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 aim of this study is to assess the performance of diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) values in predicting the response to neoadjuvant chemoradiation therapy (CRT) and outcome in patients with locally advanced rectal cancer (LARC). Materials and Methods: ninety-four patients with magnetic resonance imaging (MRI) pre- and post-neoadjuvant treatment were retrospectively enrolled. Three regions of interest (ROIs) were manually drawn on three different slices of the tumor for every DWI sequence. ROIs were positioned to include only high signal areas and avoid artifacts or necrotic areas. ROIs were automatically copied onto the corresponding ADC maps and the system derived three different ADC values, distinguishing between mean, maximum, and minimum values, and the standard deviation (SD). Only mean ADC values were considered. After surgical intervention, pTNM and the Mandard tumor regression grade (TRG) were obtained. Patients with a TRG of 1–2 were classified as responders, while patients with a TRG from 3 to 5 were classified as non-responders. Results: no correlation was found between pre-ADC values and TRG classes, while post-ADC and ΔADC values showed a significant correlation with TRG classes (r = −0.285, p = 0.002 and r = −0.290, p = 0.019, respectively). Post-ADC values were statistically different between responders and non-responders (p = 0.019). When considering the relation between overall survival (OS) and ADC values, pre-ADC showed a negative correlation with OS (r = −0.381, p = 0.001), while a positive correlation was found between ΔADC values and OS (r = 0.323, p = 0.013). According to ΔADC values, the mean OS time between responders and non-responders showed a significant difference (p = 0.030). A statistical difference was found between TRG classes and OS (p = 0.038) and by dividing patients in responders and non-responders (p = 0.019). Conclusions: the pre-ADC and ΔADC values could be used as useful predictors for patient prognosis, thus helping the treatment planning. On the other hand, the post-ADC values, thanks to their relationship with the TRG classes, could be the ideal tool to predict the histopathological response and plan a conservative approach to the treatment of rectal cancer.

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.