Computed Tomography Features Research Articles

Computed Tomography and Magnetic Resonance Imaging Features of Gracilis Myopathy in Dogs.

Gracilis myopathy is a well-recognized syndrome of weight-bearing gait abnormality; however, detailed description of computed tomography (CT) and magnetic resonance imaging (MRI) features of gracilis myopathy is lacking in the literature. This study aims to describe in detail CT and MRI features of gracilis myopathy. This is a retrospective study. Medical records of dogs with characteristic gait and available pelvic limb MRI or CT, presenting to four referral institutions, were reviewed. Seven cases with MRI (3/7) or CT (4/7) were included, all German Shepherd dogs. MRI findings in all cases included a well-defined, T1-/T2-/proton density-weighted hypointense band along the axial margin of the gracilis muscle, shorter affected muscles, thickening and T2W/STIR hyperintensity with contrast enhancement at the symphyseal tendon, and concurrent focal indentation of the axial border of the gracilis muscle. CT findings included a hyperattenuating band (4/4), single instances of contrast enhancement at the origin of the muscle, thickening of the tendon at the origin and insertion, and concurrent thickening at the musculotendinous muscle. CT and MRI can present advantages in correctly identifying the specific muscles affected within the gracilis-semitendinosus muscle complex. Both modalities can localize anatomically lesions in detail, for instance at the origin or insertion of the muscle, which could present advantages in novel treatment strategies for this condition.

Preoperative Computed Tomography Radiomics-Based Models for Predicting Microvascular Invasion of Intrahepatic Mass-Forming Cholangiocarcinoma

Objectives The aim of the study is to investigate the ability of preoperative CT (Computed Tomography)-based radiomics signature to predict microvascular invasion (MVI) of intrahepatic mass-forming cholangiocarcinoma (IMCC) and develop radiomics-based prediction models. Materials and Methods Preoperative clinical data, basic CT features, and radiomics features of 121 IMCC patients (44 with MVI and 77 without MVI) were retrospectively reviewed. The loading and display of CT images, delineation of the volume of interest, and feature extraction were performed using 3D Slicer. Radiomics features were selected by the LASSO logistic regression model. Multivariate logistic regression analysis was used to establish the radiomics model, radiologic model, and combined model in the training set (n = 85) to predict the MVI of IMCC, and then verified in the validation set (n = 36). Results Among the 3948 radiomics features extracted from multiphase dynamic enhanced CT imaging, 16 most stable features were selected. The AUC of the radiomics model for predicting MVI in the training set and validation set were 0.935 and 0.749, respectively. The AUC of the radiologic model for predicting MVI in the training set and validation set were 0.827 and 0.796, respectively. When radiomics and radiologic models are combined, the predictive performance of the combined model (constructed with shape, intratumoral vessels, portal venous phase tumor-liver CT ratio, and radscore) is optimal, with an AUC of 0.958 in the training set and 0.829 in the test set for predicting MVI. Conclusions CT radiomics signature is a powerful predictor for predicting MVI. The preoperative combined model (constructed with shape, intratumoral vessels, portal venous phase tumor-liver CT ratio, and radscore) performed well in predicting the MVI.

An insight to PDAC tumor heterogeneity across pancreatic subregions using computed tomography images

Pancreatic Ductal Adenocarcinoma (PDAC) is an exceptionally deadly form of pancreatic cancer with an extremely low survival rate. From diagnosis to treatment, PDAC is highly challenging to manage. Studies have demonstrated that PDAC tumors in distinct regions of the pancreas exhibit unique characteristics, influencing symptoms, treatment responses, and survival rates. Gaining insight into the heterogeneity of PDAC tumors based on their location in the pancreas can significantly enhance overall management of PDAC. Previous studies have explored PDAC tumor heterogeneity across pancreatic subregions based on their genetic and molecular profiles through biopsy-based histologic assessment. However, biopsy examinations are highly invasive and impractical for large populations. Abdominal imaging, such as Computed Tomography (CT) offers a completely non-invasive means to evaluate PDAC tumor heterogeneity across pancreatic subregions and an opportunity to correlate image feature of tumors with treatment outcome and monitoring. In this study, we explored the inter-tumor heterogeneity in PDAC tumors across three primary pancreatic subregions: the head, body, and tail. Utilizing contrast-enhanced abdominal CT scans and a thorough radiomic analysis of PDAC tumors, several morphological and textural tumor features were identified to be notably different between tumors in the head and those in the body and tail regions. To validate the significance of the identified features, a machine learning ML model was trained to automatically classify PDAC tumors into their respective regions i.e. head or body/tail subregion using their CT features. The study involved 200 CT abdominal scans, with 100 used for radiomic analysis and model training, and the remaining 100 for model testing. The ML model achieved an average classification accuracy, sensitivity, and specificity of 87%, 86%, and 88% on the testing scans respectively. Evaluating the heterogeneity of PDAC tumors across pancreatic subregions provides valuable insights into tumor composition and has the potential to enhance diagnosis and personalize treatment based on tumor characteristics and location.

