Computed Tomography Features Research Articles

Typical Clinical Presentation of an Autosomal Dominant Polycystic Kidney Disease Patient with an Atypical Genetic Pattern

Background: Autosomal Dominant Polycystic Kidney Disease (ADPKD) is mainly characterized by renal involvement with progressive bilateral development of renal cysts and volumetric increase in the kidneys, causing a loss of renal function, chronic kidney disease (CKD), and kidney failure. The occurrence of mosaicism may modulate the clinical course of the disease. Mosaicism is characterized by a few cell populations with different genomes. In these special cases, a genetic diagnosis could be challenging. Methods: Herein, we describe the case of a 47-year-old woman presenting with typical ultrasound and computed tomography features of ADPKD. She had stage 3b CKD and hypertension. There was no family history of ADPKD, prompting an investigation with a genetic test. Target next-generation sequencing (NGS) did not detect the presence of any genomic variants. Therefore, we carried out second-level genetic analysis to investigate the presence of a large rearrangement through a multiple ligation-dependent probe amplification (MLPA) analysis of PKD1 and PKD2 genes. Results: MLPA showed a large deletion (portion including exons 2–34 of PKD1) present in the heterozygosis with a percentage of cells close to the resolution limits of the technique used (<25–30%). We concluded that the large deletion identified was mosaicism. This variant is not reported in major ADPKD databases, but due to the type of mutation and the patient’s clinical picture, it should be considered as likely pathogenic. Conclusions: A stepwise genetic approach might be useful in those cases where standard methods do not allow one to reach a definitive diagnosis.

Early Lung Adenocarcinoma Manifesting as Irregular Subsolid Nodules: Clinical and CT Characteristics.

To explore the clinical and computed tomography (CT) characteristics of early-stage lung adenocarcinoma (LADC) that presents with an irregular shape. The CT data of 575 patients with stage IA LADC and 295 with persistent inflammatory lesion (PIL) manifesting as subsolid nodules (SSNs) were analyzed retrospectively. Among these patients, we selected 233 patients with LADC and 140 patients with PIL, who showed irregular SSNs, hereinafter referred to as irregular LADC (I-LADC) and irregular PIL (I-PIL), respectively. The incidence rates, clinical characteristics, and CT features of I-LADC and I-PIL were compared. Additionally, binary logistic regression analysis was performed to determine the independent factors for diagnosing I-LADC. The incidence rates of I-LADC and I-PIL were 40.5% (233/575) and 47.5% (140/295), respectively, with no statistically significant difference observed between the two groups (P > 0.05). Univariate analysis revealed significant differences in three clinical characteristics and 13 radiological features between I-LADC and I-PIL (all P < 0.05). Binary logistic regression indicated that the alignment of the long axis of SSN with the bronchial vascular bundle, a well-defined boundary of ground-glass opacity, lobulation, arc concave sign, and absence of knife-like change were the independent predictors of I-LADC, yielding an area under the curve and accuracy of 0.979% and 93.5%, respectively. Early LADC presenting as SSNs is associated with a high incidence of irregular shape. I-LADC and I-PIL exhibited different clinical and imaging characteristics. A good understanding of these differences may be helpful for the accurate diagnosis of I-LADC.

Predictors Based on the Radiologic Characteristics for Aggressiveness of Small (< 20 mm) Nonfunctioning Pancreatic Neuroendocrine Tumors.

To find the association between preoperative computed tomography (CT) features combined with tumor marker and known high-risk factors of small nonfunctioning pancreatic neuroendocrine tumors (NF-PNETS), thereby selecting appropriate treatment strategy for these patients. One hundred fourteen patients with NF-PNETs< 20 mm who underwent surgical operation were retrospectively analyzed from 2009 to 2023. Univariate and multivariable logistic regression analyses were performed to find the relationship between preoperative clinical psychological and CT features and high-risk factors. The overall survival (OS) rates with and without high-risk factors were compared. Of 114 patients, 29(25%) had at least one of these high-risk factors. Main pancreatic duct dilation (OR, 3.315; 95% CI, 1.079-10.184; p = 0.036), irregular tumor margin (OR, 2.955; 95% CI, 1.021-8.551; p = 0.046), positive tumor marker (OR, 6.047; 95% CI, 1.408-25.963; p = 0.015) were associated with increased odds of having any of these high-risk factors. The time to death differed significantly between patients with and without high-risk factors. Patients combining with high-risk factors were associated with lower 3- and 5-year OS (100% vs. 81.8%, 93.1% vs. 81.8%, respectively; p = 0.035 for both). Main pancreatic duct dilation, irregular tumor margin and positive tumor marker could screen a subset of patients recommended for surgery.

