Single CT Research Articles

Automated AI-based coronary calcium scoring using retrospective CT data from SCAPIS is accurate and correlates with expert scoring.

Evaluation of the correlation and agreement between AI and semi-automatic evaluations of calcium scoring CT (CSCT) examinations using extensive data from the Swedish CardioPulmonary bio-Image study (SCAPIS). In total, 5057 CSCT examinations were performed on one CT system at Linköping University Hospital between October 8, 2015, and June 12, 2018. AI evaluations were compared to semi-automatic CSCT results from expert reader evaluations rendered within SCAPIS. Pearson correlation, intraclass correlation coefficients (ICC), and Bland-Altman analysis were applied for Agatston (AS), volume (VS), mass scores (MS), number of lesions and lesion location. Agreement of Agatston score classifications into cardiovascular (CV) risk categories was evaluated with weighted kappa analysis. The evaluation included 4567 subjects, 2229 (48.8%) male, 2338 (51.2%) female, 50-64 years of age (mean 57.3 ± 4.4). The AS ranged from 0 to 2871 in the cohort, with 2846 subjects having an AS of 0. Mean and median AS were 51.4 and 0.0, respectively. Total AS, VS, MS and number of lesions ICCs were 0.994, 0.994, 0.994, 0.960 (p < 0.001), respectively. Bland-Altman analyses rendered mean differences ± 1.96 SD upper and lower limits of agreement for AS -0.04, -52.5 to 52.4, VS -0.44, -46.51 to 45.63, and MS -0.07, -9.62 to 9.48. Weighted kappa analysis for CV risk category classifications was 0.913, and overall accuracy was 91.2%. There was excellent correlation and agreement between AI and semi-automatic evaluations for all calcium scores, number of lesions and lesion location. High degrees of agreement and accuracy were found for the CV risk categorization. Question Can AI function as a tool for enhancing the efficiency and accuracy of Coronary Artery Calcium Score (CACS) evaluations in clinical radiology practice? Findings This study confirms the robustness of AI-derived CACS results across extensive datasets, though its generalizability is limited by data acquisition from a single CT system. Clinical relevance This study suggests that AI holds significant promise as a tool for enhancing the efficiency and accuracy of CACS evaluations, with implications for improving patient diagnostics and reducing radiologist workload in clinical practice.

Deep learning from head CT scans to predict elevated intracranial pressure.

Elevated intracranial pressure (ICP) resulting from severe head injury or stroke poses a risk of secondary brain injury that requires neurosurgical intervention. However, currently available noninvasive monitoring techniques for predicting ICP are not sufficiently advanced. We aimed to develop a minimally invasive ICP prediction model using simple CT images to prevent secondary brain injury caused by elevated ICP. We used the following three methods to determine the presence or absence of elevated ICP using midbrain-level CT images: (1) a deep learning model created using the Python (PY) programming language; (2) a model based on cistern narrowing and scaling of brainstem deformities and presence of hydrocephalus, analyzed using the statistical tool Prediction One (PO); and (3) identification of ICP by senior residents (SRs). We compared the accuracy of the validation and test data using fivefold cross-validation and visualized or quantified the areas of interest in the models. The accuracy of the validation data for the PY, PO, and SR methods was 83.68% (83.42%-85.13%), 85.71% (73.81%-88.10%), and 66.67% (55.96%-72.62%), respectively. Significant differences in accuracy were observed between the PY and SR methods. Test data accuracy was 77.27% (70.45%-77.2%), 84.09% (75.00%-85.23%), and 61.36% (56.82%-68.18%), respectively. Overall, the outcomes suggest that these newly developed models may be valuable tools for the rapid and accurate detection of elevated ICP in clinical practice. These models can easily be applied to other sites, as a single CT image at the midbrain level can provide a highly accurate diagnosis.

Patient radiation risk reduction by controlling the tube start angle in single and dual source spiral CT scans: A simulation study.

Organ doses in spiral CT scans depend on the tube startangle. To determine the effective dose in single source CT (SSCT) and dual source CT (DSCT) scans as a function of tube start angle and spiral pitch value to identify the dose reduction potential by selecting the optimal startangle. Using Monte Carlo simulations, dose values for different tube positions with an angular increment of and a longitudinal increment of were simulated over a range of with collimations of , , and . The simulations were performed for the thorax region of six adult patients based on clinical CT data. From the resulting dose distributions, organ doses and effective dose were determined as a function of tube angle and longitudinal position. Using these per-view dose data, the individual organ doses, as well as the total effective dose, were determined for spiral scans with and without tube current modulation (TCM) with pitch values ranging from 0.5 to 1.5 for SSCT and up to 3.0 for DSCT. The dose of the best and worst tube start angle in terms of dose was determined and compared to the mean dose over all tube startangles. With increasing pitch and collimation, the dose variations from the effective dose averaged over all start angles increase. While for a collimation of , the variations from the mean dose value stay below for SSCT, we find that for a spiral scan with a pitch of 3.0 for DSCT with TCM and collimation of , the dose for the best starting angle is on average lower than the mean value and lower than the maximumvalue. Variation of the tube start angle in spiral scans exhibits substantial differences in radiation dose especially for high pitch values and for high collimations. Therefore, we suggest to control the tube start angle to minimize patientrisk.

