Application of machine learning algorithms for multiparametric MRI-based evaluation of murine colitis

Machine Learning-based Prediction Models for Diagnosis and Prognosis in Inflammatory Bowel Diseases: A Systematic Review

BACKGROUND AND AIMS

There is increasing interest in machine learning-based prediction models in inflammatory bowel diseases [IBD]. We synthesised and critically appraised studies comparing machine learning vs traditional statistical models, using routinely available clinical data for risk prediction in IBD.

METHODS

Through a systematic review till January 1, 2021, we identified cohort studies that derived and/or validated machine learning models, based on routinely collected clinical data in patients with IBD, to predict the risk of harbouring or developing adverse clinical outcomes, and reported its predictive performance against a traditional statistical model for the same outcome. We appraised the risk of bias in these studies using the Prediction model Risk of Bias ASsessment [PROBAST] tool.

RESULTS

We included 13 studies on machine learning-based prediction models in IBD, encompassing themes of predicting treatment response to biologics and thiopurines and predicting longitudinal disease activity and complications and outcomes in patients with acute severe ulcerative colitis. The most common machine learning models used were tree-based algorithms, which are classification approaches achieved through supervised learning. Machine learning models outperformed traditional statistical models in risk prediction. However, most models were at high risk of bias, and only one was externally validated.

CONCLUSIONS

Machine learning-based prediction models based on routinely collected data generally perform better than traditional statistical models in risk prediction in IBD, though frequently have high risk of bias. Future studies examining these approaches are warranted, with special focus on external validation and clinical applicability.

Effective bowel motion reduction in mouse abdominal MRI using hyoscine butylbromide

PURPOSE

Bowel motion is a significant source of artifacts in mouse abdominal MRI. Fasting and administration of hyoscine butylbromide (BUSC) have been proposed for bowel motion reduction but with inconsistent results and limited efficacy assessments. Here, we evaluate these regimes for mouse abdominal MRI at high field.

METHODS

Thirty-two adult C57BL/6J mice were imaged on a 9.4T scanner with a FLASH sequence, acquired over 90 min with ~19 s temporal resolution. During MRI acquisition, 8 mice were injected with a low-dose and 8 mice with a high-dose bolus of BUSC (0.5 and 5 mg/kg, respectively). Eight mice were food deprived for 4.5-6.5 hours before MRI and another group of eight mice was injected with saline during MRI acquisition. Two expert readers reviewed the images and classified bowel motion, and quantitative voxel-wise analyses were performed for identification of moving regions. After defining the most effective protocol, high-resolution T₂ -weighted and diffusion-weighted images were acquired from 4 mice.

RESULTS

High-dose BUSC was the most effective protocol for bowel motion reduction, for up to 45 min. Fasting and saline protocols were not effective in suppressing bowel motion. High-resolution abdominal MRI clearly demonstrated improved image quality and ADC quantification with the high-dose BUSC protocol.

CONCLUSION

Our data show that BUSC administration is advantageous for abdominal MRI in the mouse. Specifically, it endows significant bowel motion reduction, with relatively short onset timings after injection (~8.5 min) and relatively long duration of the effect (~45 min). These features improve the quality of high-resolution images of the mouse abdomen.