Combination Of Computed Tomography Research Articles

Interpretable machine learning model based on CT semantic features and radiomics features to preoperatively predict Ki-67 expression in gastrointestinal stromal tumors

To develop and validate a machine learning (ML) model which combined computed tomography (CT) semantic and radiomics features to preoperatively predict Ki-67 expression in gastrointestinal stromal tumors (GISTs) patients. We retrospectively collected the clinical, imaging and pathological data of 149 GISTs patients. We randomly assigned the patients in a ratio of 7:3 to a training set (104 cases) and a validation (45 cases) set. We divided the patients into low and high Ki-67 expression group according to postoperative pathology. CT semantic features were analyzed from preoperative enhancement CT images and radiomics features were extracted from venous phase-enhanced images. We used intraclass correlation coefficient, maximal relevance and minimal redundancy and least absolute shrinkage and selection operator method to screen radiomics features and build radiomics label. 6 ML models were used for model construction. Receiver operating characteristic curves were used to evaluate the predictive efficiency of ML models. SHAP analysis was used to explain the contribution of different variables and their risk threshold. AUC of radscores in predicting Ki-67 expression of GIST patients were 0.749 and 0.729 in training and validation set. Among the 6 ML models, SVM exhibited best prediction accuracy. AUC of SVM model in predicting Ki-67 expression of GIST patients were 0.840, 0.767 and 0.832 in training, validation and test set. SHAP analysis showed that radscores and tumor diameter had highly positive contribution to the model. Therefore, the interpretable SVM model can predict Ki-67 expression of GISTs patients individually before surgery, which can provide reliable imaging biomarkers for clinical treatment decisions.

Predicting malignant potential of solitary pulmonary nodules in patients with COVID-19 infection: a comprehensive analysis of CT imaging and tumor markers

ObjectiveTo analyze the value of combining computed tomography (CT) with serum tumor markers in the differential diagnosis of benign and malignant solitary pulmonary nodules (SPNs).MethodsThe case data of 267 patients diagnosed with SPNs in the First Affiliated Hospital of Zhengzhou University from March 2020 to January 2023 were retrospectively analyzed. All individuals diagnosed with coronavirus disease 2019 (COVID-19) were confirmed via respiratory specimen viral nucleic acid testing. The included cases underwent CT, serum tumor marker testing and pathological examination. The diagnostic efficacy and clinical significance of CT, serum tumor marker testing and a combined test in identifying benign and malignant SPNs were analyzed using pathological histological findings as the gold standard. Finally, a nomogram mathematical model was established to predict the malignant probability of SPNs.ResultsOf the 267 patients with SPNs, 91 patients were not afflicted with COVID-19, 36 exhibited malignant characteristics, whereas 55 demonstrated benign features. Conversely, within the cohort of 176 COVID-19 patients presenting with SPNs, 62 were identified as having malignant SPNs, and the remaining 114 were diagnosed with benign SPNs. CT scans revealed statistically significant differences between the benign and malignant SPNs groups in terms of CT values (P<0.001), maximum nodule diameter (P<0.001), vascular convergence sign (P<0.001), vacuole sign (P = 0.0007), air bronchogram sign (P = 0.0005), and lobulation sign (P = 0.0005). Malignant SPNs were associated with significantly higher levels of carcinoembryonic antigen (CEA) and neuron-specific enolase (NSE) compared to benign SPNs (P < 0.05), while no significant difference was found in carbohydrate antigen 125 (CA125) levels (P = 0.054 for non-COVID-19; P = 0.072 for COVID-19). The sensitivity (95.83%), specificity (95.32%), and accuracy (95.51%) of the comprehensive diagnosis combining serum tumor markers and CT were significantly higher than those of CT alone (70.45%, 79.89%, 76.78%) or serum tumor marker testing alone (56.52%, 73.71%, 67.79%) (P < 0.05). A visual nomogram predictive model for malignant pulmonary nodules was constructed.ConclusionCombining CT with testing for CEA, CA125, and NSE levels offers high diagnostic accuracy and sensitivity, enables precise differentiation between benign and malignant nodules, particularly in the context of COVID-19, thereby reducing the risk of unnecessary surgical interventions.

