Background: Accurately assessing the activity of Crohn's disease (CD) is crucial for determining prognosis and guiding treatment strategies for CD patients.

Objective: This study aimed to develop and validate a nomogram for assessing CD activity.

Methods: The semi-automatic segmentation method and PyRadiomics software were employed to segment and extract radiomics features from the spectral CT enterography images of lesions in 107 CD patients. The radiomic score (rad-score) was calculated using the radiomic signature formula. Multivariate logistic regression analysis identified the independent risk factors of erythrocyte sedimentation rate, fecal calprotectin, and Inflammatory Bowel Disease Questionnaire (IBDQ), and a nomogram was constructed in combination with rad-score. The nomogram underwent evaluation and testing in the training set (n = 84) and validation set (n = 23), respectively.

Results: The discrimination performance of the combined (AUC 0.877) was marginally superior to that of IBDQ + clinical (AUC 0.854). However, there was no significant difference in AUC between the two models in the validation set ( = 0.206). IBDQ + clinical outperformed clinical (AUC 0.808), clinical outperformed IBDQ (AUC 0.746), and IBDQ outperformed radiomic signature (AUC 0.688). Significant differences in AUC were observed between the two models (radiomic signature vs clinical, = 0.026; radiomic signature vs IBDQ + clinical, = 0.011; radiomic signature vs combined, = 0.008; in the validation set).

Conclusion: The nomogram, combined with laboratory data, IBDQ and rad-score, presents an accurate and reliable method for assessing CD activity.

Clinical Impact: The nomogram enhances the potential for personalized treatment plans and better disease management, making it a valuable tool for clinical practice.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11720638PMC
http://dx.doi.org/10.2147/JIR.S491043DOI Listing

Publication Analysis

Top Keywords

radiomic signature
20
ibdq clinical
12
laboratory data
8
inflammatory bowel
8
bowel disease
8
disease questionnaire
8
crohn's disease
8
validation set
8
clinical auc
8
clinical outperformed
8

Similar Publications

Background: Previous studies mostly use single-type features to establish a prediction model. We aim to develop a comprehensive prediction model that effectively identify patients with poor prognosis for single hepatocellular carcinoma (HCC) based on artificial intelligence (AI). : 236 single HCC patients were studied to establish a comprehensive prediction model.

View Article and Find Full Text PDF

Background/objectives: Accurate diagnosis is essential to avoid unnecessary procedures for thyroid incidentalomas (TIs). Advances in radiomics and machine learning applied to medical imaging offer promise for assessing thyroid nodules. This study utilized radiomics analysis on F-18 FDG PET/CT to improve preoperative differential diagnosis of TIs.

View Article and Find Full Text PDF

Objectives: To compare an MRI-based radiomics signature with the programmed cell death ligand 1 (PD-L1) expression score for predicting immunotherapy response in nasopharyngeal carcinoma (NPC).

Methods: Consecutive patients with NPC who received immunotherapy between January 2019 and June 2022 were divided into training (n = 111) and validation (n = 66) sets. Tumor radiomics features were extracted from pretreatment MR images.

View Article and Find Full Text PDF

Background: Hepatocellular carcinoma (HCC) is one of the most common tumors worldwide. Various factors in the tumor environment (TME) can lead to the activation of endoplasmic reticulum stress (ERS), thereby affecting the occurrence and development of tumors. The objective of our study was to develop and validate a radiogenomic signature based on ERS to predict prognosis and systemic combination therapy response.

View Article and Find Full Text PDF

Introduction: This study predicted HRD score and status based on intra- and peritumoral radiomics in patients with ovarian cancer (OC) for better guiding the use of PARPi in clinical.

Methods: A total of 106 and 95 patients with OC were included between January 2022 and November 2023 for predicting HRD score and status, respectively. Radiomics features were extracted and quantitatively analyzed from intra- and peri-tumor regions in the CT image.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!