Optimal FDG PET/CT volumetric parameters for risk stratification in patients with locally advanced non-small cell lung cancer: results from the ACRIN 6668/RTOG 0235 trial
Purpose In recent years, multiple studies have demonstrated the value of volumetric FDG-PET/CT parameters as independent prognostic factors in patients with non-small cell lung cancer (NSCLC). We aimed to determine the optimal cut-off points of pretreatment volumetric FDG-PET/CT parameters in predic...
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| Veröffentlicht in: | European journal of nuclear medicine and molecular imaging 2017-11, Vol.44 (12), p.1969-1983 |
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| container_issue | 12 |
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| container_title | European journal of nuclear medicine and molecular imaging |
| container_volume | 44 |
| creator | Salavati, Ali Duan, Fenghai Snyder, Bradley S. Wei, Bo Houshmand, Sina Khiewvan, Benjapa Opanowski, Adam Simone, Charles B. Siegel, Barry A. Machtay, Mitchell Alavi, Abass |
| description | Purpose
In recent years, multiple studies have demonstrated the value of volumetric FDG-PET/CT parameters as independent prognostic factors in patients with non-small cell lung cancer (NSCLC). We aimed to determine the optimal cut-off points of pretreatment volumetric FDG-PET/CT parameters in predicting overall survival (OS) in patients with locally advanced NSCLC and to recommend imaging biomarkers appropriate for routine clinical applications.
Methods
Patients with inoperable stage IIB/III NSCLC enrolled in ACRIN 6668/RTOG 0235 were included. Pretreatment FDG-PET scans were quantified using semiautomatic adaptive contrast-oriented thresholding and local-background partial-volume-effect-correction algorithms. For each patient, the following indices were measured: metabolic tumor volume (MTV), total lesion glycolysis (TLG), SUVmax, SUVmean, partial-volume-corrected TLG (pvcTLG), and pvcSUVmean for the whole-body, primary tumor, and regional lymph nodes. The association between each index and patient outcome was assessed using Cox proportional hazards regression. Optimal cut-off points were estimated using recursive binary partitioning in a conditional inference framework and used in Kaplan-Meier curves with log-rank testing. The discriminatory ability of each index was examined using time-dependent receiver operating characteristic (ROC) curves and corresponding area under the curve (AUC(t)).
Results
The study included 196 patients. Pretreatment whole-body and primary tumor MTV, TLG, and pvcTLG were independently prognostic of OS. Optimal cut-off points were 175.0, 270.9, and 35.5 cm
3
for whole-body TLG, pvcTLG, and MTV, and were 168.2, 239.8, and 17.4 cm
3
for primary tumor TLG, pvcTLG, and MTV, respectively. In time-dependent ROC analysis, AUC(t) for MTV and TLG were uniformly higher than that of SUV measures over all time points. Primary tumor and whole-body parameters demonstrated similar patterns of separation for those patients above versus below the optimal cut-off points in Kaplan-Meier curves and in time-dependent ROC analysis.
Conclusion
We demonstrated that pretreatment whole-body and primary tumor volumetric FDG-PET/CT parameters, including MTV, TLG, and pvcTLG, are strongly prognostic for OS in patients with locally advanced NSCLC, and have similar discriminatory ability. Therefore, we believe that, after validation in future trials, the derived optimal cut-off points for primary tumor volumetric FDG-PET/CT parameters, or their more refined v |