Operative outcomes 24 hours after retrograde intrarenal surgery for solitary renal calculi using a flexible and navigable suction ureteral access sheath. A prospective global multicenter study by the European Association of Urology Section on Urolithiasis.

Suction techniques showed potential to improve outcomes of retrograde intra-renal surgery (RIRS). We assessed the 24-hour stone-free rate (SFR) and complications after RIRS using flexible and navigable suction ureteral access sheaths (FANS-UAS). Sixteen centers prospectively contributed to data (August 2023-October 2023). Inclusion criteria: age ≥18 years, single renal stone, pre and 24-hour post-RIRS CT scan. Exclusion criteria were: ureteral stone, anomalous kidney, multiple stones. SFR was divided into: 1) grade A - no fragments; 2) grade B - fragments ≤2 mm; 3) grade C - fragments 2.1-4 mm; and 4) grade D - fragments >4 mm. A multivariable logistic regression analysis model was performed to assess factors associated with the odds of having grade A stone-free status. Data are expressed as median (interquartile range), absolute numbers and frequencies, odds ratio (OR), and 95% confidence interval (CI). One hundred forty-two patients with a median age of 52 years (40-61) were enrolled. 61.3% were males. Median stone volume was 1165 mm3 (656-1936). Median operative time was 48.5 (36.25-71.75) min. Transient fever (37°C-37.5°C) occurred in 10 (7%) patients. No sepsis case occurred. 96.5% of patients were stone-free (Grade A+B). Grade A SFR was 52.8%. All patients were discharged within 48 hours. Bone window (OR 3.156 95% CI 1.177-9.130, P=0.027) was the only factor significantly associated with higher odds of 100% SFR, while stone volume (OR 0.999, 95% CI 0.999-1.000, P=0.007) was significantly associated with lower odds. Imaging and clinical evidence demonstrate excellent perioperative outcomes just 24 hours post RIRS with FANS-UAS. The technique demonstrates a good safety profile, ability for immediate high SFR, and a low rate of infective complications.

Prospective study of dual-phase 99mTc-MIBI SPECT/CT nomogram for differentiating non-small cell lung cancer from benign pulmonary lesions

ObjectivesTo establish and validate a technetium 99m sestamibi (99mTc-MIBI) single-photon emission computed tomography/computed tomography (SPECT/CT) nomogram for predicting non-small cell lung cancer (NSCLC). Comparing the diagnostic performance of early and delayed SPECT/CT nomogram, and compare the diagnostic performance of SPECT/CT radiomics models with single SPECT and CT radiomics models. MethodsThis prospective study included 119 lesions (NSCLC: n = 92, benign pulmonary lesions: n = 27) from 103 patients (mean age: 59.68 ± 8.94 years). Patients underwent dual-phase 99mTc-MIBI SPECT/CT imaging. They were divided into the training (n = 83) and validation (n = 36) cohorts. Logistic regression, support vector machine, random forest, and light-gradient boosting machine were applied to train and determine the optimal machine learning model. Then, combining radiomics score and clinical factors, establish nomograms for diagnosing NSCLC. ResultCYFRA21-1 was selected for constructing the clinical model. In early imaging, the areas under the curve (AUCs) of the clinical model, radiomics model, and nomogram were 0.571, 0.830, and 0.875, respectively. The nomogram performed better than the clinical model and similarly to the radiomics model (P=0.020, P=0.216), and there are no statistically significant differences in the predictive performance between the radiomics model and the clinical model (P=0.103). In delayed imaging, the AUC was 0.643, 0.888, and 0.893, respectively. The predictive performance of the nomogram was superior compared to the clinical model and comparable to the radiomics model (P=0.042, P=0.480), and the radiomics model also demonstrated superior diagnostic performance compared to the clinical model (P=0.049). Compared to early SPECT/CT results, the AUC values of the nomogram and radiomics models in the delayed phase were higher, although no statistical differences were found (P=0.831, P=0.568). In delayed imaging, the AUC of the radiomics models for CT and SPECT was 0.696 and 0.768, respectively, SPECT/CT radiomics exhibited significant differences compared with CT and SPECT alone (P=0.042, P=0.038). ConclusionDual-phase 99mTc-MIBI SPECT/CT nomograms and radiomics models can effectively predict NSCLC, providing an economically and non-invasive imaging method for diagnosing NSCLC, moreover, these findings provide a basis for early diagnosis and treatment strategies in NSCLC patients. Delayed-phase SPECT/CT imaging may offer greater practical value than early-phase imaging for diagnosing NSCLC. However, this novel approach necessitates further validation in larger, multi-center cohorts. Clinical relevanceRadiomics nomogram based on SPECT/CT for discriminating NSCLC from benign lung lesions helps to aid early diagnosis and guide treatment. Key pointsNomograms, based on dual-phase SPECT/CT, was constructed to discriminate between non-small cell lung cancer and benign lesions.SPECT/CT radiomics model has better predictive performance than SPECT and CT radiomics model.

