Computed Tomography Imaging Parameters Research Articles

Enhancing the differential diagnosis of small pulmonary nodules: a comprehensive model integrating plasma methylation, protein biomarkers, and LDCT imaging features

BackgroundAccurate differentiation between malignant and benign pulmonary nodules, especially those measuring 5–10 mm in diameter, continues to pose a significant diagnostic challenge. This study introduces a novel, precise approach by integrating circulating cell-free DNA (cfDNA) methylation patterns, protein profiling, and computed tomography (CT) imaging features to enhance the classification of pulmonary nodules.MethodsBlood samples were collected from 419 participants diagnosed with pulmonary nodules ranging from 5 to 30 mm in size, before any disease-altering procedures such as treatment or surgical intervention. High-throughput bisulfite sequencing was used to conduct DNA methylation profiling, while protein profiling was performed utilizing the Olink proximity extension assay. The dataset was divided into a training set and an independent test set. The training set included 162 matched cases of benign and malignant nodules, balanced for sex and age. In contrast, the test set consisted of 46 benign and 49 malignant nodules. By effectively integrating both molecular (DNA methylation and protein profiling) and CT imaging parameters, a sophisticated deep learning-based classifier was developed to accurately distinguish between benign and malignant pulmonary nodules.ResultsOur results demonstrate that the integrated model is both accurate and robust in distinguishing between benign and malignant pulmonary nodules. It achieved an AUC score 0.925 (sensitivity = 83.7%, specificity = 82.6%) in classifying test set. The performance of the integrated model was significantly higher than that of individual methylation (AUC = 0.799, P = 0.004), protein (AUC = 0.846, P = 0.009), and imaging models (AUC = 0.866, P = 0.01). Importantly, the integrated model achieved a higher AUC of 0.951 (sensitivity = 83.9%, specificity = 89.7%) in 5–10 mm small nodules. These results collectively confirm the accuracy and robustness of our model in detecting malignant nodules from benign ones.ConclusionsOur study presents a promising noninvasive approach to distinguish the malignancy of pulmonary nodules using multiple molecular and imaging features, which has the potential to assist in clinical decision-making.Trial registration: This study was registered on ClinicalTrials.gov on 01/01/2020 (NCT05432128). https://classic.clinicaltrials.gov/ct2/show/NCT05432128.

Effect of X‐Ray Computed Tomography Imaging Parameters on Quantification of Petrophysical Properties

AbstractThree‐dimensional (3D) X‐ray computed tomography (X‐ray CT) imaging has emerged as a nondestructive means of microstructural characterization. However, obtaining and processing high‐quality and high‐resolution images is time‐consuming and often requires high‐performance computing, particularly with a high number of projections. This work evaluates the effect of 3D X‐ray CT imaging parameters on pore connectivity and surface area quantification in sandstone samples of varied composition. Samples from Bentheimer and Torrey Buff formations are imaged via 3D X‐ray CT imaging at resolutions ranging from 1.25 to 15 μm, bin sizes 1, 2, and 4, and number of projections from 400 to 4,500. Collected images are processed and analyzed using ImageJ and MATLAB to discern petrophysical properties and the results are compared with each other and Mercury Intrusion Capillary Pressure (MICP) results. Overall, little variation in bulk porosity with changing scanning parameters is observed. However, for low resolution and projection numbers, connected porosity is lower compared to bulk porosity due to a failure to capture microfeatures. Overall, mineral surface area is observed to decrease with increasing bin size, voxel size, and projection numbers, except an observed increase with projection numbers for Torrey Buff. The Torrey Buff samples contains comparatively more clays and even the highest resolution (1.25 μm) fails to separate the micrograins, which is reflected in the pore size distribution. Identifying these variations are helpful as discrepancies in imaged pore connectivity and surface area can largely impact assessments of fluid flow and transport in reactive transport simulations informed by this data.

Effect of X-ray Computed Tomography Imaging Parameters on Petrophysical Properties

Effect of X-ray Computed Tomography Imaging Parameters on Petrophysical Properties

Development and Application of a Virtual Imaging Trial framework for Airway Quantifications via CT.

