BMC Medical Imaging最新文献

Objective: Lung neuroendocrine neoplasms (L-NENs) are increasingly recognized, yet reliable preoperative assessment of the Ki-67 proliferation index remains invasive and subject to sampling variability. We aimed to develop and validate a clinical-radiomics nomogram that uses routine chest CT to estimate Ki-67 status in patients with L-NENs.

Methods: In this retrospective multicenter study, 199 patients with histologically confirmed L-NENs from four hospitals between January 2014 and April 2024 were enrolled, all of whom underwent preoperative dual-phase contrast-enhanced CT. Following manual 3D tumor segmentation, a total of 1,874 radiomics features were extracted from fused non-contrast and arterial/venous phase images. Feature selection was performed using Pearson correlation analysis (removing redundant features with correlation coefficients > 0.8), followed by further variable compression via LASSO regression to identify discriminative radiomics features. Based on the selected features, five classification models were constructed, and the best-performing one was combined with clinical predictors identified through univariate and multivariate analyses to develop a radiomics-based nomogram. The model's discriminative ability, calibration, and clinical utility were evaluated in the training set (n = 116), internal test set (n = 50), and external validation set (n = 33) using the area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis (DCA), respectively.

Results: The LR-based radiomics model demonstrated high discriminatory ability, achieving AUCs of 0.912 (95% CI: 0.858-0.965) in the training set and 0.943 (0.887-0.999) in the testing set, significantly outperforming other models. Consequently, it was combined with independent clinical predictors-largest tumor diameter, smoking history, and age-to build a nomogram. The final combined model exhibited excellent performance across all datasets, with AUCs of 0.958 (0.925-0.990) in training, 0.930 (0.865-0.995) in testing, and 0.911 (0.867-0.955) in external validation, accompanied by good calibration and a superior net benefit on decision curve analysis.

Conclusion: The CT-based clinical-radiomics nomogram provides an accurate, non-invasive tool for pre-operative Ki-67 estimation in L-NENs, potentially guiding treatment decisions. Prospective, larger-scale validation is warranted.

Clinical trial number: Not applicable.

Background and purpose: A noninvasive and accurate indicator for evaluating direct renal effects after remote ischemia preconditioning (RIPC) is currently lacking. To explore the feasibility of R₂' mapping in evaluating the direct effect of RIPC on rabbit kidneys and to investigate the mechanisms underlying renal changes induced by RIPC.

Methods: Eighteen healthy New Zealand rabbits were used (RIPC group, N = 12; control group, N = 6). RIPC was achieved with three cycles of bilateral hindlimb ischemia (10 min/cycle, 60 min total). Magnetic resonance imaging was performed at 1 and 24 hours after RIPC. The R₂' values of the renal cortex, outer medulla, and inner medulla were then recorded. Femoral arterial blood was collected for blood gas analysis and measurements of electrolytes. Enzyme-linked immunosorbent assay was used to detect the levels of myeloperoxidase (MPO), malondialdehyde (MDA), and superoxide dismutase (SOD). Immunohistochemical staining was used to detect the average optical density (AOD) of hypoxia-inducible factor 1 alpha (HIF1α). One-way analysis of variance or the Kruskal-Wallis test was used to assess differences among the groups. Correlations were evaluated using the Spearman rank correlation coefficient.

Results: The R₂' values of the renal cortex, outer medulla, and inner medulla in the RIPC groups were significantly lower than those in the control group (RIPC 1 h group: each P < .001; RIPC 24 h group: P = .002, P = .002, P < .001, respectively). MPO levels in the RIPC 1 h and 24 h groups were significantly lower than those in the control group (P = .02, P = .004, respectively). SOD levels in the RIPC 1 h group were significantly higher than in the control group (P = .001). HIF1α AOD in the RIPC 1 h and 24 h groups were significantly higher than those in the control group (both P < .001). The R₂' values of the renal cortex, outer medulla, and inner medulla positively correlated with myeloperoxidase level (r_s=0.78, P < .001; r_s=0.78, P < .001; r_s=0.78, P < .001), and negatively correlated with superoxide dismutase level (r_s=-0.81, P < .001; r_s=-0.74, P < .001; r_s=-0.69, P = .002), and HIF1α AOD (r_s=-0.74, P < .001; r_s=-0.55, P = .02; r_s=-0.71, P < .001).

Conclusion: R₂' mapping can quantitatively assess kidney effects after remote ischemia preconditioning, and remote ischemia preconditioning can effectively enhance renal antioxidant capacity and oxygen uptake.

Background: This study aims to investigate the feasibility of coronary artery calcium scoring (CACS) calculating from PureCalcium virtual non-iodine algorithm on photon-counting detector CT (PCD-CT) and the potential impact of different section thickness, level of virtual monoenergetic images (VMIs), and quantum iterative reconstruction (QIR) on the accuracy of CACS quantification.

Materials and methods: A total of 123 patients who underwent coronary CT angiography on PCD-CT with a separate true non-contrast CACS (CACS_TNC) scan were prospectively included. Agatston scores were calculated from the PureCalcium algorithm (CACS_PC) using a section thickness of 3 mm-1.5 mm, different VMI (55-75 kilo-electron volt (keV)) and QIR (strength 1,4) levels, respectively. CACS_TNC at 70 keV and QIR 2 were used as reference standards. Differences in CACS of different reconstructions section thicknesses, various keV levels, and QIR strength were compared using the Wilcoxon rank sum test with Bonferroni correction. The intraclass correlation coefficients (ICCs) and Bland-Altman analysis were conducted to assessed the agreement. The agreement of plaque burden groups (based on CACS) at different reconstruction parameters was evaluated using weighted Cohen kappa.

Results: At all investigated section thickness, VMI, and QIR levels, the CACS_PC were strongly correlated with CACS_TNC (ICC: 0.94-0.98, P < 0.001 for all). There were no statistical differences in CACS between CACS_PC at 3 mm section thickness, 60/65 keV (QIR1/4), and at 1.5 mm section thickness with 55 keV (QIR1/4), compared with CACS_TNC. The smallest CACS bias was observed at a 1.5 mm section thickness, 55 keV, QIR 1, with mean bias of 2.4; LoA (IQR: -182.7, 187.4). CACS_PC correctly identified 105 of 123 participants (85.4%) into the corresponding plaque burden group using CACS_TNC as the referent standard (excellent agreement, κ = 0.904).

Conclusion: CACS derived from the PureCalcium algorithm with optimized reconstruction parameters shows excellent correlation with true non-contrast scans derived values. Thus, it is may possible to use the PureCalcium virtual non-iodine algorithm to replace the true non-contrast scans for CACS quantification, without additional radiation dose exposure.