Deep learning integration of chest computed tomography and plasma proteomics to identify novel aspects of severe COVID-19 pneumonia
Background: Heterogeneity is a critical characteristic of severe coronavirus disease 2019 (COVID-19) pneumonia. Integrating chest computed tomography (CT) imaging and plasma proteomics holds the potential to elucidate Image-Expression Axes (IEAs) that can effectively address this disease heterogenei...
Saved in:
| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-07-01
|
| Series: | Journal of Intensive Medicine |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667100X2400121X |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849426779111948288 |
|---|---|
| author | Yucai Hong Lin Chen Yang Yu Ziyue Zhao Ronghua Wu Rui Gong Yandong Cheng Lingmin Yuan Shaojun Zheng Cheng Zheng Ronghai Lin Jianping Chen Kangwei Sun Ping Xu Li Ye Chaoting Han Xihao Zhou Yaqing Liu Jianhua Yu Yaqin Zheng Jie Yang Jiajie Huang Juan Chen Junjie Fang Chensong Chen Bo Fan Honglong Fang Baning Ye Xiyun Chen Xiaoli Qian Junxiang Chen Haitao Yu Jun Zhang Xi-Ming Pan Yi-Xing Zhan You-Hai Zheng Zhang-Hong Huang Chao Zhong Ning Liu Hongying Ni Gengsheng Zhang Zhongheng Zhang |
| author_facet | Yucai Hong Lin Chen Yang Yu Ziyue Zhao Ronghua Wu Rui Gong Yandong Cheng Lingmin Yuan Shaojun Zheng Cheng Zheng Ronghai Lin Jianping Chen Kangwei Sun Ping Xu Li Ye Chaoting Han Xihao Zhou Yaqing Liu Jianhua Yu Yaqin Zheng Jie Yang Jiajie Huang Juan Chen Junjie Fang Chensong Chen Bo Fan Honglong Fang Baning Ye Xiyun Chen Xiaoli Qian Junxiang Chen Haitao Yu Jun Zhang Xi-Ming Pan Yi-Xing Zhan You-Hai Zheng Zhang-Hong Huang Chao Zhong Ning Liu Hongying Ni Gengsheng Zhang Zhongheng Zhang |
| author_sort | Yucai Hong |
| collection | DOAJ |
| description | Background: Heterogeneity is a critical characteristic of severe coronavirus disease 2019 (COVID-19) pneumonia. Integrating chest computed tomography (CT) imaging and plasma proteomics holds the potential to elucidate Image-Expression Axes (IEAs) that can effectively address this disease heterogeneity. Methods: A cohort of subjects diagnosed with severe COVID-19 pneumonia at 12 participating hospitals between December 2022 and March 2023 was prospectively screened for eligibility. Context-aware self-supervised representation learning (CSRL) was employed to extract intricate features from CT images. Quantification of plasma proteins was achieved using the Olink® inflammation panel. A deep learning model was meticulously trained, with CSRL features serving as input and the proteomic data as the target. This trained model facilitated the construction of IEAs, offering a representation of the underlying disease heterogeneity. The potential of these IEAs for prognostic and predictive enrichment was subsequently explored via conventional regression models. Results: The study cohort comprised 1979 eligible patients, who were stratified into a training set of 630 individuals and a testing set of 1349 individuals. Three distinct IEAs were identified: IEA1 was correlated with shock conditions, IEA2 was associated with the systemic inflammatory response syndrome (SIRS), and IEA3 was reflective of the coagulation profile. Notably, IEA1 (odds ratio [OR]= 0.52, 95 % confidence interval [CI]: 0.40 to 0.67, P < 0.001) and IEA2 (OR=0.74, 95 % CI: 0.62 to 0.90, P=0.002) exhibited significant associations with the risk of mortality. Intriguingly, patients characterized by lower IEA1 values (<-2, indicative of more severe shock) demonstrated a reduced mortality risk when administered with steroids. Conversely, patients with higher IEA2 values seemed to benefit from a judicious approach to fluid infusion. Conclusions: Our comprehensive approach, seamlessly integrating advanced deep learning techniques, proteomic profiling, and clinical data, has unraveled intricate interdependencies between IEAs, protein abundance patterns, therapeutic interventions, and ultimate patient outcomes in the context of severe COVID-19 pneumonia. These discoveries make a significant contribution to the rapidly advancing field of precision medicine, paving the way for tailored therapeutic strategies that can significantly impact patient care. |
| format | Article |
