CT-based radiomics signature of visceral adipose tissue and bowel lesions for identifying patients with Crohn's disease resistant to infliximab
© 2024. The Author(s)..
PURPOSE: To develop a CT-based radiomics model combining with VAT and bowel features to improve the predictive efficacy of IFX therapy on the basis of bowel model.
METHODS: This retrospective study included 231 CD patients (training cohort, n = 112; internal validation cohort, n = 48; external validation cohort, n = 71) from two tertiary centers. Machine-learning VAT model and bowel model were developed separately to identify CD patients with primary nonresponse to IFX. A comprehensive model incorporating VAT and bowel radiomics features was further established to verify whether CT features extracted from VAT would improve the predictive efficacy of bowel model. Area under the curve (AUC) and decision curve analysis were used to compare the prediction performance. Clinical utility was assessed by integrated differentiation improvement (IDI).
RESULTS: VAT model and bowel model exhibited comparable performance for identifying patients with primary nonresponse in both internal (AUC: VAT model vs bowel model, 0.737 (95% CI, 0.590-0.854) vs. 0.832 (95% CI, 0.750-0.896)) and external validation cohort [AUC: VAT model vs. bowel model, 0.714 (95% CI, 0.595-0.815) vs. 0.799 (95% CI, 0.687-0.885)), exhibiting a relatively good net benefit. The comprehensive model incorporating VAT into bowel model yielded a satisfactory predictive efficacy in both internal (AUC, 0.840 (95% CI, 0.706-0.930)) and external validation cohort (AUC, 0.833 (95% CI, 0.726-0.911)), significantly better than bowel alone (IDI = 4.2% and 3.7% in internal and external validation cohorts, both p < 0.05).
CONCLUSION: VAT has an effect on IFX treatment response. It improves the performance for identification of CD patients at high risk of primary nonresponse to IFX therapy with selected features from RM.
CRITICAL RELEVANCE STATEMENT: Our radiomics model (RM) for VAT-bowel analysis captured the pathophysiological changes occurring in VAT and whole bowel lesion, which could help to identify CD patients who would not response to infliximab at the beginning of therapy.
KEY POINTS: • Radiomics signatures with VAT and bowel alone or in combination predicting infliximab efficacy. • VAT features contribute to the prediction of IFX treatment efficacy. • Comprehensive model improved the performance compared with the bowel model alone.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:15 |
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| Enthalten in: |
Insights into imaging - 15(2024), 1 vom: 30. Jan., Seite 28 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Wang, Yangdi [VerfasserIn] |
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| Links: |
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| Themen: |
Computed tomography enterography |
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| Anmerkungen: |
Date Revised 02.02.2024 published: Electronic Citation Status PubMed-not-MEDLINE |
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| doi: |
10.1186/s13244-023-01581-9 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM367797577 |
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| 245 | 1 | 0 | |a CT-based radiomics signature of visceral adipose tissue and bowel lesions for identifying patients with Crohn's disease resistant to infliximab |
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| 500 | |a Date Revised 02.02.2024 | ||
| 500 | |a published: Electronic | ||
| 500 | |a Citation Status PubMed-not-MEDLINE | ||
| 520 | |a © 2024. The Author(s). | ||
| 520 | |a PURPOSE: To develop a CT-based radiomics model combining with VAT and bowel features to improve the predictive efficacy of IFX therapy on the basis of bowel model | ||
| 520 | |a METHODS: This retrospective study included 231 CD patients (training cohort, n = 112; internal validation cohort, n = 48; external validation cohort, n = 71) from two tertiary centers. Machine-learning VAT model and bowel model were developed separately to identify CD patients with primary nonresponse to IFX. A comprehensive model incorporating VAT and bowel radiomics features was further established to verify whether CT features extracted from VAT would improve the predictive efficacy of bowel model. Area under the curve (AUC) and decision curve analysis were used to compare the prediction performance. Clinical utility was assessed by integrated differentiation improvement (IDI) | ||
| 520 | |a RESULTS: VAT model and bowel model exhibited comparable performance for identifying patients with primary nonresponse in both internal (AUC: VAT model vs bowel model, 0.737 (95% CI, 0.590-0.854) vs. 0.832 (95% CI, 0.750-0.896)) and external validation cohort [AUC: VAT model vs. bowel model, 0.714 (95% CI, 0.595-0.815) vs. 0.799 (95% CI, 0.687-0.885)), exhibiting a relatively good net benefit. The comprehensive model incorporating VAT into bowel model yielded a satisfactory predictive efficacy in both internal (AUC, 0.840 (95% CI, 0.706-0.930)) and external validation cohort (AUC, 0.833 (95% CI, 0.726-0.911)), significantly better than bowel alone (IDI = 4.2% and 3.7% in internal and external validation cohorts, both p < 0.05) | ||
| 520 | |a CONCLUSION: VAT has an effect on IFX treatment response. It improves the performance for identification of CD patients at high risk of primary nonresponse to IFX therapy with selected features from RM | ||
| 520 | |a CRITICAL RELEVANCE STATEMENT: Our radiomics model (RM) for VAT-bowel analysis captured the pathophysiological changes occurring in VAT and whole bowel lesion, which could help to identify CD patients who would not response to infliximab at the beginning of therapy | ||
| 520 | |a KEY POINTS: • Radiomics signatures with VAT and bowel alone or in combination predicting infliximab efficacy. • VAT features contribute to the prediction of IFX treatment efficacy. • Comprehensive model improved the performance compared with the bowel model alone | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Computed tomography enterography | |
| 650 | 4 | |a Crohn’s disease | |
| 650 | 4 | |a Infliximab therapy | |
| 650 | 4 | |a Primary nonresponse | |
| 650 | 4 | |a Radiomics | |
| 700 | 1 | |a Luo, Zixin |e verfasserin |4 aut | |
| 700 | 1 | |a Zhou, Zhengran |e verfasserin |4 aut | |
| 700 | 1 | |a Zhong, Yingkui |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Ruonan |e verfasserin |4 aut | |
| 700 | 1 | |a Shen, Xiaodi |e verfasserin |4 aut | |
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| 700 | 1 | |a Huang, Qiapeng |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Haipeng |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Zhuya |e verfasserin |4 aut | |
| 700 | 1 | |a Mao, Ren |e verfasserin |4 aut | |
| 700 | 1 | |a Feng, Shi-Ting |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Xuehua |e verfasserin |4 aut | |
| 700 | 1 | |a Huang, Bingsheng |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Zhoulei |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Jian |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Zhihui |e verfasserin |4 aut | |
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