| doi_str_mv | 10.1007/s00259-017-3753-x |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_pubme</sourceid><recordid>TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_5648620</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>1981653254</sourcerecordid><originalsourceid>FETCH-LOGICAL-c470t-88c0b77adecc4489afae2bdb186e1204f2811fa93e708101b9661a53f694a6133</originalsourceid><addsrcrecordid>eNp1UsFuEzEQtRAVLYEP4IIsceGyxPbuem0OSFVoQ6WKoCqcLa_Xm7h47WB7Q_tLfCVepUSlEj6MR5o3b-bZD4A3GH3ACDXziBCpeYFwU5RNXRZ3z8AZppgXDWL8-TFv0Cl4GeMtQpgRxl-AU8Io44ThM_B7tUtmkBZefl7Cbxfr-WIN996Og07BKLiTQeZUhwh7H2Aw8QeMKchkeqNy9A4al1HJaJci_GXSFlqvpLX3UHZ76ZTuoPOuiHmGhUrnYEe3gWoqhY8w6Dja3NkHP8C01fB8cXP1FVJK2fxmvVpCRMoa5l2kfQVOemmjfv1wz8D3y4v14ktxvVpeLc6vC1U1KBWMKdQ2jey0UlXFuOylJm3XYkY1Jqjqs27cS17q_EoY4ZZTimVd9pRXkuKynIFPB97d2A66U1lZkFbsQn6ncC-8NOLfijNbsfF7UdOKUYIywfsHguB_jjomMZg4SZdO-zEKzHFDaTmdGXj3BHrrx-CyvIximNYlqauMwgeUCj7GoPvjMhiJyQni4ASRnSAmJ4i73PP2sYpjx9-vzwByAMRcchsdHo3-L-sfU2S_6g</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1981653254</pqid></control><display><type>article</type><title>Optimal FDG PET/CT volumetric parameters for risk stratification in patients with locally advanced non-small cell lung cancer: results from the ACRIN 6668/RTOG 0235 trial</title><source>SpringerNature Journals</source><creator>Salavati, Ali ; Duan, Fenghai ; Snyder, Bradley S. ; Wei, Bo ; Houshmand, Sina ; Khiewvan, Benjapa ; Opanowski, Adam ; Simone, Charles B. ; Siegel, Barry A. ; Machtay, Mitchell ; Alavi, Abass</creator><creatorcontrib>Salavati, Ali ; Duan, Fenghai ; Snyder, Bradley S. ; Wei, Bo ; Houshmand, Sina ; Khiewvan, Benjapa ; Opanowski, Adam ; Simone, Charles B. ; Siegel, Barry A. ; Machtay, Mitchell ; Alavi, Abass</creatorcontrib><description>Purpose
In recent years, multiple studies have demonstrated the value of volumetric FDG-PET/CT parameters as independent prognostic factors in patients with non-small cell lung cancer (NSCLC). We aimed to determine the optimal cut-off points of pretreatment volumetric FDG-PET/CT parameters in predicting overall survival (OS) in patients with locally advanced NSCLC and to recommend imaging biomarkers appropriate for routine clinical applications.
Methods
Patients with inoperable stage IIB/III NSCLC enrolled in ACRIN 6668/RTOG 0235 were included. Pretreatment FDG-PET scans were quantified using semiautomatic adaptive contrast-oriented thresholding and local-background partial-volume-effect-correction algorithms. For each patient, the following indices were measured: metabolic tumor volume (MTV), total lesion glycolysis (TLG), SUVmax, SUVmean, partial-volume-corrected TLG (pvcTLG), and pvcSUVmean for the whole-body, primary tumor, and regional lymph nodes. The association between each index and patient outcome was assessed using Cox proportional hazards regression. Optimal cut-off points were estimated using recursive binary partitioning in a conditional inference framework and used in Kaplan-Meier curves with log-rank testing. The discriminatory ability of each index was examined using time-dependent receiver operating characteristic (ROC) curves and corresponding area under the curve (AUC(t)).
Results
The study included 196 patients. Pretreatment whole-body and primary tumor MTV, TLG, and pvcTLG were independently prognostic of OS. Optimal cut-off points were 175.0, 270.9, and 35.5 cm
3
for whole-body TLG, pvcTLG, and MTV, and were 168.2, 239.8, and 17.4 cm
3
for primary tumor TLG, pvcTLG, and MTV, respectively. In time-dependent ROC analysis, AUC(t) for MTV and TLG were uniformly higher than that of SUV measures over all time points. Primary tumor and whole-body parameters demonstrated similar patterns of separation for those patients above versus below the optimal cut-off points in Kaplan-Meier curves and in time-dependent ROC analysis.