Computed tomography-based contrast features for distinguishing extra-gastrointestinal stromal tumors from intra-abdominal fibromatosis.

This study aims to define the computed tomography (CT) criteria that distinguish extra-gastrointestinal stromal tumors (eGISTs) from intra-abdominal fibromatosis (IAF). Retrospective analysis was conducted on CT images obtained from 31 pathologically confirmed cases, including 17 cases of eGISTs and 14 of IAF. Various parameters [e.g., lesion location, contour characteristics, border delineation, enhancement patterns, presence of intralesional necrosis, vessels, air, fat, and hemorrhage, the long diameter (LD), LD/short diameter (SD) ratio, and volume (LD × SD × height diameter)] were meticulously evaluated. In addition, the degree of enhancement during arterial and portal venous phases and the lesion-to-aorta CT attenuation ratio during both phases were quantified. Statistical analysis was performed using Fisher's exact test, the Student's t-test, and the receiver operating characteristic curve to identify significant CT criteria. Sensitivity and specificity assessments were conducted for single and combined CT criteria. Significant differentiators between eGISTs and IAF include non-mesenteric localization, irregular contour, well-defined borders, heterogeneous enhancement, presence of intralesional necrosis and vessels, and absence of intralesional fat, with LD exceeding 9.6 cm, an LD/SD ratio >1.22, and volume surpassing 603.3 cm3 (P < 0.05). A combination of seven or more of these criteria yielded a specificity of 100%. Ten distinct CT criteria have been identified to distinguish eGISTs from IAF, notably encompassing non-mesenteric localization, irregular contour, well-defined borders, heterogeneous enhancement, presence of intralesional necrosis and vessels, absence of intralesional fat, LD >9.6 cm, an LD/SD ratio >1.22, and volume surpassing 603.3 cm3. The current findings establish CT criteria to distinguish eGISTs from IAF in a clinical setting.

Imaging Spectrum of COVID-19 Associated Cranio-Facial Mucormycosis

Abstract Background The post COVID craniofacial mucormycosis was found to emerge since the second wave of the COVID pandemics. It was called black fungus as it was associated with high mortality rate. Objective To describe combined computed tomography (CT) and magnetic resonance imaging (MRI) characteristics of post COVID craniofacial mucormycosis for accurate diagnosis and determination of the extent of involvement. Patients and Methods The main source of data for this study was the archived CT and MRI imaging of the patients referred to the department of Radiology, Ain Shams University Hospitals. Results Our study population presented with craniofacial symptoms within 1-8 weeks from COVID-19 infection. The study revealed strong association between history of ICU admission, high doses of corticosteroids, oxygen administration and sever imaging MRI features of fungal sinusitis. Fungal invasion is not necessary to be through bony destruction. Conclusion The severest form of fungal sinusitis was found to be more commonly associated with ICU admission, usage of high doses of corticosteroids as well as patients who needed O2 therapy, the usage of contrast enhanced MRI should be combined with CT for assessment of disease extension as well as bony invasion.

Predictive value of bronchoscopy combined with CT score for refractory mycoplasma pneumoniae pneumonia in children

