Background To assess the additive value of dual-energy CT (DECT) over single-energy CT (SECT) to radiomics-based response prediction in patients with metastatic melanoma preceding immunotherapy. Material and methods A total of 140 consecutive patients with melanoma (58 female, 63±16 years) for whom baseline DECT tumor load assessment revealed stage IV and who were subsequently treated with immunotherapy were included. Best response was determined using the clinical reports (81 responders: 27 complete response, 45 partial response, 9 stable disease). Individual lesion response was classified manually analogous to RECIST 1.1 through 1291 follow-up examinations on a total of 776 lesions (6.7±7.2 per patient). The patients were sorted chronologically into a study and a validation cohort (each n=70). The baseline DECT was examined using specialized tumor segmentation prototype software, and radiomic features were analyzed for response predictors. Significant features were selected using univariate statistics with Bonferroni correction and multiple logistic regression. The area under the receiver operating characteristic curve of the best subset was computed (AUROC). For each combination (SECT/DECT and patient response/lesion response), an individual random forest classifier with 10-fold internal cross-validation was trained on the study cohort and tested on the validation cohort to confirm the predictive performance. Results We performed manual RECIST 1.1 response analysis on a total of 6533 lesions. Multivariate statistics selected significant features for patient response in SECT (min. brightness, R²=0.112, padj. ≤0.001) and DECT (textural coarseness, R²=0.121, padj. ≤0.001), as well as lesion response in SECT (mean absolute voxel intensity deviation, R²=0.115, padj. ≤0.001) and DECT (iodine uptake metrics, R²≥0.12, padj. ≤0.001). Applying the machine learning models to the validation cohort confirmed the additive predictive power of DECT (patient response AUROC SECT=0.5, DECT=0.75; lesion response AUROC SECT=0.61, DECT=0.85; p<0.001). Conclusion The new method of DECT-specific radiomic analysis provides a significant additive value over SECT radiomics approaches for response prediction in patients with metastatic melanoma preceding immunotherapy, especially on a lesion-based level. As mixed tumor response is not uncommon in metastatic melanoma, this lends a powerful tool for clinical decision-making and may potentially be an essential step toward individualized medicine.

PET/CT scanners with a long axial field-of-view (LAFOV) provide increased sensitivity, enabling the adjustment of imaging parameters by reducing the injected activity or shortening the acquisition time. This study aimed to evaluate the limitations of reduced [¹⁸F]FDG activity doses on image quality, lesion detectability, and the quantification of lesion uptake in the Biograph Vision Quadra, as well as to assess the benefits of the recently introduced ultra-high sensitivity mode in a clinical setting. A number of 26 patients who underwent [¹⁸F]FDG-PET/CT (3.0 MBq/kg, 5 min scan time) were included in this analysis. The PET raw data was rebinned for shorter frame durations to simulate 5 min scans with lower activities in the high sensitivity (HS) and ultra-high sensitivity (UHS) modes. Image quality, noise, and lesion detectability (n = 82) were assessed using a 5-point Likert scale. The coefficient of variation (CoV), signal-to-noise ratio (SNR), tumor-to-background ratio (TBR), and standardized uptake values (SUV) including SUV_mean, SUV_max, and SUV_peak were evaluated. Subjective image ratings were generally superior in UHS compared to the HS mode. At 0.5 MBq/kg, lesion detectability decreased to 95% (HS) and to 98% (UHS). SNR was comparable at 1.0 MBq/kg in HS (5.7 ± 0.6) and 0.5 MBq/kg in UHS (5.5 ± 0.5). With lower doses, there were negligible reductions in SUV_mean and SUV_peak, whereas SUV_max increased steadily. Reducing the [¹⁸F]FDG activity to 1.0 MBq/kg (HS/UHS) in a LAFOV PET/CT provides diagnostic image quality without statistically significant changes in the uptake parameters. The UHS mode improves image quality, noise, and lesion detectability compared to the HS mode.

Background: This study investigated whether a machine-learning-based combination of radiomics and clinical parameters was superior to the use of clinical parameters alone in predicting therapy response after three months, and overall survival after six and twelve months, in stage-IV malignant melanoma patients undergoing immunotherapy with PD-1 checkpoint inhibitors and CTLA-4 checkpoint inhibitors. Methods: A random forest model using clinical parameters (demographic variables and tumor markers = baseline model) was compared to a random forest model using clinical parameters and radiomics (extended model) via repeated 5-fold cross-validation. For this purpose, the baseline computed tomographies of 262 stage-IV malignant melanoma patients treated at a tertiary referral center were identified in the Central Malignant Melanoma Registry, and all visible metastases were three-dimensionally segmented (n = 6404). Results: The extended model was not significantly superior compared to the baseline model for survival prediction after six and twelve months (AUC (95% CI): 0.664 (0.598, 0.729) vs. 0.620 (0.545, 0.692) and AUC (95% CI): 0.600 (0.526, 0.667) vs. 0.588 (0.481, 0.629), respectively). The extended model was not significantly superior compared to the baseline model for response prediction after three months (AUC (95% CI): 0.641 (0.581, 0.700) vs. 0.656 (0.587, 0.719)). Conclusions: The study indicated a potential, but non-significant, added value of radiomics for six-month and twelve-month survival prediction of stage-IV melanoma patients undergoing immunotherapy.

