mutation in Clear Cell Renal Cell Carcinoma.

Radiogenomics, a burgeoning field in biomedical research, explores the correlation between imaging features and genomic data, aiming to link macroscopic manifestations with molecular characteristics. In this review, we examine existing radiogenomics literature in clear cell renal cell carcinoma (ccRCC), the predominant renal cancer, and von Hippel-Lindau (VHL) gene mutation, the most frequent genetic mutation in ccRCC. A thorough examination of the literature was conducted through searches on the PubMed, Medline, Cochrane Library, Google Scholar, and Web of Science databases. Inclusion criteria encompassed articles published in English between 2014 and 2022, resulting in 10 articles meeting the criteria out of 39 initially retrieved articles. Most of these studies applied computed tomography (CT) images obtained from open source and institutional databases. This literature review investigates the role of radiogenomics, with and without texture analysis, in predicting VHL gene mutation in ccRCC patients. Radiogenomics leverages imaging modalities such as CT and magnetic resonance imaging (MRI), to analyze macroscopic features and establish connections with molecular elements, providing insights into tumor heterogeneity and biological behavior. The investigations explored diverse mutations, with a specific focus on VHL mutation, and applied CT imaging features for radiogenomic analysis. Moreover, radiomics and machine learning techniques were employed to predict VHL gene mutations based on CT features, demonstrating promising results. Additional studies delved into the relationship between VHL mutation and body composition, revealing significant associations with adipose tissue distribution. The review concludes by highlighting the potential role of radiogenomics in guiding targeted and selective therapies.

The efficacy of clinicopathological and imaging features in differentiating benign from malignant parotid micro- and minitumors: a retrospective analysis

ObjectiveTo compare clinicopathological and imaging and features of micro- and minitumors of the parotid gland and provide a reference for preoperative prediction of benign vs. malignant status. Study DesignPatients with parotid gland tumors treated surgically were selected. Relevant clinicopathological and imaging data were collected for patients with maximum tumor diameters ≤ 20 mm on preoperative computed tomography (CT). The lesions were divided into two groups, microtumors and minitumors, based on maximum tumor diameter. CT imaging features of benign and malignant tumors were compared through binary logistic regression analysis. ResultsMicrotumors and minitumors were categorized by maximum diameters < 10 mm (n = 74) and 10-20 mm (n = 611), respectively. Benign and malignant minitumors exhibited significant differences in boundary, tumor density, margin morphology, spiculation margin, and CT values in the plain and arterial phase (P < 0.05), resembling those found in typical malignant parotid gland tumors. However, no significant differences were observed between benign and malignant microtumors. Logistic regression analysis identified boundary, margin morphology, and spiculation margin as independent predictors of malignancy. The prediction model excelled in identifying benign lesions but was less successful in identifying malignancies. ConclusionParotid gland minitumors had imaging features similar to typical larger malignant tumors. Active exclusion of the malignant risk and early surgical treatment is recommended for these tumors. Statement of Clinical RelevanceThis study analyzed the clinicopathological and imaging features of micro- and minitumors of the parotid gland to provide a reference for preoperative prediction and treatment. Active exclusion of malignant risk and early surgical treatment is recommended for small parotid tumors.

CT imaging features of calcium pyrophosphate dihydrate crystal deposition involving spine

