CT Scans Of Patients Research Articles

Granulomatosis with polyangiitis with salivary glands involvement: Presenting a case and describing its clinical, pathophysiological, and therapeutic aspects

Key Clinical MessageGranulomatosis with polyangiitis (GPA), a rare form of small vessel vasculitis, may be manifested by multisystem involvement misleading its definitive diagnosis. The involvement of salivary glands is a very rare characteristic of GPA. Herein, we described a case of GPA with submandibular salivary gland involvement followed by reviewing the literature on similar cases. The case was a 31‐year‐old man, a known case of seronegative peripheral arthritis that referred recently with bilateral enlargement of the parotid and submandibular glands. Pulmonary nodules were also evident in the patient's CT scan. Fine‐needle aspiration under ultrasound guidance indicated the presence of degenerated squamoid cells, giant cells, and inflammatory cells with a priority of neutrophils in the submandibular gland, as well as the presence of a cyst containing fluid without the evidence of malignancy in the parotid gland. The positivity for the Anti‐neutrophil Cytoplasmic Antibody (C‐ANCA) marker was also revealed. The patient was treated with methotrexate, prednisolone, and rituximab which led to a gradual reduction in the size of the glands and the improvement of the patient's clinical symptoms within 1 month after the treatment. Enlargement of salivary glands in the context of inflammatory disorders can raise doubts about the existence of GPA, and therefore imaging evaluation and histopathological assessment with an ANCA test will be necessary to confirm or rule out it.

Assessment of C-reactive protein levels as an indicator for lung infiltrates in patients with COVID-19 pneumonia.

Lung infiltrates are frequently observed in patients with COVID-19 infection and require specialized management. Identifying reliable laboratory parameters to reduce the need for chest CT scans in non-desaturation patients is of great interest. This study aimed to investigate the potential of C-reactive protein (CRP) as an indicator to identify the presence of lung infiltrates in early COVID-19 infection. The study was conducted at Al-Azhar University hospitals from May 2021 to March 2022 and included 210 patients with COVID-19 infection confirmed by positive PCR, all of whom were previously healthy, non-smokers, and non-hypoxemic. CRP levels were assessed and correlated with lung infiltrates observed in CT chest examinations. The mean value of CRP was 40.3±14.3 mg/L in males and 36.6±15.2 mg/L among females. One hundred sixty-two patients had pneumonic infiltrates, while 48 had no infiltrates. The mean value of CRP was 45.02±10.2 mg/L in patients with radiological infiltrates and 18.8±7.8 mg/L in patients without radiological infiltrates. Based on our findings, a CRP value greater than 29.8 mg/L was suggested as a cut-off value to indicate the presence of lung infiltrates. CRP is a simple laboratory marker that, at certain limits, may point to the presence of pneumonic infiltrates in early non-hypoxemic patients with COVID-19 infection.

Computed Tomography and 3-D Face Scan Fusion for IoT-Based Diagnostic Solutions

In clinical diagnosis, multimodal medical image fusion is meaningful and necessary, for the reason that some diseases need to be diagnosed in combination with the situation of different tissues of patients. Spiral computed tomography realizes the high-precision and smooth reconstruction of bone tissue, while it can not represent the color and texture information in soft tissue reconstruction with high accuracy. The face scan precisely records the color and shape of the maxillofacial region. The diagnosis of some diseases (like cavernous hemangioma and jaw deformity caused by idiopathic condylar resorption) needs to combine the information of maxillofacial soft tissue and bone, so it is of great significance to fuse spiral CT and face scan images. In this paper, a novel intelligent IoT scene is proposed: a multimodal medical images acquisition and fusion system, and by combining CT machine and face scan equipment, the CT and face scan of patients can be synchronously collected. Deep point neural networks are used to extract feature points and a threshold iterative closest point algorithm performing registration with deep feature points and contributed region segmentation is applied. Finally, high-precision fused modal data is output at the mobile terminal to facilitate diagnosis and analysis and improve the efficiency of doctor-patient communication. Quantitative experiments show promising results, and clinical experiments prove that our method enables patients and doctors to better understand the state of an illness and improves the efficiency of doctor-patient communication.

