CT Features Research Articles

A Rare Cause of Neck Mass in an Adult Woman- Cervical Thymic Cyst - A Case Report

Background: Cervical thymic cysts (CTCs) develop from the thymo-pharyngeal duct, representing less than 0.5% of all neck masses. Most of the cases are diagnosed in the first decade of life, being rarely described in adults. The majority of CTCs are located on the left side, the rest develop on the right side or in the midline. The patients present usually with a painless mass, rarely with compressive signs. The mechanisms of CTCs occurrence are still debated, failure of involution of thymopharyngeal duct, arrest in migration or retained thymic tissue during descent, being considered. Case Presentation: We report a case of a 49-year-old woman who presented a left laterocervical mass, with rapid growth over several months. The lump was soft, mobile and painless, no other clinical abnormalities were noticed. The ultrasound described an anechoic nodule of 57/38/63 mm at the inferior pole of the left thyroid lobe, raising the suspicion of a parathyroid cyst or branchial cyst. Contrast-enhanced CT scan confirmed the well-defined, homogeneous cystic lesion of 35/25 mm, located between the trachea and common carotid artery, being delimitated inferiorly by the left brachiocephalic vein. The aspiration of the cyst resulted in 40 ml of water-clear fluid, cytology examination confirmed the absence of thyroid or parathyroid cells. Surgery was performed, the pathological diagnosis confirmed the CTC. Conclusion: Although the thymic cyst is a very rare cause of a cervical mass in adult, the diagnosis should be kept in mind, based on clinical presentation, ultrasound and CT features, respectively aspirated fluid characteristics.

Prediction of disease severity in COPD: a deep learning approach for anomaly-based quantitative assessment of chest CT

ObjectivesTo quantify regional manifestations related to COPD as anomalies from a modeled distribution of normal-appearing lung on chest CT using a deep learning (DL) approach, and to assess its potential to predict disease severity.Materials and methodsPaired inspiratory/expiratory CT and clinical data from COPDGene and COSYCONET cohort studies were included. COPDGene data served as training/validation/test data sets (N = 3144/786/1310) and COSYCONET as external test set (N = 446). To differentiate low-risk (healthy/minimal disease, [GOLD 0]) from COPD patients (GOLD 1–4), the self-supervised DL model learned semantic information from 50 × 50 × 50 voxel samples from segmented intact lungs. An anomaly detection approach was trained to quantify lung abnormalities related to COPD, as regional deviations. Four supervised DL models were run for comparison. The clinical and radiological predictive power of the proposed anomaly score was assessed using linear mixed effects models (LMM).ResultsThe proposed approach achieved an area under the curve of 84.3 ± 0.3 (p < 0.001) for COPDGene and 76.3 ± 0.6 (p < 0.001) for COSYCONET, outperforming supervised models even when including only inspiratory CT. Anomaly scores significantly improved fitting of LMM for predicting lung function, health status, and quantitative CT features (emphysema/air trapping; p < 0.001). Higher anomaly scores were significantly associated with exacerbations for both cohorts (p < 0.001) and greater dyspnea scores for COPDGene (p < 0.001).ConclusionQuantifying heterogeneous COPD manifestations as anomaly offers advantages over supervised methods and was found to be predictive for lung function impairment and morphology deterioration.Clinical relevance statementUsing deep learning, lung manifestations of COPD can be identified as deviations from normal-appearing chest CT and attributed an anomaly score which is consistent with decreased pulmonary function, emphysema, and air trapping.Key Points• A self-supervised DL anomaly detection method discriminated low-risk individuals and COPD subjects, outperforming classic DL methods on two datasets (COPDGene AUC = 84.3%, COSYCONET AUC = 76.3%).• Our contrastive task exhibits robust performance even without the inclusion of expiratory images, while voxel-based methods demonstrate significant performance enhancement when incorporating expiratory images, in the COPDGene dataset.• Anomaly scores improved the fitting of linear mixed effects models in predicting clinical parameters and imaging alterations (p < 0.001) and were directly associated with clinical outcomes (p < 0.001).

CT predictors of sub-centimeter occult lymph node metastases in oral cavity squamous cell carcinoma: A case-control study.

