Traditional CT Research Articles

EP01.07-004 FDG-PET-CT for Staging Screen Detected Lung Cancer

As lung cancer screening programs develop globally, concern for increasing downstream investigations and cost remain. Flurodeoxyglucose positron emission tomography computed tomography (FDG-PET-CT) is routinely conducted for pre-operative staging, however its utility in screen detected lung cancer is unclear. PET-CT increases the wait time from tissue diagnosis to surgery as well as adds cost. We hypothesize for screen detected early-stage lung cancer (cT1a), PET-CT will not significantly change peri-operative management beyond clinical staging with traditional CT and linear endobronchial ultrasound staging (EBUS).

The Influence of Martial Arts on Spine CT Image Morphological Structure Based on Optimized Ant Colony Algorithm.

As the second lifeline of human body, the abnormal development of its morphological structure has a great impact on people's physical and mental health. Due to bad living habits and learning pressure, the morphological and structural development of spine in adolescents and children is abnormal to a certain extent, which can be improved by sports intervention. Therefore, this article puts forward the research on the influence of Wushu on the spinal morphological structure based on the optimization algorithm and integrates the optimization ant colony algorithm on the basis of the traditional spinal CT image segmentation method. The experimental results show that the improved CT image segmentation method based on the optimized ant colony algorithm can solve the sensitive problem of the number of clusters, improve the segmentation efficiency and quality, and provide more accurate data for the subsequent comparative experiments. At the same time, the comparative test results show that Wuqinxi Wushu can better improve the abnormal dry tilt angle, abnormal kyphosis angle, and body balance angle of teenagers, improve the activity of teenagers' spine, and help teenagers enhance the overall health of spine.

Radiomics analysis of CT imaging improves preoperative prediction of cervical lymph node metastasis in laryngeal squamous cell carcinoma.

To investigate the role of CT radiomics for preoperative prediction of lymph node metastasis (LNM) in laryngeal squamous cell carcinoma (LSCC). LSCC patients who received open surgery and lymphadenectomy were enrolled and randomized into primary and validation cohorts at a ratio of 7:3 (325 vs. 139). In the primary cohort, we extracted radiomics features from whole intratumoral regions on venous-phase CT images and constructed a radiomics signature by least absolute shrinkage and selection operator (LASSO) regression. A radiomics model incorporating the radiomic signature and independent clinical factors was established via multivariable logistic regression and presented as a nomogram. Nomogram performance was compared with a clinical model and traditional CT report with respect to its discrimination and clinical usefulness. The radiomics nomogram was internally tested in an independent validation cohort. The radiomics signature, composed of 9 stable features, was associated with LNM in both the primary and validation cohorts (both p < .001). A radiomics model incorporating independent predictors of LNM (the radiomics signature, tumor subsite, and CT report) showed significantly better discrimination of nodal status than either the clinical model or the CT report in the primary cohort (AUC 0.91 vs. 0.84 vs. 0.68) and validation cohort (AUC 0.89 vs. 0.83 vs. 0.70). Decision curve analysis confirmed that the radiomics nomogram was superior to the clinical model and traditional CT report. The CT-based radiomics nomogram may improve preoperative identification of nodal status and help in clinical decision-making in LSCC. • The radiomics model showed favorable performance for predicting LN metastasis in LSCC patients. • The radiomics model may help in clinical decision-making and define patient subsets benefiting most from neck treatment.

Malignancy risk of gastrointestinal stromal tumors evaluated with noninvasive radiomics: A multi-center study.

PurposeThis study was to investigate the diagnostic efficacy of radiomics models based on the enhanced CT images in differentiating the malignant risk of gastrointestinal stromal tumors (GIST) in comparison with the clinical indicators model and traditional CT diagnostic criteria.Materials and methodsA total of 342 patients with GISTs confirmed histopathologically were enrolled from five medical centers. Data of patients wrom two centers comprised the training group (n=196), and data from the remaining three centers constituted the validation group (n=146). After CT image segmentation and feature extraction and selection, the arterial phase model and venous phase model were established. The maximum diameter of the tumor and internal necrosis were used to establish a clinical indicators model. The traditional CT diagnostic criteria were established for the classification of malignant potential of tumor. The performance of the four models was assessed using the receiver operating characteristics curve.ReusltsIn the training group, the area under the curves(AUCs) of the arterial phase model, venous phase model, clinical indicators model, and traditional CT diagnostic criteria were 0.930 [95% confidence interval (CI): 0.895-0.965), 0.933 (95%CI 0.898-0.967), 0.917 (95%CI 0.872-0.961) and 0.782 (95%CI 0.717-0.848), respectively. In the validation group, the AUCs of the models were 0.960 (95%CI 0.930-0.990), 0.961 (95% CI 0.930-0.992), 0.922 (95%CI 0.884-0.960) and 0.768 (95%CI 0.692-0.844), respectively. No significant difference was detected in the AUC between the arterial phase model, venous phase model, and clinical indicators model by the DeLong test, whereas a significant difference was observed between the traditional CT diagnostic criteria and the other three models.ConclusionThe radiomics model using the morphological features of GISTs play a significant role in tumor risk stratification and can provide a reference for clinical diagnosis and treatment plan.

