Retrospective Study Open Access
Copyright ©The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Jun 7, 2025; 31(21): 105895
Published online Jun 7, 2025. doi: 10.3748/wjg.v31.i21.105895
Longitudinal computed tomography-based delta-radiomics of visceral adipose tissue predicts infliximab secondary loss of response in Crohn’s disease patients
Xi Li, Fu-Long Song, Shu-Min Zeng, Zhi-Chao Feng, Peng-Fei Rong, Department of Radiology, The Third Xiangya Hospital, Central South University, Changsha 410013, Hunan Province, China
Hai-Feng He, Department of Radiology, Hunan Cancer Hospital/The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha 410013, Hunan Province, China
Zhi-Chao Feng, Department of Radiology, Department of Medical Imaging, SJTU-Ruijin-UIH Institute for Medical Imaging Technology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
Zhi-Chao Feng, Department of Medical Imaging, Yueyang Central Hospital, Yueyang 414020, Hunan Province, China
ORCID number: Zhi-Chao Feng (0000-0001-6651-5195); Peng-Fei Rong (0000-0001-5473-1982).
Co-first authors: Xi Li and Fu-Long Song.
Co-corresponding authors: Zhi-Chao Feng and Peng-Fei Rong.
Author contributions: Li X, Song FL, He HF, and Zeng SM were tasked with data collection. They gathered information from various clinical sources, carefully extracted key clinical variables such as patient age, disease stage, etc., and assessed images using professional tools. Li X and Song FL conceptualized and designed the research, choosing suitable research methods and setting the direction. They analyzed data using statistical methods and segmented medical images accurately. They also took the lead in writing the manuscript and are co-first authors. Feng ZC and Rong PF participated in the overall study design and provided valuable suggestions. They supervised data analysis to ensure accuracy and revised the manuscript thoroughly. As co-corresponding authors, they coordinated the whole process. He HF helped with the literature review and looked for relevant studies, while Zeng SM validated the data. They are responsible for patient screening, enrollment, clinical data and imaging index collection. All authors read and approved the final manuscript, ensuring the quality of this research work.
Supported by the National Natural Science Foundation of China, No. 82372072 and No. 82071986.
Institutional review board statement: This retrospective study was reviewed and approved by the Institutional Ethics Review Board of the Third Xiangya Hospital of Central South University, No. Kuai20018.
Informed consent statement: The need for patient consent was waived due to the retrospective nature of the study.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Data sharing statement: Data are available upon reasonable request and pursuant to appropriate agreements from the corresponding author at rongpengfei66@163.com.
Open Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Peng-Fei Rong, Department of Radiology, The Third Xiangya Hospital, Central South University, No. 138 Tongzipo Road, Changsha 410013, Hunan Province, China. rongpengfei66@163.com
Received: February 10, 2025
Revised: April 5, 2025
Accepted: May 21, 2025
Published online: June 7, 2025
Processing time: 116 Days and 19.3 Hours

Abstract
BACKGROUND

Visceral adipose tissue (VAT) plays a role in the pathogenesis of Crohn's disease (CD) and is associated with treatment outcomes following infliximab (IFX) therapy. We developed and validated the first delta-radiomics model to quantify VAT heterogeneity as a predictive biomarker for IFX response in patients with CD.

AIM

To develop a longitudinal computed tomography (CT)-based delta-radiomics model of VAT for predicting secondary loss of response (SLR) in patients with CD.

METHODS

This retrospective study included 161 patients with CD who achieved clinical remission following IFX induction therapy between 2015 and 2023. All patients underwent CT enterography before IFX initiation and after completing induction therapy. VAT volume was delineated by two radiologists in consensus. Radiomics features were extracted from pre-treatment and post-induction CT images, and delta-radiomics features were calculated as follows: Delta features = Feature-post - Feature-pre. A radiomics model was constructed using logistic regression. Model performance was assessed using discrimination, calibration, and decision curve analyses.

RESULTS

Nine significant delta-radiomics features were used to develop the delta-radiomics model, yielding an area under the receiver operating characteristic curve (AUC) of 0.816 (95%CI: 0.737-0.896) in the training cohort and 0.750 (95%CI: 0.605-0.895) in the validation cohort. Multivariable logistic regression identified platelet count, Montreal behavior classification, and the VAT/subcutaneous adipose tissue volume ratio prior to treatment as independent risk factors for SLR. The combined model integrating clinical predictors and delta-radiomics features achieved superior predictive performance, with an AUC of 0.853 (95%CI: 0.786-0.921) in the training cohort and 0.812 (95%CI: 0.677-0.948) in the validation cohort.

CONCLUSION

We developed a predictive model based on longitudinal changes in VAT, demonstrating significant potential for identifying patients with CD at high risk of SLR to IFX therapy.