Dosimetric implications of kidney anatomical volume changes in 177Lu-DOTATATE therapy

IntroductionThis study aims to evaluate the use of CT-based whole kidney parenchyma (WKP) segmentation in 177Lu-DOTATATE dosimetry. Specifically, it investigates whether WKP volumes change during treatment and evaluates the accuracy of applying a single delineated WKP volume for dosimetry. Furthermore, it aims to determine the cause of WKP volume changes—whether caused by radiation or amino acid infusion—by comparing them with spleen volume changes as a marker for radiation-induced alterations.MethodsSPECT/CT images of 18 patients were acquired over the abdomen approximately 4 h (h) (D0), 24 h (D1), 48 h (D2) and 168 h (D7) post-administration of 177Lu-DOTATATE. CT guided WKP volumes were measured before (baseline) and during treatment. Kidney activity concentrations at each time point were derived from CT-segmented WKP overlaid on SPECT scans. The accuracy of using WKP segmentation from a single CT for all time points was assessed against the gold standard of segmenting each WKP individually. Time-integrated activity calculations were based on a tri-exponential curve fit of the kidney activity concentration over time. Kidney absorbed doses were estimated under the assumption of local energy deposition. Additionally, the impact of various partial volume correction methods on dosimetry was evaluated.ResultsWhole-kidney parenchyma (WKP) volumes, ranging from 31 to 243 mL, showed a gradual increase from baseline (mean ± SD = 130.6 ± 46.1 mL) at the initial time points D0 (138.5 ± 44.7 mL) and D1 (139.4 ± 41.6 mL), followed by a slight decrease at D2 (132.8 ± 44.5 mL) and a further decrease at D7 (129.2 ± 42.7 mL). The volume increase at D0 and D1 was statistically significant. Spleen volume did not change during treatment, suggesting that amino acid infusion rather than irradiation effects caused WKP volume changes. Bland-Altman analysis revealed WKP volume biases of 8.77% (D0 vs. BL), 10.77% (D1 vs. BL), 1.10% (D2 vs. BL), and 1.10% (D7 vs. BL), with corresponding uncertainties of 24.4%, 23.6%, 25.4%, and 25.4%, respectively. When WKP segmentation from a single CT is applied across all SPECTs, these WKP volume changes could overestimate the activity concentration and mean absorbed doses up to 4.3% and 2.5%, respectively. The absorbed dose uncertainties using a recovery coefficient (RC) of 0.85 for single-time-point WKP delineation increase the absorbed dose uncertainty by 4% compared to the use of patient-specific RCs and time specific segmentation of WKP volumes.ConclusionsKidney volume exhibited significant variation form D0 to D7, affecting the precision of dosimetry calculation, primarily due to errors in whole-kidney parenchyma (WKP) delineation. Notably, using WKP segmentation from a single CT scan applied to sequential SPECT images introduce further uncertainty and may lead to an overestimation of the absorbed dose. The fluctuations in kidney volume are most likely attributable to amino acid infusion.

Volumetric computed tomography measurements as predictors for outcomes in a cohort of Fournier's gangrene patients.

This study evaluates the prognostic value of CT findings, including volumetric measurements, in predicting outcomes for patients with Fournier gangrene (FG), focusing on mortality, ICU admission, hospital stay length, and healthcare costs. A retrospective study was conducted on 38 FG patients who underwent CT scans before surgical debridement. We analyzed demographic data, CT volumetric measurements, and clinical outcomes using logistic and linear regression models. No single CT measurement significantly predicted mortality or ICU admission. The best model for mortality prediction included age, air volume, NSTI score, and male sex, with an AUC of 0.911. Intubation likelihood was modeled with an AUC of 0.913 using age, NSTI score, and visceral to subcutaneous fat ratio. The ICU admission model achieved an AUC of 0.677. Hospital stay was predicted by air volume (β = 0.0002656, p = 0.0505) with an adjusted R-squared of 0.1287. Air volume significantly predicted hospital costs (β = 2.859, p = 0.00558), resulting in an adjusted R-squared of 0.2165. Volumetric CT findings provide valuable prognostic insights for FG patients, suggesting a basis for informed clinical decisions and resource allocation. Further validation in larger, multi-center studies is recommended to develop robust predictive models for FG outcomes.