Learning low-dose CT degradation from unpaired data with flow-based model.

There has been growing interest in low-dose computed tomography (LDCT) for reducing the X-ray radiation to patients. However, LDCT always suffers from complex noise in reconstructed images. Although deep learning-based methods have shown their strong performance in LDCT denoising, most of them require a large number of paired training data of normal-dose CT (NDCT) images and LDCT images, which are hard to acquire in the clinic. Lack of paired training data significantly undermines the practicability of supervised deep learning-based methods. To alleviate this problem, unsupervised or weakly supervised deep learning-based methods arerequired. We aimed to propose a method that achieves LDCT denoising without training pairs. Specifically, we first trained a neural network in a weakly supervised manner to simulate LDCT images from NDCT images. Then, simulated training pairs could be used for supervised deep denoisingnetworks. We proposed a weakly supervised method to learn the degradation of LDCT from unpaired LDCT and NDCT images. Concretely, LDCT and normal-dose images were fed into one shared flow-based model and projected to the latent space. Then, the degradation between low-dose and normal-dose images was modeled in the latent space. Finally, the model was trained by minimizing the negative log-likelihood loss with no requirement of paired training data. After training, an NDCT image can be input to the trained flow-based model to generate the corresponding LDCT image. The simulated image pairs of NDCT and LDCT can be further used to train supervised denoising neural networks fortest. Our method achieved much better performance on LDCT image simulation compared with the most widely used image-to-image translation method, CycleGAN, according to the radial noise power spectrum. The simulated image pairs could be used for any supervised LDCT denoising neural networks. We validated the effectiveness of our generated image pairs on a classic convolutional neural network, REDCNN, and a novel transformer-based model, TransCT. Our method achieved mean peak signal-to-noise ratio (PSNR) of 24.43dB, mean structural similarity (SSIM) of 0.785 on an abdomen CT dataset, mean PSNR of 33.88dB, mean SSIM of 0.797 on a chest CT dataset, which outperformed several traditional CT denoising methods, the same network trained by CycleGAN-generated data, and a novel transfer learning method. Besides, our method was on par with the supervised networks in terms of visualeffects. We proposed a flow-based method to learn LDCT degradation from only unpaired training data. It achieved impressive performance on LDCT synthesis. Next, we could train neural networks with the generated paired data for LDCT denoising. The denoising results are better than traditional and weakly supervised methods, comparable to supervised deep learningmethods.

A phase I study of ADXS-504, a cancer type specific immunotherapy, for patients with biochemically recurrent prostate cancer.