Correlation of preoperative CT features with intra- and postoperative parameters of endoscopic resection in patients with gastric submucosal tumor (1~3cm).

Endoscopic resection of gastric submucosal tumors (SMTs) possesses minimal trauma, expedited recovery, and reduced costs. Nonetheless, intraoperative challenges, including imprecise surgical risk assessment, prevail. Hence, we investigated the correlation between preoperative CT observation features and perioperative parameters for predicting outcomes in SMT patients. Subjects diagnosed with SMT and undergoing endoscopic surgery conducted at West China Hospital's Endoscopy Center from September 2019 to October 2023 were retrospectively selected. Endoscopic ultrasonography (EUS) and computer tomography (CT) were utilized for evaluating SMT. The study assessed the correlation between preoperative CT findings and perioperative parameters. Increased lesion size on contrast-enhanced CT correlates with increased operative duration, hospital stay, postoperative antibiotic use. Of utmost significance, we observed a significantly higher intraoperative perforation rate for patients with tumor outgrowth compared to those with lesion involvement of the lumen (96.88% vs 29.11%, P < 0.001). These patients also demonstrated an increased risk of postoperative blood stream infections (P = 0.012), necessitating higher antibiotic grade (P = 0.048), along with prolonged gastrostomy tube retention (P = 0.001) and hospitalization (P = 0.018). In addition, CT provides a more accurate and comprehensive assessment of tumor size (P = 0.037) and growth pattern (P = 0.026) than EUS. CT assessment of tumor size closer to reality than EUS. Importantly, these features can assist in pinpointing lesions with elevated surgical complexity and high risk of complications, leading to improved preoperative preparation, thereby increasing anticipation of surgical risks and reducing incidence of complications.

CT feature of irregular extensive ulceration as a predictor of liver metastasis in gastric gastrointestinal stromal tumours.

This study aimed to investigate whether the computed tomography (CT) finding of irregular extensive ulceration (IEU) can serve as a predictor of liver metastasis (LIM) in patients with gastric gastrointestinal stromal tumours (GISTs). This study retrospectively collected clinical and imaging data from 286 patients diagnosed with low-, intermediate-, or high-risk gastric GISTs, or primary lesions with LIM from three medical institutions. The patients were categorised into non-LIM and LIM groups according to whether they had synchronous or metachronous LIM. Multivariate logistic regression analyses were performed to identify significant predictors of LIM. Additionally, receiver operating characteristic (ROC) curve, subgroup, and pathologic-radiologic correlation analyses were conducted. A total of 124 patients were ultimately enroled. There were significant differences in sex, site, growth pattern, size, shape, ulceration and Ki-67 expression between LIM and non-LIM groups. ROC curve analysis demonstrated that IEU had the highest area under the curve for predicting LIM (AUC = 0.842; 95% CI: 0.760-0.924; p < 0.001). Multivariate analysis indicated that IEU was the most significant independent predictor of high LIM risk (OR = 88.62; 95% CI: 2.80-2803.54; p = 0.011). Subgroup analysis showed that IEU was more frequently associated with male sex, age ≤ 55 years, proximal sites, irregular shapes, mixed growth patterns, and a high Ki-67 expression. The CT feature of IEU serves as an independent predictor of LIM in gastric GISTs and is strongly associated with high Ki-67 expression. Question Accurate assessment of LIM risk in patients with gastric GISTs is crucial, yet current non-invasive predictors remain inadequate. Findings IEU on CT is an independent predictor of LIM, with high diagnostic accuracy and a significant association with elevated Ki-67 expression. Clinical relevance IEU on CT scans enables non-invasive risk stratification for LIM in gastric GISTs. Our study refined the assessment of ulceration types, highlighting significant heterogeneity, which may guide personalised treatment strategies.

