Validation and optimization of a web-based nomogram for predicting survival of patients with newly diagnosed glioblastoma
Purpose To optimize and validate a current (NRG [a newly constituted National Clinical Trials Network group through National Surgical Adjuvant Breast and Bowel Project [NSABP], the Radiation Therapy Oncology Group [RTOG] and the Gynecologic Oncology Group (GOG)]) nomogram for glioblastoma patients a...
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| Veröffentlicht in: | Strahlentherapie und Onkologie 2020, Vol.196 (1), p.58-69 |
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| creator | Kim, Nalee Chang, Jee Suk Wee, Chan Woo Kim, In Ah Chang, Jong Hee Lee, Hye Sun Kim, Se Hoon Kang, Seok-Gu Kim, Eui Hyun Yoon, Hong In Kim, Jun Won Hong, Chang-Ki Cho, Jaeho Kim, Eunji Kim, Tae Min Kim, Yu Jung Park, Chul-Kee Kim, Jin Wook Kim, Chae-Yong Choi, Seung Hong Kim, Jae Hyoung Park, Sung-Hye Choe, Gheeyoung Lee, Soon-Tae Kim, Il Han Suh, Chang-Ok |
| description | Purpose
To optimize and validate a current (NRG [a newly constituted National Clinical Trials Network group through National Surgical Adjuvant Breast and Bowel Project [NSABP], the Radiation Therapy Oncology Group [RTOG] and the Gynecologic Oncology Group (GOG)]) nomogram for glioblastoma patients as part of continuous validation.
Methods
We identified patients newly diagnosed with glioblastoma who were treated with temozolomide-based chemoradiotherapy between 2006 and 2016 at three large-volume hospitals. The extent of resection was determined via postoperative MRI. The discrimination and calibration abilities of the prediction algorithm were assessed; if additional factors were identified as independent prognostic factors, updated models were developed using the data from two hospitals and were externally validated using the third hospital. Models were internally validated using cross-validation and bootstrapping.
Results
A total of 837 patients met the eligibility criteria. The median overall survival (OS) was 20.0 (95% CI 18.5–21.5) months. The original nomogram was able to estimate the 6‑, 12-, and 24-month OS probabilities, but it slightly underestimated the OS values. In multivariable Cox regression analysis, MRI-defined total resection had a greater impact on OS than that shown by the original nomogram, and two additional factors—
IDH1
mutation and tumor contacting subventricular zone—were newly identified as independent prognostic values. An updated nomogram incorporating these new variables outperformed the original nomogram (C-index at 6, 12, 24, and 36 months: 0.728, 0.688, 0.688, and 0.685, respectively) and was well calibrated. External validation using an independent cohort showed C‑indices of 0.787, 0.751, 0.719, and 0.702 at 6, 12, 24, and 36 months, respectively, and was well calibrated.
Conclusion
An updated and validated nomogram incorporating the contemporary parameters can estimate individual survival outcomes in patients with glioblastoma with better accuracy. |
