Abstract
INTRODUCTION:
To develop and validate a radiomics nomogram for assessing the response of patients with Crohn's disease (CD) to infliximab.
METHODS:
Radiomics features of the spleen were extracted from computed tomography enterography images of each patient's arterial phase. The feature selection process was performed using the least absolute shrinkage and selection operator algorithm, and a radiomics score was calculated based on the radiomics signature formula. Subsequently, the radiomic model and the clinical risk factor model were separately established based on the radiomics score and clinically significant features, respectively. The performance of both models was evaluated using receiver operating characteristic curves, decision curve analysis curves, and clinical impact curves.
RESULTS:
Among the 175 patients with CD, 105 exhibited a clinical response, and 60 exhibited clinical remission after receiving infliximab treatment. Our radiomic model, comprising 20 relevant features, demonstrated excellent predictive performance. The radiomic nomogram for predicting clinical response showed good calibration and discrimination in the training cohort (area under the curve [AUC] 0.909, 95% confidence interval [CI] 0.840–0.978), the validation cohort (AUC 0.954, 95% CI 0.889–1), and the external cohort (AUC = 0.902, 95% CI 0.83–0.974). Accordingly, the nomogram was also suitable for predicting clinical remission. Decision curve analysis and clinical impact curves highlighted the clinical utility of our nomogram.
DISCUSSION:
Our radiomics nomogram is a noninvasive predictive tool constructed from radiomic features of the spleen. It also demonstrated good predictive accuracy in evaluating CD patients' response to infliximab treatment. Multicenter validation provided high-level evidence for its clinical application.
KEYWORDS: Crohn's disease, radiomics, nomogram, spleen, computed tomography enterography
INTRODUCTION
Crohn's disease (CD) comprises a group of chronic, immune-mediated diseases of the gastrointestinal tract (1). In CD, various cytokines are believed to be involved in the excessive activation of T cells in the intestinal mucosa (2,3). These inflammatory substances may affect not only the intestines but also other organs. Lymphoid tissue tends to accumulate in the spleen, consisting of a substantial number of B lymphocytes and T lymphocytes, and often enlarges during various inflammatory states, which closely correlates with immune responses (4). The spleen has a wide range of functions; for instance, it can serve as the body's larger filter of blood, produce white blood cells and antibodies, and remove microorganisms and old red blood cells (5). These different functions of the spleen predispose it to be related to many diseases, such as immunological, infectious, hematopoietic, and oncological diseases. Typically, the involvement of different diseases alters the morphology of the spleen, such as its size and margin (5,6). Although some studies have examined the correlation between disease activity and spleen size, these investigations have been limited in scope and conclusions (7,8).
Anti-TNFα therapies have been well studied in adults and have shown efficacy in both the induction and maintenance of remission (9). Anti-TNFα (infliximab [IFX]) treatment has been used for 20 years. In the ACCENT-I study, 58% of patients with CD who experienced a clinical response after the first dose of IFX were randomized, and the IFX 5-mg/kg and 10-mg/kg regimens were more effective at achieving clinical remission at week 54 than the placebo treatment (10). Although IFX is effective in patients with CD, the loss of response to IFX increases over time. A review showed that 60%–87% of patients receiving anti-TNF agents could achieve clinical remission or partial response in the induction phase, whereas less than 40% of patients maintain clinical remission at 1 year (11).
Radiomics has been found to help in assessing disease prognosis, disease activity, and diagnosis. For instance, Gao et al reported that an automated segmentation model constructed by computed tomography enterography (CTE) radiomics accurately segmented CD lesions and distinguished CD activity well (12). CTE radiomics is effective for the differential diagnosis of CD and intestinal tuberculosis; moreover, radiomics can predict surgery, postoperative anastomotic recurrence, and response to IFX during the course of CD (13–16). In summary, the use of radiomics, an emerging technology, is an effective approach for the management of CD. Radiomics can identify heterogeneity within tissues and use automated high-throughput feature extraction algorithms to convert image data into readable quantitative data (17); this transformation enables health care professionals to better comprehend the information embedded in images and apply it in clinical practice.
We used 3-dimensional (3D) slicer software to calculate radiological features of the spleen and determine whether these radiological characteristics can be used to assess disease activity and response to IFX in patients with CD. This investigation seeks to leverage imaging data to aid clinical treatment decisions.