IntroductionMycoplasma pneumoniae pneumonia (MPP) is prevalent in paediatric patients and can progress to refractory mycoplasma pneumoniae pneumonia (RMPP).ObjectiveTo assess the predictive value of bronchoscopy combined with computed tomography (CT) score in identifying RMPP in children.MethodsA retrospective analysis was conducted on 244 paediatric patients with MP, categorising them into RMPP and general mycoplasma pneumoniae pneumonia (GMPP) groups. A paired t-test compared the bronchitis score (BS) and CT score before and after treatment, supplemented by receiver operating characteristic (ROC) analysis.ResultsThe RMPP group showed higher incidences of extrapulmonary complications and pleural effusion (58.10% and 40%, respectively) compared with the GMPP group (44.60%, p = 0.037 and 18.71%, p < 0.001, respectively). The CT scores for each lung lobe were statistically significant between the groups, except for the right upper lobe (p < 0.05). Correlation analysis between the total CT score and total BS yielded r = 0.346 and p < 0.001. The ROC for BS combined with CT score, including area under the curve, sensitivity, specificity, and cut-off values, were 0.82, 0.89, 0.64, and 0.53, respectively.ConclusionThe combined BS and CT score method is highly valuable in identifying RMPP in children.

Radiologic Parameters Predicting the Histologic Invasiveness of Pure Ground-Glass Nodules

BackgroundThis study aimed to investigate the diagnostic performance of combined computed tomography (CT) and fluorine-18 fluorodeoxyglucose (FDG) positron emission tomography (PET) for predicting histologic invasiveness of pure ground-glass nodules (pGGNs). MethodsThe study analyzed 91 patients who underwent resection of pGGNs and examined the correlation of pathologic invasiveness with preoperative CT and FDG PET findings. ResultsOverall, 24, 36, and 31 patients had adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (IAD), respectively. Compared with AIS and MIA, IAD was significantly correlated with larger CT size (P = .001), maximum CT value (P = .026), and high maximum standardized uptake value (SUVmax; P < .001). Multivariable logistic analyses revealed that CT size (odds ratio [OR], 3.848; P = .019) and SUVmax (OR, 4.968; P = .009) were independent predictors of histologic invasiveness. Receiver operating characteristic curve analysis revealed that a cutoff CT size value of 18 mm predicted histologic invasiveness with a sensitivity and specificity of 65% and 80%, respectively; similarly, a cutoff SUVmax value of 1.5 predicted histologic invasiveness with a sensitivity and specificity of 61% and 90%, respectively. Of 20 lesions with CT size ≥18 mm and SUVmax ≥1.5, 16 (80%) were IAD. Of 54 lesions with CT size <18 mm and SUVmax <1.5, 46 (85%) were non-IAD lesions. Furthermore, all pGGNs with SUVmax ≥2.5 were IAD. ConclusionsCT size and SUVmax were significantly correlated with the histologic invasiveness of pGGNs. These factors may aid in determining optimal surgical procedures.

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

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

Paleoradiological and scientific investigations of the screaming woman mummy from the area beneath Senmut's (1479-1458 BC) Theban tomb (TT71).

The Screaming Mummy of Cairo Egyptian-Museum Store, is an anonymous woman with a wide-open mouth coded as CIT8, discovered beneath Theban Tomb 71 (TT71) which is the burial site of Senmut's relatives, the architect of 18th-Dynasty Queen Hatschepsut (1479-1458 BC). The study aims to evaluate if combining computed tomography (CT) with scientific investigations and archeological data of the Screaming Mummy CIT8 will reveal information about its physical appearance, health, cause of death, and mummification. We CT-scanned the mummy and created reconstructed images. Scanning-Electron-Microscope (SEM), Fourier-Transform-Infrared-Spectroscopy (FTIR), and X-ray-Diffraction-Analysis (XRD) were used to investigate mummy skin, hair, and wig samples. We compared our findings to previous data. Computed tomography estimated the age of death to be 48.1 years ±14.6 based on the pubic symphyseal surface. CT detected mild-to-moderate teeth attrition, and joints degeneration. The desiccated brain and viscera remained in situ. FTIR revealed the wig is formed of midrib date palm that shows in CT as spiral low density fibers. The wig fibers are partially coated with a thick substance that is inspected as black consolidation and identified as crystalline by XRD, comparable to material found in an ancient wig-making workshop. FTIR showed that the skin, hair, and wig samples were treated with imported juniper resin had anti-bacterial and insecticidal properties. The skin and wig samples contained frankincense, and the hair sample contained henna. Combining the advantages of paleoradiology to the scientific investigations, provided enhanced comprehension of the mummy CIT8 and ancient Egyptian wig structure and material. CT scanning non-invasively showed the mummy's inner and exterior morphology, and estimated the age of death as 48 years. CT evaluated the mummification technique based on retained viscera and absence of embalming packs. The scientific tests revealed expensive imported embalming materials, contradicting the traditional belief that the non-removal of the viscera implied poor mummification, resulting in careless embalmers sealing the mouth. The widely opened mouth could be a result of facial expression of suffering before death, fixed by cadaveric spasm. The study also explores how rigor mortis, tissue decomposition, burial techniques, and postmortem alterations may contribute to a mummy's screaming appearance.