Conclusion
We demonstrated that pretreatment whole-body and primary tumor volumetric FDG-PET/CT parameters, including MTV, TLG, and pvcTLG, are strongly prognostic for OS in patients with locally advanced NSCLC, and have similar discriminatory ability. Therefore, we believe that, after validation in future trials, the derived optimal cut-off points for primary tumor volumetric FDG-PET/CT parameters, or their more refined versions, could be incorporated into routine clinical practice, and may provide more accurate prognostication and staging based on tumor metabolic features.</description><identifier>ISSN: 1619-7070</identifier><identifier>EISSN: 1619-7089</identifier><identifier>DOI: 10.1007/s00259-017-3753-x</identifier><identifier>PMID: 28689281</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Adaptive algorithms ; Biomarkers ; Cancer ; Cardiology ; Clinical trials ; Computed tomography ; Glycolysis ; Hazard assessment ; Imaging ; Lung cancer ; Lymph nodes ; Medical prognosis ; Medicine ; Medicine & Public Health ; Metabolism ; Non-small cell lung carcinoma ; Nuclear Medicine ; Oncology ; Original Article ; Orthopedics ; Patients ; Positron emission ; Radiology ; Regression analysis ; Therapeutic applications ; Time dependence ; Tomography</subject><ispartof>European journal of nuclear medicine and molecular imaging, 2017-11, Vol.44 (12), p.1969-1983</ispartof><rights>Springer-Verlag GmbH Germany 2017</rights><rights>European Journal of Nuclear Medicine and Molecular Imaging is a copyright of Springer, (2017). All Rights Reserved.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c470t-88c0b77adecc4489afae2bdb186e1204f2811fa93e708101b9661a53f694a6133</citedby><cites>FETCH-LOGICAL-c470t-88c0b77adecc4489afae2bdb186e1204f2811fa93e708101b9661a53f694a6133</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/s00259-017-3753-x$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00259-017-3753-x$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>230,314,780,784,885,27924,27925,41488,42557,51319</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/28689281$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Salavati, Ali</creatorcontrib><creatorcontrib>Duan, Fenghai</creatorcontrib><creatorcontrib>Snyder, Bradley S.</creatorcontrib><creatorcontrib>Wei, Bo</creatorcontrib><creatorcontrib>Houshmand, Sina</creatorcontrib><creatorcontrib>Khiewvan, Benjapa</creatorcontrib><creatorcontrib>Opanowski, Adam</creatorcontrib><creatorcontrib>Simone, Charles B.</creatorcontrib><creatorcontrib>Siegel, Barry A.</creatorcontrib><creatorcontrib>Machtay, Mitchell</creatorcontrib><creatorcontrib>Alavi, Abass</creatorcontrib><title>Optimal FDG PET/CT volumetric parameters for risk stratification in patients with locally advanced non-small cell lung cancer: results from the ACRIN 6668/RTOG 0235 trial</title><title>European journal of nuclear medicine and molecular imaging</title><addtitle>Eur J Nucl Med Mol Imaging</addtitle><addtitle>Eur J Nucl Med Mol Imaging</addtitle><description>Purpose
In recent years, multiple studies have demonstrated the value of volumetric FDG-PET/CT parameters as independent prognostic factors in patients with non-small cell lung cancer (NSCLC). We aimed to determine the optimal cut-off points of pretreatment volumetric FDG-PET/CT parameters in predicting overall survival (OS) in patients with locally advanced NSCLC and to recommend imaging biomarkers appropriate for routine clinical applications.
Methods
Patients with inoperable stage IIB/III NSCLC enrolled in ACRIN 6668/RTOG 0235 were included. Pretreatment FDG-PET scans were quantified using semiautomatic adaptive contrast-oriented thresholding and local-background partial-volume-effect-correction algorithms. For each patient, the following indices were measured: metabolic tumor volume (MTV), total lesion glycolysis (TLG), SUVmax, SUVmean, partial-volume-corrected TLG (pvcTLG), and pvcSUVmean for the whole-body, primary tumor, and regional lymph nodes. The association between each index and patient outcome was assessed using Cox proportional hazards regression. Optimal cut-off points were estimated using recursive binary partitioning in a conditional inference framework and used in Kaplan-Meier curves with log-rank testing. The discriminatory ability of each index was examined using time-dependent receiver operating characteristic (ROC) curves and corresponding area under the curve (AUC(t)).
Results
The study included 196 patients. Pretreatment whole-body and primary tumor MTV, TLG, and pvcTLG were independently prognostic of OS. Optimal cut-off points were 175.0, 270.9, and 35.5 cm
3
for whole-body TLG, pvcTLG, and MTV, and were 168.2, 239.8, and 17.4 cm
3
for primary tumor TLG, pvcTLG, and MTV, respectively. In time-dependent ROC analysis, AUC(t) for MTV and TLG were uniformly higher than that of SUV measures over all time points. Primary tumor and whole-body parameters demonstrated similar patterns of separation for those patients above versus below the optimal cut-off points in Kaplan-Meier curves and in time-dependent ROC analysis.