For patients with oral cavity squamous cell carcinoma (OCSCC) without evidence of nodal metastasis (cN0) on pre-operative evaluation, there are no clear guidelines who should undergo elective neck dissection (END) versus clinical surveillance. To identify CT imaging characteristics of sub-centimeter lymph nodes that would help predict the likelihood of nodal metastases on pathology. Retrospective review of cN0 OCSCC patients at a tertiary academic medical center was performed. Inclusion criteria included elective neck dissection, pre-operative CT imaging and presence of metastatic disease within lymph nodes. Control group consisted of patients without nodal metastases on pathology. CT features that were evaluated included asymmetric size, disrupted fatty hilum, asymmetric number, presence of cortical nodule, cortical nodule size, and round/oval shape. We evaluated the associations between CT LN features and the presence of metastases using multi-level mixed-effects logistic regression models. Model evaluation was performed using 5-fold cross-validation. The positive predictive value (PPV) and negative predictive value (NPV) were calculated. 26 patients in each study and control groups were included. Three-level mixed-effects logistic regression models indicated round/oval shape (OR = 1.39, p = .01), asymmetric number (OR = 7.20, p = .005), and disrupted fatty hilum (OR = 3.31, p = .04) to be independently predictive in a 3-variable model with sensitivity = 38.0%, specificity = 92.0%, and PPV = 93.8%. In cN0 OCSCC patients undergoing END, round/oval shape, asymmetric number, and disrupted fatty hilum of lymph nodes on pre-operative CT imaging are novel and highly predictive of occult nodal disease.

Development and validation of clinical-radiomics analysis for preoperative prediction of IDH mutation status and WHO grade in diffuse gliomas: a consecutive L-[methyl-11C] methionine cohort study with two PET scanners.

Determination of isocitrate dehydrogenase (IDH) genotype is crucial in the stratification of diagnosis and prognostication in diffuse gliomas. We sought to build and validate radiomics models and clinical features incorporated nomogram for preoperative prediction of IDH mutation status and WHO grade of diffuse gliomas with L-[methyl-11C] methionine ([11C]MET) PET/CT imaging according to the 2016 WHO classification of tumors of the central nervous system. Consecutive 178 preoperative [11C]MET PET/CT images were retrospectively studied for radiomics analysis. One hundred six patients from PET scanner 1 were used as training dataset, and 72 patients from PET scanner 2 were used for validation dataset. [11C]MET PET and integrated CT radiomics features were extracted, respectively; three independent predictive models were built based on PET features, CT features, and combined PET/CT features, respectively. The SelectKBest method, Spearman correlation analysis, Least Absolute Shrinkage and Selection Operator (LASSO) regression, and machine learning algorithms were applied for feature selection and model building. After filtering the satisfactory predictive model, key clinical features were incorporated for the nomogram establishment. The combined [11C]MET PET/CT radiomics model, which consisted of four PET features and eight integrated CT features, was significantly associated with IDH genotype (p < 0.0001 for both training and validation datasets). Nomogram based on the [11C]MET PET/CT radiomics score, patients' age, and dichotomous tumor location status showed satisfactory discrimination capacity, and the AUC was 0.880 (95% CI, 0.726-0.998) in the training dataset and 0.866 (95% CI, 0.777-0.956) in the validation dataset. In IDH stratified WHO grade prediction, the final radiomics model consists of four PET features and two CT features had reasonable and stable differential efficacy of WHO grade II and III patients from grade IV patients in IDH-wildtype patients, and the AUC was 0.820 (95% CI, 0.541-1.000) in the training dataset and 0.766 (95% CI, 0.612-0.921) in the validation dataset. [11C]MET PET radiomics features could benefit non-invasive IDH genotype prediction, and integrated CT radiomics features could enhance the efficacy. Radiomics and clinical features incorporation could establish satisfactory nomogram for clinical application. This non-invasive predictive investigation based on our consecutive cohort from two PET scanners could provide the perspective to observe the differential efficacy and the stability of radiomics-based investigation in untreated diffuse gliomas.

Infectious sacroiliitis: MRI- and CT-based assessment of disease extent, complications, and anatomic correlation.