Chronic obstructive pulmonary disease (COPD) is one of the top three causes of death worldwide, characterized by emphysema and bronchitis. Airway measurements reflect the severity of bronchitis and other airway-related diseases. Airway structures can be objectively evaluated with quantitative computed tomography (CT). The accuracy of such quantifications is limited by the spatial resolution and image noise characteristics of the imaging system and can be potentially improved with the emerging photon-counting CT (PCCT) technology. This study evaluated the quantitative performance of PCCT against energy-integrating CT (EICT) systems for airway measurements, and further identified optimum CT imaging parameters for such quantifications. The study was performed using a novel virtual imaging framework by developing the first library of virtual patients with bronchitis. These virtual patients were developed based on CT images of confirmed COPD patients with varied bronchitis severity. The human models were virtually imaged at 6.3 and 12.6 mGy dose levels using a scanner-specific simulator (DukeSim), synthesizing clinical PCCT and EICT scanners (NAEOTOM Alpha, FLASH, Siemens). The projections were reconstructed with two algorithms and kernels at different matrix sizes and slice thicknesses. The CT images were used to quantify clinically relevant airway measurements ("Pi10" and "WA%") and compared against their ground truth values. Compared to EICT, PCCT provided more accurate Pi10 and WA% measurements by 63.1% and 68.2%, respectively. For both technologies, sharper kernels and larger matrix sizes led to more reliable bronchitis quantifications. This study highlights the potential advantages of PCCT against EICT in characterizing bronchitis utilizing a virtual imaging platform.

Three-dimensional prostate CT segmentation through fine-tuning of a pre-trained neural network using no reference labeling.

Accurate segmentation of the prostate on computed tomography (CT) has many diagnostic and therapeutic applications. However, manual segmentation is time-consuming and suffers from high inter- and intra-observer variability. Computer-assisted approaches are useful to speed up the process and increase the reproducibility of the segmentation. Deep learning-based segmentation methods have shown potential for quick and accurate segmentation of the prostate on CT images. However, difficulties in obtaining manual, expert segmentations on a large quantity of images limit further progress. Thus, we proposed an approach to train a base model on a small, manually-labeled dataset and fine-tuned the model using unannotated images from a large dataset without any manual segmentation. The datasets used for pre-training and fine-tuning the base model have been acquired in different centers with different CT scanners and imaging parameters. Our fine-tuning method increased the validation and testing Dice scores. A paired, two-tailed t-test shows a significant change in test score (p = 0.017) demonstrating that unannotated images can be used to increase the performance of automated segmentation models.

Differential diagnosis of renal oncocytoma and chromophobe renal cell carcinoma using CT features: a central scar-matched retrospective study.

Renal oncocytoma (RO) and chromophobe renal cell carcinoma (chRCC) have a common cellular origin and different clinical management and prognosis. To explore the utility of computed tomography (CT) in the differentiation of RO and chRCC. Twenty-five patients with RO and 73 patients with chRCC presenting with the central scar were included retrospectively. Two experienced radiologists independently reviewed the CT imaging features, including location, tumor size, relative density ratio, segmental enhancement inversion (SEI), necrosis, and perirenal fascia thickening, among others. Interclass correlation coefficient (ICC, for continuous variables) or Kappa coefficient test (for categorical variables) was used to determine intra-observer and inter-observer bias between the two radiologists. The inter- and intra-reader reproducibility of the other CT imaging parameters were nearly perfect (>0.81) except for the measurements of fat (0.662). RO differed from chRCC in the cortical or medullary side (P = 0.005), relative density ratio (P = 0.020), SEI (P < 0.001), and necrosis (P = 0.045). The logistic regression model showed that location (right kidney), hypo-density on non-enhanced CT, SEI, and perirenal fascia thickening were highly predictive of RO. The combined indicators from logistic regression model were used for ROC analysis. The area under the ROC curve was 0.923 (P < 0.001). The sensitivity and specificity of the four factors combined for diagnosing RO were 88% and 86.3%, respectively. The correlation coefficient between necrosis and tumor size in all tumors including both of RO and chRCC was 0.584, indicating a positive correlation (P < 0.001). The CT imaging features of location (right kidney), hypo-density on non-enhanced CT, SEI, and perirenal fascia thickening were valuable indicators in distinguishing RO from chRCC.

