A multiomics approach-based prediction of radiation pneumonia in lung cancer patients: impact on survival outcome
Purpose To predict the risk of radiation pneumonitis (RP), a multiomics model was built to stratify lung cancer patients. Our study also investigated the impact of RP on survival. Methods This study retrospectively collected 100 RP and 99 matched non-RP lung cancer patients treated with radiotherapy...
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| Veröffentlicht in: | Journal of cancer research and clinical oncology 2023-09, Vol.149 (11), p.8923-8934 |
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| container_title | Journal of cancer research and clinical oncology |
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| creator | Niu, Lishui Chu, Xianjing Yang, Xianghui Zhao, Hongxiang Chen, Liu Deng, Fuxing Liang, Zhan Jing, Di Zhou, Rongrong |
| description | Purpose
To predict the risk of radiation pneumonitis (RP), a multiomics model was built to stratify lung cancer patients. Our study also investigated the impact of RP on survival.
Methods
This study retrospectively collected 100 RP and 99 matched non-RP lung cancer patients treated with radiotherapy from two independent centres. They were divided into training (n = 175) and validation cohorts (n = 24). The radiomics, dosiomics and clinical features were extracted from planning CT and electronic medical records and were analysed by LASSO Cox regression. A multiomics prediction model was developed by the optimal algorithm. Overall survival (OS) between the RP, non-RP, mild RP, and severe RP groups was analysed by the Kaplan‒Meier method.
Results
Sixteen radiomics features, two dosiomics features, and one clinical feature were selected to build the best multiomics model. The optimal performance for predicting RP was the area under the receiver operating characteristic curve (AUC) of the testing set (0.94) and validation set (0.92). The RP patients were divided into mild (≤ 2 grade) and severe (> 2 grade) RP groups. The median OS was 31 months for the non-RP group compared with 49 months for the RP group (HR = 0.53, p = 0.0022). Among the RP subgroup, the median OS was 57 months for the mild RP group and 25 months for the severe RP group (HR = 3.72, p |
| doi_str_mv | 10.1007/s00432-023-04827-7 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_2811565935</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2811565935</sourcerecordid><originalsourceid>FETCH-LOGICAL-c375t-88cf7b0efd57fd376bd92cb3404b05392bea5b12e2250bca8335267b09f4b26e3</originalsourceid><addsrcrecordid>eNp9kU9rHSEUxaWkNK9pv0AXRcimm2n1Oo4z2YVH_0Ggm3Yt6txJDTM60THQb19fXtpAFlnJ9fzO0csh5B1nHzlj6lNmrBXQMBANa3tQjXpBdvxwxYWQJ2THuOKNBN6dktc537A6SwWvyKlQXLYDqB25vaRLmTcfF-8yNeuaonG_G2syjnRNOHpXxUDjRJMZvbkf1oBlicEb6gOdS7imzgSHia5Vx7DlC-qX1biNVjiXdOfvzExj2Vxc8A15OZk549uH84z8-vL55_5bc_Xj6_f95VXjhJJb0_duUpbhNEo1jUJ1dhzAWdGy1jIpBrBopOWAAJJZZ_q6MnTVMUythQ7FGflwzK0r3RbMm158djjPJmAsWUPPuezkIGRFz5-gN7GkUH9XqRa6QTJQlYIj5VLMOeGk1-QXk_5ozvShEH0sRNdC9H0h-mB6_xBd7ILjf8u_BiogjkCuUrjG9Pj2M7F_ARAzlyk</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2842695027</pqid></control><display><type>article</type><title>A multiomics approach-based prediction of radiation pneumonia in lung cancer patients: impact on survival outcome</title><source>MEDLINE</source><source>SpringerLink Journals - AutoHoldings</source><creator>Niu, Lishui ; Chu, Xianjing ; Yang, Xianghui ; Zhao, Hongxiang ; Chen, Liu ; Deng, Fuxing ; Liang, Zhan ; Jing, Di ; Zhou, Rongrong</creator><creatorcontrib>Niu, Lishui ; Chu, Xianjing ; Yang, Xianghui ; Zhao, Hongxiang ; Chen, Liu ; Deng, Fuxing ; Liang, Zhan ; Jing, Di ; Zhou, Rongrong</creatorcontrib><description>Purpose
To predict the risk of radiation pneumonitis (RP), a multiomics model was built to stratify lung cancer patients. Our study also investigated the impact of RP on survival.
