Background: FXYD3 is up-/down-regulated in different types of cancers. We examined FXYD3 expression in colorectal cancers and its relationship to biological and clinicopathological variables. Patients and Methods: Expression of FXYD3 protein was immunohistochemically examined in distant normal mucosa (n = 34), adjacent normal mucosa (n = 72), primary tumour (n = 150) and lymph node metastasis (n = 35) from colorectal cancer patients. Results: FXYD3 was highly expressed in primary tumour compared to adjacent normal mucosa (p = 0.02). FXYD3 was or tended to be positively related to the expression of ras (p = 0.02), p53 (p = 0.06), legumain (p = 0.02) and proliferating cell nuclear antigen (p = 0.03). Moreover, there was a higher frequency of strong FXYD3 expression in Dukes A–C tumours than in D tumours (p = 0.04). The strong FXYD3 expression tended to predict worse survival in the patients with Dukes A + B tumour (p = 0.07), while there was no such tendency in the patients with Dukes C + D tumour (p = 0.94). The tumours located in the colon had a higher degree of FXYD3 expression than the tumours located in the rectum (p = 0.05). Conclusion: The FXYD3 was associated with certain biological variables and may be involved in the development of the relative earlier stages of colorectal cancers.

We analyzed the effects of anti-hedgehog signaling on the (18)F-FDG uptake of pancreatic cancer xenografts (PCXs) using a clinically implemented positron emission tomography (PET)-computer tomography (CT) scanner with high-resolution reconstruction. PCXs from two pancreatic cancer cell lines were developed subcutaneously in nude mice and injected intraperitoneally with a low dose of cyclopamine for 1 week. (18)F-FDG PET-CT was performed using a new-generation clinical PET-CT scanner with minor modifications of the scanning protocol to adapt for small-animal imaging. The data set was reconstructed and quantified using a three-dimensional workstation. MiaPaCa-2 cells, which respond to cyclopamine, showed decreased (18)F-FDG uptake without a change in tumor size. For hip tumors, the maximum standardized uptake value (SUV(max)) was reduced by -24.5 ± 9.2%, the average SUV (SUV(avg)) by -33.5 ± 7.0%, and the minimum SUV (SUV(min)) by -54.4 ± 11.5% (P < .05). For shoulder tumors, SUV(max) was reduced by -14.7 ± 7.5%, SUV(avg) by -12.6 ± 6.3, and SUV(min) by -30.3 ± 16.7% (P < .05). Capan-1 cells, which do not respond to cyclopamine, did not show significant SUV changes. The new generations of clinically implemented PET-CT scanners with high-resolution reconstruction detect a minimal response of PCX to low-dose short-term cyclopamine therapy without changes in tumor size and offer potential for preclinical translational imaging.

Photon-Counting Computed Tomography (PCCT) offers significant advancements in aortic imaging, especially for vascular applications. Its Ultra-High-Resolution (UHR) mode enhances the visualization of small vascular structures, aiding surgical planning while reducing radiation exposure. This study assesses whether UHR abdominal aortic angiography with PCCT improves resolution and noise ratio compared to Energy-Integrating Computed Tomography (EICT) without increasing radiation dose. In this retrospective study, 25 patients underwent Ultra-High-Resolution abdominal CT angiography (PCCT) and were compared to 25 patients, who underwent abdominal CT angiography on an EICT scanner. Ethical approval was obtained. Image quality was assessed using Signal-to-Noise Ratio (SNR), Contrast-to-Noise Ratio (CNR), and a five-point Likert scale. Radiation doses were compared using CTDIvol, DLP, and effective dose, with statistical significance set at p < 0.05. The PCCT group showed significantly lower radiation exposure (CTDIvol: 4.7 mGy vs. 7.3 mGy, p = 0.0081; effective dose: 3.4 mSv vs. 6.5 mSv, p = 0.0057). Although PCCT had higher SNR and CNR, differences were statistically significant only for renal arteries (p = 0.0432). Subjective image quality was significantly better for PCCT (p < 0.0001). This study outlines the feasibility of UHR mode using PCCT in improving the contrast-to-noise and signal-to-noise ratio in patients undergoing abdominal CT angiography in the arterial phase as well as improved image quality of visceral arteries compared to EICT, accompanied by a significant reduction in radiation dose. This supports its recommending PCCT angiography for use in reducing patient exposure while improving image quality.