| id | doaj-art-b5ca33019a124111b36d2b8f2232bfc3 |
| institution | Kabale University |
| issn | 2667-100X |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Intensive Medicine |
| spelling | doaj-art-b5ca33019a124111b36d2b8f2232bfc32025-08-20T03:29:15ZengElsevierJournal of Intensive Medicine2667-100X2025-07-015325226110.1016/j.jointm.2024.11.001Deep learning integration of chest computed tomography and plasma proteomics to identify novel aspects of severe COVID-19 pneumoniaYucai Hong0Lin Chen1Yang Yu2Ziyue Zhao3Ronghua Wu4Rui Gong5Yandong Cheng6Lingmin Yuan7Shaojun Zheng8Cheng Zheng9Ronghai Lin10Jianping Chen11Kangwei Sun12Ping Xu13Li Ye14Chaoting Han15Xihao Zhou16Yaqing Liu17Jianhua Yu18Yaqin Zheng19Jie Yang20Jiajie Huang21Juan Chen22Junjie Fang23Chensong Chen24Bo Fan25Honglong Fang26Baning Ye27Xiyun Chen28Xiaoli Qian29Junxiang Chen30Haitao Yu31Jun Zhang32Xi-Ming Pan33Yi-Xing Zhan34You-Hai Zheng35Zhang-Hong Huang36Chao Zhong37Ning Liu38Hongying Ni39Gengsheng Zhang40Zhongheng Zhang41Department of Emergency Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, ChinaDepartment of Critical Care Medicine, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China; Department of Neurosurgery, Neurological Intensive Care Unit, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, Jinhua, Zhejiang, ChinaDepartment of Critical Care Medicine, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, ChinaDepartment of Clinical Medicine, Zhejiang University School of Medicine, Hangzhou, Zhejiang, ChinaDepartment of Radiology, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, Jinhua, Zhejiang, ChinaDepartment of Laboratory, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, Jinhua, Zhejiang, ChinaDepartment of Critical Care Medicine, Longyou County People's Hospital, Quzhou, Zhejiang, ChinaDepartment of Critical Care Medicine, Longyou County People's Hospital, Quzhou, Zhejiang, ChinaEmergency Department, Longyou County People's Hospital, Quzhou, Zhejiang, ChinaDepartment of Critical Care Medicine, Taizhou Municipal Hospital, Taizhou, Zhejiang, ChinaDepartment of Critical Care Medicine, Taizhou Municipal Hospital, Taizhou, Zhejiang, ChinaEmergency Department, Dongyang People’ Hospital of Wenzhou Medical University, Jinhua, Zhejiang, ChinaEmergency Department, Dongyang People’ Hospital of Wenzhou Medical University, Jinhua, Zhejiang, ChinaEmergency Department, Zigong Fourth People's Hospital, Zigong, Sichuan, ChinaEmergency Department, Fushun People's Hospital, Fushun, Liaoning, ChinaEmergency Department, Zigong Fourth People's Hospital, Zigong, Sichuan, ChinaDepartment of Clinical Laboratory, Fushun People's Hospital, Fushun, Liaoning, ChinaIntensive care unit, Longquan People's Hospital, Lishui, Zhejiang, ChinaIntensive care unit, Longquan People's Hospital, Lishui, Zhejiang, ChinaClinical Laboratory, Longquan People's Hospital, Lishui, Zhejiang, ChinaDepartment of Emergency Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, ChinaDepartment of Emergency Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, ChinaDepartment of Critical Care Medicine, The Affiliated Xiangshan Hospital of Wenzhou Medical University, Ningbo, Zhejiang, ChinaDepartment of Critical Care Medicine, The Affiliated Xiangshan Hospital of Wenzhou Medical University, Ningbo, Zhejiang, ChinaDepartment of Critical Care Medicine, The Affiliated Xiangshan Hospital of Wenzhou Medical University, Ningbo, Zhejiang, ChinaDepartment of Respiratory and Critical Care Medicine, First People's Hospital of Jiashan, Jiaxing, Zhejiang, ChinaDepartment of Critical Care Medicine, Quzhou People's Hospital, Quzhou, Zhejiang, ChinaDepartment of Critical Care Medicine, Guizhou Provincial People's Hospital, Guiyang, Guizhou, ChinaDepartment of Geriatric Rehabilitation, Tiantai New City Orthopedics and Traumatology Hospital, Tiantai, Taizhou, Zhejiang, ChinaDepartment of Respiratory and Critical Care Medicine, Xiaoshan District Second People's Hospital, Hangzhou, Zhejiang, ChinaDepartment of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USADepartment of Clinical Laboratory, Key Laboratory of Precision Medicine in Diagnosis and Monitoring Research of Zhejiang Province, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, ChinaDepartment