PurposeTo explore the computed tomography (CT) imaging features of the spine involved by calcium pyrophosphate dihydrate crystal deposition disease (CPPD-CDD) in order to improve the understanding of this disease. Materials and methodsAmong 79 patients diagnosed with CPPD-CDD in our hospital, 51 patients with spinal involvement were selected, 9 patients without CT examination were excluded, and 42 patients were finally included as research objects. The CT images of the 42 patients were retrospectively analyzed. The analysis contents mainly included the distribution of lesions and the characteristic imaging findings of intervertebral disc, posterior ligament complex, sacroiliac joint and atlantoaxial joint. ResultsIn this paper, 42 patients with 11 males and 31 females were studied. The onset age ranged from 62 to 95 years. There were 264 lesions in 42 patients with spinal involvement, including 177 lumbar vertebrae, 43 cervical vertebrae, 27 thoracic vertebrae and 17 sacroiliac joints (14 bilateral and 3 unilateral). Characteristic imaging findings include: disc calcification (129 cases), posterior ligament complex calcification (107 cases), sacroiliac articular cartilage calcification (17 cases), and ligaments around the odontoid process calcification (11 cases). ConclusionsThe characteristic images of CPPD-CDD involving the spine are calcification of intervertebral disc, posterior ligament complex, sacroiliac joint surface and ligaments around odontoid process, and the lesions are frequent and coexisting, so it should be differentiated from gout, rheumatoid arthritis, ankylosing spondylitis, ossification of ligamentum flavum, basic calcium phosphate and infectious spondylodiscitis.

Predictive nomogram for lymph node metastasis and survival in gastric cancer using contrast-enhanced computed tomography-based radiomics: a retrospective study.

Lymph node involvement significantly impacts the survival of gastric cancer patients and is a crucial factor in determining the appropriate treatment. This study aimed to evaluate the potential of enhanced computed tomography (CT)-based radiomics in predicting lymph node metastasis (LNM) and survival in patients with gastric cancer before surgery. Retrospective analysis of clinical data from 192 patients diagnosed with gastric carcinoma was conducted. The patients were randomly divided into a training cohort (n=128) and a validation cohort (n=64). Radiomic features of CT images were extracted using the Pyradiomics software platform, and distinctive features were further selected using a Lasso Cox regression model. Features significantly associated with LNM were identified through univariate and multivariate analyses and combined with radiomic scores to create a nomogram model for predicting lymph node involvement before surgery. The predictive performance of radiomics features, CT-reported lymph node status, and the nomogram model for LNM were compared in the training and validation cohorts by plotting receiver operating characteristic (ROC) curves. High-risk and low-risk groups were identified in both cohorts based on the cut-off value of 0.582 within the radiomics evaluation scheme, and survival rates were compared. Seven radiomic features were identified and selected, and patients were stratified into high-risk and low-risk groups using a 0.582 cut-off radiomics score. Univariate and multivariate analyses revealed that radiomics features, diabetes mellitus, Nutrition Risk Screening (NRS) 2002 score, and CT-reported lymph node status were significant predictors of LNM in patients with gastric cancer. A predictive nomogram model was developed by combining these predictors with the radiomics score, which accurately predicted LNM in gastric cancer patients before surgery and outperformed other models in terms of accuracy and sensitivity. The AUC values for the training and validation cohorts were 0.82 and 0.722, respectively. The high-risk and low-risk groups in both the training and validation cohorts showed significant differences in survival rates. The radiomics nomogram, based on contrast-enhanced computed tomography (CECT ), is a promising non-invasive tool for preoperatively predicting LNM in gastric cancer patients and postoperative survival.

Clinical value of CT imaging features in the diagnosis of acute and chronic pancreatitis: A retrospective study.

Recurrent acute pancreatitis is a common acute abdominal disease in surgery. To evaluate the radiographic features of pancreatic computed tomography (CT) imaging in the diagnosis of acute and chronic pancreatitis. 48 pancreatitis patients who met the criteria were selected in this retrospective study from 2010 to 2019. Each diagnosis was evaluated as functional abdominal pain, recurrent acute pancreatitis, or chronic pancreatitis. All clinical data were collected from the patient's medical records. 54 radiological features were extracted from each region of interest in outline the pancreas and divided into five categories: first order statistics, the gray level co-occurrence matrix (GLCM), the gray level run-length matrix (GLRLM), the neighborhood gray level difference matrix (NGTDM), and morphological features by the MATLAB program. Of the 48 patients, 16 had functional abdominal pain (33.3%), 18 had recurrent acute pancreatitis (37.5%), and 14 had chronic pancreatitis (29.2%). In the univariate analysis, nine radiological features, eight GLCM features and one NGTDM feature were significantly different between groups. Nine radiological characteristics had important reference values with AUC values ranging from 0.73-0.91. Nine radiographic features of CT imaging demonstrate good evaluation efficiency in the diagnosis of pancreatitis and can distinguish patients with functional abdominal pain, recurrent acute pancreatitis, and chronic pancreatitis.

