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Three models that predict the efficacy of immunotherapy in Chinese patients with advanced non‐small cell lung cancer
Background Many tools have been developed to predict the efficacy of immunotherapy, such as lung immune prognostic index (LIPI), EPSILoN [Eastern Cooperative Oncology Group performance status (ECOG PS), smoking, liver metastases, lactate dehydrogenase (LDH), neutrophil‐to‐lymphocyte ratio (NLR)], an...
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| Published in: | Cancer medicine (Malden, MA) MA), 2021-09, Vol.10 (18), p.6291-6303 |
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| creator | Zhao, Qian Li, Butuo Xu, Yiyue Wang, Shijiang Zou, Bing Yu, Jinming Wang, Linlin |
| description | Background
Many tools have been developed to predict the efficacy of immunotherapy, such as lung immune prognostic index (LIPI), EPSILoN [Eastern Cooperative Oncology Group performance status (ECOG PS), smoking, liver metastases, lactate dehydrogenase (LDH), neutrophil‐to‐lymphocyte ratio (NLR)], and modified lung immune predictive index (mLIPI) scores. The aim of this study was to determine the ability of three predictive scores to predict the outcomes in Chinese advanced non‐small cell lung cancer (aNSCLC) patients treated with immune checkpoint inhibitors (ICIs).
Methods
We retrospectively analyzed 429 patients with aNSCLC treated with ICIs at our institution. The predictive ability of these models was evaluated using area under the curve (AUC) in receiver operating characteristic curve (ROC) analysis. Calibration was assessed using the Hosmer–Lemeshow test (H–L test) and Spearman's correlation coefficient. Progression‐free survival (PFS) and overall survival (OS) curves were generated using the Kaplan–Meier method.
Results
The AUC values of LIPI, mLIPI, and EPSILoN scores predicting PFS at 6 months were 0.642 [95% confidence interval (CI):0.590–0.694], 0.720 (95% CI: 0.675–0.762), and 0.633 (95% CI: 0.585–0.679), respectively (p |
| doi_str_mv | 10.1002/cam4.4171 |
| format | article |
| fullrecord | <record><control><sourceid>proquest_doaj_</sourceid><recordid>TN_cdi_doaj_primary_oai_doaj_org_article_15a920c0d6c44045b8ded1c665748f46</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><doaj_id>oai_doaj_org_article_15a920c0d6c44045b8ded1c665748f46</doaj_id><sourcerecordid>2573227875</sourcerecordid><originalsourceid>FETCH-LOGICAL-c5761-3d72f9adaa756ae7d8a87ac7d3ec3fdb9b0b583f5e54579fd167cc3336d390e83</originalsourceid><addsrcrecordid>eNp1kctu1DAUhiMEolXpghdAllixmNaOb5kNUjXiUqmITVlbJ_bxxKMkDk4y1ex4BJ6RJ8HplKpd4IUv5_z67N9_Ubxl9IJRWl5a6MSFYJq9KE5LKuRKKy5ePtmfFOfjuKN5aFoqzV4XJ1zwNS1ZdVrsb5uESLrosB3J1MBEhoQu2CkfkKD3wYI9kOhJ6Lq5j7maYDiQ0JNNE3ockQwwBeynkdyFqSHg9tBbdKSP_Z9fv8cO2pZYzFM791til2Z6U7zy0I54_rCeFT8-f7rdfF3dfP9yvbm6WVmpFVtxp0u_BgegpQLUroJKg9WOo-Xe1eua1rLiXqIUUq-9Y0pbyzlXLhvEip8V10eui7AzQwodpIOJEMx9IaatgTQF26JhEtYltdQpK0T-u7py6JhVSmpReaEy6-ORNcx1h85mywnaZ9DnnT40Zhv3phJCSSUz4P0DIMWfM46T2cU59dm_KaXmZakrvag-HFU2xXFM6B9vYNQsiZslcbMknrXvnj7pUfkv3yy4PAruQouH_5PM5uqbuEf-BTaJt-U</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2573227875</pqid></control><display><type>article</type><title>Three models that predict the efficacy of immunotherapy in Chinese patients with advanced non‐small cell lung cancer</title><source>Wiley Online Library Open Access</source><source>Publicly Available Content Database</source><source>PubMed Central</source><creator>Zhao, Qian ; Li, Butuo ; Xu, Yiyue ; Wang, Shijiang ; Zou, Bing ; Yu, Jinming ; Wang, Linlin</creator><creatorcontrib>Zhao, Qian ; Li, Butuo ; Xu, Yiyue ; Wang, Shijiang ; Zou, Bing ; Yu, Jinming ; Wang, Linlin</creatorcontrib><description>Background
Many tools have been developed to predict the efficacy of immunotherapy, such as lung immune prognostic index (LIPI), EPSILoN [Eastern Cooperative Oncology Group performance status (ECOG PS), smoking, liver metastases, lactate dehydrogenase (LDH), neutrophil‐to‐lymphocyte ratio (NLR)], and modified lung immune predictive index (mLIPI) scores. The aim of this study was to determine the ability of three predictive scores to predict the outcomes in Chinese advanced non‐small cell lung cancer (aNSCLC) patients treated with immune checkpoint inhibitors (ICIs).