MATERIALS AND METHODS
Patient extraction
All patients diagnosed with CD between October 2020 and October 2022 were obtained from 2 hospitals from the First Affiliated Hospital of Nanchang University and Second Affiliated Hospital of Soochow University. The inclusion criteria were as follows: (i) patients diagnosed with CD based on clinical features, colonoscopy, imaging, and histological examination; (ii) patients with detailed CTE data recorded before patients received treatment with biological agents; and (iii) complete follow-up data. We excluded the following patients: (i) without a spleen, (ii) with incomplete or unavailable imaging data, (iii) patients with other spleen diseases, and (iv) patients with severe infectious diseases. For patients with missing clinical data such as perianal fistula data, we recorded the information through telephone follow-up. All patients in our study finished 4 treatments based on 5 mg/kg, and the response evaluation was completed in the fourth treatment. Finally, 113 patients from our center were randomly assigned to 2 groups at a 6:4 ratio: the training group (67 patients) and the testing group (46 patients); patients from the Second Affiliated Hospital of Soochow University were enrolled in the external group. The study flowchart is shown in Figure 1.
Figure 1.
The flowchart of radiomics and the whole study.
Definitions of variables
In our study, the following data were collected: (i) clinical characteristics, including age, sex, abdominal pain, diarrhea, bloody stools, fever, abdominal abscess, perianal abscess, intestinal fistula, and perianal fistula; (ii) CD-related features, including response to IFX, remission with IFX, mucosal healing, disease location, disease behavior, antituberculosis treatment, azathioprine treatment, Crohn's Disease Activity Index (CDAI) score, and duration of symptoms; and (iii) serum markers, including erythrocyte sedimentation rate (ESR), C-reactive protein (CRP), and T-SPOT. Patient characteristics are summarized in Table 1. Sex was recorded as male or female. Clinical features such as abdominal pain, diarrhea, bloody stools, and fever were classified as no or yes. Similarly, disease-related features such as response to IFX, remission with IFX, and mucosal healing were also divided into no or yes. T-SPOT results were recorded as negative or positive. ESR and CRP values were recorded as actual measurements. Montreal typing of CD included: based on age, A1: under 16 years old; A2: ages 16 to 40 years; and A3: over 40 years old; according to the location of the lesion: L1: end segment of ileum; L2: colon; L3: ileocolon; L4: upper gastrointestinal tract (clinically referring to the segment from the oral cavity to the duodenum); and divided by disease behavior: B1: non-narrow and nonpenetrating; B2: narrow; and B3: penetration. The response to IFX of patients with CD was the main variable observed in our study. According to the definition of clinical response, according to the update goals for IBD treatment (STRIDE II consensus), a clinical response was defined as a decrease of ≥50% in the abdominal pain score and stool frequency in patient-reported outcome 2 (PRO2), which was the short-term goal of IBD (18). In addition, for those who had no abdominal pain or diarrhea, we considered patients to have a clinical response if the CDAI was reduced by ≥100 (19,20). Therefore, in our study, we mainly assessed the clinical response according to the PRO2 score and adopted the standard of reducing the CDAI score by ≥100 for a small number of patients. Clinical remission in adult patients with CD was defined as a PRO2 abdominal pain score ≤1, a bowel frequency ≤3, or a Harvey-Bradshaw index, which are midterm goals. Endoscopic mucosal healing in patients with CD was defined as a simplified endoscopic score ≤3 for CD (18).
Table 1.