The Compatibility of Preoperative Temporal Bone CT Scan Interpretation and Intraoperative Findings in Chronic Suppurative Otitis Media at Dr. Zainoel Abidin Hospital Banda Aceh

Introduction: Chronic suppurative otitis media (CSOM) is middle ear and mastoid infection that causes hearing loss globally. Computed Tomography (CT)-Scan of the temporal bone is useful for assessing the pathogenesis of the disease and estimating the prognosis. In addition, evaluation of CT-Scan in patients with CSOM is useful for selecting the appropriate surgical procedure to minimize post-surgical complications. This study aims to assess the suitability of temporal CT-Scan interpretation to intraoperatif findings in CSOM patients. Methods: This is an observational study with cross-sectional design conducted at the dr. Zainoel Abidin General Hospital, Banda Aceh. The components of suitability evaluation include cholesteatoma, scutum erosion, ossicles, tegmen tympani, facial canal, posterior wall and sigmoid sinus wall. The interpretation suitability of temporal CT-Scan and intraoperatif findings was performed using the Kappa test. Results: A total of 18 CSOM patients were involved in this study with a mean age of 32.44 years and dominated by the safe type with a percentage of 55.6%. Interpretation of ossicular erosion based on temporal CT scan has very strong suitability with the intraoperatif findings with k=0.88 followed by posterior wall erosion (k=0.684), scutum erosion (k=0.679), tympanic tegmen erosion (k=0.455) and facial canal erosion (k=0.341). The findings of cholesteatoma and erosion of the sigmoid sinus wall had a very low suitability (k<0.001). Conclusion: Temporal CT scan in CSOM patients has relatively good suitability with intraoperatif findings in evaluating erosion of the ossicles, posterior wall, and scutum. Another examination modality is necessary to assess and improve the accuracy of disease pathogenesis.

The correlation between nasal and paranasal sinus anatomical variations with severity of Chronic Rhino Sinusitis Based on CT Scan Examination at Dr. Zainoel Abidin Hospital Banda Aceh

Background: Chronic rhinosinusitis (CRS) is a persistent inflammation of the nasal mucosa and paranasal sinuses. Anatomical variations of the nasal and paranasal sinuses can obstruct and impair the drainage and ventilation functions of the sinus ostium, causing CRS. Objective: To determine the correlation between nasal and paranasal sinus anatomical variations with the degree of severity of CRS based on a CT scan at Dr. Zainoel Abidin Hospital. Methods: An analytic observational study with a cross-sectional design from the results of CT scans of CRS patients in the period on October 2019-october 2022. The anatomical variations assessed were the degree of deviation of the nasal septum, type of bullous turbinate, paradoxical turbinate, hypertrophy of ethmoid bullae, Haller cells, deflection of uncinate process, type of superior insertion of the uncinate process, pneumatization of the uncinate process and frontal sinus cells to the degree of CRS using the Lund Mackay score. Data were analyzed using the Chi-Square test or Fisher's exact test. Result: Analysis of 64 CT scans consisting of 128 sides of the nose found a significant relationship between anatomic variations and the degree of CRS, namely the degree of deviation of nasal septum, type of concha bullosa, ethmoid bulla hypertrophy and deflection of the uncinate process to the severity of maxillary sinusitis (p=0.023; p=0.024; p=0.039; p=0.029) and paradoxical medial concha for the severity of maxillary sinusitis, anterior and frontal ethmoiditis (p=0.018; p=0.013; p=0.000) Conclusion: Anatomical variations of the nasal and paranasal sinuses are related to the severity of CRS.

ExtRanFS: An Automated Lung Cancer Malignancy Detection System Using Extremely Randomized Feature Selector.

Lung cancer is an abnormality where the body's cells multiply uncontrollably. The disease can be deadly if not detected in the initial stage. To address this issue, an automated lung cancer malignancy detection (ExtRanFS) framework is developed using transfer learning. We used the IQ-OTH/NCCD dataset gathered from the Iraq Hospital in 2019, encompassing CT scans of patients suffering from various lung cancers and healthy subjects. The annotated dataset consists of CT slices from 110 patients, of which 40 were diagnosed with malignant tumors and 15 with benign tumors. Fifty-five patients were determined to be in good health. All CT images are in DICOM format with a 1mm slice thickness, consisting of 80 to 200 slices at various sides and angles. The proposed system utilized a convolution-based pre-trained VGG16 model as the feature extractor and an Extremely Randomized Tree Classifier as the feature selector. The selected features are fed to the Multi-Layer Perceptron (MLP) Classifier for detecting whether the lung cancer is benign, malignant, or normal. The accuracy, sensitivity, and F1-Score of the proposed framework are 99.09%, 98.33%, and 98.33%, respectively. To evaluate the proposed model, a comparison is performed with other pre-trained models as feature extractors and also with the existing state-of-the-art methodologies as classifiers. From the experimental results, it is evident that the proposed framework outperformed other existing methodologies. This work would be beneficial to both the practitioners and the patients in identifying whether the tumor is benign, malignant, or normal.

