Predicting epidermal growth factor receptor mutations in non-small cell lung cancer through dual-layer spectral CT: a prospective study.

OBJECTIVE: To determine whether quantitative parameters of detector-derived dual-layer spectral computed tomography (DLCT) can reliably identify epidermal growth factor receptor (EGFR) mutation status in patients with non-small cell lung cancer (NSCLC). METHODS: Patients with NSCLC who underwent arterial phase (AP) and venous phase (VP) DLCT between December 2021 and November 2022 were subdivided into the mutated and wild-type EGFR groups following EGFR mutation testing. Their baseline clinical data, conventional CT images, and spectral images were obtained. Iodine concentration (IC), iodine no water (INW), effective atomic number (Zeff), virtual monoenergetic images, the slope of the spectral attenuation curve (λHU), enhancement degree (ED), arterial enhancement fraction (AEF), and normalized AEF (NAEF) were measured for each lesion. RESULTS: Ninety-two patients (median age, 61 years, interquartile range [51, 67]; 33 men) were evaluated. The univariate analysis indicated that IC, normalized IC (NIC), INW and ED for the AP and VP, as well as Zeff and λHU for the VP were significantly associated with EGFR mutation status (all p < 0.05). INW(VP) showed the best diagnostic performance (AUC, 0.892 [95% confidence interval {CI}: 0.823, 0.960]). However, neither AEF (p = 0.156) nor NAEF (p = 0.567) showed significant differences between the two groups. The multivariate analysis showed that INW(AP) and NIC(VP) were significant predictors of EGFR mutation status, with the latter showing better performance (p = 0.029; AUC, 0.897 [95% CI: 0.816, 0.951] vs. 0.774 [95% CI: 0.675, 0.855]). CONCLUSION: Quantitative parameters of DLCT can help predict EGFR mutation status in patients with NSCLC. CRITICAL RELEVANCE STATEMENT: Quantitative parameters of DLCT, especially NIC(VP), can help predict EGFR mutation status in patients with NSCLC, facilitating appropriate and individualized treatment for them. KEY POINTS: Determining EGFR mutation status in patients with NSCLC before starting therapy is essential. Quantitative parameters of DLCT can predict EGFR mutation status in NSCLC patients. NIC in venous phase is an important parameter to guide individualized treatment selection for NSCLC patients.

Utility of spectral CT with orthopedic metal artifact reduction algorithms for 125I seeds implantation in mediastinal and hepatic tumors.

Background: Virtual monochromatic image (VMI) combined with orthopedic metal artifact reduction algorithms (VMI + O-MAR) can effectively reduce artifacts caused by metal implants of different types. Nevertheless, so far, no study has systematically evaluated the efficacy of VMI + O-MAR in reducing various types of metal artifacts induced by 125I seeds. The aim of this study was to assess the effectiveness of combining spectral computed tomography (CT) images with O-MAR in reducing metal artifacts and improving the image quality affected by artifacts in patients after 125I radioactive seeds implantation (RSI). Methods: A total of 45 patients who underwent dual-layer detector spectral CT (DLCT; IQon, Philips Healthcare) scanning of mediastinal and hepatic tumors after 125I RSI were retrospectively included. Spectral data were reconstructed into conventional image (CI), VMI, CI combined with O-MAR (CI + O-MAR), and VMI + O-MAR to evaluate the de-artifact effect and image quality improvement. Objective indicators included signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and artifact index (AI) of lesions affected by artifacts. Subjective indicators included assessment of overcorrected artifacts and new artifacts, different morphology of artifacts, and overall image quality. Results: In artifact-affected lesion areas, SNR and CNR in the CI/VMI + O-MAR groups were better than those in CI groups (all P values <0.05). The AI showed a downward trend as VMI keV increased (all P values <0.001). The AI values of the CI/VMI (50-150 keV) group were all higher than the groups of CI/VMI + O-MAR (50-150 keV) (P<0.001). Overcorrection artifacts and new artifacts were concentrated in the VMI50/70 keV groups. In the evaluation of artifact morphology, as the VMI keV increased, the number of near-field banding artifacts in hyperdense artifacts gradually decreased, whereas the number of minimal or no artifacts increased, and the total number of hyperdense artifacts were decreased. The diagnostic and image quality scores of hyperdense artifacts were higher than those of hypodense artifacts as VMI keV increased. Conclusions: High VMI level combined with O-MAR substantially improve objective and subjective image quality, lesion display ability, and diagnostic confidence of CT follow-up after 125I RSI, especially at the VMI + O-MAR 150 keV level.

