Contrast-enhanced Computed Tomography Imaging Research Articles

Development and validation of a CT-based deep learning radiomics signature to predict lymph node metastasis in oropharyngeal squamous cell carcinoma: a multicenter study.

Lymph node metastasis (LNM) is a pivotal determinant that influences the treatment strategies and prognosis for oropharyngeal squamous cell carcinoma (OPSCC) patients. This study aims to establish and verify a deep learning (DL) radiomics model for the prediction of LNM in OPSCCs using contrast-enhanced computed tomography (CECT). A retrospective analysis included 279 OPSCC patients from three institutions. CECT images were used for handcrafted (HCR) and DL feature extraction. Dimensionality reduction for HCR features used recursive feature elimination and least absolute shrinkage and selection operator algorithms, whereas DL feature dimensionality reduction used variance-threshold and recursive feature elimination algorithms. Radiomics signatures were constructed using support vector machine, decision tree, random forest, k-nearest neighbor, gaussian naive bayes classifiers and light gradient boosting machine. A combined model was then constructed using the screened DL, HCR, and clinical features. The area under the receiver operating characteristic curve (AUC) served to quantify the model's performance, and calibration curves were utilized to assess its calibration. The combined model exhibited robust performance, achieving AUC values of 0.909 (95% CI: 0.861-0.957) in the training cohort, 0.884 (95% CI: 0.800-0.968) in the internal validation cohort, and 0.865 (95% CI: 0.791-0.939) in the external validation cohort. It outperformed both the clinical model and best-performing radiomics model. Moreover, calibration was deemed satisfactory. The combined model based on CECT demonstrates the potential to predict LNM in OPSCCs preoperatively, offering a valuable tool for more precise and tailored treatment strategies. This study presents a novel combined model integrating clinical factors with deep learning radiomics, significantly enhancing preoperative LNM prediction in OPSCC.

Skeletal Muscle Segmentation at the Level of the Third Lumbar Vertebra (L3) in Low-Dose Computed Tomography: A Lightweight Algorithm.

The cross-sectional area of skeletal muscles at the level of the third lumbar vertebra (L3) measured from computed tomography (CT) images is an established imaging biomarker used to assess patients' nutritional status. With the increasing prevalence of low-dose CT scans in clinical practice, accurate and automated skeletal muscle segmentation at the L3 level in low-dose CT images has become an issue to address. This study proposed a lightweight algorithm for automated segmentation of skeletal muscles at the L3 level in low-dose CT images. This study included 57 patients with rectal cancer, with both low-dose plain and contrast-enhanced pelvic CT image series acquired using a radiotherapy CT scanner. A training set of 30 randomly selected patients was used to develop a lightweight segmentation algorithm, and the other 27 patients were used as the test set. A radiologist selected the most representative axial CT image at the L3 level for both the image series for all the patients, and three groups of observers manually annotated the skeletal muscles in the 54 CT images of the test set as the gold standard. The performance of the proposed algorithm was evaluated in terms of the Dice similarity coefficient (DSC), precision, recall, 95th percentile of the Hausdorff distance (HD95), and average surface distance (ASD). The running time of the proposed algorithm was recorded. An open source deep learning-based AutoMATICA algorithm was compared with the proposed algorithm. The inter-observer variations were also used as the reference. The DSC, precision, recall, HD95, ASD, and running time were 93.2 ± 1.9% (mean ± standard deviation), 96.7 ± 2.9%, 90.0 ± 2.9%, 4.8 ± 1.3 mm, 0.8 ± 0.2 mm, and 303 ± 43 ms (on CPU) for the proposed algorithm, and 94.1 ± 4.1%, 92.7 ± 5.5%, 95.7 ± 4.0%, 7.4 ± 5.7 mm, 0.9 ± 0.6 mm, and 448 ± 40 ms (on GPU) for AutoMATICA, respectively. The differences between the proposed algorithm and the inter-observer reference were 4.7%, 1.2%, 7.9%, 3.2 mm, and 0.6 mm, respectively, for the averaged DSC, precision, recall, HD95, and ASD. The proposed algorithm can be used to segment skeletal muscles at the L3 level in either the plain or enhanced low-dose CT images.

Computed tomography radiomics-based cross-sectional detection of mandibular osteoradionecrosis in head and neck cancer survivors.