Comparative study on CT image characteristics of pneumoconiosis large shadow and primary lung cancer mass

Objective: To compare the CT image characteristics of pneumoconiosis large shadow and primary lung cancer mass, and analyze the value of CT image characteristics in the differential diagnosis of pneumoconiosis large shadow and primary lung cancer. Methods: In September 2022, 43 patients with stage Ⅲ pneumoconiosis who were hospitalized in Zibo Occupational Disease Prevention Hospital from January 2020 to June 2021 and 52 patients with primary lung cancer who were confirmed by pathology in the Affiliated Hospital of Jining Medical University during the same period were selected as the investigation objects, and the image characteristics of pneumoconiosis large shadow or lung cancer mass and surrounding tissues in the chest CT images of the two groups were compared. Univariate analysis, cluster analysis and cross analysis were used to screen out statistically significant indicators as independent variables, and pneumoconiosis and lung cancer as dependent variables for logistic regression analysis. Results: There were statistically significant differences between large shadow of pneumoconiosis and primary lung cancer mass in single factor CT imaging, such as irregular shape of lesions, CT attenuation value, calcification, cavitation, spiculation, liquefactive necrosis, satellite lesions, adjacent emphysema, short spicules, and pleural thickening (P<0.05). CT value ≥92 HU (abnormal CT attenuation value), calcification, peripheral satellite lesions, pleural thickening, parapunctal emphysema, spines on the lesion margin, irregular lesion morphology were typical features of stage Ⅲ pneumoconiosis, with multiple features of aggregation. The typical features of lung cancer were liquefaction necrosis, round or quasi-round appearance, cavitation and interlobar pleura. A logistic regression model was constructed using satellite lesions, spiculation, pleural thickening, and lesion abnormal CT attenuation value had an R(2) of 0.880 and an accuracy of 95.3% for differentiation. Conclusion: Abnormal CT attenuation value, calcification, peripheral satellite lesions, pleural thickening, spiculation at the edges, liquefaction necrosis, interlobar pleura involvement, and cavitation can distinguish the large shadow of stage Ⅲ pneumoconiosis from lung cancer mass.

CT-derived body composition and differential association with age, TNM stage and systemic inflammation in patients with colon cancer

Low skeletal muscle index/density (SMI/SMD) is prevalent in cancer, adversely prognostic and associated with tumour stage and the systemic inflammatory response (SIR). Age and SMI/SMD has not been widely studied. The present study analyses the association between age and SMI/SMD after adjustment for other clinicopathological factors. Patients undergoing resectional surgery for TNM Stage I-III disease within the West of Scotland between 2011 and 2014 were identified. A single CT slice was obtained from each patients staging CT scan. SMI and SMD were stratified normal/abnormal. The SIR was stratified using Systemic Inflammatory Grade (SIG). When stratified by age (< 50/50s/60s/70s/80+), 39%/38%/48%/62%/74% and 27%/48%/64%/82%/92% of patients had a low SMI and SMD respectively (both p < 0.001). Older age (OR 1.47, p < 0.001), female sex (OR 1.32, p = 0.032), lower socioeconomic deprivation (OR 1.15, p = 0.004), higher ASA (OR 1.30, p = 0.019), emergency presentation (OR 1.82, p = 0.003), lower BMI (OR 0.67, p < 0.002) and higher SIG (OR 1.23, p < 0.001) were independently associated with low SMI. Older age (OR 2.28, p < 0.001), female sex (OR 1.38, p = 0.038), higher ASA (OR 1.92, p < 0.001), emergency presentation (OR 1.71, p = 0.023), and higher SIG (OR 1.37, p < 0.001) were independently associated with lower SMD. Tumour factors were not independently associated with either SMI/SMD. Age was a major factor associated with low SMI/SMD in patients with colon cancer. Therefore, in these patients it is likely that this represents largely constitutional body composition as opposed to being a disease mediated effect. Adjustment for age is required when considering the cancer mediated effect on SMI/SMD in patients with colon cancer.

A micro-meso coupled model for coral reef rocks based on CT Scanning

The coral reef rock has a rich porous structure, and establishing an entity model with a realistic porous structure will do great good to study the mechanical behavior and its mechanisms. However, the pore's characterized size of coral rocks spans four orders of magnitude from micro to meso scales (1 μm to 10 mm). This requires a large specimen size and a very small element size in the model, which leads to extremely high computational costs. To overcome this fundamental challenge, this paper uses CT to scan a macro coral specimen to obtain an entity model and establishes a micro-meso scale coupled model: (1) retaining pore’s equivalent diameter larger than 1mm in the overall CT entity, and regarding the rest part as a uniformly dense matrix material with unknown material’s properties, which is called the meso model; (2) extracting a small entity containing pore’s equivalent diameter less than 1mm from the CT entity, which is called the micro model also with unknown matrix material properties; (3) the unknown matrix material parameters in meso model are derived from mechanical response of the micro model, and the unknown matrix material parameters in micro model are confined by that meso model's mechanical responses should be coincident with that of a real coral specimen. Comparing with the uniaxial compression test result, this micro-meso coupled model has high precision and retains the influence of pores at various scales. The computational result shows that it can reduce the computational complexity of a single CT entity model by four orders of magnitude. It provides a new approach for studying similar materials' mechanical behavior and mechanisms under complex loading conditions.