Basic information of extracted patients from First Affiliated Hospital of Nanchang University diagnosed in October 2020–August 2022 and from Second Affiliated Hospital of Soochow University in October 2020–August 2022
| Variables | Total | Training group | Testing group | External testing | P value |
| Total, n (%) | 175 | 67 (38.3) | 46 (26.3) | 62 (35.4) | |
| Sex, n (%) | 0.721 | ||||
| Male | 115 (65.7) | 45 (67.2) | 28 (60.9) | 42 (67.7) | |
| Female | 60 (34.3) | 22 (32.8) | 18 (39.1) | 20 (32.3) | |
| Age, n (%) | 0.534 | ||||
| ≤16 | 19 (10.9) | 10 (14.9) | 4 (8.7) | 5 (8.1) | |
| 17–40 | 133 (76) | 51 (76.1) | 35 (76.1) | 47 (75.8) | |
| ≥40 | 23 (13.1) | 6 (9.0) | 7 (15.2) | 10 (16.1) | |
| Abdominal pain, n (%) | 0.239 | ||||
| No | 44 (25.1) | 19 (28.4) | 14 (30.4) | 11 (17.7) | |
| Yes | 131 (74.9) | 48 (71.6) | 32 (69.6) | 51 (82.3) | |
| Diarrhea, n (%) | 0.492 | ||||
| No | 51 (29.1) | 23 (34.3) | 12 (26.1) | 16 (25.8) | |
| Yes | 124 (70.9) | 44 (65.7) | 34 (73.9) | 46 (74.2) | |
| Bloody stool, n (%) | 0.001 | ||||
| No | 136 (77.7) | 59 (88.1) | 38 (82.6) | 39 (62.9) | |
| Yes | 39 (22.3) | 8 (11.9) | 8 (17.4) | 23 (37.1) | |
| Fever, n (%) | 0.736 | ||||
| No | 139 (79.4) | 55 (82.1) | 35 (76.1) | 49 (79.1) | |
| Yes | 46 (26.3) | 12 (17.9) | 11 (23.9) | 13 (20.9) | |
| SESCD.0 level, n (%) | 0.748 | ||||
| 2 | 36 (20.6) | 12 (17.9) | 9 (19.6) | 15 (24.2) | |
| 3 | 82 (46.9) | 35 (52.2) | 22 (47.8) | 25 (40.3) | |
| 4 | 57 (32.5) | 20 (29.9) | 15 (32.6) | 22 (35.5) | |
| Responses, n (%) | 0.654 | ||||
| No | 70 (40) | 28 (41.8) | 20 (43.5) | 22 (34.9) | |
| Yes | 105 (60) | 39 (58.2) | 26 (56.5) | 40 (63.4) | |
| Remission, n (%) | 0.355 | ||||
| No | 115 (65.7) | 46 (68.7) | 28 (60.9) | 41 (66.1) | |
| Yes | 60 (34.3) | 21 (31.3) | 18 (39.1) | 21 (33.9) | |
| Mucosal healing, n (%) | 0.469 | ||||
| No | 165 (94.3) | 62 (92.5) | 45 (97.8) | 58 (93.5) | |
| Yes | 10 (5.7) | 5 (7.5) | 1 (2.2) | 4 (6.5) | |
| Disease location, n (%) | 0.059 | ||||
| Terminal ileum | 28 (16) | 7 (10.4) | 9 (19.6) | 12 (19.4) | |
| Colon | 37 (21.1) | 9 (13.4) | 10 (21.7) | 18 (29) | |
| Whole bowel | 90 (51.4) | 51 (76.1) | 27 (58.7) | 32 (51.6) | |
| Disease behavior, n (%) | 0.284 | ||||
| Non-narrow and nonpenetrating | 120 (68.7) | 51 (76.1) | 29 (63.0) | 40 (64.5) | |
| Narrow | 47 (26.7) | 15 (22.4) | 15 (32.6) | 17 (27.4) | |
| Penetrating | 8 (4.6) | 1 (1.5) | 2 (4.3) | 5 (8.1) | |
| Antituberculosis, n (%) | 0.307 | ||||
| No | 149 (85.1) | 59 (88.1) | 36 (78.3) | 54 (87.1) | |
| Yes | 26 (14.9) | 8 (11.9) | 10 (21.7) | 8 (12.9) | |
| AZA, n (%) | 0.667 | ||||
| No | 144 (82.3) | 57 (85.1) | 38 (82.6) | 49 (79.1) | |
| Yes | 31 (17.7) | 10 (14.9) | 8 (17.4) | 13 (20.9) | |
| T-spot, n (%) | 0.354 | ||||
| Negative | 161 (92) | 64 (95.5) | 42 (91.3) | 55 (88.7) | |
| Positive | 14 (8) | 3 (4.5) | 4 (8.7) | 7 (11.3) | |
| Abdominal abscess, n (%) | 0.043 | ||||
| No | 167 (95.4) | 65 (97.0) | 46 (100.0) | 56 (90.3) | |
| Yes | 8 (4.6) | 2 (3.0) | 0 (0.0) | 6 (9.7) | |
| Intestinal fistula, n (%) | 0.307 | ||||
| No | 167 (95.4) | 66 (98.5) | 43 (93.5) | 58 (93.5) | |
| Yes | 8 (4.6) | 1 (1.5) | 3 (6.5) | 4 (6.5) | |
| Perianal abscess, n (%) | 0.677 | ||||
| No | 152 (86.9) | 59 (88.1) | 41 (89.1) | 52 (83.9) | |
| Yes | 23 (13.1) | 8 (11.9) | 5 (10.9) | 10 (11.3) | |
| Perianal fistula, n (%) | 0.77 | ||||
| No | 74 (42.3) | 30 (44.8) | 20 (43.5) | 24 (38.7) | |
| Yes | 101 (57.7) | 37 (55.2) | 26 (56.5) | 38 (61.3) | |
| CDAI (mean ± SD) | 230.24 (65.60) | 227.67 (65.17) | 229.06 (66.94) | 241.2 (59.48) | 0.296 |
