Quantitative dual-energy CT for evaluating hepatocellular carcinoma after transarterial chemoembolization

Dual-energy CT applications on liver imaging: what radiologists and radiographers should know? A systematic review

PURPOSE

This review aims to provide a comprehensive summary of DECT techniques, acquisition workflows, and post-processing methods. By doing so, we aim to elucidate the advantages and disadvantages of DECT compared to conventional single-energy CT imaging.

METHODS

A systematic search was conducted on MEDLINE/EMBASE for DECT studies in liver imaging published between 1980 and 2024. Information regarding study design and endpoints, patient characteristics, DECT technical parameters, radiation dose, iodinated contrast agent (ICA) administration and postprocessing methods were extracted. Technical parameters, including DECT phase, field of view, pitch, collimation, rotation time, arterial phase timing (from injection), and venous timing (from injection) from the included studies were reported, along with formal narrative synthesis of main DECT applications for liver imaging.

RESULTS

Out of the initially identified 234 articles, 153 met the inclusion criteria. Extensive variability in acquisition parameters was observed, except for tube voltage (80/140 kVp combination reported in 50% of articles) and ICA administration (1.5 mL/kg at 3-4 mL/s, reported in 91% of articles). Radiation dose information was provided in only 40% of articles (range: 6-80 mGy), and virtual non-contrast imaging (VNC) emerged as a common strategy to reduce the radiation dose. The primary application of DECT post-processed images was in detecting focal liver lesions (47% of articles), with predominance of study focusing on hepatocellular carcinoma (HCC) (27%). Furthermore, a significant proportion of the articles (16%) focused on enhancing DECT protocols, while 15% explored metastasis detection.

CONCLUSION

Our review recommends using 80/140 kVp tube voltage with 1.5 mL/kg ICA at 3-4 mL/s flow rate. Post-processing should include low keV-VMI for enhanced lesion detection, IMs for tumor iodine content evaluation, and VNC for dose reduction. However, heterogeneous literature hinders protocol standardization.

Imaging in interventional oncology, the better you see, the better you treat

Imaging and image processing is the fundamental pillar of interventional oncology in which diagnostic, procedure planning, treatment and follow-up are sustained. Knowing all the possibilities that the different image modalities can offer is capital to select the most appropriate and accurate guidance for interventional procedures. Despite there is a wide variability in physicians preferences and availability of the different image modalities to guide interventional procedures, it is important to recognize the advantages and limitations for each of them. In this review, we aim to provide an overview of the most frequently used image guidance modalities for interventional procedures and its typical and future applications including angiography, computed tomography (CT) and spectral CT, magnetic resonance imaging, Ultrasound and the use of hybrid systems. Finally, we resume the possible role of artificial intelligence related to image in patient selection, treatment and follow-up.

Application of spectral CT in diagnosis, classification and prognostic monitoring of gastrointestinal cancers: progress, limitations and prospects

Gastrointestinal (GI) cancer is the leading cause of cancer-related deaths worldwide. Computed tomography (CT) is an important auxiliary tool for the diagnosis, evaluation, and prognosis prediction of gastrointestinal tumors. Spectral CT is another major CT revolution after spiral CT and multidetector CT. Compared to traditional CT which only provides single-parameter anatomical diagnostic mode imaging, spectral CT can achieve multi-parameter imaging and provide a wealth of image information to optimize disease diagnosis. In recent years, with the rapid development and application of spectral CT, more and more studies on the application of spectral CT in the characterization of GI tumors have been published. For this review, we obtained a substantial volume of literature, focusing on spectral CT imaging of gastrointestinal cancers, including esophageal, stomach, colorectal, liver, and pancreatic cancers. We found that spectral CT can not only accurately stage gastrointestinal tumors before operation but also distinguish benign and malignant GI tumors with improved image quality, and effectively evaluate the therapeutic response and prognosis of the lesions. In addition, this paper also discusses the limitations and prospects of using spectral CT in GI cancer diagnosis and treatment.

[Progress in Image-planned and Real-time Image-guided Lung Cancer Biopsy in the Detection of Biomarkers]

With the progress of targeted therapy and immunotherapy for lung cancer, the clinical demand for lung biopsy is increasing. An ideal biopsy specimen can be used not only for histopathological diagnosis, but also for biomarker detection. The ideal biopsy specimen should meet two requirements, including more than 60 mm2 of tumor tissue and containing more than 20% of tumor cells. In order to obtain ideal lung cancer biopsy specimens, advanced imaging techniques are needed to help. In this article, we reviewed the requirements for biopsy specimens based on biomarker detection, as well as the current status and research progress of using imaging techniques for preoperative planning and intraoperative real time guidance of lung cancer biopsy.
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Survival prediction of hepatocellular carcinoma by measuring the extracellular volume fraction with single-phase contrast-enhanced dual-energy CT imaging

Purpose

This study aimed to investigate the value of quantified extracellular volume fraction (fECV) derived from dual-energy CT (DECT) for predicting the survival outcomes of patients with hepatocellular carcinoma (HCC) after transarterial chemoembolization (TACE).

