MRI-Based Deep-Learning Model for Distant Metastasis-Free Survival in Locoregionally Advanced Nasopharyngeal Carcinoma
© 2020 International Society for Magnetic Resonance in Medicine..
BACKGROUND: Distant metastasis is the primary cause of treatment failure in locoregionally advanced nasopharyngeal carcinoma (LANPC).
PURPOSE: To develop a model to evaluate distant metastasis-free survival (DMFS) in LANPC and to explore the value of additional chemotherapy to concurrent chemoradiotherapy (CCRT) for different risk groups.
STUDY TYPE: Retrospective.
POPULATION: In all, 233 patients with biopsy-confirmed nasopharyngeal carcinoma (NPC) from two hospitals.
FIELD STRENGTH: 1.5T and 3T.
SEQUENCE: Axial T2 -weighted (T2 -w) and contrast-enhanced T1 -weighted (CET1 -w) images.
ASSESSMENT: Deep learning was used to build a model based on MRI images (including axial T2 -w and CET1 -w images) and clinical variables. Hospital 1 patients were randomly divided into training (n = 169) and validation (n = 19) cohorts; Hospital 2 patients were assigned to a testing cohort (n = 45). LANPC patients were divided into low- and high-risk groups according to their DMFS (P < 0.05). Kaplan-Meier survival analysis was performed to compare the DMFS of different risk groups and subgroup analysis was performed to compare patients treated with CCRT alone and treated with additional chemotherapy to CCRT in different risk groups, respectively.
STATISTICAL TESTS: Univariate analysis was performed to identify significant clinical variables. The area under the receiver operating characteristic (ROC) curve (AUC) was used to assess the model performance.
RESULTS: Our deep-learning model integrating the deep-learning signature, node (N) stage (from TNM staging), plasma Epstein-Barr virus (EBV)-DNA, and treatment regimens yielded an AUC of 0.796 (95% confidence interval [CI]: 0.729-0.863), 0.795 (95% CI: 0.540-1.000), and 0.808 (95% CI: 0.654-0.962) in the training, internal validation, and external testing cohorts, respectively. Low-risk patients treated with CCRT alone had longer DMFS than patients treated with additional chemotherapy to CCRT (P < 0.05).
DATA CONCLUSION: The proposed deep-learning model, based on MRI features and clinical variates, facilitated the prediction of DMFS in LANPC patients.
LEVEL OF EVIDENCE: 3.
TECHNICAL EFFICACY STAGE: 4.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2021 |
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| Erschienen: |
2021 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:53 |
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| Enthalten in: |
Journal of magnetic resonance imaging : JMRI - 53(2021), 1 vom: 09. Jan., Seite 167-178 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Zhang, Lu [VerfasserIn] |
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| Links: |
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| Themen: |
Chemoradiotherapy |
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| Anmerkungen: |
Date Completed 14.05.2021 Date Revised 14.05.2021 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1002/jmri.27308 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM313513066 |
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| 100 | 1 | |a Zhang, Lu |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a MRI-Based Deep-Learning Model for Distant Metastasis-Free Survival in Locoregionally Advanced Nasopharyngeal Carcinoma |
| 264 | 1 | |c 2021 | |
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| 500 | |a Date Completed 14.05.2021 | ||
| 500 | |a Date Revised 14.05.2021 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a © 2020 International Society for Magnetic Resonance in Medicine. | ||
| 520 | |a BACKGROUND: Distant metastasis is the primary cause of treatment failure in locoregionally advanced nasopharyngeal carcinoma (LANPC) | ||
| 520 | |a PURPOSE: To develop a model to evaluate distant metastasis-free survival (DMFS) in LANPC and to explore the value of additional chemotherapy to concurrent chemoradiotherapy (CCRT) for different risk groups | ||
| 520 | |a STUDY TYPE: Retrospective | ||
| 520 | |a POPULATION: In all, 233 patients with biopsy-confirmed nasopharyngeal carcinoma (NPC) from two hospitals | ||
| 520 | |a FIELD STRENGTH: 1.5T and 3T | ||
| 520 | |a SEQUENCE: Axial T2 -weighted (T2 -w) and contrast-enhanced T1 -weighted (CET1 -w) images | ||
| 520 | |a ASSESSMENT: Deep learning was used to build a model based on MRI images (including axial T2 -w and CET1 -w images) and clinical variables. Hospital 1 patients were randomly divided into training (n = 169) and validation (n = 19) cohorts; Hospital 2 patients were assigned to a testing cohort (n = 45). LANPC patients were divided into low- and high-risk groups according to their DMFS (P < 0.05). Kaplan-Meier survival analysis was performed to compare the DMFS of different risk groups and subgroup analysis was performed to compare patients treated with CCRT alone and treated with additional chemotherapy to CCRT in different risk groups, respectively | ||
| 520 | |a STATISTICAL TESTS: Univariate analysis was performed to identify significant clinical variables. The area under the receiver operating characteristic (ROC) curve (AUC) was used to assess the model performance | ||
| 520 | |a RESULTS: Our deep-learning model integrating the deep-learning signature, node (N) stage (from TNM staging), plasma Epstein-Barr virus (EBV)-DNA, and treatment regimens yielded an AUC of 0.796 (95% confidence interval [CI]: 0.729-0.863), 0.795 (95% CI: 0.540-1.000), and 0.808 (95% CI: 0.654-0.962) in the training, internal validation, and external testing cohorts, respectively. Low-risk patients treated with CCRT alone had longer DMFS than patients treated with additional chemotherapy to CCRT (P < 0.05) | ||
| 520 | |a DATA CONCLUSION: The proposed deep-learning model, based on MRI features and clinical variates, facilitated the prediction of DMFS in LANPC patients | ||
| 520 | |a LEVEL OF EVIDENCE: 3 | ||
| 520 | |a TECHNICAL EFFICACY STAGE: 4 | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Randomized Controlled Trial | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 650 | 4 | |a chemoradiotherapy | |
| 650 | 4 | |a deep learning | |
| 650 | 4 | |a distant metastasis-free survival | |
| 650 | 4 | |a induction chemotherapy | |
| 650 | 4 | |a nasopharyngeal carcinoma | |
| 700 | 1 | |a Wu, Xiangjun |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Jing |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Bin |e verfasserin |4 aut | |
| 700 | 1 | |a Mo, Xiaokai |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Qiuying |e verfasserin |4 aut | |
| 700 | 1 | |a Fang, Jin |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Fei |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Minmin |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Zhuozhi |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Shuyi |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Luyan |e verfasserin |4 aut | |
| 700 | 1 | |a You, Jingjing |e verfasserin |4 aut | |
| 700 | 1 | |a Jin, Zhe |e verfasserin |4 aut | |
| 700 | 1 | |a Tang, Binghang |e verfasserin |4 aut | |
| 700 | 1 | |a Dong, Di |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Shuixing |e verfasserin |4 aut | |
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