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

医学 队列 英夫利昔单抗 内科学 接收机工作特性 回顾性队列研究 克罗恩病 临床实习 曲线下面积 疾病 胃肠病学 物理疗法
作者
Xuehua Li,Yingkui Zhong,Chenglang Yuan,Jinjiang Lin,Xiaodi Shen,Minyi Guo,Baolan Lu,Jixin Meng,Yangdi Wang,Naiwen Zhang,Zixin Luo,Guimeng Hu,Ren Mao,Minhu Chen,Canhui Sun,Ziping Li,Qinghua Cao,Baili Chen,Zhihui Chen,Bingsheng Huang,Shi‐Ting Feng
出处
期刊:International Journal of Intelligent Systems [Wiley]
卷期号:37 (12): 11853-11870 被引量:2
标识
DOI:10.1002/int.23066
摘要

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.
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