Both Magnetic Resonance Enterography (MRE) and Computed Tomography Enterography (CTE) are crucial imaging modalities in the diagnosis and treatment of inflammatory bowel disease (IBD). CTE is often used in acute scenarios, such as when complications (such as abscesses, perforations, or bowel obstructions) are suspected. It can also help determine the degree and extent of pathological processes. Although CTE is rapid, generally accessible, and offers precise images that are useful in emergencies, it does expose patients to ionizing radiation. Nevertheless, MRE is very useful in assessing perianal illness and the small intestine, and it is frequently used in patients who need repeated follow-ups or are pregnant to minimize radiation exposure. Moreover, MRE can demonstrate oedema, fistulas, abscesses, and the thickening of the bowel wall. In addition, MRE offers superior soft tissue contrast resolution without ionizing radiation, which helps identify complications such as fistulas and abscesses. With their respective advantages and disadvantages, both approaches play essential roles in assessing IBD. The primary goal of this review is to provide an overview of the technical specifications, benefits, drawbacks, and imaging findings of CTE and MRE.

Background: The purpose of this study is to determine the relationship between the texture analysis extracted from preoperative rectal magnetic resonance (MR) studies and the response to neoadjuvant treatment. Materials and Methods: In total, 88 patients with rectal adenocarcinoma who underwent staging MR between 2017 and 2022 were retrospectively enrolled. After the completion of neoadjuvant treatment, they underwent surgical resection. The tumour regression grade (TRG) was collected. Patients with TRG 1–2 were classified as responders, while patients with TRG 3 to 5 were classified as non-responders. A texture analysis was conducted using LIFEx software (v 7.6.0), where T2-weighted MR sequences on oriented axial planes were uploaded, and a region of interest (ROI) was manually drawn on a single slice. Features with a Spearman correlation index > 0.5 have been discarded, and a LASSO feature selection has been applied. Selected features were trained using bootstrapping. Results: According to the TRG classes, 49 patients (55.8%) were considered responders, while 39 (44.2) were non-responders. Two features were associated with the responder class: GLCM_Homogeneity and Discretized Histo Entropy log 2. Regarding GLCM_Homogeneity, the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity were 0.779 (95% CIs = 0.771–0.816), 86% (80–90), and 67% (60–71). Regarding Discretized Histo Entropy log 2, we found 0.775 AUC (0.700–0.801), 80% sensitivity (74–83), and 63% specificity (58–69). Combining both radiomics features the radiomics signature diagnostic accuracy increased (AUC = 0.844). Finally, the AUC of 1000 bootstraps were 0.810. Conclusions: Texture analysis can be considered an advanced tool for determining a possible correlation between pre-surgical MR data and the response to neoadjuvant therapy.