Risk factors of severe conditions in hospitalized children with adenovirus infection and chest CT features

BackgroundChildren infected with human adenovirus (HAdV) were at particularly high risk of developing severe disease, but the risk factors of severe conditions are poorly understood.ObjectivesTo explore the risk factors for developing into severe conditions in pediatric patients with HAdV infection by analyzing baseline epidemiological data, clinical characteristics, and computed tomography (CT) imaging features.MethodsIn this retrospective study, 267 children with HAdV infection were included between October 2016 and September 2021 at Tongji Hospital, Wuhan, China. A descriptive analysis was conducted on the epidemiological and clinical data, as well as patient outcomes. CT manifestations were semi-quantitatively scored based on the presence of consolidation, ground-glass opacities, and emphysema in each lung lobe.ResultsOur analysis revealed that there was a statistically significant difference in the rate of abnormalities observed on chest CT imaging (P = 0.007) and the imaging characteristics of chest CTs (P = 0.002) when comparing severe and mild cases. We found that co-infection with two or more additional pathogens occurred more frequently in severe cases. Additionally, the proportion of lymphocytes in laboratory tests was significantly lower in patients with severe conditions. Furthermore, both the proportions and scores of consolidations were markedly higher in each lung lobe among the severe cases.ConclusionsOur findings may assist in identifying children hospitalized with HAdV who are at increased risk for severe conditions, thereby facilitating more aggressive treatment and care strategies.

Deep learning model combined with computed tomography features to preoperatively predicting the risk stratification of gastrointestinal stromal tumors

BACKGROUND Gastrointestinal stromal tumors (GIST) are prevalent neoplasm originating from the gastrointestinal mesenchyme. Approximately 50% of GIST patients experience tumor recurrence within 5 years. Thus, there is a pressing need to accurately evaluate risk stratification preoperatively. AIM To assess the application of a deep learning model (DLM) combined with computed tomography features for predicting risk stratification of GISTs. METHODS Preoperative contrast-enhanced computed tomography (CECT) images of 551 GIST patients were retrospectively analyzed. All image features were independently analyzed by two radiologists. Quantitative parameters were statistically analyzed to identify significant predictors of high-risk malignancy. Patients were randomly assigned to the training (n = 386) and validation cohorts (n = 165). A DLM and a combined DLM were established for predicting the GIST risk stratification using convolutional neural network and subsequently evaluated in the validation cohort. RESULTS Among the analyzed CECT image features, tumor size, ulceration, and enlarged feeding vessels were identified as significant risk predictors (P &lt; 0.05). In DLM, the overall area under the receiver operating characteristic curve (AUROC) was 0.88, with the accuracy (ACC) and AUROCs for each stratification being 87% and 0.96 for low-risk, 79% and 0.74 for intermediate-risk, and 84% and 0.90 for high-risk, respectively. The overall ACC and AUROC were 84% and 0.94 in the combined model. The ACC and AUROCs for each risk stratification were 92% and 0.97 for low-risk, 87% and 0.83 for intermediate-risk, and 90% and 0.96 for high-risk, respectively. Differences in AUROCs for each risk stratification between the two models were significant (P &lt; 0.05). CONCLUSION A combined DLM with satisfactory performance for preoperatively predicting GIST stratifications was developed using routine computed tomography data, demonstrating superiority compared to DLM.

CT and MRI characteristics of inflammatory pseudotumor-like follicular dendritic cell sarcoma of the spleen: a report of 11 patients with pathological correlation.