TPS5115 Background: Roughly 20%-30% of prostate cancer patients experience biochemical recurrence (BCR), rising prostate-specific antigen (PSA) levels, after definitive therapy with radical prostatectomy (RP) or radiation therapy (RT). The optimal therapy and timing of treatment for BCR is unknown, however, for patients who are not eligible for salvage radiation, androgen deprivation therapy (ADT) is the standard first-line treatment. ADT is an effective therapy but has many acute and long-term toxicities. Novel strategies to reduce ADT exposure and prolong disease control are needed. ADXS-504 is a live attenuated Listeria monocytogenes (Lm)-based immunotherapy consisting of a truncated nonhemolytic fragment of listeriolysin O (tLLO) fused to a total of 24 tumor associated antigens (TAA). ADXS-504 was designed so that nearly 20% of patients with BCR cancer will express at least one of the targeted hotspot mutation peptide antigens and that &gt;90% will express at least one of the TAAs targeted by the sequence-optimized TAA peptide antigens. By combining these shared, commonly expressed antigen targets into a single Lm-based immunotherapy, ADXS-504 provides the potential for a potent, disease-specific approach to treating patients with prostate cancer. In addition to the generation of antigen-specific T cell responses, treatment with Lm-based immunotherapies has been shown to reprogram the tumor microenvironment (TME), by reducing the frequencies and function of immunosuppressive regulatory T cells and myeloid-derived suppressor cells within the TME. A pre-clinical murine model showed that a proto-type Lm-construct could reduce the number of metastases after removal of the primary tumor by eliminating residual tumor cells. We hypothesize that ADXS-504 is safe and promotes anti-tumor immune responses that may delay or prevent the use of ADT in castration sensitive prostate cancer. Methods: This is an open-label dose-escalation phase I trial of ADXS-504 in patients with BCR of prostate cancer previously treated with RP or RT. The study will enroll up to 18 subjects with BCR with a PSA ≥ 0.3 and a PSADT ≥ 4 months without evidence of metastatic disease on traditional CT imaging and bone scans. ADXS-504 will be given by IV every 4 weeks level for 6 study treatments, followed by maintenance therapy given every 12 weeks for 4 doses for overall total of 10 doses of study treatment. DLTs will be evaluated over the first 28 days of treatment. PSA response, PSADT, and adverse events will be summarized. The Kaplan- Meier method will be used to estimate time to PSA progression and rPFS. Immunologic activity will be evaluated by ELISpot analysis, flow cytometry, and multiplex assays and blood samples will be collected for gene sequencing analysis. The study is currently open to enrollment. Clinical trial information: NCT05077098.

Metabolic complete responses (mCR) in patients with metastatic uveal melanoma (mUM) treated with image-guided injection (IGI) of PV-10.

9543 Background: Traditional CT imaging can underestimate the degree of anti-cancer treatment effect due to reliance on morphological changes of visualized tumors. In contrast, PET imaging offers information on metabolic activity using a positron emitting radiolabeled agent (e.g. FDG) but is less sensitive to changes in tumor size. FDG-PET images acquired, co-registered, and superimposed on CT images (PET-CT) allow spatial detection of anti-cancer activity. Moreoever, FDG-PET-CT can provide information regarding anti-tumor immune responses in patients receiving immunotherapy. Rose bengal (PV-10) is a small molecule autolytic immunotherapy in development for metastatic disease. When administered by intralesional injection, PV-10 can produce immunogenic cell death and a T-cell mediated immune response against treatment-refractory and immunologically-cold tumors. Herein, we report the FDG-PET-CT imaging responses of 7 metastatic uveal melanoma (mUM) patients who received percutaneous image-guided injection (IGI) of PV-10 into hepatic tumors. Methods: The Phase 1 study is evaluating safety, tolerability, and efficacy of intralesional PV-10 in hepatic tumors. PV-10 is administered percutaneously via IGI into designated tumors ≤4.9 cm in diameter. Response is assessed at Day 28, then every 3 months, using CT/MRI or PET-CT. Patients with multiple tumors may receive further IGI of PV-10 after Day 28. Results: To date, 25 mUM patients with liver metastases have been treated; 16 patients received standard of care immune checkpoint inhibitor (ICI) during or post PV-10 treatment. Seven subjects had FDG-PET-CT imaging during the study (baseline 1, follow-up 6). Two follow-up FDG-PET-CTs were performed 1 and 3 years after PV-10 injection with intervening ICI, and another was 1.5 years after PV-10, without any follow-on treatment. Four patients experienced mCR in all metastatic sites, including extrahepatic metastasis. Conclusions: FDG-PET-CT shows that PV-10 is capable of inducing mCR in injected (adscopal) and non-injected (abscopal) lesions. This pattern of response is suggestive of immunogenic cell death in mUM patients with liver metastases. Clinical trial information: NCT00986661.

Clinical predictors of survival for patients with atypical teratoid/rhabdoid tumors.