Quantification of soft tissue artifacts using CT registration and subject-specific multibody modeling

The potential use of gait analysis for quantitative preoperative planning in total hip arthroplasty (THA) has previously been demonstrated. However, the joint kinematic data measured through this process tend to be unreliable for surgical planning due to distortions caused by soft tissue artifacts (STAs). In this study, we developed a novel motion capture framework by combining computed tomography (CT)-based postural calibration and subject-specific multibody dynamics modeling to prevent the effect of STAs in measuring hip kinematics. Three subjects with femoroacetabular impingement syndrome were recruited, and CT data for each patient were collected by attaching marker clusters near the hip. A subject-specific multibody hip joint model was developed based on reconstructed CT data. Spring–dashpot network calculations were performed to minimize the distance between the anatomical landmark and its corresponding infrared reflective marker. The STAs of the thigh was described as six degrees of freedom viscoelastic bushing elements, and their parameter values were identified via smooth orthogonal decomposition. Least squares optimization was used to modify the pelvic rotations to compensate for the rigid components of STAs. The results showed that CT-assisted motion tracking enabled the successful identification of STA influences in gait and squat positions. Furthermore, STA effects were found to alter maximal pelvis tilt and hip rotations during a squat. Compared to other techniques, such as dual fluoroscopic imaging, the adopted framework does not require additional medical imaging for patients undergoing robot-assisted THA surgery and is thus a practical way of evaluating hip joint kinematics for preoperative surgical planning.

Quantitative CT features on admission combined with laboratory biomarkers for predicting severe acute pancreatitis

To assess the association of quantitative computed tomography (CT) features on admission with acute pancreatitis (AP) severity, and to explore the performance of combined CT and laboratory markers for predicting severe AP (SAP). Data from 208 AP patients were reviewed retrospectively. Pancreas volume, the area of extrapancreatic inflammation, extrapancreatic fluid collection volume, and number were calculated based on CT images on admission. Laboratory biomarkers within 24 h of admission were collected. Interobserver agreement for CT measurements was measured by calculating interclass correlation coefficient (ICC). The associations of quantitative CT features with AP severity were evaluated. Predictive models for SAP were constructed based on CT and laboratory markers. Performances of single marker and the models were evaluated using receiver operating characteristic (ROC) curve and area under the ROC curve (AUC). Pancreas volume, area of extrapancreatic inflammation, extrapancreatic fluid collection volume, and number were significantly different between severe and non-severe AP groups. In predicting SAP, the AUCs of quantitative CT indicators ranged from 0.72 to 0.79; the AUCs of laboratory biomarkers were between 0.53 and 0.66. The combined model of area of extrapancreatic inflammation, serum calcium, and haematocrit yielded an AUC of 0.84, significantly higher than that of the laboratory model, single CT, or laboratory marker. Interobserver agreements for quantitative CT indicators were excellent, with ICC ranging from 0.91 to 0.98. Quantitative CT features on admission were significantly associated with AP severity; the combination of extrapancreatic inflammation area, serum calcium, and haematocrit could be taken as a new method for predicting SAP.