This study aimed to outline the computed tomography (CT) and magnetic resonance imaging (MRI) characteristics of inflammatory pseudotumor-like follicular dendritic cell sarcoma (IPT-like FDCS) in the spleen and to link these imaging features with histopathological findings. A retrospective analysis was conducted on 11 patients (3 males, 8 females; mean age, 63.3 ± 9.66years; age range, 44-76years; median age, 62years) with histopathologically confirmed inflammatory pseudotumor-like follicular dendritic cell sarcoma of the spleen (IPT-like FDCS). The study encompassed a comprehensive review of initial clinical manifestations, macroscopic pathological alterations, along with histopathological and corresponding immunohistochemical findings. CT (n = 10) and MRI (n = 8) characteristics were meticulously assessed by two experienced radiologists, who evaluated parameters including lesion number, size, shape, margin definition, capsular integrity, attenuation, signal intensity, presence of hemosiderin deposition, and patterns of contrast enhancement. In this study, all eleven patients (11/11; 100%) presented with solitary inflammatory pseudotumor-like follicular dendritic cell sarcoma (IPT-like FDCS). The majority of these cases were incidentally detected during routine physical examinations, while one case was revealed through abdominal CT following a decline in platelet count post-radiation and chemotherapy for prostate cancer. The splenic lesions were predominantly oval in shape (10/11; 91%), with a single case exhibiting a lobulated appearance (1/11; 9%). The largest lesion diameter was 18cm, with diameters ranging from 3.3 to 18cm, averaged 6.5cm, and a median diameter of 5.0cm. On plain CT images, the lesions were primarily observed as hypodense with poorly defined margins. On contrast-enhanced CT, all lesions (10/10; 100%) promptly became sharply demarcated in the arterial phase, encircled by a perceptible, relatively hypodense capsule-like rim. On T2-weighted MRI, all lesions (8/8; 100%) displayed a characteristic rim-like low-intensity capsular margin. These low-signal/density capsular margins were pathologically confirmed to be capsules containing fibrous components. IPT-like FDCS is very rare but has characteristic imaging features that correlate with its pathology.

Machine Learning for Evaluating Vulnerable Plaque on Coronary Computed Tomography Using Spectral Imaging.

This study aimed to determine whether spectral imaging with dual-energy computed tomography (CT) can improve diagnostic performance for coronary plaque characterization. We conducted a retrospective analysis of 30 patients with coronary plaques, using coronary CT angiography (dual-layer CT) and intravascular ultrasound (IVUS) studies. Based on IVUS findings, patients were diagnosed with either vulnerable or stable plaques. We computed 7 histogram parameters for plaque CT numbers in 120 kVp images and virtual monochromatic images ranging from 40 to 140 keV at 5-keV intervals. A predictive model was developed using histogram data of optimal energy, plaque volume or stenosis, and a combination of both. The model's performance was evaluated by calculating the area under the receiver operating characteristic curve (AUC) using 5-fold cross-validation. Peak diagnostic performances for each histogram parameter were observed at various energy levels (40-110 keV) in the univariate logistic regression model. The histogram model demonstrated optimal diagnostic performance at 65 keV, with an AUC of 0.81. The combined model, incorporating histogram data and plaque volume, achieved an AUC of 0.85, which was similar to the performance of qualitative CT characteristics (AUC=0.88; P=0.70). Spectral imaging with dual-energy CT can enhance the diagnostic performance of machine learning using CT histograms for coronary plaque characterization.

Open- and closed-type congenital cholesteatomas of the middle ear: computed tomography differentiation and correlation with surgical staging.