This study aims to identify radiomic features extracted from contrast-enhanced CT scans that differentiate osteoradionecrosis (ORN) from normal mandibular bone in patients with head and neck cancer (HNC) treated with radiotherapy (RT). Contrast-enhanced CT (CECT) images were collected for 150 patients (80% train, 20% test) with confirmed ORN diagnosis at The University of Texas MD Anderson Cancer Center between 2008 and 2018. Using PyRadiomics, radiomic features were extracted from manually segmented ORN regions and the corresponding automated control regions, the later defined as the contralateral healthy mandible region. A subset of pre-selected features was obtained based on correlation analysis (r > 0.95) and used to train a Random Forest (RF) classifier with Recursive Feature Elimination. Model explainability SHapley Additive exPlanations (SHAP) analysis was performed on the 20 most important features identified by the trained RF classifier. From a total of 1316 radiomic features extracted, 810 features were excluded due to high collinearity. From a set of 506 pre-selected radiomic features, the optimal subset resulting on the best discriminative accuracy of the RF classifier consisted of 67 features. The RF classifier was well calibrated (Log Loss 0.296, ECE 0.125) and achieved an accuracy of 88% and a ROC AUC of 0.96. The SHAP analysis revealed that higher values of Wavelet-LLH First-order Mean and Median were associated with ORN of the jaw (ORNJ). Conversely, higher Exponential GLDM Dependence Entropy and lower Square First-order Kurtosis were more characteristic of normal mandibular tissue. This study successfully developed a CECT-based radiomics model for differentiating ORNJ from healthy mandibular tissue in HNC patients after RT. Future work will focus on the detection of subclinical ORNJ regions to guide earlier interventions.

Precise ablation zone segmentation on CT images after liver cancer ablation using semi‐automatic CNN‐based segmentation

AbstractBackgroundAblation zone segmentation in contrast‐enhanced computed tomography (CECT) images enables the quantitative assessment of treatment success in the ablation of liver lesions. However, fully automatic liver ablation zone segmentation in CT images still remains challenging, such as low accuracy and time‐consuming manual refinement of the incorrect regions.PurposeTherefore, in this study, we developed a semi‐automatic technique to address the remaining drawbacks and improve the accuracy of the liver ablation zone segmentation in the CT images.MethodsOur approach uses a combination of a CNN‐based automatic segmentation method and an interactive CNN‐based segmentation method. First, automatic segmentation is applied for coarse ablation zone segmentation in the whole CT image. Human experts then visually validate the segmentation results. If there are errors in the coarse segmentation, local corrections can be performed on each slice via an interactive CNN‐based segmentation method. The models were trained and the proposed method was evaluated using two internal datasets of post‐interventional CECT images ( = 22, = 145; 62 patients in total) and then further tested using an external benchmark dataset ( = 12; 10 patients).ResultsTo evaluate the accuracy of the proposed approach, we used Dice similarity coefficient (DSC), average symmetric surface distance (ASSD), Hausdorff distance (HD), and volume difference (VD). The quantitative evaluation results show that the proposed approach obtained mean DSC, ASSD, HD, and VD scores of 94.0%, 0.4 mm, 8.4 mm, 0.02, respectively, on the internal dataset, and 87.8%, 0.9 mm, 9.5 mm, and −0.03, respectively, on the benchmark dataset. We also compared the performance of the proposed approach to that of five well‐known segmentation methods; the proposed semi‐automatic method achieved state‐of‐the‐art performance on ablation segmentation accuracy, and on average, 2 min are required to correct the segmentation. Furthermore, we found that the accuracy of the proposed method on the benchmark dataset is comparable to that of manual segmentation by human experts ( = 0.55, ‐test).ConclusionsThe proposed semi‐automatic CNN‐based segmentation method can be used to effectively segment the ablation zones, increasing the value of CECT for an assessment of treatment success. For reproducibility, the trained models, source code, and demonstration tool are publicly available at https://github.com/lqanh11/Interactive_AblationZone_Segmentation.

Contrast-enhanced CT scan (CECT) for the detection of hollow viscus and mesenteric injuries in blunt trauma - an updated systematic review of the literature and meta-analysis of diagnostic test accuracy.