Key Words: Crohn’s disease; Computed tomography enterography; Infliximab; Delta-radiomics; Secondary loss of response

Core Tip: The treatment response to infliximab in patients with Crohn's disease (CD) is heterogeneous, with approximately 23%-46% of those achieving clinical remission after induction therapy experiencing secondary loss of response (SLR) within the first year. This significantly increases the risk of serious adverse outcomes. In this study, we developed a delta-radiomics model based on longitudinal changes in visceral adipose tissue assessed through computed tomography enterography. This model further integrates clinical and imaging biomarkers to identify patients with CD at high risk of SLR. This non-invasive approach holds promise as a valuable tool for optimizing personalized treatment regimens and guiding monitoring strategies.
INTRODUCTION

Crohn's disease (CD) is a chronic autoimmune inflammatory bowel disease (IBD) characterized by alternating relapsing and remitting symptoms throughout its course, often leading to progressive intestinal damage[1]. The primary clinical objective in managing CD is maintaining long-term asymptomatic remission and preventing serious complications[2,3]. Infliximab (IFX), a monoclonal antibody targeting tumor necrosis factor (TNF), has been shown to significantly improve intestinal mucosal healing and increase clinical remission rates in patients with CD[3,4]. However, approximately 23-46% of patients who achieve clinical remission following IFX induction therapy experience secondary loss of response (SLR) within the first year, increasing the risk of disease exacerbation and serious adverse outcomes[5].

Presently, the efficacy of IFX treatment is primarily monitored through periodic endoscopic evaluations. However, due to limitations such as invasiveness, cost, and patient discomfort, endoscopy is not suitable for all individuals. Although several risk factors-such as disease duration, C-reactive protein (CRP) levels, fecal calprotectin concentration, and the presence of creeping fat-have been identified in relation to IFX treatment response, many of these markers are subjective, and the underlying mechanisms remain poorly understood, which limits their widespread clinical applicability[6-8]. Therefore, there is a need for reliable predictive tools capable of identifying patients at risk of SLR to support personalized management strategies.

Growing evidence suggests that visceral adipose tissue (VAT) plays a critical role in the pathogenesis of CD and is strongly associated with disease complexity, prognosis, and the occurrence of complications[9]. VAT contributes to chronic intestinal inflammation by secreting pro-inflammatory cytokines such as TNF-α and various interleukins[10]. Several studies have employed fat attenuation techniques based on CT imaging to quantitatively assess VAT and derive metrics predictive of CD prognosis[11,12]. However, using single fat parameters to characterize the relationship between VAT and treatment response in CD has notable limitations. These methods often rely on conventional visual assessment, exhibit inter-observer variability, and lack sensitivity in detecting subtle changes in adipose tissue.

In contrast, more comprehensive analyses that capture microstructural alterations and functional impairments within VAT may offer deeper insights into therapeutic outcomes and disease progression in CD[13].

Radiomics, an emerging technique capable of non-invasively extracting extensive quantitative information from medical images, has demonstrated considerable potential in clinical diagnosis and treatment[14]. Previous studies have confirmed the significant clinical utility of VAT-based radiomic features in predicting CD progression[15]. However, in CD patients undergoing IFX induction therapy and achieving clinical remission, VAT exhibits tissue heterogeneity and subtle pathophysiological alterations that vary temporally and spatially before and after treatment. These changes may be indicative of differential therapeutic outcomes[9].

Delta-radiomic features enable precise quantification of longitudinal variations in subtle structural tissue characteristics between two time points[16], offering enhanced information for assessing the response of CD patients to IFX therapy. Compared to magnetic resonance enterography (MRE), CT provides superior spatial resolution for VAT quantification and broader clinical accessibility in routine practice, rendering it more feasible for delta-radiomic analysis. Despite this, no prior studies have assessed the potential clinical value of VAT radiomic features derived from longitudinal CT enterography for predicting SLR to IFX.

This study aims to develop a delta-radiomics model based on longitudinal changes in VAT and to integrate clinical and imaging biomarkers to investigate its predictive utility in identifying CD patients at high risk for SLR.

MATERIALS AND METHODS
Study patients

This retrospective study was approved by the Institutional Ethics Review Board of the Third Xiangya Hospital, Central South University, with the requirement for informed patient consent waived. Patients diagnosed with CD and treated with IFX between January 2015 and August 2023 at the institution were enrolled. CD diagnosis followed the European Crohn’s and Colitis Organization guidelines[3]. IFX was administered intravenously with a dosage of 5-10 mg/kg at weeks 0, 2, and 6, followed by maintenance dosing every 8 weeks.

Inclusion criteria: (1) Initial administration of IFX with subsequent regular continuation of therapy; and (2) Availability of CT enterography scans within two weeks prior to IFX initiation and following induction therapy (i.e., between weeks 0-2 and 2-6, with week 14 evaluation).