Computerized tomography features acting as predictors for invasive therapy in the management of Crohn's disease-related spontaneous intra-abdominal abscess: experience from long-term follow-up.

Decision-making in the management of Crohn's disease (CD)-related spontaneous intra-abdominal abscess (IAA) is challenging. This study aims to reveal predictive factors for percutaneous drainage and/or surgery in the treatment of CD-related spontaneous IAA through long-term follow-up. Data were collected, including clinical manifestations, radiography and treatment strategies, in Chinese patients with CD-related IAA in a tertiary medical center. Univariate and Multivariate Cox analysis were conducted to identify predictors for invasive therapy. Altogether, 48 CD patients were identified as having IAA through enhanced CT scans. The median follow-up time was 45.0 (23.3, 58.0) months. 23 (47.9%) patients underwent conservative medical treatment, and 25 (52.1%) patients underwent percutaneous drainage and/or surgical intervention (invasive treatment group). The 1-, 2-, and 5-year overall survival rates without invasive treatment were 75.0%, 56.1%, and 46.1%, respectively. On univariate Cox analysis, the computerized tomography (CT) features including nonperienteric abscess (HR: 4.22, 95% CI: 1.81-9.86, p = 0.001), max abscess diameter (HR: 1.01, 95% CI: 1.00-1.02, p<0.001) and width of sinus (HR: 1.27, 95% CI: 1.10-1.46, p = 0.001) were significantly associated with invasive treatment. Nonperienteric abscess was significantly associated with invasive treatment on multivariate Cox analysis (HR: 3.11, 95% CI: 1.25-7.71, p = 0.015). A score model was built by width of sinus, location of abscess and max abscess diameter to predict invasive treatment. The AUC of ROC, sensitivity and specificity were 0.892, 80.0% and 90.9% respectively. More than half of CD-related IAA patients needed invasive therapy within 5-year follow-up. The CT features including nonperienteric abscess, larger maximum abscess diameter and width of sinus suggested a more aggressive approach to invasive treatment.

Differentiation and characterization of non-thymomatous ocular myasthenia gravis children with CT.

To compare thymic features using unenhanced computed tomography (CT) images between children with non-thymomatous ocular myasthenia gravis (OMG) and healthy children and determine the image feature cut-off values to allow differentiation of the two populations. We conducted a retrospective study involving 66 children who underwent chest CT, with 33 having non-thymomatous OMG and 33 healthy children without any abnormality. We recorded CT features of the thymus in each child, including the mean CT attenuation value, thymus area, area ratio, anteroposterior (AP) length, width (W), thickness (T), and transverse diameter (TD). These characteristics were analyzed to distinguish OMG from healthy controls. The receiver operating characteristic (ROC) analysis was used to clarify the best assessment, and the optimal cut-off values were determined. The mean CT attenuation value, the thymus area, area ratio, and TD were significantly different (P < 0.05) between the two groups. The mean CT attenuation value was the most significant characteristic in differentiating OMG and healthy controls (area under the curve [AUC] = 0.70). Using 50.41 as the cut-off value for mean CT attenuation, a specificity of 0.70 and sensitivity of 0.82 was observed for distinguishing the two groups (P = 0.003). Optimal differentiation was achieved by combining more than one characteristic including CT attenuation, thymus area, and TD with an optional area ratio (AUC = 0.72). Non-enhanced CT of the thymus complemented the clinical workup of children with non-thymomatous OMG. The cut-off values of the CT features may allow robust differentiation of OMG children.

Quantitative texture analysis using machine learning for predicting interpretable pulmonary perfusion from non-contrast computed tomography in pulmonary embolism patients