Methods
This study retrospectively collected 100 RP and 99 matched non-RP lung cancer patients treated with radiotherapy from two independent centres. They were divided into training (n = 175) and validation cohorts (n = 24). The radiomics, dosiomics and clinical features were extracted from planning CT and electronic medical records and were analysed by LASSO Cox regression. A multiomics prediction model was developed by the optimal algorithm. Overall survival (OS) between the RP, non-RP, mild RP, and severe RP groups was analysed by the Kaplan‒Meier method.
Results
Sixteen radiomics features, two dosiomics features, and one clinical feature were selected to build the best multiomics model. The optimal performance for predicting RP was the area under the receiver operating characteristic curve (AUC) of the testing set (0.94) and validation set (0.92). The RP patients were divided into mild (≤ 2 grade) and severe (> 2 grade) RP groups. The median OS was 31 months for the non-RP group compared with 49 months for the RP group (HR = 0.53, p = 0.0022). Among the RP subgroup, the median OS was 57 months for the mild RP group and 25 months for the severe RP group (HR = 3.72, p < 0.0001).
Conclusions
The multiomics model contributed to improving the accuracy of RP prediction. Compared with the non-RP patients, the RP patients displayed longer OS, especially the mild RP patients.</description><identifier>ISSN: 0171-5216</identifier><identifier>ISSN: 1432-1335</identifier><identifier>EISSN: 1432-1335</identifier><identifier>DOI: 10.1007/s00432-023-04827-7</identifier><identifier>PMID: 37154927</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Biological analysis ; Cancer Research ; Electronic medical records ; Hematology ; Humans ; Internal Medicine ; Lung cancer ; Lung Neoplasms - radiotherapy ; Medicine ; Medicine & Public Health ; Multiomics ; Oncology ; Patients ; Pneumonitis ; Prediction models ; Radiation ; Radiation Pneumonitis - diagnosis ; Radiation Pneumonitis - etiology ; Radiation therapy ; Radiomics ; Retrospective Studies ; Risk Factors ; Survival</subject><ispartof>Journal of cancer research and clinical oncology, 2023-09, Vol.149 (11), p.8923-8934</ispartof><rights>The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</rights><rights>2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c375t-88cf7b0efd57fd376bd92cb3404b05392bea5b12e2250bca8335267b09f4b26e3</citedby><cites>FETCH-LOGICAL-c375t-88cf7b0efd57fd376bd92cb3404b05392bea5b12e2250bca8335267b09f4b26e3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s00432-023-04827-7$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00432-023-04827-7$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,41488,42557,51319</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/37154927$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Niu, Lishui</creatorcontrib><creatorcontrib>Chu, Xianjing</creatorcontrib><creatorcontrib>Yang, Xianghui</creatorcontrib><creatorcontrib>Zhao, Hongxiang</creatorcontrib><creatorcontrib>Chen, Liu</creatorcontrib><creatorcontrib>Deng, Fuxing</creatorcontrib><creatorcontrib>Liang, Zhan</creatorcontrib><creatorcontrib>Jing, Di</creatorcontrib><creatorcontrib>Zhou, Rongrong</creatorcontrib><title>A multiomics approach-based prediction of radiation pneumonia in lung cancer patients: impact on survival outcome</title><title>Journal of cancer research and clinical oncology</title><addtitle>J Cancer Res Clin Oncol</addtitle><addtitle>J Cancer Res Clin Oncol</addtitle><description>Purpose
To predict the risk of radiation pneumonitis (RP), a multiomics model was built to stratify lung cancer patients. Our study also investigated the impact of RP on survival.
Methods
This study retrospectively collected 100 RP and 99 matched non-RP lung cancer patients treated with radiotherapy from two independent centres. They were divided into training (n = 175) and validation cohorts (n = 24). The radiomics, dosiomics and clinical features were extracted from planning CT and electronic medical records and were analysed by LASSO Cox regression. A multiomics prediction model was developed by the optimal algorithm. Overall survival (OS) between the RP, non-RP, mild RP, and severe RP groups was analysed by the Kaplan‒Meier method.