Exclusion criteria: (1) History of prior bowel resection or previous treatment of biological agents; (2) Lack of clinical response after IFX induction treatment; (3) Poor CT enterography image quality; and (4) Incomplete clinical baseline data and discontinued IFX treatment for reasons other than SLR.

The patient selection process is detailed in Figure 1. Enrolled patients were randomly assigned to the training or validation cohorts in a 7:3 ratio.

Figure 1
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Figure 1 Flowchart of patient recruitment. CD: Crohn’s disease; IFX: Infliximab; CT: Computed tomography; SLR: Secondary loss of response.

Baseline data included age, sex, height, weight, smoking history, neutrophil, lymphocyte, platelet counts, serum albumin, creatinine, erythrocyte sedimentation rate, and CRP levels. Additionally, CD location and behavior were documented according to the Montreal classification. Body mass index was calculated as weight (kg) divided by height squared (m²), and the neutrophil-lymphocyte ratio was calculated by dividing the neutrophil count by the lymphocyte count.

Outcomes and definitions

Generally, SLR is defined as the recurrence of clinical symptoms between 14 and 54 weeks following an initial response to IFX[17,18]. In this study, SLR outcomes were evaluated by an experienced multidisciplinary team based on clinical symptoms, radiological and endoscopic findings, and subsequent treatment modifications. These included initiating alternative biological agents, requiring immunosuppressants or corticosteroids, IFX dose escalation, and CD-related surgery. Additional criteria included a CD Activity Index > 150 or a reduction of less than 70% from baseline and/or evidence of mucosal recurrence, defined as a reduction in the Simple Endoscopic Score for CD (SES-CD) of < 50% or an SES-CD ≥ 3.

CT image acquisition

CT enterography examinations were performed using a 64-slice multidetector CT scanner (Philips Brilliance; Philips) or a 256-slice scanner (GE Revolution; GE Healthcare), following standardized bowel preparation protocols. Patients fasted prior to imaging and consumed 1200-1500 mL of 2.5% isotonic mannitol solution in three divided doses at 15-minute intervals before the scan.

Scanning was conducted in the supine position, from the diaphragm to the symphysis pubis. After the acquisition of non-enhanced images, a nonionic contrast agent (320 mg/mL; Loversol, Jiangsu Hengrui Medicine Corp Ltd) was administered intravenously at a dose of 1.5-2.0 mL/kg, with an average injection rate of 3.0-4.0 mL/s. Using automatic bolus-tracking, arterial phase images were obtained either 15 seconds after the attenuation value in the abdominal aorta reached 100 HU or with a fixed delay of 32-35 seconds following contrast administration. Portal venous phase images were acquired 35 seconds after the arterial phase, or with a total delay of 70 seconds post-injection.

Detailed scanner parameters are provided in Supplementary Table 1. All CT enterography scans were performed within two weeks before IFX initiation and following the completion of the induction phase. For radiomic analysis, images from the portal venous phase were selected.

Volume of interests segmentation and feature extraction

Volumes of VAT used for radiomics analysis were defined from the top of the diaphragm to the superior margin of the symphysis pubis[15]. Based on tissue-specific attenuation thresholds ranging from -150 to -50 HU[19], two abdominal radiologists-blinded to all clinical information- initially delineated the VAT volumes using the 3D semi-automated segmentation module of an open-source software platform (3D Slicer V4.11.2, https://www.slicer.org/).

Subsequently, manual corrections and verification were performed to finalize the Volume of interests (VOIs). Any discrepancies between the two radiologists were thoroughly reviewed and resolved through consensus. A representative example of the VOI segmentation process is provided in Supplementary Figure 1.

To evaluate reproducibility, the two radiologists independently segmented 30 randomly selected cases after a one-month interval. The intra-class correlation coefficient (ICC) was calculated to assess both intra- and inter-observer reliability, with the methodology illustrated in Figure 2.

Figure 2
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Figure 2 The study design and workflow of radiomics analysis. IFX: Infliximab; CT: Computed tomography; VOI: Volume of interest; LASSO: Least absolute shrinkage and selection operator; ROC: Receiver operating characteristic curves.

The Artificial Intelligent Kit (version 3.2.2; GE Healthcare) was employed to resample CT enterography images. To address differences in voxel resolution across various CT scanners, the voxel dimensions of the portal venous phase images were isotropically resampled to 1 mm × 1 mm × 1 mm (x, y, z) using a linear interpolation algorithm. Hounsfield unit values were discretized into 64 bins to standardize intensity levels.

