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?

Differentiation of pathological subtypes and Ki-67 and TTF-1 expression by dual-energy CT (DECT) volumetric quantitative analysis in non-small cell lung cancer

BACKGROUND

To explore the value of dual-energy computed tomography (DECT) in differentiating pathological subtypes and the expression of immunohistochemical markers Ki-67 and thyroid transcription factor 1 (TTF-1) in patients with non-small cell lung cancer (NSCLC).

METHODS

Between July 2022 and May 2024, patients suspected of lung cancer who underwent two-phase contrast-enhanced DECT were prospectively recruited. Whole-tumor volumetric and conventional spectral analysis were utilized to measure DECT parameters in the arterial and venous phase. The DECT parameters model, clinical-CT radiological features model, and combined prediction model were developed to discriminate pathological subtypes and predict Ki-67 or TTF-1 expression. Multivariate logistic regression analysis was used to identify independent predictors. The diagnostic efficacy was assessed by the area under the receiver operating characteristic curve (AUC) and compared using DeLong's test.

RESULTS

This study included 119 patients (92 males and 27 females; mean age, 63.0 ± 9.4 years) who was diagnosed with NSCLC. When applying the DECT parameters model to differentiate between adenocarcinoma and squamous cell carcinoma, ROC curve analysis indicated superior diagnostic performance for conventional spectral analysis over volumetric spectral analysis (AUC, 0.801 vs. 0.709). Volumetric spectral analysis exhibited higher diagnostic efficacy in predicting immunohistochemical markers compared to conventional spectral analysis (both P < 0.05). For Ki-67 and TTF-1 expression, the combined prediction model demonstrated optimal diagnostic performance with AUC of 0.943 and 0.967, respectively.

CONCLUSIONS

The combined predictive model based on volumetric quantitative analysis in DECT offers valuable information to discriminate immunohistochemical expression status, facilitating clinical decision-making for patients with NSCLC.

Differentiating the Invasiveness of Lung Adenocarcinoma Manifesting as Ground Glass Nodules: Combination of Dual-energy CT Parameters and Quantitative-semantic Features

RATIONALE AND OBJECTIVES

To evaluate the diagnostic performance of dual-energy CT (DECT) parameters and quantitative-semantic features for differentiating the invasiveness of lung adenocarcinoma manifesting as ground glass nodules (GGNs).

MATERIALS AND METHODS

Between June 2022 and September 2023, 69 patients with 74 surgically resected GGNs who underwent DECT examinations were included. CT numbers on virtual monochromatic images were calculated at 40-130 keV generated from DECT. Quantitative morphological measurements and semantic features were evaluated on unenhanced CT images and compared between pathologically confirmed adenocarcinoma in situ (AIS)-minimally invasive adenocarcinoma (MIA) and invasive lung adenocarcinoma (IAC). Multivariable logistic regression analysis was used to identify independent predictors. The diagnostic performance was assessed by the area under the receiver operating characteristic curve (AUC) and compared using DeLong's test.

RESULTS

Monochromatic CT numbers at 40-130 keV were significantly higher in IAC than in AIS-MIA (all P < 0.05). Multivariate logistic analysis revealed that CT number of 130 keV (odds ratio [OR] = 1.02, P = 0.013), maximum cross-sectional long diameter (OR =1.40, P = 0.014), deep or moderate lobulation sign (OR =19.88, P = 0.005), and abnormal intranodular vessel morphology (OR = 25.57, P = 0.017) were independent predictors of IAC. The combined prediction model showed a favorable differentiation performance with an AUC of 0.966 (95.2% sensitivity, 94.3% specificity, 94.8% accuracy), which was significantly higher than that for each risk factor (AUC = 0.791-0.822, all P < 0.05).

CONCLUSION

A multi-parameter combined prediction model integrating monochromatic CT numbers from DECT and quantitative-semantic features is promising for the preoperative discrimination of IAC and AIS-MIA in GGN-predominant lung adenocarcinoma.

Thoracic Applications of Spectral CT Scan

TOPIC IMPORTANCE

Thoracic imaging with CT scan has become an essential component in the evaluation of respiratory and thoracic diseases. Providers have historically used conventional single-energy CT; however, prevalence of dual-energy CT (DECT) is increasing, and as such, it is important for thoracic physicians to recognize the utility and limitations of this technology.