Results
Sixteen radiomics features, two dosiomics features, and one clinical feature were selected to build the best multiomics model. The optimal performance for predicting RP was the area under the receiver operating characteristic curve (AUC) of the testing set (0.94) and validation set (0.92). The RP patients were divided into mild (≤ 2 grade) and severe (> 2 grade) RP groups. The median OS was 31 months for the non-RP group compared with 49 months for the RP group (HR = 0.53, p = 0.0022). Among the RP subgroup, the median OS was 57 months for the mild RP group and 25 months for the severe RP group (HR = 3.72, p < 0.0001).
Conclusions
The multiomics model contributed to improving the accuracy of RP prediction. Compared with the non-RP patients, the RP patients displayed longer OS, especially the mild RP patients.</description><subject>Biological analysis</subject><subject>Cancer Research</subject><subject>Electronic medical records</subject><subject>Hematology</subject><subject>Humans</subject><subject>Internal Medicine</subject><subject>Lung cancer</subject><subject>Lung Neoplasms - radiotherapy</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Multiomics</subject><subject>Oncology</subject><subject>Patients</subject><subject>Pneumonitis</subject><subject>Prediction models</subject><subject>Radiation</subject><subject>Radiation Pneumonitis - diagnosis</subject><subject>Radiation Pneumonitis - etiology</subject><subject>Radiation therapy</subject><subject>Radiomics</subject><subject>Retrospective Studies</subject><subject>Risk Factors</subject><subject>Survival</subject><issn>0171-5216</issn><issn>1432-1335</issn><issn>1432-1335</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>8G5</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>GUQSH</sourceid><sourceid>M2O</sourceid><recordid>eNp9kU9rHSEUxaWkNK9pv0AXRcimm2n1Oo4z2YVH_0Ggm3Yt6txJDTM60THQb19fXtpAFlnJ9fzO0csh5B1nHzlj6lNmrBXQMBANa3tQjXpBdvxwxYWQJ2THuOKNBN6dktc537A6SwWvyKlQXLYDqB25vaRLmTcfF-8yNeuaonG_G2syjnRNOHpXxUDjRJMZvbkf1oBlicEb6gOdS7imzgSHia5Vx7DlC-qX1biNVjiXdOfvzExj2Vxc8A15OZk549uH84z8-vL55_5bc_Xj6_f95VXjhJJb0_duUpbhNEo1jUJ1dhzAWdGy1jIpBrBopOWAAJJZZ_q6MnTVMUythQ7FGflwzK0r3RbMm158djjPJmAsWUPPuezkIGRFz5-gN7GkUH9XqRa6QTJQlYIj5VLMOeGk1-QXk_5ozvShEH0sRNdC9H0h-mB6_xBd7ILjf8u_BiogjkCuUrjG9Pj2M7F_ARAzlyk</recordid><startdate>20230901</startdate><enddate>20230901</enddate><creator>Niu, Lishui</creator><creator>Chu, Xianjing</creator><creator>Yang, Xianghui</creator><creator>Zhao, Hongxiang</creator><creator>Chen, Liu</creator><creator>Deng, Fuxing</creator><creator>Liang, Zhan</creator><creator>Jing, Di</creator><creator>Zhou, Rongrong</creator><general>Springer Berlin Heidelberg</general><general>Springer Nature B.V</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7TO</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8C1</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>8G5</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>GUQSH</scope><scope>H94</scope><scope>K9.</scope><scope>M0S</scope><scope>M1P</scope><scope>M2O</scope><scope>MBDVC</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>Q9U</scope><scope>7X8</scope></search><sort><creationdate>20230901</creationdate><title>A multiomics approach-based prediction of radiation pneumonia in lung cancer patients: impact on survival outcome</title><author>Niu, Lishui ; Chu, Xianjing ; Yang, Xianghui ; Zhao, Hongxiang ; Chen, Liu ; Deng, Fuxing ; Liang, Zhan ; Jing, Di ; Zhou, Rongrong</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c375t-88cf7b0efd57fd376bd92cb3404b05392bea5b12e2250bca8335267b09f4b26e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Biological analysis</topic><topic>Cancer Research</topic><topic>Electronic medical records</topic><topic>Hematology</topic><topic>Humans</topic><topic>Internal Medicine</topic><topic>Lung cancer</topic><topic>Lung Neoplasms - radiotherapy</topic><topic>Medicine</topic><topic>Medicine & Public Health</topic><topic>Multiomics</topic><topic>Oncology</topic><topic>Patients</topic><topic>Pneumonitis</topic><topic>Prediction models</topic><topic>Radiation</topic><topic>Radiation Pneumonitis - diagnosis</topic><topic>Radiation Pneumonitis - etiology</topic><topic>Radiation therapy</topic><topic>Radiomics</topic><topic>Retrospective