Sequential CT Research Articles

Optimising ovarian tumor classification using a novel CT sequence selection algorithm

Gynaecological cancers, especially ovarian cancer, remain a critical public health issue, particularly in regions like India, where there are challenges related to cancer awareness, variable pathology, and limited access to screening facilities. These challenges often lead to the diagnosis of cancer at advanced stages, resulting in poorer outcomes for patients. The goal of this study is to enhance the accuracy of classifying ovarian tumours, with a focus on distinguishing between malignant and early-stage cases, by applying advanced deep learning methods. In our approach, we utilized three pre-trained deep learning models—Xception, ResNet50V2, and ResNet50V2FPN—to classify ovarian tumors using publicly available Computed Tomography (CT) scan data. To further improve the model’s performance, we developed a novel CT Sequence Selection Algorithm, which optimises the use of CT images for a more precise classification of ovarian tumours. The models were trained and evaluated on selected TIFF images, comparing the performance of the ResNet50V2FPN model with and without the CT Sequence Selection Algorithm. Our experimental results show the Comparative evaluation against the ResNet50V2 FPN model, both with and without the CT Sequence Selection Algorithm, demonstrates the superiority of the proposed algorithm over existing state-of-the-art methods. This research presents a promising approach for improving the early detection and management of gynecological cancers, with potential benefits for patient outcomes, especially in areas with limited healthcare resources.

Imaging of Cervical Spine Trauma: Update of Techniques and Clinical Relevance.

Imaging of cervical spine trauma most commonly begins with computed tomography (CT) for initial osseous and basic soft tissue evaluation, followed by magnetic resonance imaging (MRI) for complementary evaluation of the neural structures (i.e., spinal cord, nerves) and soft tissues (i.e., ligaments). Although CT and conventional MRI sequences have been the mainstay of trauma imaging for decades, there have been significant advances in CT processing, imaging sequences and techniques made possible by hardware and software development, and artificial intelligence. These advancements may provide advantages in increasing sensitivity for detection of pathology as well as in decreasing imaging and interpretation time. Unquestionably, the most important role of imaging is to provide information to help direct patient care, including diagnosis, next steps in treatment plan, and prognosis. As such, there has been a growing body of research investigating the clinical relevance of imaging findings to clinical outcomes in the setting of spinal cord injury. This article will focus on these recent advances in imaging of cervical spinal trauma.

A Cross Spatio-Temporal Pathology-based Lung Nodule Dataset

Recently, intelligent analysis of lung nodules with the assistant of computer aided diagnosis (CAD) techniques can improve the accuracy rate of lung cancer diagnosis. However, existing CAD systems and pulmonary datasets mainly focus on Computed Tomography (CT) images from one single period, while ignoring the cross spatio-temporal features associated with the progression of nodules contained in imaging data from various captured periods of lung cancer. If the evolution patterns of nodules across various periods in the patients’ CT sequences can be explored, it will play a crucial role in guiding the precise screening identification of lung cancer. Therefore, a cross spatio-temporal lung nodule dataset with pathological information for nodule identification and diagnosis is constructed, which contains 317 CT sequences and 2,295 annotated nodules from 109 patients. This comprehensive database is intended to drive research in the field of CAD towards more practical and robust methods, and also contribute to the further exploration of precision medicine related field. To ensure patient confidentiality, we have removed sensitive information from the dataset.

Expression of Tenascin-C Is Upregulated in the Early Stages of Radiation Pneumonitis/Fibrosis in a Novel Mouse Model.

The lung is a major dose-limiting organ for radiation therapy (RT) for cancer in the thoracic region, and the clarification of radiation-induced lung damage (RILD) is important. However, there have been few reports containing a detailed comparison of radiographic images with the pathological findings of radiation pneumonitis (RP)/radiation fibrosis (RF). We recently reported the upregulated expression of tenascin-C (TNC), an inflammation-associated extracellular matrix molecule, in surgically resected lung tissue, and elevated serum levels were elevated in a RILD patient. Therefore, we have developed a novel mouse model of partial lung irradiation and studied it with special attention paid to the computed tomography (CT) images and immunohistological findings. The right lungs of mice (BALB/c) were irradiated locally at 30 Gy/1fr, and the following two groups were created. In Group 1, sequential CT was performed to confirm the time-dependent changes in RILD. In Group 2, the CT images and histopathological findings of the lung were compared. RP findings were detected histologically at 16 weeks after irradiation; they were also observed on the CT images from 20 weeks. The immunostaining of TNC was observed before the appearance of RP on the CT images. The findings suggest that TNC could be an inflammatory marker preceding lung fibrosis.