| doi_str_mv | 10.1007/s00066-019-01512-y |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_2286940055</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2286940055</sourcerecordid><originalsourceid>FETCH-LOGICAL-c290y-aec5ac28d1ee1a23b70184659843c074cf301b28eef7d7c7f9f8cf7b1ca085773</originalsourceid><addsrcrecordid>eNp9kUFv1iAch4nRuNfNL-DBkOyyS_VPSwscl2VTkyVe1HgjlEJlaaGDdm-6T-Nn8ZPJu06XePBACPD8fhAehN4QeEcA2PsEAE1TABF51KQs1mdoR2iVl0J8f452QJgoGKn5EXqV0g0AaaigL9FRRSgXtGY7dP9NDa5TswseK9_hMM1udPfbRrBY_fq5N23RqmQ67MMY-qhGbEPEUzSd07PzPU5LvHN3ajgEphw1fk547-Yf2Jv9sOLOqd6HQ0M_uNAOKs1hVCfohVVDMq8f52P09eryy8XH4vrzh08X59eFLgWshTK6VrrkHTGGqLJqGRBOm1pwWmlgVNsKSFtyYyzrmGZWWK4ta4lWwGvGqmN0tvVOMdwuJs1ydEmbYVDehCXJsuSNoAB1ndHTf9CbsESfX3egKM-fXJFMlRulY0gpGiun6EYVV0lAHszIzYzMZuSDGbnm0NvH6qUdTfc38kdFBqoNSPnI9yY-3f2f2t-OEJz7</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2284851231</pqid></control><display><type>article</type><title>Validation and optimization of a web-based nomogram for predicting survival of patients with newly diagnosed glioblastoma</title><source>SpringerLink Journals</source><creator>Kim, Nalee ; Chang, Jee Suk ; Wee, Chan Woo ; Kim, In Ah ; Chang, Jong Hee ; Lee, Hye Sun ; Kim, Se Hoon ; Kang, Seok-Gu ; Kim, Eui Hyun ; Yoon, Hong In ; Kim, Jun Won ; Hong, Chang-Ki ; Cho, Jaeho ; Kim, Eunji ; Kim, Tae Min ; Kim, Yu Jung ; Park, Chul-Kee ; Kim, Jin Wook ; Kim, Chae-Yong ; Choi, Seung Hong ; Kim, Jae Hyoung ; Park, Sung-Hye ; Choe, Gheeyoung ; Lee, Soon-Tae ; Kim, Il Han ; Suh, Chang-Ok</creator><creatorcontrib>Kim, Nalee ; Chang, Jee Suk ; Wee, Chan Woo ; Kim, In Ah ; Chang, Jong Hee ; Lee, Hye Sun ; Kim, Se Hoon ; Kang, Seok-Gu ; Kim, Eui Hyun ; Yoon, Hong In ; Kim, Jun Won ; Hong, Chang-Ki ; Cho, Jaeho ; Kim, Eunji ; Kim, Tae Min ; Kim, Yu Jung ; Park, Chul-Kee ; Kim, Jin Wook ; Kim, Chae-Yong ; Choi, Seung Hong ; Kim, Jae Hyoung ; Park, Sung-Hye ; Choe, Gheeyoung ; Lee, Soon-Tae ; Kim, Il Han ; Suh, Chang-Ok</creatorcontrib><description>Purpose
To optimize and validate a current (NRG [a newly constituted National Clinical Trials Network group through National Surgical Adjuvant Breast and Bowel Project [NSABP], the Radiation Therapy Oncology Group [RTOG] and the Gynecologic Oncology Group (GOG)]) nomogram for glioblastoma patients as part of continuous validation.
Methods
We identified patients newly diagnosed with glioblastoma who were treated with temozolomide-based chemoradiotherapy between 2006 and 2016 at three large-volume hospitals. The extent of resection was determined via postoperative MRI. The discrimination and calibration abilities of the prediction algorithm were assessed; if additional factors were identified as independent prognostic factors, updated models were developed using the data from two hospitals and were externally validated using the third hospital. Models were internally validated using cross-validation and bootstrapping.
Results
A total of 837 patients met the eligibility criteria. The median overall survival (OS) was 20.0 (95% CI 18.5–21.5) months. The original nomogram was able to estimate the 6‑, 12-, and 24-month OS probabilities, but it slightly underestimated the OS values. In multivariable Cox regression analysis, MRI-defined total resection had a greater impact on OS than that shown by the original nomogram, and two additional factors—
IDH1
mutation and tumor contacting subventricular zone—were newly identified as independent prognostic values. An updated nomogram incorporating these new variables outperformed the original nomogram (C-index at 6, 12, 24, and 36 months: 0.728, 0.688, 0.688, and 0.685, respectively) and was well calibrated. External validation using an independent cohort showed C‑indices of 0.787, 0.751, 0.719, and 0.702 at 6, 12, 24, and 36 months, respectively, and was well calibrated.