Subsequently, 1702 radiomics features-including both pre-treatment and post-induction treatment data-were extracted for each patient using the “PyRadiomics” plugin in the 3D Slicer software. To quantify longitudinal changes in VAT, delta-radiomics features were calculated as the relative net differences between the pre-treatment feature values (Featurepre) and the post-induction treatment values (Featurepost) as follows: Delta features = Feature-post - Feature-pre. A total of 851 delta-radiomics features could be obtained per patient, encompassing shape features (n = 14), texture features (n = 75), histogram features (n = 18), and wavelet-based features (n = 744).

Measurement of other imaging parameters of adipose tissue

Additional adipose tissue parameters were incorporated into the study, including VAT volume (mm3), subcutaneous adipose tissue (SAT) volume (mm3), VAT/SAT volume ratio, VAT area (mm2) and SAT area (mm2) measured at the L3 Lumbar vertebral level, and VAT/SAT area ratio prior to treatment initiation and after induction therapy. All measurements were automatically obtained from CT enterography images using 3D slicer software. The VAT/SAT volume or area ratio was calculated as VAT volume or area divided by SAT volume or area. VAT and SAT areas and volumes were normalized by patient height to improve data comparability. The VAT/SAT ratio was further categorized as either < 1.0 or ≥ 1.0.

Model development and validation

To construct the clinical model, univariable and multivariable logistic regression analyses were performed in the training cohort to identify potential clinical predictors. Model performance was assessed using the minimal Akaike Information Criterion (AIC) to optimize fit.

A multi-step feature selection process was implemented to prevent overfitting in the delta-radiomics model. First, all delta-radiomics features were standardized using the z-score method to ensure data uniformity. Features with an ICC ≥ 0.75 were retained for further analysis. In the univariable analysis, features with P values < 0.05 were selected. The final set of significant features was determined using the least absolute shrinkage and selection operator (LASSO) method with tenfold cross-validation (Supplementary Figure 2), and these were used to construct a delta-radiomics score (delta-radscore).

A nomogram was then developed by integrating the selected clinical predictors with the delta-rad score through logistic regression. Model discrimination was evaluated using receiver operating characteristic (ROC) curve analysis, and the areas under the curves (AUCs) were compared using the DeLong test. Model calibration and clinical utility were assessed with a calibration and decision curve analysis (DCA).

Statistical analysis

Statistical analyses were conducted using R statistical software (version 4.3.1; https//www.r-project.org). Continuous variables were expressed as mean ± SD for normally distributed data or as median with interquartile range for non-normally distributed data. These were analyzed using the Student’s t-test and the Mann-Whitney U test, respectively. Categorical variables were presented as frequencies or percentages and compared using the χ2 test or Fisher’s exact test, as appropriate. A two-sided P value < 0.05 was considered statistically significant.

RESULTS
Patient characteristics and construction of the clinical model

A total of 161 eligible patients were included in the final analysis (129 men, 32 women; mean age, 29 years). Demographic and clinical data for the training cohort (n = 112) and validation cohort (n = 49) are summarized in Table 1. Among them, 38 (33.9%) patients in the training cohort and 16 (32.7%) in the validation cohort experienced SLR to IFX during treatment. There were no significant differences in demographic or clinical characteristics between the two cohorts (all P > 0.05).