Computed Tomography Optic Nerve Sheath Diameter-to-Eyeball Transverse Diameter Ratio as a Novel Noninvasive Parameter for Prognostication in Traumatic Brain Injury.

Background Traumatic brain injury (TBI) remains a foremost cause of death and disability globally, with elevated intracranial pressure (ICP) being a crucial factor in patient outcomes. While invasive monitoring is the gold standard for assessing ICP, it carries risks and is not always feasible. This study proposes a novel noninvasive parameter using computed tomography (CT) imaging. Aims and objectives The study aims to determine the efficacy of the optic nerve sheath diameter (ONSD)-to-eyeball transverse diameter (ETD) ratio from CT scans in predicting TBI patients' prognosis. The primary objective is to study the ONSD/ETD ratio's efficacy in assessing TBI's severity. The secondary objective is to correlate the ONSD/ETD ratio with the Glasgow Coma Scale (GCS) and Rotterdam computed tomography scoring (RCTS) and assess its clinical benefit. Materials and methods This combined retrospective and prospective analytical study included 308 consecutive patients who underwent CT imaging for TBI at a tertiary care center with a dedicated trauma and neurosurgical unit. We evaluated bilateral ONSD and ETD using axial CT scans. The ONSD/ETD ratio correlated with the GCS, RCTS, and clinical outcomes. Results The cut-off values for elevated ICP were ONSD of >5.17 mm, ETD of <22.2 mm, and ONSD/ETD ratio of >0.21. Variables between GCS (<12 and >12) and the ONSD/ETD ratio (<0.21 and >0.21) were statistically significant (chi-square {χ2} = 18.52, p = 0.000). The ONSD shows a strong positive correlation with RCTS (r = 0.82, p = 0.01), ETD shows a moderate negative correlation with RCTS (r = -0.50), and the ONSD/ETD ratio shows a strong negative correlation with GCS (r = -0.783, p = 0.01). The area under the curve for the ONSD/ETD ratio (0.920) was higher than that for ONSD (0.932) and ETD (0.490). The ONSD/ETD ratio's sensitivity, specificity, positive predictive value, and negative predictive value were 100%, 95.6%, 72.0%, and 100%, respectively, demonstrating that it is an excellent predictor of raised ICP. Conclusion The CT-ONSD/ETD ratio correlates with the severity of TBI as assessed by GCS and RCTS. It could serve as a noninvasive parameter for monitoring ICP and guiding the need for sequential CT in TBI patients, potentially aiding in prognostication and clinical management.

Evaluation of Progressive Architectural Distortion in Idiopathic Pulmonary Fibrosis Using Deformable Registration of Sequential CT Images.

Monitoring the progression of idiopathic pulmonary fibrosis (IPF) using CT primarily focuses on assessing the extent of fibrotic lesions, without considering the distortion of lung architecture. To evaluate three-dimensional average displacement (3D-AD) quantification of lung structures using deformable registration of serial CT images as a parameter of local lung architectural distortion and predictor of IPF prognosis. Patients with IPF evaluated between January 2016 and March 2017 who had undergone CT at least twice were retrospectively included (n = 114). The 3D-AD was obtained by deformable registration of baseline and follow-up CT images. A computer-aided quantification software measured the fibrotic lesion volume. Cox regression analysis evaluated these variables to predict mortality. The 3D-AD and the fibrotic lesion volume change were significantly larger in the subpleural lung region (5.2 mm (interquartile range (IQR): 3.6-7.1 mm) and 0.70% (IQR: 0.22-1.60%), respectively) than those in the inner region (4.7 mm (IQR: 3.0-6.4 mm) and 0.21% (IQR: 0.004-1.12%), respectively). Multivariable logistic analysis revealed that subpleural region 3D-AD and fibrotic lesion volume change were independent predictors of mortality (hazard ratio: 1.12 and 1.23; 95% confidence interval: 1.02-1.22 and 1.10-1.38; p = 0.01 and p < 0.001, respectively). The 3D-AD quantification derived from deformable registration of serial CT images serves as a marker of lung architectural distortion and a prognostic predictor in patients with IPF.