To describe the frequency of MR and CT features of infectious sacroiliitis (ISI) and assess its extent and complications MATERIALS AND METHODS: This retrospective study included patients with ISI who were evaluated between 2008 and 2021 in a single center. Two radiologists reviewed MRI and CT images to determine the anatomical distribution (unilateral/bilateral, iliac/sacral bone, proximal/middle/distal), severity (bone marrow edema [BME]/periostitis/erosions), concurrent infection (vertebral/nonvertebral), and complications (abscess/probable adjacent osteomyelitis/cavitation/devitalized areas/sequestrum/pelvic venous thrombosis) of ISI. Interobserver reproducibility was assessed. Correlation analysis evaluated the effect of the causative microorganism on severity. Two human bodies were dissected to outline possible ways that ISI can spread. Forty patients with ISI (40 years ± 22; 26 women) were evaluated. Ten patients had bilateral ISI. Concurrent vertebral infection was associated in 15% of cases. Reproducibility of sacral BME, periostitis, and reactive locoregional abnormalities was perfect (κ = 1). Reproducibility was low for erosion count (κ = 0.52[0.52-0.82]) and periarticular osteopenia (κ = 0.50[0.18-0.82]). Inflammatory changes were BME (42/42 joints), muscle edema (38/42), and severe periostitis along the ilium (33/37). Destructive structural changes occurred with confluent erosions (iliac, 20/48; sacral, 13/48), sequestrum (20/48), and cavitation (12/48). Complications occurred in 75% of cases, including periarticular abscesses (n = 30/47), probable adjacent osteomyelitis (n = 16/37), and pelvic thrombophlebitis (n = 3). Tuberculous ISI (6/40) correlated with sclerosis (rs = 0.45[0.16; 0.67]; p < 10-2) and bone devitalization (rs = 0.38[0.16; 0.67]; p = .02). The anatomical study highlighted the shared venous vascularization of sacroiliac joints, pelvic organs, and mobile spine. Complications of ISI are frequent, including abscesses, adjacent osteomyelitis, and periostitis. ISI had bilateral involvement nonrarely and is commonly associated with another spinal infection.

Stereotactic radiotherapy for advanced canine anal sac adenocarcinoma: an exploratory study.

For dogs with anal sac adenocarcinoma (ASAC), metastasis to intra-abdominal and pelvic lymph nodes occurs early in the disease course. Death is usually related to locoregional progression. Surgical excision is the treatment of choice, but may not be possible in advanced cases. Dogs treated with RT in the gross disease setting showed a 38%-75% overall response rate, but side effects to organs at risk in this area (especially the colon, bladder, and spinal cord) were reported. Stereotactic radiation therapy (SRT) utilizes highly conformal treatment planning with rapid dose fall-off and hypofractionation. SRT may help to reduce the risk of late side effects of radiation while also creating a larger biological effect on ASACs. A primary aim of this prospective, descriptive, exploratory study was to describe the safety and feasibility of an SRT protocol in a small sample of dogs with ASAC, using objective and subjective measures to monitor acute and late side effects. A secondary aim was to describe the anti-tumor response of the SRT protocol using CT at 3- and 6 months posttreatment. Five dogs completed the radiation protocol. Four had follow-up CT characteristics of complete response (1), partial response (2), and stable disease (1). Minimal acute side effects were observed. Despite some large tumor volumes, constraints for OAR were achieved in all but the spinal cord for one patient. Findings indicated that SRT is a safe and feasible treatment for dogs with ACAC. Future studies are warranted to compare patient outcomes for SRT versus other treatments.

Possibility of Using Conventional Computed Tomography Features and Histogram Texture Analysis Parameters as Imaging Biomarkers for Preoperative Prediction of High-Risk Gastrointestinal Stromal Tumors of the Stomach.

The objective of this study is to determine the morphological computed tomography features of the tumor and texture analysis parameters, which may be a useful diagnostic tool for the preoperative prediction of high-risk gastrointestinal stromal tumors (HR GISTs). This is a prospective cohort study that was carried out in the period from 2019 to 2022. The study included 79 patients who underwent CT examination, texture analysis, surgical resection of a lesion that was suspicious for GIST as well as pathohistological and immunohistochemical analysis. Textural analysis pointed out min norm (p = 0.032) as a histogram parameter that significantly differed between HR and LR GISTs, while min norm (p = 0.007), skewness (p = 0.035) and kurtosis (p = 0.003) showed significant differences between high-grade and low-grade tumors. Univariate regression analysis identified tumor diameter, margin appearance, growth pattern, lesion shape, structure, mucosal continuity, enlarged peri- and intra-tumoral feeding or draining vessel (EFDV) and max norm as significant predictive factors for HR GISTs. Interrupted mucosa (p < 0.001) and presence of EFDV (p < 0.001) were obtained by multivariate regression analysis as independent predictive factors of high-risk GISTs with an AUC of 0.878 (CI: 0.797-0.959), sensitivity of 94%, specificity of 77% and accuracy of 88%. This result shows that morphological CT features of GIST are of great importance in the prediction of non-invasive preoperative metastatic risk. The incorporation of texture analysis into basic imaging protocols may further improve the preoperative assessment of risk stratification.

