A Novel Radiomics Nomogram for the Prediction of Secondary Loss of Response to Infliximab in Crohn’s Disease

References

• Ng SC, Tang W, Ching JY, et al. Incidence and phenotype of inflammatory bowel disease based on results from the Asia-pacific Crohn’s and colitis epidemiology study. Gastroenterology. 2013;145(1):158–165.e2. doi:10.1053/j.gastro.2013.04.007
   PubMed Web of Science ®Google Scholar
• Singh S, Fumery M, Sandborn WJ, et al. Systematic review and network meta-analysis: first- and second-line biologic therapies for moderate-severe Crohn’s disease. Aliment Pharmacol Ther. 2018;48(4):394–409. doi:10.1111/apt.14852
   PubMed Web of Science ®Google Scholar
• Torres J, Bonovas S, Doherty G, et al. ECCO guidelines on therapeutics in Crohn’s disease: medical treatment. J Crohns Colitis. 2020;14(1):4–22. doi:10.1093/ecco-jcc/jjz180
   PubMed Web of Science ®Google Scholar
• Gisbert JP, Panés J. Loss of response and requirement of infliximab dose intensification in Crohn’s disease: a review. Am J Gastroenterol. 2009;104(3):760–767. doi:10.1038/ajg.2008.88
   PubMed Web of Science ®Google Scholar
• Allez M, Karmiris K, Louis E, et al. Report of the ECCO pathogenesis workshop on anti-TNF therapy failures in inflammatory bowel diseases: definitions, frequency and pharmacological aspects. J Crohns Colitis. 2010;4(4):355–366. doi:10.1016/j.crohns.2010.04.004
   PubMed Web of Science ®Google Scholar
• Chaparro M, Panes J, García V, et al. Long-term durability of infliximab treatment in Crohn’s disease and efficacy of dose “escalation” in patients losing response. J Clin Gastroenterol. 2011;45(2):113–118. doi:10.1097/MCG.0b013e3181ebaef9
   PubMed Web of Science ®Google Scholar
• Harper JW, Sinanan MN, Zisman TL. Increased body mass index is associated with earlier time to loss of response to infliximab in patients with inflammatory bowel disease. Inflamm Bowel Dis. 2013;19(10):2118–2124. doi:10.1097/MIB.0b013e31829cf401
   PubMed Web of Science ®Google Scholar
• Billiet T, Papamichael K, de Bruyn M, et al. A matrix-based model predicts primary response to infliximab in Crohn’s disease. J Crohns Colitis. 2015;9(12):1120–1126. doi:10.1093/ecco-jcc/jjv156
   PubMed Web of Science ®Google Scholar
• Sprakes MB, Ford AC, Warren L, et al. Efficacy, tolerability, and predictors of response to infliximab therapy for Crohn’s disease: a large single centre experience. J Crohns Colitis. 2012;6(2):143–153. doi:10.1016/j.crohns.2011.07.011
   PubMed Web of Science ®Google Scholar
• Maser EA, Villela R, Silverberg MS, et al. Association of trough serum infliximab to clinical outcome after scheduled maintenance treatment for Crohn’s disease. Clin Gastroenterol Hepatol. 2006;4(10):1248–1254. doi:10.1016/j.cgh.2006.06.025
   PubMed Web of Science ®Google Scholar
• Zapater P, Almenara S, Gutiérrez A, et al. Actual anti-TNF Trough levels relate to serum IL-10 in drug-responding patients with Crohn’s disease. Inflamm Bowel Dis. 2019;25(8):1357–1366. doi:10.1093/ibd/izz012
   PubMed Web of Science ®Google Scholar
• Leal RF, Planell N, Kajekar R, et al. Identification of inflammatory mediators in patients with Crohn’s disease unresponsive to anti-TNFα therapy. Gut. 2015;64(2):233–242. doi:10.1136/gutjnl-2013-306518
   PubMed Web of Science ®Google Scholar
• Matsuoka K, Hamada S, Shimizu M, et al. Factors predicting the therapeutic response to infliximab during maintenance therapy in Japanese patients with Crohn’s disease. PLoS One. 2018;13(10):e0204632. doi:10.1371/journal.pone.0204632
   PubMed Web of Science ®Google Scholar
• Bashir U, Siddique MM, McLean E, et al. Imaging heterogeneity in lung cancer: techniques, applications, and challenges. AJR Am J Roentgenol. 2016;207(3):534–543. doi:10.2214/AJR.15.15864
   PubMed Web of Science ®Google Scholar
• Comelli A, Stefano A, Coronnello C, et al. Radiomics: A New Biomedical Workflow to Create a Predictive Model. Cham: Springer International Publishing; 2020.