Sarcopenia is independently associated with poor preoperative physical fitness in patients undergoing colorectal cancer surgery.

Accurate preoperative risk assessment for major colorectal cancer (CRC) surgery remains challenging. Body composition (BC) and cardiopulmonary exercise testing (CPET) can be used to evaluate risk. The relationship between BC and CPET in patients undergoing curative CRC surgery is unclear. Consecutive patients undergoing CPET prior to CRC surgery between 2010 and 2020 were identified between two different UK hospitals. Body composition phenotypes such as sarcopenia, myosteatosis, and visceral obesity were defined using widely accepted thresholds using preoperative single axial slice CT image at L3 vertebrae. Relationships between clinicopathological, BC, and CPET variables were investigated using linear regression analysis. Two hundred eighteen patients with stage I-III CRC were included. The prevalence of sarcopenia, myosteatosis, and visceral obesity was 62%, 33%, and 64%, respectively. The median oxygen uptake at anaerobic threshold (VO2 at AT) was 12.2mL/kg/min (IQR 10.6-14.2), and oxygen uptake at peak exercise (VO2 peak) was 18.8mL/kg/min (IQR 15.4-23). On univariate linear regression analysis, male sex (P<0.001) was positively associated with VO2 at AT. While ASA grade (P<0.001) and BMI (P=0.007) were negatively associated with VO2 at AT, on multivariate linear regression analysis, these variables remained significant (P<0.05). On univariate linear regression analysis, male sex (P<0.001) was positively associated with VO2 peak, whereas age (P<0.001), ASA grade (P<0.001), BMI (P=0.003), sarcopenia (P=0.015), and myosteatosis (P<0.001) were negatively associated with VO2 peak. On multivariate linear regression analysis age (P<0.001), ASA grade (P<0.001), BMI (P<0.001), and sarcopenia (P=0.006) were independently and negatively associated with VO2 peak. The novel finding that sarcopenia is independently associated with reduced VO2 peak performance in CPET supports the supposition that reduced muscle mass relates to poor physical function in CRC patients. Further work should be undertaken to assess whether sarcopenia diagnosed on CT can act as suitable surrogate for CPET to further enhance personalized risk stratification.

An Image-Based Prior Knowledge-Free Approach for a Multi-Material Decomposition in Photon-Counting Computed Tomography.

Photon-counting CT systems generally allow for acquiring multiple spectral datasets and thus for decomposing CT images into multiple materials. We introduce a prior knowledge-free deterministic material decomposition approach for quantifying three material concentrations on a commercial photon-counting CT system based on a single CT scan. We acquired two phantom measurement series: one to calibrate and one to test the algorithm. For evaluation, we used an anthropomorphic abdominal phantom with inserts of either aqueous iodine solution, aqueous tungsten solution, or water. Material CT numbers were predicted based on a polynomial in the following parameters: Water-equivalent object diameter, object center-to-isocenter distance, voxel-to-isocenter distance, voxel-to-object center distance, and X-ray tube current. The material decomposition was performed as a generalized least-squares estimation. The algorithm provided material maps of iodine, tungsten, and water with average estimation errors of 4% in the contrast agent maps and 1% in the water map with respect to the material concentrations in the inserts. The contrast-to-noise ratio in the iodine and tungsten map was 36% and 16% compared to the noise-minimal threshold image. We were able to decompose four spectral images into iodine, tungsten, and water.

Predictive value of preoperative CT enhancement rate and CT perfusion parameters in colorectal cancer

