In this article, the authors discuss the technical background and summarize the current body of literature regarding virtual monoenergetic (VM) images derived from dual-energy CT data, which can be reconstructed between 40 and 200 keV. Substantially improved iodine attenuation at lower kiloelectron volt levels and reduced beam-hardening artifacts at higher kiloelectron volt levels have been demonstrated from all major manufacturers of dual-energy CT units. Improved contrast attenuation with VM imaging at lower kiloelectron volt levels enables better delineation and diagnostic accuracy in the detection of various vascular or oncologic abnormalities. Low-kiloelectron-volt VM imaging may be useful for salvaging CT studies with suboptimal contrast material delivery or providing additional information on the arterial vasculature obtained from venous phase acquisitions. For patients with renal impairment, substantial reductions in the use of iodinated contrast material can be achieved by using lower-energy VM imaging. The authors recommend routine reconstruction of VM images at 50 keV when using dual-energy CT to exploit the increased contrast properties. For reduction of beam-hardening artifacts, VM imaging at 120 keV is useful for the initial assessment.

https://pubs.rsna.org/doi/pdf/10.1148/radiol.2019182297

Review of Clinical Applications for Virtual Monoenergetic Dual-Energy CT.

Virtual monochromatic spectral imaging with fast kilovoltage switching : improved image quality as compared with that obtained with conventional 120-kVp CT

This review summarizes current applications and clinical utility of diffusion-weighted imaging (DWI) for rectal cancer and in addition provides a brief overview of more recent developments (including intravoxel incoherent motion imaging, diffusion kurtosis imaging, and novel postprocessing tools) that are still in more early stages of research. More than 140 papers have been published in the last decade, during which period the use of DWI have slowly moved from mainly qualitative (visual) image interpretation to increasingly advanced methods of quantitative analysis. So far, the largest body of evidence exists for assessment of tumour response to neoadjuvant treatment. In this setting, particularly the benefit of DWI for visual assessment of residual tumour in post-radiation fibrosis has been established and is now increasingly adopted in clinics. Quantitative DWI analysis (mainly the apparent diffusion coefficient) has potential, both for response prediction as well as for tumour prognostication, but protocols require standardization and results need to be prospectively confirmed on larger scale. The role of DWI for further clinical tumour and nodal staging is less well-defined, although there could be a benefit for DWI to help detect lymph nodes. Novel methods of DWI analysis and post-processing are still being developed and optimized; the clinical potential of these tools remains to be established in the upcoming years.

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Diffusion-weighted imaging in rectal cancer: current applications and future perspectives

To develop a T2-weighted (T2W) image-based radiomics signature for the individual prediction of KRAS mutation status in patients with rectal cancer. Three hundred four consecutive patients from center I with pathologically diagnosed rectal adenocarcinoma (training dataset, n = 213; internal validation dataset, n = 91) were enrolled in our retrospective study. The patients from center II (n = 86) were selected as an external validation dataset. A total of 960 imaging features were extracted from high-resolution T2W images for each patient. Five steps, mainly univariate statistical tests, were applied for feature selection. Subsequently, three classification methods, i.e., logistic regression (LR), decision tree (DT), and support vector machine (SVM) algorithm, were applied to develop the radiomics signature for KRAS prediction in the training dataset. The predictive performance was evaluated by receiver operating characteristics curve (ROC) analysis, calibration curve, and decision curve analysis (DCA). Seven radiomics features were screened as a KRAS-associated radiomics signature of rectal cancer. Our best prediction model was obtained with SVM classifiers with AUC of 0.722 (95%CI, 0.654–0.790) in the training dataset. This was validated in the internal and external validation datasets with good calibration, and the corresponding AUCs were 0.682 (95% CI, 0.569–0.794) and 0.714 (95% CI, 0.602–0.827), respectively. DCA confirmed its clinical usefulness. The proposed T2WI-based radiomics signature has a moderate performance to predict KRAS status, and may be useful for supplementing genomic analysis to determine KRAS expression in rectal cancer patients. • T2WI-based radiomics showed a moderate diagnostic significance for KRAS status.
 • The best prediction model was obtained with SVM classifier.
 • The baseline clinical and histopathological characteristics were not associated with KRAS mutation.