Preoperative differentiation of gastric schwannomas and gastrointestinal stromal tumors based on computed tomography: a retrospective multicenter observational study.

Gastric schwannoma is a rare benign tumor accounting for only 1-2% of alimentary tract mesenchymal tumors. Owing to their low incidence rate, most cases are misdiagnosed as gastrointestinal stromal tumors (GISTs), especially tumors with a diameter of less than 5 cm. Therefore, this study aimed to develop and validate a diagnostic nomogram based on computed tomography (CT) imaging features for the preoperative prediction of gastric schwannomas and GISTs (diameters = 2-5 cm). Gastric schwannomas in 47 patients and GISTs in 230 patients were confirmed by surgical pathology. Thirty-four patients with gastric schwannomas and 167 with GISTs admitted between June 2009 and August 2022 at Hospital 1 were retrospectively analyzed as the test and training sets, respectively. Seventy-six patients (13 with gastric schwannomas and 63 with GISTs) were included in the external validation set (June 2017 to September 2022 at Hospital 2). The independent factors for differentiating gastric schwannomas from GISTs were obtained by multivariate logistic regression analysis, and a corresponding nomogram model was established. The accuracy of the nomogram was evaluated using receiver operating characteristic and calibration curves. Logistic regression analysis showed that the growth pattern (odds ratio [OR] 3.626; 95% confidence interval [CI] 1.105-11.900), absence of necrosis (OR 4.752; 95% CI 1.464-15.424), presence of tumor-associated lymph nodes (OR 23.978; 95% CI 6.499-88.466), the difference between CT values during the portal and arterial phases (OR 1.117; 95% CI 1.042-1.198), and the difference between CT values during the delayed and portal phases (OR 1.159; 95% CI 1.080-1.245) were independent factors in differentiating gastric schwannoma from GIST. The resulting individualized prediction nomogram showed good discrimination in the training (area under the curve [AUC], 0.937; 95% CI, 0.900-0.973) and validation (AUC, 0.921; 95% CI, 0.830-1.000) datasets. The calibration curve showed that the probability of gastric schwannomas predicted using the nomogram agreed well with the actual value. The proposed nomogram model based on CT imaging features can be used to differentiate gastric schwannoma from GIST before surgery.

Correlation of CT parameters and PD-L1 expression status in gastric cancer.

We aimed to explore the correlation between routine computed tomography (CT) imaging features and programmed cell death ligand-1(PD-L1) expression status in gastric cancer and evaluate the predictive value of imaging parameters for this immunotherapy biomarker. Patients with gastric adenocarcinoma who underwent abdominal CT three-stage enhanced scan and PD-L1 immunohistochemical testing before treatment were retrospectively examined. All diagnoses were confirmed through pathology. According to the expression status of PD-L1, they were divided into the positive (CPS ≥ 5) or negative group (CPS < 5). Baseline CT imaging features were collected. Diagnostic performances of the different variables were evaluated using receiver operating characteristic (ROC) curve. In total, 67 patients (17 women and 50 men; mean age: 59.55 ± 10.22years) with gastric adenocarcinoma were included in the study. The overall stages, probability of maximum lymph node short diameter > 1cm and peak of lesion enhancement occurring in the arterial phase were statistically significant between the two groups (p < 0.05). Moreover, the arterial enhancement fraction (AEF) was significantly higher in the positive group than that in the negative group (p < 0.05), and ROC curve analysis showed that the AEF exhibited a high evaluation efficacy (area under the curve [AUC] = 0.724 [95% confidence interval (CI): 0.602-0.826]). The combined parameters had the best diagnostic efficacy (AUC = 0.825 [95%CI: 0.716-0.933]), sensitivity (75.00%), and specificity (81.40%). These findings confirm a correlation between CT imaging features and PD-L1 expression status in gastric cancer, and AEF may help evaluate high PD-L1 expression and select patients suitable for immunotherapy.