Low-iodine-dose computed tomography coupled with an artificial intelligence-based contrast-boosting technique in children: a retrospective study on comparison with conventional-iodine-dose computed tomography

BackgroundLow-iodine-dose computed tomography (CT) protocols have emerged to mitigate the risks associated with contrast injection, often resulting in decreased image quality.ObjectiveTo evaluate the image quality of low-iodine-dose CT combined with an artificial intelligence (AI)-based contrast-boosting technique in abdominal CT, compared to a standard-iodine-dose protocol in children.Materials and methodsThis single-center retrospective study included 35 pediatric patients (mean age 9.2 years, range 1–17 years) who underwent sequential abdominal CT scans—one with a standard-iodine-dose protocol (standard-dose group, Iobitridol 350 mgI/mL) and another with a low-iodine-dose protocol (low-dose group, Iohexol 240 mgI/mL)—within a 4-month interval from January 2022 to July 2022. The low-iodine CT protocol was reconstructed using an AI-based contrast-boosting technique (contrast-boosted group). Quantitative and qualitative parameters were measured in the three groups. For qualitative parameters, interobserver agreement was assessed using the intraclass correlation coefficient, and mean values were employed for subsequent analyses. For quantitative analysis of the three groups, repeated measures one-way analysis of variance with post hoc pairwise analysis was used. For qualitative analysis, the Friedman test followed by post hoc pairwise analysis was used. Paired t-tests were employed to compare radiation dose and iodine uptake between the standard- and low-dose groups.ResultsThe standard-dose group exhibited higher attenuation, contrast-to-noise ratio (CNR), and signal-to-noise ratio (SNR) of organs and vessels compared to the low-dose group (all P-values < 0.05 except for liver SNR, P = 0.12). However, noise levels did not differ between the standard- and low-dose groups (P = 0.86). The contrast-boosted group had increased attenuation, CNR, and SNR of organs and vessels, and reduced noise compared with the low-dose group (all P < 0.05). The contrast-boosted group showed no differences in attenuation, CNR, and SNR of organs and vessels (all P > 0.05), and lower noise (P = 0.002), than the standard-dose group. In qualitative analysis, the contrast-boosted group did not differ regarding vessel enhancement and lesion conspicuity (P > 0.05) but had lower noise (P < 0.05) and higher organ enhancement and artifacts (all P < 0.05) than the standard-dose group. While iodine uptake was significantly reduced in low-iodine-dose CT (P < 0.001), there was no difference in radiation dose between standard- and low-iodine-dose CT (all P > 0.05).ConclusionLow-iodine-dose abdominal CT, combined with an AI-based contrast-boosting technique exhibited comparable organ and vessel enhancement, as well as lesion conspicuity compared to standard-iodine-dose CT in children. Moreover, image noise decreased in the contrast-boosted group, albeit with an increase in artifacts.Graphical

ViTR-SP: A CT-based vision transformer model for prediction of pneumonitis in patients with non-small cell lung cancer who received thoracic radiotherapy and immunotherapy.

e20034 Background: The combination of thoracic radiotherapy (TRT) and immunotherapy accompanied by the accumulation of pulmonary toxicity. This study aimed to construct a deep radiomics model based on sequential computed tomography (CT) images for radioimmune-associated pneumonitis prediction in non-small cell lung cancer (NSCLC) patients treated with TRT and immune checkpoint inhibitors (ICIs). Methods: Three models were constructed based on a vision transformer (ViT) network using sequential chest CT images, tumor-mask and lung-mask as inputs, respectively. NSCLC patients treated with TRT and ICIs at Shandong Cancer Hospital and Qilu Hospital were included for model training and external validation, respectively. The image-level model was trained based on ViT to identify predictive images, and the image-level probability distribution were performed. Then, a ViT-Recursive Neural Network (ViT-RNN) model framework was constructed at the patient-level to integrate the image-level information to derive the final prediction, which was evaluated in an independent validation cohort. Results: A total of 230 NSCLC patients from Shandong Cancer Hospital were randomly assigned in a 7:3 ratio for model training and validation, respectively. Another 42 patients from Qilu Hospital were used as an independent external validation cohort. The ViT-RNN model for symptomatic pneumonitis (ViTR-SP) based on sequential CT images achieved better area under curve (AUC), sensitivity, and specificity of 0.725, 0.750, and 0.886 than lung-mask based model (0.575, 0.625, and 0.705) and tumor-mask based model (0.658, 0.667, and 0.773) in the internal validation set. In the external validation set, the proposed model achieved the AUC, sensitivity, and specificity of 0.654, 0.818, 0.727, while the lung-mask and tumor-mask based models had the AUC, sensitivity, and specificity of 0.540, 0.545, 0.677, and 0.630, 0.545, 0.742, respectively. The performance of the model was improved after integrating clinical features, with internal and external validation AUC of 0.782 and 0.672, respectively. Conclusions: In conclusion, the clinical combined ViTR-SP neural network risk prediction model based on sequential chest CT images can be used for predicting the risk of developing radioimmune-associated pneumonitis in NSCLC patients.