Despite improved awareness of blunt traumatic hollow viscus and mesenteric injuries (THVMI), the accuracy of contrast-enhanced CT (CECT) varies considerably among studies. This systematic review and meta-analysis of test accuracy aims to explore the diagnostic performance of CECT in detecting THVMI in blunt trauma. The study was conducted according to the Cochrane recommendations searching the PubMed, Scopus, and Cochrane Library datasets from 2000 to 8 September 2023 (PROSPERO ID: CRD42023473041). Surgical exploration, autopsy, and discharge from the hospital after monitoring were set as reference standard. To explore the diagnostic accuracy of CECT in detecting THVMI hierarchical models were developed. The risk of bias in individual studies was assessed with the QUADAS-2 tool. Sensitivity analysis was conducted to detect sources of heterogeneity. Twelve studies, for a total of 4537 patients, were deemed eligible. After identification of outliers and sensitivity analysis, the pooled sensitivity, specificity, positive and negative likelihood ratios, and diagnostic odds ratio were 0.85 (95% CI: 0.69-0.93), 0.94 (95% CI: 0.8-0.98), 14.65 (95% CI: 4.22-50.85), 0.16 (95% CI: 0.07-0.34), 92.3 (95% CI: 29.75-286.34), respectively. The Area under the HSROC curve was 0.95 (95% CI: 0.92-0.96). Meta-regression analysis identified the year of publication as a covariate significantly associated with heterogeneity. A high risk of bias was detected in the "patient selection" domains. CECT has a fundamental role in identifying THVMI with high specificity but suboptimal sensitivity. Clinical criteria are still of paramount importance, especially in cases of ambiguous initial CECT images.

Outcome of the novel description of arterial position changes after major liver resections: retrospective study.

After major liver resections, anatomical shifts due to liver parenchymal hypertrophy and organ displacement can happen. The aim of this study was to evaluate the impact of these anatomical changes on the main abdominal arteries (coeliac trunk and superior mesenteric artery) and on patient outcomes. All patients who underwent major liver resections (between January 2010 and July 2021) and who underwent preoperative and postoperative arterial-phase contrast-enhanced abdominal CT imaging were studied. Observed arterial position changes were classified into three groups: no position changes; class I position changes (vessel displacement with or without kinking with a vessel angle greater than 105°); and class II position changes (kinking less than or equal to 105°). The Mann-Whitney test and the Kruskal-Wallis test were used to compare continuous variables and the chi-squared test and Fisher's exact test were used to compare categorical variables. Univariable and multivariable logistic regression analyses were used to identify the risk factors for morbidity and mortality. A total of 265 patients (149 men and median age of 59 years) were enrolled. Arterial position changes were detected in a total of 145 patients (54.7%) (99 patients (37%) with class I position changes and 46 patients (18%) with class II position changes) and were observed more often after extended resection and right-sided resection (P < 0.001). Major complications were seen in 94 patients (35%) and the rate of mortality was 15% (40 patients died). Post-hepatectomy liver failure (P = 0.030), major complications (P < 0.001), and mortality (P = 0.004) occurred more frequently in patients with class II position changes. In multivariable analysis, arterial position change was an independent risk factor for post-hepatectomy liver failure (OR 2.86 (95% c.i. 1.06 to 7.72); P = 0.038), major complications (OR 2.10 (95% c.i. 1.12 to 3.93); P = 0.020), and mortality (OR 2.39 (95% c.i. 1.03 to 5.56); P = 0.042). Arterial position changes post-hepatectomy are observed in the majority of patients and are significantly related to postoperative morbidities and mortality.

Machine learning-assisted diagnosis of parotid tumor by using contrast-enhanced CT imaging features

PurposeThis study aims to develop a machine learning diagnostic model for parotid gland tumors based on preoperative contrast-enhanced CT imaging features to assist in clinical decision-making. Materials and methodsClinical data and contrast-enhanced CT images of 144 patients with parotid gland tumors from the Peking University School of Stomatology Hospital, collected from January 2019 to December 2022, were gathered. The 3D slicer software was utilized to accurately annotate the tumor regions, followed by exploring the correlation between multiple preoperative contrast-enhanced CT imaging features and the benign or malignant nature of the tumor, as well as the type of benign tumor. A prediction model was constructed using the k-nearest neighbors (KNN) algorithm. ResultsThrough feature selection, four key features—morphology, adjacent structure invasion, boundary, and suspicious cervical lymph node metastasis—were identified as crucial in preoperative discrimination between benign and malignant tumors. The KNN prediction model achieved an accuracy rate of 94.44 %. Additionally, six features including arterial phase CT value, age, delayed phase CT value, pre-contrast CT value, venous phase CT value, and gender, were also significant in the classification of benign tumors, with a KNN prediction model accuracy of 95.24 %. ConclusionThe machine learning model based on preoperative contrast-enhanced CT imaging features can effectively discriminate between benign and malignant parotid gland tumors and classify benign tumors, providing valuable reference information for clinicians.