BackgroundPulmonary embolism (PE) is life-threatening and requires timely and accurate diagnosis, yet current imaging methods, like computed tomography pulmonary angiography, present limitations, particularly for patients with contraindications to iodinated contrast agents. We aimed to develop a quantitative texture analysis pipeline using machine learning (ML) based on non-contrast thoracic computed tomography (CT) scans to discover intensity and textural features correlated with regional lung perfusion (Q) physiology and pathology and synthesize voxel-wise Q surrogates to assist in PE diagnosis.MethodsWe retrospectively collected 99mTc-labeled macroaggregated albumin Q-SPECT/CT scans from patients suspected of PE, including an internal dataset of 76 patients (64 for training, 12 for testing) and an external testing dataset of 49 patients. Quantitative CT features were extracted from segmented lung subregions and underwent a two-stage feature selection pipeline. The prior-knowledge-driven preselection stage screened for robust and non-redundant perfusion-correlated features, while the data-driven selection stage further filtered features by fitting ML models for classification. The final classification model, trained with the highest-performing PE-associated feature combination, was evaluated in the testing cohorts based on the Area Under the Curve (AUC) for subregion-level predictability. The voxel-wise Q surrogate was then synthesized using the final selected feature maps (FMs) and model score maps (MSMs) to investigate spatial distributions. The Spearman correlation coefficient (SCC) and Dice similarity coefficient (DSC) were used to assess the spatial consistency between FMs or MSMs and Q-SPECT scans.ResultsThe optimal model performance achieved an AUC of 0.863 during internal testing and 0.828 on the external testing cohort. The model identified a combination containing 14 intensity and textural features that were non-redundant, robust, and capable of distinguishing between high- and low-functional lung regions. Spatial consistency assessment in the internal testing cohort showed moderate-to-high agreement between MSMs and reference Q-SPECT scans, with median SCC of 0.66, median DSCs of 0.86 and 0.64 for high- and low-functional regions, respectively.ConclusionsThis study validated the feasibility of using quantitative texture analysis and a data-driven ML pipeline to generate voxel-wise lung perfusion surrogates, providing a radiation-free, widely accessible alternative to functional lung imaging in managing pulmonary vascular diseases.Clinical trial numberNot applicable.

Aggressiveness classification of clear cell renal cell carcinoma using registration-independent radiology-pathology correlation learning.

Renal cell carcinoma (RCC) is a common cancer that varies in clinical behavior. Clear cell RCC (ccRCC) is the most common RCC subtype, with both aggressive and indolent manifestations. Indolent ccRCC is often low-grade without necrosis and can be monitored without treatment. Aggressive ccRCC is often high-grade and can cause metastasis and death if not promptly detected and treated. While most RCCs are detected on computed tomography (CT) scans, aggressiveness classification is based on pathology images acquired from invasive biopsy orsurgery. CT imaging-based aggressiveness classification would be an important clinical advance, as it would facilitate non-invasive risk stratification and treatment planning. Here, we present a novel machine learning method, Correlated Feature Aggregation By Region (CorrFABR), for CT-based aggressiveness classification ofccRCC. CorrFABR is a multimodal fusion algorithm that learns from radiology and pathology images, and clinical variables in a clinically-relevant manner. CorrFABR leverages registration-independent radiology (CT) and pathology image correlations using features from vision transformer-based foundation models to facilitate aggressiveness assessment on CT images. CorrFABR consists of three main steps: (a) Feature aggregation where region-level features are extracted from radiology and pathology images at widely varying image resolutions, (b) Fusion where radiology features correlated with pathology features (pathology-informed CT biomarkers) are learned, and (c) Classification where the learned pathology-informed CT biomarkers, together with clinical variables of tumor diameter, gender, and age, are used to distinguish aggressive from indolent ccRCC using multi-layer perceptron-based classifiers. Pathology images are only required in the first two steps of CorrFABR, and are not required in the prediction module. Therefore, CorrFABR integrates information from CT images, pathology images, and clinical variables during training, but for inference, it relies solely on CT images and clinical variables, ensuring its clinical applicability. CorrFABR was trained with heterogenous, publicly-available data from 298 ccRCC tumors (136 indolent tumors, 162 aggressive tumors) in a five-fold cross-validation setup and evaluated on an independent test set of 74 tumors with a balanced distribution of aggressive and indolent tumors. Ablation studies were performed to test the utility of each component ofCorrFABR. CorrFABR outperformed the other classification methods, achieving an ROC-AUC (area under the curve) of 0.855±0.0005 (95% confidence interval: 0.775, 0.947), F1-score of 0.793±0.029, sensitivity of 0.741±0.058, and specificity of 0.876±0.032 in classifying ccRCC as aggressive or indolent subtypes. It was found that pathology-informed CT biomarkers learned through registration-independent correlation learning improves classification performance over using CT features alone, irrespective of the kind of features or the classification model used. Tumor diameter, gender, and age provide complementary clinical information, and integrating pathology-informed CT biomarkers with these clinical variables further improvesperformance. CorrFABR provides a novel method for CT-based aggressiveness classification of ccRCC by enabling the identification of pathology-informed CT biomarkers, and integrating them with clinical variables. CorrFABR enables learning of these pathology-informed CT biomarkers through a novel registration-independent correlation learning module that considers unaligned radiology and pathology images at widely varying imageresolutions.