Value of high-density sign on CT images after mechanical thrombectomy for large vessel occlusion in predicting hemorrhage and unfavorable outcome.

Cerebral hyperdensities can appear on head computed tomography (CT) images performed early after endovascular treatment (EVT) in patients with acute ischemic stroke and may be secondary to contrast staining or hemorrhagic transformation. The aim of this study was to determine how the high-density sign on CT affects mortality and clinical outcome and whether CT parameters predict hemorrhagic conversion or unfavorable outcome. We retrospectively reviewed a database of patients who underwent EVT with mechanical thrombectomy for acute ischemic stroke over 7 years. Included were acute stroke patients with a CT examination within 24 h post-EVT with mechanical thrombectomy, demonstrating areas of hyperdensity. We evaluated morphologic characteristics of these lesions, location, CT Hounsfield units and largest area, as well as patient demographics, EVT methods and patient outcome. A total of 29 patients met the strict inclusion criteria. Complete recanalization was achieved in 58.6% (17/29). Seventeen (58.6%) cases of post-intervention cerebral hyperdensities were related to contrast staining and 12 (41.4%) cases to contrast staining and hemorrhage. Patient mortality was significantly higher in the hemorrhagic group (50.0% versus 5.9%, p = 0.003). The increased density on CT was associated with higher hemorrhagic risk (odds ratio 1.05, p = 0.036). Patients with the high-density sign on CT images after mechanical thrombectomy for acute ischemic stroke demonstrated increased mortality and worse clinical outcome, primarily when these hyperdensities were related to hemorrhage. CT imaging parameters as higher density areas can help in the differentiation of hemorrhage from contrast staining.

Assessment of CT Imaging Protocols Impacts on Calculation of Point Dose in Water Phantom Using Radiotherapy Treatment Planning System

Purpose: Point dose calculation in the Treatment Planning System (TPS) is performed using Computed Tomography (CT) images because CT images data have the tissue electron density information. The effect of CT imaging protocols on the calculation of point doses in TPS is one of the most important subjects that was evaluated in this study. Materials and Methods: CT scan imaging was performed from cylindrical water phantom using three scanner systems and different imaging technical parameters. The CT images data were irradiated in TPS to delivering a 200 cGy radiation dose to the center of the phantom with 6 and 15MV X-Ray photon energy with multiple radiation fields and Monitor Unit (MU) were separately calculated. In the TPS, a virtual water phantom with the same characteristic as CT image phantom was simulated and irradiated with similar conditions. The difference in MU values obtained from two irradiation methods in TPS was compared with Wilcoxon nonparametric test. Results: Variations of mA, kV, Pitch, slice thickness, and kernel as CT imaging parameters have not significantly affected radiotherapy point dose calculation (&lt;2%). CT imaging protocols as a thin slice, 80 kV, and sharp kernel have the greatest difference between CT image-based calculation and designed phantom calculation in TPS where wedge field and 6 MV photon energy were used. Conclusion: The use of CT images obtained with multiple protocols can be used without having a significant effect on the dose calculations of the treatment planning system.