Machine learning models from computed tomography to diagnose thymic epithelial tumors requiring combined resection.

Minimally invasive approaches have been a standard choice of surgery for noninvasive thymic epithelial tumors (TETs), but we sometimes experience cases requiring combined resection of adjacent structures. We develop and validate machine learning models to predict combined resection based on preoperative contrast-enhanced computed tomography (CT). This study included 212 patients with TETs (140 in the training cohort and 72 in the validation cohort) who underwent radical surgery. Radiomics features were extracted from contrast-enhanced CT and predicted with five feature selection methods and seven machine learning models in nested cross validation. The clinical utility of the models was analyzed by a decision curve analysis (DCA). Fifty-five patients in the training cohort and 28 in the validation cohort required combined resection. The classifiers random forest (RF), gradient boosting (GB), and eXtreme Gradient Boosting (XGB) indicated high predictive performance, with the XGB classifier based on features selected by GB performing the best, with an area under the curve (AUC) of 0.797. In the validation cohort, the classifier had an AUC of 0.817. The DCA showed the validity of the model with a threshold range of 15-72%. When restricted to combined pulmonary and pericardial resection, the respective AUCs were 0.736 and 0.674 for the training cohort and 0.806 and 0.924 for the validation cohort. The machine learning model based on preoperative CT images was able to diagnose TETs requiring combined resection with high accuracy. The DCA demonstrated a wide range of model validity and may aid in surgical approach selection.

Study on the Prediction of Liver Injury in Acute Pancreatitis Patients by Radiomic Model Based on Contrast-Enhanced Computed Tomography.

to predict liver injury in acute pancreatitis (AP) patients by establishing a radiomics model based on contrast-enhanced computed tomography (CECT). a total of 1223 radiomic features were extracted from late arterial-phase pancreatic CECT images of 209 AP patients (146 in the training cohort and 63 in the test cohort), and the optimal radiomic features retained after dimensionality reduction by least absolute shrinkage and selection operator (LASSO) were used to construct a radiomic model through logistic regression analysis. In addition, clinical features were collected to develop a clinical model, and a joint model was established by combining the best radiomic features and clinical features to evaluate the practicality and application value of the radiomic models, clinical model and combined model. four potential features were selected from the pancreatic parenchyma to construct the radiomic model, and the area under the receiver operating characteristic curve (AUC) of the radiomic model was significantly greater than that of the clinical model for both the training cohort (0.993 vs. 0.653, p = 0.000) and test cohort (0.910 vs. 0.574, p = 0.000). The joint model had a greater AUC than the radiomics model for both the training cohort (0.997 vs. 0.993, p = 0.357) and test cohort (0.925 vs. 0.910, p = 0.302). the radiomic model based on CECT has good performance in predicting liver injury in AP patients and can guide clinical decision-making and improve the prognosis of patients with AP.</p>.

Baseline tumour vessel perfusion as a non-invasive predictive biomarker for immune checkpoint therapy in non-small-cell lung cancer

ObjectiveCurrent biomarkers for predicting immunotherapy response in non-small-cell lung cancer (NSCLC) are derived from invasive procedures with limited predictive accuracy. Thus, identifying a non-invasive predictive biomarker would improve patient stratification...

Peripheral liver metastases are more frequently missed than central metastases in contrast-enhanced CT: insights from a 25-reader performance study.

Subtle liver metastases may be missed in contrast enhanced CT imaging. We determined the impact of lesion location and conspicuity on metastasis detection using data from a prior reader study. In the prior reader study, 25 radiologists examined 40 CT exams each and circumscribed all suspected hepatic metastases. CT exams were chosen to include a total of 91 visually challenging metastases. The detectability of a metastasis was defined as the fraction of radiologists that circumscribed it. A conspicuity index was calculated for each metastasis by multiplying metastasis diameter with its contrast, defined as the difference between the average of a circular region within the metastasis and the average of the surrounding circular region of liver parenchyma. The effects of distance from liver edge and of conspicuity index on metastasis detectability were measured using multivariable linear regression. The median metastasis was 1.4cm from the edge (interquartile range [IQR], 0.9-2.1cm). Its diameter was 1.2cm (IQR, 0.9-1.8cm), and its contrast was 38 HU (IQR, 23-68 HU). An increase of one standard deviation in conspicuity index was associated with a 6.9% increase in detectability (p = 0.008), whereas an increase of one standard deviation in distance from the liver edge was associated with a 5.5% increase in detectability (p = 0.03). Peripheral liver metastases were missed more frequently than central liver metastases, with this effect depending on metastasis size and contrast.