Reproducibility in Radiomics: A Comparison of Feature Extraction Methods and Two Independent Datasets.

Radiomics involves the extraction of information from medical images that are not visible to the human eye. There is evidence that these features can be used for treatment stratification and outcome prediction. However, there is much discussion about the reproducibility of results between different studies. This paper studies the reproducibility of CT texture features used in radiomics, comparing two feature extraction implementations, namely the MATLAB toolkit and Pyradiomics, when applied to independent datasets of CT scans of patients: (i) the open access RIDER dataset containing a set of repeat CT scans taken 15 min apart for 31 patients (RIDER Scan 1 and Scan 2, respectively) treated for lung cancer; and (ii) the open access HN1 dataset containing 137 patients treated for head and neck cancer. Gross tumor volume (GTV), manually outlined by an experienced observer available on both datasets, was used. The 43 common radiomics features available in MATLAB and Pyradiomics were calculated using two intensity-level quantization methods with and without an intensity threshold. Cases were ranked for each feature for all combinations of quantization parameters, and the Spearman's rank coefficient, rs, calculated. Reproducibility was defined when a highly correlated feature in the RIDER dataset also correlated highly in the HN1 dataset, and vice versa. A total of 29 out of the 43 reported stable features were found to be highly reproducible between MATLAB and Pyradiomics implementations, having a consistently high correlation in rank ordering for RIDER Scan 1 and RIDER Scan 2 (rs > 0.8). 18/43 reported features were common in the RIDER and HN1 datasets, suggesting they may be agnostic to disease site. Useful radiomics features should be selected based on reproducibility. This study identified a set of features that meet this requirement and validated the methodology for evaluating reproducibility between datasets.

Comparison of 3D-conformal and intensity-modulated radiation therapy for left-sided breast cancer

Background: We compared 3D-conformal radiation therapy (3D-CRT) and intensity-modulated radiation therapy (IMRT) planning for left-sided post-mastectomy patients. Aim: To compare the dose coverage of the planning target volume (PTV) and dose delivered to organs at risk (OAR) of 3D-CRT and IMRT plans. Setting: Department of Oncology, central South Africa. Methods: Twenty-six archived CT scans of patients with left-sided breast cancer were included. The 3D-CRT and IMRT plans were designed for each patient and compared using the Monaco © planning system (version 5.11.02). Statistical analysis was performed for PTV coverage (V 95% , V 98% , V 105% ) and radiation doses to the heart, ipsilateral lung, combined lungs, contralateral breast, and oesophagus. Results: The V 98% and V 105% target volume dose coverage for the 3D-CRT plans were 67.07% and 0.21%, respectively, compared to 92.32% and 1.10% of the IMRT plans. However, the IMRT plans’ mean volume of PTV, receiving 95% of the prescribed dose (PD), was 7.68% compared to the 3D-CRT’s 32.93%. The IMRT plans resulted in a V 22 Gy < 10% for the heart, with a value of 4.15%. The V 18.87 Gy < 45% values for the ipsilateral and combined lungs were 28.09% and 13.70%, respectively. The 3D-CRT plans showed a lower dose to the oesophagus (5.07 Gy) and contralateral breast (V 5 Gy < 15% = 3.51%). Conclusion: It was shown that 3D-CRT and IMRT treatment planning can effectively achieve clinical goals for post-mastectomy left-sided breast cancer radiotherapy. Contribution: The findings underscore the continuing relevance of 3D-CRT planning in oncology for optimal PTV dose coverage and low OAR dose.