Abstract 256: Proposal of multimodal CT‐based scoring system in prediction of hemorrhagic transformation in acute ischemic stroke

Introduction The routinely used computed tomography (CT)‐based workup in the setting of acute ischemic stroke (AIS) includes non‐contrast brain CT, CT angiography (CTA), and CT perfusion. Several CT, CTA, CTP‐based radiological biomarkers of hemorrhagic transformation (HT) were reported. To assess the predictive value of the combined multimodal CT parameters for HT after AIS and proposal of predictive scoring scale. Methods The source images of the NCCT, CTA and CTP of 282 AIS patients involving the anterior circulation (HT = 91, non‐HT = 191) were retrospectively reviewed and the following biomarkers were recorded and analyzed: Early subtle ischemic signs, hyperdense middle cerebral artery sign (HMCAS) and Alberta Stroke Program Early CT Score (ASPECTS) &lt; 7 in NCCT, large‐vessel occlusion (LVO), clot burden score (CBS) &lt; 6, large‐vessel occlusion, poor collateral score (CS) and Tmax &gt; 6 s ≥ 56.5 ml. A scoring system to predict HT based on these biomarkers was developed. Each biomarker counts for a single point with the total score ranging from 0 to 7. Results All the aforementioned multimodal CT biomarkers and the selected cut offs were significantly associated with higher HT risk. The calculated scores were statistically significant different between the HT and the non‐HT groups with AUC 0.761 (95% CI 0.703–0.819, P &lt; 0.0000001). Rates of HT were approximately five times higher in patients with score ≥ 3. Conclusion Multimodal CT‐based scoring system may provide highly reliable predictive model of hemorrhagic transformation in acute ischemic stroke.

A New Proposed Combined CT and MRI Staging System for Covid-19-Associated Rhino-Orbito-Cerebral Fungal Infection: A Multi-center Study with Pathological Correlation

To propose a combined computed tomography (CT) and magnetic resonance imaging (MRI) based classification system in the management of COVID-19-associated rhino-orbito-cerebral (C-ROC) fungal infection and to assess the reliability of such proposed staging system. This was a multi-center prospective study conducted on 122 adults with previously confirmed COVID-19 infection. CT and contrast-enhanced MRI (CE-MRI) were performed for all patients. Three radiologists (with experience of 8, 10, and 14 years) independently assessed all images. Then, each patient was assigned a radiological stage based on the five stages of the proposed system according to the radiological extent of the fungal infection. The intra-class correlation coefficient (ICC) test assessed the inter-rater agreement. Based on the pathological evaluation of post-operative specimens, a diagnosis of fungal infection was documented. The most prevalent severity stage among all raters was stage IV in 29.5-31.1% patients. The overall inter-rater agreement of the proposed staging system was excellent (ICC 0.971, 95% CI;0.960-0.979). Moreover, the most common detected pathogen was Mucormycosis (n=87, 71.3%). Furthermore, there was a statistically significant association between the patients' outcome and the severity stage (P value 0.001) and there was no statistically significant association between ethmoid and sphenoid sinus affection and cranial extension (P value 0.081). Our proposed combined CT and MRI severity staging system has a high inter-rater agreement. Moreover, it can aid in the early detection of the C-ROC fungal infection, improve preoperative planning, and subsequently improve the patient's outcome.

CT-derived extracellular volume and liver volumetry can predict posthepatectomy liver failure in hepatocellular carcinoma