SMARCA4-deficient non-small cell lung carcinoma: clinicodemographic, computed tomography, and positron emission tomography-computed tomography features.

SMARCA4-deficient non-small cell lung carcinoma (SD-NSCLC) is a relatively rare tumor, which occurs in 5-10% of NSCLC. Based on World Health Organization thoracic tumor classification system, SMARCA4-deficient undifferentiated tumor (SD-UT) is recognized as a separate entity from SD-NSCLC. Differentiation between SD-NSCLC and SD-UT is often difficult due to shared biological continuum, but often required for choosing appropriate treatment regimen. Therefore, the aim of our study was to identify the clinicopathologic, computed tomography (CT), and positron emission tomography (PET)-CT imaging features of SD-NSCLC. Nine patients of pathologically confirmed SD-NSCLC were included in our analysis. We reviewed electronic medical records for clinical information, demographic features, CT, and PET-CT imaging features were analyzed. Smoking history and male predominance are observed in all patients with SD-NSCLC (n=9). On CT, SD-NSCLC appeared as relatively well-defined masses with lobulated contour (n=8) and peripheral location (n=7). Invasion of adjacent pleura or chest wall (n=7) were frequently observed, regardless of small tumor size. Four cases showed lymph node metastases. Among nine patients, three patients showed multiple bone metastases, and one patient showed lung-to-lung metastases. In patient with SD-NSCLC, there was tendency for male smokers, peripheral location and invasion of adjacent pleural or chest wall invasion regardless of small tumor size, when compared to SD-UT.

Feasibility analysis of arterial CT radiomics model to predict the risk of local and metastatic recurrence after radical cystectomy for bladder cancer

PurposeTo construct a radiomics-clinical nomogram model for predicting the risk of local and metastatic recurrence within 3 years after radical cystectomy (RC) of bladder cancer (BCa) based on the radiomics features and important clinical risk factors for arterial computed tomography (CT) images and to evaluate its efficacy.MethodsPreoperative CT datasets of 134 BCa patients (24 recurrent) who underwent RC were collected and divided into training (n = 93) and validation sets (n = 41). Radiomics features were extracted from a 1.5 mm CT layer thickness image in the arterial phase. A radiomics score (Rad-Score) model was constructed using the feature dimension reduction method and a logistic regression model. Combined with important clinical factors, including gender, age, tumor size, tumor number and grade, pathologic T stage, lymph node stage and histology type of the archived lesion, and CT image signs, a radiomics-clinical nomogram was developed, and its performance was evaluated in the training and validation sets. Decision curve analyses (DCA) the potential clinical usefulness.ResultsThe radiomics model is finally linear combined by 8 features screened by LASSO regression, and after coefficient weighting, achieved good predictive results. The radiomics nomogram developed by combining two independent predictors, Rad-Score and pathologic T stage, was developed in the training set [AUC, 0.840; 95% confidence interval (CI) 0.743–0.937] and validation set (AUC, 0.883; 95% CI 0.777–0.989). The calibration curve showed good agreement between the predicted probability of the radiomics-clinical model and the actual recurrence rate within 3 years after RC for BCa. DCA show the clinical application value of the radiomics-clinical model.ConclusionThe radiomics-clinical nomogram model constructed based on the radiomics features of arterial CT images and important clinical risk factors is potentially feasible for predicting the risk of recurrence within 3 years after RC for BCa.

Diagnosis of pulmonary tuberculosis with 3D neural network based on multi-scale attention mechanism.