REVIEW FINDINGS

The technical aspects of DECT are presented, and practical approaches to using DECT are provided. Imaging at multiple energy spectra allows for postprocessing of the data and the possibility of creating multiple distinct image reconstructions based on the clinical question being asked. The data regarding utility of DECT in pulmonary vascular disorders, ventilatory defects, and thoracic oncology are presented. A pictorial essay is provided to give examples of the strengths associated with DECT.

SUMMARY

DECT has been most heavily studied in chronic thromboembolic pulmonary hypertension; however, it is increasingly being used across a wide spectrum of thoracic diseases. DECT combines morphologic and functional assessments in a single imaging acquisition, providing clinicians with a powerful diagnostic tool. Its role in the evaluation and treatment of thoracic diseases will likely continue to expand in the coming years as clinicians become more experienced with the technology.

Stress does not induce a general transcription of transposable elements in Drosophila

Transposable elements, also known as "jumping genes," have the ability to hop within the host genome. Nonetheless, this capacity is kept in check by the host cell defense systems to avoid unbridled TE mobilization. Different types of stressors can activate TEs in Drosophila, suggesting that TEs may play an adaptive role in the stress response, especially in generating genetic variability for adaptive evolution. TE activation by stressors may also lead to the notion, usually found in the literature, that any form of stress could activate all or the majority of TEs. In this review, we define what stress is. We then present and discuss RNA sequencing results from several studies demonstrating that stress does not trigger TE transcription broadly in Drosophila. An explanation for the LTR order of TEs being the most overexpressed is also proposed.

Dual Attention Mechanisms and Feature Fusion Networks Based Method for Predicting LncRNA-Disease Associations

LncRNAs play a part in numerous momentous processes of biology such as disease diagnoses, preventions and treatments. The associations between various diseases and lncRNAs are one of the crucial approaches to learn the role and status of lncRNAs in human diseases. With the researches on lncRNA and diseases, multiple methods based on neural network have been employed to predict these associations. However, the deep and complicated characteristic representations of lncRNA-disease associations were failed to be extracted, and the discriminative contributions of the interactions, correlations, and similarities among miRNAs diseases, and lncRNAs for the correlation predictions were ignored. In this paper, based on the multibiology premise of lncRNAs, miRNAs, and diseases, a dual attention network was proposed to predict the model of lncRNA-disease associations for miRNAs, the disease characteristic matrix, and lncRNAs. Through two attention modules, we enable the model to learn the nonlinear, more complex and useful features of lncRNA, miRNA, and disease characteristic matrix. For the feature embedding matrix composed of lncRNA-disease, the connection between lncRNA-disease feature embedding matrix and lncRNA, miRNA, and disease characteristic matrix was enhanced through deconvolution and feature fusion layer. Compared with several latest methods, the method proposed in this paper can produce better performance. Researches on the cases of osteosarcoma, lung cancer, and gastric cancer have confirmed the effective recognition of potential lncRNA-disease associations.

Differentiation Between Solitary Pulmonary Inflammatory Lesions and Solitary Cancer Using Gemstone Spectral Imaging

BACKGROUND

The distinction between solitary inflammatory lesion and solitary lung cancer remains a challenge because of their considerable overlapping computed tomography (CT) imaging features.

PURPOSE

This study aimed to verify whether spectral CT parameters can differentiate solitary lung cancer from solitary inflammatory lesions and to find their correlations with lesion size.

METHODS

A total of 78 patients with solitary lung lesions were included in our study. All of them underwent enhanced CT scans with Gemstone Spectral Imaging (GSI) mode, which was one of the dual-energy imaging technologies. According to maximum diameter (Dmax) of the lesion, regions of interest were collected and divided into inflammatory (group I: <3 cm [IA], n = 17; ≥3 cm [IB], n = 14) and cancer groups (group II: <3 cm [IIA], n = 20; ≥3 cm [IIB], n = 27). Computed tomography values (HU40keV, HU70keV), effective atomic number (Zeff), iodine concentration (IC), normalized IC (NIC), and spectral curve slopes (λ30, λ40) of each region of interest were calculated. The NIC was defined as the IC ratio of the lesion to the descending aorta. Mann-Whitney U test was used for intergroup (I vs II, IA vs IIA, IB vs IIB) and intragroup (IA vs IB, IIA vs IIB) comparisons, and receiver operating characteristic curve analysis was performed. Correlation analysis was applied to find the relationship between Dmax and GSI parameters.