ObjectivesPosthepatectomy liver failure (PHLF) is a severe complication of liver resection. We aimed to develop and validate a model based on extracellular volume (ECV) and liver volumetry derived from computed tomography (CT) for preoperative predicting PHLF in resectable hepatocellular carcinoma (HCC) patients.MethodsA total of 393 resectable HCC patients from two hospitals were enrolled and underwent multiphasic contrast-enhanced CT before surgery. A total of 281 patients from our hospital were randomly divided into a training cohort (n = 181) and an internal validation cohort (n = 100), and 112 patients from another hospital formed the external validation cohort. CT-derived ECV was measured on nonenhanced and equilibrium phase images, and liver volumetry was measured on portal phase images. The model is composed of independent predictors of PHLF. The under the receiver operator characteristic curve (AUC) and calibration curve were used to reflect the predictive performance and calibration of the model. Comparison of AUCs used the DeLong test.ResultsCT-derived ECV, measured future liver remnant (mFLR) ratio, and serum albumin were independent predictors for PHLF in resectable HCC patients. The AUC of the model was significantly higher than that of the ALBI score in the training cohort, internal validation cohort, and external validation cohort (all p < 0.001). The calibration curve of the model showed good consistency in the training cohort and the internal and external validation cohorts.ConclusionsThe novel model contributes to the preoperative prediction of PHLF in resectable HCC patients.Critical relevance statementThe novel model combined CT–derived extracellular volume, measured future liver remnant ratio, and serum albumin outperforms the albumin–bilirubin score for predicting posthepatectomy liver failure in patients with resectable hepatocellular carcinoma.Key points• CT-derived ECV correlated well with the fibrosis stage of the background liver.• CT-derived ECV and mFLR ratio were independent predictors for PHLF in HCC.• The AUC of the model was higher than the CT-derived ECV and mFLR ratio.• The model showed a superior predictive performance than that of the ALBI score.Graphical

Dual-energy CT Virtual Non-Calcium Image is Beneficial for the Detection of Non-displaced Knee Fractures.

Early and accurate diagnosis is vital for avoiding the development of nondisplaced fractures to displaced fractures. Dual-energy CT (Computed Tomography) can detect bone marrow edema (BME), which may help to detect non-displaced fractures. To evaluate the value of DECT (Dual-Energy Computed Tomography) VNCa (Virtual noncalcium) images for improving diagnostic performance and confidence in acute non-displaced knee fractures. 125 patients with clinical suspicion of knee fractures underwent both DECT and MR. Conventional linear-blended CT and VNCa images were obtained from DECT. First, five readers with varying levels of experience evaluated the presence of fractures on conventional linear-blended CT and graded their diagnostic confidence on a scale of 1 to 10. Then BME with VNCa images was evaluated and compared with MR. Finally, the VNCa images combined with conventional linear-blended CT images were used to reassess the presence of fractures and diagnostic confidence. Diagnostic performance and matched pair analyses were performed. 20 non-displaced knee fractures were detected. The consistency test of VNCa images and MR by five radiologists showed Kappa values are 0.76, 0.79, 0.81,0.85,and 0.90,respectively. The diagnostic performance of all readers was improved when using VNCa images combined with conventional linear-blended CT compared with that with conventional linear-blended CT alone. Diagnostic confidence was improved with combined conventional linear-blended CT and VNCa images (median score:8,8,9,9, and 10, respectively) compared with conventional linear-blended CT alone (median score:7,7,8,9, and 9). DECT VNCa images could improve the radiologists' diagnostic performance and confidence with varying levels of experience in the detection of non-displaced knee fractures.

Diagnostic Reliability of Computed Tomography in Predicting Posterior Ligamentous Complex Injury in Traumatic Lower Lumbar Fracture

ObjectiveThough magnetic resonance imaging (MRI) is the primary modality of investigation for determining the extent of PLC injuries in lower lumbar fractures (L3-L5), the reliability of computed tomography (CT) has not been well defined. The main objective of this study is to analyze the diagnostic accuracy of combined CT findings for detecting posterior ligamentous complex injury in patients with lower lumbar fractures. MethodsWe retrospectively analyzed data from 108 patients who presented with traumatic lower lumbar fractures. CT parameters like loss of vertebral body height, local kyphosis, retropulsion of fracture fragment, interlaminar distance (ILD), interspinous distance (ISD), supraspinous distance (SSD), interpedicular distance (IPD), canal compromise, facet joint diastasis in axial images (FJDA) and sagittal images (FJDS), presence of lamina and spinous process fracture were calculated using axial and sagittal CT images. The presence or absence of PLC injury was determined using MRI as a reference standard. ResultsAmong 108 patients PLC injury was identified in 57 (52.8%). On univariate analysis local kyphosis, retropulsion of fracture fragment, ILD, IPD, FJDS, FJDA, and the presence of spinous process fracture were found to be significant (P < 0.05) in predicting PLC injury. Whereas on multivariate logistic regression analysis, FJDS (P= 0.039), and FJDA (P= 0.003) were found to be variables independently associated with PLC injury. ConclusionAmong the various CT parameters, facet joint diastasis (FJDS > 4.2 mm and FJDA > 3.5 mm) is the most reliable factor in determining PLC injury.