Correlation between CT-based phenotypes and serum biomarker in interstitial lung diseases

BackgroundThe quantitative analysis of computed tomography (CT) and Krebs von den Lungen-6 (KL-6) serum level has gained importance in the diagnosis, monitoring, and prognostication of interstitial lung disease (ILD). However, the associations between quantitative analysis of CT and serum KL-6 level remain poorly understood.MethodsIn this retrospective observational study conducted at tertiary hospital between June 2020 and March 2022, quantitative analysis of CT was performed using the deep learning-based method including reticulation, ground glass opacity (GGO), honeycombing, and consolidation. We investigated the associations between CT-based phenotypes and serum KL-6 measured within three months of the CT scan. Furthermore, we evaluated the performance of the combined CT-based phenotypes and KL-6 levels in predicting hospitalizations due to respiratory reasons of ILD patients.ResultsA total of 131 ILD patients (104 males) with a median age of 67 years were included in this study. Reticulation, GGO, honeycombing, and consolidation extents showed a positive correlation with KL-6 levels. [Reticulation, correlation coefficient (r) = 0.567, p < 0.001; GGO, r = 0.355, p < 0.001; honeycombing, r = 0.174, p = 0.046; and consolidation, r = 0.446, p < 0.001]. Additionally, the area under the ROC of the combined reticulation and KL-6 for hospitalizations due to respiratory reasons was 0.810 (p < 0.001).ConclusionsQuantitative analysis of CT features and serum KL-6 levels ascertained a positive correlation between the two. In addition, the combination of reticulation and KL-6 shows potential for predicting hospitalizations of ILD patients due to respiratory causes. The combination of reticulation, focusing on phenotypic change in lung parenchyma, and KL-6, as an indicator of lung injury extent, could be helpful for monitoring and predicting the prognosis of various types of ILD.

Constructing a nomogram to predict overall survival of colon cancer based on computed tomography characteristics and clinicopathological factors.

The colon cancer prognosis is influenced by multiple factors, including clinical, pathological, and non-biological factors. However, only a few studies have focused on computed tomography (CT) imaging features. Therefore, this study aims to predict the prognosis of patients with colon cancer by combining CT imaging features with clinical and pathological characteristics, and establishes a nomogram to provide critical guidance for the individualized treatment. To establish and validate a nomogram to predict the overall survival (OS) of patients with colon cancer. A retrospective analysis was conducted on the survival data of 249 patients with colon cancer confirmed by surgical pathology between January 2017 and December 2021. The patients were randomly divided into training and testing groups at a 1:1 ratio. Univariate and multivariate logistic regression analyses were performed to identify the independent risk factors associated with OS, and a nomogram model was constructed for the training group. Survival curves were calculated using the Kaplan-Meier method. The concordance index (C-index) and calibration curve were used to evaluate the nomogram model in the training and testing groups. Multivariate logistic regression analysis revealed that lymph node metastasis on CT, perineural invasion, and tumor classification were independent prognostic factors. A nomogram incorporating these variables was constructed, and the C-index of the training and testing groups was 0.804 and 0.692, respectively. The calibration curves demonstrated good consistency between the actual values and predicted probabilities of OS. A nomogram combining CT imaging characteristics and clinicopathological factors exhibited good discrimination and reliability. It can aid clinicians in risk stratification and postoperative monitoring and provide important guidance for the individualized treatment of patients with colon cancer.

Clinical, pathological, and computed tomography morphological features of lung cancer with spread through air spaces.