Visual Image Annotation for Bowel Obstruction: Repeatability and Agreement with Manual Annotation and Neural Networks

Bowel obstruction is a common cause of acute abdominal pain. The development of algorithms for automated detection and characterization of bowel obstruction on CT has been limited by the effort required for manual annotation. Visual image annotation with an eye tracking device may mitigate that limitation. The purpose of this study is to assess the agreement between visual and manual annotations for bowel segmentation and diameter measurement, and to assess agreement with convolutional neural networks (CNNs) trained using that data. Sixty CT scans of 50 patients with bowel obstruction from March to June 2022 were retrospectively included and partitioned into training and test data sets. An eye tracking device was used to record 3-dimensional coordinates within the scans, while a radiologist cast their gaze at the centerline of the bowel, and adjusted the size of a superimposed ROI to approximate the diameter of the bowel. For each scan, 59.4 ± 15.1 segments, 847.9 ± 228.1 gaze locations, and 5.8 ± 1.2 m of bowel were recorded. 2d and 3d CNNs were trained using this data to predict bowel segmentation and diameter maps from the CT scans. For comparisons between two repetitions of visual annotation, CNN predictions, and manual annotations, Dice scores for bowel segmentation ranged from 0.69 ± 0.17 to 0.81 ± 0.04 and intraclass correlations [95% CI] for diameter measurement ranged from 0.672 [0.490–0.782] to 0.940 [0.933–0.947]. Thus, visual image annotation is a promising technique for training CNNs to perform bowel segmentation and diameter measurement in CT scans of patients with bowel obstruction.

Radiomics to predict checkpoint inhibitor pneumonitis based on chest CT scans in patients with advanced cancer.

e20550 Background: Checkpoint inhibitor pneumonitis (CIP) is a concerning immune-related adverse event responsible for treatment discontinuation, hospitalization, with acknowledged possible fatal cases. The aim of our study was to assess the potential role of machine learning models, combining pre-treatment CT lung radiomic features with clinical data, to predict the risk of developing CIP. Methods: A cohort of 116 patients with different type advanced malignancies (81% lung cancer, 14% melanoma, 8% other solid tumors) treated with immune checkpoint inhibitors (ICIs) were retrospectively enrolled in this study and divided into a CIP (n = 35) and a control group (n = 81). Each patients had a chest CT scan at baseline and associated clinical covariates. Lungs and lobes were segmented semi-automatically on pre-treatment CT scans. We extracted radiomics features and selected them by the Minimum Redundancy Maximum Relevance (mRMR) method. Clinical-only, radiomics-only, and clinical-radiomics models (both lung- and lobe-based) were built using generalized linear (GLM) and random forest (RF) models to predict the occurrence of CIP. A nested 5-fold cross-validation scheme was used in the training, validation and test sets. Model performances were assessed in terms of Area under the Curve (AUC). Results: The most accurate clinical (RF) and the best radiomic (lobe-based GLM) models achieved AUCs of 0.72 (95%CI:0.51-0.92) and 0.71 (95%CI:0.58-0.84) on the test set, respectively. No statistically significant differences have been found between lung- and lobe-based radiomic models. The RF model combining lung-based radiomic and clinical features showed the best performances overall with an AUC of 0.75 (95%CI:0.62-0.88) on the test set. Conclusions: CT-based lung radiomic, and clinical models alone show moderate to good performances at identifying patients at risk of developing CIP. Nevertheless, we demonstrated the added value of machine learning models built combining clinical and radiomic features to better stratify the risk of developing CIP in patients with advanced malignancies treated with ICIs.

A novel three-biomarker signature (CA-62, CEA, CYFRA 21-1) and early-stage NSC lung cancer detection.