of Clinical Laboratory, Key Laboratory of Precision Medicine in Diagnosis and Monitoring Research of Zhejiang Province, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, ChinaDepartment of Emergency Medicine, The People's Hospital of Suichang County, Lishui, Zhejiang, ChinaDepartment of Emergency Medicine, The People's Hospital of Suichang County, Lishui, Zhejiang, ChinaDepartment of Emergency Medicine, The People's Hospital of Suichang County, Lishui, Zhejiang, ChinaIntensive Care Unit, The People's Hospital of Suichang County, Lishui, Zhejiang, ChinaIntensive care unit, Ninghai First Hospital, Ningbo, Zhejiang, ChinaDepartment of Emergency Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, ChinaDepartment of Critical Care Medicine, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, Jinhua, Zhejiang, China; Corresponding authors: Hongying Ni, Department of Critical Care Medicine, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, Jinhua, Zhejiang 321000, China. Gengsheng Zhang, Department of Critical Care Medicine, Second Affiliated Hospital, Zhejiang University School of Medicine, Key Laboratory of Multiple Organ Failure (Zhejiang University), Ministry of Education, Hangzhou, Zhejiang 310000, China. Zhongheng Zhang, Department of Emergency Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310000, China.Department of Critical Care Medicine, Second Affiliated Hospital, Zhejiang University School of Medicine, Key Laboratory of Multiple Organ Failure (Zhejiang University), Ministry of Education, Hangzhou, Zhejiang, China; Corresponding authors: Hongying Ni, Department of Critical Care Medicine, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, Jinhua, Zhejiang 321000, China. Gengsheng Zhang, Department of Critical Care Medicine, Second Affiliated Hospital, Zhejiang University School of Medicine, Key Laboratory of Multiple Organ Failure (Zhejiang University), Ministry of Education, Hangzhou, Zhejiang 310000, China. Zhongheng Zhang, Department of Emergency Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310000, China.Department of Emergency Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China; Provincial Key Laboratory of Precise Diagnosis and Treatment of Abdominal Infection, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China; School of Medicine, Shaoxing University, Shaoxing, Zhejiang, China.; Corresponding authors: Hongying Ni, Department of Critical Care Medicine, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, Jinhua, Zhejiang 321000, China. Gengsheng Zhang, Department of Critical Care Medicine, Second Affiliated Hospital, Zhejiang University School of Medicine, Key Laboratory of Multiple Organ Failure (Zhejiang University), Ministry of Education, Hangzhou, Zhejiang 310000, China. Zhongheng Zhang, Department of Emergency Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310000, China.Background: Heterogeneity is a critical characteristic of severe coronavirus disease 2019 (COVID-19) pneumonia. Integrating chest computed tomography (CT) imaging and plasma proteomics holds the potential to elucidate Image-Expression Axes (IEAs) that can effectively address this disease heterogeneity. Methods: A cohort of subjects diagnosed with severe COVID-19 pneumonia at 12 participating hospitals between December 2022 and March 2023 was prospectively screened for eligibility. Context-aware self-supervised representation learning (CSRL) was employed to extract intricate features from CT images. Quantification of plasma proteins was achieved using the Olink® inflammation panel. A deep learning model was meticulously trained, with CSRL features serving as input and the proteomic data as the target. This trained model facilitated the construction of IEAs, offering a representation of the underlying disease heterogeneity. The potential of these IEAs for prognostic and predictive enrichment was subsequently explored via conventional regression models. Results: The study cohort comprised 1979 eligible patients, who were stratified into a training set of 630 individuals and a testing set of 1349 individuals. Three distinct IEAs were identified: IEA1 was correlated with shock conditions, IEA2 was associated with the systemic inflammatory