Increasing the rate of datasets amenable to CTFFR and quantitative plaque analysis: Value of software for reducing stair-step artifacts demonstrated in photon-counting detector CT

PurposeTo determine the value of an algorithm for reducing stair-step artifacts for advanced coronary analyses in sequential mode coronary CT angiography (CCTA). MethodsForty patients undergoing sequential mode photon-counting detector CCTA with at least one stair-step artifact were included. Twenty patients (14 males; mean age 57±17years) with 45 segments showing stair-step artifacts and without atherosclerosis were included for CTFFR analysis. Twenty patients (20 males; mean age 74±13years) with 22 segments showing stair-step artifacts crossing an atherosclerotic plaque were included for quantitative plaque analysis. Artifacts were graded, and CTFFR and quantitative coronary plaque analyses were performed in standard reconstructions and in those reconstructed with a software (entitled ZeeFree) for artifact reduction. ResultsStair-step artifacts were significantly reduced in ZeeFree compared to standard reconstructions (p<0.05). In standard reconstructions, CTFFR was not feasible in 3/45 (7 %) segments but was feasible in all ZeeFree reconstructions. In 9/45 (20 %) segments without atherosclerosis, the ZeeFree algorithm led to a change of CTFFR values from pathologic in standard to physiologic values in ZeeFree reconstructions. In one segment (1/22, 5 %), quantitative plaque analysis was not feasible in standard but only in ZeeFree reconstruction. The mean overall plaque volume (111±60 mm3), the calcific (77±47 mm3), fibrotic (31±28 mm3), and lipidic (4±3 mm3) plaque components were higher in standard than in ZeeFree reconstructions (overall 75±50 mm3, p<0.001; calcific 51±42 mm3, p<0.001; fibrotic 22±19 mm3, p<0.05; lipidic 3±3 mm3, p=0.055). ConclusionDespite the lack of reference standard modalities for CTFFR and coronary plaque analysis, initial evidence indicates that an algorithm for reducing stair-step artifacts in sequential mode CCTA increases the rate and quality of datasets amenable to advanced coronary artery analysis, hereby potentially improving patient management.

Does on-site computed tomography matter? A cross-sectional study of stroke patients' door-to-scan-time in rural hospitals.

Many rural Australian hospitals lack on-site computed tomography (CT). These hospitals often refer patients to local off-site private radiology clinics or to central hospitals, challenging the achievement of time-sensitive scans. For stroke patients, timely access to CT affects treatment options. This study questions whether on-site CT matters in rural hospitals by investigating stroke patients' door-to-scan-time (DTST) and CT scan sequence referrals. A retrospective chart audit was completed across four rural hospitals; two with on-site CT and two without. Adult emergency stroke presentations were randomly sampled. Comparisons between on-site and off-site CT hospitals were made for DTST and CT sequence referrals using Mann-Whitney U-tests and Fisher's exact tests. A total of 120 charts were audited (on-site CT, n = 60; off-site CT, n = 60). DTST was longer for off-site vs. on-site CT hospitals (median = 4.30 h vs. median = 0.70 h; U = 338, p < 0.001) regardless of whether presentations occurred in business hours or out of hours (p < 0.001). Off-site CT hospitals ordered less CT angiography or perfusion scanning (32% vs. 85%, p < 0.001). Off-site CT hospital patients had longer DTST and received less angiography or perfusion scanning. These findings suggest that on-site CT matters to rural stroke patients by improving equitable access to CT and appropriate scan referrals.