| ESR (mm/hr), M (P25, P75) | 34.00 (17.00–61.00) | 37.00 (13.50–61.50) | 32.50 (23.25–59.25) | 24 (9.5–56.4) | 0.071 |
| CRP (mg/L), M (P25, P75) | 21.65 (3.73–56.30) | 21.65 (5.44–55.79) | 28.10 (2.41–69.38) | 16.3 (5.8–56.3) | 0.064 |
| Duration of symptoms (mo), M (P25, P75) | 10.00 (3.00–16.00) | 13.00 (3.00–30.00) | 8.00 (3.25–13.00) | 5.5 (1–26) | 0.157 |
The P value was from the t test, nonparametric Wilcoxon test, or χ2 test for comparing baseline characteristics of all patients.
AZA, azathioprine; CD, Crohn's disease; CDAI, Crohn's Disease Activity Index; CRP, C-reactive protein; ESR, erythrocyte sedimentation rate; T-SPOT, tuberculosis spot test; SES-CD, simplified endoscopic score for CD.
CTE image acquisition, segmentation, and extraction of radiomic features
The radiomic workflow is illustrated in Figure 1. In this study, all patients underwent contrast-enhanced abdominal CTE scans that covered the entire spleen. The CTE examinations were performed using a Siemens SOMATOM Definition AS 128-slice spiral CT scanner. The CTE scan was conducted with the following parameters: 120 kV, 200 effective mAs, a collimation of 640.6 mm, a matrix of 512 × 512, a pitch of 0.8, and a gantry rotation time of 0.5 seconds. After the nonenhanced CT scan, 80–100 mL of nonionic contrast agent (370 mg I/mL, Pamir iodine, Bracco) was intravenously injected at a rate of 3.5 mL/s, followed by a saline flush (20 mL), after which a dynamic contrast-enhanced CT scan was performed. Images in the arterial phase and venous phase were obtained at 30 and 60 seconds, respectively. The slice thickness of the images was 1.0 mm. The CTE images in the arterial phase were retrieved for image feature extraction. The region of interest (ROI) for spleen lesions was semiautomatically segmented using 3D Slicer 5.0.3, and the ROI was selected on the slice with the largest spleen area. Texture extraction was performed using the radiomic tool 3D Slicer 5.0.3 to extract imaging features from 2-dimensional (2D) images of the spleen. ROI segmentation for spleen imaging was conducted by 2 clinicians with extensive experience in CT examinations. Then, the readers segmented the spleen and saved the results in the nrrd format. Then, another author used software to extract features. Furthermore, intraclass and interclass correlation coefficients were calculated to assess the consistency of the 2 readers in radiomic feature extraction.
Feature selection and radiomic signature construction
For the feature extraction process, we first drew the ROI and downloaded the SlicerRadiomics data set. During analysis, we selected manual customization because we can determine feature classes such as firstorder, gldm, and glrlm. Then, we checked the wave-based features and resampled the voxel size to filter out some useless features. Finally, we output the data table. The LASSO algorithm was applied through the glmnet package in R software. According to this principle, we calculated the lambda (logλ = −2.15) and deleted the variables with a coefficient value of 0. The remaining variables were subsequently included in the model. A formula, which is shown in the Supplementary Digital Content (see Supplementary Material, http://links.lww.com/CTG/B97), was generated using a linear combination of selected features according to their respective least absolute shrinkage and selection operator coefficients; then, the formula was used to determine the risk score (named the radiomic score) for each patient to reflect the response to IFX in patients with CD.