Atypical teratoid/rhabdoid tumors (AT/RTs) are malignant central nervous system (CNS) neoplasms of the young. Our study analyzed a large AT/RT cohort from the National Cancer Database (NCDB) to elucidate predictors of short-term mortality and overall survival (OS). Information was collected on patients with histologically confirmed AT/RT using the NCDB (2004-2016). Kaplan-Meier analysis indicated OS. Prognostic factors for 30-day mortality, 90-day mortality, and OS were determined via multivariate Cox proportional hazards (CPH) and logistic regression models. Our cohort of 189 patients had a median age of 1year (IQR [1, 4]) and tumor size of 4.7 ± 2.0cm at diagnosis. Seventy-two percent were under 3years old; 55.6% were male and 71.0% were Caucasian. Fifty (27.2%) patients received only surgery (S) (OS = 5.91months), 51 (27.7%) received surgery and chemotherapy (S + CT) (OS = 11.2months), and 9 (4.89%) received surgery and radiotherapy (S + RT) (OS = 10.3months). Forty-five (24.5%) received S + CT + RT combination therapy (OS = 45.4months), 13 (17.1%) received S + CT + BMT/SCT (bone marrow or stem cell transplant) (OS = 55.5months), and 16 (8.70%) received S + CT + RT + BMT/SCT (OS = 68.4months). Bivariate analysis of dichotomized age (HR = 0.550, 95% CI [0.357, 0.847], p = 0.0067) demonstrated significantly increased patient survival if diagnosed at or above 1year old. On multivariate analysis, administration of S + CT + RT, S + CT + BMT/SCT, or S + CT + RT + BMT/SCT combination therapy predicted significantly (p < 0.05) increased OS compared to surgery alone. AT/RTs are CNS tumors where those diagnosed under 1year old have a significantly worse prognosis. Our study demonstrates that while traditional CT, RT, and BMT/SCT combination regimens prolong life, overall survival in this population is still low.

Analysis of Applicants’ Perspectives of Cardiothoracic Surgery Fellowship Program Websites

BackgroundCardiothoracic (CT) surgery fellowship websites help applicants determine where they apply and/or accept an interview. However, relevant information from programs is not communicated in a standardized way. MethodsWe used Fellow and Residency Electronic Interactive Database Access (FREIDA) Online to identify residency programs with traditional CT fellowships. Program-specific variables included presence or absence of tracks, track duration, and annual cardiac and thoracic cases. Resident-specific variables included number of resident(s) a program accepts and case numbers per fellow. Current CT residents completed an online survey in which they rated how important they deemed the presence of these variables in program websites. ResultsAccording to FREIDA Online, 74 traditional CT surgery fellowship websites were analyzed. Among the websites listed on FREIDA, only 16 (22%) linked directly to the CT fellowship page. Surveys were sent to all trainees enrolled in the 74 programs, and 24 responded. There were marked deficiencies in the availability of information on program websites that was highly valued by trainees. Only 31% of websites reported annual program volume, and 14% reported resident case numbers, while this data was highly valued by >60% of respondents. Similarly, 11% of program websites described their education curriculum, while 81% of respondents highly valued this information. One-quarter of respondents were dissatisfied with the overall information provided by program websites. ConclusionsCT fellowship program websites lack crucial content that is deemed highly valued by applicants. This study suggests the possible need for a single comprehensive data repository or a standardized method for communicating information through program websites.

Deep Learning-Enabled Clinically Applicable CT Planbox for Stroke With High Accuracy and Repeatability.

BackgroundComputed tomography (CT) plays an essential role in classifying stroke, quantifying penumbra size and supporting stroke-relevant radiomics studies. However, it is difficult to acquire standard, accurate and repeatable images during follow-up. Therefore, we invented an intelligent CT to evaluate stroke during the entire follow-up.MethodsWe deployed a region proposal network (RPN) and V-Net to endow traditional CT with intelligence. Specifically, facial detection was accomplished by identifying adjacent jaw positions through training and testing an RPN on 76,382 human faces using a preinstalled 2-dimensional camera; two regions of interest (ROIs) were segmented by V-Net on another training set with 295 subjects, and the moving distance of scanning couch was calculated based on a pre-generated calibration table. Multiple cohorts including 1,124 patients were used for performance validation under three clinical scenarios.ResultsCranial Automatic Planbox Imaging Towards AmeLiorating neuroscience (CAPITAL)-CT was invented. RPN model had an error distance of 4.46 ± 0.02 pixels with a success rate of 98.7% in the training set and 100% with 2.23 ± 0.10 pixels in the testing set. V-Net-derived segmentation maintained a clinically tolerable distance error, within 3 mm on average, and all lines presented with a tolerable angle error, within 3° on average in all boundaries. Real-time, accurate, and repeatable automatic scanning was accomplished with and a lower radiation exposure dose (all P < 0.001).ConclusionsCAPITAL-CT generated standard and reproducible images that could simplify the work of radiologists, which would be of great help in the follow-up of stroke patients and in multifield research in neuroscience.