To investigate the differences in computed tomography (CT) features between closed-type congenital cholesteatoma (CCC) and open-type congenital cholesteatoma (OCC) of the middle ear and to evaluate the usefulness of preoperative CT examination for staging workup of congenital cholesteatoma (CC) in correlation with the surgical findings. We retrospectively reviewed the preoperative CT scans of the temporal bone obtained from 80 patients with surgically confirmed CC of the middle ear. All patients had a solitary lesion, except for one patient with two lesions, resulting in 81 CCs, which formed the basis of this study. We compared the CT features between CCCs and OCCs, focusing on their morphological characteristics, such as size, shape, location, and bone change. Based on the Potsic classification, the stage of CCs was determined at CT and surgery, and the results were compared between CCCs and OCCs. Of the 81 CCs, surgery revealed 43 CCCs and 38 OCCs. On CT scans, CCC was frequently seen as a small (median: 3.15 mm), round to oval (65.1%) mass, most commonly located in the anterosuperior quadrant (74.4%) of the middle ear with less frequent ossicular erosion (14.0%). In contrast, OCC was frequently seen as a large (median: 6.70 mm), irregular (94.7%) mass, most commonly located in the posterosuperior quadrant (68.4%) of the middle ear with frequent ossicular erosion (55.3%). The size, shape, location, and presence of ossicular erosion were significantly different between the two types. Overall, the CT and surgical stages of CCs demonstrated good agreement (kappa value: 0.77) and the CT and surgical stages of OCCs were statistically significantly higher than those of CCCs (P < 0.001 in both). CT is useful for preoperative determination of the types and staging of CC of the middle ear. Preoperative differentiation between CCC and OCC is important to avoid reoperation and prevent an extensive surgery. By providing valuable information on the morphology and extent of the lesions, CT is useful for not only the accurate preoperative determination of the type of CCs but also the accurate prediction of staging of the lesion, which should be important to preparing optimal treatment plans.

Papillary Renal Neoplasm With Reverse Polarity: CT and MR Imaging Characteristics in 26 Patients.

To improve the diagnostic recognition of papillary renal neoplasm with reverse polarity (PRNRP) through comprehensive analysis of computed tomography (CT) and magnetic resonance imaging (MRI) findings. A retrospective multi-center study was conducted on patients with pathologically confirmed PRNRPs from 2019 to 2024, encompassing six institutions. Clinical and pathological data were meticulously documented. Preoperative CT (n=23) and MRI (n=9) features were independently evaluated in consensus by two genitourinary radiologists, focusing on tumor location, morphologic features, attenuation, signal intensity, and enhancement patterns. Postoperative outcomes were assessed through medical record review or telephone follow-up. The study cohort comprised 26 patients (mean age 62±12 years, 13 men) with 26 well-defined PRNRPs (mean diameter 2.2±1.0cm) were included. 15 cases (58%) were situated in the right kidney, 18(69%) were exophytic, and 23(88%) were quasi-spherical. A pseudocapsule was identified in eight cases (89%) on MRI. All cases demonstrated iso- or slight hyperattenuation (43.1±13.6 HU) on non-contrast CT, hypointensity on T2-weighted imaging (T2WI), and mild diffusion restriction on diffusion-weighted imaging (DWI). All cases exhibited mild or moderate enhancement in the corticomedullary phase, followed by progressive enhancement in the nephrographic and excretory phases. Concomitant renal cysts were found in 17 cases (65%). All cases showed no evidence of recurrence or metastasis. PRNRP typically presents as a small hypovascular renal mass, and characterized by a pseudocapsule, iso- or slight hyperattenuation on non-contrast CT, heterogeneous T2WI hypointensity, and mild diffusion restriction on DWI.

The value of thoracoscopic ultrasound for the localization of ground-glass opacities with incomplete lung collapse in video-assisted thoracoscopic surgery.

Video-assisted thoracoscopic surgery (VATS) has been widely used for the resection of lung lesions. However, it is difficult to palpate or see small lesions, especially ground-glass opacities (GGOs) during VATS. Thoracoscopic ultrasound has definite value in locating pulmonary parenchymal nodules. However, due to the air in the lung parenchyma, its wide application is limited. This study investigated the value of thoracoscopic ultrasound for the localization of GGOs with incomplete lung collapse in VATS. A retrospective analysis was conducted on patients diagnosed with ground-glass nodules (GGNs) on computed tomography (CT) at Liaoning Province Tumor Hospital from November 2018 to August 2019, who underwent thoracoscopic ultrasound localization and VATS. Screening was conducted for patients who did not achieve complete collapse of the lungs after natural collapse during surgery, the success rate was calculated, and preoperative CT features and thoracoscopic ultrasound features of GGNs were summarized and analyzed. The success rate of GGOs' localization by thoracoscopic ultrasound in incomplete collapse lung was 56.67%. Of all preoperative CT features, only the distance between the nodule and the pleura was statistically different (P=0.001). The closer the GGO was to the pleura, the easier it was to be detected. Thoracoscopic ultrasound could effectively locate GGO in the condition of incomplete lung collapse and not affect the surgical field, especially when the GGO is close to the pleura, which provides an alternative method for GGO localization in patients who cannot achieve complete lung collapse during VATS.