Proposal of multimodal computed tomography-based scoring system in prediction of hemorrhagic transformation in acute ischemic stroke.

The routinely used computed tomography (CT)-based workup in the setting of acute ischemic stroke (AIS) includes non-contrast brain CT, CT angiography (CTA), and CT perfusion. Several CT, CTA, CTP-based radiological biomarkers of hemorrhagic transformation (HT) were reported. To assess the predictive value of the combined multimodal CT parameters for HT after AIS and proposal of predictive scoring scale. The source images of the NCCT, CTA and CTP of 282 AIS patients involving the anterior circulation (HT = 91, non-HT = 191) were retrospectively reviewed and the following biomarkers were recorded and analyzed: Early subtle ischemic signs, hyperdense middle cerebral artery sign (HMCAS) and Alberta Stroke Program Early CT Score (ASPECTS) < 7 in NCCT, large-vessel occlusion (LVO), clot burden score (CBS) < 6, large-vessel occlusion, poor collateral score (CS) and Tmax > 6s ≥ 56.5ml. A scoring system to predict HT based on these biomarkers was developed. Each biomarker counts for a single point with the total score ranging from 0 to 7. All the aforementioned multimodal CT biomarkers and the selected cut offs were significantly associated with higher HT risk. The calculated scores were statistically significant different between the HT and the non-HT groups with AUC 0.761 (95% CI 0.703-0.819, P < 0.0000001). Rates of HT were approximately five times higher in patients with score ≥ 3. Multimodal CT-based scoring system may provide highly reliable predictive model of hemorrhagic transformation in acute ischemic stroke.

Network analysis of pore structure of coral reef limestone and its implications for seepage flow

Determining the seepage characteristics of coral reef limestone is highly significant for engineering activities on relevant strata. In this paper, the pore structure information of two types of coral reef limestones with distinct pore characteristics was obtained by combining computed tomography (CT) scanning technology and pore network modeling. We regarded the pore structure of coral reef limestone as a complex network, and the seepage inside is analogous to information transmission. Network analysis was performed on this network considering both global and individual network indicators. It was shown that the pore network of coral reef limestones has a short seepage distance and strong connectivity, and exhibits a prominent clustering effect. The centrality of network nodes indicated that very few pore nodes have extremely high centrality. Based on the Dijkstra algorithm, the shortest percolation paths along three orthogonal directions were obtained and visualized for both samples. This showed that the shortest percolation paths always converge to a few paths in all directions for a pore network of coral reef limestone with coarse pores. There existed a preferential seepage path characterized by more intensive shortest percolated paths. Some pore nodes with higher betweenness centrality were located at a crucial location in a few shortest percolation paths. A new indicator VB considering both the volume and betweenness centrality of the pore node was proposed. Those pore nodes with large VB values were identified as key pore nodes in the network, as seepage simulations showed that blockage of these key nodes causes a dramatic decrease in permeability.

