The Effects of Iodine Attenuation on Pulmonary Nodule Volumetry using Novel Dual-Layer Computed Tomography Reconstructions

Impact on Image Quality and Diagnostic Performance of Dual-Layer Detector Spectral CT for Pulmonary Subsolid Nodules: Comparison With Hybrid and Model-Based Iterative Reconstruction

OBJECTIVE

To evaluate the image quality and diagnostic performance of pulmonary subsolid nodules on conventional iterative algorithms, virtual monoenergetic images (VMIs), and electron density mapping (EDM) using a dual-layer detector spectral CT (DLSCT).

METHODS

This retrospective study recruited 270 patients who underwent DLSCT scan for lung nodule screening or follow-up. All CT examinations with subsolid nodules (pure ground-glass nodules [GGNs] or part-solid nodules) were reconstructed with hybrid and model-based iterative reconstruction, VMI at 40, 70, 100, and 130 keV levels, and EDM. The CT number, objective image noise, signal-to-noise ratio, contrast-to-noise ratio, diameter, and volume of subsolid nodules were measured for quantitative analysis. The overall image quality, image noise, visualization of nodules, artifact, and sharpness were subjectively rated by 2 thoracic radiologists on a 5-point scale (1 = unacceptable, 5 = excellent) in consensus. The objective image quality measurements, diameter, and volume were compared among the 7 groups with a repeated 1-way analysis of variance. The subjective scores were compared with Kruskal-Wallis test.

RESULTS

A total of 198 subsolid nodules, including 179 pure GGNs, and 19 part-solid nodules were identified. Based on the objective analysis, EDM had the highest signal-to-noise ratio (164.71 ± 133.60; P < 0.001) and contrast-to-noise ratio (227.97 ± 161.96; P < 0.001) among all image sets. Furthermore, EDM had a superior mean subjective rating score (4.80 ± 0.42) for visualization of GGNs compared to other reconstructed images (all P < 0.001), although the model-based iterative reconstruction had superior subjective scores of overall image quality. For pure GGNs, the measured diameter and volume did not significantly differ among different reconstructions (both P > 0.05).

CONCLUSIONS

EDM derived from DLSCT enabled improved image quality and lesion conspicuity for the evaluation of lung subsolid nodules compared to conventional iterative reconstruction algorithms and VMIs.

Factors influencing the outcome of volumetry tools for pulmonary nodule analysis: a systematic review and attempted meta-analysis

Health systems worldwide are implementing lung cancer screening programmes to identify early-stage lung cancer and maximise patient survival. Volumetry is recommended for follow-up of pulmonary nodules and outperforms other measurement methods. However, volumetry is known to be influenced by multiple factors. The objectives of this systematic review (PROSPERO CRD42022370233) are to summarise the current knowledge regarding factors that influence volumetry tools used in the analysis of pulmonary nodules, assess for significant clinical impact, identify gaps in current knowledge and suggest future research. Five databases (Medline, Scopus, Journals@Ovid, Embase and Emcare) were searched on the 21st of September, 2022, and 137 original research studies were included, explicitly testing the potential impact of influencing factors on the outcome of volumetry tools. The summary of these studies is tabulated, and a narrative review is provided. A subset of studies (n = 16) reporting clinical significance were selected, and their results were combined, if appropriate, using meta-analysis. Factors with clinical significance include the segmentation algorithm, quality of the segmentation, slice thickness, the level of inspiration for solid nodules, and the reconstruction algorithm and kernel in subsolid nodules. Although there is a large body of evidence in this field, it is unclear how to apply the results from these studies in clinical practice as most studies do not test for clinical relevance. The meta-analysis did not improve our understanding due to the small number and heterogeneity of studies testing for clinical significance. CRITICAL RELEVANCE STATEMENT: Many studies have investigated the influencing factors of pulmonary nodule volumetry, but only 11% of these questioned their clinical relevance in their management. The heterogeneity among these studies presents a challenge in consolidating results and clinical application of the evidence. KEY POINTS: • Factors influencing the volumetry of pulmonary nodules have been extensively investigated. • Just 11% of studies test clinical significance (wrongly diagnosing growth). • Nodule size interacts with most other influencing factors (especially for smaller nodules). • Heterogeneity among studies makes comparison and consolidation of results challenging. • Future research should focus on clinical applicability, screening, and updated technology.

Diagnostic Accuracy of a Convolutional Neural Network Assessment of Solitary Pulmonary Nodules Compared With PET With CT Imaging and Dynamic Contrast-Enhanced CT Imaging Using Unenhanced and Contrast-Enhanced CT Imaging

BACKGROUND

Solitary pulmonary nodules (SPNs) measuring 8 to 30 mm in diameter require further workup to determine the likelihood of malignancy.

RESEARCH QUESTION

What is the diagnostic performance of a lung cancer prediction convolutional neural network (LCP-CNN) in SPNs using unenhanced and contrast-enhanced CT imaging compared with the current clinical workup?