3038 Background: A double-blind clinical study was conducted on 304 clinically verified blood serum samples, including 141 non-small cell lung cancer (NSCLC), 133 healthy volunteers and 30 patients with chronic obstructive pulmonary disease (COPD). The objectives of the study were: The assessment of various cancer markers and their possible combinations for detection of early (I-II) stages of non-small cell lung cancer (NSCLC) and evaluation of the best cancer markers panel as a pre-screening tool for NSCLC before Low-Dose CT scan. Methods: Quantitative measurement of various tumor markers for serum samples was carried out using electrochemiluminescent immunoassays Elecsys CA-125, ELECSYS CA 19-9, ELECSYS CYFRA 21-1 and ELECSYS SCC (COBAS, Roche Diagnostics GmbH, Germany, EU), enzyme immunoassay CA15-3-ELISA-BEST, CEA-ELISA-BEST, NSE-ELISA-BEST (JSC "VECTOR-BEST", RF) and immunochemiluminescent analysis CLIA-CA-62 (LLC "JVS Diagnostics", RF). The results were compared to the literature data for other biomarkers and their panels. Results: Unlike other tumor markers, which are expressed proportionally to tumor size, the marker for epithelial carcinomas CA-62 is expressed from Stage I and demonstrated the highest diagnostic results in detecting early stages (I-II) of NSCLC. The biomarkers CYFRA 21-1 and SCC do not have sufficient sensitivity and specificity to diagnose asymptomatic lung cancer. However, the addition of cytokeratin 19 CYFRA 21-1 to glycoproteins CA-62 and CEA increases the specificity by eliminating false positive results, which significantly improves the diagnostic value of the cancer marker signature (CA-62, CEA and CYFRA 21-1): 100% Specificity, 93% Sensitivity, 100% PPV, 94% NPV and 94% test accuracy. Conclusions: The results of the study demonstrated that using the biomarkers signature (CA-62, CEA and CYFRA 21-1) allows increasing the specificity of CT scan for patients with suspicious abnormalities on the tomogram, improving the interpretation of visualized localized lesion, and improving the accuracy of differential diagnosis at detecting early stages of LC up to 94%. In the prospective, adding cancer markers panel signature (CA-62, CEA and CYFRA 21-1) to the current lung cancer risk assessment system as a pre-screening tool for LDCT may improve the quality of early-stage Lung cancer detection by significant increasing the sensitivity and by reducing the proportion of false positive results. [Table: see text]

OC-087 THE MESH AND THE MACHINE: DEEP LEARNING TO IDENTIFY HERNIA MESHES ON CT SCANS

Abstract Aim To identify the presence of meshes in CT scans of patients who have previously undergone hernia repairs using our novel deep learning algorithms. Materials & Methods We annotated mesh data from 30 ventral hernia CT scans of patients who had undergone previous ventral hernia repairs using computer vision tools. The annotated slices from the CT scans qualified as the ground truth. 80% of the data was used for training and validation. 20% data was used for testing. The novel deep learning model we used had previously identified meshes with lower accuracy levels due to inferior data sets. Results Our deep learning model was able to identify the mesh with an accuracy of 70% when compared to a manual annotator. Retraining our model with more varied mesh data gave us improved results. Conclusions Identification of the mesh is a challenge for surgeons and radiologists. The evolution of deep learning techniques made the identification of obscure structures possible. Meshes present a unique challenge as they constitute a foreign tissue which integrates into native tissue. Our team's novel deep learning algorithms have been effective in identifying meshes, This could play a major role in automated hernia reporting. The surgeon will be benefited from additional information about the plane of mesh and level of integration while planning surgeries for recurrence or mesh explantation.

Correlation between clinical and brain computed tomography findings of stroke patients: A cross-sectional study.

In developing countries, the burden of stroke is growing and causing significant morbidity and disability with high mortality rates. Neuroimaging plays a crucial role in differentiating ischemic stroke from an intracerebral hemorrhage, as well as entities other than stroke. This study sought to determine the correlation between the clinical and brain CT scan findings of stroke patients attending three hospitals in Kampala, Uganda. This was a cross-sectional study of clinically suspected stroke patients who were sent for brain CT scan at three selected hospitals in Kampala, Uganda. All brain CT scans of patients with suspected stroke were evaluated and the Alberta stroke program early CT score (ASPECTS) was used for middle cerebral artery (MCA) strokes. Univariate analysis was used to describe the clinico-demographic and brain CT features of stroke and summarized them as percentages. Bivariate and multivariate analysis were used to determine the adjusted odds ratios as a measure of association with a 95% confidence interval (CI). Of the 270 study participants, 141 (52.2%) were male. 162 (60%) had CT findings of stroke, and 90 (33.3%) had normal brain CT findings. Eighteen (6.7%) had other CT findings like tumor, dural hemorrhage, epidermoid cyst, and others. Ischemic stroke, hemorrhagic stroke, and subarachnoid hemorrhage accounted for 124 (45.9%), 34 (12.6%), and 4 (1.5%) respectively. Limb weakness (55.2%), headache (41.1%), and loss of consciousness (39.3%) were associated with stroke findings on CT. Among the acute ischemic strokes, 30 (73.2%) had a worse (0-7) ASPECT score. Those aged ≥65 years were associated with a worse ASPECTS [AOR: 22.01, (95% CI: 1.58-306.09) p = 0.021]. More than a third of patients with a clinical diagnosis of stroke had either no CT features of stroke or had other findings. The most commonly affected vascular territory was left MCA. Old age was strongly associated with having the worst ASPECTS score.