Development and validation of a MRI-based radiomics signature for prediction of KRAS mutation in rectal cancer

PURPOSE Mutation of the Kirsten Ras (KRAS) oncogene is present in 30%-40% of colorectal cancers and has prognostic significance in rectal cancer. In this study, we examined the ability of radiomics features extracted from T2-weighted magnetic resonance (MR) images to differentiate between tumors with mutant KRAS and wild-type KRAS. Materials and Methods Sixty patients with primary rectal cancer (25 with mutant KRAS, 35 with wild-type KRAS) were retrospectively enrolled. Texture analysis was performed in all regions of interest on MR images, which were manually segmented by two independent radiologists. We identified potentially useful imaging features using the two-tailed t test and used them to build a discriminant model with a decision tree to estimate whether KRAS mutation had occurred. RESULTS Three radiomic features were significantly associated with KRASmutational status (p < 0.05). The mean (and standard deviation) skewness with gradient filter value was significantly higher in the mutant KRAS group than in the wild-type group (2.04±0.94 vs. 1.59±0.69). Higher standard deviations for medium texture (SSF3 and SSF4) were able to differentiate mutant KRAS (139.81±44.19 and 267.12±89.75, respectively) and wild-type KRAS (114.55±29.30 and 224.78±62.20). The final decision tree comprised three decision nodes and four terminal nodes, two of which designated KRAS mutation. The sensitivity, specificity, and accuracy of the decision tree was 84%, 80%, and 81.7%, respectively. CONCLUSION Using MR-based texture analysis, we identified three imaging features that could differentiate mutant from wild-type KRAS. T2-weighted images could be used to predict KRAS mutation status preoperatively in patients with rectal cancer.

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Magnetic Resonance-Based Texture Analysis Differentiating KRAS Mutation Status in Rectal Cancer.

To investigate potential relationships between diffusion kurtosis imaging (DKI)-derived parameters using whole-tumour volume histogram analysis and clinicopathological prognostic factors in patients with rectal adenocarcinoma. 79 consecutive patients who underwent MRI examination with rectal adenocarcinoma were retrospectively evaluated. Parameters D, K and conventional ADC were measured using whole-tumour volume histogram analysis. Student’s t-test or Mann-Whitney U-test, receiver operating characteristic curves and Spearman’s correlation were used for statistical analysis. Almost all the percentile metrics of K were correlated positively with nodal involvement, higher histological grades, the presence of lymphangiovascular invasion (LVI) and circumferential margin (CRM) (p<0.05), with the exception of between K10th, K90th and histological grades. In contrast, significant negative correlations were observed between 25th, 50th percentiles and mean values of ADC and D, as well as ADC10th, with tumour T stages (p< 0.05). Meanwhile, lower 75th and 90th percentiles of ADC and D values were also correlated inversely with nodal involvement (p< 0.05). Kmean showed a relatively higher area under the curve (AUC) and higher specificity than other percentiles for differentiation of lesions with nodal involvement. DKI metrics with whole-tumour volume histogram analysis, especially K parameters, were associated with important prognostic factors of rectal cancer. • K correlated positively with some important prognostic factors of rectal cancer.
 • K
 mean
 showed higher AUC and specificity for differentiation of nodal involvement.
 • DKI metrics with whole-tumour volume histogram analysis depicted tumour heterogeneity.

Whole-tumour diffusion kurtosis MR imaging histogram analysis of rectal adenocarcinoma: Correlation with clinical pathologic prognostic factors

Fibroblast activation protein-α (FAP) is a type II integral serine protease that is specifically expressed by activated fibroblasts. Cancer-associated fibroblasts (CAFs) in the tumor stroma have abundant and stable expression of FAP, which plays an important role in promoting tumor growth, invasion, metastasis, and immunosuppression. For example, in women with a high incidence of breast cancer, CAFs account for 50–70% of the cells in the tumor’s microenvironment. CAF overexpression of FAP promotes tumor development and metastasis by influencing extracellular matrix remodeling, intracellular signaling, angiogenesis, epithelial-to-mesenchymal transition, and immunosuppression. This review covers the basic biological characteristics of FAP and its applications in the diagnosis and treatment of various cancers. Furthermore, we review the emerging basic and clinical research data regarding the use of nanomaterials that target FAP.