STUDY DESIGN AND METHODS

This was a post hoc analysis of the Single Pulmonary Nodule Investigation: Accuracy and Cost-Effectiveness of Dynamic Contrast Enhanced Computed Tomography in the Characterisation of Solitary Pulmonary Nodules trial, a prospective multicenter study comparing the diagnostic accuracy of dynamic contrast-enhanced (DCE) CT imaging with PET imaging in SPNs. The LCP-CNN was designed and validated in an external cohort. LCP-CNN-generated risk scores were created from the noncontrast and contrast-enhanced CT scan images from the DCE CT imaging. The gold standard was histologic analysis or 2 years of follow-up. The area under the receiver operating characteristic curves (AUC) were calculated using LCP-CNN score, maximum standardized uptake value, and DCE CT scan maximum enhancement and were compared using the DeLong test.

RESULTS

Two hundred seventy participants (mean ± SD age, 68.3 ± 8.8 years; 49% women) underwent PET with CT scan imaging and DCE CT imaging with CT scan data available centrally for LCP-CNN analysis. The accuracy of the LCP-CNN on the noncontrast images (AUC, 0.83; 95% CI, 0.79-0.88) was superior to that of DCE CT imaging (AUC, 0.76; 95% CI, 0.69-0.82; P = .03) and equal to that of PET with CT scan imaging (AUC, 0.86; 95% CI, 0.81-0.90; P = .35). The presence of contrast resulted in a small reduction in diagnostic accuracy, with the AUC falling from 0.83 (95% CI, 0.79-0.88) on the noncontrast images to 0.80 to 0.83 after contrast (P < .05 for 240 s after contrast only).

INTERPRETATION

An LCP-CNN algorithm provides an AUC equivalent to PET with CT scan imaging in the diagnosis of solitary pulmonary nodules.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier; No.: NCT02013063.

State of the Art: Lung Cancer Staging Using Updated Imaging Modalities

Lung cancer is among the most common mortality causes worldwide. This scientific article is a comprehensive review of current knowledge regarding screening, subtyping, imaging, staging, and management of treatment response for lung cancer. The traditional imaging modality for screening and initial lung cancer diagnosis is computed tomography (CT). Recently, a dual-energy CT was proven to enhance the categorization of variable pulmonary lesions. The National Comprehensive Cancer Network (NCCN) recommends usage of fluorodeoxyglucose positron emission tomography (FDG PET) in concert with CT to properly stage lung cancer and to prevent fruitless thoracotomies. Diffusion MR is an alternative to FDG PET/CT that is radiation-free and has a comparable diagnostic performance. For response evaluation after treatment, FDG PET/CT is a potent modality which predicts survival better than CT. Updated knowledge of lung cancer genomic abnormalities and treatment regimens helps to improve the radiologists’ skills. Incorporating the radiologic experience is crucial for precise diagnosis, therapy planning, and surveillance of lung cancer.

Added value of spectral parameters for the assessment of lymph node metastasis of lung cancer with dual-layer spectral detector computed tomography

BACKGROUND

Lymph node (LN) metastasis is an important factor affecting the treatment of lung cancer. The purpose of this article was to investigate the benefits of dual-layer spectral detector computed tomography (SDCT) for the evaluation of metastatic LNs in lung cancer.

METHODS

Data from 93 patients with lung cancer who underwent dual-phase enhanced scanning with SDCT were retrospectively analyzed. According to the pathological findings, 166 LNs were grouped as metastatic (n=80) or non-metastatic (n=86). LNs in station 4 (n=80) and station 7 (n=35) accounted for the majority of the LNs (approximately 69.23%). The short-axis diameter of the LN, arterial enhancement fraction (AEF), normalized iodine concentration (NIC), and the slope of the spectral Hounsfield unit curve (λHU) during the arterial phase (AP) and venous phase (VP) were measured. The Mann-Whitney U test was used to statistically compare these quantitative parameters. Receiver operating characteristic (ROC) curves were plotted to identify the cutoff values, and decision curve analysis (DCA) was performed to determine the net benefit of each parameter. The diagnostic performance, obtained by combining the short-axis diameter with each of the above parameters, was also studied.

RESULTS

The short-axis LN diameter, AEF, NIC, and λHU during the AP and VP all showed significant differences between the metastatic and non-metastatic groups (P<0.05). Of the parameters, the AEF had the greatest diagnostic efficiency for metastatic LNs [area under the ROC curve (AUC)AEF =0.885] with a threshold of 86.40%. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and 95% confidence interval were 90.00%, 89.53%, 88.89%, 90.59%, and 0.830-0.944, respectively. When the quantitative parameters were combined with the short-axis diameter, the AUCs of the parameters, except the AEF, were significantly improved (P<0.05).

CONCLUSIONS

The iodine quantitative parameters from SDCT, such as the AEF, demonstrated high diagnostic performances in the differentiation of metastatic and non-metastatic LNs.