Conclusion
We demonstrated that pretreatment whole-body and primary tumor volumetric FDG-PET/CT parameters, including MTV, TLG, and pvcTLG, are strongly prognostic for OS in patients with locally advanced NSCLC, and have similar discriminatory ability. Therefore, we believe that, after validation in future trials, the derived optimal cut-off points for primary tumor volumetric FDG-PET/CT parameters, or their more refined versions, could be incorporated into routine clinical practice, and may provide more accurate prognostication and staging based on tumor metabolic features.</description><subject>Adaptive algorithms</subject><subject>Biomarkers</subject><subject>Cancer</subject><subject>Cardiology</subject><subject>Clinical trials</subject><subject>Computed tomography</subject><subject>Glycolysis</subject><subject>Hazard assessment</subject><subject>Imaging</subject><subject>Lung cancer</subject><subject>Lymph nodes</subject><subject>Medical prognosis</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Metabolism</subject><subject>Non-small cell lung carcinoma</subject><subject>Nuclear Medicine</subject><subject>Oncology</subject><subject>Original Article</subject><subject>Orthopedics</subject><subject>Patients</subject><subject>Positron emission</subject><subject>Radiology</subject><subject>Regression analysis</subject><subject>Therapeutic applications</subject><subject>Time dependence</subject><subject>Tomography</subject><issn>1619-7070</issn><issn>1619-7089</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp1UsFuEzEQtRAVLYEP4IIsceGyxPbuem0OSFVoQ6WKoCqcLa_Xm7h47WB7Q_tLfCVepUSlEj6MR5o3b-bZD4A3GH3ACDXziBCpeYFwU5RNXRZ3z8AZppgXDWL8-TFv0Cl4GeMtQpgRxl-AU8Io44ThM_B7tUtmkBZefl7Cbxfr-WIN996Og07BKLiTQeZUhwh7H2Aw8QeMKchkeqNy9A4al1HJaJci_GXSFlqvpLX3UHZ76ZTuoPOuiHmGhUrnYEe3gWoqhY8w6Dja3NkHP8C01fB8cXP1FVJK2fxmvVpCRMoa5l2kfQVOemmjfv1wz8D3y4v14ktxvVpeLc6vC1U1KBWMKdQ2jey0UlXFuOylJm3XYkY1Jqjqs27cS17q_EoY4ZZTimVd9pRXkuKynIFPB97d2A66U1lZkFbsQn6ncC-8NOLfijNbsfF7UdOKUYIywfsHguB_jjomMZg4SZdO-zEKzHFDaTmdGXj3BHrrx-CyvIximNYlqauMwgeUCj7GoPvjMhiJyQni4ASRnSAmJ4i73PP2sYpjx9-vzwByAMRcchsdHo3-L-sfU2S_6g</recordid><startdate>20171101</startdate><enddate>20171101</enddate><creator>Salavati, Ali</creator><creator>Duan, Fenghai</creator><creator>Snyder, Bradley S.</creator><creator>Wei, Bo</creator><creator>Houshmand, Sina</creator><creator>Khiewvan, Benjapa</creator><creator>Opanowski, Adam</creator><creator>Simone, Charles B.</creator><creator>Siegel, Barry A.</creator><creator>Machtay, Mitchell</creator><creator>Alavi, Abass</creator><general>Springer Berlin Heidelberg</general><general>Springer Nature B.V</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7RV</scope><scope>7TK</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</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>KB0</scope><scope>LK8</scope><scope>M0S</scope><scope>M1P</scope><scope>M7P</scope><scope>NAPCQ</scope><scope>P5Z</scope><scope>P62</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7X8</scope><scope>5PM</scope></search><sort><creationdate>20171101</creationdate><title>Optimal FDG PET/CT volumetric parameters for risk stratification in patients with locally advanced non-small cell lung cancer: results from the ACRIN 6668/RTOG 0235 trial</title><author>Salavati, Ali ; Duan, Fenghai ; Snyder, Bradley S. ; Wei, Bo ; Houshmand, Sina ; Khiewvan, Benjapa ; Opanowski, Adam ; Simone, Charles B. ; Siegel, Barry A. ; Machtay, Mitchell ; Alavi, Abass</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c470t-88c0b77adecc4489afae2bdb186e1204f2811fa93e708101b9661a53f694a6133</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Adaptive algorithms</topic><topic>Biomarkers</topic><topic>Cancer</topic><topic>Cardiology</topic><topic>Clinical trials</topic><topic>Computed tomography</topic><topic>Glycolysis</topic><topic>Hazard assessment</topic><topic>Imaging</topic><topic>Lung cancer</topic><topic>Lymph nodes</topic><topic>Medical prognosis</topic><topic>Medicine</topic><topic>Medicine & Public Health</topic><topic>Metabolism</topic><topic>Non-small cell lung carcinoma</topic><topic>Nuclear Medicine</topic><topic>Oncology</topic><topic>Original Article</topic><topic>Orthopedics</topic><topic>Patients</topic><topic>Positron emission</topic><topic>Radiology</topic><topic>Regression analysis</topic><topic>Therapeutic applications</topic><topic>Time dependence</topic><topic>Tomography</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Salavati, Ali</creatorcontrib><creatorcontrib>Duan, Fenghai</creatorcontrib><creatorcontrib>Snyder, Bradley S.