D-dimer correlation as a marker of coagulopathy with neuroimaging abnormalities on head CT scans of COVID-19 patients

Background: COVID-19 is a coronavirus disease with the main symptoms in the respiratory tract which can cause hypoxia. The duration and severity of the hypoxia can lead to oxidative stress that can affect the brain. The phenomenon of thromboembolism was also found in the form of an increase in coagulopathy markers which can cause abnormalities in small blood vessels in various organs, including the brain. Methods: We conducted a prospective crossectional study of 23 samples that met the inclusion criteria. Duration of hypoxia, degree of hypoxia based on oxygen saturation, and D-Dimer level as independent variables underwent Spearman correlation test with neuroimaging abnormalities on head CT scan. Results: The sample population consisted of 14 men (60.90%) and 9 women (39.10%), with the highest age group being 61 – 80 years old (56.50%) who underwent a head CT scan without contrast administration. Eighteen samples were found to have brain infarction, while 5 samples were without abnormalities. The Spearman correlation test found a correlation (p=0.003) with moderate correlation (r=0.558) between the D-Dimer level as a coagulopathy marker and abnormal findings on the head CT scan of samples. Where 15 samples with high D-Dimer levels (D-Dimer values above the normal level) and 3 samples with normal D-Dimer levels were found to have brain infarction. Meanwhile, the duration and degree of hypoxia based on the oxygen saturation value did not show a significant correlation with neuroimaging abnormalities (p>0.05). Conclusion: There was a moderate correlation between the D-Dimer value. No significant correlation was found between the duration of hypoxia and the degree of oxygen saturation toward neuroimaging abnormalities in COVID-19 patients.

Prediction of hemorrhagic transformation via pre-treatment CT radiomics in acute ischemic stroke patients receiving endovascular therapy.

This study aimed to extract radiomics features (RFs) from pre-treatment CT scans in patients with acute ischemic stroke (AIS), and to establish a radiomics model to predict hemorrhagic transformation (HT) after endovascular therapy (EVT). A total of 105 patients who were diagnosed with AIS [with occlusion of the M1 segment of the middle cerebral artery (MCA) and/or internal carotid artery] and received EVT were enrolled. They were randomly divided into the development cohort (n = 73) and the validation cohort (n = 32). The clinicoradiological data of all patients, including pre-treatment cranial CT without contrast enhancement, CT perfusion, and CT angiography, were obtained. The MCA territory on pre-treatment CT images was segmented to extract RFs associated with HT after EVT. Then, a CT radiomics model based on the selected RFs was constructed to predict HT after EVT. The sensitivity, specificity, and area under the curve of the CT radiomics model for predicting HT after EVT based on pre-treatment CT RFs was 0.806, 0.649, and 0.781 (95% confidence interval (CI): 0.675-0.886), respectively, in the development cohort. The sensitivity, specificity, and area under the curve in the validation cohort was 0.625, 0.875, and 0.797 (95% CI: 0.642-0.951), respectively. CT radiomics analysis is a valuable tool for predicting HT in AIS patients receiving EVT. It may guide the selection of patients in practice and improve procedural safety and effectiveness. Identification of the importance of pre-treatment CT radiomics in the prediction of HT in AIS patients after EVT.