BackgroundAngiogenesis is a critical step in colorectal cancer growth, progression and metastasization. CT are routine imaging examinations for preoperative clinical evaluation in colorectal cancer patients. This study aimed to investigate the predictive value of preoperative CT enhancement rate (CER) and CT perfusion parameters on angiogenesis in colorectal cancer, as well as the association of preoperative CER and CT perfusion parameters with serum markers.MethodsThis retrospective analysis included 42 patients with colorectal adenocarcinoma. Median of microvessel density (MVD) as the cut-off value, it divided 42 patients into high-density group (MVD ≥ 35/field, n = 24) and low-density group (MVD < 35/field, n = 18), and 25 patients with benign colorectal lesions were collected as the control group. Statistical analysis of CER, CT perfusion parameters, serum markers were performed in all groups. Receiver operating curves (ROC) were plotted to evaluate the diagnostic efficacy of relevant CT perfusion parameters for tumor angiogenesis; Pearson correlation analysis explored potential association between CER, CT perfusion parameters and serum markers.ResultsCER, blood volume (BV), blood flow (BF), permeability surface (PS) and carbohydrate antigen 19 − 9 (CA19-9), carbohydrate antigen 125 (CA125), carcinoembryonic antigen (CEA), trefoil factor 3 (TFF3), vascular endothelial growth factor (VEGF) in colorectal adenocarcinoma were significantly higher than those in the control group, the parameters in high-density group were significantly higher than those in the low-density group (P < 0.05); however, the time to peak (TTP) of patients in colorectal adenocarcinoma were significantly lower than those in the control group, and the high-density group showed a significantly lower level compared to the low-density group (P < 0.05). The combined parameters BF + TTP + PS and BV + BF + TTP + PS demonstrated the highest area under the curve (AUC), both at 0.991. Pearson correlation analysis showed that the serum levels of CA19-9, CA125, CEA, TFF3, and VEGF in patients showed positive correlations with CER, BV, BF, and PS (P < 0.05), while these indicators exhibited negative correlations with TTP (P < 0.05).ConclusionsSome single and joint preoperative CT perfusion parameters can accurately predict tumor angiogenesis in colorectal adenocarcinoma. Preoperative CER and CT perfusion parameters have certain association with serum markers.

Experimental measurement of local noise power spectrum (NPS) in photon counting detector-CT (PCD-CT) using a single data acquisition.

Accurate noise power spectra (NPS) measurement in clinical X-ray CT exams is challenging due to the need for repeated scans, which expose patients to high radiation risks. A reliable method for single CT acquisition NPS estimation is thus highly desirable. To develop a method for estimating local NPS from a single photon counting detector-CT (PCD-CT)acquisition. A novel nearly statistical bias-free estimator was constructed from the raw counts data of PCD-CT scan to estimate the variance of sinogram projection data. An analytical algorithm is employed to reconstruct point-wise covariance between any two image pixel/voxel locations and . A Fourier transform is applied to obtain the desired point-wise NPS for any chosen location . The method was validated using experimental data acquired from a benchtop PCD-CT system with various physical phantoms, and the results were compared with the conventional local NPS measurement method using repeated scans and statistical ensembleaveraging. The experimental results demonstrate that (1) the proposed method can achieve pointwise/local NPS measurement for a region of interest (ROI) located at any chosen position, accurately characterizing the NPS with spatial structures resulting from image content heterogeneity; (2) the local NPS measured using the proposed method show a higher precision in the measured NPS compared to the conventional measurement method; (3) spatial averaging of the local NPS yields the conventional NPS for a given localROI. A new method was developed to enable local NPS from a single PCD-CTacquisition.

Abstract PO1-11-11: A Novel Body Composition Risk Score (B-Score) and Overall Survival among Patients with Early-Stage Breast Cancer

Abstract Background: Measurements (amount, distribution, and radiodensity) of muscle and adipose tissue were reported to be individually associated with overall survival in breast cancer survivors, but they were not typically combined to develop an overall risk score. Such a score can identify patients at high risk of death and prioritize patients in need of lifestyle interventions. Objectives: We developed a novel body composition risk score (B-Score) by combining multiple tissue measurements. Methods: We included 3105 patients with stage II or III breast cancer (diagnosed from 2000 to 2013) at Kaiser Permanente Northern California and Dana Farber Cancer Institute. From CT scans at diagnosis, we used the third lumber vertebrae as the landmark to assess areas (cm2) and radiodensity (HU) of muscle and adipose tissue. We divided the area by height2 (m2) to derive the index and measured the average HU of the tissue area as the radiodensity, which acts as a marker of tissue 'quality' to be indicative of lipid content, inflammation, and angiogenesis. Out of all tissue measurements, we considered skeletal muscle index (SMI), subcutaneous adipose tissue index (SATI) and SAT radiodensity as they were independent predictors for overall survival. Each measurement was dichotomized using optimal stratification, with low SMI (&amp;lt; 40.1cm2/m2), high SATI (≥75.7cm2/m2), and high SAT radiodensity (≥ -97.2HU) considered risk factors. We calculated B-Score as the sum of these factors and estimated its association with overall survival using Cox proportional hazards regression with adjustment for clinicopathologic factors. Results: Mean (standard deviation) age at diagnosis was 53.9 (11.8) years, 2161 (70.1%) were Non-Hispanic White, 1880 (60.5%) were stage II, and 919 (29.6%) died over a median follow-up of 10.7 years. Most patients (60.6%) had only one body composition risk factor (B-Score = 1). Compared to those with no body composition risk factors (B-Score = 0), the risk of death increased with more body composition risk factors: the adjusted hazard ratios were 1.10 (95% CI: 0.85, 1.42), 1.47 (95% CI: 1.12, 1.92), and 2.11 (95% CI: 1.26, 3.53) for B-Scores of 1, 2, and 3, respectively (Ptrend &amp;lt; 0.001). Conclusions: More unfavorable body composition characteristics were associated with increased risks of overall mortality in a dose-response manner. Considering body composition measurements together as a composite score (B-Score) may prove a useful tool to identify patients at high risk of death following breast cancer diagnosis. Table. Adjusted Associations of the Body Composition Risk Score (B-Score) with Overall Mortality Among Patients with Early-Stage Breast Cancer Abbreviations: HR, hazard ratio. a Areas (cm2) and radiodensity (HU) of skeletal muscle and subcutaneous adipose tissue (SAT) were extracted from the single axial CT slice at the third lumbar vertebra. The body composition risk score was defined as the sum of three risk factors: low skeletal muscle index (SMI; &amp;lt;40.1cm2/m2), high SAT index (SATI; ≥75.7cm2/m2), and high SAT radiodensity (≥ -97.2HU). b Adjusted for age (years), race and ethnicity (Asian, Black, Hispanic, Non-Hispanic White, and Others), stage (II, III), ER (ER+, ER-), PR (PR+, PR-), HER2 (HER2+, HER2-, unknown), smoking (current, former, never), Charlson comorbidity index (0, 1-2, ≥3), and BMI (&amp;lt;18.5, 28.5-24.9, 25-29.9, and ≥30 kg/m2). For the trend test, B-Score was considered as a continuous variable with adjustment for the above covariates. Citation Format: En Cheng, Bette Caan, Wendy Chen, Carla Prado, Elizabeth Cespedes Feliciano. A Novel Body Composition Risk Score (B-Score) and Overall Survival among Patients with Early-Stage Breast Cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO1-11-11.