Studies</topic><topic>Risk Factors</topic><topic>Survival</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Niu, Lishui</creatorcontrib><creatorcontrib>Chu, Xianjing</creatorcontrib><creatorcontrib>Yang, Xianghui</creatorcontrib><creatorcontrib>Zhao, Hongxiang</creatorcontrib><creatorcontrib>Chen, Liu</creatorcontrib><creatorcontrib>Deng, Fuxing</creatorcontrib><creatorcontrib>Liang, Zhan</creatorcontrib><creatorcontrib>Jing, Di</creatorcontrib><creatorcontrib>Zhou, Rongrong</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Oncogenes and Growth Factors Abstracts</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Public Health Database</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Research Library (Alumni Edition)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>Research Library Prep</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Research Library</collection><collection>Research Library (Corporate)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central Basic</collection><collection>MEDLINE - Academic</collection><jtitle>Journal of cancer research and clinical oncology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Niu, Lishui</au><au>Chu, Xianjing</au><au>Yang, Xianghui</au><au>Zhao, Hongxiang</au><au>Chen, Liu</au><au>Deng, Fuxing</au><au>Liang, Zhan</au><au>Jing, Di</au><au>Zhou, Rongrong</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A multiomics approach-based prediction of radiation pneumonia in lung cancer patients: impact on survival outcome</atitle><jtitle>Journal of cancer research and clinical oncology</jtitle><stitle>J Cancer Res Clin Oncol</stitle><addtitle>J Cancer Res Clin Oncol</addtitle><date>2023-09-01</date><risdate>2023</risdate><volume>149</volume><issue>11</issue><spage>8923</spage><epage>8934</epage><pages>8923-8934</pages><issn>0171-5216</issn><issn>1432-1335</issn><eissn>1432-1335</eissn><abstract>Purpose
To predict the risk of radiation pneumonitis (RP), a multiomics model was built to stratify lung cancer patients. Our study also investigated the impact of RP on survival.
Methods
This study retrospectively collected 100 RP and 99 matched non-RP lung cancer patients treated with radiotherapy from two independent centres. They were divided into training (n = 175) and validation cohorts (n = 24). The radiomics, dosiomics and clinical features were extracted from planning CT and electronic medical records and were analysed by LASSO Cox regression. A multiomics prediction model was developed by the optimal algorithm. Overall survival (OS) between the RP, non-RP, mild RP, and severe RP groups was analysed by the Kaplan‒Meier method.
Results
Sixteen radiomics features, two dosiomics features, and one clinical feature were selected to build the best multiomics model. The optimal performance for predicting RP was the area under the receiver operating characteristic curve (AUC) of the testing set (0.94) and validation set (0.92). The RP patients were divided into mild (≤ 2 grade) and severe (> 2 grade) RP groups. The median OS was 31 months for the non-RP group compared with 49 months for the RP group (HR = 0.53, p = 0.0022). Among the RP subgroup, the median OS was 57 months for the mild RP group and 25 months for the severe RP group (HR = 3.72, p < 0.0001).
Conclusions
The multiomics model contributed to improving the accuracy of RP prediction. Compared with the non-RP patients, the RP patients displayed longer OS, especially the mild RP patients.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><pmid>37154927</pmid><doi>10.1007/s00432-023-04827-7</doi><tpages>12</tpages></addata></record> |
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| subjects | Biological analysis Cancer Research Electronic medical records Hematology Humans Internal Medicine Lung cancer Lung Neoplasms - radiotherapy Medicine Medicine & Public Health Multiomics Oncology Patients Pneumonitis Prediction models Radiation Radiation Pneumonitis - diagnosis Radiation Pneumonitis - etiology Radiation therapy Radiomics Retrospective Studies Risk Factors Survival |
| title | A multiomics approach-based prediction of radiation pneumonia in lung cancer patients: impact on survival outcome |
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