Methods
We retrospectively analyzed 429 patients with aNSCLC treated with ICIs at our institution. The predictive ability of these models was evaluated using area under the curve (AUC) in receiver operating characteristic curve (ROC) analysis. Calibration was assessed using the Hosmer–Lemeshow test (H–L test) and Spearman's correlation coefficient. Progression‐free survival (PFS) and overall survival (OS) curves were generated using the Kaplan–Meier method.
Results
The AUC values of LIPI, mLIPI, and EPSILoN scores predicting PFS at 6 months were 0.642 [95% confidence interval (CI):0.590–0.694], 0.720 (95% CI: 0.675–0.762), and 0.633 (95% CI: 0.585–0.679), respectively (p < 0.001 for all models). The AUC values of LIPI, mLIPI, and EPSILON scores predicting objective response rate (ORR) were 0.606 (95% CI: 0.546–0.665), 0.683 (95% CI: 0.637–0.727), and 0.666 (95% CI: 0.620–0.711), respectively (p < 0.001 for all models). The C‐indexes of LIPI, mLIPI, and EPSILoN scores for PFS were 0.627 (95% CI 0.611–6.643), 0.677 (95% CI 0.652–0.682), and 0.631 (95% CI 0.617–0.645), respectively.
Conclusions
As mLIPI scores had the highest accuracy when used to predict the outcomes in Chinese aNSCLC patients, this tool could be used to guide clinical immunotherapy decision‐making.
Many tools have been developed to predict the efficacy of immunotherapy, such as lung immune prognostic index (LIPI), EPSILoN [Eastern Cooperative Oncology Group performance status (ECOG PS), smoking, liver metastases, lactate dehydrogenase (LDH), neutrophil‐to‐lymphocyte ratio (NLR)], and modified lung immune predictive index (mLIPI) scores. The aim of this study was to determine the ability of to predict outcomes in Chinese aNSCLC patients treated with immune checkpoint inhibitors (ICIs).</description><identifier>ISSN: 2045-7634</identifier><identifier>EISSN: 2045-7634</identifier><identifier>DOI: 10.1002/cam4.4171</identifier><identifier>PMID: 34390218</identifier><language>eng</language><publisher>United States: John Wiley & Sons, Inc</publisher><subject>Aged ; Biomarkers ; Blood platelets ; Cancer therapies ; Carcinoma, Non-Small-Cell Lung - diagnosis ; Carcinoma, Non-Small-Cell Lung - drug therapy ; Carcinoma, Non-Small-Cell Lung - mortality ; Chemotherapy ; China - epidemiology ; Clinical Cancer Research ; Clinical medicine ; Decision making ; Dehydrogenases ; FDA approval ; Feasibility Studies ; Female ; Histology ; Humans ; Immune checkpoint inhibitors ; Immune Checkpoint Inhibitors - therapeutic use ; Immunotherapy ; Kaplan-Meier Estimate ; L-Lactate dehydrogenase ; Lactic acid ; Leukocytes (neutrophilic) ; Liver ; Lung cancer ; Lung Neoplasms - diagnosis ; Lung Neoplasms - drug therapy ; Lung Neoplasms - mortality ; Lymphocytes ; Male ; Medical prognosis ; Metastases ; Metastasis ; Middle Aged ; Mutation ; Neutrophils ; Non-small cell lung carcinoma ; non‐small cell lung cancer (NSCLC) ; Oncology ; Patients ; predictive ; Prognosis ; Progression-Free Survival ; Radiation ; Retrospective Studies ; Risk Assessment - methods ; ROC Curve ; score ; Software</subject><ispartof>Cancer medicine (Malden, MA), 2021-09, Vol.10 (18), p.6291-6303</ispartof><rights>2021 The Authors. published by John Wiley & Sons Ltd.