Utility of Dark-Blood Dual-Energy CT Images for Predicting Vascular Involvement and R0 Resection in Patients With Pancreatic Cancer

The preoperative prediction of lymph node metastasis of resectable pancreatic ductal adenocarcinoma using dual-layer spectral computed tomography

OBJECTIVES

To investigate the value of dual-layer spectral computed tomography (DLCT) parameters derived from primary tumors in predicting lymph node metastasis (LNM) of resectable pancreatic ductal adenocarcinoma (PDAC).

MATERIALS AND METHODS

In this retrospective study, patients with resectable PDAC who underwent DLCT within 2-week intervals before surgery were enrolled and randomly divided into training and validation sets at a 7:3 ratio. The patients' clinical data, CT morphological features, and DLCT parameters were analyzed. Univariate and multivariate logistic analyses were used to identify the predictors and construct a predictive model, and receiver operator characteristic (ROC) curves were programmed to evaluate the predictive efficacy.

RESULTS

We enrolled 107 patients (44 patients with LNM and 63 patients without LNM). Among all variables, iodine concentration in the venous phase, extracellular volume, and tumor size were identified as independent predictors of LNM. The nomogram model, incorporating the two DLCT parameters and the morphological feature, achieved an area under the curve (AUC) of 0.877 (95% confidence interval [CI]: 0.803-0.952) and 0.842 (95% CI: 0.707-0.977) for predicting LNM in the training and validation sets, respectively. Furthermore, the AUC of the nomogram model was greater than that of morphological features of lymph nodes in the training (AUC = 0.877 vs. 0.570) and validation (AUC = 0.842 vs. 0.583) sets.

CONCLUSIONS

DLCT has the potential to predict LNM in patients with resectable PDAC and show a better predictive value than morphological features of lymph nodes.

KEY POINTS

Question Morphological features of lymph nodes are of limited value in detecting metastatic lymph nodes in pancreatic ductal adenocarcinoma (PDAC). Findings Dual-layer spectral computed tomography (DLCT) parameters and morphological features derived from PDAC lesions show good preoperatively predictive efficacy for lymph node metastasis. Clinical relevance The proposed DLCT-based nomogram model may serve as an effective and convenient tool for preoperatively predicting lymph node metastasis of resectable PDAC.

A Visual Aid Tool for Detection of Pancreatic Tumour-Vessel Contact on Staging CT: A Retrospective Cohort Study

Purpose: In pancreatic adenocarcinoma, the difficult distinction between normal and affected pancreas on CT studies may lead to discordance between the pre-surgical assessment of vessel involvement and intraoperative findings. We hypothesize that a visual aid tool could improve the performance of radiology residents when detecting vascular invasion in pancreatic adenocarcinoma patients. Methods: This study consisted of 94 pancreatic adenocarcinoma patient CTs. The visual aid compared the estimated body fat density of each patient with the densities surrounding the superior mesenteric artery and mapped them onto the CT scan. Four radiology residents annotated the locations of perceived vascular invasion on each scan with the visual aid overlaid on alternating scans. Using 3 expert radiologists as the reference standard, we quantified the area under the receiver operating characteristic curve to determine the performance of the tool. We then used sensitivity, specificity, balanced accuracy ((sensitivity + specificity)/2), and spatial metrics to determine the performance of the residents with and without the tool. Results: The mean area under the curve was 0.80. Radiology residents' sensitivity/specificity/balanced accuracy for predicting vascular invasion were 50%/85%/68% without the tool and 81%/79%/80% with it compared to expert radiologists, and 58%/85%/72% without the tool and 78%/77%/77% with it compared to the surgical pathology. The tool was not found to impact the spatial metrics calculated on the resident annotations of vascular invasion. Conclusion: The improvements provided by the visual aid were predominantly reflected by increased sensitivity and accuracy, indicating the potential of this tool as a learning aid for trainees.

Detectability of intracranial vessel wall atherosclerosis using black-blood spectral CT: a phantom and clinical study

BACKGROUND

Computed tomography (CT) is the usual modality for diagnosing stroke, but conventional CT angiography reconstructions have limitations.

METHODS

A phantom with tubes of known diameters and wall thickness was scanned for wall detectability, wall thickness, and contrast-to-noise ratio (CNR) on conventional and spectral black-blood (SBB) images. The clinical study included 34 stroke patients. Diagnostic certainty and conspicuity of normal/abnormal intracranial vessels using SBB were compared to conventional. Sensitivity/specificity/accuracy of SBB and conventional were compared for plaque detectability. CNR of the wall/lumen and quantitative comparison of remodeling index, plaque burden, and eccentricity were obtained for SBB imaging and high-resolution magnetic resonance imaging (hrMRI).

RESULTS

The phantom study showed improved detectability of tube walls using SBB (108/108, 100% versus conventional 81/108, 75%, p < 0.001). CNRs were 75.9 ± 62.6 (mean ± standard deviation) for wall/lumen and 22.0 ± 17.1 for wall/water using SBB and 26.4 ± 15.3 and 101.6 ± 62.5 using conventional. Clinical study demonstrated (i) improved certainty and conspicuity of the vessels using SBB versus conventional (certainty, median score 3 versus 0; conspicuity, median score 3 versus 1 (p < 0.001)), (ii) improved sensitivity/specificity/accuracy of plaque (≥ 1.0 mm) detectability (0.944/0.981/0.962 versus 0.239/0.743/0.495) (p < 0.001), (iii) higher wall/lumen CNR of SBB of (78.3 ± 50.4/79.3 ± 96.7) versus hrMRI (18.9 ± 8.4/24.1 ± 14.1) (p < 0.001), and (iv) excellent reproducibility of remodeling index, plaque burden, and eccentricity using SBB versus hrMRI (intraclass correlation coefficient 0.85-0.94).