2.5D UNet with context-aware feature sequence fusion for accurate esophageal tumor semantic segmentation

Segmenting esophageal tumor from computed tomography (CT) sequence images can assist doctors in diagnosing and treating patients with this malignancy. However, accurately extracting esophageal tumor features from CT images often present challenges due to their small area, variable position, and shape, as well as the low contrast with surrounding tissues. This results in not achieving the level of accuracy required for practical applications in current methods. To address this problem, we propose a 2.5D context-aware feature sequence fusion UNet (2.5D CFSF-UNet) model for esophageal tumor segmentation in CT sequence images. Specifically, we embed intra-slice multiscale attention feature fusion (Intra-slice MAFF) in each skip connection of UNet to improve feature learning capabilities, better expressing the differences between anatomical structures within CT sequence images. Additionally, the inter-slice context fusion block (Inter-slice CFB) is utilized in the center bridge of UNet to enhance the depiction of context features between CT slices, thereby preventing the loss of structural information between slices. Experiments are conducted on a dataset of 430 esophageal tumor patients. The results show an 87.13% dice similarity coefficient, a 79.71% intersection over union and a 2.4758 mm Hausdorff distance, which demonstrates that our approach can improve contouring consistency and can be applied to clinical applications.

A deep learning-based automatic image quality assessment method for respiratory phase on computed tomography chest images.

Computed tomography (CT) chest scans have become commonly used in clinical diagnosis. Image quality assessment (IQA) for CT images plays an important role in CT examination. It is worth noting that IQA is still a manual and subjective process, and even experienced radiologists make mistakes due to human limitations (fatigue, perceptual biases, and cognitive biases). There are also kinds of biases because of poor consensus among radiologists. Excellent IQA methods can reliably give an objective evaluation result and also reduce the workload of radiologists. This study proposes a deep learning (DL)-based automatic IQA method, to assess whether the image quality of respiratory phase on CT chest images are optimal or not, so that the CT chest images can be used in the patient's physical condition assessment. This retrospective study analysed 212 patients' chest CT images, with 188 patients allocated to a training set (150 patients), validation set (18 patients), and a test set (20 patients). The remaining 24 patients were used for the observer study. Data augmentation methods were applied to address the problem of insufficient data. The DL-based IQA method combines image selection, tracheal carina segmentation, and bronchial beam detection. To automatically select the CT image containing the tracheal carina, an image selection model was employed. Afterward, the area-based approach and score-based approach were proposed and used to further optimize the tracheal carina segmentation and bronchial beam detection results, respectively. Finally, the score about the image quality of the patient's respiratory phase images given by the DL-based automatic IQA method was compared with the mean opinion score (MOS) given in the observer study, in which four blinded experienced radiologists took part. The DL-based automatic IQA method achieved good performance in assessing the image quality of the respiratory phase images. For the CT sequence of the same patient, the DL-based IQA method had an accuracy of 92% in the assessment score, while the radiologists had an accuracy of 88%. The Kappa value of the assessment score between the DL-based IQA method and radiologists was 0.75, with a sensitivity of 85%, specificity of 91%, positive predictive value (PPV) of 92%, negative predictive value (NPV) of 93%, and accuracy of 88%. This study develops and validates a DL-based automatic IQA method for the respiratory phase on CT chest images. The performance of this method surpassed that of the experienced radiologists on the independent test set used in this study. In clinical practice, it is possible to reduce the workload of radiologists and minimize errors caused by human limitations.

Manufacturing errors detection and visualization system for parts with complex internal cavity structural through industrial CT image

To address the challenge of detection and visualization manufacturing errors in part with complex internal cavity structures, this paper present a method based on industrial computed tomography (CT) image. First, a two-dimensional (2D) CT sequence slice images are obtained using fan-beam high energy industrial CT scanning, which is then reconstructed into a three-dimensional (3D) measurement model. The measurement 3D model is registered with the original design model, and the surface information of the part is obtained by segmenting the surfaces. The error between reference surface of original design model and test surface of measurement model is calculated. By comparing the error value with the tolerance, the detection result is obtained and analyzed. The developed software system enables areas that do not meet the tolerance requirements to be visualized. This method can detect manufacturing errors for part with complex internal cavity structures, such as planeness error, cylindricity error, verticality error, and parallelism error. This method provides a basis for assessing the manufacturing and processing quality of part with complex internal cavity structures.

Sequential Brain CT Image Captioning Based on the Pre-Trained Classifiers and a Language Model

Intracerebral hemorrhage (ICH) is a severe cerebrovascular disorder that poses a life-threatening risk, necessitating swift diagnosis and treatment. While CT scans are the most effective diagnostic tool for detecting cerebral hemorrhage, their interpretation typically requires the expertise of skilled professionals. However, in regions with a shortage of such experts or situations with time constraints, delays in diagnosis may occur. In this paper, we propose a method that combines a pre-trained CNN classifier and GPT-2 to generate text for sequentially acquired ICH CT images. Initially, CNN undergoes fine-tuning by learning the presence of ICH in publicly available single CT images, and subsequently, it extracts feature vectors (i.e., matrix) from 3D ICH CT images. These vectors are input along with text into GPT-2, which is trained to generate text for consecutive CT images. In experiments, we evaluated the performance of four models to determine the most suitable image captioning model: (1) In the N-gram-based method, ReseNet50V2 and DenseNet121 showed relatively high scores. (2) In the embedding-based method, DenseNet121 exhibited the best performance. (3) Overall, the models showed good performance in BERT score. Our proposed method presents an automatic and valuable approach for analyzing 3D ICH CT images, contributing to the efficiency of ICH diagnosis and treatment.