Spread through air spaces (STAS) is significantly associated with decreased overall survival (OS) and reduced recurrence-free survival. However, there are no reliable methods to confirm the presence of STAS before surgery. The sensitivity and specificity of the intraoperative frozen section diagnosis of STAS are not satisfactory. This study sought to determine the clinical, pathological, and computed tomography (CT) features of lung cancer with STAS before surgery to guide treatment decisions. The data of 121 patients who were positive for STAS and 121 who were negative for STAS as confirmed by surgery and pathology were collected at Jiangsu Cancer Hospital from January 2020 to December 2022. The differences between the two groups in terms of the clinical, pathological, and CT characteristics were compared. STAS occurred not only in lung adenocarcinoma (LUAD) (106 of 121, 87.6%), but also in other pathological types of lung cancer (15 of 121, 12.4%). STAS was significantly correlated with pathological invasiveness [pathological differentiation, tumor, node, metastasis (TNM) staging, vascular invasion, and pleural invasion; all P<0.05]. STAS was most common in solid tumors (95 of 121, 78.51%). The receiver operating characteristic (ROC) curve showed that the optimal cut-off value for diagnosing STAS based on diameter is 1.55 cm with a sensitivity of 73.3% and a specificity of 47.9%. The percentage of solid components (PSC) is an independent influencing factor of lung cancer with STAS [odds ratio (OR) =111.27; P<0.05] with an optimal cut-off value of 63%, a sensitivity of 92.5%, and a specificity of 72.7%. In the part-solid nodules, the occurrence rate of STAS increased as the PSC increased. STAS was only observed in part-solid nodules with a PSC greater than 25%. Among the CT morphological features, lobulation was an independent influencing factor of lung cancer with STAS (OR =3.513; P<0.05), and persistent indistinct margin ground-glass opacity around the primary lesion of lung cancer (21 of 121, 17.36%) and satellite foci (9 of 121, 7.44%) strongly indicated the existence of STAS. The clinical, pathological and CT features of STAS may guide clinicians to develop appropriate strategies and improve the survival rate of patients.

Comparison of the Patient Demographics, High-Resolution Computed Tomography Features of the Pulmonary Ground-Glass Opacity and Its Diagnostic Value Analysis

Introduction Pulmonary ground-glass opacity (GGO) observed on computed tomography (CT) is widely regarded as a diagnostic feature of lung adenocarcinoma; however, a significant radiological predictive sign remains controversial. In this retrospective study, we conducted a comprehensive analysis of 206 patients with GGO to establish a correlation analysis model between CT images and diagnosis of GGO nodules. Methods Histopathologic specimens were obtained from 206 patients (130 women, 76 men; age range 24–77). The clinical data, pathologic findings, and thin-section CT features of solid, pure GGO (pGGO), and mixed GGO (mGGO) nodules were compared using rigorous statistical methods, such as t-test, Fisher’s exact test, or univariate logistic regression analysis. Results Significant differences were observed among the 3 groups in terms of patient demographics, including gender (P = 0.016), smoking history (P = 0.002), and nodule size (P = 0.002). Morphologic CT characteristics, such as the presence of spiculated sign, lobulated sign, vascular sign, bubble-lucency sign, or pleural retraction, exhibited significant differences among the solid nodule, pGGO, and mGGO groups. However, no significant differences were observed in terms of air-bronchogram sign. Notably, the incidence of malignancy was significantly higher in pGGO nodules (76.52%) than in solid (48.48%) and mGGO (73.86%) nodules. Patients with mGGO had up to 2.988 times higher hazard of malignant lesions than those with solid nodules [P = 0.036, hazard ratio (HR) = 2.988]. Similarly, the hazard of malignant lesions was 2.941 times higher in patients with pGGO than in those with solid nodules (P = 0.007, HR = 2.941). Conclusion Clinical, pathological, and thin-section CT features of solid, pGGO and mGGO nodules were found to be significantly different. Based on our comprehensive analysis, patients presenting with a mGGO or pGGO on CT scans were more likely to be diagnosed with lung cancer.

EP.07G.01 Computed Tomography Features of Pathological Stage IA Lung Adenocarcinoma Showing Spread Through Air Spaces.

EP.07G.01 Computed Tomography Features of Pathological Stage IA Lung Adenocarcinoma Showing Spread Through Air Spaces.

Chest CT features and risk factors for patients with Omicron variant pneumonia: a multicenter retrospective clinical study.