Prediction of CD8+T lymphocyte infiltration levels in gastric cancer from contrast-enhanced CT and clinical factors using machine learning.

CD8+ T lymphocyte infiltration is closely associated with the prognosis and immunotherapy response of gastric cancer (GC). For now, the examination of CD8 infiltration levels relies on endoscopic biopsy, which is invasive and unsuitable for longitude assessment during anti-tumor therapy. This work aims to develop and validate a noninvasive workflow based on contrast-enhanced CT (CECT) images to evaluate the CD8+ T-cell infiltration profiles of GC. GC patients were retrospectively and consecutively enrolled and randomly assigned to the training (validation) or test cohort at a 7:3 ratio. All patients were binary classified into the CD8-high (infiltrated proportion≥20%) or CD8-low group (infiltrated proportion<20%) group. A total of 1170 radiomics features were extracted from each presurgical CECT series. After feature selection, fifteen radiomics features were transmitted to three independent machine-learning models for the computation of predictive radiological scores. Multilayer perceptron (MLP) was applied to merge the radiological scores with clinical factors. The predictive efficacy of the radiological scores and of the combined model was evaluated by receiver operating characteristic curve, calibration curve, and decision curve analysis in both the training and test cohorts. A total of 210 patients were enrolled in this study (mean age: 63.22±8.74years, 151 men), and were randomly assigned to the training set (n=147) or the test set (n=63). The merged radiological score was correlated with CD8 infiltration in both the training (p=1.8e-10) and test cohorts (p=0.00026). The combined model integrating the radiological scores and clinical features achieved an area under the curve (AUC) value of 0.916 (95% CI: 0.872-0.960) in the training set and 0.844 (95% CI: 0.742-0.946) in the test set for classifying CD8-high GCs. The model was well-calibrated and exhibited net benefit over "treat-all" and"treat-none" strategies in decision curve analysis. Artificial intelligent systems combining radiological features and clinical factors could accurately predict CD8 infiltration levels of GC, which may benefit personalized treatment of GC in the context of immunotherapy.

Deep Learning-Based Prediction of Post-treatment Survival in Hepatocellular Carcinoma Patients Using Pre-treatment CT Images and Clinical Data.

The objective of this study was to develop and evaluate a model for predicting post-treatment survival in hepatocellular carcinoma (HCC) patients using their CT images and clinical information, including various treatment information. We collected pre-treatment contrast-enhanced CT images and clinical information including patient-related factors, initial treatment options, and survival status from 692 patients. The patient cohort was divided into a training cohort (n = 507), a testing cohort (n = 146), and an external CT cohort (n = 39), which included patients who underwent CT scans at other institutions. After model training using fivefold cross-validation, model validation was performed on both the testing cohort and the external CT cohort. Our cascaded model employed a 3D convolutional neural network (CNN) to extract features from CT images and derive final survival probabilities. These probabilities were obtained by concatenating previously predicted probabilities for each interval with the patient-related factors and treatment options. We utilized two consecutive fully connected layers for this process, resulting in a number of final outputs corresponding to the number of time intervals, with values representing conditional survival probabilities for each interval. Performance was assessed using the concordance index (C-index), the mean cumulative/dynamic area under the receiver operating characteristics curve (mC/D AUC), and the mean Brier score (mBS), calculated every 3months. Through an ablation study, we found that using DenseNet-121 as the backbone network and setting the prediction interval to 6months optimized the model's performance. The integration of multimodal data resulted in superior predictive capabilities compared to models using only CT images or clinical information (C index 0.824 [95% CI 0.822-0.826], mC/D AUC 0.893 [95% CI 0.891-0.895], and mBS 0.121 [95% CI 0.120-0.123] for internal test cohort; C index 0.750 [95% CI 0.747-0.753], mC/D AUC 0.819 [95% CI 0.816-0.823], and mBS 0.159 [95% CI 0.158-0.161] for external CT cohort, respectively). Our CNN-based discrete-time survival prediction model with CT images and clinical information demonstrated promising results in predicting post-treatment survival of patients with HCC.