response syndrome (SIRS), and IEA3 was reflective of the coagulation profile. Notably, IEA1 (odds ratio [OR]= 0.52, 95 % confidence interval [CI]: 0.40 to 0.67, P < 0.001) and IEA2 (OR=0.74, 95 % CI: 0.62 to 0.90, P=0.002) exhibited significant associations with the risk of mortality. Intriguingly, patients characterized by lower IEA1 values (<-2, indicative of more severe shock) demonstrated a reduced mortality risk when administered with steroids. Conversely, patients with higher IEA2 values seemed to benefit from a judicious approach to fluid infusion. Conclusions: Our comprehensive approach, seamlessly integrating advanced deep learning techniques, proteomic profiling, and clinical data, has unraveled intricate interdependencies between IEAs, protein abundance patterns, therapeutic interventions, and ultimate patient outcomes in the context of severe COVID-19 pneumonia. These discoveries make a significant contribution to the rapidly advancing field of precision medicine, paving the way for tailored therapeutic strategies that can significantly impact patient care.http://www.sciencedirect.com/science/article/pii/S2667100X2400121XCovid-19Systemic inflammatory response syndromeHeterogeneitySelf-supervised representation learning |
| spellingShingle | Yucai Hong Lin Chen Yang Yu Ziyue Zhao Ronghua Wu Rui Gong Yandong Cheng Lingmin Yuan Shaojun Zheng Cheng Zheng Ronghai Lin Jianping Chen Kangwei Sun Ping Xu Li Ye Chaoting Han Xihao Zhou Yaqing Liu Jianhua Yu Yaqin Zheng Jie Yang Jiajie Huang Juan Chen Junjie Fang Chensong Chen Bo Fan Honglong Fang Baning Ye Xiyun Chen Xiaoli Qian Junxiang Chen Haitao Yu Jun Zhang Xi-Ming Pan Yi-Xing Zhan You-Hai Zheng Zhang-Hong Huang Chao Zhong Ning Liu Hongying Ni Gengsheng Zhang Zhongheng Zhang Deep learning integration of chest computed tomography and plasma proteomics to identify novel aspects of severe COVID-19 pneumonia Journal of Intensive Medicine Covid-19 Systemic inflammatory response syndrome Heterogeneity Self-supervised representation learning |
| title | Deep learning integration of chest computed tomography and plasma proteomics to identify novel aspects of severe COVID-19 pneumonia |
| title_full | Deep learning integration of chest computed tomography and plasma proteomics to identify novel aspects of severe COVID-19 pneumonia |
| title_fullStr | Deep learning integration of chest computed tomography and plasma proteomics to identify novel aspects of severe COVID-19 pneumonia |
| title_full_unstemmed | Deep learning integration of chest computed tomography and plasma proteomics to identify novel aspects of severe COVID-19 pneumonia |
| title_short | Deep learning integration of chest computed tomography and plasma proteomics to identify novel aspects of severe COVID-19 pneumonia |
| title_sort | deep learning integration of chest computed tomography and plasma proteomics to identify novel aspects of severe covid 19 pneumonia |
| topic | Covid-19 Systemic inflammatory response syndrome Heterogeneity Self-supervised representation learning |
| url | http://www.sciencedirect.com/science/article/pii/S2667100X2400121X |
| work_keys_str_mv | AT yucaihong deeplearningintegrationofchestcomputedtomographyandplasmaproteomicstoidentifynovelaspectsofseverecovid19pneumonia AT linchen deeplearningintegrationofchestcomputedtomographyandplasmaproteomicstoidentifynovelaspectsofseverecovid19pneumonia AT yangyu deeplearningintegrationofchestcomputedtomographyandplasmaproteomicstoidentifynovelaspectsofseverecovid19pneumonia AT ziyuezhao deeplearningintegrationofchestcomputedtomographyandplasmaproteomicstoidentifynovelaspectsofseverecovid19pneumonia AT ronghuawu deeplearningintegrationofchestcomputedtomographyandplasmaproteomicstoidentifynovelaspectsofseverecovid19pneumonia AT ruigong deeplearningintegrationofchestcomputedtomographyandplasmaproteomicstoidentifynovelaspectsofseverecovid19pneumonia AT yandongcheng deeplearningintegrationofchestcomputedtomographyandplasmaproteomicstoidentifynovelaspectsofseverecovid19pneumonia AT lingminyuan deeplearningintegrationofchestcomputedtomographyandplasmaproteomicstoidentifynovelaspectsofseverecovid19pneumonia AT shaojunzheng deeplearningintegrationofchestcomputedtomographyandplasmaproteomicstoidentifynovelaspectsofseverecovid19pneumonia