Staging of breast cancer in the breast and regional lymph nodes using contrast-enhanced photon-counting detector CT: accuracy and potential impact on patient management.

To describe the feasibility and evaluate the performance of multiphasic photon-counting detector (PCD) CT for detecting breast cancer and nodal metastases with correlative dynamic breast MRI and digital mammography as the reference standard. Adult females with biopsy-proven breast cancer undergoing staging breast MRI were prospectively recruited to undergo a multiphasic PCD-CT using a 3-phase protocol: a non-contrast ultra-high-resolution (UHR) scan and 2 intravenous contrast-enhanced scans with 50 and 180 s delay. Three breast radiologists compared CT characteristics of the index malignancy, regional lymphadenopathy, and extramammary findings to MRI. Thirteen patients underwent both an MRI and PCD-CT (mean age: 53 years, range: 36-75 years). Eleven of thirteen cases demonstrated suspicious mass or non-mass enhancement on PCD-CT when compared to MRI. All cases with metastatic lymphadenopathy (3/3 cases) demonstrated early avid enhancement similar to the index malignancy. All cases with multifocal or multicentric disease on MRI were also identified on PCD-CT (3/3 cases), including a 4 mm suspicious satellite lesion. Four of five patients with residual suspicious post-biopsy calcifications on mammograms were detected on the UHR PCD-CT scan. Owing to increased field-of-view at PCD-CT, a 5 mm thoracic vertebral metastasis was identified at PCD-CT and not with the breast MRI. A 3-phase PCD-CT scan protocol shows initial promising results in characterizing breast cancer and regional lymphadenopathy similar to MRI and detects microcalcifications in 80% of cases. UHR and spectral capabilities of PCD-CT may allow for comprehensive characterization of breast cancer and may represent an alternative to breast MRI in select cases.

Expert consensus on the management of interstitial lung disease during the COVID-19 epidemic

Expert consensus on the management of interstitial lung disease during the COVID-19 epidemic

18F-FDG PET/CT-based deep learning radiomics predicts 5-years disease-free survival after failure to achieve pathologic complete response to neoadjuvant chemotherapy in breast cancer

BackgroundThis study aimed to assess whether a combined model incorporating radiomic and depth features extracted from PET/CT can predict disease-free survival (DFS) in patients who failed to achieve pathologic complete response (pCR) after neoadjuvant chemotherapy.ResultsThis study retrospectively included one hundred and five non-pCR patients. After a median follow-up of 71 months, 15 and 7 patients experienced recurrence and death, respectively. The primary tumor volume underwent feature extraction, yielding a total of 3644 radiomic features and 4096 depth features. The modeling procedure employed Cox regression for feature selection and utilized Cox proportional-hazards models to make predictions on DFS. Time-dependent receiver operating characteristic (ROC) curves and the area under the ROC curve (AUC) were utilized to evaluate and compare the predictive performance of different models. 2 clinical features (RCB, cT), 4 radiomic features, and 7 depth features were significant predictors of DFS and were included to develop models. The integrated model incorporating RCB, cT, and radiomic and depth features extracted from PET/CT images exhibited the highest accuracy for predicting 5-year DFS in the training (AUC 0.943) and the validation cohort (AUC 0.938).ConclusionThe integrated model combining radiomic and depth features extracted from PET/CT images can accurately predict 5-year DFS in non-pCR patients. It can help identify patients with a high risk of recurrence and strengthen adjuvant therapy to improve survival.