Novel biomarkers based on dual-energy computed tomography for risk stratification of very early distant metastasis in colorectal cancer after surgery.

Background: Very early distant metastasis (VEDM) for patients with colorectal cancer (CRC) following surgery suggests failure of local treatment strategy and few biomarkers are available for its effective risk stratification. This study aimed to explore the potential of quantitative dual-energy computed tomography (DECT) spectral parameters and build models to predict VEDM. Methods: Consecutive patients suspected of having CRC and with a clinical indication for enhanced CT from April 2021 to July 2022 at a single institution were prospectively enrolled to undertake spectral CT scanning. The spectral features were extracted by two reviewers and intraclass correlation coefficient (ICC) was used for interobserver agreement evaluation. A total of 16 spectral parameters, including unenhanced effective atomic number, triphasic iodine concentrations (ICs)/normalized ICs (NICs)-A/V/E/1/NIC-A/V/E/spectral curve slopes (λ-A/V/E), two arterial enhancement fractions (AEFs), and venous enhancement fraction (VEF), were determined for analysis. Patients with and without VEDM after surgery were matched using propensity score matching (PSM). The diagnostic performance was assessed using the area under the curve (AUC). Models of multiple modalities were generated. Results: In total, 222 patients were included (141 males, age range, 32-83 years) and 13 patients developed VEDM. Interobserver agreement ranged from good to excellent (ICC, 0.773-0.964). A total of three spectral parameters (VEF, λ-V, and 1/NIC-V) exhibited significant discriminatory ability (P<0.05) in predicting VEDM, with AUCs of 0.822 [95% confidence interval (CI): 0.667-0.926], 0.738 (95% CI: 0.573-0.866), and 0.713 (95% CI: 0.546-0.846) and optimal cutoff points of 67.16%, 2.46, and 2.44, respectively. The performance of these spectral parameters was validated in the entire cohort; the combined spectral model showed comparable efficiency to the combined clinical model [AUC, 0.771 (95% CI: 0.622-0.919) vs. 0.779 (95% CI: 0.663-0.894), P>0.05]; the clinical-spectral model achieved further improved AUC of 0.887 (95% CI: 0.812-0.962), which was significantly higher than the combined clinical model (P=0.015), yet not superior to the combined spectral model (P=0.078). Conclusions: Novel spectral parameters showed potential in predicting VEDM in CRC following surgery in this preliminary study, which were closely related with spectral perfusion in the venous phase. However, further studies with larger samples are warranted.

Dual-layer spectral computed tomography aortography using a seventy-five-percent-reduced iodine dose protocol and multiparameter spectral imaging: comparison with conventional computed tomography imaging.

Background: Computed tomography angiography (CTA) is the recommended diagnostic and follow-up imaging modality for acute aortic dissection (AD). However, the high-contrast medium burden associated with repeated CT aortography follow-ups remains a significant concern. This prospective study aimed to assess whether an ultra-low contrast dose (75% cutoff) aortic CTA protocol on dual-layer spectral CT could achieve comparable image quality with the full dose protocol. We also investigated the image quality of the virtual noncontrast (VNC) images derived from the ultra-low dose protocol. Methods: This study included 37 consecutive patients who were referred to aortic CTA from May 2022 to August 2022. The enrolled patients underwent full-dose contrast CTA and ultra-low dose (reduced to 25% of conventional) contrast CTA on dual-layer spectral CT in 1 day. Virtual monochromatic images (VMIs) were reconstructed with 40 and 70 keV. The VNC images were reconstructed for both protocols. Objective image quality evaluation, recorded as signal-to-noise ratios (SNRs) and contrast-to-noise ratios (CNRs), was compared between the groups using 1-way analysis of variance and post hoc analysis with Bonferroni correction. Subjective image quality was also compared between the groups. Finally, VNC images derived from the low-dose (VNClow) and full-dose (VNCfull) protocols were compared to the true noncontrast (TNC) images. Results: Neither CNR nor SNR was lower for the 40-keV images reconstructed from the ultra-low dose group compared to the conventional images. Both were significantly higher than those of the 70-keV images. Regarding subjective image quality, vessel enhancement was not significantly different between the 40-keV VMI and full-dose images [ascending aorta (AAO): 4.37±0.46 vs. 4.57±0.48, P=0.096; brachiocephalic arteries: 4.34±0.45 vs. 4.51±0.49, P=0.152; abdominal aortic side branch: 4.42±0.48 vs. 4.51±0.49, P=0.480]. The VNClow images were similar to the TNC images but significantly different from the VNCfull images (P<0.001). Conclusions: Ultra-low contrast aortic CTA with a 75%-reduced iodine dose using dual-layer spectral CT and the derived VNC achieved image quality comparable to that of conventional CTA and TNC images.