Correlation between preoperative CT imaging parameters and clinical outcome of lumbar spinal stenosis treated with endoscopic transforaminal decompression

ObjectiveTo evaluate clinical effects, and their correlations with preoperative computed tomography imaging parameters, in cases of lumbar spinal stenosis treated by endoscopic transforaminal decompression.MethodsThis retrospective study included orthopaedic patients who had undergone percutaneous endoscopic lumbar discectomy (PELD) for lumbar spinal stenosis. Clinical symptoms were evaluated by visual analogue scale (VAS), Oswestry Disability Index (ODI) and claudication distance. Overall clinical efficacy was evaluated by Macnab score.ResultsA total of 87 patients were included. Postoperative wound healing was good without complications. Macnab scores following PELD were ‘excellent’ in 41 cases (47.12%), ‘good’ in 30 cases (34.48%), ‘generally good’ in seven cases (8.04%), and ‘poor’ in nine cases (10.34%). The overall rate of optimal surgery was 81.60%. Postoperative pain (VAS) and ODI scores, and claudication distance, were significantly improved versus preoperative values. The soft tissue invasion ratio of the vertebral canal and invasion ratio of the nerve root canal were correlated with clinical efficacy.ConclusionPositive correlations were observed between clinical efficacy of endoscopic transforaminal decompression and preoperative vertebral canal soft tissue invasion ratio and nerve root canal invasion ratio in patients with lumbar spinal stenosis.

Comparison of spectral computed tomography imaging parameters between squamous cell carcinoma and adenocarcinoma at the gastroesophageal junction.

To compare the spectral computed tomography (CT) imaging parameters between squamous cell carcinoma (SCC) and adenocarcinoma (AC) at the gastroesophageal junction (GEJ). A total of 80 patients were enrolled in this retrospective study. Among them, 35 were diagnosed with SCC (SCC group) and 45 were diagnosed with AC (AC group). All patients underwent an enhanced scan with spectral CT. The following CT imaging parameters were evaluated: iodine concentration (IC), water content (WC), effective atomic number (Eff-Z) and slope of the spectral HU curve (λHU) of lesions. Receiver operating characteristic (ROC) curve was used to analyze the predictive value of spectral CT imaging parameters for diagnosis of SCC and AC. Patients with SCC had lower IC, Eff-Z, and λHU in arterial phase and venous phase compared with AC (p< 0.05). There were no significant differences in WC between the two groups. ROC curve analyses revealed that IC, Eff-Z, and λHU in arterial phase and venous phase were predictors for diagnosis of SCC and AC (AUC > 0.5). Moreover, the IC, Eff-Z and λHU in venous phase had better differential diagnostic performances than that in arterial phase. Spectral CT could be useful in the differential diagnosis of SCC and AC at the GEJ. Therefore, a routine spectral CT scan is recommended for patients with carcinoma of the GEJ.

Spectral CT imaging parameters and Ki-67 labeling index in lung adenocarcinoma.

ObjectiveTo explore the correlation between the spectral computed tomography (CT) imaging parameters and the Ki-67 labeling index in lung adenocarcinoma.MethodsSpectral CT imaging parameters [iodine concentrations of lesions (ICLs) in the arterial phase (ICLa) and venous phase (ICLv), normalized IC in the aorta (NICa/NICv), slope of the spectral HU curve (λHUa/λHUv) and monochromatic CT number enhancement on 40 keV and 70 keV images (CT40keVa/v, CT70keVa/v)] in 34 lung adenocarcinomas were analyzed, and common molecular markers, including the Ki-67 labeling index, were detected with immunohistochemistry. Different Ki-67 labeling indexes were measured and grouped into four grades according to the number of positive-stained cells (grade 0, ≤1%; 1%<grade 1≤10%; 10%<grade 2≤30%; and grade 3, >30%). One-way analysis of variance (ANOVA) was used to compare the four different grades, and the Bonferroni method was used to correct the P value for multiple comparisons. A Spearman correlation analysis was performed to further research a quantitative correlation between the Ki-67 labeling index and spectral CT imaging parameters.ResultsCT40keVa, CT40keVv, CT70keVa and CT70keVv increased as the grade increased, and CT70keVa and CT70keVv were statistically significant (P<0.05). These four parameters and the Ki-67 labeling index showed a moderate positive correlation with lung adenocarcinoma nodules. ICL, NIC and λHU in the arterial and venous phases were not significantly different among the four grades.ConclusionsThe spectral CT imaging parameters CT40keVa, CT40keVv, CT70keVa and CT70keVv gradually increased with Ki-67 expression and showed a moderate positive correlation with lung adenocarcinomas. Therefore, spectral CT imaging parameter-enhanced monochromatic CT numbers at 70 keV may indicate the extent of proliferation of lung adenocarcinomas.

