Radiomics signature based on computed tomography images for the preoperative prediction of lymph node metastasis at individual stations in gastric cancer : A multicenter study
Copyright © 2021 Elsevier B.V. All rights reserved..
BACKGROUND: Specific diagnosis and treatment of gastric cancer (GC) require accurate preoperative predictions of lymph node metastasis (LNM) at individual stations, such as estimating the extent of lymph node dissection. This study aimed to develop a radiomics signature based on preoperative computed tomography (CT) images, for predicting the LNM status at each individual station.
METHODS: We enrolled 1506 GC patients retrospectively from two centers as training (531) and external (975) validation cohorts, and recruited 112 patients prospectively from a single center as prospective validation cohort. Radiomics features were extracted from preoperative CT images and integrated with clinical characteristics to construct nomograms for LNM prediction at individual lymph node stations. Performance of the nomograms was assessed through calibration, discrimination and clinical usefulness.
RESULTS: In training, external and prospective validation cohorts, radiomics signature was significantly associated with LNM status. Moreover, radiomics signature was an independent predictor of LNM status in the multivariable logistic regression analysis. The radiomics nomograms revealed good prediction performances, with AUCs of 0.716-0.871 in the training cohort, 0.678-0.768 in the external validation cohort and 0.700-0.841 in the prospective validation cohort for 12 nodal stations. The nomograms demonstrated a significant agreement between the actual probability and predictive probability in calibration curves. Decision curve analysis showed that nomograms had better net benefit than clinicopathologic characteristics.
CONCLUSION: Radiomics nomograms for individual lymph node stations presented good prediction accuracy, which could provide important information for individual diagnosis and treatment of gastric cancer.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2021 |
|---|---|
| Erschienen: |
2021 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:165 |
|---|---|
| Enthalten in: |
Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology - 165(2021) vom: 01. Dez., Seite 179-190 |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Sun, Zepang [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
Computed tomography |
|---|
| Anmerkungen: |
Date Completed 30.12.2021 Date Revised 30.12.2021 published: Print-Electronic Citation Status MEDLINE |
|---|
| doi: |
10.1016/j.radonc.2021.11.003 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM333124138 |
|---|
| LEADER | 01000naa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM333124138 | ||
| 003 | DE-627 | ||
| 005 | 20231225221009.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 231225s2021 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1016/j.radonc.2021.11.003 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1110.xml |
| 035 | |a (DE-627)NLM333124138 | ||
| 035 | |a (NLM)34774652 | ||
| 035 | |a (PII)S0167-8140(21)08795-8 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Sun, Zepang |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Radiomics signature based on computed tomography images for the preoperative prediction of lymph node metastasis at individual stations in gastric cancer |b A multicenter study |
| 264 | 1 | |c 2021 | |
| 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 30.12.2021 | ||
| 500 | |a Date Revised 30.12.2021 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Copyright © 2021 Elsevier B.V. All rights reserved. | ||
| 520 | |a BACKGROUND: Specific diagnosis and treatment of gastric cancer (GC) require accurate preoperative predictions of lymph node metastasis (LNM) at individual stations, such as estimating the extent of lymph node dissection. This study aimed to develop a radiomics signature based on preoperative computed tomography (CT) images, for predicting the LNM status at each individual station | ||
| 520 | |a METHODS: We enrolled 1506 GC patients retrospectively from two centers as training (531) and external (975) validation cohorts, and recruited 112 patients prospectively from a single center as prospective validation cohort. Radiomics features were extracted from preoperative CT images and integrated with clinical characteristics to construct nomograms for LNM prediction at individual lymph node stations. Performance of the nomograms was assessed through calibration, discrimination and clinical usefulness | ||
| 520 | |a RESULTS: In training, external and prospective validation cohorts, radiomics signature was significantly associated with LNM status. Moreover, radiomics signature was an independent predictor of LNM status in the multivariable logistic regression analysis. The radiomics nomograms revealed good prediction performances, with AUCs of 0.716-0.871 in the training cohort, 0.678-0.768 in the external validation cohort and 0.700-0.841 in the prospective validation cohort for 12 nodal stations. The nomograms demonstrated a significant agreement between the actual probability and predictive probability in calibration curves. Decision curve analysis showed that nomograms had better net benefit than clinicopathologic characteristics | ||
| 520 | |a CONCLUSION: Radiomics nomograms for individual lymph node stations presented good prediction accuracy, which could provide important information for individual diagnosis and treatment of gastric cancer | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Multicenter Study | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 650 | 4 | |a Computed tomography | |
| 650 | 4 | |a Gastric cancer | |
| 650 | 4 | |a Individual stations prediction | |
| 650 | 4 | |a Lymph node metastasis | |
| 650 | 4 | |a Radiomics | |
| 700 | 1 | |a Jiang, Yuming |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Chuanli |e verfasserin |4 aut | |
| 700 | 1 | |a Zheng, Huan |e verfasserin |4 aut | |
| 700 | 1 | |a Huang, Weicai |e verfasserin |4 aut | |
| 700 | 1 | |a Xu, Benjamin |e verfasserin |4 aut | |
| 700 | 1 | |a Tang, Weijing |e verfasserin |4 aut | |
| 700 | 1 | |a Yuan, Qingyu |e verfasserin |4 aut | |
| 700 | 1 | |a Zhou, Kangneng |e verfasserin |4 aut | |
| 700 | 1 | |a Liang, Xiaokun |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Hao |e verfasserin |4 aut | |
| 700 | 1 | |a Han, Zhen |e verfasserin |4 aut | |
| 700 | 1 | |a Feng, Hao |e verfasserin |4 aut | |
| 700 | 1 | |a Yu, Shitong |e verfasserin |4 aut | |
| 700 | 1 | |a Hu, Yanfeng |e verfasserin |4 aut | |
| 700 | 1 | |a Yu, Jiang |e verfasserin |4 aut | |
| 700 | 1 | |a Zhou, Zhiwei |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Wei |e verfasserin |4 aut | |
| 700 | 1 | |a Xu, Yikai |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Guoxin |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 165(2021) vom: 01. Dez., Seite 179-190 |w (DE-627)NLM012598305 |x 1879-0887 |7 nnns |
| 773 | 1 | 8 | |g volume:165 |g year:2021 |g day:01 |g month:12 |g pages:179-190 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1016/j.radonc.2021.11.003 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 165 |j 2021 |b 01 |c 12 |h 179-190 | ||