Combining computed tomography features of left atrial epicardial and pericoronary adipose tissue with the triglyceride-glucose index to predict the recurrence of atrial fibrillation after radiofrequency catheter ablation: a machine learning study.

Radiofrequency catheter ablation (RFCA) represents an important treatment option for atrial fibrillation (AF); however, the recurrence rate following surgery is relatively high. This study aimed to predict the recurrence of AF after RFCA using interpretable machine learning models that combined the triglyceride-glucose (TyG) index and the quantification of left atrial epicardial and pericoronary adipose tissue. This retrospective study included 325 patients with AF who underwent their first successful RFCA, among whom 79 had confirmed recurrence. The preoperative clinical data of patients were collected, the TyG index was calculated, and computed tomography (CT) image features were quantitatively measured. Multivariate Cox regression analysis was used to identify the independent risk factors for RFCA recurrence, and adjustments being made for various confounding factors. Post-hoc subgroup analysis was conducted to evaluate the predictive value of the TyG index for recurrence in different patient subgroups. Prediction models based on six machine learning algorithms were constructed. The optimal model's features were evaluated using Shapley additive explanations (SHAP). After adjustment were made for various confounding factors such as comorbidities of AF, Cox regression showed that the volume of left atrial epicardial adipose tissue (LA-EAT), LA-EAT attenuation, left circumflex coronary artery fat attenuation index (LCX-FAI), and the TyG index were independent risk factors for recurrence after RFCA (P<0.001). The support vector machine (SVM) model based on these combined indicators had the best predictive performance, with an area under the curve of 0.793 [95% confidence interval (CI): 0.782-0.805] in the validation set, while its accuracy and positive predictive value were 0.804 and 0.710, respectively. The predictive efficiency of the TyG index appeared to be independent of type 2 diabetes mellitus (T2DM) status (Pinteraction=0.660). The SVM model that integrated the TyG index and quantitative CT imaging variables demonstrated good predictive ability for post-RFCA recurrence in patients with AF. Furthermore, the TyG index appeared capable of predicting recurrence independently of T2DM status.

Enhancing Radiomics Reproducibility: Deep Learning-Based Harmonization of Abdominal Computed Tomography (CT) Images.

We assessed the feasibility of using deep learning-based image harmonization to improve the reproducibility of radiomics features in abdominal CT scans. In CT imaging, harmonization adjusts images from different institutions to ensure consistency despite variations in scanners and acquisition protocols. This process is essential because such differences can lead to variability in radiomics features, affecting reproducibility and accuracy. Harmonizing images minimizes these inconsistencies, supporting more reliable and clinically applicable results across diverse settings. A pre-trained harmonization algorithm was applied to 63 dual-energy abdominal CT images, which were reconstructed into four different types, and 10 regions of interest (ROIs) were analyzed. From the original 455 radiomics features per ROI, 387 were used after excluding redundant features. Reproducibility was measured using the intraclass correlation coefficient (ICC), with a threshold of ICC ≥ 0.85 indicating acceptable reproducibility. The region-based analysis revealed significant improvements in reproducibility post-harmonization, especially in vessel features, which increased from 14% to 69%. Other regions, including the spleen, kidney, muscle, and liver parenchyma, also saw notable improvements, although air reproducibility slightly decreased from 95% to 94%, impacting only a few features. In patient-based analysis, reproducible features increased from 18% to 65%, with an average of 179 additional reproducible features per patient after harmonization. These results demonstrate that deep learning-based harmonization can significantly enhance the reproducibility of radiomics features in abdominal CT, offering promising potential for advancing radiomics development and its clinical applications.

