Objectives: Metal artifacts remain a challenge in computed tomography. We investigated the potential of photon-counting computed tomography (PCD-CT) for metal artifact reduction using an iterative metal artifact reduction (iMAR) algorithm alone and in combination with high keV monoenergetic images (140 keV) in patients with dental hardware. Material and methods: Consecutive patients with dental implants were prospectively included in this study and received PCD-CT imaging of the craniofacial area. Four series were reconstructed (standard [PCD-CT_std], monoenergetic at 140 keV [PCD-CT_140keV], iMAR corrected [PCD-CT_iMAR], combination of iMAR and 140 keV monoenergetic [PCD-CT_iMAR+140keV]). All reconstructions were assessed qualitatively by four radiologists (independent and blinded reading on a 5-point Likert scale [5 = excellent; no artifact]) regarding overall image quality, artifact severity, and delineation of adjacent and distant anatomy. To assess signal homogeneity and evaluate the magnitude of artifact reduction, we performed quantitative measures of coefficient of variation (CV) and a region of interest (ROI)–based relative change in artifact reduction [PCD-CT/PCD-CT_std]. Results: We enrolled 48 patients (mean age 66.5 ± 11.2 years, 50% (n = 24) males; mean BMI 25.2 ± 4.7 kg/m²; mean CTDI_vol 6.2 ± 6 mGy). We found improved overall image quality, reduced artifacts and superior delineation of both adjacent and distant anatomy for the iMAR vs. non-iMAR reconstructions (all p < 0.001). No significant effect of the different artifact reduction approaches on CV was observed (p = 0.42). The ROI-based analysis indicated the most effective artifact reduction for the iMAR reconstructions, which was significantly higher compared to PCD-CT_140keV (p < 0.001). Conclusion: PCD-CT offers highly effective approaches for metal artifact reduction with the potential to overcome current diagnostic challenges in patients with dental implants. Clinical relevance statement: Metallic artifacts pose a significant challenge in CT imaging, potentially leading to missed findings. Our study shows that PCD-CT with iMAR post-processing reduces artifacts, improves image quality, and can possibly reveal pathologies previously obscured by artifacts, without additional dose application. Key Points: • Photon-counting detector CT (PCD-CT) offers highly effective approaches for metal artifact reduction in patients with dental fillings/implants. • Iterative metal artifact reduction (iMAR) is superior to high keV monoenergetic reconstructions at 140 keV for artifact reduction and provides higher image quality. • Signal homogeneity of the reconstructed images is not affected by the different artifact reduction techniques.

Purpose: Artifacts caused by metallic implants remain a challenge in computed tomography (CT). We investigated the impact of photon-counting detector computed tomography (PCD-CT) for artifact reduction in patients with orthopedic implants with respect to image quality and diagnostic confidence using different artifact reduction approaches. Material and methods: In this prospective study, consecutive patients with orthopedic implants underwent PCD-CT imaging of the implant area. Four series were reconstructed for each patient (clinical standard reconstruction [PCD-CT_Std], monoenergetic images at 140 keV [PCD-CT_140keV], iterative metal artifact reduction (iMAR) corrected [PCD-CT_iMAR], combination of iMAR and 140 keV monoenergetic [PCD-CT_140keV+iMAR]). Subsequently, three radiologists evaluated the reconstructions in a random and blinded manner for image quality, artifact severity, anatomy delineation (adjacent and distant), and diagnostic confidence using a 5-point Likert scale (5 = excellent). In addition, the coefficient of variation [CV] and the relative quantitative artifact reduction potential were obtained as objective measures. Results: We enrolled 39 patients with a mean age of 67.3 ± 13.2 years (51%; n = 20 male) and a mean BMI of 26.1 ± 4 kg/m². All image quality measures and diagnostic confidence were significantly higher for the iMAR vs. non-iMAR reconstructions (all p < 0.001). No significant effect of the different artifact reduction approaches on CV was observed (p = 0.26). The quantitative analysis indicated the most effective artifact reduction for the iMAR reconstructions, which was higher than PCD-CT_140keV (p < 0.001). Conclusion: PCD-CT allows for effective metal artifact reduction in patients with orthopedic implants, resulting in superior image quality and diagnostic confidence with the potential to improve patient management and clinical decision making.

Background: Remarkable differences exist in the outcome of systemic cancer therapies. Lymphomas and leukemias generally respond well to systemic chemotherapies, while solid cancers often fail. We engineered different human cancer cells lines to uniformly express a modified herpes simplex virus thymidine kinase TK.007 as a suicide gene when ganciclovir (GCV) is applied, thus in theory achieving a similar response in all cell lines. Methods: Fifteen different cell lines were engineered to express the TK.007 gene. XTT-cell proliferation assays were performed and the IC₅₀-values were calculated. Functional kinome profiling, mRNA sequencing, and bottom-up proteomics analysis with Ingenuity pathway analysis were performed. Results: GCV potency varied among cell lines, with lymphoma and leukemia cells showing higher susceptibility than solid cancer cells. Functional kinome profiling implies a contribution of the SRC family kinases and decreased overall kinase activity. mRNA sequencing highlighted alterations in the MAPK pathways and bottom-up proteomics showed differences in apoptotic and epithelial junction signaling proteins. Conclusions: The histogenetic origin of cells influenced the susceptibility of human malignant cells towards cytotoxic agents with leukemias and lymphomas being more sensitive than solid cancer cells.

PURPOSE: Most low-dose computed tomography (LDCT) scans in lung cancer (LC) screening are negative, yet contain prognostic information. We developed a fully automated 3D deep learning model for thoracic body composition (BC) quantification from LDCT and evaluated its prognostic value for overall and cause-specific mortality. MATERIALS AND METHODS: Baseline and first follow-up LDCTs from the National Lung Screening Trial were analyzed. A 3D convolutional neural network was trained for full thoracic volumetric segmentation of subcutaneous adipose tissue (SAT) and skeletal muscle (SM) and extraction of attenuation values. BC metrics were categorized into sex-specific percentile groups (80%). Kaplan-Meier and Cox regression assessed the association between baseline BC/1-year BC changes and all-cause mortality (primary end point) and atherosclerotic cardiovascular disease (ASCVD) and LC mortality (secondary end points) adjusted for age, sex, ethnicity, BMI, smoking status, diabetes, hypertension, and history of stroke or heart disease. RESULTS: Among 23,195 participants (mean age 61.4 ± 5 years; 41.5% women), 1,601 (6.9%) deaths occurred over 6.5 years. Low SM volume and density were independently associated with increased all-cause mortality (adjusted hazard ratio [aHR], 1.40 [95% CI, 1.25 to 1.57]; aHR, 1.80 [95% CI, 1.60 to 2.02]; both P < .001). Similar associations were found for low SAT volume, high SAT density, and low SM density with ASCVD and LC mortality. A >10% 1-year decline in SAT or SM measures further increased mortality risk, strongest for reduced SAT density (aHR, 2.13 [95% CI, 1.54 to 2.95]; P < .001). CONCLUSION: Automated 3D thoracic BC quantification from LDCT enables opportunistic, artificial intelligence–driven risk stratification beyond traditional factors, supporting personalized prevention in high-risk populations.