Conclusion
An updated and validated nomogram incorporating the contemporary parameters can estimate individual survival outcomes in patients with glioblastoma with better accuracy.</description><identifier>ISSN: 0179-7158</identifier><identifier>EISSN: 1439-099X</identifier><identifier>DOI: 10.1007/s00066-019-01512-y</identifier><identifier>PMID: 31489457</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Algorithms ; Brain cancer ; Calibration ; Hospitals ; Identification methods ; Mathematical models ; Medical prognosis ; Medicine ; Medicine & Public Health ; Mutation ; Nomograms ; Oncology ; Optimization ; Original Article ; Parameter estimation ; Predictions ; Radiation therapy ; Radiotherapy ; Regression analysis ; Survival ; Tumors</subject><ispartof>Strahlentherapie und Onkologie, 2020, Vol.196 (1), p.58-69</ispartof><rights>Springer-Verlag GmbH Germany, part of Springer Nature 2019</rights><rights>Strahlentherapie und Onkologie is a copyright of Springer, (2019). All Rights Reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c290y-aec5ac28d1ee1a23b70184659843c074cf301b28eef7d7c7f9f8cf7b1ca085773</citedby><cites>FETCH-LOGICAL-c290y-aec5ac28d1ee1a23b70184659843c074cf301b28eef7d7c7f9f8cf7b1ca085773</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/s00066-019-01512-y$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00066-019-01512-y$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27901,27902,41464,42533,51294</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/31489457$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Kim, Nalee</creatorcontrib><creatorcontrib>Chang, Jee Suk</creatorcontrib><creatorcontrib>Wee, Chan Woo</creatorcontrib><creatorcontrib>Kim, In Ah</creatorcontrib><creatorcontrib>Chang, Jong Hee</creatorcontrib><creatorcontrib>Lee, Hye Sun</creatorcontrib><creatorcontrib>Kim, Se Hoon</creatorcontrib><creatorcontrib>Kang, Seok-Gu</creatorcontrib><creatorcontrib>Kim, Eui Hyun</creatorcontrib><creatorcontrib>Yoon, Hong In</creatorcontrib><creatorcontrib>Kim, Jun Won</creatorcontrib><creatorcontrib>Hong, Chang-Ki</creatorcontrib><creatorcontrib>Cho, Jaeho</creatorcontrib><creatorcontrib>Kim, Eunji</creatorcontrib><creatorcontrib>Kim, Tae Min</creatorcontrib><creatorcontrib>Kim, Yu Jung</creatorcontrib><creatorcontrib>Park, Chul-Kee</creatorcontrib><creatorcontrib>Kim, Jin Wook</creatorcontrib><creatorcontrib>Kim, Chae-Yong</creatorcontrib><creatorcontrib>Choi, Seung Hong</creatorcontrib><creatorcontrib>Kim, Jae Hyoung</creatorcontrib><creatorcontrib>Park, Sung-Hye</creatorcontrib><creatorcontrib>Choe, Gheeyoung</creatorcontrib><creatorcontrib>Lee, Soon-Tae</creatorcontrib><creatorcontrib>Kim, Il Han</creatorcontrib><creatorcontrib>Suh, Chang-Ok</creatorcontrib><title>Validation and optimization of a web-based nomogram for predicting survival of patients with newly diagnosed glioblastoma</title><title>Strahlentherapie und Onkologie</title><addtitle>Strahlenther Onkol</addtitle><addtitle>Strahlenther Onkol</addtitle><description>Purpose
To optimize and validate a current (NRG [a newly constituted National Clinical Trials Network group through National Surgical Adjuvant Breast and Bowel Project [NSABP], the Radiation Therapy Oncology Group [RTOG] and the Gynecologic Oncology Group (GOG)]) nomogram for glioblastoma patients as part of continuous validation.
Methods
We identified patients newly diagnosed with glioblastoma who were treated with temozolomide-based chemoradiotherapy between 2006 and 2016 at three large-volume hospitals. The extent of resection was determined via postoperative MRI. The discrimination and calibration abilities of the prediction algorithm were assessed; if additional factors were identified as independent prognostic factors, updated models were developed using the data from two hospitals and were externally validated using the third hospital. Models were internally validated using cross-validation and bootstrapping.
Results
A total of 837 patients met the eligibility criteria. The median overall survival (OS) was 20.0 (95% CI 18.5–21.5) months. The original nomogram was able to estimate the 6‑, 12-, and 24-month OS probabilities, but it slightly underestimated the OS values. In multivariable Cox regression analysis, MRI-defined total resection had a greater impact on OS than that shown by the original nomogram, and two additional factors—
IDH1
mutation and tumor contacting subventricular zone—were newly identified as independent prognostic values. An updated nomogram incorporating these new variables outperformed the original nomogram (C-index at 6, 12, 24, and 36 months: 0.728, 0.688, 0.688, and 0.685, respectively) and was well calibrated. External validation using an independent cohort showed C‑indices of 0.787, 0.751, 0.719, and 0.702 at 6, 12, 24, and 36 months, respectively, and was well calibrated.
