Radiomic analysis of MRI for prediction of response to induction chemotherapy in nasopharyngeal carcinoma patients
Copyright © 2023. Published by Elsevier Ltd..
AIM: To establish and validate radiomic models for response prediction to induction chemotherapy (IC) in nasopharyngeal carcinoma (NPC) using the radiomic features from pretreatment MRI.
MATERIALS AND METHODS: This retrospective analysis included 184 consecutive NPC patients, 132 in the primary cohort and 52 in the validation cohort. Radiomic features were derived from contrast-enhanced T1-weighted imaging (CE-T1) and T2-weighted imaging (T2-WI) for each subject. The radiomic features were then selected and combined with clinical characteristics to build radiomic models. The potential of the radiomic models was evaluated based on its discrimination and calibration. To measure the performance of these radiomic models in predicting the treatment response to IC in NPC, the area under the receiver operating characteristic curve (AUC), and sensitivity, specificity, and accuracy were used.
RESULTS: Four radiomic models were constructed in the present study including the radiomic signature of CE-T1, T2-WI, CE-T1 + T2-WI, and the radiomic nomogram of CE-T1. The radiomic signature of CE-T1 + T2-WI performed well in distinguishing response and non-response to IC in patients with NPC, which yielded an AUC of 0.940 (95% CI, 0.885-0.974), sensitivity of 83.1%, specificity of 91.8%, and accuracy of 87.1% in the primary cohort, and AUC of 0.952 (95% CI, 0.855-0.992), sensitivity of 74.2%, specificity of 95.2%, and accuracy of 82.7% in the validation cohort.
CONCLUSION: MRI-based radiomic models could be helpful for personalised risk stratification and treatment in NPC patients receiving IC.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:78 |
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| Enthalten in: |
Clinical radiology - 78(2023), 9 vom: 12. Sept., Seite e644-e653 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Wang, A [VerfasserIn] |
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| Anmerkungen: |
Date Completed 07.08.2023 Date Revised 08.08.2023 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1016/j.crad.2023.05.012 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM358333970 |
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| 245 | 1 | 0 | |a Radiomic analysis of MRI for prediction of response to induction chemotherapy in nasopharyngeal carcinoma patients |
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| 500 | |a Date Revised 08.08.2023 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Copyright © 2023. Published by Elsevier Ltd. | ||
| 520 | |a AIM: To establish and validate radiomic models for response prediction to induction chemotherapy (IC) in nasopharyngeal carcinoma (NPC) using the radiomic features from pretreatment MRI | ||
| 520 | |a MATERIALS AND METHODS: This retrospective analysis included 184 consecutive NPC patients, 132 in the primary cohort and 52 in the validation cohort. Radiomic features were derived from contrast-enhanced T1-weighted imaging (CE-T1) and T2-weighted imaging (T2-WI) for each subject. The radiomic features were then selected and combined with clinical characteristics to build radiomic models. The potential of the radiomic models was evaluated based on its discrimination and calibration. To measure the performance of these radiomic models in predicting the treatment response to IC in NPC, the area under the receiver operating characteristic curve (AUC), and sensitivity, specificity, and accuracy were used | ||
| 520 | |a RESULTS: Four radiomic models were constructed in the present study including the radiomic signature of CE-T1, T2-WI, CE-T1 + T2-WI, and the radiomic nomogram of CE-T1. The radiomic signature of CE-T1 + T2-WI performed well in distinguishing response and non-response to IC in patients with NPC, which yielded an AUC of 0.940 (95% CI, 0.885-0.974), sensitivity of 83.1%, specificity of 91.8%, and accuracy of 87.1% in the primary cohort, and AUC of 0.952 (95% CI, 0.855-0.992), sensitivity of 74.2%, specificity of 95.2%, and accuracy of 82.7% in the validation cohort | ||
| 520 | |a CONCLUSION: MRI-based radiomic models could be helpful for personalised risk stratification and treatment in NPC patients receiving IC | ||
| 650 | 4 | |a Journal Article | |
| 700 | 1 | |a Xu, H |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, C |e verfasserin |4 aut | |
| 700 | 1 | |a Ren, J |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, J |e verfasserin |4 aut | |
| 700 | 1 | |a Zhou, P |e verfasserin |4 aut | |
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