RESULTS

No significant correlation was found between GSI parameters and Dmax in the inflammatory group, whereas inverse correlations were found in the cancer group. Gemstone spectral imaging parameters (except HU70keV) of group IIA were significantly higher than those of group IIB. There were significant differences in HU40keV, IC, NIC, λ30, and λ40 between groups IB and IIB under both arterial and venous phase (P values < 0.05), whereas the area under the curve for λ30 under venous phase was largest, and sensitivity and specificity were 96.32% and 85.71%, respectively. However, only HU40keV and HU70keV values under the arterial phase of IIA were significantly higher than those of IA.

CONCLUSIONS

Quantitative parameters of GSI demonstrated an inverse correlation with the lesion size of solitary lung cancer, and GSI parameters can be new ways to differentiate solitary lung cancer from solitary inflammatory lesions.

Benign and malignant pulmonary part-solid nodules: differentiation via thin-section computed tomography

BACKGROUND

Pulmonary part-solid nodules (PSNs) reportedly have a high possibility of malignancy, while benign PSNs are common. This study aimed to reveal the differences between benign and malignant PSNs by comparing their thin-section computed tomography (CT) features.

METHODS

Patients with PSNs confirmed by postoperative pathological examination or follow-up (at the same period) were retrospectively enrolled from March 2016 to January 2020. The clinical data of patients and CT features of benign and malignant PSNs were reviewed and compared. Binary logistic regression analysis was performed to reveal the predictors of malignant PSNs.

RESULTS

A total of 119 PSNs in 117 patients [age (mean ± standard deviation), 56±11 years; 70 women] were evaluated. Of the 119 PSNs, 44 (37.0%) were benign, and 75 (63.0%) were malignant (12 adenocarcinomas in situ, 22 minimally invasive adenocarcinomas, and 41 invasive adenocarcinomas). There were significant differences in the patients' age and smoking history between benign and malignant PSNs. In terms of CT characteristics, malignant and benign lesions significantly differed in the following CT features: whole nodule, internal solid component, and peripheral ground-glass opacity. The binary logistic regression analysis revealed that well-defined border [odds ratio (OR), 4.574; 95% confidence interval (CI), 1.186-17.643; P=0.027] and lobulation (OR, 61.739; 95% CI, 5.230-728.860; P=0.001) of the nodule, as well as irregular shape (OR, 9.502; 95% CI, 1.788-50.482; P=0.008) and scattered distribution (OR, 13.238; 95% CI, 1.359-128.924; P=0.026) of the internal solid components were significant independent predictors distinguishing malignant PSNs. However, the lesion shape, density, and margin were similar between malignant and benign lesions.

CONCLUSIONS

Well-defined and lobulated PSNs with irregular and scattered solid components are highly likely to be malignant.

Comparison of Spectral and Perfusion Computed Tomography Imaging in the Differential Diagnosis of Peripheral Lung Cancer and Focal Organizing Pneumonia

Objective

To investigate the spectral and perfusion computed tomography (CT) findings of peripheral lung cancer (PLC) and focal organizing pneumonia (FOP) and to compare the accuracy of spectral and perfusion CT imaging in distinguishing PLC from FOP.

Materials and Methods

Patients who were suspected of having lung tumor and underwent “one-stop” chest spectral and perfusion CT, with their diagnosis confirmed pathologically, were prospectively enrolled from September 2020 to March 2021. Patients who were suspected of having lung tumor and underwent “one-stop” chest spectral and perfusion CT, with their diagnosis confirmed pathologically, were prospectively enrolled from September 2020 to March 2021. A total of 57 and 35 patients with PLC and FOP were included, respectively. Spectral parameters (CT_40keV, CT_70keV, CT_100keV, iodine concentration [IC], water concentration [WC], and effective atomic number [Zeff]) of the lesions in the arterial and venous phases were measured in both groups. The slope of the spectral curve (K_70keV) was calculated. The perfusion parameters, including blood volume (BV), blood flow (BF), mean transit time (MTT), and permeability surface (PS), were measured simultaneously in both groups. The differences in the spectral and perfusion parameters between the groups were examined. Receiver operating characteristic (ROC) curves were generated to calculate and compare the area under the curve (AUC), sensitivity, specificity, and accuracy of both sets of parameters in both groups.

