Highlights:GGO with crazy-paving patterns or interlobular septa thickening were common signs Fewer lesions were identified in the younger and adolescent age groups Distribution of lesions in the lungs showed age-related differences ABSTRACT J o u r n a l P r e -p r o o f Purpose: We aimed to compare chest HRCT lung signs identified in scans of differently aged patients with COVID-19 infections. Methods: Case data of patients diagnosed with COVID-19 infection in Hangzhou City, Zhejiang Province in China were collected, and chest HRCT signs of infected patients in four age groups (<18 years, 18-44 years, 45-59 years, ≥60 years) were compared.Results: Small patchy, ground-glass opacity (GGO), and consolidations were the main HRCT signs in 98 patients with confirmed COVID-19 infections. Patients aged 45-59 years and aged ≥60 years had more bilateral lung, lung lobe, and lung field involvement, and greater lesion numbers than patients <18 years. GGO accompanied with the interlobular septa thickening or a crazy-paving pattern, consolidation, and air bronchogram sign were more common in patients aged 45-59 years, and ≥60 years, than in those aged <18 years, and aged 18-44 years. Conclusions:Chest HRCT manifestations in patients with COVID-19 are related to patient's age, and HRCT signs may be milder in younger patients.

The current study aims to investigate the possibility of using solid lipid nanoparticles (SLNs)-enhanced magnetic resonance (MR) colonography to diagnose colorectal cancer. Gd-FITC-SLNs were synthesized by loading gadolinium diethylenetriaminepentaacetic acid (Gd-DTPA) and fluorescein isothiocyanate (FITC) simultaneously. Twenty mice received azoxymethane/dextran sulfate sodium (AOM/DSS) to induce adenocarcinoma of the colon and were divided into 4 groups, and 5 in per group. MR colonography were performed at different time periods before and after enema or intravenous injection of Gd-FITC-SLNs or Gd-DTPA. The results demonstrated SNR (signal-to-noise ratio) significantly increased from 1.56- to 1.76-fold within the colorectal tumors after the enema of Gd-FITC-SLNs (p < 0.001). No differences in SNR were observed after the enema of Gd-DTPA (p > 0.05). Besides, SNR increased from 1.54- to 1.72-fold within the colorectal tumors after the intravenous injection of Gd-FITC-SLNs (p < 0.001) while SNR increased from 1.39to 1.57-fold within the colorectal tumors after the injection of Gd-DTPA (p < 0.001). In addition, SNR within colorectal tumors significantly increased ranging from 20th to 140th min, and lasted for about 120 min (p < 0.05) after the enema of Gd-FITC-SLNs and SNR within colorectal tumors also significantly increased ranging from 0th hour to 8th hour, lasted for about 8 hour (p < 0.05) after the injection of Gd-FITC-SLNs. However, after the injection of Gd-DTPA, SNR within colorectal tumors significantly increased only ranging from 0th min to 20th min after administration (p < 0.01). Furthermore, hematoxylin and eosin (H&E) staining revealed that all mice developed adenocarcinoma of the colon. In summary, it is feasible by using Gd-FITC-SLNs in MR colonography to diagnose colorectal cancer. Enema of Gd-FITC-SLNs can provide marked enhancement of colorectal tumors quickly, and safer while intravenous injection of Gd-FITC-SLNs can provide a long-lasting enhancement of colorectal tumors in MR colonography. These findings present a potential clinical application of Gd-FITC-SLNs on MR colonography.

Purpose To develop an iron-based solid lipid nanoparticle (SLN) absorbable by the intestinal wall and assess the differential diagnostic value of intestinal lesions in magnetic resonance imaging (MRI). Methods SLNs were prepared with the simultaneous loading of trivalent Fe ions (Fe 3+ ), levodopa methyl ester (DM), and fluorescein isothiocyanate (FITC). We evaluated the particle size, loading rate, encapsulation efficiency, and cytotoxicity of SLNs. The T 1 contrast effects of the FeDM-FITC-SLNs and gadolinium-based contrast agent (GBCA) were compared in different mouse models: acute ulcerative colitis (AUC), chronic ulcerative colitis (CUC), colon adenocarcinoma (COAD), and normal control. MRI was performed in the same mouse with intravenous injection of GBCA on day 1 and enema of FeDM-FITC-SLNs on day 2. The signal-to-noise ratios (SNRs) were compared using one-way analysis of variance. Tissues were then collected for histology. Results The average particle size of FeDM-FITC-SLN was 220 nm. The mean FeDM loading rate was 94.3%, and the encapsulation efficiency was 60.3%. The relaxivity was 4.02 mM −1 ·s −1 . After enema with FeDM-FITC-SLNs, MRI showed the following contrast enhancement duration: AUC = COAD > normal > CUC. Confocal fluorescence microscopy confirmed that FeDM-FITC-SLNs were mainly distributed in the intestinal mucosa and tumor capsule. Conclusion Iron-based SLNs are promising alternatives for contrast enhancement at T1-weighted MRI and will help in the differential diagnosis of intestinal bowel diseases (IBDs).

Purpose To establish and verify the ability of a radiomics prediction model to distinguish invasive adenocarcinoma (IAC) and minimal invasive adenocarcinoma (MIA) presenting as ground-glass nodules (GGNs).MethodsWe retrospectively analyzed 118 lung GGN images and clinical data from 106 patients in our hospital from March 2016 to April 2019. All pathological classifications of lung GGN were confirmed as IAC or MIA by two pathologists. R language software (version 3.5.1) was used for the statistical analysis of the general clinical data. ITK-SNAP (version 3.6) and A.K. software (Analysis Kit, American GE Company) were used to manually outline the regions of interest of lung GGNs and collect three-dimensional radiomics features. Patients were randomly divided into training and verification groups (ratio, 7:3). Random forest combined with hyperparameter tuning was used for feature selection and prediction modeling. The receiver operating characteristic curve and the area under the curve (AUC) were used to evaluate model prediction efficacy. The calibration curve was used to evaluate the calibration effect.ResultsThere was no significant difference between IAC and MIA in terms of age, gender, smoking history, tumor history, and lung GGN location in both the training and verification groups (P&gt;0.05). For each lung GGN, the collected data included 396 three-dimensional radiomics features in six categories. Based on the training cohort, nine optimal radiomics features in three categories were finally screened out, and a prediction model was established. We found that the training group had a high diagnostic efficacy [accuracy, sensitivity, specificity, and AUC of the training group were 0.89 (95%CI, 0.73 - 0.99), 0.98 (95%CI, 0.78 - 1.00), 0.81 (95%CI, 0.59 - 1.00), and 0.97 (95%CI, 0.92-1.00), respectively; those of the validation group were 0.80 (95%CI, 0.58 - 0.93), 0.82 (95%CI, 0.55 - 1.00), 0.78 (95%CI, 0.57 - 1.00), and 0.92 (95%CI, 0.83 - 1.00), respectively]. The model calibration curve showed good consistency between the predicted and actual probabilities.ConclusionsThe radiomics prediction model established by combining random forest with hyperparameter tuning effectively distinguished IAC from MIA presenting as GGNs and represents a noninvasive, low-cost, rapid, and reproducible preoperative prediction method for clinical application.