Objective: The aim of the study is to assess the correlation between artificial intelligence (AI)–based low attenuation volume percentage (LAV%) with forced expiratory volume in the first second to forced vital capacity (FEV1/FVC) and visual emphysema grades in routine chest computed tomography (CT). Furthermore, optimal LAV% cutoff values for predicting a FEV1/FVC < 70% or moderate to more extensive visual emphysema grades were calculated. Methods: In a retrospective study of 298 consecutive patients who underwent routine chest CT and spirometry examinations, LAV% was quantified using an AI-based software with a threshold < −950 HU. The FEV1/FVC was derived from spirometry, with FEV1/FVC < 70% indicating airway obstruction. The mean time interval of CT from spirometry was 3.87 ± 4.78 days. Severity of emphysema was visually graded by an experienced chest radiologist using an established 5-grade ordinal scale (Fleischner Society classification system). Spearman correlation coefficient between LAV% and FEV1/FVC was calculated. Receiver operating characteristic determined the optimal LAV% cutoff values for predicting a FEV1/FVC < 70% or a visual emphysema grade of moderate or higher (Fleischner grade 3–5). Results: Significant correlation between LAV% and FEV1/FVC was found (ϱ = −0.477, P < 0.001). Increasing LAV% corresponded to higher visual emphysema grades. For patients with absent visual emphysema, mean LAV% was 2.98 ± 3.30, for patients with trace emphysema 3.22 ± 2.75, for patients with mild emphysema 3.90 ± 3.33, for patients with moderate emphysema 6.41 ± 3.46, for patients with confluent emphysema 9.02 ± 5.45, and for patients with destructive emphysema 16.90 ± 8.19. Optimal LAV% cutoff value for predicting a FEV1/FVC < 70 was 6.1 (area under the curve = 0.764, sensitivity = 0.773, specificity = 0.665), while for predicting a visual emphysema grade of moderate or higher, it was 4.7 (area under the curve = 0.802, sensitivity = 0.766, specificity = 0.742). Furthermore, correlation between visual emphysema grading and FEV1/FVC was found. In patients with FEV1/FVC < 70% a high proportion of subjects had emphysema grade 3 (moderate) or higher, whereas in patients with FEV1/FVC ≥ 70%, a larger proportion had emphysema grade 3 (moderate) or lower. The sensitivity for visual emphysema grading predicting a FEV1/FVC < 70% was 56.3% with an optimal cutoff point at a visual grade of 4 (confluent), demonstrating a lower sensitivity compared with LAV% (77.3%). Conclusions: A significant correlation between AI-based LAV% and FEV1/FVC as well as visual CT emphysema grades can be found in routine chest CT suggesting that AI-based LAV% measurement might be integrated as an add-on functional parameter in the evaluation of chest CT in the future.

Background: Low attenuation volume percentage (LAV%) has been identified as a quantitative imaging biomarker for emphysema with good correlation with spirometry. The influence of intravenous contrast agent on LAV% and its correlation with spirometry is not well known. Purpose: To evaluate the influence of intravenous contrast agent on artificial intelligence (AI)-based LAV% in correlation with spirometric Tiffeneau-Pinelli Index (TI). Material and Methods: In a retrospective study, two groups of 47 patients (mean age 68.04 ± 12.64 and 67.89 ± 11.54 years) with either non-enhanced chest computed tomography (CT) or contrast-enhanced CT were compared. Using an AI-based software, LAV% was quantified using a threshold <−950 HU. TI was calculated from spirometry and pathologic airway obstruction was considered with a TI <70. The effect of contrast agent on LAV% and the relationship between TI and LAV% was analyzed. Correlation coefficients between TI and LAV% were compared for both groups. Results: Patients with non-enhanced CT had a mean LAV% of 9.07 ± 7.53. Of them, 22 patients had a TI <70% and 25 patients a TI ≥70%. Patients with contrast-enhanced CT had a mean LAV% of 6.54 ± 4.62. Of them, 20 patients had a TI <70% and 27 patients had a TI ≥70%. Contrast agent did not show a major effect on LAV% ( P = 0.099) and the relationship between TI and LAV% ( P = 0.88). In both groups, a significant correlation between TI and LAV% was found (ρ = −0.317 for non-enhanced CT; ρ = −0.514 for contrast-enhanced CT). Difference between correlation coefficients was insignificant. Conclusion: Our findings suggest that contrast agent does not influence LAV% nor its correlation with TI.

This study evaluates ultra-low-dose computed tomography (ULDCT) protocols concerning the detectability of pulmonary nodules. The influence of tube current settings, kernels, strength levels of third-generation iterative reconstruction algorithms, and pitch was investigated. A chest phantom with artificial spherical nodules of different densities and diameters was examined with a third-generation dual-source CT. Scanning and post-processing protocols, tube current levels, and ultra-high and non-high pitch modes were applied. Images were reconstructed with filtered back-projection (FBP) or advanced model-based iterative reconstruction (ADMIRE) algorithms. Sharp (Bl57) or medium-soft (Br36) convolution kernels were applied. The reading was performed by an experienced and an inexperienced reader. The highest observer sensitivity was found using a non-high pitch protocol at tube currents of 120 mAs and 90 mAs with the sharp kernel and iterative reconstruction level of 5. Non-high pitch protocols showed better detectability of solid nodules. Combinations with the medium-soft kernel achieved slightly higher observer sensitivity than with the sharp kernel. False positives (FP) occurred more often for subsolid nodules, at a tube current level of 120 mAs, and with the sharp kernel. A tube current level of 90 mAs combined with the highest iterative reconstruction level achieved the highest accuracy in lung nodule detection regardless of size, density, and reader experience.