Rationale and objectives To compare radiation dose and image quality of coronary artery reconstructions obtained from non–coronary-dedicated transcatheter aortic valve implantation (TAVI) planning CT scans using ultra-high-pitch photon-counting CT (PCCT) and ultra-high-pitch energy-integrating detector dual-source CT (EID-DSCT; hereafter referred to as DSCT), including assessment of the feasibility of coronary artery evaluation according to the Coronary Artery Disease Reporting and Data System (CAD-RADS). Materials and methods Retrospective study on consecutive patients with aortic valve stenosis (AS) undergoing contrast-enhanced TAVI planning CT, either PCCT or DSCT between August 2021 and June 2023. CT dose index (CTDI) and dose length product (DLP) were used to calculate the effective radiation dose (eRD). Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated using standardized region-of-interest (ROI) analysis. Coronary arteries were evaluated by three independent radiologists using 5-point Likert scales followed by a classification according to the CAD-RADS system. Results 187 patients (83 female, median age 82, IQR: 76–84) underwent 190 CT-examinations, 103 PCCT (54.2%) and 87 (45.8%) DSCT. Three patients underwent both PCCT and DSCT for clinical reasons. Mean CTDI and DLP were significantly lower in PCCT compared to DSCT (21.9 vs. 52.8 and 607.9 vs. 906.8, respectively; both p < 0.001), with a correspondingly lower mean effective radiation dose (eRD) in PCCT compared to DSCT (10.3 ± 5.4 mSv vs. 15.4 ± 5.6 mSv, p < 0.001). While both SNR and CNR were lower in PCCT than in DSCT (SNR: 35.28 ± 11.63 in PCCT vs. 45.6 ± 16.3 in DSCT, p < 0.001; CNR: 49.25 ± 13.31 in PCCT vs. 57.42 v ± 22.55 in DSCT, p = 0.004), PCCT outperformed DSCT in every one of the eleven visual image categories. Overall image quality was rated as good or excellent in 71.8% (74/103) of PCCT vs. 19.5% (17/87) of DSCT scans. Coronary reconstructions were rated as good or excellent in 72.8% (75/103) of PCCT vs. 20.7% of DSCT datasets. CAD-RADS [1–5] rating was possible in 68.9% (71/103) of PCCT vs. 39.1% (34/87) of DSCT datasets. Conclusion Photon-counting computed tomography (PCCT) allows for significantly improved assessability of the coronary arteries in TAVI planning CT compared with dual-source CT (DSCT), while achieving a substantially lower radiation dose.
Introduction Moderate coronary stenosis (CAD-RADS 3) is a diagnostic gray zone in coronary CT angiography (CCTA). We evaluated whether super-resolution deep learning reconstruction (SR-DLR), particularly with a 1024 matrix, modifies CAD-RADS classification and inter-reader agreement. Materials and methods: Thirty-nine CCTA examinations from two centers, each containing at least one 50–69% stenosis identified on the routine normal-resolution deep learning reconstruction (NR-DLR), were reconstructed with NR-DLR and SR-DLR (512 and 1024 matrices). Five experienced readers blindly assigned CAD-RADS categories. Agreement was assessed with Gwet’s AC2 and paired reclassification with exact McNemar tests. Subgroup analyses (plaque composition, initial image quality) were exploratory. Results Forty-six lesions were analyzed (19 calcified, 16 mixed, 10 non-calcified, one in-stent). Agreement remained excellent (AC2 0.89 [0.86–0.92], 0.87 [0.83–0.90] and 0.84 [0.81–0.88] for NR-DLR, SR-DLR-512 and SR-DLR-1024). Consensus CAD-RADS 2 rose from 6.5% to 21.7% with SR-DLR-1024 (p = 0.016; 10 lesions downgraded, 2 upgraded), predominantly in lesions with non-calcified components. Per-reader CAD-RADS 2 rates ranged from 13% to 30%, increasing significantly for two of five readers (p = 0.004). Conclusion SR-DLR-1024 modified CAD-RADS classification in about one fifth of intermediate lesions while preserving excellent agreement. Without an invasive reference standard, these reclassifications reflect a change in reader interpretation rather than a proven gain in accuracy.
Purpose To assess the inter-reader agreement in background parenchymal enhancement (BPE) categorization on ultrafast dynamic contrast-enhanced (DCE) MRI before and after training and its relationship with breast cancer prognostic factors. Materials and methods We retrospectively analyzed the cases of 220 patients (median, age 60 years; 220 breast cancers) who underwent ultrafast DCE MRI. BPE was qualitatively classified as minimal, mild, moderate, or marked by three readers: Reader1 (R1), a radiology resident in the second year of training; Reader2 (R2), an experienced general radiologist with 28 years of experience in diagnostic radiology; and Reader3 (R3), an experienced breast radiologist with 25 years of experience in breast MRI. Inter-reader agreement was evaluated using weighted kappa (κ) values and was reassessed between R1 and R3 after intensive training. Associations between BPE levels and prognostic factors were also evaluated. Results Inter-reader agreement was substantial with weighted κ values of 0.679–0.742 for R1 versus R2, 0.612–0.665 for R1 versus R3, and 0.626–0.689 for R2 versus R3. Agreement between R1 and R3 improved slightly after training but remained substantial. No significant associations were observed between visual BPE classification and prognostic factors, including hormone receptor status, HER2 status, and proliferation markers. Conclusion Inter-reader agreement for BPE assessment on ultrafast DCE MRI was substantial and improved slightly after training. No significant relationship was observed between ultrafast BPE and breast cancer prognostic factors.
Purpose To evaluate the efficacy of radiomics from dual-layer spectral detector CT (DLCT) iodine density maps, combined with clinicopathological characteristics and spectral parameters, in predicting pathological complete response (pCR) following neoadjuvant chemoimmunotherapy (nCIT) in patients with esophageal squamous cell carcinoma (ESCC). Materials and methods A total of 129 patients with ESCC undergoing nCIT were allocated to training (n = 86) and validation (n = 43) groups. Patients were categorised into pCR and non-pCR groups based on postoperative tumor regression grade. Multiple parameter maps were reconstructed from pre-treatment DLCT images for quantitative spectral analysis. Radiomic features extracted from the venous-phase iodine density maps were used to develop a radiomics score (Radscore). Univariate and multivariate logistic regression analyses were used to identify independent predictors of pCR, leading to the development of clinical, spectral, radiomics, and combined models. The model performance was evaluated using receiver operating characteristic (ROC) curves, decision curve analysis, and calibration curves. Results Multivariate logistic regression analysis identified differentiation grade, venous-phase normalized iodine density (NIDV), and Radscore as independent predictors of pCR. The combined model demonstrated superior predictive performance compared to the individual models, with areas under the curve of 0.943 (training) and 0.833 (validation). Calibration curves showed good agreement between the predicted and observed outcome values, whereas decision curves confirmed a superior net clinical benefit in both cohorts. Conclusions The combined model incorporating radiomics features from DLCT iodine density maps, clinicopathological characteristics, and spectral parameters demonstrated robust predictive capability in predicting pCR to nCIT in ESCC, which is anticipated to be a valuable tool for personalized treatment decision-making.