Results

The patients’ demographic and clinical characteristics were similar in both groups (P > 0.05). In the arterial and venous phases, the values of spectral parameters (CT_40keV, CT_70keV, spectral curve K_70keV, IC, and Zeff) were greater in the FOP group than in the PLC group (P < 0.05). In contrast, the values of the perfusion parameters (BV, BF, MTT, and PS) were smaller in the FOP group than in the PLC group (P < 0.05). The AUC of the combination of the spectral parameters was larger than that of the perfusion parameters. For the former imaging method, the AUC, sensitivity, and specificity were 0.89 (95% confidence interval [CI]: 0.82–0.96), 0.86, and 0.83, respectively. For the latter imaging method, the AUC, sensitivity, and specificity were 0.80 (95% CI: 0.70–0.90), 0.71, and 0.83, respectively. There was no significant difference in AUC between the two imaging methods (P > 0.05).

Conclusion

Spectral and perfusion CT both has the capability to differentiate PLC and FOP. However, compared to perfusion CT imaging, spectral CT imaging has higher diagnostic efficiency in distinguishing them.

A wavelet features derived radiomics nomogram for prediction of malignant and benign early-stage lung nodules

This study was to develop a radiomics nomogram mainly using wavelet features for identifying malignant and benign early-stage lung nodules for high-risk screening. A total of 116 patients with early-stage solitary pulmonary nodules (SPNs) (≤ 3 cm) were divided into a training set (N = 70) and a validation set (N = 46). Radiomics features were extracted from plain LDCT images of each patient. A radiomics signature was then constructed with the LASSO with the training set. Combined with independent risk factors, a radiomics nomogram was built with a multivariate logistic regression model. This radiomics signature, consisting of one original and nine wavelet features, achieved favorable predictive efficacy than Mayo Clinic Model. The radiomics nomogram with radiomics signature and age also showed good calibration and discrimination in the training set (AUC 0.9406; 95% CI 0.8831-0.9982) and the validation set (AUC 0.8454; 95% CI 0.7196-0.9712). The decision curve indicated the clinical usefulness of our nomogram. The presented radiomics nomogram shows favorable predictive accuracy for identifying malignant and benign lung nodules in early-stage patients and is much better than the Mayo Clinic Model.

Differentiating pulmonary metastasis from benign lung nodules in thyroid cancer patients using dual-energy CT parameters

OBJECTIVES

To explore the importance of quantitative characteristics of dual-energy CT (DECT) between pulmonary metastasis and benign lung nodules in thyroid cancer.

METHODS

In this retrospective study, we identified 63 patients from our institution's database with pathologically proven thyroid cancer who underwent DECT to assess pulmonary metastasis. Among these patients, 22 had 55 pulmonary metastases, and 41 had 97 benign nodules. If nodules showed increased iodine uptake on I-131 single-photon emission computed tomography-computed tomography or increased size in follow-up CT, they were considered metastatic. We compared the clinical findings and DECT parameters of both groups and performed a receiver operating characteristic analysis to evaluate the optimal cutoff values of the DECT parameters.

RESULTS

Patients with metastases were significantly older than patients with benign nodules (p = 0.048). The DECT parameters of the metastatic nodules were significantly higher than those of the benign nodules (iodine concentration [IC], 5.61 ± 2.02 mg/mL vs. 1.61 ± 0.98 mg/mL; normalized IC [NIC], 0.60 ± 0.20 vs. 0.16 ± 0.11; NIC using pulmonary artery [NIC_PA], 0.60 ± 0.44 vs. 0.15 ± 0.11; slope of the spectral attenuation curves [λHU], 5.18 ± 2.54 vs. 2.12 ± 1.39; and Z-effective value [Z_eff], 10.0 ± 0.94 vs. 8.79 ± 0.75; all p < 0.001). In the subgroup analysis according to nodule size, all DECT parameters of the metastatic nodules in all subgroups were significantly higher than those of the benign nodules (all p < 0.05). The cutoff values for IC, NIC, λHU, NIC_PA, and Z_eff for diagnosing metastases were 3.10, 0.29, 3.57, 0.28, and 9.34, respectively (all p < 0.001).

CONCLUSIONS

DECT parameters can help to differentiate metastatic and benign lung nodules in thyroid cancer.