Establishment of Age- and Sex-Specific Reference Cerebral Ventricle Volumes

The diagnosis of hydrocephalus is dependent on clinical symptoms and radiographic findings including ventriculomegaly. Our goal was to generate a data set of ventricular volume utilizing non-pathologic computed tomography (CT) scans for adults to help define reference ventricle size. We performed a retrospective analysis of non-contrast head CTs for adults at a single institution to identify patients who had undergone imaging and did not have a diagnosis of hydrocephalus, history of ventriculoperitoneal shunting, or treatments for hydrocephalus. A convolutional neural network was trained on hand-segmented scans from a variety of age ranges and then utilized to automate the segmentation of the entire data set. Ventricles on 866 CT scans were segmented to generate a reference range of volumes for both male and female individuals ranging in age from 18-99 years. The generated data were binned by age ranges. We have developed a convolutional neural network that can segment the ventricles on CT scans of adult patients over a range of ages. This network was used to measure the ventricular volume of non-pathologic head CTs to produce reference ranges for several age bins. This data set could be utilized to aid in the diagnosis of hydrocephalus by comparing potentially pathologic scans to reference ventricular volumes.

The application of gradient dose segmented analysis of in-vivo EPID images for patients undergoing VMAT in a resource-constrained environment.

The gamma analysis metric is a commonly used metric for VMAT plan evaluation. The major drawback of this is the lack of correlation between gamma passing rates and DVH values. The novel GDSAmean metric was developed by Steers etal. to quantify changes in the PTV mean dose (Dmean ) for VMAT patients. The aim of this work is to apply the GDSA retrospectively on head-and-neck cancer patients treated on the newly acquired Varian Halcyon, to assess changes in GDSAmean , and to evaluate the cause of day-to-day changes in the time-plot series. In-vivo EPID transmission images of head-and-neck cancer patients treated between August 2019 and July 2020 were analyzed retrospectively. The GDSAmean was determined for all patients treated. The changes in patient anatomy and rotational errors were quantified using the daily CBCT images and added to a time-plot with the daily change in GDSAmean . Over 97% of the delivered treatment fractions had a GDSAmean <3%. Thirteen of the patients received at least one treatment fraction where the GDSAmean >3%. Most of these deviations occurred for the later fractions of radiotherapy treatment. Additionally, 92% of these patients were treated for malignancies involving the larynx and oropharynx. Notable deviations in the effective separation diameters were observed for 62% of the patients where the change in GDSAmean >3%. For the other five cases with GDSAmean <3%, the mean pitch, roll, and yaw rotational errors were 0.90°, 0.45°, and 0.43°, respectively. A GDSAmean >3% was more likely due to a change in separation, whereas a GDSAmean <3% was likely caused by rotational errors. Pitch errors were shown to be the most dominant. The GDSAmean is easily implementable and can aid in scheduling new CT scans for patients before significant deviations in dose delivery occur.

The Swirl Sign and Its Relationship to Patient Outcomes in Extradural Hematomas: A Retrospective Investigation

Introduction:" Swirl sign" is a relatively uncommon sign, seen on non-contrast CT scans in patients with head trauma who develop extradural hematoma the prognostic significance of which is controversial. This research aims to evaluate the predictive significance of the swirl sign in patients with an extradural hemorrhage. Material and Methods: It was a retrospective study and included 145 patients, with traumatic brain injury, who underwent surgical treatment at the Department of Neurosurgery Holy Family Hospital, Rawalpindi, Pakistan, between January 2022 and January 2023 and had traumatic EDH identified by computed tomography scan. Patients who did not undergo surgery or had combined or open craniocerebral injuries were eliminated. A Glasgow Outcome Scale score was used to evaluate outcomes after three months of traumatic brain injury. Mann-Whitney U test, the Chi-square test, and multivariate logistic regression were applied for descriptive and inferential analysis. Results: A total of 145 cases were evaluated, 19 (13.1%) demonstrated the sign on a CT scan of the brain. Analysis displayed a notable association between the incidence of the swirl sign and pupillary size, preoperative Glasgow Coma Scale score, time between trauma and first CT scan, and volume of hematoma measured intraoperatively. Patients displaying the swirl sign showed an increased mortality rate (25%) compared to patients without the swirl sign (5%) and worse outcomes at 3 months. Conclusion: The presence of the swirl sign on the CT scan had a significant association with worse outcomes. Early detection and prompt surgical evacuation are important for patients with this sign.