Table 1 Characteristics of patients in the training and validation cohorts, n (%).
Variables
Total (n = 161)
Training (n = 112)
Validation (n = 49)
P value
Age (years)29 (22, 38)27 (21, 38)31 (24, 38)0.308
Male gender129 (80.1)90 (80.4)39 (79.6)0.999
BMI (kg/m²)18.70 (16.60, 21.50)19.10 (16.60, 21.50)17.60 (16.60, 20.10)0.282
Smoking49 (30.4)33 (29.5)16 (32.7)0.827
Platelet count (× 109 cells/L)299 (299, 368)298 (227, 377)311 (234, 358)0.834
Albumin (g/dL)35.2 (32.3, 40.1)35.2 (32.4, 39.9)35.0 (31.7, 40.1)0.827
ESR (mm/hour)40.0 (22.0, 74.0)42.0 (22.0,75.0)37.0 (23.0,70.0)0.887
CRP (mg/dL)20.2 (5.0, 50.0)21.6 (5.4, 49.9)17.6 (5.0, 51.2)0.779
Cr (μmol/L)67 (59, 76)68 (61, 77)66 (58, 74)0.309
NLR4.3 (2.7, 5.8)4.3 (2.7, 5.7)4.0 (2.7, 6.0)0.977
CDAI159.0 (107.9, 234.2)158.7 (99.5, 235.4)159.0 (118.0, 230.0)0.786
Montreal location classification0.380
    L1 (ileal disease)58 (36.0)41 (36.6)17 (34.7)
    L2 (colonic disease)15 (9.3)13 (11.6)2 (4.1)
    L3 (ileocolonic disease)88 (54.7)58 (51.8)30 (61.2)
Montreal behaviour classification0.554
    B1 (non stricturing, non-penetrating)72 (44.7)47 (42.0)25 (51.0)
    B2 (stricturing)79 (49.1)58 (51.8)21 (42.9)
    B3 (penetrating)10 (6.2)7 (6.2)3 (6.1)
Pre-IFX treatment
    SAT area (cm2/m2)20.0 (7.9, 33.4)21.7 (8.3, 33.4)19.2 (7.2, 33.4)0.511
    VAT area (cm2/m2)8.1 (3.2, 25.9)7.4 (3.2, 24.5)9.7 (3.1, 28.0)0.790
    SAT volume (cm3/m3)423.2 (166.0, 773.3)438.2 (170.7, 758.5)359.8 (155.1, 806.2)0.901
    VAT volume (cm3/m3)187.3 (83.1, 421.4)169.6 (79.15, 410.3)214.7 (93.6, 468.1)0.866
    VAT/SAT area ratio (≥ 1)45 (28.0)28 (25.0)17 (34.7)0.210
    VAT/SAT volume ratio (≥ 1)27 (16.8)19 (17.0)8 (16.3)0.921
Post-IFX treatment
    SAT area (cm2/m2)22.7 (12.9, 35.6)23.9 (13.7, 39.7)19.8 (12.1, 31.7)0.068
    VAT area (cm2/m2)11.0 (5.2, 24.9)11.7 (6.1, 26.7)9.8 (4.2, 20.3)0.219
    SAT volume (cm3/m3)310.5 (166.1, 596.4)321.5 (181.8, 627.6)297.6 (156.8, 535.2)0.368
    VAT volume (cm3/m3)436.2 (244.5, 759.9)469.1 (272.9, 824.0)359.6 (214.8, 626.8)0.131
    VAT/SAT area ratio (≥ 1)37 (23.0)25 (22.3)12 (24.5)0.765
    VAT/SAT volume ratio (≥ 1)18 (11.2)12 (10.7)6 (12.2)0.778
Data are expressed as median (interquartile range), (SD) or number (percentage). BMI: Body mass index; ESR: Erythrocyte sedimentation rate; CRP: C-reactive protein; Cr: Creatinine; NLR: Neutrophil-lymphocyte ratio; CDAI: Crohn’s disease activity index; IFX: Infliximab; SAT: Subcutaneous adipose tissue; VAT: Visceral adipose tissue.

In the training cohort, multivariable logistic regression analysis identified the following as independent predictors of SLR:

Platelet count [odds ratio (OR) = 1.005, 95%CI: 1.001-1.008, P = 0.011], Montreal behavior classification (OR = 2.307, 95%CI: 1.108-4.803, P = 0.025), VAT/SAT volume ratio before IFX treatment (OR = 3.977, 95%CI: 1.320-11.980, P = 0.014).

These three variables were incorporated into a clinical model developed using logistic regression (Table 2).