KEY POINTS

• DECT parameters can help to differentiate metastatic and benign lung nodules in patients with thyroid cancer. • DECT parameters showed a significant difference between benign lung nodules and lung metastases, even for nodules with diameters ≥ 3 mm and < 5 mm. • Among the DECT parameters, the highest diagnostic accuracy for differentiating pulmonary metastases from benign lung nodules was achieved with the NIC and IC, followed by the NIC_PA and λHU, and their cutoff values were 0.29, 3.10, 0.28, and 3.57, respectively.

Significance of intra-nodular vessel sign in differentiating benign and malignant pulmonary ground-glass nodules

BACKGROUND

The presence of pulmonary vessels inside ground-glass nodules (GGNs) of different nature is a very common occurrence. This study aimed to reveal the significance of pulmonary vessels displayed in GGNs in their diagnosis and differential diagnosis.

RESULTS

A total of 149 malignant and 130 benign GGNs confirmed by postoperative pathological examination were retrospectively enrolled in this study. There were significant differences in size, shape, nodule-lung interface, pleural traction, lobulation, and spiculation (each p < 0.05) between benign and malignant GGNs. Compared with benign GGNs, intra-nodular vessels were more common in malignant GGNs (67.79% vs. 54.62%, p = 0.024), while the vascular categories were similar (p = 0.663). After adjusting the nodule size and the distance between the nodule center and adjacent pleura [radius-distance ratio, RDR], the occurrences of internal vessels between them were similar. The number of intra-nodular vessels was positively correlated with nodular diameter and RDR. Vascular changes were more common in malignant than benign GGNs (52.48% vs. 18.31%, p < 0.0001), which mainly manifested as distortion and/or dilation of pulmonary veins (61.19%). The occurrence rate, number, and changes of internal vessels had no significant differences among all the pre-invasive and invasive lesions (each p > 0.05).

CONCLUSIONS

The incidence of internal vessels in GGNs is mainly related to their size and the distance between nodule and pleura rather than the pathological nature. However, GGNs with dilated or distorted internal vessels, especially pulmonary veins, have a higher possibility of malignancy.

The regulatory function of mixed lineage kinase 3 in tumor and host immunity

Protein kinases are the second most sought-after G-protein coupled receptors as drug targets because of their overexpression, mutations, and dysregulated catalytic activities in various pathological conditions. Till 2019, 48 protein kinase inhibitors have received FDA approval for the treatment of multiple illnesses, of which the majority of them are indicated for different malignancies. One of the attractive sub-group of protein kinases that has attracted attention for drug development is the family members of MAPKs that are recognized to play significant roles in different cancers. Several inhibitors have been developed against various MAPK members; however, none of them as monotherapy has shown sustainable efficacy. One of the MAPK members, called Mixed Lineage Kinase 3 (MLK3), has attracted considerable attention due to its role in inflammation and neurodegenerative diseases; however, its role in cancer is an emerging area that needs more investigation. Recent advances have shown that MLK3 plays a role in cancer cell survival, migration, drug resistance, cell death, and tumor immunity. This review describes how MLK3 regulates different MAPK pathways, cancer cell growth and survival, apoptosis, and host's immunity. We also discuss how MLK3 inhibitors can potentially be used along with immunotherapy for different malignancies.

The application of dual-layer spectral detector computed tomography in solitary pulmonary nodule identification

BACKGROUND

Differentiating between malignant solitary pulmonary nodules (SPNs) and other lung diseases remains a substantial challenge. The latest generation of dual-energy computed tomography (CT), which realizes dual-energy technology at the detector level, has clinical potential for distinguishing lung cancer from other benign SPNs. This study aimed to evaluate the performance of dual-layer spectral detector CT (SDCT) for the differentiation of SPNs.

METHODS

Spectral images of 135 SPNs confirmed by pathology were retrospectively analyzed in both the arterial phase (AP) and the venous phase (VP). Patients were classified into two groups [the malignant group (n=93) and the benign group (n=42)], with the malignant group further divided into small cell lung cancer (SCLC, n=30) and non-small cell lung cancer (NSCLC, n=63) subtypes. The slope of the spectral Hounsfield Unit (HU) curve (λHU), normalized iodine concentration (NIC), CT values of 40 keV monochromatic images (CT40keV), and normalized arterial enhancement fraction (NAEF) in contrast-enhanced images were calculated and compared between the benign and malignant groups, as well as between the SCLC and NSCLC subgroups. ROC curve analysis was performed to assess the diagnostic performance of the above parameters. Seventy cases were randomly selected and independently measured by two radiologists, and intraclass correlation coefficient (ICC) and Bland-Altman analyses were performed to calculate the reliability of the measurements.