Utility of radiomic features in predicting clinical outcomes in stage II-III pancreatic cancer.

74 Background: We identified computed tomography (CT)-derived radiomic features predictive of tumor progression within three months, then examined their ability to prognosticate overall survival (OS) along with clinical features in pancreatic cancer. We evaluated these features in patients with unresected pancreatic cancer who underwent stereotactic body radiation therapy (SBRT) in sequence with chemotherapy, but not surgery. Methods: In this retrospective study, we examined a cohort of 101 patients with stage II-III pancreatic cancer who underwent SBRT with sequential chemotherapy at a single institution (Stanford Health Care) between 1999-2020. From their pre-SBRT contrast-enhanced CT images with segmented tumors, delineating regions-of-interest, we extracted 900 radiomic (quantitative pixel-level imaging characteristic) features. In the first phase, we identified radiomic features that predicted rapid tumor progression within three months following SBRT. We divided the dataset into a training set (n = 53) for model development and a test set (n = 48) for evaluation. Using logistic regression with the Least Absolute Shrinkage and Selection Operator algorithm for feature selection and classification, we built a binary prediction model on the training set to identify patients at risk of progression within three months of SBRT. To fine-tune parameters, we performed five-fold cross-validation (CV) on the training set, repeating each set of parameters five times. We assessed model performance on the test set using the area under the curve (AUC). We selected the model with the best AUC, generating the predictive radiomic feature set. In the second phase, we conducted univariate and multivariate Cox regression analyses to assess the relationship between OS and individual clinical variables (age, sex, stage, vessel involvement, tumor location, performance status, body mass index, biological equivalent dose of radiation) and the radiomic feature set as high versus low risk. Results: Our cohort consisted of 48 men (mean age, 70 years ± 11 [SD]) and 53 women (mean age, 67 years ± 13 [SD]). From the first phase, 32 textural features comprised the radiomic feature set that best predicted rapid tumor progression, with mean AUCs of 0.852 (CV, n=53) and 0.814 (test, n=48). In the univariate Cox model, only the radiomic feature set was predictive of OS (hazard ratio, HR, 1.724, p=0.011). In the multivariate Cox model, radiomic features and age were significant predictors of OS, with HR of 1.819 (p=0.007) and 1.024 (p=0.024), respectively. Conclusions: CT-derived radiomic features predict rapid tumor progression following SBRT, confer nearly a twofold increase in mortality risk, and, along with patient age, enhance the identification of patients with stage II-III pancreatic cancer with poor OS. [Table: see text]

Radiological assessment of the dissection area in supraomohyoid neck dissection

PurposeThe current supraomohyoid neck dissection (SOHND) is performed above the omohyoid muscle to dissect levels I, II, and III in the levels of cervical lymph nodes. However, the anatomical boundary between levels III and IV is the inferior border of the cricoid cartilage. We investigated the anatomical relationship between the omohyoid muscle and cricoid cartilage using contrast-enhanced CT (CE-CT) images to assess the validity of the current SOHND.MethodsCE-CT images of the head and neck regions in patients were reviewed. The patients were divided into two groups: “malignant tumors” and “others”. The vertebral levels corresponding to the positions of anatomical structures such as the intersection of the omohyoid muscle and internal jugular vein (OM-IJ), and the inferior border of the cricoid cartilage (CC), were recorded.ResultsThe OM-IJ was located around the seventh cervical to the first thoracic vertebra. There was a significant difference between the malignant tumor and others groups in females (p = 0.036). The CC was located around the sixth to seventh cervical vertebrae. There was a significant sex difference in each group (malignant tumor: p < 0.0001; others: p = 0.008). Both sexes tended to have lower OM-IJ than CC, and females had significantly lower OM-IJ than males.ConclusionThis study provides clear anatomical evidence showing the difference between the SOHND dissection area and levels I, II, and III. It could be considered that in most cases SOHND invades level IV, not just levels I, II, and III, especially in female patients.

Textural heterogeneity of liver lesions in CT imaging - comparison of colorectal and pancreatic metastases.