Observational Study of the Natural Growth History of Peripheral Small-Cell Lung Cancer on CT Imaging.

BACKGROUND: This study aimed to investigate the natural growth history of peripheral small-cell lung cancer (SCLC) using CT imaging. METHODS: A retrospective study was conducted on 27 patients with peripheral SCLC who underwent at least two CT scans. Two methods were used: Method 1 involved direct measurement of nodule dimensions using a calliper, while Method 2 involved tumour lesion segmentation and voxel volume calculation using the "py-radiomics" package in Python. Agreement between the two methods was assessed using the intraclass correlation coefficient (ICC). Volume doubling time (VDT) and growth rate (GR) were used as evaluation indices for SCLC growth, and growth distribution based on GR and volume measurements were depicted. We collected potential factors related to imaging VDT and performed a differential analysis. Patients were classified into slow-growing and fast-growing groups based on a VDT cut-off point of 60 days, and univariate analysis was used to identify factors influencing VDT. RESULTS: Median VDT calculated by the two methods were 61 days and 71 days, respectively, with strong agreement. All patients had continuously growing tumours, and none had tumours that decreased in size or remained unchanged. Eight patients showed possible growth patterns, with six possibly exhibiting exponential growth and two possibly showing Gompertzian growth. Tumours deeper in the lung grew faster than those adjacent to the pleura. CONCLUSIONS: Peripheral SCLC tumours grow rapidly and continuously without periods of nongrowth or regression. Tumours located deeper in the lung tend to grow faster, but further research is needed to confirm this finding.

Predicting coronary atherosclerosis heart disease with pericoronary adipose tissue attenuation parameters based on dual-layer spectral detector computed tomography: a preliminary exploration.

Background: Coronary atherosclerosis is a chronic inflammatory condition. Pericoronary adipose tissue (PCAT) attenuation is closely related to coronary inflammation. This study aimed to investigate the relationship between PCAT attenuation parameters and coronary atherosclerotic heart disease (CAD) using dual-layer spectral detector computed tomography (SDCT). Methods: This cross-sectional study included eligible patients who underwent coronary computed tomography angiography using SDCT at the First Affiliated Hospital of Harbin Medical University between April 2021 and September 2021. Patients were classified as CAD (with coronary artery atherosclerotic plaque) or non-CAD (without coronary artery atherosclerotic plaque). Propensity score matching was used to match the two groups. The fat attenuation index (FAI) was used to quantify PCAT attenuation. The FAI was measured on conventional images (120 kVp) and virtual monoenergetic images (VMI) by semiautomatic software. The slope of the spectral attenuation curve (λ) was calculated. Regression models were established to evaluate the predictive value of PCAT attenuation parameters for CAD. Results: A total of 45 patients with CAD and 45 patients without CAD were enrolled. The PCAT attenuation parameters in the CAD group were significantly higher than those in the non-CAD group (all P values <0.05). The PCAT attenuation parameters of vessels with or without plaques in the CAD group were higher than those of vessels without plaques in the non-CAD group (all P values <0.05). In the CAD group, the PCAT attenuation parameters of vessels with plaques were slightly higher than those of vessels without plaques (all P values >0.05). In receiver operating characteristic curve analysis, the FAIVMI model achieved an area under the curve (AUC) of 0.8123 for discriminating between patients with and without CAD, which was higher than those of the FAI120 kVp model (AUC =0.7444) and the λ model (AUC =0.7230). However, the combined model of FAIVMI, FAI120 kVp, and λ obtained the best performance (AUC =0.8296) of all the models. Conclusions: PCAT attenuation parameters obtained using dual-layer SDCT can aid in distinguishing patients with and without CAD. By detecting increases in PCAT attenuation parameters, it might be possible to predict the formation of atherosclerotic plaques before they appear.