Experimental assessment of diffusible iodine-based contrast-enhanced computed tomography (diceCT) protocols.

Diffusible iodine-based contrast-enhanced computed tomography (diceCT) is an increasingly used digital complement, supplement, or alternative to traditional dissection-based anatomical research. The diceCT protocol, which has evolved and expanded over the past decade, employs passive diffusion of Lugol's iodine (KI3) to increase soft tissue radiodensity and improve structure contrast in the CT or microCT imaging of specimens. The development and application of diceCT has focused largely on specimens under 1 kg, and the varying reporting of methods on studies of both small and large specimens has initiated, but not yet established, an effective diceCT protocol for larger specimens based on monitored experiments of several fundamental variables (e.g., Lugol's iodine concentration, duration, and impacts of Lugol's iodine on tissues). In this study, we have experimentally assessed the efficacy of diceCT protocols for imaging whole-body specimens of the 1-4.5 kg Australian brushtail possum (Trichosurus vulpecula) using sequential CT imaging assessment across experimental conditions. We assessed the impact of varying Lugol's iodine concentration, the presence/absence of skin, solution volume and agitation on tissue radiodensity changes through weekly CT-based monitoring of tissue radiodensities over an 8-week experimental period. We have also quantified tissue volumetric changes across our experiment to assess the impact of diceCT applications on subsequent analyses of imaging datasets. Our results indicate that substantial changes in both soft-tissue radiodensity and soft-tissue volume occur within the first 28 days of Lugol's iodine treatment, followed by a slower rate of progressive soft-tissue radiodensity and volume changes across the experiment duration. Our results demonstrate the negligible benefit of skinning larger specimens to improve solution diffusion, and document significant soft-tissue volumetric changes with high concentration solutions (e.g., 10%) and long-duration exposure (e.g., beyond 5 weeks) that should guide individual diceCT protocol design and/or quantification and analysis for mammal specimens above 1 kg.

Comparison between ultrasonography and computed tomography for measuring skeletal muscle mass in critically ill patients with different body mass index

Among critical patients, there is an early onset of changes in both the quantity and quality of muscle mass. It is essential to find tools that promptly identify this muscle mass loss. The aim of this study was to compare the ultrasonography of the quadriceps femoris to the gold standard, thigh computed tomography (CT) for assessing the musculature of critically ill patients with different body mass index who have suffered traumatic brain injury. This is a prospective validation study in an Intensive Care Unit (ICU) specialized in trauma care, located at a tertiary teaching hospital. Our study involved a convenience sample of patients. Sequential ultrasound and CT scans were performed at three distinct time intervals: upon admission, between 24 and 96h' post-admission, and finally, between 96 and 168h' post-admission. For all ultrasound measurements, we conducted simultaneous quadriceps CT measurements. The correlation between measurements obtained by ultrasound and computed tomography at three different times and in three BMI ranges was analyzed, in individuals with normal weight, overweight and obese. Results: We analyzed 252 images in 49 patients in time 1, 40 patients in time 2, and 37 in time 3 to compare the thickness quadriceps muscle using US and CT. Of these, 18 patients had a BMI ≤ 24.9kg/m2 (normal weight), 18 patients from 25 to 29.9kg/m2 (overweight), and 8 patients had a BMI ≥ 30kg/m2 (obese). The mean age was 37 years, the majority (94%) were male and the main comorbidities were: hypertension 12%, diabetes 4% and 14% smoking. The results revealed minor discrepancies between measurements obtained through the two methods, these changes were not influenced by the body mass index, with these variations being practically insignificant in the context of clinical application. Thus, the correlation and concordance between the values obtained found a strong positive correlation with good limits of agreement. The Spearman's correlation coefficients obtained were r=0.89, 0.91 and 0.88, p<0.01at T1, T2 and T3 respectively for normal weight, r=0.91, 0.80 and 0.81, p<0.01at T1, T2 and T3 respectively for overweight and r=0.89, 0.94 and 0.84, p<0.01at T1, T2 and T3 respectively for obesity. In addition to a positive correlation, we observed a high agreement between the methods. The Bland & Altman analysis at time 1 showed, respectively, the bias of 1.46, 2.03 and 0.76. At time 2, the bias was 0.42, 3.11 and 2.12. At time 3, the bias was 2.26, 3.38 and 2.11mm. Our findings suggest that measure femoral quadriceps muscle thickness ultrasound-based exhibits a comparable performance to thigh CT. This conclusion stems from the excellent correlation and good agreement observed between ultrasound and CT, which is considered the gold standard for muscle assessment in critically ill patients. This clinical trial is registered at REBEC https://ensaiosclinicos.gov.br/ identifier: RBR-2bzspnz. The protocol was approved, on July 30, 2019, by the Research Ethics Committee of the Hospital das Clínicas, Faculdade de Medicina de Ribeirão Preto - Trial Registration Number: 3,475,851.