Machine Learning Methods Based on CT Features Differentiate G1/G2 From G3 Pancreatic Neuroendocrine Tumors

To identify CT features for distinguishing grade 1 (G1)/grade 2 (G2) from grade 3 (G3) pancreatic neuroendocrine tumors (PNETs) using different machine learning (ML) methods. A total of 147 patients with 155 lesions confirmed by pathology were retrospectively included. Clinical-demographic and radiological CT features was collected. The entire cohort was separated into training and validation groups at a 7:3 ratio. Least absolute shrinkage and selection operator (LASSO) algorithm and principal component analysis (PCA) were used to select features. Three ML methods, namely logistic regression (LR), support vector machine (SVM), and K-nearest neighbor (KNN) were used to build a differential model. Receiver operating characteristic (ROC) curves and precision-recall curves for each ML method were generated. The area under the curve (AUC), accuracy rate, sensitivity, and specificity were calculated. G3 PNETs were more likely to present with invasive behaviors and lower enhancement than G1/G2 PNETs. The LR classifier yielded the highest AUC of 0.964 (95% confidence interval [CI]: 0.930, 0.972), with 95.4% accuracy rate, 95.7% sensitivity, and 92.9% specificity, followed by SVM (AUC: 0.957) and KNN (AUC: 0.893) in the training group. In the validation group, the SVM classier reached the highest AUC of 0.952 (95% CI: 0.860, 0.981), with 91.5% accuracy rate, 97.3% sensitivity, and 70% specificity, followed by LR (AUC: 0.949) and KNN (AUC: 0.923). The LR and SVM classifiers had the best performance in the training group and validation group, respectively. ML method could be helpful in differentiating between G1/G2 and G3 PNETs.

A Patient with 38 Years of Smoking History and Severe Shortness of Breath

Aim: to present a clinical case of post-COVID bronchiolitis in an adult.Key points. A 54-year-old female patient with a long history of smoking was hospitalized in the pulmonology department of the Clinic with a leading complaint of progressive shortness of breath after a COVID-19 infection. The diagnosis was established: bilateral bronchiolitis associated with a new coronavirus infection (SARS-CoV-2 polymerase chain reaction — positive); chronic obstructive pulmonary disease stage II according to GOLD (Global Initiative for Chronic Obstructive Lung Disease), exacerbation. Against the background of the therapy, there was a significant positive dynamics in well-being and in CT-picture of bronchiolitis. The patient was discharged for outpatient follow-up treatment. One of the most common symptoms of post-COVID syndrome is shortness of breath. Diagnosis of bronchiolitis, in which this symptom may be the only one, is difficult, especially in patients with a long history of smoking and the presence of comorbid pulmonary pathology.Conclusion. A clinical case of bilateral bronchiolitis in the framework of the post-COVID syndrome is presented. The key to successful diagnosis of bronchiolitis is a thorough differential analysis of the totality of anamnestic and clinical laboratory data, as well as a characteristic CT pattern.

Retrospective cohort study on the correlation analysis among peri-procedural factors, complications, and local tumor progression of lung tumors treated with CT-guided microwave ablation.

Despite adherence to guidelines, recurrence of lesions remains possible in lung tumor microwave ablation (MWA) even when termination is enabled by 5-10 mm ground glass changes. Limited evidence exists regarding the correlation between timely management of perioperative complications (including pneumothorax, pleural effusion, hemorrhage, cavity formation, and infection) and local tumor progression. This retrospective study aimed to investigate the relationship among peri-procedural factors, complications, and local tumor progression in 164 cases of lung tumors treated with computed tomography-guided MWA (CT-MWA), and improve the local prognosis and reduce the complication rate of CT-guided lung tumor ablation. We reviewed 164 consecutive patients who underwent CT-MWA at Fudan University Shanghai Cancer Center's Minimally Invasive Therapy Center for lung cancer from September 2019 to May 2020. Correlative analysis was performed between peri-procedural factors, complications and outcomes (local tumor progression rates). Patients who have had prior surgery or previous MWA were excluded. Ablation was the first treatment of choice, and all patients who have had other treatments were excluded. Patients were followed every 3 months with CT. Outcomes of ablation including complications and local tumor progression were evaluated. Peri-procedural factors included demographical factors, tumor features, ablation parameters, management of intra-procedural pneumothorax, and CT features. Complications included pneumothorax, post-procedural refractory infection, and pleural effusion. The study included 98 males and 68 females, with an average age of 56.1 years. Local tumor progression rate was negatively correlated with intra-procedural management of pneumothorax (R=-0.550, P=0.0003) and Hounsfield unit (HU) difference between HU before and after procedure (R=-0.855, P=0.006), and positively correlated with the average HU value of immediate post-procedural CT at the measurement points (R=0.857, P=0.00002). The correlation analysis results also showed a positive correlation between infection after procedure and pneumothorax (R=0.340, P=0.0001). A greater difference between HU before and after the procedure or a decrease in CT values immediately after ablation may predict a higher rate of local complete ablation. Prompt management of intraoperative pneumothorax may lower local tumor progression rates and decrease incidence of post-procedural infection.

