Identifying patients with Crohn's disease at high risk of primary nonresponse to infliximab using a radiomic‐clinical model

Abstract

Approximately 13%–40% of patients with Crohn's disease (CD) show a primary loss of response to infliximab (IFX) therapy. Therefore, differentiating potential responders from primary nonresponders is clinically important. In this double‐center study, we developed and validated a computed tomography enterography (CTE)‐based radiomic signature (RS) for identification of CD patients at high risk of primary nonresponse (PNR) to IFX therapy, and demonstrated its incremental value to the clinical model. A total of 244 patients (training cohort, n = 119; test cohort 1, n = 51; test cohort 2, n = 74) were retrospectively recruited. Their clinical data and pretreatment CTE were retrieved and analyzed. All patients underwent IFX induction therapy. Reliability of clinical factors and radiomic‐based features were assessed with the area under the receiver operating characteristic curve (AUC). In all, 1130 radiomic features were extracted from the whole inflamed gut in CTE images. In training cohort and test cohorts 1 and 2, the RS that discriminated PNR to IFX therapy yielded AUCs of 0.848, 0.789, and 0.789, respectively (all p < 0.05). By combining the clinical predictors (C‐reactive protein, albumin, and body mass index) and RS, the radiomic‐clinical model showed an increase in predicting performance (AUCs: 0.864, 0.794, and 0.791, respectively; all p < 0.05). Decision curve analysis and net reclassification improvement demonstrated the clinical usefulness of the radiomic‐clinical model. In this study, the proposed RS showed potential as a clinical aid for the accurate identification of CD patients at high risk of PNR to IFX therapy before treatment. A combination of the RS and existing clinical factors might enable a step forward precise medicine.

CT-based radiomics signature of visceral adipose tissue and bowel lesions for identifying patients with Crohn’s disease resistant to infliximab

“…Consistent with previous study [ 17 ], bowel RM served as a promising technique in tailoring treatment strategy in CD patients, exhibiting satisfactory performance in predicting effectiveness and robustness, while peri-lesion microenvironment such as VAT was ignored [ 17 ]. In our study, the combined model consisting of VAT and bowel radiomics features outperformed bowel RM alone for identifying CD patients at high risk of PNR for IFX both in training and testing cohort.…”

“…In our previous study [ 17 ], a radiomics model based on the entire inflamed bowel in CTE images (bowel model) was established for identification of PNR to IFX therapy. To further improve its performance, radiomics features of VAT would be added to the bowel model to construct a comprehensive (VAT-bowel) radiomics model.…”

“…Previous studies have quantitative analyzed metrics of VAT volume to clarify the relationship between adipose and therapeutic response [ 15 ]. Furthermore, radiomics can efficiently extract numerous imaging features imperceptible to the naked eyes [ 16 ] and allow more accurate identification of the features of bowel lesions in CD patients [ 17 ]. Therefore, the use of CT-based radiomics to extract effective features from both intestinal lesions and VAT may potentially enhanced pharmacotherapy response prediction.…”

Computed tomography‐based radiomics nomogram using machine learning for predicting 1‐year surgical risk after diagnosis of Crohn's disease

“…making it impossible to clinically translate their usage 36 . By comparing with studies that adopted the same handcrafted radiomic features, 9,25,37 the texture features retained in our study are only partially the same as those in other researches, which implies that subsequent research relies on additional validation of histopathologic findings and immunohistochemistry.…”