Integrating Preoperative Computed Tomography and Clinical Factors for Lymph Node Metastasis Prediction in Esophageal Squamous Cell Carcinoma by Feature-Wise Attentional Graph Neural Network

This study aimed to propose a regional lymph node (LN) metastasis prediction model for patients with esophageal squamous cell carcinoma (ESCC) that can learn and adaptively integrate preoperative computed tomography (CT) image features and nonimaging clinical parameters. Contrast-enhanced CT scans taken 2 weeks before surgery and 20 clinical factors, including general, pathologic, hematological, and diagnostic information, were collected from 357 patients with ESCC between October 2013 and November 2018. There were 999 regional LNs (857 negative, 142 positive) with pathologically confirmed status after surgery. All LNs were randomly divided into a training set (n=738) and a validation set (n=261) for testing. The feature-wise attentional graph neural network (FAGNN) was composed of (1) deep image feature extraction by the encoder of 3-dimensional UNet and high-level nonimaging factor representation by the clinical parameter encoder; (2) a feature-wise attention module for feature embedding with learnable adaptive weights; and (3) a graph attention layer to integrate the embedded features for final LN level metastasis prediction. Among the 4 models we constructed, FAGNN using both CT and clinical parameters as input is the model with the best performance, and the area under the curve (AUC) reaches 0.872, which is better than manual CT diagnosis method, multivariable model using CT only (AUC=0.797), multivariable model with combined CT and clinical parameters (AUC=0.846), and our FAGNN using CT only (AUC=0.853). Our adaptive integration model improved the metastatic LN prediction performance based on CT and clinical parameters. Our model has the potential to foster effective fusion of multisourced parameters and to support early prognosis and personalized surgery or radiation therapy planning in patients with ESCC.

Traumatic rib fracture patterns associated with bone mineral density statuses derived from CT images.

The impact of decreased bone mineral density (BMD) on traumatic rib fractures remains unknown. We combined computed tomography (CT) and artificial intelligence (AI) to measure BMD and explore its impact on traumatic rib fractures and their patterns. The retrospective cohort comprised patients who visited our hospital from 2017-2018; the prospective cohort (control group) was consecutively recruited from the same hospital from February-June 2023. All patients had blunt chest trauma and underwent CT. Volumetric BMD of L1 vertebra was measured by using an AI software. Analyses were done by using BMD categorized as osteoporosis (<80 mg/cm3), osteopenia (80-120 mg/cm3), or normal (>120 mg/cm3). Pearson's χ2, Fisher's exact, or Kruskal-Wallis tests and Bonferroni correction were used for comparisons. Negative binomial, and logistic regression analyses were used to assess the associations and impacts of BMD status. Sensitivity analyses were also performed. The retrospective cohort included 2,076 eligible patients, of whom 954 (46%) had normal BMD, 806 (38.8%) had osteopenia, and 316 (15.2%) had osteoporosis. After sex- and age-adjustment, osteoporosis was significantly associated with higher rib fracture rates, and a higher likelihood of fractures in ribs 4-7. Furthermore, both the osteopenia and osteoporosis groups demonstrated a significantly higher number of fractured ribs and fracture sites on ribs, with a higher likelihood of fractures in ribs 1-3, as well as flail chest. The prospective cohort included 205 eligible patients, of whom 92 (44.9%) had normal BMD, 74 (36.1%) had osteopenia, and 39 (19.0%) had osteoporosis. The findings observed within this cohort were in concurrence with those in the retrospective cohort. Traumatic rib fractures are associated with decreased BMD. CT-AI can help to identify individuals who have decreased BMD and a greater rib fracture rate, along with their fracture patterns.

A Projection-Based Augmented Reality System for Medical Applications

The aim of this paper was to present the development of an Augmented Reality (AR) system which uses a 2D video projector to project a 3D model of blood vessels, built by combining Computed Tomography (CT) slices of a human brain, onto a model of a human head. The difficulty in building this system is that the human head contains, not flat surfaces, but non-regular curved surfaces. Using a 2D projector to project a 3D model onto non-regular curved 3D surfaces would result in serious distortions of the projection if the image was not uncorrected first. This paper proposed a method of correcting the projection, not only based on the curvatures of the surfaces, but also on the viewing position of the observer. Experimental results of this system showed that an average positional deviation error of 2.065 mm could be achieved under various test conditions.