Purpose: The purpose of this study was to test the technical feasibility and the impact on the image quality of a deep learning-based super-resolution reconstruction algorithm in 1.5 T abdominopelvic MR imaging. Methods: 44 patients who underwent abdominopelvic MRI were retrospectively included, of which 4 had to be subsequently excluded. After the acquisition of the conventional volume interpolated breath-hold examination (VIBE_Std), images underwent postprocessing, using a deep learning-based iterative denoising super-resolution reconstruction algorithm for partial Fourier acquisitions (VIBE_SR). Image analysis of 40 patients with a mean age of 56 years (range 18–84 years) was performed qualitatively by two radiologists independently using a Likert scale ranging from 1 to 5, where 5 was considered the best rating. Results: Image analysis showed an improvement of image quality, noise, sharpness of the organs and lymph nodes, and sharpness of the intestine for pre- and postcontrast images in VIBE_SR compared to VIBE_Std (each p < 0.001). Lesion detectability was better for VIBE_SR (p < 0.001), while there were no differences concerning the number of lesions. Average acquisition time was 16 s (±1) for the upper abdomen and 15 s (±1) for the pelvis for VIBE_Std, and 15 s (±1) for the upper abdomen and 14 s (±1) for the pelvis for VIBE_SR. Conclusion: This study demonstrated the technical feasibility of a deep learning-based super-resolution algorithm including partial Fourier technique in abdominopelvic MR images and illustrated a significant improvement of image quality, noise, and sharpness while reducing TA.

(1) Background: To evaluate the effects of an AI-based denoising post-processing software solution in low-dose whole-body computer tomography (WBCT) stagings; (2) Methods: From 1 January 2019 to 1 January 2021, we retrospectively included biometrically matching melanoma patients with clinically indicated WBCT staging from two scanners. The scans were reconstructed using weighted filtered back-projection (wFBP) and Advanced Modeled Iterative Reconstruction strength 2 (ADMIRE 2) at 100% and simulated 50%, 40%, and 30% radiation doses. Each dataset was post-processed using a novel denoising software solution. Five blinded radiologists independently scored subjective image quality twice with 6 weeks between readings. Inter-rater agreement and intra-rater reliability were determined with an intraclass correlation coefficient (ICC). An adequately corrected mixed-effects analysis was used to compare objective and subjective image quality. Multiple linear regression measured the contribution of “Radiation Dose”, “Scanner”, “Mode”, “Rater”, and “Timepoint” to image quality. Consistent regions of interest (ROI) measured noise for objective image quality; (3) Results: With good–excellent inter-rater agreement and intra-rater reliability (Timepoint 1: ICC ≥ 0.82, 95% CI 0.74–0.88; Timepoint 2: ICC ≥ 0.86, 95% CI 0.80–0.91; Timepoint 1 vs. 2: ICC ≥ 0.84, 95% CI 0.78–0.90; all p ≤ 0.001), subjective image quality deteriorated significantly below 100% for wFBP and ADMIRE 2 but remained good–excellent for the post-processed images, regardless of input (p ≤ 0.002). In regression analysis, significant increases in subjective image quality were only observed for higher radiation doses (≥0.78, 95%CI 0.63–0.93; p < 0.001), as well as for the post-processed images (≥2.88, 95%CI 2.72–3.03, p < 0.001). All post-processed images had significantly lower image noise than their standard counterparts (p < 0.001), with no differences between the post-processed images themselves. (4) Conclusions: The investigated AI post-processing software solution produces diagnostic images as low as 30% of the initial radiation dose (3.13 ± 0.75 mSv), regardless of scanner type or reconstruction method. Therefore, it might help limit patient radiation exposure, especially in the setting of repeated whole-body staging examinations.

(1) This study evaluates the impact of an AI denoising algorithm on image quality, diagnostic accuracy, and radiological workflows in pediatric chest ultra-low-dose CT (ULDCT). (2) Methods: 100 consecutive pediatric thorax ULDCT were included and reconstructed using weighted filtered back projection (wFBP), iterative reconstruction (ADMIRE 2), and AI denoising (PixelShine). Place-consistent noise measurements were used to compare objective image quality. Eight blinded readers independently rated the subjective image quality on a Likert scale (1 = worst to 5 = best). Each reader wrote a semiquantitative report to evaluate disease severity using a severity score with six common pathologies. The time to diagnosis was measured for each reader to compare the possible workflow benefits. Properly corrected mixed-effects analysis with post-hoc subgroup tests were used. Spearman’s correlation coefficient measured inter-reader agreement for the subjective image quality analysis and the severity score sheets. (3) Results: The highest noise was measured for wFBP, followed by ADMIRE 2, and PixelShine (76.9 ± 9.62 vs. 43.4 ± 4.45 vs. 34.8 ± 3.27 HU; each p < 0.001). The highest subjective image quality was measured for PixelShine, followed by ADMIRE 2, and wFBP (4 (4–5) vs. 3 (4–5) vs. 3 (2–4), each p < 0.001) with good inter-rater agreement (r ≥ 0.790; p ≤ 0.001). In diagnostic accuracy analysis, there was a good inter-rater agreement between the severity scores (r ≥ 0.764; p < 0.001) without significant differences between severity score items per reconstruction mode (F (5.71; 566) = 0.792; p = 0.570). The shortest time to diagnosis was measured for the PixelShine datasets, followed by ADMIRE 2, and wFBP (2.28 ± 1.56 vs. 2.45 ± 1.90 vs. 2.66 ± 2.31 min; F (1.000; 99.00) = 268.1; p < 0.001). (4) Conclusions: AI denoising significantly improves image quality in pediatric thorax ULDCT without compromising the diagnostic confidence and reduces the time to diagnosis substantially.