AT chengzheng deeplearningintegrationofchestcomputedtomographyandplasmaproteomicstoidentifynovelaspectsofseverecovid19pneumonia AT ronghailin deeplearningintegrationofchestcomputedtomographyandplasmaproteomicstoidentifynovelaspectsofseverecovid19pneumonia AT jianpingchen deeplearningintegrationofchestcomputedtomographyandplasmaproteomicstoidentifynovelaspectsofseverecovid19pneumonia AT kangweisun deeplearningintegrationofchestcomputedtomographyandplasmaproteomicstoidentifynovelaspectsofseverecovid19pneumonia AT pingxu deeplearningintegrationofchestcomputedtomographyandplasmaproteomicstoidentifynovelaspectsofseverecovid19pneumonia AT liye deeplearningintegrationofchestcomputedtomographyandplasmaproteomicstoidentifynovelaspectsofseverecovid19pneumonia AT chaotinghan deeplearningintegrationofchestcomputedtomographyandplasmaproteomicstoidentifynovelaspectsofseverecovid19pneumonia AT xihaozhou deeplearningintegrationofchestcomputedtomographyandplasmaproteomicstoidentifynovelaspectsofseverecovid19pneumonia AT yaqingliu deeplearningintegrationofchestcomputedtomographyandplasmaproteomicstoidentifynovelaspectsofseverecovid19pneumonia AT jianhuayu deeplearningintegrationofchestcomputedtomographyandplasmaproteomicstoidentifynovelaspectsofseverecovid19pneumonia AT yaqinzheng deeplearningintegrationofchestcomputedtomographyandplasmaproteomicstoidentifynovelaspectsofseverecovid19pneumonia AT jieyang deeplearningintegrationofchestcomputedtomographyandplasmaproteomicstoidentifynovelaspectsofseverecovid19pneumonia AT jiajiehuang deeplearningintegrationofchestcomputedtomographyandplasmaproteomicstoidentifynovelaspectsofseverecovid19pneumonia AT juanchen deeplearningintegrationofchestcomputedtomographyandplasmaproteomicstoidentifynovelaspectsofseverecovid19pneumonia AT junjiefang deeplearningintegrationofchestcomputedtomographyandplasmaproteomicstoidentifynovelaspectsofseverecovid19pneumonia AT chensongchen deeplearningintegrationofchestcomputedtomographyandplasmaproteomicstoidentifynovelaspectsofseverecovid19pneumonia AT bofan deeplearningintegrationofchestcomputedtomographyandplasmaproteomicstoidentifynovelaspectsofseverecovid19pneumonia AT honglongfang deeplearningintegrationofchestcomputedtomographyandplasmaproteomicstoidentifynovelaspectsofseverecovid19pneumonia AT baningye deeplearningintegrationofchestcomputedtomographyandplasmaproteomicstoidentifynovelaspectsofseverecovid19pneumonia AT xiyunchen deeplearningintegrationofchestcomputedtomographyandplasmaproteomicstoidentifynovelaspectsofseverecovid19pneumonia AT xiaoliqian deeplearningintegrationofchestcomputedtomographyandplasmaproteomicstoidentifynovelaspectsofseverecovid19pneumonia AT junxiangchen deeplearningintegrationofchestcomputedtomographyandplasmaproteomicstoidentifynovelaspectsofseverecovid19pneumonia AT haitaoyu deeplearningintegrationofchestcomputedtomographyandplasmaproteomicstoidentifynovelaspectsofseverecovid19pneumonia AT junzhang deeplearningintegrationofchestcomputedtomographyandplasmaproteomicstoidentifynovelaspectsofseverecovid19pneumonia AT ximingpan deeplearningintegrationofchestcomputedtomographyandplasmaproteomicstoidentifynovelaspectsofseverecovid19pneumonia AT yixingzhan deeplearningintegrationofchestcomputedtomographyandplasmaproteomicstoidentifynovelaspectsofseverecovid19pneumonia AT youhaizheng deeplearningintegrationofchestcomputedtomographyandplasmaproteomicstoidentifynovelaspectsofseverecovid19pneumonia AT zhanghonghuang deeplearningintegrationofchestcomputedtomographyandplasmaproteomicstoidentifynovelaspectsofseverecovid19pneumonia AT chaozhong deeplearningintegrationofchestcomputedtomographyandplasmaproteomicstoidentifynovelaspectsofseverecovid19pneumonia AT ningliu deeplearningintegrationofchestcomputedtomographyandplasmaproteomicstoidentifynovelaspectsofseverecovid19pneumonia AT hongyingni deeplearningintegrationofchestcomputedtomographyandplasmaproteomicstoidentifynovelaspectsofseverecovid19pneumonia AT gengshengzhang deeplearningintegrationofchestcomputedtomographyandplasmaproteomicstoidentifynovelaspectsofseverecovid19pneumonia AT zhonghengzhang deeplearningintegrationofchestcomputedtomographyandplasmaproteomicstoidentifynovelaspectsofseverecovid19pneumonia |