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) Omicron variant infection has become widespread in China as a result of the alterations in epidemic control and prevention policies. We identified the clinical characteristics and lung computed tomography (CT) imaging characteristics of patients infected during the early stage of the Omicron BA.5 wave in Shanghai to provide a guide to the diagnosis, treatment, and prognosis of infection. Clinical information and lung CT imaging characteristics of patients with Omicron variant infection admitted to three designated hospitals in Shanghai from March to June 2022 were analyzed retrospectively. A total of 958 patients were included in the analysis. Among the patients, 169 (17.64%) had pneumonia confirmed by CT, of whom 70.41% (119/169) had lesions in < 10% of the lung area. Older age, unvaccinated status, and comorbid chronic lung disease, cerebrovascular disease, kidney disease, or Alzheimer`s disease were associated with poor prognosis. In patients with coronavirus disease 2019 (COVID-19) pneumonia, a large lesion size was associated with a poor prognosis. Age ≥ 65 years, unvaccinated status, fever > 5 days, and lymphocyte count < 0.5×109/L were risk factors for pneumonia. Age ≥ 65 years, unvaccinated status, fever > 5 days, and lymphocyte count < 0.5×109/L can be used to identify high-risk individuals who warrant a CT scan to screen for COVID-19 pneumonia, especially during the period of Omicron variant predominance. Concurrently, the importance of immunization should be emphasized to help people withstand the effects of Omicron variant infection.

Pericholecystic Fat Stranding as a Predictive Factor of Length of Stays of Patients with Acute Cholecystitis: A Novel Scoring Model.

Background: The 2018 Tokyo Guidelines (TG18) are used to classify the severity of acute cholecystitis (AC) but insufficient to predict the length of hospital stay (LOS). Methods: For patients with AC, clinical factors and computed tomography features, including our proposed grading system of pericholecystic fat stranding were used for predicting an LOS of ≥7 days in the logistic regression models. Results: Our multivariable model showed age ≥ 65 years (OR: 2.56, p < 0.001), C-reactive protein (CRP) ≥ 2 mg/dL (OR: 1.97, p = 0.013), gamma-glutamyltransferase levels (OR: 2.460, p = 0.001), TG18 grade (OR: 2.89 per grade, p < 0.001), and moderate to severe pericholecystic fat stranding (OR: 2.14, p = 0.012) exhibited prolonged LOS ≥ 7 days. Conclusions: We developed a scoring model, including TG18 grades (score of 1-3 per grade), our grading system of fat stranding (score of 1), CRP (score of 1), and gamma-glutamyltransferase (score of 1), and a cutoff of >3 had highest diagnostic performance.

A Novel Framework for Multimodal Brain Tumor Detection with Scarce Labels.

Brain tumor detection has advanced significantly with the development of deep learning technology. Although multimodal data, such as Magnetic Resonance Imaging (MRI) and Computed Tomography (CT), has potential advantages in diagnostics, most existing studies rely solely on a single modality. This is because common fusion methods may lead to the loss of critical information when attempting multimodal fusion. Therefore, effectively integrating multimodal data has become a significant challenge. Additionally, medical image analysis requires large amounts of annotated data, and labeling images is a resourceintensive task that demands experienced professionals to spend a considerable amount of time. To address these challenges, this paper introduces a new unsupervised learning framework named Double-SimCLR. This framework builds on the foundation of contrastive learning and features a dual-branch structure, enabling direct and simultaneous processing of MRI and CT images for multimodal feature fusion. Given the "weak feature" characteristics of CT images (e.g., low soft tissue contrast and low resolution), we incorporated adaptive weight masking technology to enhance CT feature extraction. Moreover, we introduced a multimodal attention mechanism, which ensures that the model focuses on salient information, thereby elevating the precision and robustness of brain tumor detection. Even without substantial labeled data, experimental results demonstrate that Double-SimCLR achieves 93.458% accuracy, 92.463% precision, and a 93.058% F1-score, outperforming state-of-the-art (SOTA) models by 2.871%, 2.643%, and 3.098%, respectively.

A retrospective study differentiating nontuberculous mycobacterial pulmonary disease from pulmonary tuberculosis on computed tomography using radiomics and machine learning algorithms