Is there any correlation between spectral CT imaging parameters and PD-L1 expression of lung adenocarcinoma?

BackgroundThe aim of this study was to explore whether spectral computed tomography (CT) imaging parameters are associated with PD‐L1 expression of lung adenocarcinoma.MethodsSpectral CT imaging parameters (iodine concentrations [IC] of lesion in arterial phase [ICLa] and venous phase [ICLv], normalized IC [NICa/NICv]‐normalized to the IC in the aorta, slope of the spectral HU curve [λHUa/λHUv] and enhanced monochromatic CT number [CT40keVa/v, CT70keVa/v] on 40 and 70 keV images) were analyzed in 34 prospectively enrolled lung adenocarcinoma patients with common molecular pathological markers including PD‐L1 expression detected with immunohistochemistry. Patients were divided into two groups: positive PD‐L1 expression and negative PD‐L1 expression groups. Two‐sample Mann‐Whitney U test was used to test the difference of spectral CT imaging parameters between the two groups.ResultsThe CT40keVa (127.03 ± 37.92 vs. −54.69 ± 262.04), CT40keVv (124.39 ± 34.71 vs. −45.73 ± 238.97), CT70keVa (49.56 ± 11.76 vs. −136.51 ± 237.08) and CT70keVv (46.13 ± 15.81 vs. −133.10 ± 230.72) parameters in the positive PD‐L1 expression group of lung adenocarcinoma were significantly higher than the negative PD‐L1 expression group (all P < 0.05). There was no difference detected in IC, NIC and λHU of the arterial and venous phases between both groups (all P > 0.05).ConclusionCT40keVa, CT40keVv, CT70keVa and CT70keVv were increased in positive PD‐L1 expression. These parameters may be used to distinguish the PD‐L1 expression state of lung adenocarcinoma.

Evaluation of Texture Analysis for the Differential Diagnosis of Mass-Forming Pancreatitis From Pancreatic Ductal Adenocarcinoma on Contrast-Enhanced CT Images.

Purpose: To investigate the potential of computed tomography (CT) imaging features and texture analysis to differentiate between mass-forming pancreatitis (MFP) and pancreatic ductal adenocarcinoma (PDAC).Materials and Methods: Thirty patients with pathologically proved MFP and 79 patients with PDAC were included in this study. Clinical data and CT imaging features of the two lesions were evaluated. Texture features were extracted from arterial and portal phase CT images using commercially available software (AnalysisKit). Multivariate logistic regression analyses were used to identify relevant CT imaging and texture parameters to discriminate MFP from PDAC. Receiver operating characteristic curves were performed to determine the diagnostic performance of predictions.Results: MFP showed a larger size compared to PDAC (p = 0.009). Cystic degeneration, pancreatic ductal dilatation, vascular invasion, and pancreatic sinistral portal hypertension were more frequent and duct penetrating sign was less frequent in PDAC compared to MFP. Arterial CT attenuation, arterial, and portal enhancement ratios of MFP were higher than PDAC (p < 0.05). In multivariate analysis, arterial CT attenuation and pancreatic duct penetrating sign were independent predictors. Texture features in arterial phase including SurfaceArea, Percentile40, InverseDifferenceMoment_angle90_offset4, LongRunEmphasis_angle45_offset4, and uniformity were independent predictors. Texture features in portal phase including LongRunEmphasis_angle135_offset7, VoxelValueSum, LongRunEmphasis_angle135_offset4, and GLCMEntropy_angle45_offset1 were independent predictors. Areas under the curve of imaging feature-based, texture feature-based in arterial and portal phases, and the combined models were 0.84, 0.96, 0.93, and 0.98, respectively.Conclusions: CT texture analysis demonstrates great potential to differentiate MFP from PDAC.

Whole-tumor perfusion CT using texture analysis in unresectable stage IIIA/B non-small cell lung cancer treated with recombinant human endostatin.

