Deep Learning-Based Automatic CT Quantification of Coronavirus Disease 2019 Pneumonia : An International Collaborative Study
Copyright © 2022 Wolters Kluwer Health, Inc. All rights reserved..
OBJECTIVE: We aimed to develop and validate the automatic quantification of coronavirus disease 2019 (COVID-19) pneumonia on computed tomography (CT) images.
METHODS: This retrospective study included 176 chest CT scans of 131 COVID-19 patients from 14 Korean and Chinese institutions from January 23 to March 15, 2020. Two experienced radiologists semiautomatically drew pneumonia masks on CT images to develop the 2D U-Net for segmenting pneumonia. External validation was performed using Japanese (n = 101), Italian (n = 99), Radiopaedia (n = 9), and Chinese data sets (n = 10). The primary measures for the system's performance were correlation coefficients for extent (%) and weight (g) of pneumonia in comparison with visual CT scores or human-derived segmentation. Multivariable logistic regression analyses were performed to evaluate the association of the extent and weight with symptoms in the Japanese data set and composite outcome (respiratory failure and death) in the Spanish data set (n = 115).
RESULTS: In the internal test data set, the intraclass correlation coefficients between U-Net outputs and references for the extent and weight were 0.990 and 0.993. In the Japanese data set, the Pearson correlation coefficients between U-Net outputs and visual CT scores were 0.908 and 0.899. In the other external data sets, intraclass correlation coefficients were between 0.949-0.965 (extent) and between 0.978-0.993 (weight). Extent and weight in the top quartile were independently associated with symptoms (odds ratio, 5.523 and 10.561; P = 0.041 and 0.016) and the composite outcome (odds ratio, 9.365 and 7.085; P = 0.021 and P = 0.035).
CONCLUSIONS: Automatically quantified CT extent and weight of COVID-19 pneumonia were well correlated with human-derived references and independently associated with symptoms and prognosis in multinational external data sets.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2022 |
|---|---|
| Erschienen: |
2022 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:46 |
|---|---|
| Enthalten in: |
Journal of computer assisted tomography - 46(2022), 3 vom: 01. Mai, Seite 413-422 |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Yoo, Seung-Jin [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
|---|
| Anmerkungen: |
Date Completed 18.05.2022 Date Revised 20.05.2022 published: Print-Electronic Citation Status MEDLINE |
|---|
| doi: |
10.1097/RCT.0000000000001303 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM339353155 |
|---|
| LEADER | 01000naa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM339353155 | ||
| 003 | DE-627 | ||
| 005 | 20231226002717.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 231226s2022 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1097/RCT.0000000000001303 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1131.xml |
| 035 | |a (DE-627)NLM339353155 | ||
| 035 | |a (NLM)35405709 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Yoo, Seung-Jin |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Deep Learning-Based Automatic CT Quantification of Coronavirus Disease 2019 Pneumonia |b An International Collaborative Study |
| 264 | 1 | |c 2022 | |
| 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 18.05.2022 | ||
| 500 | |a Date Revised 20.05.2022 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Copyright © 2022 Wolters Kluwer Health, Inc. All rights reserved. | ||
| 520 | |a OBJECTIVE: We aimed to develop and validate the automatic quantification of coronavirus disease 2019 (COVID-19) pneumonia on computed tomography (CT) images | ||
| 520 | |a METHODS: This retrospective study included 176 chest CT scans of 131 COVID-19 patients from 14 Korean and Chinese institutions from January 23 to March 15, 2020. Two experienced radiologists semiautomatically drew pneumonia masks on CT images to develop the 2D U-Net for segmenting pneumonia. External validation was performed using Japanese (n = 101), Italian (n = 99), Radiopaedia (n = 9), and Chinese data sets (n = 10). The primary measures for the system's performance were correlation coefficients for extent (%) and weight (g) of pneumonia in comparison with visual CT scores or human-derived segmentation. Multivariable logistic regression analyses were performed to evaluate the association of the extent and weight with symptoms in the Japanese data set and composite outcome (respiratory failure and death) in the Spanish data set (n = 115) | ||
| 520 | |a RESULTS: In the internal test data set, the intraclass correlation coefficients between U-Net outputs and references for the extent and weight were 0.990 and 0.993. In the Japanese data set, the Pearson correlation coefficients between U-Net outputs and visual CT scores were 0.908 and 0.899. In the other external data sets, intraclass correlation coefficients were between 0.949-0.965 (extent) and between 0.978-0.993 (weight). Extent and weight in the top quartile were independently associated with symptoms (odds ratio, 5.523 and 10.561; P = 0.041 and 0.016) and the composite outcome (odds ratio, 9.365 and 7.085; P = 0.021 and P = 0.035) | ||
| 520 | |a CONCLUSIONS: Automatically quantified CT extent and weight of COVID-19 pneumonia were well correlated with human-derived references and independently associated with symptoms and prognosis in multinational external data sets | ||
| 650 | 4 | |a Journal Article | |
| 700 | 1 | |a Qi, Xiaolong |e verfasserin |4 aut | |
| 700 | 1 | |a Inui, Shohei |e verfasserin |4 aut | |
| 700 | 1 | |a Kim, Hyungjin |e verfasserin |4 aut | |
| 700 | 1 | |a Jeong, Yeon Joo |e verfasserin |4 aut | |
| 700 | 1 | |a Lee, Kyung Hee |e verfasserin |4 aut | |
| 700 | 1 | |a Lee, Young Kyung |e verfasserin |4 aut | |
| 700 | 1 | |a Lee, Bae Young |e verfasserin |4 aut | |
| 700 | 1 | |a Kim, Jin Yong |e verfasserin |4 aut | |
| 700 | 1 | |a Jin, Kwang Nam |e verfasserin |4 aut | |
| 700 | 1 | |a Lim, Jae-Kwang |e verfasserin |4 aut | |
| 700 | 1 | |a Kim, Yun-Hyeon |e verfasserin |4 aut | |
| 700 | 1 | |a Kim, Ki Beom |e verfasserin |4 aut | |
| 700 | 1 | |a Jiang, Zicheng |e verfasserin |4 aut | |
| 700 | 1 | |a Shao, Chuxiao |e verfasserin |4 aut | |
| 700 | 1 | |a Lei, Junqiang |e verfasserin |4 aut | |
| 700 | 1 | |a Zou, Shengqiang |e verfasserin |4 aut | |
| 700 | 1 | |a Pan, Hongqiu |e verfasserin |4 aut | |
| 700 | 1 | |a Gu, Ye |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Guo |e verfasserin |4 aut | |
| 700 | 1 | |a Goo, Jin Mo |e verfasserin |4 aut | |
| 700 | 1 | |a Yoon, Soon Ho |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Journal of computer assisted tomography |d 1985 |g 46(2022), 3 vom: 01. Mai, Seite 413-422 |w (DE-627)NLM00030963X |x 1532-3145 |7 nnns |
| 773 | 1 | 8 | |g volume:46 |g year:2022 |g number:3 |g day:01 |g month:05 |g pages:413-422 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1097/RCT.0000000000001303 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 46 |j 2022 |e 3 |b 01 |c 05 |h 413-422 | ||