Magnetic resonance colonography with intestine-absorbable nanoparticle contrast agents in evaluation of colorectal inflammation

Dark-Lumen Magnetic Resonance Image Based on Artificial Intelligence Algorithm in Differential Diagnosis of Colon Cancer

This research was aimed o investigate the application value and diagnostic effect of dark-lumen magnetic resonance imaging (dark-lumen MRI) based on artificial intelligence algorithm on colon cancer. A total of 98 patients with ulcerated colon cancer were selected as the study subjects. All patients underwent colonic endoscopy. The patients were divided into algorithm group (artificial intelligence algorithm processing image group) and control group (conventional method processing image group) according to different dark-lumen MRI processing methods. The detection efficiency of colon cancer was compared between the two groups. It showed that the diagnostic effect of dark-lumen MRI based on artificial intelligence algorithm was significant. The apparent diffusion coefficient (ADC) in the control group was 0.92 ± 0.14 mm²/s (minimum: 0.74, maximum: 1.30), ADC in the algorithm group was 1.55 ± 0.31 mm²/s (minimum: 1.22, maximum: 2.42). The ADC of patients in algorithm group was significantly higher than that of patients in control group, with statistical difference (t = 7.827, P < 0.001). The correct number of cases was 46 and the diagnostic error number was 3 in algorithm group, with accuracy of 93%. The correct number of cases was 41 and the diagnostic error number was 8 in control group, with accuracy of 83%. In comparison, the correct rate was 10% higher in algorithm group, indicating that the diagnostic effect was better in algorithm group. The mean value of invasion depth was 10.42 in the algorithm group and 5.27 in the control group, indicating that the algorithm group was more accurate in the judgment of invasion depth, had a good prospect of clinical application, and had guiding significance for the diagnosis of colon cancer.

Development of a Novel MR Colonography via Iron-Based Solid Lipid Nanoparticles

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 (Fe3+), levodopa methyl ester (DM), and fluorescein isothiocyanate (FITC). We evaluated the particle size, loading rate, encapsulation efficiency, and cytotoxicity of SLNs. The T1 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).