To observe the dynamic changes of blood perfusion with whole-tumor computed tomography (CT) perfusion imaging using texture analysis in patients with unresectable stage IIIA/B non-small cell lung cancer (NSCLC) treated with recombinant human endostatin (Endostar). This phase II clinical trial recruited 11 patients diagnosed with stage IIIA/B NSCLC. Histological examination prior to treatment revealed squamous cell carcinoma in 4 cases and adenocarcinoma in 7 cases. All patients underwent contrast-enhanced perfusion CT at baseline and a second CT scan 1 week after treatment initiation with Endostar. CT perfusion images including blood flow (BF), blood volume (BV), and permeability (PMB) were imported into OmniKinetics software to quantitatively assess the texture features. Skewness, kurtosis, and entropy were calculated at baseline and after anti-angiogenic therapy. Changes in tumor were analyzed using Wilcoxon signed-rank test. The association of parameters with survival was evaluated using Cox proportional hazards regression model. There were no statistical differences in the mean values of BF, BV, and PMB before and after treatment (P=0.594, 0.477 and 0.328, respectively). The skewness on BF images demonstrated significant differences at baseline and after treatment (0.6±2.7 vs. 1.0±2.6, P=0.010), while skewness of BV and PMB showed no significant variation (P=0.477 and 0.213, respectively). The kurtosis and entropy for BF, BV and PMB showed no significant differences (all P>0.05). In adenocarcinoma, the mean BF showed no significant differences at baseline and after treatment (76.5±25.7 vs. 101.2±46.4, P=0.398), while skewness for BF was significantly higher after treatment than at baseline (-0.19±3.3 vs. 0.59±3.2, P=0.028). No significant associations were found between perfusion CT imaging parameters and progression-free survival. These results suggested that blood perfusion showed improvement with whole-tumor perfusion CT using texture analysis in patients with stage IIIA/B NSCLC treated by Endostar.

Computerized Tomography Criteria as a Tool for Simplifying the Assessment of Locally Advanced Rectal Cancer

Rectal cancer represents a leading cause of mortality worldwide. Staging defines the local and distant extent of the disease, guides management, and predicts prognosis. Different modalities are available for staging including TRUS (transrectal ultrasound), CT (computed tomography), and MRI (magnetic resonance imaging). The objective of this study was to screen and isolate CT imaging parameters suggestive of advanced rectal cancer and its utility as a tool in simplifying the staging protocol making further imaging studies unnecessary. Retrospective, single center study. Seventy-five patients with rectal carcinoma were included and were divided into two groups according to their T score and nodal involvement status, as diagnosed by TRUS. Group 1 (n = 15) "local disease" (T1/T2 N0) and group 2 (n = 60) "locally advanced disease" are both eligible for neoadjuvant treatment (N/any T or T3/any N). For each patient, three CT imaging parameters that represent locally advanced disease, i.e., perirectal fat infiltration, local lymphadenopathy, and rectal wall thickening, were evaluated and compared between the two groups. The capability of CT imaging to accurately predict locally advanced rectal carcinoma. Rectal wall thickening on CT was found to have 92% PPV and perirectal lymphadenopathy 96% PPV for predicting a locally advanced stage. A combination of those two parameters results in a predictive PPV of 98%. This was a single center retrospective study, with a relatively small cohort. CT is a valuable tool in the assessment and management of rectal carcinoma as it can identify locally advanced rectal cancer. This enables treatment without any further unnecessary evaluation.

Linear Accelerator-Based Radiotherapy Simulation Using On-Board Kilovoltage Cone-Beam Computed Tomography for 3-Dimensional Volumetric Planning and Rapid Treatment in the Palliative Setting.