</creatorcontrib><creatorcontrib>Wei, Bo</creatorcontrib><creatorcontrib>Houshmand, Sina</creatorcontrib><creatorcontrib>Khiewvan, Benjapa</creatorcontrib><creatorcontrib>Opanowski, Adam</creatorcontrib><creatorcontrib>Simone, Charles B.</creatorcontrib><creatorcontrib>Siegel, Barry A.</creatorcontrib><creatorcontrib>Machtay, Mitchell</creatorcontrib><creatorcontrib>Alavi, 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Abass</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Optimal FDG PET/CT volumetric parameters for risk stratification in patients with locally advanced non-small cell lung cancer: results from the ACRIN 6668/RTOG 0235 trial</atitle><jtitle>European journal of nuclear medicine and molecular imaging</jtitle><stitle>Eur J Nucl Med Mol Imaging</stitle><addtitle>Eur J Nucl Med Mol Imaging</addtitle><date>2017-11-01</date><risdate>2017</risdate><volume>44</volume><issue>12</issue><spage>1969</spage><epage>1983</epage><pages>1969-1983</pages><issn>1619-7070</issn><eissn>1619-7089</eissn><abstract>Purpose
In recent years, multiple studies have demonstrated the value of volumetric FDG-PET/CT parameters as independent prognostic factors in patients with non-small cell lung cancer (NSCLC). We aimed to determine the optimal cut-off points of pretreatment volumetric FDG-PET/CT parameters in predicting overall survival (OS) in patients with locally advanced NSCLC and to recommend imaging biomarkers appropriate for routine clinical applications.
Methods
Patients with inoperable stage IIB/III NSCLC enrolled in ACRIN 6668/RTOG 0235 were included. Pretreatment FDG-PET scans were quantified using semiautomatic adaptive contrast-oriented thresholding and local-background partial-volume-effect-correction algorithms. For each patient, the following indices were measured: metabolic tumor volume (MTV), total lesion glycolysis (TLG), SUVmax, SUVmean, partial-volume-corrected TLG (pvcTLG), and pvcSUVmean for the whole-body, primary tumor, and regional lymph nodes. The association between each index and patient outcome was assessed using Cox proportional hazards regression. Optimal cut-off points were estimated using recursive binary partitioning in a conditional inference framework and used in Kaplan-Meier curves with log-rank testing. The discriminatory ability of each index was examined using time-dependent receiver operating characteristic (ROC) curves and corresponding area under the curve (AUC(t)).
Results
The study included 196 patients. Pretreatment whole-body and primary tumor MTV, TLG, and pvcTLG were independently prognostic of OS. Optimal cut-off points were 175.0, 270.9, and 35.5 cm
3
for whole-body TLG, pvcTLG, and MTV, and were 168.2, 239.8, and 17.4 cm
3
for primary tumor TLG, pvcTLG, and MTV, respectively. In time-dependent ROC analysis, AUC(t) for MTV and TLG were uniformly higher than that of SUV measures over all time points. Primary tumor and whole-body parameters demonstrated similar patterns of separation for those patients above versus below the optimal cut-off points in Kaplan-Meier curves and in time-dependent ROC analysis.
Conclusion
We demonstrated that pretreatment whole-body and primary tumor volumetric FDG-PET/CT parameters, including MTV, TLG, and pvcTLG, are strongly prognostic for OS in patients with locally advanced NSCLC, and have similar discriminatory ability. Therefore, we believe that, after validation in future trials, the derived optimal cut-off points for primary tumor volumetric FDG-PET/CT parameters, or their more refined versions, could be incorporated into routine clinical practice, and may provide more accurate prognostication and staging based on tumor metabolic features.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><pmid>28689281</pmid><doi>10.1007/s00259-017-3753-x</doi><tpages>15</tpages><oa>free_for_read</oa></addata></record> |
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| source | SpringerNature Journals |
| subjects | Adaptive algorithms Biomarkers Cancer Cardiology Clinical trials Computed tomography Glycolysis Hazard assessment Imaging Lung cancer Lymph nodes Medical prognosis Medicine Medicine & Public Health Metabolism Non-small cell lung carcinoma Nuclear Medicine Oncology Original Article Orthopedics Patients Positron emission Radiology Regression analysis Therapeutic applications Time dependence Tomography |
| title | Optimal FDG PET/CT volumetric parameters for risk stratification in patients with locally advanced non-small cell lung cancer: results from the ACRIN 6668/RTOG 0235 trial |
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