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Fibroblast Activation Protein-α as a Target in the Bench-to-Bedside Diagnosis and Treatment of Tumors: A Narrative Review.

To investigate the feasibility of using Spectral CT imaging with low contrast medium in abdominal CT angiography (CTA). 70 consecutive patients (40 men, 42.6 ± 20.4 years; 30 women, 46.7 ± 18.8 years) with suspected abdominal focal lesions were referred to CTA exam. They were randomly assigned into two groups. Group A: 35 patients underwent conventional CT scan of Tube voltage 120 kVp, automatic current modulation with a Noise Index of 12, ASIR 30%, and injected with Iohexol (350 mgI/ml). Group B: 35 patients underwent Spectral CT Imaging, with Tube current of 600 mA, injected with Iodixanol (270 mgI/ml). The optimal mono-energy keV was achieved using the optimal contrast noise ratio in abdominal aorta at the renal artery level relative to the erector spine muscle. Both groups were injected with an injection rate of 3.5 ml/s, and a contrast volume of 1.5 ml/kg body weight. The Hounsfield units (HU) and noise of the bilateral renal arteries and muscle of both groups, as well as the optimal monochromatic image set of Group B were measured. Two radiologists assessed all images with a 5-points scale. CTDIvol and DLP were recorded. Data were analyzed using student t test. The total iodine intake of Group B was 28% lower than that of Group A. The CNR of abdomen artery, celiac trunk, superior mesenteric artery, and renal artery in spectral group (at the best mono-energy of 53.0 keV) were higher than those in conventional CTA group (p   0.05). With 28% contrast medium reduction and reduced radiation dose, CT angiography using spectral imaging and lower concentration contrast agent provided better image quality than conventional CTA.

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The initial experience of the upper abdominal CT angiography using low-concentration contrast medium on dual energy spectral CT.

Objective: To develop and validate a radiomics predictive model based on multiparameter MR imaging features and clinical features to predict lymph node metastasis (LNM) in patients with cervical cancer Material and Methods: A total of 168 consecutive patients with cervical cancer from two centers were enrolled in our retrospective study A total of 3,930 imaging features were extracted from T2-weighted (T2W), ADC, and contrast-enhanced T1-weighted (cT1W) images for each patient Four-step procedures, mainly minimum redundancy maximum relevance (MRMR) and least absolute shrinkage and selection operator (LASSO) regression, were applied for feature selection and radiomics signature building in the training set from center I (n = 115) Combining clinical risk factors, a radiomics nomogram was then constructed The models were then validated in the external validation set comprising 53 patients from center II The predictive performance was determined by its calibration, discrimination, and clinical usefulness Results: The radiomics signature derived from the combination of T2W, ADC, and cT1W images, composed of six LN-status-related features, was significantly associated with LNM and showed better predictive performance than signatures derived from either of them alone in both sets Encouragingly, the radiomics signature also showed good discrimination in the MRI-reported LN-negative subgroup, with AUC of 0825 (95% CI: 0732-0919) The radiomics nomogram that incorporated radiomics signature and MRI-reported LN status also showed good calibration and discrimination in both sets, with AUCs of 0865 (95% CI: 0794-0936) and 0861 (95% CI: 0733-0990), respectively Decision curve analysis confirmed its clinical usefulness Conclusion: The proposed MRI-based radiomics nomogram has good performance for predicting LN metastasis in cervical cancer and may be useful for improving clinical decision making

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Lei Xin

Papers

Development and validation of a MRI-based radiomics signature for prediction of KRAS mutation in rectal cancer

Whole-tumour diffusion kurtosis MR imaging histogram analysis of rectal adenocarcinoma: Correlation with clinical pathologic prognostic factors

Fibroblast Activation Protein-α as a Target in the Bench-to-Bedside Diagnosis and Treatment of Tumors: A Narrative Review.

The initial experience of the upper abdominal CT angiography using low-concentration contrast medium on dual energy spectral CT.

Radiomics Analysis of Multiparametric MRI for the Preoperative Prediction of Lymph Node Metastasis in Cervical Cancer.