This paper presents a novel multi-scale attention residual network (MAResNet) for diagnosing patients with pulmonary tuberculosis (PTB) by computed tomography (CT) images. First, a three-dimensional (3D) network structure is applied in MAResNet based on the continuity and correlation of nodal features on different slices of CT images. Secondly, MAResNet incorporates the residual module and Convolutional Block Attention Module (CBAM) to reuse the shallow features of CT images and focus on key features to enhance the feature distinguishability of images. In addition, multi-scale inputs can increase the global receptive field of the network, extract the location information of PTB, and capture the local details of nodules. The expression ability of both high-level and low-level semantic information in the network can also be enhanced. The proposed MAResNet shows excellent results, with overall 94% accuracy in PTB classification. MAResNet based on 3D CT images can assist doctors make more accurate diagnosis of PTB and alleviate the burden of manual screening. In the experiment, a called Grad-CAM was employed to enhance the class activation mapping (CAM) technique for analyzing the model's output, which can identify lesions in important parts of the lungs and make transparent decisions.

Assessing the reproducibility, stability, and biological interpretability of multimodal computed tomography image features for prognosis in advanced non‐small cell lung cancer

AbstractBackgroundDespite the existence of proposed prognostic features on computed tomography (CT) for patients with advanced‐stage non‐small cell lung cancer (NSCLC), including radiologists' handcrafted (RaH) features, radiomics features, and deep learning features, comprehensive studies that examine their reproducibility, stability, and biological interpretability remain limited.MethodsThe Image Biomarker Standardization Initiative‐reported tolerance, Kappa, interclass correlation coefficient, and coefficient of variance were employed to identify reproducible features among RaH, radiomics, and deep learning features derived from NSCLC phantoms. The reproducible features were then input into six artificial intelligence algorithms to develop prognostic models for targeted therapy and immunotherapy using real‐world patients with advanced‐stage NSCLC to assess their capability and stability. Pathway enrichment was also conducted to explore the underlying biological pathways associated with these reproducible features.ResultsReproducible features in advanced NSCLC included RaH features (9/9, 100%), radiomics features (572/1835, 31.17%), and deep learning features (3442/4096, 84.03%). Among the six artificial intelligence‐based prognostic methods, the RaH features exhibited least variability. We also observed that the optimal CT‐based prognostic approach differed depending on treatment regimens for advanced NSCLC. In analysis using the Cancer Genome Atlas Program lung adenocarcinoma dataset, the identified reproducible prognostic features, specifically tumor size‐derived radiomics and RaH features, showed significant associations with five key signaling pathways involved in NSCLC survival outcomes (false‐discovery rate p &lt; 0.05).ConclusionsBy elucidating the reproducibility, stability, and biological associations of prognostic CT features, our study provides valuable evidence for future NSCLC studies and modeling approaches.

Retrospective study of BRAFV600E mutation and CT features of papillary thyroid carcinoma.

This study aimed to examine the correlation between BRAFV600E status and computed tomography (CT) imaging characteristics in papillary thyroid carcinoma (PTC) and determine if suspicious CT imaging features could predict BRAFV600E status. This retrospective study included patients with pathologically confirmed PTC at the Department of Thyroid Surgery of Zhongshan Hospital, Xiamen University, between July 2020 and June 2022. We compared the clinicopathologic factors and CT findings of nodules with and without the mutation, and the multiple logistical regression test was used to determine independent parameters of the BRAFV600E mutation. This study included 381 patients with PTC, among them, BRAFV600E mutation was detected in 314 patients (82.4%). Multivariate logistic regression analysis showed that gender (OR = 0.542, 95% CI [0.296-0.993], P = 0.047) and shape (OR = 0.510, 95% CI [0.275-0.944], P = 0.032) were associated with BRAFV600E mutation. Compared to BRAFV600E mutation-negative, BRAFV600E-positive PTC lesions were more likely to be found in female patients and were characterized by irregular shape. However, the CT imaging finding is not enough to predict BRAFV600E status, but an indication.