Tensor radiomics: paradigm for systematic incorporation of multi-flavoured radiomics features.

Radiomics features hold significant value as quantitative imaging biomarkers for diagnosis, prognosis, and treatment response assessment. To generate radiomics features and ultimately develop signatures, various factors can be manipulated, including image discretization parameters (e.g., bin number or size), convolutional filters, segmentation perturbation, or multi-modality fusion levels. Typically, only one set of parameters is employed, resulting in a single value or "flavour" for each radiomics feature. In contrast, we propose "tensor radiomics" (TR) where tensors of features calculated using multiple parameter combinations (i.e., flavours) are utilized to optimize the creation of radiomics signatures. We provide illustrative instances of TR implementation in positron emission tomography-computed tomography (PET-CT), magnetic resonance imaging (MRI), and CT by leveraging machine learning (ML) and deep learning (DL) methodologies, as well as reproducibility analyses: (I) to predict overall survival (OS) in lung cancer (CT) and head and neck cancer (PET-CT), TR was employed by varying bin sizes. This approach involved use of a hybrid deep neural network called 'TR-Net' and two ML-based techniques for combining different flavours. (II) TR was constructed by incorporating different segmentation perturbations and various bin sizes to classify the response of late-stage lung cancer to first-line immunotherapy using CT images. (III) In MRI of glioblastoma (GBM), TR was implemented to generate multi-flavour radiomics features, enabling enhanced analysis and interpretation. (IV) TR was employed via multiple PET-CT fusions in head and neck cancer. Flavours based on different fusions were created using Laplacian pyramids and wavelet transforms. Our findings demonstrated that TR outperformed conventional radiomics features in lung cancer CT and head and neck cancer PET-CT images, significantly enhancing OS prediction accuracy. TR also improved classification of lung cancer response to therapy and exhibited notable advantages in reproducibility compared to single-flavour features in MR imaging of GBM. Moreover, in head and neck cancer, TR through multiple PET-CT fusions exhibited improved performance in predicting OS. We conclude that the proposed TR paradigm has significant potential to improve performance in different medical imaging tasks. By incorporating multiple flavours of radiomics features, TR overcomes limitations associated with individual features and shows promise in enhancing prognostic capabilities in clinical settings.

Potential Role of Intrapulmonary Concomitant Lesions in Differentiating Non-Neoplastic and Neoplastic Ground Glass Nodules.

To determine the value of intrapulmonary concomitant lesions in differentiating non-neoplastic and neoplastic ground-glass nodules (GGNs). From January 2014 to March 2022, 395 and 583 patients with confirmed non-neoplastic and neoplastic GGNs were retrospectively enrolled. Their clinical and chest CT data were evaluated. The CT features of target GGNs and intrapulmonary concomitant lesions in these two groups were analyzed and compared, and the role of intrapulmonary concomitant lesions in improving differentiation was evaluated. The intrapulmonary concomitant lesions were more common in patients with non-neoplastic GGNs than in those with neoplastic ones (87.88% vs 82.18%, P = 0.015). Specifically, patients with non-neoplastic GGNs had a higher incidence of multiple solid nodules (SNs), patchy ground-glass opacity/consolidation, and fibrosis/calcification in any lung fields (each P < 0.05). Logistic regression analysis indicated that patients < 44 years old, diameter < 7.35 mm, irregular shape, and coarse margin or ill-defined boundary for target GGN, pleural thickening, and concomitant SNs in the same lobe and fibrosis or calcification in any lung field were independent indicators for predicting non-neoplastic GGNs. The AUC of the model for predicting non-neoplastic GGNs increased from 0.894 to 0.926 (sensitivity, 83.10%; specificity, 87.10%) after including the concomitant lesions in the patients' clinical characteristics and CT features of target GGNs (P < 0.0001). Besides the patients' clinical characteristics and CT features of target GGNs, the concomitant multiple SNs in the same lobe and fibrosis/calcification in any lung field should be considered in further differentiating non-neoplastic and neoplastic GGNs.

