Identifying patients with Crohn's disease at high risk of primary nonresponse to infliximab using a radiomic‐clinical model. Issue 12 (14th September 2022)

• Record Type: Journal Article
• Authors: Li, Xuehua; Zhong, Yingkui; Yuan, Chenglang; Lin, Jinjiang; Shen, Xiaodi; Guo, Minyi; Lu, Baolan; Meng, Jixin; Wang, Yangdi; Zhang, Naiwen; Luo, Zixin; Hu, Guimeng; Mao, Ren; Chen, Minhu; Sun, Canhui; Li, Ziping; Cao, Qing‐hua; Chen, Baili; Chen, Zhihui; Huang, Bingsheng; Feng, Shi‐Ting
• Is Part Of: International journal of intelligent systems. Volume 37:Issue 12(2022)
• Page Start: 11853
• Page End: 11870
• Publication Date: 2022-09-14
• Subjects: computed tomography enterography -- Crohn's disease -- infliximab therapy -- primary nonresponse -- radiomics; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; 006.3
• DOI: 10.1002/int.23066
• Languages: English
• ISSNs: 0884-8173

Abstract

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.