Statistical analysis
For statistical analysis, patients extracted from our center were first divided into a training group and a testing group at a 6:4 ratio, whereas patients from another center were included for external validation. In addition, features were compared among the 3 groups. Differences in continuous variables were compared using the Mann-Whitney U test or independent t test, whereas comparisons of categorical variables were conducted using the χ2 test or Fisher exact test. To construct the nomogram, we simultaneously constructed a radiomic model and clinical feature model. For the model's performance and discriminative ability, we tested the Hosmer-Lemeshow goodness-of-fit test and plotted receiver operating characteristic curves to evaluate the model's classification ability. We used decision curve analysis (DCA) and a clinical impact curve (CIC) to evaluate the clinical net benefit of the predictive models. In addition, we conducted the DeLong test to calculate the significant differences in the models. All the statistical analyses were performed using R software, and the relevant software packages were obtained from the R software program website (https://cran.r-project.org/). A P value less than 0.05 was considered to indicate statistical significance for all analyses.
Our study was approved by Ethics Committee of the First Affiliated Hospital of Nanchang University ((2022)CDYFYYLK(06–025)). Informed consent was obtained from all the participants. All methods were performed in accordance with relevant guidelines and regulations.
RESULTS
Basic patient information
As shown by the flowchart (see Supplementary Figure 1, http://links.lww.com/CTG/B96), we enrolled 303 patients with CD treated with IFX as the first biological agent and excluded 128 patients based on our main aim. However, for some missing values, we imputed by performing multiple imputation, decreasing the loss of data. The detailed information is listed in Table 1. As shown in Table 1, 175 patients with CD were included. For sexual frequency, the distribution of patients with CD was mainly male. The number of patients with CD aged 17–40 years was obviously greater than that of patients with CD aged ≤16 or ≥40 years. Interestingly, more than 50% of patients with CD had abdominal pain and diarrhea. However, the frequency of bloody stool and fever was not as high as the incidence of abdominal pain. The incidence of intestinal fistula and abdominal abscess in patients with CD was very low, at less than 50%. Nevertheless, 13.1% of the patients had perianal abscess, and the percentage of patients with perianal fistula was greater (57.7%). In our study, the ratio of patients without narrow or penetrating bowel was the largest, followed by patients with narrow bowel, whereas patients with penetrating bowel were the lowest. The percentage of positive T-SPOT cells was 8%. Serological indicators such as the ESR and CRP concentration were obviously greater than the normal values. As shown by the CDAI, the mean CDAI score was 230.24, indicating that the patients with CD exhibited a moderate-to-severe activity period. Furthermore, the duration of symptoms was up to 10 months, and some patients may even experience symptoms within 3 years. In our study, the rate of clinical response after treatment with IFX was 60%, the rate of clinical remission was 34.3%, and the rate of endoscopic mucosal healing was only 5.7%.
Establishment of the nomogram
As shown in Figure 1, we performed our study according to the flowchart. Our patients were divided into training, testing, and external groups. The detailed characteristics of the patients in the training set and 2 validation sets are shown in Table 1. There were 67 patients, 46 patients, and 62 patients in the training group, testing group, and external group, respectively. Table 1 shows that the distribution was almost random, except for the number of patients with bloody stool and abdominal abscess because the P value was greater than 0.05. We used the Radiomics tool of 3D Slicer software to read the information of the 2D lesion, and we subsequently identified 500 imaging features for analysis. The interclass correlation coefficients of the readers ranged from 0.692 to 0.893, indicating favorable feature extraction reproducibility. Then, we performed least absolute shrinkage and selection operator logistic regression to identify the features, as shown in Figure 2a,b. A total of 20 features were used to construct the model. Next, we constructed a radiomic score calculation formula according to the weight coefficient of the features, which is presented in Supplementary Digital Content (see Supplementary Material 1, http://links.lww.com/CTG/B97). Finally, we determined the Akaike information criterion value of the nomogram by stepwise regression and selected the best Akaike information criterion value (Figure 2c). Furthermore, as shown in Table 2, we performed multivariate logistic regression analysis using clinical features and found that disease location, disease behavior, and duration of symptoms were associated with the response to IFX; simple terminal ileum type (L1) was associated with treatment with IFX, whereas penetrating (B3) and longer duration of symptoms were associated with poorer effects of IFX (Table 2). We subsequently constructed a nomogram plot for visualization (Figure 2d). For the interpretation of the nomogram built by CTE, each patient had several features, such as our formula, which were read by a 3D slicer; then, the radiomics score was determined according to our formula. Next, we constructed a vertical straight line and observed the risk of CD. Finally, we can diagnose CD with a concrete accuracy. Similarly, the interpretations of the nomograms were similar.