THU526 Long-term Follow-up Of A Functional Gastroenteropancreatic Tumor Secreting Both Somatostatin And Gastrin

Abstract Disclosure: R. Zafar: None. J. Syeda: None. K. Rajamani: None. Introduction: There have been fewer than 200 cases of somatostatinoma reported since 1977. We report a case of a functional Gastroenteropancreatic neuroendocrine tumor (GEP-NET) secreting both somatostatin and gastrin. Clinical Case: A 55-year-old male with diabetes mellitus, chronic diarrhea, gastric ulcer, and cholelithiasis was referred to the endocrinology clinic for poorly controlled diabetes mellitus. He had a history of B-cell lymphoma of the thyroid treated by chemotherapy and radiation. He was found to have two paraduodenal masses by PET CT scan during surveillance for recurrence of lymphoma. Laboratory evaluation revealed elevated levels of insulin, glucagon, gastrin, somatostatin, and chromogranin A on multiple occasions both fasting and random. Ultrasound-guided endoscopic biopsy of the paraduodenal nodules was initially read as a neuroendocrine tumor. Subsequent immunohistochemical staining was positive for both somatostatin and gastrin. The stains were negative for glucagon and insulin. The Ki-67 proliferation index was less than 1%. The patient declined surgical treatment of the paraduodenal tumors. He underwent cholecystectomy for cholelithiasis. Gastric ulcers resolved with the use of proton pump inhibitors. His blood glucose was well controlled with insulin. Sequential CT/PET scans showed no change in the size of paraduodenal masses over a follow-up period of 14 years. Conclusion: GEP-NETs may secrete more than one type of hormone simultaneously. This case highlights a functional GEP-NET with clinical manifestations due to elevated levels of somatostatin and gastrin. Characterization of GEP-NETs by immuno-histochemical staining and hormonal analysis may help direct clinical management. Although many GEP-NETs are managed surgically, this case suggests that the Ki-67 proliferation index and sequential PET or CT scans may guide the nonsurgical management of functional GEP-NETs.

Relationship of Sarcopenia to Tolerance of Chemotherapy During Definitive Treatment of Primary Anal Squamous Cell Carcinoma

Primary therapy for anal squamous cell carcinoma consists of definitive radiation with concurrent chemotherapy, most commonly 5-fluorouracil and mitomycin-C. This regimen is associated with significant cutaneous, hematologic, and gastrointestinal toxicity. Sarcopenia, or low skeletal muscle mass, has been described as an adverse prognostic factor and predictor of poor tolerance to therapy across a range of malignancies, including breast, gynecologic and head and neck cancers. Here, we sought to determine if the presence of sarcopenia at the initiation of therapy is associated with increased rates of toxicity and poorer tolerance of definitive treatment for primary anal squamous cell carcinoma. This was a review of patients treated with curative intent for anal squamous cell carcinoma at a single academic medical center between 2007 and 2017, median follow up was 38 months. Analysis was limited to patients receiving definitive radiation with concurrent chemotherapy. Sarcopenia was objectively defined, using previously validated methods, as a skeletal muscle index (SMI) <41 cm2/m2 for female patients regardless of body mass index (BMI); for male patients sarcopenia was defined as SMI<43cm2/m2 for BMI<25 and <53 cm2/m2 for BMI >25. SMI calculations were performed using the average skeletal muscle cross sectional areas of two sequential axial CT slices at the L3 vertebral level obtained from radiation planning computed tomography scans. The image analysis was performed by a single trained observer using Sliceomatic software with a Hounsfield unit threshold of -29 to 150 to define skeletal muscle. Sarcopenia as a predictor for treatment toxicity was analyzed using Chi-Square testing. Overall survival and progression free survival were compared with 2-sample t tests. Sixty-one patients were included in the analysis with 49% meeting criteria for sarcopenia. Stage did not differ although sarcopenic patients were significantly more elderly than those without sarcopenia (mean 72 vs 58 years, p<0.001). Patients with sarcopenia were significantly more likely to have chemotherapy breaks, chemotherapy dose reductions, and incomplete chemotherapy courses compared to non-sarcopenic patients (33.3% vs 9.7%; p = 0.02). Sarcopenia was not predictive of more radiation treatment breaks, hematologic toxicity, progression free survival or overall survival. Sarcopenia is predictive of reduced tolerance to chemotherapy among patients receiving definitive combined modality therapy for primary anal carcinoma. We found no association between sarcopenia and tolerance of radiation therapy or survival outcomes.