Table 2 Univariable and multivariable logistic regression analysis for prediction of secondary loss of response to infliximab in Crohn’s disease patients in the training cohort, n (%).
VariablesNon-SLR (n = 74)SLR (n = 38)Univariable analysis
Multivariable analysis
OR (95CI)
P value
OR (95%CI)
P value
Age (years)27 (21, 38)27 (21, 38)1.004 (0.677-1.489)0.990
Male gender61 (82.4)29 (76.3)1.456 (0.323-6.573)0.603
BMI (kg/m2)19.25 (16.62, 21.10)18.95 (16.65, 23.02)0.968 (0.292-3.210)0.794
Smoking23 (31.1)10 (26.3)1.263 (0.453-3.521)0.760
Platelet count (× 109 cells/L)291 (197, 368)323 (272, 406)0.997 (0.021-46.938)0.0491.005 (1.001-1.008)0.011
Albumin (g/dL)35.7 (32.7, 40.3)34.3 (32.3, 39.7)1.020 (0.289-3.593)0.480
ESR (mm/hour)39.5 (24.0, 74.8)47.0 (18.3, 75.5)1.000 (0.972-1.029)0.703
CRP (mg/dL)22.1 (6.7, 51.2)16.1 (5.0, 44.4)1.006 (0.116-8.735)0.296
Cr (μmol/L)69 (61,78)65 (58, 74)1.020 (0.078-13.332)0.167
NLR4.3 (2.7, 5.7)4.3 (3.1, 5.8)1.033 (0.360-2.964)0.825
CDAI156.5 (98.5, 242.4)166.4 (124.8, 233.5)1.001 (0.590-1.695)0.890
Montreal location classification0.786 (0.090-6.874)0.401
    L1 (ileal disease)29 (39.2)12 (31.5)
    L2 (colonic disease)8 (10.8)5 (13.2)
    L3 (ileocolonic disease)37 (50.5)21 (55.3)
Montreal behaviour classification0.472 (0.007-32.379)0.0312.307 (1.108-4.803)0.025
    B1 (non stricturing, non-penetrating)36 (48.6)11 (29.0)
    B2 (stricturing)35 (47.3)23 (60.5)
    B3 (penetrating)3 (4.1)4 (10.5)
Pre-IFX treatment
    SAT area (cm2/m2)21.2 (7.5, 33.4)21.7 (9.2, 33.4)0.999 (0.873-1.143)0.98
    VAT area (cm2/m2)6.9 (3.0, 21.6)9.7 (3.6, 26.9)0.994 (0.320-3.088)0.405
    SAT volume (cm3/m3)453.3 (165.2, 761.2)409.8 (179.3, 745.3)1.000 (0.840-1.190)0.815
    VAT volume (cm3/m3)168.5 (75.5, 394.8)234.1 (97.3, 415.2)0.999 (0.210-4.761)0.424
    VAT/SAT area ratio (≥ 1)17 (57.0)11 (28.9)1.366 (0.353-5.282)0.491
    VAT/SAT volume ratio (≥ 1)8 (10.8)11 (28.9)3.361 (0.034-330.690)0.0163.977 (1.320-11.980)0.014
Post-IFX treatment
    SAT area (cm2/m2)26.4 (14.3, 39.8)20.2 (12.9, 39.3)1.007 (0.272-3.731)0.386
    VAT area (cm2/m2)11.8 (6.1, 28.6)11.1 (6.2, 23.0)1.009 (0.222-4.577)0.681
    SAT volume (cm3/m3)481.9 (289.0, 782.3)408.1 (271.2, 836.1)1.000 (0.226-4.427)0.435
    VAT volume (cm3/m3)319.2 (187.9, 624.6)434.9 (170.6, 612.7)1.000 (0.544-1.838)0.975
    VAT/SAT area ratio (≥ 1)18 (24.3)7 (18.4)0.703 (0.175-2.815)0.479
    VAT/SAT volume ratio (≥ 1)6 (8.1)6 (15.8)2.125 (0.193-23.398)0.215
Data are expressed as median (interquartile range), (SD) or number (percentage). IFX: Infliximab; SLR: Secondary loss of response; BMI: Body mass index; ESR: Erythrocyte sedimentation rate; CRP: C-reactive protein; Cr: Creatinine; NLR: Neutrophil-lymphocyte ratio; CDAI: Crohn’s disease activity index; SAT: Subcutaneous adipose tissue; VAT: Visceral adipose tissue; OR: Odds ratio.
Establishment of the delta-radiomics model and clinical-radiomics combined model

A total of 851 delta-radiomics features were extracted from CT enterography images. Following intra- and inter-observer reliability assessment (ICCs > 0.75) and univariate correlation analysis, 193 delta-radiomics features showed statistically significant differences between the SLR and non-SLR groups in the training cohort (P < 0.05).

After applying LASSO selection, 9 delta-radiomics features with nonzero coefficients were obtained to construct a delta-radiomics model based on logistic regression. The model was expressed through the following formula:

Delta-radscore = -1.150

+ 0.452 × Original-Shape-MajorAxisLength

+ 0.448 × Original-Shape-MinorAxisLength

- 1.202 × Wavelet-LHH-Ngtdm-Busyness

- 0.584 × Wavelet-LLH-Gldm-LargeDependenceLowGrayLevelEmphasis

+ 0.436 × Wavelet-LHH-Ngtdm-Contrast

- 0.591 × Wavelet-LHL-Firstorder-Skewness

- 0.193 × Wavelet-HLL-Ngtdm-Contrast

+ 1.118 × Wavelet-HHL-Glcm-JointEnergy

- 0.125 × Wavelet-LLL-Glcm-DifferenceAverage

To integrate significant clinical parameters and delta-radscore, we further established a clinical-radiomics combined model. Based on the combined model, we developed a visual nomogram for simplified application in clinical practice (Figure 3A).