Materials and methods

A total of 63 patients with HCC who underwent DECT before treatment were retrospectively included. Virtual monochromatic images (VMI) (70 keV) and iodine density images (IDI) during the equilibrium phase (EP) were generated. The tumor VMI-fECV and IDI-fECV were measured and calculated on the whole tumor (Whole) and maximum enhancement of the tumor (Maximum), respectively. Univariate and multivariate Cox models were used to evaluate the effects of clinical and imaging predictors on overall survival (OS) and progression-free survival (PFS).

Results

The correlation between tumor VMI-fECV and IDI-fECV was strong (both p< 0.001). The Bland-Altman plot between VMI-fECV and IDI-fECV showed a bias of 5.16% for the Whole and 6.89% for the Maximum modalities, respectively. Increasing tumor VMI-fECV and IDI-fECV were positively related to the effects on OS and PFS (both p< 0.05). The tumor IDI-fECV-Maximum was the only congruent independent predictor in patients with HCC after TACE in the multivariate analysis on OS (p = 0.000) and PFS (p = 0.028). Patients with higher IDI-fECV-Maximum values had better survival rates above the optimal cutoff values, which were 35.42% for OS and 29.37% for PFS.

Conclusion

The quantified fECV determined by the equilibrium-phase contrast-enhanced DECT can potentially predict the survival outcomes of patients with HCC following TACE treatment.

Physiological iodine uptake of the spine's bone marrow in dual-energy computed tomography - using artificial intelligence to define reference values based on 678 CT examinations of 189 individuals

Purpose

The bone marrow's iodine uptake in dual-energy CT (DECT) is elevated in malignant disease. We aimed to investigate the physiological range of bone marrow iodine uptake after intravenous contrast application, and examine its dependence on vBMD, iodine blood pool, patient age, and sex.

Method

Retrospective analysis of oncological patients without evidence of metastatic disease. DECT examinations were performed on a spectral detector CT scanner in portal venous contrast phase. The thoracic and lumbar spine were segmented by a pre-trained neural network, obtaining volumetric iodine concentration data [mg/ml]. vBMD was assessed using a phantomless, CE-certified software [mg/cm3]. The iodine blood pool was estimated by ROI-based measurements in the great abdominal vessels. A multivariate regression model was fit with the dependent variable "median bone marrow iodine uptake". Standardized regression coefficients (β) were calculated to assess the impact of each covariate.

Results

678 consecutive DECT exams of 189 individuals (93 female, age 61.4 ± 16.0 years) were evaluated. AI-based segmentation provided volumetric data of 97.9% of the included vertebrae (n=11,286). The 95^th percentile of bone marrow iodine uptake, as a surrogate for the upper margin of the physiological distribution, ranged between 4.7-6.4 mg/ml. vBMD (p <0.001, mean β=0.50) and portal vein iodine blood pool (p <0.001, mean β=0.43) mediated the strongest impact. Based thereon, adjusted reference values were calculated.

Conclusion

The bone marrow iodine uptake demonstrates a distinct profile depending on vBMD, iodine blood pool, patient age, and sex. This study is the first to provide the adjusted reference values.

Spectral CT: Current Liver Applications

Using two different energy levels, dual-energy computed tomography (DECT) allows for material differentiation, improves image quality and iodine conspicuity, and allows researchers the opportunity to determine iodine contrast and radiation dose reduction. Several commercialized platforms with different acquisition techniques are constantly being improved. Furthermore, DECT clinical applications and advantages are continually being reported in a wide range of diseases. We aimed to review the current applications of and challenges in using DECT in the treatment of liver diseases. The greater contrast provided by low-energy reconstructed images and the capability of iodine quantification have been mostly valuable for lesion detection and characterization, accurate staging, treatment response assessment, and thrombi characterization. Material decomposition techniques allow for the non-invasive quantification of fat/iron deposition and fibrosis. Reduced image quality with larger body sizes, cross-vendor and scanner variability, and long reconstruction time are among the limitations of DECT. Promising techniques for improving image quality with lower radiation dose include the deep learning imaging reconstruction method and novel spectral photon-counting computed tomography.