Tumoral heterogeneity poses a challenge for personalized cancer treatments. Especially in metastasized cancer, it remains a major limitation for successful targeted therapy, often leading to drug resistance due to tumoral escape mechanisms. This work explores a non-invasive radiomics-based approach to capture textural heterogeneity in liver lesions and compare it between colorectal cancer (CRC) and pancreatic cancer (PDAC). In this retrospective single-center study 73 subjects (42 CRC, 31 PDAC) with 1291 liver metastases (430 CRC, 861 PDAC) were segmented fully automated on contrast-enhanced CT images by a UNet for medical images. Radiomics features were extracted using the Python package Pyradiomics. The mean coefficient of variation (CV) was calculated patient-wise for each feature to quantify the heterogeneity. An unpaired t-test identified features with significant differences in feature variability between CRC and PDAC metastases. In both colorectal and pancreatic liver metastases, interlesional heterogeneity in imaging can be observed using quantitative imaging features. 75second-order features were extracted to compare the varying textural characteristics. In total, 18 radiomics features showed a significant difference (p < 0.05) in their expression between the two malignancies. Out of these, 16 features showed higher levels of variability within the cohort of pancreatic metastases, which, as illustrated in a radar plot, suggests greater textural heterogeneity for this entity. Radiomics has the potential to identify the interlesional heterogeneity of CT texture among individual liver metastases. In this proof-of-concept study for the quantification and comparison of imaging-related heterogeneity in liver metastases a variation in the extent of heterogeneity levels in CRC and PDAC liver metastases was shown.

Anatomy-constrained synthesis for spleen segmentation improvement in unpaired mouse micro-CT scans with 3D CycleGAN

Objective. Auto-segmentation in mouse micro-CT enhances the efficiency and consistency of preclinical experiments but often struggles with low-native-contrast and morphologically complex organs, such as the spleen, resulting in poor segmentation performance. While CT contrast agents can improve organ conspicuity, their use complicates experimental protocols and reduces feasibility. We developed a 3D Cycle Generative Adversarial Network (CycleGAN) incorporating anatomy-constrained U-Net models to leverage contrast-enhanced CT (CECT) insights to improve unenhanced native CT (NACT) segmentation. Approach. We employed a standard CycleGAN with an anatomical loss function to synthesize virtual CECT images from unpaired NACT scans at two different resolutions. Prior to training, two U-Nets were trained to automatically segment six major organs in NACT and CECT datasets, respectively. These pretrained 3D U-Nets were integrated during the CycleGAN training, segmenting synthetic images, and comparing them against ground truth annotations. The compound loss within the CycleGAN maintained anatomical fidelity. Full image processing was achieved for low-resolution datasets, while high-resolution datasets employed a patch-based method due to GPU memory constraints. Automated segmentation was applied to original NACT and synthetic CECT scans to evaluate CycleGAN performance using the Dice Similarity Coefficient (DSC) and the 95th percentile Hausdorff Distance (HD95p). Main results. High-resolution scans showed improved auto-segmentation, with an average DSC increase from 0.728 to 0.773 and a reduced HD95p from 1.19 mm to 0.94 mm. Low-resolution scans benefited more from synthetic contrast, showing a DSC increase from 0.586 to 0.682 and an HD95p reduction from 3.46 mm to 1.24 mm. Significance. Implementing CycleGAN to synthesize CECT scans substantially improved the visibility of the mouse spleen, leading to more precise auto-segmentation. This approach shows the potential in preclinical imaging studies where contrast agent use is impractical.

Exploring bone density analysis on routine CT scans as a tool for opportunistic osteoporosis screening