Background:Palliation of advanced disease using radiotherapy can create difficult clinical situations where standard computed tomography simulation and immobilization techniques are not feasible. We developed a linear accelerator-based radiotherapy simulation technique using nonstandard patient positioning for head and neck palliation using on-board kilovoltage cone-beam computed tomography for 3-D volumetric planning and rapid treatment. Material and Methods: We proved cone-beam computed tomography simulation feasibility for semi-upright patient positioning using an anthropomorphic phantom on a clinical Elekta-Synergy linear accelerator. Cone-beam computed tomography imaging parameters were optimized for high-resolution image reconstruction and to ensure mechanical clearance. The patient was simulated using a cone-beam computed tomography–based approach and the cone-beam computed tomography digital imaging and communications in medicine file was imported to the treatment planning software to generate radiotherapy target volumes. Rapid planning was achieved by using a 3-level bulk density correction for air, soft tissue, and bone set at 0, 1.0, and 1.4 g/cm3, respectively.Results:Patient volumetric imaging was obtained through cone-beam computed tomography simulation and treatment was delivered as planned without incident. Bulk density corrections were verified against conventionally simulated patients where differences were less than 1%. Conclusion: We successfully developed and employed a semi-upright kilovoltage cone-beam computed tomography–based head and neck simulation and treatment planning method for 3-D conformal radiotherapy delivery. This approach provides 3-D documentation of the radiotherapy plan and allows tabulation of quantitative spatial dose information which is valuable if additional palliative treatments are needed in the future. This is a potentially valuable technique that has broad clinical applicability for benign and palliative treatments across multiple disease sites—particularly where standard supine simulation and immobilization techniques are not possible.

Can spectral computed tomography imaging improve the differentiation between malignant and benign pulmonary lesions manifesting as solitary pure ground glass, mixed ground glass, and solid nodules?

BackgroundThis study quantitatively assessed the efficacy of spectral computed tomography (CT) imaging parameters for differentiating the malignancy and benignity of solitary pulmonary nodules (SPNs) manifesting as ground glass nodules (GGNs) and solid nodules (SNs).MethodsThe study included 114 patients with SPNs (61 GGNs, and 53 SNs) who underwent CT plain and enhanced scans in the arterial (a) and venous (v) phases using the spectral imaging mode. The spectral CT imaging parameters included: iodine concentrations (IC) of lesions in the arterial (ICLa) and venous (ICLv) phases; normalized IC (NICa/NICv, normalized to the IC in the aorta); the slope of the spectral Hounsfield unit (HU) curve (λHUa/λHUv); and monochromatic CT number (CT40keVa/v, CT70keVa/v) enhancement on 40 and 70 keV images. The two‐sample Mann–Whitney U test was used to compare quantitative parameters between malignant and benign SPNs, SNs, and GGNs.ResultsPathology revealed 75 lung cancer cases, 3 metastatic nodules, 14 benign nodules, and 22 inflammatory nodules. Among the 53 SNs there were 37 malignant and 16 benign nodules. Among the 61 GGNs there were 41 malignant and 20 benign nodules. Overall, the CT40keVa, λHUa, CT40keVv, λHUv, and ICLv of benign SPNs were all greater than those of malignant SPNs (all P < 0.05). For GGNs, CT40keVa/v, CT70keVa/v, λHUa/λHUv, and ICLv of malignant GGNs were all lower than those of benign GGNs.ConclusionSpectral CT imaging is a more promising method for distinguishing malignant from benign nodules, especially in nodules manifesting as GGNs in contrast‐enhanced scanning.

Computed Tomography Perfusion Derived Blood-Brain Barrier Permeability Does Not Yet Improve Prediction of Hemorrhagic Transformation