Photothermal Responsive Digital Polymerase Chain Reaction Resolving Exosomal microRNAs Expression in Liver Cancer

Exosomal microRNAs have been studied as a good source of noninvasive biomarkers due to their functions in genetic exchange between cells and have been already well documented in many biological activities; however, inaccuracy remains a key challenge for liver cancer surveillance. Herein, a versatile duplex photothermal digital polymerase chain reaction (PCR) strategy combined with a lipid nanoparticle-based exosome capture approach is proposed to profile microRNAs expression through a 3-h easy-to-operate process. The microfluidically-generated molybdenum disulfide-nanocomposite-doped gelatin microcarriers display attractive properties as a 2-4 °C s^-1 ramping-up rate triggered by near-infrared and reversible sol-gel transforming in step with PCR activation. To achieve PCR thermocycling, the corresponding irradiation coordinating with fan cooling are automatically performed via a homemade control module with programs. Thus, taking the multiplexing capability of dual-color labeling, 19-31 folds higher in exosomal microRNA-200b-3p and microRNA-21-5p, and tenfold lower in microRNA-22-3p expressions relative to the control microRNA-26a-5p are quantified in two liver cancer cells (Huh7 and HepG2) than in those from the healthy cells. It is believed that this exosomal microRNA genotyping method would be highly applicable for liver cancer diagnostics.

Role of iodine density value on dual-energy CT for detection of high tumor cell proportion region in lung cancer during CT-guided transthoracic biopsy

OBJECTIVE

This study aimed to identify regions with at least 20% tumor cell content in lung cancer tumors by using spectral parameters from dual-layer spectral detector computed tomography (SDCT) to design the puncture path for transthoracic lung biopsy (TTLB).

MATERIALS AND METHODS

This prospective study recruited patients with suspected lung cancer. Forty-one patients were enrolled to identify the high tumor cell proportion region (HTPR) and then another 15 patients to validate the accuracy of the HTPR. In each of the 41 patients, the suspected regions with high or low tumor cell proportions were punctured according to local iodine density (IoD) values for separate biopsies. The tumor cell proportions of 82 specimens were assessed and classified into high and low tumor cell proportions based on the threshold value of 20 %. The performance of spectral parameters was analyzed to distinguish the HTPR (tumor cell proportion ≥ 20 %) from the low tumor cell proportion region (LTPR). The cutoff value of optimal spectral parameter was used to prospectively guide the biopsy of the HTPR in 15 cases for further validation, and then the accuracy was calculated.

RESULTS

The AUC values of spectral parameters were all higher than those of CT_conventional in identifying the HTPR (all P < 0.05). The IoD with a cutoff value of 0.59 mg/mL in arterial phase (AP) yielded good performance (specificity: 97.10 %) in identifying the HTPR. It was applied to 15 cases for validation, and the accuracy rate was 100 %.

CONCLUSION

Spectral CT parameters can be used to identify regions with at least 20% tumor cell content in lung cancer for biopsies.

Comparison of image quality of two versions of deep-learning image reconstruction algorithm on a rapid kV-switching CT: a phantom study

BACKGROUND

To assess the impact of the new version of a deep learning (DL) spectral reconstruction on image quality of virtual monoenergetic images (VMIs) for contrast-enhanced abdominal computed tomography in the rapid kV-switching platform.

METHODS

Two phantoms were scanned with a rapid kV-switching CT using abdomen-pelvic CT examination parameters at dose of 12.6 mGy. Images were reconstructed using two versions of DL spectral reconstruction algorithms (DLSR V1 and V2) for three reconstruction levels. The noise power spectrum (NSP) and task-based transfer function at 50% (TTF₅₀) were computed at 40/50/60/70 keV. A detectability index (d') was calculated for enhanced lesions at low iodine concentrations: 2, 1, and 0.5 mg/mL.

RESULTS

The noise magnitude was significantly lower with DLSR V2 compared to DLSR V1 for energy levels between 40 and 60 keV by -36.5% ± 1.4% (mean ± standard deviation) for the standard level. The average NPS frequencies increased significantly with DLSR V2 by 23.7% ± 4.2% for the standard level. The highest difference in TTF₅₀ was observed at the mild level with a significant increase of 61.7% ± 11.8% over 40-60 keV energy with DLSR V2. The d' values were significantly higher for DLSR V2 versus DLSR V1.

CONCLUSIONS

The DLSR V2 improves image quality and detectability of low iodine concentrations in VMIs compared to DLSR V1. This suggests a great potential of DLSR V2 to reduce iodined contrast doses.