The Predictive Accuracy of the Delayed Spot Sign for Haematoma Expansion in Spontaneous Supratentorial Intracerebral Haemorrhage: A Systematic Review and Meta-Analysis

Previous studies have shown that spot signs on imaging modalities such as CT perfusion, delayed phase CTA or post contrast CT imaging reportedly have greater ability to predict haematoma expansion (HE) than the traditional CT angiography spot sign. We performed a systematic review and meta-analysis of the diagnostic accuracy of the spot sign on delayed imaging modalities in predicting haematoma expansion.Pubmed, Excerpta Medica Database, and the Cochrane library were searched on the 11 November 2019. The search strategy utilised the following terms: CT angiography OR post contrast CT OR CT perfusion OR CT AND intracerebral haemorrhage (or synonyms) AND spot sign OR delayed spot sign OR dynamic spot sign. The area under the summary of receiver operating curves for diagnostic accuracy of delayed spot sign in predicting HE was calculated using bivariate random effects meta-analysis.501 articles were identified, with 10 meeting inclusion criteria. The studies included 711 patients overall, with 272 (38%) demonstrating a spot sign. The presence of a delayed spot sign was associated with HE with a diagnostic odds ratio of 25.4 (12.7-50.9). Pooled sensitivity was 0.81 (0.72-0.88), with a pooled specificity of 0.82 (0.76-0.88). Pooled positive likelihood ratio was 4.30, with a pooled negative likelihood ratio of 0.26. The area under the receiver operating curve (AUC) was 0.88.The delayed spot sign has greater diagnostic accuracy in predicting haematoma expansion than the traditional CT angiography spot sign. Further research could determine the delayed imaging technique that has the greatest diagnostic accuracy.

Abstract 131: Multiphase CT Angiography Perfusion Vs. CT Perfusion In Predicting Final Infarction In Acute Stroke Patients

Background: In acute ischemic stroke (AIS), Computed Tomography Perfusion (CT, CTP) is the most widely used technique for determining extent of tissue likely to die even after successful reperfusion. However, CTP results in higher radiation dose to the patient, is affected by motion, and is not widely available. Multiphase CT Angiography (mCTA), by contrast, is a low radiation extension to the ubiquitous CT Angiography workflow. Here, we evaluate StrokeSENS mCTA Perfusion, a software tool that uses mCTA to estimate brain tissue perfusion, and compare it to CTP in its ability to predict final infarction. Methods: 551 subjects with baseline mCTA, Non-contrast CT (NCCT), and CTP were included. Of these, 480 were part of the development dataset used to derive the mCTA Perfusion algorithm while the remaining 71 were included in the test dataset. T max and CBF perfusion maps were generated on CTP using GE CTP-4D Perfusion, and on mCTA using StrokeSENS mCTA Perfusion. Final infarction was manually segmented on 24-48h MRI/NCCT by 2 experts using ITK-SNAP. Voxel values from CTP and mCTA were assessed in their ability to predict final infarction at an individual patient level (AUC calculated for each patient, then averaged across patients) and for the combined patient data (voxels pooled across patients, then one AUC calculated), then compared (two-sided difference test p-value, p). Results: At patient level, mCTA Perfusion T max AUC was 77.7% (95% c.i.: [74%, 82%]) while CTP T max AUC was 74.6% (95% c.i.: [71%, 79%]), p=0.15. mCTA Perfusion CBF AUC was 68.5% (95% c.i.: [65%, 72%]) while CTP CBF AUC was 69.8% (95% c.i.: [67%, 73%]), p=0.43. In combined patient data analysis, mCTA perfusion T max AUC was 84.13% while CTP T max AUC was 81.36%, p=0. mCTA perfusion CBF AUC was 74.44% while CTP CBF AUC was 72.51%, p=0. Conclusion: StrokeSENS mCTA Perfusion software is similar to traditional CT Perfusion in its ability to predict final infarction in patients with acute ischemic stroke.

Application of Artificial Intelligence Nuclear Medicine Automated Images Based on Deep Learning in Tumor Diagnosis.

In order to correctly obtain normal tissues and organs and tumor lesions, the research on multimodal medical image segmentation based on deep learning fully automatic segmentation algorithm is more meaningful. This article aims to study the application of deep learning-based artificial intelligence nuclear medicine automated images in tumor diagnosis. This paper studies the methods to improve the accuracy of the segmentation algorithm from the perspective of boundary recognition and shape changeable adaptive capabilities, studies the active contour model based on boundary constraints, and proposes a superpixel boundary-aware convolution network to realize the automatic CT cutting algorithm. In this way, the tumor image can be cut more accurately. The experimental results in this paper show that the improved algorithm in this paper is more robust than the traditional CT algorithm in terms of accuracy and sensitivity, an increase of about 12%, and a slight increase in the negative prediction rate of 3%. In the comparison of cutting images of malignant tumors, the cutting effect of the algorithm in this paper is about 34% higher than that of the traditional algorithm.