Introduction: Hemorrhagic transformation (HT) in acute ischemic stroke can occur as a result of reperfusion treatment. While withholding treatment may be warranted in patients with increased risk of HT, prediction of HT remains difficult. Nonlinear regression analysis can be used to estimate blood-brain barrier permeability (BBBP). The aim of this study was to identify a combination of clinical and imaging variables, including BBBP estimations, that can predict HT. Materials and Methods: From the Dutch acute stroke study, 545 patients treated with intravenous recombinant tissue plasminogen activator and/or intra-arterial treatment were selected, with available admission extended computed tomography (CT) perfusion and follow-up imaging. Patient admission treatment characteristics and CT imaging parameters regarding occlusion site, stroke severity, and BBBP were recorded. HT was assessed on day 3 follow-up imaging. The association between potential predictors and HT was analyzed using univariate and multivariate logistic regression. To compare the added value of BBBP, areas under the curve (AUCs) were created from 2 models, with and without BBBP. Results: HT occurred in 57 patients (10%). In univariate analysis, older age (OR 1.03, 95% CI 1.006–1.05), higher admission National Institutes of Health Stroke Scale (NIHSS; OR 1.13, 95% CI 1.08–1.18), higher clot burden (OR 1.28, 95% CI 1.16–1.41), poor collateral score (OR 3.49, 95% CI 1.85–6.58), larger Alberta Stroke Program Early CT Score cerebral blood volume deficit size (OR 1.26, 95% CI 1.14–1.38), and increased BBBP (OR 2.22, 95% CI 1.46–3.37) were associated with HT. In multivariate analysis with age and admission NIHSS, the addition of BBBP did not improve the AUC compared to both independent predictors alone (AUC 0.77, 95% CI 0.71–0.83). Conclusion: BBBP predicts HT but does not improve prediction with age and admission NIHSS.

Accounting for reconstruction kernel-induced variability in CT radiomic features using noise power spectra.

Large variability in computed tomography (CT) radiomics feature values due to CT imaging parameters can have subsequent implications on the prognostic or predictive significance of these features. Here, we investigated the impact of pitch, dose, and reconstruction kernel on CT radiomic features. Moreover, we introduced correction factors to reduce feature variability introduced by reconstruction kernels. The credence cartridge radiomics and American College of Radiology (ACR) phantoms were scanned on five different scanners. ACR phantom was used for 3-D noise power spectrum (NPS) measurements to quantify correlated noise. The coefficient of variation (COV) was used as the variability assessment metric. The variability in texture features due to different kernels was reduced by applying the NPS peak frequency and region of interest (ROI) maximum intensity as correction factors. Most texture features were dose independent but were strongly kernel dependent, which is demonstrated by a significant shift in NPS peak frequency among kernels. Percentage improvement in robustness was calculated for each feature from original and corrected %COV values. Percentage improvements in robustness of 19 features were in the range of 30% to 78% after corrections. We show that NPS peak frequency and ROI maximum intensity can be used as correction factors to reduce variability in CT texture feature values due to reconstruction kernels.

Correlation between dual-energy spectral CT imaging parameters and pathological grades of non-small cell lung cancer

To investigate the correlation between pathological grades of non-small cell lung cancers (NSCLCs) and quantitative parameters generated in dual-energy spectral computed tomography (CT). Fifty-three patients with NSCLCs who underwent preoperative dual-energy spectral CT imaging and surgical resection were evaluated retrospectively. These patients were divided into a low-grade group and a high-grade group based on their histopathological differentiation. In the arterial phase (AP) and venous phase (VP), iodine concentration (IC) in cancers was measured in iodine-based material decomposition images, and normalised to the IC in the aorta to calculate the normalised iodine concentration (NIC), the spectral CT curve was generated from the monochromatic images to calculate the slope of the spectral curve (λHU). Differences in quantitative parameters (NIC and λHU) were compared using the two-sample t-test. The correlations between spectral CT parameters and tumour grades were evaluated using the Spearman rank correlation analysis. Receiver operating characteristic (ROC) curves were generated to calculate their diagnostic efficacies. The NIC and λHU in the low-grade NSCLC group were significantly higher than those in the high-grade NSCLC group both in AP and VP (all p<0.001). There was a significant negative correlation between spectral CT parameters and pathological grades by the Spearman rank correlation (all p<0.001). ROC analysis indicated that λHU in VP provided the best diagnostic performance in distinguishing high-grade cancers from low-grade cancers (area under the ROC curve [AUC], 0.914; sensitivity, 85.7%; specificity, 84.4%). The quantitative parameters in dual-energy spectral CT imaging provide useful information to differentiate the pathological grades of NSCLCs.