   Google Scholar
• Varghese BA, Cen SY, Hwang DH, et al. Texture analysis of imaging: what radiologists need to know. AJR Am J Roentgenol. 2019;212(3):520–528. doi:10.2214/AJR.18.20624
   PubMed Web of Science ®Google Scholar
• Zhang H, Graham CM, Elci O, et al. Locally advanced squamous cell carcinoma of the head and neck: CT texture and histogram analysis allow independent prediction of overall survival in patients treated with induction chemotherapy. Radiology. 2013;269(3):801–809. doi:10.1148/radiol.13130110
   PubMed Web of Science ®Google Scholar
• Miles KA, Ganeshan B, Griffiths MR, et al. Colorectal cancer: texture analysis of portal phase hepatic CT images as a potential marker of survival. Radiology. 2009;250(2):444–452. doi:10.1148/radiol.2502071879
   PubMed Web of Science ®Google Scholar
• Li Z, Yu L, Wang X, et al. Diagnostic performance of mammographic texture analysis in the differential diagnosis of benign and malignant breast tumors. Clin Breast Cancer. 2018;18(4):e621–e27. doi:10.1016/j.clbc.2017.11.004
   PubMed Web of Science ®Google Scholar
• Maaser C, Sturm A, Vavricka SR, et al. ECCO-ESGAR guideline for diagnostic assessment in IBD part 1: initial diagnosis, monitoring of known IBD, detection of complications. J Crohns Colitis. 2019;13(2):144–164.
   PubMed Web of Science ®Google Scholar
• Towbin AJ, Sullivan J, Denson LA, et al. CT and MR enterography in children and adolescents with inflammatory bowel disease. Radiographics. 2013;33(7):1843–1860. doi:10.1148/rg.337105140
   PubMed Web of Science ®Google Scholar
• Jensen MD, Ormstrup T, Vagn-Hansen C, et al. Interobserver and intermodality agreement for detection of small bowel Crohn’s disease with MR enterography and CT enterography. Inflamm Bowel Dis. 2011;17(5):1081–1088. doi:10.1002/ibd.21534
   PubMed Web of Science ®Google Scholar
• Gomollón F, Dignass A, Annese V, et al. 3rd European evidence-based consensus on the diagnosis and management of Crohn’s disease 2016: part 1: diagnosis and medical management. J Crohns Colitis. 2017;11(1):3–25. doi:10.1093/ecco-jcc/jjw168
   PubMed Web of Science ®Google Scholar
• Yu L, Yang X, Xia L, et al. Infliximab preferentially induces clinical remission and mucosal healing in short course Crohn’s disease with luminal lesions through balancing abnormal immune response in gut mucosa. Mediators Inflamm. 2015;2015.
   Web of Science ®Google Scholar
• Bruining DH, Zimmermann EM, Loftus EV, et al. Consensus recommendations for evaluation, interpretation, and utilization of computed tomography and magnetic resonance enterography in patients with small bowel Crohn’s disease. Gastroenterology. 2018;154(4):1172–1194. doi:10.1053/j.gastro.2017.11.274
   PubMed Web of Science ®Google Scholar
• Nioche C, Orlhac F, Boughdad S, et al. LIFEx: a freeware for radiomic feature calculation in multimodality imaging to accelerate advances in the characterization of tumor heterogeneity. Cancer Res. 2018;78(16):4786–4789. doi:10.1158/0008-5472.CAN-18-0125
   PubMed Web of Science ®Google Scholar
• Hu Y, Zhao X, Zhang J, et al. Value of (18)F-FDG PET/CT radiomic features to distinguish solitary lung adenocarcinoma from tuberculosis. Eur J Nucl Med Mol Imaging. 2021;48(1):231–240. doi:10.1007/s00259-020-04924-6
   PubMed Web of Science ®Google Scholar
• Wang B. A two-stage peak alignment algorithm for two-dimensional gas chromatography time-of-flight mass spectrometry-based metabolomics. Comput Struct Biotechnol J. 2013;7:e201304002. doi:10.5936/csbj.201304002
   PubMedGoogle Scholar
• She Y, Zhang L, Zhu H, et al. The predictive value of CT-based radiomics in differentiating indolent from invasive lung adenocarcinoma in patients with pulmonary nodules. Eur Radiol. 2018;28(12):5121–5128. doi:10.1007/s00330-018-5509-9
   PubMed Web of Science ®Google Scholar
• Collins GS, Reitsma JB, Altman DG, et al. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. Circulation. 2015;131:211–219.