Low-dose CT of the abdomen: Initial experience on a novel photon-counting detector CT and comparison with energy-integrating detector CT

PurposeTo analyze the quantitative and qualitative image quality of low-dose CT scans of the abdomen on a novel photon-counting detector CT (PCD-CT) in comparison with a traditional energy-integrating detector CT (EID-CT). MethodsConsecutive patients with clinically indicated low-dose CT were scanned on a PCD-CT and compared with a BMI-matched EID-CT-cohort scanned during the same timeframe. Radiation dose, image noise, and signal-to-noise ratio (SNR) were measured for each patient. Furthermore, image quality and conspicuity of abdominal structures (adrenal glands, mesenteric vessels, ureters, and renal pelvis) were assessed on 5-point Likert-scales (1 = very poor quality/not detectable; 5 = excellent quality/differentiability). ResultsTwenty patients (mean age 46.2 [range: 19–77]; 13 men) were included. Image noise was significantly lower (24.9 ± 3.3 vs. 31.4 ± 5.6 SD HU, p < 0.001) and SNR significantly higher (2.1 ± 0.3 vs. 1.5 ± 0.4; p < 0.001) on the PCD-CT. Subjective image quality was substantially higher (4.0 ± 0.3 vs. 3.1 ± 0.6; p < 0.001) and conspicuity better for the renal pelvis, ureters, and mesenteric vessels on the PCD-CT. There was no significant difference in the conspicuity of the adrenal glands. With increasing BMI (1st–4th BMI quartile), noise increased, and SNR decreased more strongly on the EID-CT than on the PCD-CT (ΔNoise: 39% vs. 2%, ΔSNR: −33% vs. −7% for EID-CT vs. PCD-CT, respectively) while radiation dose increased comparably (70 vs. 59%). ConclusionsLow-dose CT scans of the abdomen performed on a novel PCD-CT exhibit reduced noise, higher SNR, increased subjective image quality, and superior conspicuity of abdominal fine structures compared to scans in comparable patients on an EID-CT.

Quantitative CT imaging features for COVID-19 evaluation: The ability to differentiate COVID-19 from non- COVID-19 (highly suspected) pneumonia patients during the epidemic period.

COVID-19 and Non-Covid-19 (NC) Pneumonia encountered high CT imaging overlaps during pandemic. The study aims to evaluate the effectiveness of image-based quantitative CT features in discriminating COVID-19 from NC Pneumonia. 145 patients with highly suspected COVID-19 were retrospectively enrolled from four centers in Sichuan Province during January 23 to March 23, 2020. 88 cases were confirmed as COVID-19, and 57 patients were NC. The dataset was randomly divided by 3:2 into training and testing sets. The quantitative CT radiomics features were extracted and screened sequentially by correlation analysis, Mann-Whitney U test, the least absolute shrinkage and selection operator (LASSO) logistic regression (LR) and backward stepwise LR with minimum AIC methods. The selected features were used to construct the LR model for differentiating COVID-19 from NC. Meanwhile, the differentiation performance of traditional quantitative CT features such as lesion volume ratio, ground glass opacity (GGO) or consolidation volume ratio were also considered and compared with Radiomics-based method. The receiver operating characteristic curve (ROC) analysis were conducted to evaluate the predicting performance. Compared with traditional CT quantitative features, radiomics features performed best with the highest Area Under Curve (AUC), sensitivity, specificity and accuracy in the training (0.994, 0.942, 1.0 and 0.965) and testing sets (0.977, 0.944, 0.870, 0.915) (Delong test, P < 0.001). Among CT volume-ratio based models using lesion or GGO component ratio, the model combining CT lesion score and component ratio performed better than others, with the AUC, sensitivity, specificity and accuracy of 0.84, 0.692, 0.853, 0.756 in the training set and 0.779, 0.667, 0.826, 0.729 in the testing set. The significant difference of the most selected wavelet transformed radiomics features between COVID-19 and NC might well reflect the CT signs. The differentiation between COVID-19 and NC could be well improved by using radiomics features, compared with traditional CT quantitative values.

Added value of iodine-specific imaging and virtual non-contrast imaging for gastrointestinal assessment using dual-energy computed tomography.