Objective To evaluate the effectiveness of a machine learning based on computed tomography (CT) radiomics to distinguish nontuberculous mycobacterial pulmonary disease (NTM-PD) from pulmonary tuberculosis (PTB). Methods In this retrospective analysis, medical records of 99 individuals afflicted with NTM-PD and 285 individuals with PTB in Zhejiang Chinese and Western Medicine Integrated Hospital were examined. Random numbers generated by a computer were utilized to stratify the study cohort, with 80% designated as the training cohort and 20% as the validation cohort. A total of 2153 radiomics features were extracted using Python (Pyradiomics package) to analyse the CT characteristics of the large disease areas. The identification of significant factors was conducted through the least absolute shrinkage and selection operator (LASSO) regression. The following four supervised learning classifier models were developed: random forest (RF), support vector machine (SVM), logistic regression (LR), and extreme gradient boosting (XGBoost). For assessment and comparison of the predictive performance among these models, receiver-operating characteristic (ROC) curves and the areas under the ROC curves (AUCs) were employed. Results The Student’s t-test, Levene test, and LASSO algorithm collectively selected 23 optimal features. ROC analysis was then conducted, with the respective AUC values of the XGBoost, LR, SVM, and RF models recorded to be 1, 0.9044, 0.8868, and 0.7982 in the training cohort. In the validation cohort, the respective AUC values of the XGBoost, LR, SVM, and RF models were 0.8358, 0.8085, 0.87739, and 0.7759. The DeLong test results noted the lack of remarkable variation across the models. Conclusion The CT radiomics features can help distinguish between NTM-PD and PTB. Among the four classifiers, SVM showed a stable performance in effectively identifying these two diseases.

Leveraging denoising diffusion probabilistic model to improve the multi-thickness CT segmentation

Organs-at-risk (OARs) segmentation in computed tomography (CT) is a fundamental step in the radiotherapy workflow, which has been prone as a time-consuming and labor-intensive task. Deep neural networks (DNNs) have gained significant popularity in the field of OAR segmentation tasks, achieving remarkable progress in clinical practice. Typically, OARs are distributed throughout different areas of the body and require varying thicknesses of CT scans for better diagnosis and segmentation in clinical. Most DNN-based segmentation focuses on single-thickness CT scans, limiting their applicability to varying thicknesses due to a lack of diverse thickness-related feature learning. While pre-training with the denoising diffusion probabilistic model (DDPM) offers an effective solution for dense feature learning, current works are constrained in addressing feature diversity, as exemplified by scenarios such as multi-thickness CT. To address the above challenges, this paper introduces a novel pre-training approach called DiffMT. This approach leverages the DDPM to extract valuable features from multi-thickness CT images. By transferring the pre-trained DDPM to the downstream segmentation for fine-tuning, the model gains proficiency in learning diverse multi-thickness CT features, leading to precise segmentation across varied thicknesses. We explore DiffMT’s feature learning capacity through experiments involving pre-trained models of varying sizes and different denoising thicknesses. Subsequently, thorough experiments comparing DDPM-based segmentation with other state-of-the-art (SOTA) CT segmentation methods, along with assessments on diverse OARs and modalities, empirically demonstrate that the proposed DiffMT method outperforms the control methods. The codes are available at https://github.com/ychengrong/DiffMT.

The relationship between lung CT features and serum cryptococcal antigen titers in localized pulmonary cryptococcosis patients

BackgroundTo explore the associations of computed tomography (CT) image features with the serum cryptococcal antigen (CrAg) titers measured by the lateral flow assay (LFA) in localized pulmonary cryptococcosis patients.MethodsA retrospective analysis of patients with pathologically confirmed pulmonary cryptococcosis admitted to the First Affiliated Hospital of Xiamen University from January 2016 to December 2022 was performed. Clinical data, CT results, serum CrAg-LFA test results, and follow-up data were collected and analyzed.ResultsA total of 107 patients with localized pulmonary cryptococcosis were included, of which 31 had a single lesion in chest CT and the other 76 had multiple lesions. The positivity rate was (94.74% vs 64.52%) and titers of serum CrAg-LFA (1.77 ± 0.87 vs 0.91 ± 0.98) in the multiple lesion group were higher than those in the single lesion group, respectively. Multivariate linear regression analysis showed that the serum CrAg titers were positively associated with the number of lesions (β, 0.08; 95% CI, 0.05 to 0.12) and the lesion size (β, 0.40; 95% CI, 0.31 to 0.50) after adjusting other covariates. The serum CrAg-LFA titers of 60 pulmonary cryptococcosis patients showed a decreasing trend with the reduction in pulmonary lesion size after effective therapy.ConclusionIn pulmonary cryptococcosis patients, the number and size of lung lesions are positively correlated with the titers of the serum CrAg-LFA test. The CrAg-LFA test could be a useful tool for the diagnosis, severity assessment, and therapeutic monitoring of localized pulmonary cryptococcosis patients.