Quantitative Dixon and intravoxel incoherent motion diffusion magnetic resonance imaging parameters in lumbar vertebrae for differentiating aplastic anemia and acute myeloid leukemia.

Objective: We sought to evaluate the use of quantitative Dixon (Q-Dixon) and intravoxel incoherent motion diffusion imaging (IVIM) for the differential diagnosis of aplastic anemia (AA) and acute myeloid leukemia (AML). Methods: Between August 2021 and October 2023, we enrolled 68 diagnosed patients, including 36 patients with AA and 32 patients with AML, as well as 26 normal controls. All patients underwent 3-Tesla magnetic resonance imaging, which included IVIM and T2*-corrected Q-Dixon imaging at the L2-4 level. The iliac crest biopsy's pathology was used as the diagnostic criterion. The interobserver measurement repeatability was evaluated using the intraclass correlation coefficient (ICC). One-way analysis of variance, Spearman analysis, and receiver operating characteristic curve analysis were used. Results: The fat fraction (FF) and perfusion fraction (f) values were statistically significantly different between the three groups (p < 0.001 and p = 0.007). The FF and f values in the AA group were higher than those in the AML group. The true apparent diffusion coefficient (D) value was substantially negatively correlated to the FF and R2* values (r = -0.601, p < 0.001; r = -0.336, p = 0.002). The f value was positively correlated with both FF and pseudo-apparent diffusion coefficient (D*) values (r = 0.376, p < 0.001; r = 0.263, p = 0.017) and negatively correlated with the D value (r = -0.320, p = 0.003). The FF and f values were negatively correlated with the degree of myelodysplasia (r = -0.597, p < 0.001; r = -0.454, p = 0.004), and the D value was positively correlated with the degree of myelodysplasia (r = 0.395, p = 0.001). For the differential diagnosis of AA and AML, the Q-Dixon model's sensitivity (93.75%) and specificity (84%) confirmed that it outperformed the IVIM model. Conclusion: Q-Dixon parameters have the potential to be used as new biomarkers to differentiate AA from AML.

Application of colony-stimulating factor 3 in determining the prognosis of high-grade gliomas based on magnetic resonance imaging radiomics.

Rationale and objectives: Radiomics is a promising, non-invasive method for determining the prognosis of high-grade glioma (HGG). The connection between radiomics and the HGG prognostic biomarker is still insufficient. Materials and methods: In this study, we collected the pathological, clinical, RNA-sequencing, and enhanced MRI data of HGG from TCIA and TCGA databases. We characterized the prognostic value of CSF3. Kaplan-Meier (KM) analysis, univariate and multivariate Cox regression, subgroup analysis, Spearman analysis, and gene set variation analysis enrichment were used to elucidate the prognostic value of the CSF3 gene and the correlation between CSF3 and tumor features. We used CIBERSORT to analyze the correlation between CSF3 and cancer immune infiltrates. Logistic regression (LR) and support vector machine methods (SVM) were used to build the radiomics models for the prognosis prediction of HGG based on the expression of CSF3. Results: Based on the radiomics score calculated from LR model, 182 patients with HGG from TCGA database were divided into radiomics score (RS) high and low groups. CSF3 expression varied between tumor and normal group tissues. CSF3 expression was found to be a significant risk factor for survival outcomes. A positive association was found between CSF3 expression and immune infiltration. The radiomics model based on both LR and SVM methods showed high clinical practicability. Conclusion: The results showed that CSF3 has a prognostic value in HGG. The developed radiomics models can predict the expression of CSF3, and further validate the predictions of the radiomics models for HGG.

Exploration of CT manifestations of different clinical types of novel coronavirus pneumonia.

BACKGROUND: To explore computed tomography (CT) characteristics of the 2019 novel coronavirus (COVID-19) pneumonia and explore variations among the different clinical types. METHODS: Clinical and CT imaging data of 43 patients diagnosed with COVID-19 in our hospital and the cooperative hospital between January 15-30, 2020 were collected (27 male and 16 female). Patients were classified as common type (26 cases, 60%), severe type (14 cases, 33%) or critical type (three cases, 7%) according to the new coronavirus pneumonia treatment scheme (sixth edition). Patient clinical data and CT images were analyzed and evaluated. RESULTS: Fever was the main symptom in common type COVID-19 cases (23/26, 88.46%). Both severe and critical type COVID-19 patients had fever and cough symptoms, and dyspnea was observed in all three critical COVID-19 patients. CT manifestations in the common type COVID-19 cohort were bilateral involvement (20/26, 71%), multiple lesions (14/26, 54%), ground-glass density shadow (17/26, 65%), and some cases were accompanied by local consolidation (9/26, 35%), which is consistent with early stage COVID-19 CT performance. CT manifestations in the severe and critical types involved both lungs. Severe COVID-19 cases predominantly consisted of multiple mixed-density lesions (10/14, 71%), and a few patients showed diffuse lung glass density shadows in both lungs (4/14, 29%), which is consistent with the progression stage COVID-19 CT performance. Critical COVID-19 cases exhibited mixed-density lesions, and two cases displayed "white lung", which is the CT manifestation at the severe COVID-19 stage. Only one critical COVID-19 patient had pleural effusion. CONCLUSIONS: The CT manifestations of COVID-19 are specific and there are variations between different clinical types. Thus, CT is an important clinical tool for early diagnosis and assessment of the severity of COVID-19.