Figure 3
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Figure 3 Establishment of the combined model and predictive performance of models for predicting secondary loss of response. A: The developed nomogram of combined model scaled by the proportional regression coefficient of each predictor; B and C: Receiver operating characteristic curves of the clinical model, delta-radiomics model and combined model in the training cohort (B) and the validation cohort (C). VAT: Visceral adipose tissue; SAT: Subcutaneous adipose tissue; AUC: Area under the receiver operating characteristic curve. 1VAT/SAT volume ratio prior to infliximab treatment.
Performance and clinical utility assessment of models

The predictive performance of each model was assessed using ROC analysis, yielding metrics such as the AUC, sensitivity, specificity, positive predictive value, and negative predictive value, as shown in Table 3. In the training cohort (Figure 3B), the AUCs for the clinical model, delta-radiomics model, and combined model in predicting SLR to IFX were 0.767 (95%CI: 0.678-0.857), 0.816 (95%CI: 0.737-0.896), and 0.853 (95%CI: 0.786-0.921), respectively. In the validation cohort (Figure 3C), the corresponding AUCs were 0.707 (95%CI: 0.539-0.876), 0.750 (95%CI: 0.605-0.895), and 0.812 (95%CI: 0.677-0.948), respectively. Among the three models, the combined model exhibited the lowest AIC value (109.28 vs 135.10 vs 121.61), indicating a superior model fit. The DeLong test demonstrated that the combined model significantly improved predictive performance compared to the clinical model in both the training cohort (P = 0.023) and validation cohort (P = 0.048).

Table 3 Performance of models for predicting secondary loss of response in the training and validation cohorts.
AUC (95%CI)
Sensitivity
Specificity
PPV
NPV
P alue1
Accuracy
Training cohort (n = 112)
    Clinical model0.767 (0.678-0.857)85.1% (63/74)36.8% (14/38)72.4% (63/87)56.0% (14/25)-68.8% (77/112)
    Radiomics model0.816 (0.737-0.896)86.5% (64/74)47.4% (18/38)76.2% (64/84)64.3% (18/28)0.13373.2% (82/112)
    Combined model0.853 (0.786-0.921)85.1% (63/74)52.6% (20/38)77.8% (63/81)64.5% (20/31)0.02374.1% (83/112)
Validation cohort (n = 49)
    Clinical model0.707 (0.539-0.876)96.97% (32/33)31.3% (5/16)74.4% (32/43)83.3% (5/6)-79.9% (37/49)
    Radiomics model0.750 (0.605-0.895)84.8% (28/33)50.0% (8/16)77.8% (28/36)61.5% (8/13)0.20473.5% (36/49)
    Combined model0.812 (0.677-0.948)93.9% (31/33)50.0% (8/16)79.5% (31/39)80.0% (8/10)0.04879.6% (39/49)
1P value indicates the significance level of the comparison of areas under the curves with the clinical model as the reference using DeLong test in the training and validation cohort, respectively.
SLR: Secondary loss of response AUC: Area under the curve NPV: Negative predictive value PPV: Positive predictive value.

Calibration plots of the combined model showed excellent agreement between the predicted probabilities and observed outcomes in both the training (Figure 4A) and validation (Figure 4B) cohorts. DCA revealed that the combined model outperformed the clinical and delta-radiomics models, offering greater overall net benefit for predicting SLR to IFX treatment in CD patients across the most clinically relevant threshold probability ranges in both the training (Figure 4C) and validation (Figure 4D) cohorts.

Figure 4
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Figure 4 Calibration and clinical utility of models for predicting secondary loss of response to infliximab in patients with Crohn’s disease. A and B: Calibration plots for clinical model, delta-radiomics model and combined model in the training cohort (A) and the validation cohort (B); C and D: Decision curve analysis for these three models in the training cohort (C) and the validation cohort (D).
DISCUSSION

Early and accurate identification of SLR to IFX treatment is critical for effectively managing CD. This study utilized baseline clinical parameters and longitudinal delta-radiomics features to develop and validate a predictive model for identifying patients at high risk of SLR. The model demonstrated satisfactory predictive performance in both the training and validation cohorts. Our findings further support the utility of delta-radiomics in capturing temporal and spatial alterations associated with pathophysiological and microstructural changes in VAT, providing valuable insights for predicting treatment response to IFX.

Numerous studies have established that VAT, a complex organ with multifaceted endocrine and immune functions, secretes various pro-inflammatory cytokines and plays a pivotal role in the pathogenesis of CD. It is strongly associated with disease progression and suboptimal therapeutic outcomes[20]. In CD, mesenteric adipose tissue exhibits structural disorganization and functional impairment, characterized by increased deposition of inflammatory mediators and infiltration of immune cells, thereby contributing to intestinal inflammation[21,22]. Radiomics techniques can non-invasively capture these pathophysiological alterations in VAT and transform them into quantifiable imaging features for analysis. A multicenter study by Li et al[15] demonstrated that a VAT-based radiomics model accurately identified patients at elevated risk of disease progression. Additionally, a VAT-based deep learning radiomics model effectively differentiated CD from ulcerative colitis[23], showing the presence of distinct microstructural and metabolic characteristics in VAT associated with CD.