Dual-energy computed tomography (DECT) has become increasingly available and can be readily incorporated into clinical practice. Although DECT can provide a wide variety of spectral imaging reconstructions, most clinically valuable information is available from a limited number of standard image reconstructions including virtual non-contrast and iodine overlay. The combination of these standard reconstructions can be used for specific diagnostic tasks that provide added value over traditional CT protocols. In this pictorial essay, the added value of these standard reconstructed images will be demonstrated by case examples for diseases specifically related to the gastrointestinal system.

Generating XACT images with a single-shot X-ray pulse in nanoseconds

X-ray induced acoustic computed tomography can image the body at faster speeds and with smaller doses of radiation compared to traditional CT imaging

Dosimetric Feasibility of HA-WBRT With an MRI-Guided Linear Accelerator

Whole brain radiotherapy with hippocampal avoidance (HA-WBRT) is a technique utilized to treat extensive metastatic brain disease while preserving memory and neurocognitive function. We hypothesized that the treatment planning and delivery of HA-WBRT plans is feasible with an MRI-guided linear accelerator (Linac) and compared plan results with clinical non-MRI-guided C-Arm Linac plans.Twelve patients treated with HA-WBRT plans on a non-MRI-guided C-Arm Linac were selected for retrospective analysis. Treatment plans were developed for comparison using a 0.35T MRI-guided Linac. Hippocampi were contoured by the physician on a T1 MRI image fused to a planning CT image with a 5mm expansion to the hippocampi for the avoidance structure. The PTV was expanded from the CTV excluding the hippocampal avoidance structure. Treatment planning goals were defined as provided in the Phase II Trial NRG CC001. MRI-guided treatment (MRgRT) plans were developed by a dosimetrist and compared with clinical plans. QA plans were developed and a subset were delivered on the MRI-guided Linac and measured with a cylindrical diode detector array. PTV coverage was normalized to ∼95% to provide a control point for comparison of dose to the organs at risk.MRgRT plans were deliverable and met all clinical goals. Mean values demonstrated that the clinical plans were cooler than MRgRT plans with mean V37.5 of 0.00% and 0.66%, respectively. Average hippocampi maximum doses were 14.20 ± 2.04 Gy and 14.33 ± 1.19 Gy for clinical vs MRgRT plans, respectively. Average hippocampi D100% were 8.63 ± 0.52 Gy and 7.87 ± 0.23 Gy, respectively. Average maximum doses to 0.03 cc of optic structures were 30.93 ± 1.10 Gy and 31.78 ± 0.66 Gy, respectively. The gamma analysis comparing planned and measured doses resulted in a mean of 99.9% ± 0.14% of passing points (3%/2mm criteria). MRgRT plans had an average of 38.33 beams with total delivery time ranging from 10.7-15.7 minutes and 4.03 minutes for total beam on time. Clinical plans had average delivery times of 3-7 minutes depending on the number of coplanar arcs.This study demonstrates that HA-WBRT can be treated using an MRI-guided linear accelerator with comparable treatment plan quality and delivery accuracy. Given the equitable dosimetric outcomes to traditional CT based plans, the use of MRgRT for hippocampal sparing whole brain radiotherapy opens the possibility of radiomic analysis and potential adaptive treatments (i.e., boost) to lesions based on disease response.

Clinical Workflow for Planning and Treating Palliative Patients on Diagnostic Images

Care coordination for inpatients requiring palliative radiotherapy remains a challenge. One such challenge is timely CT simulation. Previously published data demonstrate that rapid radiotherapy delivery is associated with improved patient satisfaction and reduced duration of hospitalization. The purpose of this study was to assess the feasibility of eliminating RT simulation from the current clinical workflow for palliative patients by using diagnostic images as the primary image dataset.A cohort of 11 previously treated palliative patients were identified. Treatment sites evaluated included spine (4), sacrum (2), shoulder (1), lung (1), humerus (1), pelvis (1) and whole brain (1). Diagnostic CT images on a curved couch without RT immobilization were used to generate diagnostic plans. The diagnostic plans were copied and recalculated on corresponding simulation CT images, which were the clinically used datasets for planning and treatment. For the whole brain patient who only had a diagnostic MRI, a synthetic CT scan was generated with brain, muscle, adipose and bone overridden following ICRU 44. Anatomic surrogates were aligned to couch indexing notches to avoid large shift during patient setup. The quality of diagnostic imaging based treatment plans were compared against traditional CT simulation based plans by a group of physicians.All diagnostic plans, except the shoulder, were clinically acceptable. Minor dose distribution deviation from the previously approved clinical plans were noted, but were not clinically significant, as illustrated in the table. The average change in maximum point dose was 0.1 Gy, ranged between -0.8 Gy and 0.5 Gy. Change in PTV D95 and V95 ranged from -1.3% to 0.5%, and from 0% to 2.8%, respectively. The rejected shoulder case was secondary to a limited field of view (FOV) on the diagnostic images. However, a limited FOV may be corrected through estimation of missing tissue or more appropriate diagnostic image selection. The impact from CT calibration and anatomy change on different couches was negligible.A clinical workflow to generate treatment plans on diagnostic imaging was developed. This workflow eliminates the need for formal CT simulation, allowing for expedited radiotherapy. Diagnostic imaging based treatment plans were deemed to be clinically acceptable after careful quality and dosimetric assessment against traditional CT simulation based treatment plans. Limitations of the new workflow were identified and iterative improvements are currently underway.

