Expanding the role of radiomics and artificial intelligence in the management of inflammatory bowel disease: Insights, opportunities, and challenges

Table 1 Key studies on radiomics, artificial intelligence, digital health, and predictive models in inflammatory bowel disease

Refs	Study focus	Methodology/cohort	Key findings
Li et al[33], 2025	Radiomics & SLOR to infliximab in CD	220 CD patients, 2-center, retrospective; intestinal wall + creeping fat radiomics; combined clinical-radiomics model	Combined model achieved best prediction of SLOR (AUC: 0.87 training, 0.85 validation); outperforming clinical or radiomics alone
Cai et al[15], 2024	Fusion models for predicting IFX response in CD	263 CD patients, 3-center retrospective; CTE-based radiomics, DL, clinical, and fusion models	Early fusion (radiomics + DL + clinical) had highest accuracy (AUC: 0.85–0.91) and best calibration; outperformed single-modality models
Chirra et al[17], 2024	Detecting inflammation & fibrosis in stricturing CD	51 CD patients undergoing MRE before resection; radiomics vs radiologist vs histopathology	Radiomics differentiated inflammation vs fibrosis (AUC: 0.67–0.83); combined radiomics + radiologist scoring improved accuracy (AUC: Approximately 0.79)
Colombel et al[46], 2017	Long-term safety of vedolizumab in IBD	Pooled safety data: 2830 patients, > 4800 patient-years exposure	Vedolizumab safe long-term; low rates of serious infection (≤ 0.6%), infusion reactions, and malignancy (< 1%). No PML reported
Kennedy et al[28], 2019	Predictors of anti-TNF failure (PANTS study)	1610 anti-TNF-naïve CD patients, prospective UK-wide; IFX & ADA	Low week-14 drug levels predicted nonresponse and nonremission; immunogenicity frequent (63% IFX, 29% ADA); immunomodulators reduced antibody risk
Liu et al[18], 2024	ML diagnosis of ileal CD (radiomics + clinical)	135 patients, T2-weighted MRE radiomics; compared to radiologists	Radiomics model (AUC: 0.95) outperformed 2/3 radiologists; ensemble radiomics + clinical model highest performance (AUC: 0.98, 93.5% accuracy)
Maccioni et al[49], 2000	MRI correlation with CD activity	20 CD patients, 1.5T MRI vs biomarkers	MRI signals (wall T2, gadolinium enhancement, fibrofatty proliferation) strongly correlated with biologic activity, even in clinical remission
Peters et al[45], 2023	Transcriptomic temporal signatures in IBD	Mouse colitis models + IBD patient validation; ML-based temporal classifier	Temporal gene expression & splicing signatures predicted histopathology, distinguishing acute vs chronic phases; translational potential in IBD
Qiu et al[44], 2025	Predicting long-term IFX response	746 CD patients, multicenter; ML models (XGBoost, SHAP, LCMM)	XGBoost best (AUC: 0.91 train, 0.71 test); key predictors: Hb, WBC, ESR, albumin, platelets; identified distinct patient clusters
Rimola et al[29], 2011	MRI activity index validation	48 CD patients; MRI vs ileocolonoscopy (CDEIS)	MRI activity index validation strongly correlated with CDEIS (r = 0.80); validated thresholds for active (≥ 7) and severe (> 11) disease with high sensitivity/specificity
Song et al[16], 2024	CTE radiomics + body composition to predict IFX failure	137 CD patients; CTE + muscle/fat indices; 1-year IFX outcomes	Combined radiomics + skeletal muscle index + creeping fat model best (AUC: 0.88 train, 0.83 validation); strong clinical utility
Waljee et al[50], 2017	ML to predict hospitalization/steroid use	20368 VHA IBD patients; logistic regression vs random forest	RF model outperformed logistic regression (AUC: 0.87 vs 0.68); key predictors: Age, albumin, immunosuppressive use, platelets, prior hospitalizations/steroid use
Yang et al[12], 2024	Pancreatic radiomics to predict SLOR to IFX	184 biologic-naïve CD patients; pancreatic texture + clinical model	Combined clinical–radiomics nomogram best (AUC: 0.87); first pancreatic-based model for SLOR prediction
Yueying et al[19], 2023	MRE-based model for IFX response	188 bio-naïve CD patients; pretreatment MRE radiomics	Radiomic model achieved AUC: 0.88; robust across centers/scanners; high-risk group more likely to lose response
Zhang et al[21], 2024	MRI radiomics for intestinal fibrosis	123 refractory CD patients; MR-based fibrosis models vs radiologists	Radiomics models (AUC ≤ 0.93) outperformed visual reads and clinical markers; enhanced fibrosis characterization
Zhen et al[48], 2021	Digital health monitoring in IBD	59 IBD patients using HealthPROMISE app for 1 year	ER visits/hospitalizations reduced from 25% to 3% (P = 0.03); improved patient understanding of disease; engagement limited (~54%)
Zhu et al[27], 2022	CTE radiomics nomogram for mucosal healing	106 CD patients on IFX; training + test cohorts	Clinical–radiomics nomogram predicted MH (AUC: 0.88 both cohorts); suggested utility for noninvasive MH monitoring
Zhu et al[26], 2024	DECT radiomics for mucosal healing	106 CD patients, 221 segments; iodine maps + mesenteric fat	Combined iodine radiomics model best (AUC: 0.99 train, 0.95 test); high-risk patients had more progression
Zhu et al[25], 2023	CTE radiomics nomogram for SLOR to IFX	181 CD patients; multicenter; 3 cohorts	Radiomics nomogram predicted SLOR with high accuracy (AUC: 0.86-0.95 across cohorts); supported early biologic switching
Aggeletopoulou et al[20], 2023	Review of creeping fat in CD	Review of surgical, histologic, radiologic approaches	Creeping fat strongly linked to CD outcomes; novel CT-based creeping fat index proposed; ML + radiomics may clarify mechanistic role

SLOR: Secondary loss of response; CD: Crohn’s disease; AUC: Area under the curve; DL: Deep learning; MRE: Magnetic resonance enterography; IBD: Inflammatory bowel disease; PML: Progressive multifocal leukoencephalopathy; TNF: Tumor necrosis factor; PANTS: Personalised anti-tumor necrosis factor therapy in Crohn’s disease; IFX: Infliximab; ADA: Antidrug antibodies; ML: Machine learning; MRI: Magnetic resonance imaging; XGBoost: Extreme gradient boosting; SHAP: Shapley additive explanations; LCMM: Latent class mixed model; Hb: Hemoglobin; WBC: White blood cell; ESR: Erythrocyte sedimentation rate; CDEIS: Crohn’s Disease Endoscopic Index of Severity; CTE: Computed tomography enterography; VHA: Veterans Health Administration; RF: Random forest; MR: Magnetic resonance; ER: Emergency room; MH: Mucosal healing; DECT: Dual-energy computed tomography; CT: Computed tomography.