A nomogram for predicting lymphovascular invasion in lung adenocarcinoma: a retrospective study

BackgroudLymphovascular invasion (LVI) was histological factor that was closely related to prognosis of lung adenocarcinoma (LAC).The primary aim was to investigate the value of a nomogram incorporating clinical and computed tomography (CT) factors to predict LVI in LAC, and validating the predictive efficacy of a clinical model for LVI in patients with lung adenocarcinoma with lesions ≤ 3 cm.MethodsA total of 450 patients with LAC were retrospectively enrolled. Clinical data and CT features were analyzed to identify independent predictors of LVI. A nomogram incorporating the independent predictors of LVI was built. The performance of the nomogram was evaluated by assessing its discriminative ability and clinical utility.We took 321 patients with tumours ≤ 3 cm in diameter to continue constructing the clinical prediction model, which was labelled subgroup clinical model.ResultsCarcinoembryonic antigen (CEA) level, maximum tumor diameter, spiculation, and vacuole sign were independent predictors of LVI. The LVI prediction nomogram showed good discrimination in the training set [area under the curve (AUC), 0.800] and the test set (AUC, 0.790), the subgroup clinical model also owned the stable predictive efficacy for preoperative prediction of LVI in lung adenocarcinoma patients, and both training and test set AUC reached 0.740.ConclusionsThe nomogram developed in this study could predict the risk of LVI in LAC patients, facilitate individualized risk-stratification, and help inform treatment decision-makin, and the subgroup clinical model also had good predictive performance for lung cancer patients with lesion ≤ 3 cm in diameter.

Computed Tomographic Findings in Dogs with Hepatic Bacterial Parenchymal Infection and Abscessation.

Bacterial liver parenchymal infections in dogs are rarely documented, and their imaging characteristics are scarce in the veterinary literature, especially in Computed Tomography (CT). This retrospective multicentric study aimed to describe the CT characteristics of parenchymal bacterial liver infection and abscessation in dogs and compare them with the human literature. Twenty dogs met the inclusion criteria. All dogs, except one, showed discrete hepatic lesions consistent with pyogenic liver abscess (19/20). A single case showed diffuse liver changes, which was diagnosed with granulomatous bacterial hepatitis (1/20). Multifocal lesions were associated with the presence of abdominal pain (p = 0.023). CT characteristics of pyogenic liver abscesses in our study resemble those described in the human literature, with multifocal (14/19) or single (5/19), round or ovoid (19/19), hypoattenuating hepatic lesions, which are better visualised in post-contrast images. Pyogenic liver abscesses can also show features such as the "cluster sign" (8/19), transient arterial segmental enhancement (6/10), rim enhancement (6/19), and intralesional gas (4/19). Additional CT findings, such as local lymphadenomegaly (18/20), peritoneal fat stranding (14/20), and peritoneal fluid (13/20), are also commonly observed.

Inter-observer and intra-observer agreement of bone reporting and data system (Bone-RADS) in the interpretation of bone tumors on computed tomography

ObjectiveTo assess inter-observer & intra-observer agreement of the American College of Radiology (ACR) bone reporting and data system (Bone-RADS) in the interpretation of bone tumors on computed tomography (CT). MethodsThis retrospective study included 273 bone tumors 184 (67.4 %) benign and 89 (32.6 %) malignant. Two blinded radiologists independently reviewed the CT images to assess the defined CT features of bone lesions and assign a bone-RADS category. A third observer reviewed the CT images twice with one month interval and reported the specified CT features and final bone-RADS category for bone lesions. Inter-observer and intra-observer agreement of bone-RADS were analyzed. ResultsThere was almost perfect inter-observer agreement between the two reviewers for all defined variables as well as bone-RADS categories (Kappa = 92.2 %). Overall intra-observer agreement was also perfect for all defined CT features and bone RADS categories with higher percentage than inter-observer one (κ = 98.3 %). ConclusionBone-RADS is an effective clinical tool for practicing radiologists in the risk assessment and management of bone tumors with perfect agreement among observers. To our knowledge, no studies have been conducted for assessing reliability or validity of the recent ACR Bone-RADS using CT. Hence, this study could serve as a cornerstone for further upcoming studies to assess the reproducibility and validity of the ACR Bone-RADS.

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.