Assessing Osteolytic Lesion Size on Sequential CT Scans Is a Reliable Study Endpoint for Bone Remineralization in Newly Diagnosed Multiple Myeloma.

Multiple myeloma (MM) frequently induces persisting osteolytic manifestations despite hematologic treatment response. This study aimed to establish a biometrically valid study endpoint for bone remineralization through quantitative and qualitative analyses in sequential CT scans. Twenty patients (seven women, 58 ± 8 years) with newly diagnosed MM received standardized induction therapy comprising the anti-SLAMF7 antibody elotuzumab, carfilzomib, lenalidomide, and dexamethasone (E-KRd). All patients underwent whole-body low-dose CT scans before and after six cycles of E-KRd. Two radiologists independently recorded osteolytic lesion sizes, as well as the presence of cortical destruction, pathologic fractures, rim and trabecular sclerosis. Bland-Altman analyses and Krippendorff's α were employed to assess inter-reader reliability, which was high for lesion size measurement (standard error 1.2 mm) and all qualitative criteria assessed (α ≥ 0.74). After six cycles of E-KRd induction, osteolytic lesion size decreased by 22% (p < 0.001). While lesion size response did not correlate with the initial lesion size at baseline imaging (Pearson's r = 0.144), logistic regression analysis revealed that the majority of responding osteolyses exhibited trabecular sclerosis (p < 0.001). The sum of osteolytic lesion sizes on sequential CT scans defines a reliable study endpoint to characterize bone remineralization. Patient level response is strongly associated with the presence of trabecular sclerosis.

Correlation Between Optic Nerve Sheath Diameter at Initial Head CT and the Rotterdam CT Score.

Introduction Intracranial findings on imaging have long been used in assessing the severity of traumatic brain injury (TBI); the Rotterdam CT scoring (RCTS) is a more recent tool. Estimating the optic nerve sheath diameter (ONSD) at computed tomography (CT) can be another valuable predictor of the severity of the injury, especially as both ONSD and the RCTS are proven to be independent predictors of raised intracranial pressure (ICP).The study objective was to determine the correlation between ONSD at initial head CT and RCTS. Material and methods We observed 40 consecutive confirmed TBI cases at their initial head CT examinations in the emergency department for ONSD and the presence of other intracranial findings necessary to derive RCTS. The data were prospectively collected and analyzed, with statistical significance set at p ≤0.05 at 95% CI. Results The mean ONSD positively correlated with the Rotterdam CT score (r=0.368, p=0.019). A cut-off value of 6.83 mm was extrapolated from the receiver operator characteristic (ROC) curve as the mean binocular ONSD that best predicted severe RCTS (≥4) (sensitivity: 73.3%, specificity: 80%, positive predictive value: 68.7%, negative predictive value: 83.3%). The area under the curve (AUC) was 0.780 (p=0.003). Binary logistic regression analysis revealed an odd ratio (OR) of 11.000(95% CI: 2.438-49.627; p=0.002). Conclusion TBI patients with high RCTS have wide mean binocular ONSD. Those with average binocular ONSD above the cut-off value are likelier to have severe TBI. With the documented good correlation, ONSDmay become very useful in informing the clinical decision for sequential CT scans in TBI patients and, therefore, reducing the cumulative radiation burden from needless exposures. Furthermore, the non-invasive nature of its assessment will have more clinical relevance in resource-limited settings, where the skills and equipment for ICP monitoring are either not readily available or too expensive to be used routinely.