</rights><rights>2021 The Authors. Cancer Medicine published by John Wiley & Sons Ltd.</rights><rights>2021. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c5761-3d72f9adaa756ae7d8a87ac7d3ec3fdb9b0b583f5e54579fd167cc3336d390e83</citedby><cites>FETCH-LOGICAL-c5761-3d72f9adaa756ae7d8a87ac7d3ec3fdb9b0b583f5e54579fd167cc3336d390e83</cites><orcidid>0000-0002-2231-6642</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/2573227875/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2573227875?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,11562,25753,27924,27925,37012,44590,46052,46476,53791,53793,75126</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/34390218$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Zhao, Qian</creatorcontrib><creatorcontrib>Li, Butuo</creatorcontrib><creatorcontrib>Xu, Yiyue</creatorcontrib><creatorcontrib>Wang, Shijiang</creatorcontrib><creatorcontrib>Zou, Bing</creatorcontrib><creatorcontrib>Yu, Jinming</creatorcontrib><creatorcontrib>Wang, Linlin</creatorcontrib><title>Three models that predict the efficacy of immunotherapy in Chinese patients with advanced non‐small cell lung cancer</title><title>Cancer medicine (Malden, MA)</title><addtitle>Cancer Med</addtitle><description>Background
Many tools have been developed to predict the efficacy of immunotherapy, such as lung immune prognostic index (LIPI), EPSILoN [Eastern Cooperative Oncology Group performance status (ECOG PS), smoking, liver metastases, lactate dehydrogenase (LDH), neutrophil‐to‐lymphocyte ratio (NLR)], and modified lung immune predictive index (mLIPI) scores. The aim of this study was to determine the ability of three predictive scores to predict the outcomes in Chinese advanced non‐small cell lung cancer (aNSCLC) patients treated with immune checkpoint inhibitors (ICIs).
Methods
We retrospectively analyzed 429 patients with aNSCLC treated with ICIs at our institution. The predictive ability of these models was evaluated using area under the curve (AUC) in receiver operating characteristic curve (ROC) analysis. Calibration was assessed using the Hosmer–Lemeshow test (H–L test) and Spearman's correlation coefficient. Progression‐free survival (PFS) and overall survival (OS) curves were generated using the Kaplan–Meier method.
Results
The AUC values of LIPI, mLIPI, and EPSILoN scores predicting PFS at 6 months were 0.642 [95% confidence interval (CI):0.590–0.694], 0.720 (95% CI: 0.675–0.762), and 0.633 (95% CI: 0.585–0.679), respectively (p < 0.001 for all models). The AUC values of LIPI, mLIPI, and EPSILON scores predicting objective response rate (ORR) were 0.606 (95% CI: 0.546–0.665), 0.683 (95% CI: 0.637–0.727), and 0.666 (95% CI: 0.620–0.711), respectively (p < 0.001 for all models). The C‐indexes of LIPI, mLIPI, and EPSILoN scores for PFS were 0.627 (95% CI 0.611–6.643), 0.677 (95% CI 0.652–0.682), and 0.631 (95% CI 0.617–0.645), respectively.
Conclusions
As mLIPI scores had the highest accuracy when used to predict the outcomes in Chinese aNSCLC patients, this tool could be used to guide clinical immunotherapy decision‐making.