RESULTS

Except for NAEF (P=0.23), the values of the parameters were higher in the malignant group than in the benign group (all P<0.05). NIC, λHU, and CT40keV performed better in the VP (NICVP, λVPHU, and CTVP40keV) (P<0.001), with an area under the ROC curve (AUC) of 0.93, 0.89, and 0.89 respectively. With respective cutoffs of 0.31, 1.83, and 141.00 HU, the accuracy of NICVP, λVPHU, and CTVP40keV was 91.11%, 85.19%, and 88.15%, respectively. In the subgroup differentiating NSCLC and SCLC, the diagnostic performances of NICAP (AUC =0.89) were greater than other parameters. NICAP had an accuracy of 86.02% when the cutoff was 0.14. ICC and Bland-Altman analyses indicated that the measurement of SDCT has great reproducibility.

CONCLUSIONS

Quantitative measures from SDCT can help to differentiate benign from malignant SPNs and may help with the further subclassification of malignant cancer into SCLC and NSCLC.

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.

Improved differentiation between primary lung cancer and pulmonary metastasis by combining dual-energy CT-derived biomarkers with conventional CT attenuation

OBJECTIVES

To assess the clinical utility of dual-energy CT (DE-CT)-derived iodine concentration (IC) and effective Z (Z^eff) in addition to conventional CT attenuation (HU) for the discrimination between primary lung cancer (LC) and pulmonary metastases (PM) from different primary malignancies.

METHODS

DE-CT scans of 79 patients with LC (3 histopathologic subgroups) and 89 patients with PM (5 histopathologic subgroups) were evaluated. Quantitative IC, Z^eff, and conventional HU values were extracted and normalized to the thoracic aorta. Differences between groups were assessed by pairwise Welch's t test. Correlation and linear regression analyses were used to examine the relationship of imaging parameters in LC and PM. Diagnostic accuracy was measured by the area under receiver operator characteristic curve (AUC) and validated based on resampling methods.

RESULTS

Significant differences between subgroups of LC and PMs were noted for all imaging parameters, with the highest number of significant pairs for IC. In univariate analysis, only IC was a significant diagnostic feature for discriminating LC from PM (p = 0.03). All quantitative imaging parameters correlated significantly (p < 0.0001, respectively), with the highest correlation between IC and Z^eff (r = 0.91), followed by IC and HU (r = 0.76) and Z^eff and HU (r = 0.73). Diagnostic models combining IC or Z^eff with HU (IC+HU: AUC = 0.73; Z^eff+HU: AUC = 0.69; IC+Z^eff+HU: AUC = 0.73) were not significantly different and outperformed individual parameters (IC: AUC = 0.57; Z^eff: AUC = 0.57; HU: AUC = 0.55) in diagnostic accuracy (p < 0.05, respectively).

CONCLUSION

DE-CT-derived IC or Z^eff and conventional HU represent complementary imaging parameters, which, if used in combination, may improve the differentiation between LC and PM.

KEY POINTS

• Individual quantitative imaging parameters derived from DE-CT (iodine concentration, effective Z) and conventional CT (HU) provide complementary diagnostic information for the differentiation of primary lung cancer and pulmonary metastases. • A combination of conventional HU and DE-CT parameters enhances the diagnostic utility of individual parameters.

Utility of Iodine Density Perfusion Maps From Dual-Energy Spectral Detector CT in Evaluating Cardiothoracic Conditions: A Primer for the Radiologist

OBJECTIVE. The purpose of this article is to outline the utility of iodine density maps for evaluating cardiothoracic disease and abnormalities. Multiple studies have shown that the variety of images generated from dual-energy spectral detector CT (SDCT) improve identification of cardiothoracic conditions. CONCLUSION. Understanding the technique of SDCT and being familiar with the features of different cardiothoracic conditions on iodine density map images help the radiologist make a better diagnosis.

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

Background

The aim of this study was to explore whether spectral computed tomography (CT) imaging parameters are associated with PD‐L1 expression of lung adenocarcinoma.

Methods

Spectral 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.

Results

The 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).

Conclusion

CT40keVa, 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.