A deep learning MR-based radiomic nomogram may predict survival for nasopharyngeal carcinoma patients with stage T3N1M0
Copyright © 2020 Elsevier B.V. All rights reserved..
PURPOSE: To estimate the prognostic value of deep learning (DL) magnetic resonance (MR)-based radiomics for stage T3N1M0 nasopharyngeal carcinoma (NPC) patients receiving induction chemotherapy (ICT) prior to concurrent chemoradiotherapy (CCRT).
METHODS: A total of 638 stage T3N1M0 NPC patients (training cohort: n = 447; test cohort: n = 191) were enrolled and underwent MRI scans before receiving ICT + CCRT. From the pretreatment MR images, DL-based radiomic signatures were developed to predict disease-free survival (DFS) in an end-to-end way. Incorporating independent clinical prognostic parameters and radiomic signatures, a radiomic nomogram was built through multivariable Cox proportional hazards method. The discriminative performance of the radiomic nomogram was assessed using the concordance index (C-index) and the Kaplan-Meier estimator.
RESULTS: Three DL-based radiomic signatures were significantly correlated with DFS in the training (C-index: 0.695-0.731, all p < 0.001) and test (C-index: 0.706-0.755, all p < 0.001) cohorts. Integrating radiomic signatures with clinical factors significantly improved the predictive value compared to the clinical model in the training (C-index: 0.771 vs. 0.640, p < 0.001) and test (C-index: 0.788 vs. 0.625, p = 0.001) cohorts. Furthermore, risk stratification using the radiomic nomogram demonstrated that the high-risk group exhibited short-lived DFS compared to the low-risk group in the training cohort (hazard ratio [HR]: 6.12, p < 0.001), which was validated in the test cohort (HR: 6.90, p < 0.001).
CONCLUSIONS: Our DL-based radiomic nomogram may serve as a noninvasive and useful tool for pretreatment prognostic prediction and risk stratification in stage T3N1M0 NPC.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2020 |
|---|---|
| Erschienen: |
2020 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:151 |
|---|---|
| Enthalten in: |
Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology - 151(2020) vom: 30. Okt., Seite 1-9 |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Zhong, Lian-Zhen [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
Deep learning |
|---|
| Anmerkungen: |
Date Completed 14.04.2021 Date Revised 14.04.2021 published: Print-Electronic Citation Status MEDLINE |
|---|
| doi: |
10.1016/j.radonc.2020.06.050 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM312120680 |
|---|
| LEADER | 01000naa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM312120680 | ||
| 003 | DE-627 | ||
| 005 | 20231225143729.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 231225s2020 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1016/j.radonc.2020.06.050 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1040.xml |
| 035 | |a (DE-627)NLM312120680 | ||
| 035 | |a (NLM)32634460 | ||
| 035 | |a (PII)S0167-8140(20)30387-X | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Zhong, Lian-Zhen |e verfasserin |4 aut | |
| 245 | 1 | 2 | |a A deep learning MR-based radiomic nomogram may predict survival for nasopharyngeal carcinoma patients with stage T3N1M0 |
| 264 | 1 | |c 2020 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Completed 14.04.2021 | ||
| 500 | |a Date Revised 14.04.2021 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Copyright © 2020 Elsevier B.V. All rights reserved. | ||
| 520 | |a PURPOSE: To estimate the prognostic value of deep learning (DL) magnetic resonance (MR)-based radiomics for stage T3N1M0 nasopharyngeal carcinoma (NPC) patients receiving induction chemotherapy (ICT) prior to concurrent chemoradiotherapy (CCRT) | ||
| 520 | |a METHODS: A total of 638 stage T3N1M0 NPC patients (training cohort: n = 447; test cohort: n = 191) were enrolled and underwent MRI scans before receiving ICT + CCRT. From the pretreatment MR images, DL-based radiomic signatures were developed to predict disease-free survival (DFS) in an end-to-end way. Incorporating independent clinical prognostic parameters and radiomic signatures, a radiomic nomogram was built through multivariable Cox proportional hazards method. The discriminative performance of the radiomic nomogram was assessed using the concordance index (C-index) and the Kaplan-Meier estimator | ||
| 520 | |a RESULTS: Three DL-based radiomic signatures were significantly correlated with DFS in the training (C-index: 0.695-0.731, all p < 0.001) and test (C-index: 0.706-0.755, all p < 0.001) cohorts. Integrating radiomic signatures with clinical factors significantly improved the predictive value compared to the clinical model in the training (C-index: 0.771 vs. 0.640, p < 0.001) and test (C-index: 0.788 vs. 0.625, p = 0.001) cohorts. Furthermore, risk stratification using the radiomic nomogram demonstrated that the high-risk group exhibited short-lived DFS compared to the low-risk group in the training cohort (hazard ratio [HR]: 6.12, p < 0.001), which was validated in the test cohort (HR: 6.90, p < 0.001) | ||
| 520 | |a CONCLUSIONS: Our DL-based radiomic nomogram may serve as a noninvasive and useful tool for pretreatment prognostic prediction and risk stratification in stage T3N1M0 NPC | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 650 | 4 | |a Deep learning | |
| 650 | 4 | |a Induction chemotherapy | |
| 650 | 4 | |a MRI-based treatment planning | |
| 650 | 4 | |a Nasopharyngeal cancer | |
| 650 | 4 | |a Survival analysis | |
| 700 | 1 | |a Fang, Xue-Liang |e verfasserin |4 aut | |
| 700 | 1 | |a Dong, Di |e verfasserin |4 aut | |
| 700 | 1 | |a Peng, Hao |e verfasserin |4 aut | |
| 700 | 1 | |a Fang, Meng-Jie |e verfasserin |4 aut | |
| 700 | 1 | |a Huang, Cheng-Long |e verfasserin |4 aut | |
| 700 | 1 | |a He, Bing-Xi |e verfasserin |4 aut | |
| 700 | 1 | |a Lin, Li |e verfasserin |4 aut | |
| 700 | 1 | |a Ma, Jun |e verfasserin |4 aut | |
| 700 | 1 | |a Tang, Ling-Long |e verfasserin |4 aut | |
| 700 | 1 | |a Tian, Jie |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology |d 1993 |g 151(2020) vom: 30. Okt., Seite 1-9 |w (DE-627)NLM012598305 |x 1879-0887 |7 nnns |
| 773 | 1 | 8 | |g volume:151 |g year:2020 |g day:30 |g month:10 |g pages:1-9 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1016/j.radonc.2020.06.050 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 151 |j 2020 |b 30 |c 10 |h 1-9 | ||