Does dual-layer spectral detector CT provide added value in predicting spread through air spaces in lung adenocarcinoma? A preliminary study.

To examine the predictive value of dual-layer spectral detector CT (DLCT) for spread through air spaces (STAS) in clinical lung adenocarcinoma. A total of 225 lung adenocarcinoma cases were retrospectively reviewed for demographic, clinical, pathological, traditional CT, and spectral parameters. Multivariable logistic regression analysis was carried out based on three logistic models, including a model using traditional CT features (traditional model), a model using spectral parameters (spectral model), and an integrated model combining traditional CT and spectral parameters (integrated model). Receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA) were performed to assess these models. Univariable analysis showed significant differences between the STAS and non-STAS groups in traditional CT features, including nodule density (p < 0.001), pleural indentation types (p = 0.006), air-bronchogram sign (p = 0.031), the presence of spiculation (p < 0.001), long-axis diameter of the entire nodule (LD) (p < 0.001), and consolidation/tumor ratio (CTR) (p < 0.001). Multivariable analysis revealed that LD > 20mm (odds ratio [OR] = 2.271, p = 0.025) and CTR (OR = 24.208, p < 0.001) were independent predictors in the traditional model, while electronic density (ED) in the venous phase was an independent predictor in the spectral (OR = 1.062, p < 0.001) and integrated (OR = 1.055, p < 0.001) models. The area under the curve (AUC) for the integrated model (0.84) was the highest (spectral model, 0.83; traditional model, 0.80), and the difference between the integrated and traditional models was statistically significant (p = 0.015). DCA showed that the integrated model had superior clinical value versus the traditional model. DLCT has added value for STAS prediction in lung adenocarcinoma. Spectral CT has added value for spread through air spaces prediction in lung adenocarcinoma so may impact treatment planning in the future. • Electronic density may be a potential spectral index for predicting spread through air spaces in lung adenocarcinoma. • A combination of spectral and traditional CT features enhances the performance of traditional CT for predicting spread through air spaces.

Optimization of Intracranial Hemorrhage Using CT Scan Images and Feature Extraction

Intracranial bleeding is among the most severe forms of brain stroke. The neurologic effects and artery rupture cause bleeding in the brain and the tissue around it. Haemorrhage is classified based on where the bleeding occurs on the brain. This paper depicts the application of multiple machine-learning approaches to separate CT scan images into normal and pathological categories. Separate analysis is conducted on the functionality of the features extracted from the various texturing approaches, such as the Grey Level Co-occurrence Matrix (GLCM) and Grey Level Run-Length Matrix (GRLM). Particle Swarm Optimisation (PSO) and K-Nearest Neighbours are used to choose relevant characteristics that increase the classification accuracy for feature extraction. The findings demonstrate that these texture features have excellent discrimination accuracy

CT features combined with RECIST 1.1 criteria improve progression assessments of sunitinib-treated gastrointestinal stromal tumors.