Many tools have been developed to predict the efficacy of immunotherapy, such as lung immune prognostic index (LIPI), EPSILoN [Eastern Cooperative Oncology Group performance status (ECOG PS), smoking, liver metastases, lactate dehydrogenase (LDH), neutrophil‐to‐lymphocyte ratio (NLR)], and modified lung immune predictive index (mLIPI) scores. The aim of this study was to determine the ability of to predict outcomes in Chinese aNSCLC patients treated with immune checkpoint inhibitors (ICIs).</description><subject>Aged</subject><subject>Biomarkers</subject><subject>Blood platelets</subject><subject>Cancer therapies</subject><subject>Carcinoma, Non-Small-Cell Lung - diagnosis</subject><subject>Carcinoma, Non-Small-Cell Lung - drug therapy</subject><subject>Carcinoma, Non-Small-Cell Lung - mortality</subject><subject>Chemotherapy</subject><subject>China - epidemiology</subject><subject>Clinical Cancer Research</subject><subject>Clinical medicine</subject><subject>Decision making</subject><subject>Dehydrogenases</subject><subject>FDA approval</subject><subject>Feasibility Studies</subject><subject>Female</subject><subject>Histology</subject><subject>Humans</subject><subject>Immune checkpoint inhibitors</subject><subject>Immune Checkpoint Inhibitors - therapeutic use</subject><subject>Immunotherapy</subject><subject>Kaplan-Meier Estimate</subject><subject>L-Lactate dehydrogenase</subject><subject>Lactic acid</subject><subject>Leukocytes (neutrophilic)</subject><subject>Liver</subject><subject>Lung cancer</subject><subject>Lung Neoplasms - diagnosis</subject><subject>Lung Neoplasms - drug therapy</subject><subject>Lung Neoplasms - mortality</subject><subject>Lymphocytes</subject><subject>Male</subject><subject>Medical prognosis</subject><subject>Metastases</subject><subject>Metastasis</subject><subject>Middle Aged</subject><subject>Mutation</subject><subject>Neutrophils</subject><subject>Non-small cell lung carcinoma</subject><subject>non‐small cell lung cancer (NSCLC)</subject><subject>Oncology</subject><subject>Patients</subject><subject>predictive</subject><subject>Prognosis</subject><subject>Progression-Free Survival</subject><subject>Radiation</subject><subject>Retrospective Studies</subject><subject>Risk Assessment - methods</subject><subject>ROC Curve</subject><subject>score</subject><subject>Software</subject><issn>2045-7634</issn><issn>2045-7634</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>24P</sourceid><sourceid>PIMPY</sourceid><sourceid>DOA</sourceid><recordid>eNp1kctu1DAUhiMEolXpghdAllixmNaOb5kNUjXiUqmITVlbJ_bxxKMkDk4y1ex4BJ6RJ8HplKpd4IUv5_z67N9_Ubxl9IJRWl5a6MSFYJq9KE5LKuRKKy5ePtmfFOfjuKN5aFoqzV4XJ1zwNS1ZdVrsb5uESLrosB3J1MBEhoQu2CkfkKD3wYI9kOhJ6Lq5j7maYDiQ0JNNE3ockQwwBeynkdyFqSHg9tBbdKSP_Z9fv8cO2pZYzFM791til2Z6U7zy0I54_rCeFT8-f7rdfF3dfP9yvbm6WVmpFVtxp0u_BgegpQLUroJKg9WOo-Xe1eua1rLiXqIUUq-9Y0pbyzlXLhvEip8V10eui7AzQwodpIOJEMx9IaatgTQF26JhEtYltdQpK0T-u7py6JhVSmpReaEy6-ORNcx1h85mywnaZ9DnnT40Zhv3phJCSSUz4P0DIMWfM46T2cU59dm_KaXmZakrvag-HFU2xXFM6B9vYNQsiZslcbMknrXvnj7pUfkv3yy4PAruQouH_5PM5uqbuEf-BTaJt-U</recordid><startdate>202109</startdate><enddate>202109</enddate><creator>Zhao, Qian</creator><creator>Li, Butuo</creator><creator>Xu, Yiyue</creator><creator>Wang, Shijiang</creator><creator>Zou, Bing</creator><creator>Yu, Jinming</creator><creator>Wang, Linlin</creator><general>John Wiley & Sons, Inc</general><general>John Wiley and Sons Inc</general><general>Wiley</general><scope>24P</scope><scope>WIN</scope><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>7X7</scope><scope>7XB</scope><scope>8FE</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>LK8</scope><scope>M0S</scope><scope>M7P</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>5PM</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0002-2231-6642</orcidid></search><sort><creationdate>202109</creationdate><title>Three models that predict the efficacy of immunotherapy in Chinese patients with advanced non‐small cell lung cancer</title><author>Zhao, Qian ; Li, Butuo ; Xu, Yiyue ; Wang, Shijiang ; Zou, Bing ; Yu, Jinming ; Wang, Linlin</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c5761-3d72f9adaa756ae7d8a87ac7d3ec3fdb9b0b583f5e54579fd167cc3336d390e83</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Aged</topic><topic>Biomarkers</topic><topic>Blood platelets</topic><topic>Cancer therapies</topic><topic>Carcinoma, Non-Small-Cell Lung - diagnosis</topic><topic>Carcinoma, Non-Small-Cell Lung - drug therapy</topic><topic>Carcinoma, Non-Small-Cell Lung - mortality</topic><topic>Chemotherapy</topic><topic>China - epidemiology</topic><topic>Clinical Cancer Research</topic><topic>Clinical medicine</topic><topic>Decision making</topic><topic>Dehydrogenases</topic><topic>FDA approval</topic><topic>Feasibility Studies</topic><topic>Female</topic><topic>Histology</topic><topic>Humans</topic><topic>Immune checkpoint inhibitors</topic><topic>Immune Checkpoint Inhibitors - therapeutic use</topic><topic>Immunotherapy</topic><topic>Kaplan-Meier Estimate</topic><topic>L-Lactate dehydrogenase</topic><topic>Lactic acid</topic><topic>Leukocytes (neutrophilic)</topic><topic>Liver</topic><topic>Lung cancer</topic><topic>Lung Neoplasms - diagnosis</topic><topic>Lung Neoplasms - drug therapy</topic><topic>Lung Neoplasms - mortality</topic><topic>Lymphocytes</topic><topic>Male</topic><topic>Medical prognosis</topic><topic>Metastases</topic><topic>Metastasis</topic><topic>Middle Aged</topic><topic>Mutation</topic><topic>Neutrophils</topic><topic>Non-small cell lung carcinoma</topic><topic>non‐small cell lung cancer (NSCLC)</topic><topic>Oncology</topic><topic>Patients</topic><topic>predictive</topic><topic>Prognosis</topic><topic>Progression-Free Survival</topic><topic>Radiation</topic><topic>Retrospective Studies</topic><topic>Risk Assessment - methods</topic><topic>ROC Curve</topic><topic>score</topic><topic>Software</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zhao, Qian</creatorcontrib><creatorcontrib>Li, Butuo</creatorcontrib><creatorcontrib>Xu, Yiyue</creatorcontrib><creatorcontrib>Wang, Shijiang</creatorcontrib><creatorcontrib>Zou, Bing</creatorcontrib><creatorcontrib>Yu, Jinming</creatorcontrib><creatorcontrib>Wang, Linlin</creatorcontrib><collection>Wiley Online Library Open Access</collection><collection>Wiley Online Library Free Content</collection><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>ProQuest Health and Medical</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>ProQuest Natural Science Collection</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>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>ProQuest Biological Science Collection</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Biological Science Database</collection><collection>Publicly Available Content Database</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 China</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>Cancer medicine (Malden, MA)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zhao, Qian</au><au>Li, Butuo</au><au>Xu, Yiyue</au><au>Wang, Shijiang</au><au>Zou, Bing</au><au>Yu, Jinming</au><au>Wang, Linlin</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Three models that predict the efficacy of immunotherapy in Chinese patients with advanced non‐small cell lung cancer</atitle><jtitle>Cancer medicine (Malden, MA)</jtitle><addtitle>Cancer Med</addtitle><date>2021-09</date><risdate>2021</risdate><volume>10</volume><issue>18</issue><spage>6291</spage><epage>6303</epage><pages>6291-6303</pages><issn>2045-7634</issn><eissn>2045-7634</eissn><abstract>Background
Many tools have been developed to predict the efficacy of immunotherapy, such as lung immune prognostic index (LIPI), EPSILoN [Eastern Cooperative Oncology Group performance status (ECOG PS), smoking, liver metastases, lactate dehydrogenase (LDH), neutrophil‐to‐lymphocyte ratio (NLR)], and modified lung immune predictive index (mLIPI) scores. The aim of this study was to determine the ability of three predictive scores to predict the outcomes in Chinese advanced non‐small cell lung cancer (aNSCLC) patients treated with immune checkpoint inhibitors (ICIs).