Figure 2.
Texture feature selection was determined by LASSO logistic regression and conducted a nomogram. (a) Selection of the tuning parameter (λ) according to LASSO model through 10-fold cross-validation based on minimum criteria. Binomial deviances from the LASSO regression cross-validation procedure were plotted as a function of log (λ). The y axis indicates binomial deviances. The lower x axis indicates the log (λ). Numbers along the upper x axis represent the average number of predictors. Red dots indicate average deviance values for each model with a given λ, and vertical bars through the red dots show the upper and lower values of the deviances. The vertical black lines define the optimal values of λ, where the model provides its best fit to the data. The optimal λ value of 0.025 with log (λ) = 5.23 was selected. (b) LASSO coefficient profiles of the 850 texture features. The dotted vertical line was plotted at the value selected using 10-fold cross-validation in (a). (c) Radiomics nomogram predicts response of patients with CD in the training group. (d) Nomogram built by clinical risk factors predicts the response of patients with CD in the training group. CD, Crohn's disease; CTE, computed tomography enterography; LASSO, least absolute shrinkage and selection operator.
Table 2.
Univariate and multivariate logistic regression model for exploring the potential favorable factors of response for infliximab in patients with Crohn's disease
| Variable | OR (univariable) | OR (multivariable) | |
| age | <40 | — | |
| ≥40 | 3.97 (0.44–36.04, P = 0.220) | ||
| Sex | Male | ||
| Female | 0.80 (0.29–2.24, P = 0.671) | ||
| ESR | Normal | — | |
| High | 0.71 (0.25–2.04, P = 0.529) | ||
| CRP | Normal | — | |
| High | 1.84 (0.60–5.57, P = 0.284) | ||
| Intestinal fistula | No | — | |
| Yes | 4,242,598.95 (0.00–Inf, P = 0.992) | ||
| Abdominal pain | No | — | |
| Yes | 0.75 (0.25–2.24, P = 0.606) | ||
| Perianal fistula | No | — | |
| Yes | 1.44 (0.54–3.82, P = 0.467) | ||
| Diarrhea | No | — | |
| Yes | 0.48 (0.16–1.39, P = 0.177) | ||
| Disease location | L1 | — | |
| L2 | 1.06 (0.20–3.54, P = 0.057) | 0.98 (0.19–2.91, P = 0.07) | |
| L3 | 0.69 (0.20–2.88, P = 0.038) | 0.79 (0.36–3.58, P = 0.041) | |
| Disease behavior | B1 | — | |
| B2-B3 | 0.89 (0.54–3.37, P = 0.048) | 0.76 (0.34–2.97, P = 0.058) | |
| Duration of symptoms | <12 | — | |
| ≥12 | 0.22 (0.08–0.64, P = 0.005) | 0.24 (0.08–0.69, P = 0.008) |
CRP, C-reactive protein; ESR, erythrocyte sedimentation rate; OR, odds ratio.
Validation of the nomogram and comparison of the CTE model and clinical risk model
For the calibration plot, using the bootstrap validation method (n = 1,000), the nomogram model based on clinical risk factors showed good agreement with the actual values (Figure 3a); however, after validating the model in the testing group and external group, we found that the model had poorer consistency between the actual value and the predictive value (P > 0.05) (Figure 3b,c). Nevertheless, compared with the clinical risk model, both in the training group and in the 2 validation groups, our nomogram built by CTE showed good consistency between the actual value and the predictive value (P < 0.05; Figure 3d–f). Next, we compared the AUC of the receiver operating characteristic between the training set and validation set (Figure 3e,f). For both the training group and the validation group, the AUC of the nomogram built by CTE in the training group was almost the same as that in the testing group (training group, 0.909 [0.840–0.978]; testing group, 0.954 [0.889–1]; and external group, 0.902 [0.83–0.974]) (Figure 3g–i). In comparison, the nomogram built with clinical risk factors had poorer sensitivity and specificity both in the training set and in the validation set (training group, 0.712 [0.586–0.839]; testing group, 0.591 [0.412–0.770]; and external group, 0.697 [0.564–0.831], P < 0.05) (Figure 3g–i). As shown in Figure 4, DCA graphically showed that the use of the nomogram built by the CTE to predict response had remarkable predictive power (Figure 4a–c). In addition, the CICs of the nomogram indicated that the CTE model (Figure 4g–I) had more remarkable predictive power than the other models (Figure 4d–f). Finally, to investigate the clinical efficacy of our model, we analyzed the predictive value of the 2 models for clinical remission (Figure 5). Compared with that of the CTE model, the AUC of the prediction model of the nomogram built with clinical risk factors was significantly lower in the training cohort (AUC, 0.826 [0.706–0.899] vs 0.716 [0.528–0.801], P < 0.05) (Figure 5a), whereas the difference in AUC was not significantly meaningful in the validation cohort (AUC, 0.654 [0.506–0.731] vs 0.649 [0.518–0.713], P > 0.05) (Figure 5b). In the external cohort, the differences between the CTE radiomics model and clinical risk model were significant (AUC, 0.852 [0.0.751–0.932] vs 0.51 [0.316–0.66], P < 0.05) (Figure 5c).