   PubMed Web of Science ®Google Scholar
• Feng T, Chen B, Li L, et al. Serum interleukin 9 levels predict disease severity and the clinical efficacy of infliximab in patients with Crohn’s disease. Inflamm Bowel Dis. 2017;23(10):1817–1824. doi:10.1097/MIB.0000000000001172
   PubMed Web of Science ®Google Scholar
• Boschetti G, Garnero P, Moussata D, et al. Accuracies of serum and fecal S100 proteins (calprotectin and calgranulin C) to predict the response to TNF antagonists in patients with Crohn’s disease. Inflamm Bowel Dis. 2015;21(2):331–336. doi:10.1097/MIB.0000000000000273
   PubMed Web of Science ®Google Scholar
• Sakuraba H, Ishiguro Y, Hasui K, et al. Prediction of maintained mucosal healing in patients with Crohn’s disease under treatment with infliximab using diffusion-weighted magnetic resonance imaging. Digestion. 2014;89(1):49–54. doi:10.1159/000356220
   PubMed Web of Science ®Google Scholar
• Ganeshan B, Miles KA. Quantifying tumour heterogeneity with CT. Cancer Imaging. 2013;13(1):140–149. doi:10.1102/1470-7330.2013.0015
   PubMed Web of Science ®Google Scholar
• Song B, Zhang G, Lu H, et al. Volumetric texture features from higher-order images for diagnosis of colon lesions via CT colonography. Int J Comput Assist Radiol Surg. 2014;9(6):1021–1031. doi:10.1007/s11548-014-0991-2
   PubMed Web of Science ®Google Scholar
• Huang YQ, Liang CH, He L, et al. Development and validation of a radiomics nomogram for preoperative prediction of lymph node metastasis in colorectal cancer. J Clin Oncol. 2016;34(18):2157–2164. doi:10.1200/JCO.2015.65.9128
   PubMed Web of Science ®Google Scholar
• Kim J, Choi SJ, Lee SH, et al. Predicting survival using pretreatment CT for patients with hepatocellular carcinoma treated with transarterial chemoembolization: comparison of models using radiomics. AJR Am J Roentgenol. 2018;211(5):1026–1034. doi:10.2214/AJR.18.19507
   PubMed Web of Science ®Google Scholar
• Dennie C, Thornhill R, Souza CA, et al. Quantitative texture analysis on pre-treatment computed tomography predicts local recurrence in stage I non-small cell lung cancer following stereotactic radiation therapy. Quant Imaging Med Surg. 2017;7(6):614–622. doi:10.21037/qims.2017.11.01
   PubMed Web of Science ®Google Scholar
• Wang X, Yuan M, Mi H, et al. The feasibility of differentiating colorectal cancer from normal and inflammatory thickening colon wall using CT texture analysis. Sci Rep. 2020;10(1):6346. doi:10.1038/s41598-020-62973-1
   PubMed Web of Science ®Google Scholar
• Horvat N, Tavares CC, Andrade AR, et al. Inter- and intraobserver agreement in computed tomography enterography in inflammatory bowel disease. World J Gastroenterol. 2016;22(45):10002–10008. doi:10.3748/wjg.v22.i45.10002
   PubMed Web of Science ®Google Scholar
• Hazlewood GS, Rezaie A, Borman M, et al. Comparative effectiveness of immunosuppressants and biologics for inducing and maintaining remission in Crohn’s disease: a network meta-analysis. Gastroenterology. 2015;148(2):344–354. doi:10.1053/j.gastro.2014.10.011
   PubMed Web of Science ®Google Scholar