Comparison of novel machine vision spinal image guidance system with existing 3D fluoroscopy-based navigation system: a randomized prospective study

BACKGROUND CONTEXTThe use of spinal image guidance systems (IGS) has increased patient safety, accuracy, operative efficiency, and reduced revision rates in pedicle screw placement procedures. Traditional intraoperative 3D fluoroscopy or CT imaging produces potentially harmful ionizing radiation and increases operative time to register the patient. An IGS, FLASH Navigation, uses machine vision through high resolution stereoscopic cameras and structured visible light to build a 3D topographical map of the patient's bony surface anatomy enabling navigation use without ionizing radiation. PURPOSEWe aimed to compare FLASH navigation system to a widely used 3D fluoroscopic navigation (3D) platform by comparing radiation exposure and pedicle screw accuracy. DESIGNA randomized prospective comparative cohort study of consecutive patients undergoing open posterior lumbar instrumented fusion. PATIENT SAMPLEAdults diagnosed with spinal pathology requiring surgical treatment and planning for open posterior lumbar fusion with pedicle screws implanted into 1-4 vertebral levels. OUTCOME MEASURESOutcome measures included mean intraoperative fluoroscopy time and dose, mean CT dose length product (DLP) for preoperative and day 2 CT, pedicle screw accuracy by CT, estimated blood loss and revision surgery rate. METHODSConsecutive patients were randomized 1:1 to FLASH or 3D and underwent posterior lumbar instrumented fusion. Radiation doses were recorded from pre- and postoperative CT and intraoperative 3D fluoroscopy. 2 independent blinded radiologists reviewed pedicle screw accuracy on CT. RESULTSA total of 429 (n=210 FLASH, n=219 3D) pedicle screws were placed in 90 patients (n=45 FLASH, n=45 3D) over the 18-month study period. Mean age and indication for surgery were similar between both groups, with a non-significantly higher ratio of males in the 3D group.Mean intraoperative fluoroscopy time and doses were significantly reduced in FLASH compared to 3D (4.51±3.71s vs 79.6±23.0s, p<.001 and 80.9±68.1cGycm2 vs 3704.1±3442.4 cGycm2, p<.001, respectively). This represented a relative reduction of 94.3% in the total intraoperative radiation time and a 97.8% reduction in the total intraoperative radiation dose. Mean preoperative CT DLP and mean day 2 postoperative CT DLP were significantly reduced in FLASH compared to 3D (662.0±440.4mGy-cm vs 1008.9±616.3 mGy-cm, p<.001 and 577.9±294.3 mGy-cm vs 980.7±441.6 mGy-cm, p<.001, respectively). This represented relative reductions of 34.4% and 41.0% in the preoperative CT dose and postoperative total DLP, respectively.The FLASH group required an average of 1.2 registrations in each case with an average of 2447 (±961.3) data points registered with a mean registration time of 106s (±52.1). A rapid re-registration mechanism was utilized in 22% (n=10/45) of cases and took 22.7s (±11.3). Re-registration was used in 7% (n=3/45) in the 3D group.Pedicle screw accuracy was high in FLASH (98.1%) and 3D (97.3%) groups with no pedicle breach >2mm in either group (p<.001). EBL was not statistically different between the groups (p=.38). No neurovascular injuries occurred, and no patients required return to theatre for screw repositioning. CONCLUSIONSFLASH and 3D IGS demonstrate high accuracy for pedicle screw placement. FLASH showed significant reduction in intraoperative radiation time and dose with lower but non-significant blood loss. FLASH showed significant reduction in preoperative and postoperative radiation, but this may be associated to the lower number of males/females preponderance in this group. FLASH provides similar accuracy to contemporary IGS without requiring 3D-fluoroscopy or radiolucent operating tables. Reducing registration time and specialized equipment may reduce costs.