Figure 3.
Validation of nomogram. (a-c) Calibration plot of the nomogram built by clinical risk factors in the training group, testing group, and external group, respectively. (d-f) Calibration plot of the nomogram built by CTE for predicting response of patients with CD in the training group, testing group, and external group, respectively. (g-i) ROC plot of the 2 nomograms for predicting response of patients with CD in the training group, testing group, and external group, respectively. CD, Crohn's disease; CTE, computed tomography enterography; ROC, receiver operating characteristic.
Figure 4.
DCA and CIC were performed in training and validation groups. (a-c) DCA of the 2 nomograms in the training group, testing group, and external group, respectively. (d-f) CIC of the nomograms built by clinical risk in the training group, testing group, and external group, respectively. (g-i) CIC of the nomograms built by CTE in the training group, testing group, and external group, respectively. CTE, computed tomography enterography; CIC, clinical impact curve; DCA, decision curve analysis curves.
Figure 5.
ROC curves were performed to assess the value of radiomics nomogram model for predicting the clinical remission of patients with CD in the training group (a), testing group (b), and external group (c). CD, Crohn’s disease; ROC, receiver operating characteristic.
DISCUSSION
Although biologics, such as IFX, are considered effective at treating CD, patients are more prone to unresponsiveness because up to 30% of patients with CD experience primary unresponsiveness, and up to 13% of patients with CD experience secondary unresponsiveness annually (21). This study investigated the value of splenic radiomic features in evaluating the response of patients with CD to IFX treatment. Our results demonstrated that, in both the training and validation sets, the splenic radiomic nomogram outperformed the clinical risk factor model in identifying CD patients' response to IFX. Moreover, DCA and the CIC also confirmed the clinical applicability of the model, providing compelling evidence for clinical decision-making and personalized treatment. Notably, the CTE model was independent of the location, phenotype, and disease activity of CD, which shows greater robustness for identifying the response to IFX only according to features of CTE imaging. Hence, our radiomic model is more objective than the other models and has excellent stability and generalizability.
The clinical goals for treating CD include improving laboratory markers, alleviating clinical symptoms, and achieving mucosal healing under colonoscopy (18). Considering the potential side effects and partial nonresponsiveness of IFX, not all patients can benefit from this treatment (22). Approximately 13%–40% of patients exhibit primary nonresponse to anti-TNF therapy, often driven by immunogenic factors (11). In our center, 40% of patients with CD demonstrated no response to IFX treatment. Therefore, predicting the response of patients with CD to IFX is highly important. Previous studies have shown that the quantification of membrane-bound TNFα expression during endoscopy predicts the response to anti-TNF therapy (23). However, because of intestinal stricture, fistula, and possible complications during endoscopic examinations in patients with CD, some studies have explored noninvasive biomarkers for predicting the response of patients with CD to IFX. One study indicated that low drug concentrations at week 14 were the only independent factor associated with primary nonresponse (24).