Development of a combined radiomics and CT feature-based model for differentiating malignant from benign subcentimeter solid pulmonary nodules

BackgroundWe aimed to develop a combined model based on radiomics and computed tomography (CT) imaging features for use in differential diagnosis of benign and malignant subcentimeter (≤ 10 mm) solid pulmonary nodules (SSPNs).MethodsA total of 324 patients with SSPNs were analyzed retrospectively between May 2016 and June 2022. Malignant nodules (n = 158) were confirmed by pathology, and benign nodules (n = 166) were confirmed by follow-up or pathology. SSPNs were divided into training (n = 226) and testing (n = 98) cohorts. A total of 2107 radiomics features were extracted from contrast-enhanced CT. The clinical and CT characteristics retained after univariate and multivariable logistic regression analyses were used to develop the clinical model. The combined model was established by associating radiomics features with CT imaging features using logistic regression. The performance of each model was evaluated using the area under the receiver-operating characteristic curve (AUC).ResultsSix CT imaging features were independent predictors of SSPNs, and four radiomics features were selected after a dimensionality reduction. The combined model constructed by the logistic regression method had the best performance in differentiating malignant from benign SSPNs, with an AUC of 0.942 (95% confidence interval 0.918–0.966) in the training group and an AUC of 0.930 (0.902–0.957) in the testing group. The decision curve analysis showed that the combined model had clinical application value.ConclusionsThe combined model incorporating radiomics and CT imaging features had excellent discriminative ability and can potentially aid radiologists in diagnosing malignant from benign SSPNs.Relevance statementThe model combined radiomics features and clinical features achieved good efficiency in predicting malignant from benign SSPNs, having the potential to assist in early diagnosis of lung cancer and improving follow-up strategies in clinical work.Key points• We developed a pulmonary nodule diagnostic model including radiomics and CT features.• The model yielded the best performance in differentiating malignant from benign nodules.• The combined model had clinical application value and excellent discriminative ability.• The model can assist radiologists in diagnosing malignant from benign pulmonary nodules.Graphical

Computed tomography imaging characteristics of neck paragangliomas: A retrospective analysis

Background/Aim: Paragangliomas are rare neuroendocrine tumours arising from paraganglia of the autonomic nervous system. Computed to-mography (CT) imaging plays a crucial role in the evaluation and character-isation of neck paragangliomas. This retrospective study aimed to analyse the CT imaging features of neck paragangliomas to enhance diagnostic accuracy and delineate the radiological characteristics associated with these tumours. Methods: A retrospective review of CT imaging studies of patients diagnosed with neck paragangliomas from March 2021 to October 2023 was conducted. Imaging characteristics including tumour location, size, enhancement pattern, vascularity, calcifications, adjacent tissue involvement and relationship with surrounding structures were analysed. Results: A total of 87 patients with histologically confirmed neck paragangliomas were included in the study. CT imaging revealed typical findings of neck paragangliomas ie well-defined hyper-vascular masses with avid contrast enhancement, commonly located at the carotid bifurcation or along the carotid sheath. In addition, characteristic flow voids and the presence of feeding vessels were observed on CT angiography in a significant number of cases. The imaging analysis also identified calcifications and encasement of adjacent structures as frequent features of advanced stage paragangliomas. Conclusions: CT imaging of neck paragangliomas demonstrated consistent radiological features, including hypervascularity, contrast enhancement and distinct anatomic locations. Knowledge of these imaging characteristics is essential for accurate diagnosis and preoperative planning. Recognition of these features on CT imaging can aid in differentiating paragangliomas from other neck masses and facilitate appropriate management strategies.

CT Imaging Findings of Extra-adrenal Abdominal Paragangliomas: A Case Series

Extra-adrenal paragangliomas are rare neuroendocrine tumours originating from paraganglia outside the adrenal glands. The present case series illustrates the Computed Tomography (CT) imaging features of three instances of extra-adrenal abdominal paragangliomas. The discussed cases include Retroperitoneal paraganglioma with Inferior Vena Cava (IVC) infiltration and associated hepatic, pancreatic, and skeletal metastasis; Inter-aortocaval paraganglioma; and Organ of Zuckerkandl Paraganglioma. The CT imaging features of the lesions and their relation with the adjacent vascular structures are described. The differential diagnosis and histopathological correlations are discussed. These cases underscore the importance of considering paragangliomas in the differential diagnosis of soft-tissue masses in the abdomen and highlight the role of Contrast-enhanced CT (CECT) imaging in their evaluation. Understanding the diverse imaging characteristics of extra-adrenal paragangliomas aids in accurate diagnosis and appropriate management. Further studies are needed to expand the authors knowledge of these rare tumours and optimise their imaging evaluation.