Quantitative dual-energy computed tomography texture analysis predicts the response of primary small hepatocellular carcinoma to radiofrequency ablation

BACKGROUND

Radiofrequency ablation (RFA) is one of the effective therapeutic modalities in patients with hepatocellular carcinoma (HCC). However, there is no proper method to evaluate the HCC response to RFA. This study aimed to establish and validate a clinical prediction model based on dual-energy computed tomography (DECT) quantitative-imaging parameters, clinical variables, and CT texture parameters.

METHODS

We enrolled 63 patients with small HCC. Two to four weeks after RFA, we performed DECT scanning to obtain DECT-quantitative parameters and to record the patients' clinical baseline variables. DECT images were manually segmented, and 56 CT texture features were extracted. We used LASSO algorithm for feature selection and data dimensionality reduction; logistic regression analysis was used to build a clinical model with clinical variables and DECT-quantitative parameters; we then added texture features to build a clinical-texture model based on clinical model.

RESULTS

A total of six optimal CT texture analysis (CTTA) features were selected, which were statistically different between patients with or without tumor progression (P < 0.05). When clinical variables and DECT-quantitative parameters were included, the clinical models showed that albumin-bilirubin grade (ALBI) [odds ratio (OR) = 2.77, 95% confidence interval (CI): 1.35-6.65, P = 0.010], λ_{AP (40-100 keV)} (OR = 3.21, 95% CI: 3.16-5.65, P = 0.045) and IC_AP (OR = 1.25, 95% CI: 1.01-1.62, P = 0.028) were associated with tumor progression, while the clinical-texture models showed that ALBI (OR = 2.40, 95% CI: 1.19-5.68, P = 0.024), λ_{AP (40-100 keV)} (OR = 1.43, 95% CI: 1.10-2.07, P = 0.019), and CTTA-score (OR = 2.98, 95% CI: 1.68-6.66, P = 0.001) were independent risk factors for tumor progression. The clinical model, clinical-texture model, and CTTA-score all performed well in predicting tumor progression within 12 months after RFA (AUC = 0.917, 0.962, and 0.906, respectively), and the C-indexes of the clinical and clinical-texture models were 0.917 and 0.957, respectively.

CONCLUSIONS

DECT-quantitative parameters, CTTA, and clinical variables were helpful in predicting HCC progression after RFA. The constructed clinical prediction model can provide early warning of potential tumor progression risk for patients after RFA.

Role of gadoxetic acid-enhanced liver magnetic resonance imaging in the evaluation of hepatocellular carcinoma after locoregional treatment

Locoregional treatments, as alternatives to surgery, play a key role in the management of hepatocellular carcinoma (HCC). Liver magnetic resonance imaging (MRI) enables a multiparametric assessment, going beyond the traditional dynamic computed tomography approach. Moreover, the use of hepatobiliary agents can improve diagnostic accuracy and are becoming important in the diagnosis and follow-up of HCC. However, the main challenge is to quickly identify classical responses to loco-regional treatments in order to determine the most suitable management strategy for each patient. The aim of this review is to provide a summary of the most common and uncommon liver MRI findings in patients who underwent loco-regional treatments for HCC, with a special focus on ablative therapies (radiofrequency, microwaves and cryoablation), trans-arterial chemoembolization, trans-arterial radio-embolization and stereotactic ablative radiotherapy techniques, considering the usefulness of gadoxetate disodium (Gd-EOB-DTPA) contrast agent.

Deep Learning and Domain-Specific Knowledge to Segment the Liver from Synthetic Dual Energy CT Iodine Scans

We map single energy CT (SECT) scans to synthetic dual-energy CT (synth-DECT) material density iodine (MDI) scans using deep learning (DL) and demonstrate their value for liver segmentation. A 2D pix2pix (P2P) network was trained on 100 abdominal DECT scans to infer synth-DECT MDI scans from SECT scans. The source and target domain were paired with DECT monochromatic 70 keV and MDI scans. The trained P2P algorithm then transformed 140 public SECT scans to synth-DECT scans. We split 131 scans into 60% train, 20% tune, and 20% held-out test to train four existing liver segmentation frameworks. The remaining nine low-dose SECT scans tested system generalization. Segmentation accuracy was measured with the dice coefficient (DSC). The DSC per slice was computed to identify sources of error. With synth-DECT (and SECT) scans, an average DSC score of 0.93±0.06 (0.89±0.01) and 0.89±0.01 (0.81±0.02) was achieved on the held-out and generalization test sets. Synth-DECT-trained systems required less data to perform as well as SECT-trained systems. Low DSC scores were primarily observed around the scan margin or due to non-liver tissue or distortions within ground-truth annotations. In general, training with synth-DECT scans resulted in improved segmentation performance with less data.