Radiomics has provided a novel approach for clinical diagnosis and prognosis and has been used to predict the response of patients with CD to IFX treatment (16,25). The intestines are hollow organs, and lesion segmentation can be influenced by intestinal contents, leading to variations in radiomic feature extraction and reducing model accuracy. The spleen is closely associated with the occurrence and progression of inflammation, and previous studies have assessed the relationship between spleen size and CD activity (7,8). Hence, our study used splenic radiomics to predict the baseline response of patients with CD to IFX. Simultaneously, we incorporated various clinical baseline data, and through univariate and multivariate logistic regression analyses, we identified independent predictive factors and established a clinical feature model. Multivariate analysis revealed that patients with lesions located at the terminal ileum, nonstructuring nonpenetrating disease behavior, and shorter disease duration were more likely to respond to standard IFX treatment, consistent with the findings of Jessen et al (26,27). Our study demonstrated that the radiomic model based on the spleen outperforms the clinical risk factor model, with AUCs of 0.909 (0.840–0.978) and 0.712 (0.586–0.839). In addition, the results of the clinical effect in the training cohort indicated that the CTE radiomics model showed significant superiority over the clinical factor model, as did the 2 validation cohorts.
However, our radiomic model has several limitations. First, the limited sample size of 175 patients was a drawback. However, 175 patients were from 3 centers, which to some extent reduced the selection bias. The second limitation was the retrospective nature of the study, especially for the evaluation of the models' effectiveness in the 2 test cohorts. The data from 2 centers increase the reliability of our model, and a further prospective study is needed to strengthen our model. Third, CTE is a time-consuming and expensive technique that has high operator technology and patient status requirements, which limits the application of our model. Another limitation is that we outlined only the 2D structure of the spleen ROI in the arterial phase rather than in the venous phase, which weakens the representativeness of the images and prevents the collection of imaging information. Moreover, important indicators, such as drug concentration and fecal calprotectin concentration, were missing, and serological inflammatory markers and CDAI scores were not included in the related research in the clinical model, decreasing the predictive value of the TSVR. Finally, in our study, most patients exhibited moderate-to-severe activity, which weakened the universality of the model. Therefore, further studies with larger sample sizes are needed to address this question. In addition, manual VOI segmentation is time-consuming and inaccurate, and the development of automatic approaches could lead to the development of new methods based on deep learning in the future.
In conclusion, this study developed a radiomic nomogram based on splenic radiomic features. This nomogram obtained at baseline has the ability to predict the clinical response of patients with CD after receiving IFX treatment. A more accurate and stable model comprising various radiomic and clinicopathologic characteristics and molecular biomarkers should be investigated in the future.
CONFLICTS OF INTEREST
Guarantor of the article: You-Xiang Chen, MD.
Specific author contributions: C.-T.T.: experiment performing, data analysis, and manuscript writing. Y.Y., F.Y., and Z.L.: sample collecting and data analysis. S.L., Y.C., C.Z., and Y.C.: project development.
Financial support: The study was supported by grants of Jiangxi Clinical Research Center for Gastroenterology (Grant No. 20223BCG74011, PI: Y.C.). This study was supported by grants from the National Natural Science Foundation of China (Grant No. 82060448 and 82360112, PI: Y.C.); the project was supported by Jiangxi Provincial Natural Science Foundation (Grant No. 20232BAB216021, PI: C.T.), Youth Talent Research and Cultivation Fund of First Affiliated Hospital of Nanchang University (YFYPY202254), China Postdoctoral Science Foundation (2023M741522), and Advanced PhD research project of the Second Affiliated Hospital of Soochow University (SDFEYBS2207 231375).
Potential competing interests: None to report.
Availability of data and material: All data generated or analyzed during this study are included in this published article and its supplementary information file.
Study Highlights.
WHAT IS KNOWN
✓ Infliximab is effective for relieving the inflammation of bowel in patients with Crohn's disease (CD); however, the effectiveness is not satisfactory, and more patients are more prone to unresponsiveness. There is no predicting model for the response of infliximab.
WHAT IS NEW HERE
✓ Our radiomics nomogram is a noninvasive predictive tool constructed from radiomic features of the spleen, and it has good predictive accuracy in evaluating CD patients' response to infliximab treatment.
✓ How can this study help patient care?
✓ We could use the radiomics nomogram to assess the response to infliximab of patients with CD.
Supplementary Material
Footnotes
SUPPLEMENTARY MATERIAL accompanies this paper at http://links.lww.com/CTG/B96 and http://links.lww.com/CTG/B97.
Chao-Tao Tang, Fang Yin and Yitian Yin contributed equally to this work.
Contributor Information
Chao-Tao Tang, Email: chaotao_tang@163.com.
Fang Yin, Email: yinfang1997@163.com.
Zide Liu, Email: liuzide_1997@163.com.
Shunhua Long, Email: longshunhua@163.com.
Yong Chen, Email: chenyong1991@163.com.
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