Prediction of Pathological Risk Stratification using Computed Tomography Features in Gastrointestinal Stromal Tumours: A Retrospective observational Study

Introduction: Gastrointestinal Stromal Tumours (GISTs) are the most common mesenchymal tumours of gastrointestinal tract. A high postsurgical recurrence and metastatic rate have created a need for a presurgical risk profile identification system. Aim: To assess the association between morphological Computed Tomography (CT) parameters with the pathological risk profile and analyse which CT features can predict the risk grading of GISTs. Materials and Methods: This was a retrospective cohort study based on imaging and histopathological data of 26 patients with pathologically proven GISTs presenting to the Department of Radiodiagnosis of a tertiary hospital in the northern Indian Himalayan foothills over a period of five years from July 2018 to June 2023. CT imaging features including size, growth pattern, margins, enhancement, calcifications, necrosis, intralesional haemorrhage, enlarged feeding vessels, direct organ invasion, and associations such as ascites and lymphadenopathy were studied. All lesions were classified as per Miettinen risk classification into no risk, very low-risk, low, moderate, and high-risk lesions. Analysis was done by the Chi-square test. Predictive analysis was carried out by computing the odds ratio and performing regression analysis on significantly associated imaging features. Results: Out of 26 patients, the study group comprised 16 males (61.54%) and 10 females (38.46%). The most common decade of presentation was the 6th decade with the mean age of presentation being 55.81±4.23 years. Twelve patients were grouped under intermediate to high-risk grading. Lesion size &gt;5 cm (p-value=0.0171, OR=19.12), ill-defined margins (p-value=0.0048, OR=18.33), intralesional necrosis (p-value=0.0053, OR=19.8), and enlarged feeding vessels (p-value=0.012, OR=21.27) were identified as imaging features with significant association and predictive ability for high-risk lesions. The strongest predictive ability for a high-risk profile was shown by ill-defined margins (R2 =0.381) and intralesional necrosis (R2 =0.3287). Conclusion: A preoperative Contrast Enhanced Computed Tomography (CECT) assessment provides a comprehensive imaging profile for GISTs as well as a fair accuracy of risk profile prediction via various singular and clustered morphological parameters.

Morphological Predictors of Primary Lung Cancer among Part-Solid Ground-Grass Nodules on High-Resolution CT.

Recent advancements in computed tomography (CT) scanning have improved the detection rates of peripheral pulmonary nodules, including those with ground-glass opacities (GGOs). This study focuses on part-solid pure ground-glass nodules (GGNs) and aims to identify imaging predictors that can reliably differentiate primary lung cancer from nodules with other diagnoses among part-solid GGNs on high-resolution CT (HRCT). A retrospective study was conducted on 609 patients who underwent surgical treatment or observation for lung nodules. Radiological findings from pre-operative HRCT scans were reviewed and several CT imaging features of part-solid GGNs were examined for their positive predictive value to identify primary lung cancer. The proportions of the nodules with a final diagnosis of primary lung cancer were significantly higher in part-solid GGNs (91.9%) compared with solid nodules (70.3%) or pure GGNs (66.7%). Among CT imaging features of part-solid GGNs that were evaluated, consolidation-to-tumor ratio (CTR) < 0.5 (98.1%), pleural indentation (96.4%), and clear tumor border (96.7%) had high positive predictive value to identify primary lung cancer. When two imaging features were combined, the combination of CTR < 0.5 and a clear tumor border was identified to have 100% positive predictive values with a sensitivity of 40.8%. Thus we conclude that part-solid GGNs with a CTR < 0.5 accompanied by a clear tumor border evaluated by HRCT are very likely to be primary lung cancers with an acceptable sensitivity. Preoperative diagnostic procedures to obtain a pathological diagnosis may potentially be omitted in patients harboring such part-solid GGNs.