BACKGROUND: We established and evaluated a radiomics nomogram based on multislice computed tomography (MSCT) arterial phase contrast-enhanced images to distinguish between Crohn's disease (CD) and ulcerative colitis (UC) objectively, quantitatively, and reproducibly. METHODS: MSCT arterial phase-enhancement images of 165 lesions (99 CD, 66 UC) in 87 patients with inflammatory bowel disease (IBD) confirmed by endoscopy or surgical pathology were retrospectively analyzed. A total of 132 lesions (80%) were selected as the training cohort and 33 lesions (20%) as the test cohort. A total of 1648 radiomic features were extracted from each region of interest (ROI), and the Pearson correlation coefficient and tree-based method were used for feature selection. Five machine learning classifiers, including logistic regression (LR), support vector machine (SVM), random forest (RF), stochastic gradient descent (SGD), and linear discriminative analysis (LDA), were trained. The best classifier was evaluated and obtained, and the results were transformed into the Rscore. Three clinical factors were screened out from 8 factors by univariate analysis. The logistic regression method was used to synthesize the significant clinical factors and the Rscore to generate the nomogram, which was compared with the clinical model and LR model. RESULTS: Among all machine learning classifiers, LR performed the best (AUC =0.8077, accuracy =0.697, sensitivity =0.8, specificity =0.5385), SGD model had the second best performance (AUC =0.8, accuracy =0.6667, sensitivity =0.75, specificity =0.5385), and the DeLong test results showed that there was no significant difference between LR and SGD (P=0.465>0.05), while the other models performed poorly. Texture features had the greatest impact on classification results among all imaging features. The significant features of the LR model were used to calculate the Rscore. The 3 significant clinical factors were perienteric edema or inflammation, CT value of arterial phase-enhancement (AP-CT value), and lesion location. Finally, a nomogram was constructed based on the 3 significant clinical factors and the Rscore, whose AUC (0.8846) was much higher than that of the clinical model (0.6154) and the LR model (0.8077). CONCLUSIONS: The nomogram is expected to provide a new auxiliary tool for radiologists to quickly identify CD and UC. 2021 Annals of Translational Medicine. All rights reserved.
BACKGROUND: We established and evaluated a radiomics nomogram based on multislice computed tomography (MSCT) arterial phase contrast-enhanced images to distinguish between Crohn's disease (CD) and ulcerative colitis (UC) objectively, quantitatively, and reproducibly. METHODS: MSCT arterial phase-enhancement images of 165 lesions (99 CD, 66 UC) in 87 patients with inflammatory bowel disease (IBD) confirmed by endoscopy or surgical pathology were retrospectively analyzed. A total of 132 lesions (80%) were selected as the training cohort and 33 lesions (20%) as the test cohort. A total of 1648 radiomic features were extracted from each region of interest (ROI), and the Pearson correlation coefficient and tree-based method were used for feature selection. Five machine learning classifiers, including logistic regression (LR), support vector machine (SVM), random forest (RF), stochastic gradient descent (SGD), and linear discriminative analysis (LDA), were trained. The best classifier was evaluated and obtained, and the results were transformed into the Rscore. Three clinical factors were screened out from 8 factors by univariate analysis. The logistic regression method was used to synthesize the significant clinical factors and the Rscore to generate the nomogram, which was compared with the clinical model and LR model. RESULTS: Among all machine learning classifiers, LR performed the best (AUC =0.8077, accuracy =0.697, sensitivity =0.8, specificity =0.5385), SGD model had the second best performance (AUC =0.8, accuracy =0.6667, sensitivity =0.75, specificity =0.5385), and the DeLong test results showed that there was no significant difference between LR and SGD (P=0.465>0.05), while the other models performed poorly. Texture features had the greatest impact on classification results among all imaging features. The significant features of the LR model were used to calculate the Rscore. The 3 significant clinical factors were perienteric edema or inflammation, CT value of arterial phase-enhancement (AP-CT value), and lesion location. Finally, a nomogram was constructed based on the 3 significant clinical factors and the Rscore, whose AUC (0.8846) was much higher than that of the clinical model (0.6154) and the LR model (0.8077). CONCLUSIONS: The nomogram is expected to provide a new auxiliary tool for radiologists to quickly identify CD and UC. 2021 Annals of Translational Medicine. All rights reserved.
Authors: Christian Maaser; Andreas Sturm; Stephan R Vavricka; Torsten Kucharzik; Gionata Fiorino; Vito Annese; Emma Calabrese; Daniel C Baumgart; Dominik Bettenworth; Paula Borralho Nunes; Johan Burisch; Fabiana Castiglione; Rami Eliakim; Pierre Ellul; Yago González-Lama; Hannah Gordon; Steve Halligan; Konstantinos Katsanos; Uri Kopylov; Paulo G Kotze; Eduards Krustinš; Andrea Laghi; Jimmy K Limdi; Florian Rieder; Jordi Rimola; Stuart A Taylor; Damian Tolan; Patrick van Rheenen; Bram Verstockt; Jaap Stoker Journal: J Crohns Colitis Date: 2019-02-01 Impact factor: 9.071
Authors: Flavius F Guglielmo; Sudha A Anupindi; Joel G Fletcher; Mahmoud M Al-Hawary; Jonathan R Dillman; David J Grand; David H Bruining; Manjil Chatterji; Kassa Darge; Jeff L Fidler; Namita S Gandhi; Michael S Gee; Joseph R Grajo; Chenchan Huang; Tracy A Jaffe; Seong Ho Park; Jordi Rimola; Jorge A Soto; Bachir Taouli; Stuart A Taylor; Mark E Baker Journal: Radiographics Date: 2020-01-17 Impact factor: 5.333
Authors: Fernando Magro; Paolo Gionchetti; Rami Eliakim; Sandro Ardizzone; Alessandro Armuzzi; Manuel Barreiro-de Acosta; Johan Burisch; Krisztina B Gecse; Ailsa L Hart; Pieter Hindryckx; Cord Langner; Jimmy K Limdi; Gianluca Pellino; Edyta Zagórowicz; Tim Raine; Marcus Harbord; Florian Rieder Journal: J Crohns Colitis Date: 2017-06-01 Impact factor: 10.020