To explore the auxiliary value of combining CT features with existing response evaluation criteria in the prediction of progressive disease (PD) in gastrointestinal stromal tumors (GIST) patients treated with sunitinib. Eighty-one patients with GISTs who received sunitinib were included in this retrospective multicenter study and divided into training and external validation cohorts. Progression at six months was determined as a reference standard. The predictive performance of the RECIST 1.1 and Choi criteria was compared. CT features at baseline and the first follow-up were analyzed. Logistic regression analyses were used to determine the most significant predictors and develop modified criteria. A total of 216 lesions showed a good response and 107 showed a poor response in 81 patients. The RECIST 1.1 criteria performed better than the Choi criteria in predicting progression (AUC, 0.75 vs. 0.69, p= 0.04). The expanded/intensified high-enhancement area, blurred tumor-tissue interface, and progressive enlarged vessels feeding or draining the mass (EVFDM) differed significantly between lesions with good and poor responses in the training cohort (p= 0.001, 0.003, and 0.000, respectively). Multivariate analysis revealed that the expanded/intensified high-enhancement area (p= 0.001), progressive EVFDM (p= 0.000), and RECIST PD (p= 0.000) were independent predictive factors. Modified RECIST (mRECIST) criteria were developed and showed significantly higher AUCs in the training and external validation cohorts than the RECIST 1.1 criteria (training: 0.81 vs. 0.73, p= 0.002; validation: 0.82 vs. 0.77, p= 0.04). The mRECIST criteria, combining CT features with the RECIST 1.1 criteria, demonstrated superior performance in the prediction of early progression in GIST patients receiving sunitinib. The mRECIST criteria, which combine CT features with the RECIST 1.1 criteria, may facilitate the early detection of progressive disease in GIST patients treated with sunitinib, thereby potentially guiding the timely switch to late-line medications or combination with surgical excision. • The RECIST 1.1 criteria outperformed the Choi criteria in identifying progression of GISTs in patients treated with sunitinib. • GISTs displayed different morphologic features on CT depending on how they responded to sunitinib. • Combining CT morphologic features with the RECIST 1.1 criteria allowed for the prompt and accurate identification of progressing GIST lesions.

Parathyroid carcinoma: Imaging features of initial presentation and recurrence. A single center experience.

Parathyroid carcinoma is the rarest endocrine neoplasm with very few published data discussing its imaging appearance with primary focus on ultrasound imaging features. We present our 23 years institutional experience to highlight multimodality imaging evaluation at presentation and at recurrence. Retrospective chart review of clinical and pathological diagnosis of parathyroid carcinoma patients presented for initial or recurrent disease management at M.D. Anderson Cancer Center between the period of January 2000 and February 2023 was performed. Imaging findings on US, CT, PET/CT, and technetium-99m sestamibi were analyzed for initial and local recurrent parathyroid carcinoma. We further assess patterns of distant recurrence and its location. Twenty three patients with pathological and clinical diagnosis of initial (14 patients) or recurrent parathyroid carcinoma (14 patients) were included in this study. US findings of parathyroid carcinoma were larger lesions, increased/irregular vascularity, and non-circumscribed margins. Multiphasic CT findings of parathyroid carcinoma included an arterially enhancing lesion that is hypoenhancing relative to the thyroid and demonstrates no washout on delayed imaging. Highly suggestive findings for recurrent disease included a hypoechoic solid nodule (91.67%) with increased vascularity on ultrasound (81.8%) with corresponding enhancement on CT. Parathyroid carcinoma is a rare malignancy often diagnosed after surgical resection. We provided CT and US imaging features that are helpful in suggesting the diagnosis of parathyroid carcinoma and detection of early local recurrence.

Exploring published and novel pre-treatment CT and PET radiomics to stratify risk of progression among early-stage non-small cell lung cancer patients treated with stereotactic radiation

PurposeDisease progression after definitive stereotactic body radiation therapy (SBRT) for early-stage non-small cell lung cancer (NSCLC) occurs in 20–40% of patients. Here, we explored published and novel pre-treatment CT and PET radiomics features to identify patients at risk of progression. Materials/MethodsPublished CT and PET features were identified and explored along with 15 other CT and PET features in 408 consecutively treated early-stage NSCLC patients having CT and PET < 3 months pre-SBRT (training/set-aside validation subsets: n = 286/122). Features were associated with progression-free survival (PFS) using bootstrapped Cox regression (Bonferroni-corrected univariate predictor: p ≤ 0.002) and only non-strongly correlated predictors were retained (|Rs|<0.70) in forward-stepwise multivariate analysis. ResultsTumor diameter and SUVmax were the two most frequently reported features associated with progression/survival (in 6/20 and 10/20 identified studies). These two features and 12 of the 15 additional features (CT: 6; PET: 6) were candidate PFS predictors. A re-fitted model including diameter and SUVmax presented with the best performance (c-index: 0.78; log-rank p-value < 0.0001). A model built with the two best additional features (CTspiculation1 and SUVentropy) had a c-index of 0.75 (log-rank p-value < 0.0001). ConclusionsA re-fitted pre-treatment model using the two most frequently published features – tumor diameter and SUVmax – successfully stratified early-stage NSCLC patients by PFS after receiving SBRT.