Methods
We retrospectively analyzed 429 patients with aNSCLC treated with ICIs at our institution. The predictive ability of these models was evaluated using area under the curve (AUC) in receiver operating characteristic curve (ROC) analysis. Calibration was assessed using the Hosmer–Lemeshow test (H–L test) and Spearman's correlation coefficient. Progression‐free survival (PFS) and overall survival (OS) curves were generated using the Kaplan–Meier method.
Results
The AUC values of LIPI, mLIPI, and EPSILoN scores predicting PFS at 6 months were 0.642 [95% confidence interval (CI):0.590–0.694], 0.720 (95% CI: 0.675–0.762), and 0.633 (95% CI: 0.585–0.679), respectively (p < 0.001 for all models). The AUC values of LIPI, mLIPI, and EPSILON scores predicting objective response rate (ORR) were 0.606 (95% CI: 0.546–0.665), 0.683 (95% CI: 0.637–0.727), and 0.666 (95% CI: 0.620–0.711), respectively (p < 0.001 for all models). The C‐indexes of LIPI, mLIPI, and EPSILoN scores for PFS were 0.627 (95% CI 0.611–6.643), 0.677 (95% CI 0.652–0.682), and 0.631 (95% CI 0.617–0.645), respectively.
Conclusions
As mLIPI scores had the highest accuracy when used to predict the outcomes in Chinese aNSCLC patients, this tool could be used to guide clinical immunotherapy decision‐making.
Many tools have been developed to predict the efficacy of immunotherapy, such as lung immune prognostic index (LIPI), EPSILoN [Eastern Cooperative Oncology Group performance status (ECOG PS), smoking, liver metastases, lactate dehydrogenase (LDH), neutrophil‐to‐lymphocyte ratio (NLR)], and modified lung immune predictive index (mLIPI) scores. The aim of this study was to determine the ability of to predict outcomes in Chinese aNSCLC patients treated with immune checkpoint inhibitors (ICIs).</abstract><cop>United States</cop><pub>John Wiley & Sons, Inc</pub><pmid>34390218</pmid><doi>10.1002/cam4.4171</doi><tpages>13</tpages><orcidid>https://orcid.org/0000-0002-2231-6642</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Aged Biomarkers Blood platelets Cancer therapies Carcinoma, Non-Small-Cell Lung - diagnosis Carcinoma, Non-Small-Cell Lung - drug therapy Carcinoma, Non-Small-Cell Lung - mortality Chemotherapy China - epidemiology Clinical Cancer Research Clinical medicine Decision making Dehydrogenases FDA approval Feasibility Studies Female Histology Humans Immune checkpoint inhibitors Immune Checkpoint Inhibitors - therapeutic use Immunotherapy Kaplan-Meier Estimate L-Lactate dehydrogenase Lactic acid Leukocytes (neutrophilic) Liver Lung cancer Lung Neoplasms - diagnosis Lung Neoplasms - drug therapy Lung Neoplasms - mortality Lymphocytes Male Medical prognosis Metastases Metastasis Middle Aged Mutation Neutrophils Non-small cell lung carcinoma non‐small cell lung cancer (NSCLC) Oncology Patients predictive Prognosis Progression-Free Survival Radiation Retrospective Studies Risk Assessment - methods ROC Curve score Software |
| title | Three models that predict the efficacy of immunotherapy in Chinese patients with advanced non‐small cell lung cancer |
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