CONCLUSIONS

SBB can enhance the detectability of intracranial plaques with an accuracy similar to that of hrMRI.

RELEVANCE STATEMENT

This new spectral black-blood technique for the detection and characterization of intracranial vessel atherosclerotic disease could be a time-saving and cost-effective diagnostic step for clinical stroke patients. It may also facilitate prevention strategies for atherosclerosis.

KEY POINTS

• Blooming artifacts can blur vessel wall morphology on conventional CT angiography. • Spectral black-blood (SBB) images are generated from material decomposition from spectral CT. • SBB images reduce blooming artifacts and noise and accurately detect small plaques.

Clinical Application of Dark-blood Imaging in Head and Neck CT Angiography: Effect on Image Quality and Plaque Visibility

RATIONALE AND OBJECTIVES

The aim of this study was to explore the potential of a newly developed dark-blood imaging technique to improve image quality and plaque visibility in head and neck computed tomography (CT) angiography.

MATERIALS AND METHODS

Patients who underwent triphasic head and neck CT angiography scans from August 2021 to March 2023 were retrospectively enrolled (mean age 67.23 ± 10.81 [SD] years, range 43-85 years, 64.7% male). The CT protocol consists of pre-contrast, arterial and delayed phases. Dark-blood images were postprocessed with the contrast-enhancement boost (CE-boost) technique. The quantitative assessment involved evaluating the CT value, image noise, contrast-to-noise ratio (CNR), and signal-to-noise ratio (SNR) of calcified plaque and non-calcified plaque. The plaque CNR relative to the vessel lumen (CNRplaque-lumen), vessel wall (CNRplaque-wall), and adjacent muscle (CNRplaque-muscle) was respectively calculated. Two experienced radiologists independently evaluated the CT images (5, best; 1, worst) by four characteristics including calcified plaque visibility, non-calcified plaque visibility, diagnostic confidence, and overall image quality. Inter-rater variability was also evaluated. The artery stenosis rate and plaque burden on dark-blood images were measured and compared with arterial phases. The intraclass correlation coefficient (ICC) was used for consistency analysis. The diagnostic accuracy of dark-blood images for the stenosis rate was evaluated by the area under the curve (AUC).

RESULTS

A total of 43 patients with 54 calcified plaques and 34 non-calcified plaques were assessed in this study. When compared with pre-contrast and delayed phase, dark-blood images yielded significantly higher CNRplaque-lumen and CNRplaque-muscle of calcified (219.79 ± 159.20 and 181.23 ± 112.12, respectively) and non-calcified (30.30 ± 29.11 and 6.28 ± 4.75, respectively) plaques (all p < 0.001). Calcified plaque SNR of dark-blood showed equal or slightly lower than other phases (p > 0.05 or p = 0.02). A major increase was observed in the non-calcified plaque SNR of dark-blood compared to the arterial phase (5.56 ± 3.71 vs. 4.23 ± 3.56, p = 0.02), although there were no apparent differences compared to pre-contrast and delayed phases (p > 0.05). In subjective analyzes, the calcified plaque visibility (4.99 ± 0.07), non-calcified plaque visibility (4.62 ± 0.48), overall image quality (4.81 ± 0.34), and diagnostic confidence (4.74 ± 0.36) in dark-blood images dominated the highest scores (p < 0.001). The subjective scores of radiologists exhibited good consistency (all kappa value>0.7). The dark-blood image and the arterial phase image exhibited good consistency in identifying the stenosis rate (p < 0.001). In the evaluation of plaque burden, the interobserver agreement for dark-blood images was higher compared to arterial phase images (ICC = 0.870 vs. 0.729).

CONCLUSIONS

Compared to conventional triphasic head and neck CT angiography, the CE-boost derived dark-blood imaging demonstrated a significant improvement in image quality and visibility for both calcified and non-calcified plaque assessment.

Dual-Energy CT in Oncologic Imaging

Dual-energy CT (DECT) is an innovative technology that is increasingly widespread in clinical practice. DECT allows for tissue characterization beyond that of conventional CT as imaging is performed using different energy spectra that can help differentiate tissues based on their specific attenuation properties at different X-ray energies. The most employed post-processing applications of DECT include virtual monoenergetic images (VMIs), iodine density maps, virtual non-contrast images (VNC), and virtual non-calcium (VNCa) for bone marrow edema (BME) detection. The diverse array of images obtained through DECT acquisitions offers numerous benefits, including enhanced lesion detection and characterization, precise determination of material composition, decreased iodine dose, and reduced artifacts. These versatile applications play an increasingly significant role in tumor assessment and oncologic imaging, encompassing the diagnosis of primary tumors, local and metastatic staging, post-therapy evaluation, and complication management. This article provides a comprehensive review of the principal applications and post-processing techniques of DECT, with a specific focus on its utility in managing oncologic patients.

[Current CT developments in imaging of pancreatic diseases]

BACKGROUND

Diseases of the pancreas are often diagnosed late and can have fatal consequences for patients.

PURPOSE

Current computed tomography (CT) developments in imaging of pancreatic diseases.

MATERIALS AND METHODS

Evaluation of numerous studies, especially considering modern CT techniques such as dual-energy CT and photon-counting CT but also artificial intelligence (AI) algorithms for disease detection.

RESULTS

Spectral imaging using dual-energy CT and photon-counting CT offers numerous advantages in the detection of pancreatic disease and can thus improve diagnostic performance but also provide additional information on any therapeutic response. Likewise, advances in the development of AI algorithms are improving diagnostic performance.

CONCLUSION

In the future, we can expect increasingly improved detection of pancreatic diseases, thereby enabling patients to be treated more quickly, which will consequently result in improved outcomes.