Considering that IFX treatment may influence VAT evolution in CD patients[24], we conducted delta-radiomics analysis using CT enterography images obtained before and after IFX induction. Longitudinal changes in VAT between the pre-treatment and post-induction phases were extracted and quantified into nine delta-radiomics features using LASSO regression to predict long-term response to IFX. The resulting delta-radiomics model demonstrated strong predictive capability in identifying high-risk SLR patients. Notably, the nomogram derived from this model offers clinicians a practical tool for optimizing therapeutic strategies, thereby enabling personalized management of CD patients in clinical settings.

In our study, a VAT/SAT volume ratio ≥ 1.0 prior to IFX treatment independently predicted SLR in CD patients. This finding aligns with previous research. Gu et al[11] reported that a higher visceral fat index-the ratio of visceral to SAT-was associated with an increased risk of surgery within 6 months of initiating IFX therapy. Other studies have similarly shown that a higher baseline percentage of intra-abdominal VAT relative to total adipose tissue correlates with reduced rates of corticosteroid-free deep remission or endoscopic remission following biologic therapy[9], as well as with increased disease activity and the occurrence of comorbidities[25]. A prospective cohort study also reported that a higher VAT/SAT volume ratio was associated with more severe disease behavior[26].

Despite these findings, our study did not detect a significant correlation between the VAT/SAT area ratio and SLR to IFX. This discrepancy may be attributed to the limitation of measuring fat area from a single axial slice at the L3 vertebral level, which may not fully reflect overall fat distribution[27]. We also observed that patients with complicated disease behaviors, such as stricturing or penetrating phenotypes, were more likely to experience SLR. This is likely because these more complex disease forms are associated with heightened intestinal inflammation, and initiating anti-TNF therapy after developing such phenotypes may result in reduced efficacy and an increased risk of surgical intervention[28,29].

Abnormalities in platelet number and function have been well documented in IBD[30,31]. As pro-inflammatory cells, platelets release various inflammatory mediators that can initiate or amplify the inflammatory response through diverse cellular and molecular mechanisms, often involving classical immune cells in IBD[30]. Platelets play a critical role in both acute and chronic phases of inflammation. Furthermore, activated platelets secrete profibrogenic factors that contribute to the development of fibrosis in gastrointestinal disorders[32]. In patients with ulcerative colitis who have achieved mucosal healing, elevated platelet counts have been associated with a higher risk of disease recurrence[33].

The platelet-to-lymphocyte ratio has emerged as a promising biomarker for predicting the therapeutic response to anti-TNF treatment in both CD and ulcerative colitis[34,35]. Moreover, an elevated platelet-to-albumin ratio has been identified as an independent risk factor for surgery in CD patients with stricturing disease of the small bowel[36]. In our study, increased platelet count was associated with SLR to IFX, supporting its role as a significant predictive factor in multivariate prediction models. By integrating delta-radiomics features with clinical predictors to construct a combined model, we enhanced the overall predictive performance, offering valuable insights for evaluating treatment response to IFX in clinical practice.

Our study has several limitations. First, to enhance the stability and practical applicability of the model, we did not independently include radiomics features extracted from VAT before and after treatment; their clinical utility requires further investigation. Second, although this study confirmed the clinical value of longitudinal VAT changes derived from CT enterography for predicting IFX treatment outcomes using radiomics techniques, CT imaging raises concerns regarding radiation exposure, particularly in adolescent patients. While some studies have utilized MRE to analyze adipose tissue[37], most have been limited to assessments at the level of the third lumbar vertebra. Further research is needed to clarify MRE’s potential for evaluating the entire VAT compartment and its feasibility for radiomics applications, thereby helping to reduce radiation-related risks.

Additionally, our study's sample size was relatively limited due to strict inclusion and exclusion criteria. Although the model demonstrated robust performance in internal validation, this limitation may restrict its generalizability to broader patient populations. Multicenter prospective studies with larger cohorts must validate our findings and support clinical translation. Finally, although our LASSO-based model exhibited strong performance and practical utility, exploring alternative feature selection methods may yield further insights and should be considered in future research.

CONCLUSION

We developed and validated a comprehensive model that integrates delta-radiomics features derived from longitudinal changes in VAT with clinical predictors, enabling accurate identification of CD patients at high risk of SLR to IFX treatment. This model holds the potential for guiding individualized treatment planning and monitoring strategies in the clinical management of CD.

Footnotes

Provenance and peer review: Unsolicited article; Externally peer reviewed.

Peer-review model: Single blind

Specialty type: Gastroenterology and hepatology

Country of origin: China

Peer-review report’s classification

Scientific Quality: Grade A, Grade B, Grade B, Grade C

Novelty: Grade A, Grade B, Grade C, Grade C

Creativity or Innovation: Grade A, Grade B, Grade C, Grade C

Scientific Significance: Grade A, Grade B, Grade B, Grade C

P-Reviewer: Lin Y; Wang CL; Zhu MY S-Editor: Li L L-Editor: A P-Editor: Yu HG

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