ECR 2024 Abstract Highlights

The following highlights spotlight selected abstracts presented at the European Congress of Radiology (ECR) 2024. Covering a range of topics, from deep learning applications in neurological disorders to using a single cranial CT slice for identifying deceased individuals, these highlights present the latest cutting-edge developments in radiology.

An adult and pediatric size-based contrast administration reduction phantom study for single and dual-energy CT through preservation of contrast-to-noise ratio.

Global shortages of iodinated contrast media (ICM) during COVID-19 pandemic forced the imaging community to use ICM more strategically in CT exams. The purpose of this work is to provide a quantitative framework for preserving iodine CNR while reducing ICM dosage by either lowering kV in single-energy CT (SECT) or using lower energy virtual monochromatic images (VMI) from dual-energy CT (DECT) in a phantom study. In SECT study, phantoms with effective diameters of 9.7, 15.9, 21.1, and 28.5cm were scanned on SECT scanners of two different manufacturers at a range of tube voltages. Statistical based iterative reconstruction and deep learning reconstruction were used. In DECT study, phantoms with effective diameters of 20, 29.5, 34.6, and 39.7cm were scanned on DECT scanners from three different manufacturers. VMIs were created from 40 to 140keV. ICM reduction by lowering kV levels for SECT or switching from SECT to DECT was calculated based on the linear relationship between iodine CNR and its concentration under different scanning conditions. On SECT scanner A, while matching CNR at 120kV, ICM reductions of 21%, 58%, and 72% were achieved at 100, 80, and 70kV, respectively. On SECT scanner B, 27% and 80% ICM reduction was obtained at 80 and 100kV. On the Fast-kV switch DECT, with CNR matched at 120kV, ICM reductions were 35%, 30%, 23%, and 15% with VMIs at 40, 50, 60, and 68keV, respectively. On the dual-source DECT, ICM reductions were 52%, 48%, 42%, 33%, and 22% with VMIs at 40, 50, 60, 70, and 80keV. On the dual-layer DECT, ICM reductions were 74%, 62%, 45%, and 22% with VMIs at 40, 50, 60, and 70keV. Our work provided a quantitative baseline for other institutions to further optimize their scanning protocols to reduce the use of ICM.

Projected effectiveness of lung cancer screening and concurrent smoking cessation support in the Netherlands

The NELSON trial demonstrated a 24% intention-to-screen reduction in lung cancer mortality from regular screening with low-dose computed tomography. Implementation efforts in Europe are ongoing, but still await country-specific and NELSON-adapted estimates of the benefits and harms of screening. We use the MISCAN-Lung microsimulation model, calibrated to individual-level outcomes from the NELSON trial, to estimate the effectiveness under 100% compliance of biennial lung cancer screening with concomitant smoking cessation support for Dutch cohorts 1942-1961. The model simulates smoking behaviour, lung cancer incidence and the effects of screening and smoking cessation on lung- and other-cause mortality. We find biennial screening with eligibility criteria equal to those of the 4-IN-THE-LUNG-RUN implementation trial to reduce lung cancer mortality by 16.9% among the eligible population, equivalent to 1076 LC deaths prevented per year in the next two decades. Eligible individuals constitute 21.5% of the cohorts studied, and stand to face 61% of the projected lung cancer mortality burden in the absence of screening. 10.3 life-years are gained per prevented LC death, for 14.9 screens per life year gained. Concomitant smoking cessation interventions may increase the expected gains in life years from screening by up to 20%. Policy makers should imminently consider the implementation of lung cancer screening in Europe, paired with effective smoking cessation interventions. Smoking cessation interventions on their own are not estimated to yield a gain in remaining life expectancy of the magnitude offered by even a single CT screen. European UnionHorizon 2020 grant 848294: 4-IN-THE-LUNG-RUN.