The primary aim of this study was to evaluate computed tomography (CT)-based bone density analysis at the level of thoracic vertebra 12 (Th12) as a screening method for decreased bone density in patients admitted to the intensive care unit (ICU). Interobserver variability was analyzed. Secondary aims were to assess the prevalence of CT-based low bone density upon ICU admission in a cohort of COVID-19 patients and to assess the potential effect of long-term ICU stay on bone density in these patients. Retrospective single-center cohort study. ICU of the Leiden University Medical Center (LUMC), the Netherlands. Patients admitted to the ICU of the LUMC between March 1st, 2020 and February 1st, 2022 with a diagnosis of COVID-19, and a length of ICU stay of ≥ 21 days. In the included patients both baseline chest CT scans (obtained upon ICU admission) and follow-up chest CT scans (obtained ≥ 21 days after ICU admission) were available for analysis. A total of 118 CT scans in 38 patients were analyzed. There was a good interobserver variability, with an overall mean absolute difference (between measurements of three observers) of 9.7 Hounsfield Units (HU) and an intraclass correlation coefficient (ICC) of 0.93 (95% CI 0.88–0.96). The effect of intravenous contrast administration on bone density measurements was small (+ 7.5 HU (95% CI 3.4–11.5 HU)) higher in contrast enhanced CT images compared to non contrast enhanced CT images). Thirty-seven percent of patients had a bone density < 140 HU, suggestive of osteoporosis. No significant difference was found between bone density upon ICU admission and bone density at follow-up (≥ 21 days after ICU admission). Vertebral CT-based bone density analysis using routine CT scans is an easily applicable method to identify ICU patients with decreased bone density, which could enable enrollment in osteoporosis prevention programs. A high prevalence of low bone density was found in our cohort of ICU patients. There were no changes observed in bone density between baseline and follow-up measurements.

Extrahepatic Portal Venous Gas Is the Strongest Predictor of Mortality in Patients with Portal Venous Gas and Pneumatosis Intestinalis.

Very few studies have examined the association between contrast-enhanced computed tomography (CT) findings observed in portal venous gas (PVG) and pneumatosis intestinalis (PI) and the underlying diseases in these conditions. In this study, we analyzed this association and report the findings for predicting mortality. Overall, 50 patients diagnosed with PVG or PI, observed on contrast-enhanced CT, underwent treatment at our hospital. Based on the underlying disease, we divided the patients into three groups, those with ischemic disease, infectious disease, or gastrointestinal dilatation. Furthermore, cases that underwent surgical treatment or needed surgery but were inoperable were assigned to the high risk group (n=16) and patients who received conservative treatment were assigned to the low risk group (n=34). We reviewed the patients' medical charts, laboratory data, and CT images retrospectively, and analyzed the relationship between CT findings, underlying disease, and association with the high risk or low risk group in each case. Poor enhancement of the intestinal wall, mesenteric fat stranding, extrahepatic PVG, advanced age, and renal disease were significantly associated with ischemic disease (p=0.02, p=0.02, p=0.005, p=0.008 and p=0.049, respectively). PI alone was strongly associated with gastrointestinal dilatation (p=0.009). Patients in the low risk group had more favorable outcomes with conservative treatment. In multivariate analysis, extrahepatic PVG was the only factor associated with the high risk group (p=0.002). Extrahepatic PVG associated with ischemic disease was the strongest predictive factor of mortality. Other CT findings, though useful in diagnosing the underlying disease, were not significant predictive factors.

Bone metastasis prediction in non-small-cell lung cancer: primary CT-based radiomics signature and clinical feature

BackgroundRadiomics provided opportunities to quantify the tumor phenotype non-invasively. This study extracted contrast-enhanced computed tomography (CECT) radiomic signatures and evaluated clinical features of bone metastasis in non-small-cell lung cancer (NSCLC). With the combination of the revealed radiomics and clinical features, the predictive modeling on bone metastasis in NSCLC was established.MethodsA total of 318 patients with NSCLC at the Tianjin Medical University Cancer Institute & Hospital was enrolled between January 2009 and December 2019, which included a feature-learning cohort (n = 223) and a validation cohort (n = 95). We trained a radiomics model in 318 CECT images from feature-learning cohort to extract the radiomics features of bone metastasis in NSCLC. The Kruskal-Wallis and the least absolute shrinkage and selection operator regression (LASSO) were used to select bone metastasis-related features and construct the CT radiomics score (Rad-score). Multivariate logistic regression was performed with the combination of the Rad-score and clinical data. A predictive nomogram was subsequently developed.ResultsRadiomics models using CECT scans were significant on bone metastasis prediction in NSCLC. Model performance was enhanced with each information into the model. The radiomics nomogram achieved an AUC of 0.745 (95% confidence interval [CI]: 0.68,0.80) on predicting bone metastasis in the training set and an AUC of 0.808(95% confidence interval [CI]: 0.71,0.88) in the validation set.ConclusionThe revealed invisible image features were of significance on guiding bone metastasis prediction in NSCLC. Based on the combination of the image features and clinical characteristics, the predictive nomogram was established. Such nomogram can be used for the auxiliary screening of bone metastasis in NSCLC.