Application of Dual-Energy Spectral Computed Tomography to Thoracic Oncology Imaging

Computed tomography (CT) is an important imaging modality in evaluating thoracic malignancies. The clinical utility of dual-energy spectral computed tomography (DESCT) has recently been realized. DESCT allows for virtual monoenergetic or monochromatic imaging, virtual non-contrast or unenhanced imaging, iodine concentration measurement, and effective atomic number (Z_eff map). The application of information gained using this technique in the field of thoracic oncology is important, and therefore many studies have been conducted to explore the use of DESCT in the evaluation and management of thoracic malignancies. Here we summarize and review recent DESCT studies on clinical applications related to thoracic oncology.

Semiautomated Renal Cortex Volumetry in Spectral Computed Tomography: Effect of Monoenergetic Reconstructions on Measurement Precision and Interobserver Variability

OBJECTIVE

The aim of this study was to determine the influence of virtual monoenergetic images (vMEIs) on renal cortex volumetry (RCV) and estimation of split-renal function.

METHODS

Twenty-five patients (mean ± SD, 64.7 ± 9.9 years) underwent a contrast-enhanced dual-layer spectral detector computed tomography. Images were reconstructed with a reference standard (iterative model reconstruction, IMRRef), a newly spectral detector computed tomography algorithm (SPcon) and vMEI at 40, 60, 80, 100, and 120 keV. Two blinded independent readers performed RCV on all data sets with a semiautomated tool.

RESULTS

Total kidney volume was up to 15% higher in vMEI at 40/60 keV compared with IMRRef (P < 0.001). Total kidney volume with vMEI at 80/100 keV was similar to IMRRef (P < 0.001). Split-renal function was similar in all reconstructions at approximately 50% ± 3%. Bland-Altman analysis showed no significant differences (P > 0.05), except for 40 keV versus SPcon (P < 0.05). The time required to perform RCV was reasonable, approximately 4 minutes, and showed no significant differences among reconstructions. Interreader agreement was greatest with vMEI at 80 keV (r = 0.68; 95% confidence interval, 0.39-0.85; P < 0.0002) followed by IMRRef images (r = 0.67; 95% confidence interval, 0.37-0.84; P < 0.0003). IMRRef showed the highest mean Hounsfield unit for cortex/medulla of 223.4 ± 73.7/62.5 ± 19.7 and a ratio of 3.7.

CONCLUSIONS

Semiautomated RCV performed with vMEI and IMRRef/SPcon is feasible and showed no clinically relevant differences with regard to split-renal function. Low-kiloelectron volt vMEI showed greater tissue contrast and total kidney volume but no benefit for RCV. Moderate-kiloelectron volt vMEI (80 keV) results were similar to IMRRef with a faster postprocessing time.

Low-keV virtual monoenergetic imaging reconstructions of excretory phase spectral dual-energy CT in patients with urothelial carcinoma: A feasibility study

OBJECTIVES

To compare objective and subjective image quality between low keV virtual monoenergetic images (VMI) of the excretory phase and conventional venous phase images derived from spectral dual-energy CT (DECT) in the assessment of urothelial carcinoma.

METHODS

26 consecutive patients with histologically confirmed urothelial carcinoma who received clinically indicated venous- and excretory phase abdominal CT scans were included retrospectively. Attenuation, image noise as well as signal- and contrast-to-noise-ratio (SNR, CNR) in venous and excretory phase CT and excretory phase VMI from 40 to 70 keV were obtained from ROI-based measurements in the following regions: urothelial carcinoma, liver, pancreas, renal cortex, subcutaneous fat, renal vein/artery, portal vein, urinary bladder wall, lymph nodes, prostate/uterus. Subjective vessel contrast and delineation of primary tumor manifestations and distant metastases were rated on 5-point Likert scales.

RESULTS

In comparison to venous phase CT, attenuation and SNR in excretory phase VMI40keV were higher (p < 0.001), except for liver parenchyma, where they were comparable (p = 0.07 and p = 0.17, respectively). Regarding image noise, no significant difference was found between venous phase CT and excretory phase VMI40keV (p-range: 0.08-1.00), except for liver, portal vein and renal artery, where it was lower in VMI40keV (p < 0.05). CNR of urothelial carcinoma to circumjacent bladder wall was significantly higher in excretory phase VMI40keV compared to venous phase CT. Subjective vessel contrast and delineation of primary tumor and distant metastases received equivalent or higher Likert scores in excretory phase VMI40keV than in venous phase CT.

CONCLUSION

This feasibility study indicates that in the assessment of urothelial carcinoma, virtual monoenergetic excretory phase images at 40 keV acquired with spectral DECT could be feasible to maintain subjective and objective image quality as provided by conventional venous phase images. Still, equivalence with regards to metastatic lesion detection requires further investigation before employing this technique in a potential signal-scan, single-bolus approach.