Tumor conspicuity enhancement-based segmentation model for liver tumor segmentation and RECIST diameter measurement in non-contrast CT images

Background and objectiveLiver tumor segmentation (LiTS) accuracy on contrast-enhanced computed tomography (CECT) images is higher than that on non-contrast computed tomography (NCCT) images. However, CECT requires contrast medium and repeated scans to obtain multiphase enhanced CT images, which is time-consuming and cost-increasing. Therefore, despite the lower accuracy of LiTS on NCCT images, which still plays an irreplaceable role in some clinical settings, such as guided brachytherapy, ablation, or evaluation of patients with renal function damage. In this study, we intend to generate enhanced high-contrast pseudo-color CT (PCCT) images to improve the accuracy of LiTS and RECIST diameter measurement on NCCT images. MethodsTo generate high-contrast CT liver tumor region images, an intensity-based tumor conspicuity enhancement (ITCE) model was first developed. In the ITCE model, a pseudo color conversion function from an intensity distribution of the tumor was established, and it was applied in NCCT to generate enhanced PCCT images. Additionally, we design a tumor conspicuity enhancement-based liver tumor segmentation (TCELiTS) model, which was applied to improve the segmentation of liver tumors on NCCT images. The TCELiTS model consists of three components: an image enhancement module based on the ITCE model, a segmentation module based on a deep convolutional neural network, and an attention loss module based on restricted activation. Segmentation performance was analyzed using the Dice similarity coefficient (DSC), sensitivity, specificity, and RECIST diameter error. ResultsTo develop the deep learning model, 100 patients with histopathologically confirmed liver tumors (hepatocellular carcinoma, 64 patients; hepatic hemangioma, 36 patients) were randomly divided into a training set (75 patients) and an independent test set (25 patients). Compared with existing tumor automatic segmentation networks trained on CECT images (U-Net, nnU-Net, DeepLab-V3, Modified U-Net), the DSCs achieved on the enhanced PCCT images are both improved compared with those on NCCT images. We observe improvements of 0.696–0.713, 0.715 to 0.776, 0.748 to 0.788, and 0.733 to 0.799 in U-Net, nnU-Net, DeepLab-V3, and Modified U-Net, respectively, in terms of DSC values. In addition, an observer study including 5 doctors was conducted to compare the segmentation performance of enhanced PCCT images with that of NCCT images and showed that enhanced PCCT images are more advantageous for doctors to segment tumor regions. The results showed an accuracy improvement of approximately 3%–6%, but the time required to segment a single CT image was reduced by approximately 50 %. ConclusionsExperimental results show that the ITCE model can generate high-contrast enhanced PCCT images, especially in liver regions, and the TCELiTS model can improve LiTS accuracy in NCCT images.

Evaluating Mixed Reality as a Tool for Patient Education of Facial Fractures.

Background: Patients may be educated about facial fractures using two-dimensional computed tomography (2DCT); however, three-dimensional mixed reality (3DMR) goggles may improve patient education by delivering content in an immersive environment. Objective: To compare the effectiveness of 2DCT and 3DMR formats used for patient education on facial fractures, as measured by surveys. Methods: In this prospective, randomized, crossover study, video tutorials intended for facial fracture informed consent were created in 2DCT and 3DMR formats from a single CT data set of a zygomaticomaxillary complex (ZMC) fracture. Subjects were randomly assigned into two groups. Group 1 (n = 25) viewed the 2DCT tutorial, answered self-assessment and information recall surveys, viewed the 3DMR tutorial, repeated the prior surveys as well as a comparison survey. Group 2 (n = 25) followed the same sequence but viewed the 3DMR tutorial and then the 2DCT tutorial. Results: Participants (n = 50) had no differences in age (group 1-51.9 years/standard deviation [SD] 20.9; group 2-44.7 years/SD 17.6 years; p = 0.223), gender (group 1-10 male/15 female; group 2-11 male/14 female; p = 0.999), college education level (group 1-25 yes; group 2-25 yes; p = 0.844), or prior 2DCT or 3DMR experience (group 1-9 yes/16 no; group 2-13 yes/12 no; p = 0.393). The 3DMR format was preferred over 2DCT (p < 0.05), and it was reported to enhance understanding as compared to 2DCT (p < 0.05). No differences for information recall were noted (p = 0.753). Conclusion: In this study, participants preferred 3DMR goggles over 2DCT for a simulated ZMC fracture-informed consent.