325 Evaluating the Utility of Repeat CT Scans in Patients With Isolated Traumatic Subarachnoid Hemorrhage

INTRODUCTION: Subarachnoid hemorrhage (SAH) is a relatively common consequence of head trauma. Treatment of traumatic SAH (tSAH) patients often involves serial CT imaging to assess for expansile hemorrhage. However, evidence suggests that tSAH rarely leads to neurologic deterioration or necessitates neurosurgical intervention, particularly in patients who present neurologically intact. Thus, the current standard of care may be overly extensive. METHODS: Patients presenting to Mass General Brigham Hospitals with tSAH between 2000 and 2021 were eligible for inclusion. Patients were excluded if: (a) their SAH was non-traumatic, (b) they experienced another form of intracerebral hemorrhage, or (c) they had a documented GCS of 12 or lower. Univariate and multivariate regression models were used to analyze the retrospectively collected data. RESULTS: Overall, 293 patients were included, 130 of which were male. The most common mechanism of trauma was fall from standing (n = 171). 131 patients presented with other significant traumatic injuries in addition to their tSAH. The mean number of total CTs for all patients was 2.3, with 230 patients (78.50%) receiving at least two CT scans and 92 patients (31.40%) receiving three or more scans. Of the 230 patients receiving multiple scans, only 12 (5.22%) had any degree of documented hemorrhagic expansion. No patients developed acute neurologic deterioration or required neurosurgical intervention related to their bleed. CONCLUSIONS: The standard of care for tSAH patients centers on close observation and repeat CT imaging. However, these repeat CTs rarely demonstrate any degree of hemorrhagic expansion for patients who present neurologically intact. Excessive CT imaging is thus unlikely to affect patient management and potentially presents an unnecessary burden to both the patient and the hospital staff.

3D-Printed Iodine-Ink CT Phantom for Radiomics Feature Extraction - Advantages and Challenges.

To test and validate novel CT techniques, such as texture analysis in radiomics, repeat measurements are required. Current anthropomorphic phantoms lack fine texture and true anatomic representation. 3D-printing of iodinated ink on paper is a promising phantom manufacturing technique. Previously acquired or artificially created CT data can be used to generate realistic phantoms. To present the design process of an anthropomorphic 3D-printed iodine ink phantom, highlighting the different advantages and pitfalls in its use. To analyze the phantom's x-ray attenuation properties, and the influences of the printing process on the imaging characteristics, by comparing it to the original input dataset. Two patient CT scans and artificially generated test patterns were combined in a single dataset for phantom printing and cropped to a size of 26×19×30 cm3 . This DICOM dataset was printed on paper using iodinated ink. The phantom was CT-scanned and compared to the original image dataset used for printing the phantom. The water-equivalent diameter of the phantom was compared to that of a patient cohort (N = 104). Iodine concentrations in the phantom were measured using dual-energy CT. 86 radiomics features were extracted from 10 repeat phantom scans and the input dataset. Features were compared using a histogram analysis and a PCA individually and overall, respectively. The frequency content was compared using the normalized spectrum modulus. Low density structures are depicted incorrectly, while soft tissue structures show excellent visual accordance with the input dataset. Maximum deviations of around 30 HU between the original dataset and phantom HU values were observed. The phantom has x-ray attenuation properties comparable to a lightweight adult patient (∼54 kg, BMI 19 kg/m2 ). Iodine concentrations in the phantom varied between 0 and 50 mg/ml. PCA of radiomics features shows different tissue types separate in similar areas of PCA representation in the phantom scans as in the input dataset. Individual feature analysis revealed systematic shift of first order radiomics features compared to the original dataset, while some higher order radiomics features did not. The normalized frequency modulus |f(ω)| of the phantom data agrees well with the original data. However, all frequencies systematically occur more frequently in the phantom compared to the maximum of the spectrum modulus than in the original data set, especially for mid-frequencies (e.g. for ω = 0.3942 mm-1 , |f(ω)|original = 0.09 * |fmax |original and |f(ω)|phantom = 0.12 * |fmax |phantom ). 3D-iodine-ink-printing technology can be used to print anthropomorphic phantoms with a water-equivalent diameter of a lightweight adult patient. Challenges include small residual air enclosures and the fidelity of HU values. For soft tissue, there is a good agreement between the HU values of the phantom and input data set. Radiomics texture features of the phantom scans are similar to the input data set, but systematic shifts of radiomics features in first order features, due to differences in HU values, need to be considered. The paper substrate influences the spatial frequency distribution of the phantom scans. This phantom type is of very limited use for dual-energy CT analyses. This article is protected by copyright. All rights reserved.