Conclusion
An updated and validated nomogram incorporating the contemporary parameters can estimate individual survival outcomes in patients with glioblastoma with better accuracy.</description><subject>Algorithms</subject><subject>Brain cancer</subject><subject>Calibration</subject><subject>Hospitals</subject><subject>Identification methods</subject><subject>Mathematical models</subject><subject>Medical prognosis</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Mutation</subject><subject>Nomograms</subject><subject>Oncology</subject><subject>Optimization</subject><subject>Original Article</subject><subject>Parameter estimation</subject><subject>Predictions</subject><subject>Radiation therapy</subject><subject>Radiotherapy</subject><subject>Regression analysis</subject><subject>Survival</subject><subject>Tumors</subject><issn>0179-7158</issn><issn>1439-099X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><recordid>eNp9kUFv1iAch4nRuNfNL-DBkOyyS_VPSwscl2VTkyVe1HgjlEJlaaGDdm-6T-Nn8ZPJu06XePBACPD8fhAehN4QeEcA2PsEAE1TABF51KQs1mdoR2iVl0J8f452QJgoGKn5EXqV0g0AaaigL9FRRSgXtGY7dP9NDa5TswseK9_hMM1udPfbRrBY_fq5N23RqmQ67MMY-qhGbEPEUzSd07PzPU5LvHN3ajgEphw1fk547-Yf2Jv9sOLOqd6HQ0M_uNAOKs1hVCfohVVDMq8f52P09eryy8XH4vrzh08X59eFLgWshTK6VrrkHTGGqLJqGRBOm1pwWmlgVNsKSFtyYyzrmGZWWK4ta4lWwGvGqmN0tvVOMdwuJs1ydEmbYVDehCXJsuSNoAB1ndHTf9CbsESfX3egKM-fXJFMlRulY0gpGiun6EYVV0lAHszIzYzMZuSDGbnm0NvH6qUdTfc38kdFBqoNSPnI9yY-3f2f2t-OEJz7</recordid><startdate>2020</startdate><enddate>2020</enddate><creator>Kim, Nalee</creator><creator>Chang, Jee Suk</creator><creator>Wee, Chan Woo</creator><creator>Kim, In Ah</creator><creator>Chang, Jong Hee</creator><creator>Lee, Hye Sun</creator><creator>Kim, Se Hoon</creator><creator>Kang, Seok-Gu</creator><creator>Kim, Eui Hyun</creator><creator>Yoon, Hong In</creator><creator>Kim, Jun Won</creator><creator>Hong, Chang-Ki</creator><creator>Cho, Jaeho</creator><creator>Kim, Eunji</creator><creator>Kim, Tae Min</creator><creator>Kim, Yu Jung</creator><creator>Park, Chul-Kee</creator><creator>Kim, Jin Wook</creator><creator>Kim, Chae-Yong</creator><creator>Choi, Seung Hong</creator><creator>Kim, Jae Hyoung</creator><creator>Park, Sung-Hye</creator><creator>Choe, Gheeyoung</creator><creator>Lee, Soon-Tae</creator><creator>Kim, Il Han</creator><creator>Suh, Chang-Ok</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>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8C1</scope><scope>8FE</scope><scope>8FG</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>KB0</scope><scope>M0S</scope><scope>M1P</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></search><sort><creationdate>2020</creationdate><title>Validation and optimization of a web-based nomogram for predicting survival of patients with newly diagnosed glioblastoma</title><author>Kim, Nalee ; Chang, Jee Suk ; Wee, Chan Woo ; Kim, In Ah ; Chang, Jong Hee ; Lee, Hye Sun ; Kim, Se Hoon ; Kang, Seok-Gu ; Kim, Eui Hyun ; Yoon, Hong In ; Kim, Jun Won ; Hong, Chang-Ki ; Cho, Jaeho ; Kim, Eunji ; Kim, Tae Min ; Kim, Yu Jung ; Park, Chul-Kee ; Kim, Jin Wook ; Kim, Chae-Yong ; Choi, Seung Hong ; Kim, Jae Hyoung ; Park, Sung-Hye ; Choe, Gheeyoung ; Lee, Soon-Tae ; Kim, Il Han ; Suh, Chang-Ok</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c290y-aec5ac28d1ee1a23b70184659843c074cf301b28eef7d7c7f9f8cf7b1ca085773</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Algorithms</topic><topic>Brain cancer</topic><topic>Calibration</topic><topic>Hospitals</topic><topic>Identification methods</topic><topic>Mathematical models</topic><topic>Medical prognosis</topic><topic>Medicine</topic><topic>Medicine & Public Health</topic><topic>Mutation</topic><topic>Nomograms</topic><topic>Oncology</topic><topic>Optimization</topic><topic>Original Article</topic><topic>Parameter estimation</topic><topic>Predictions</topic><topic>Radiation therapy</topic><topic>Radiotherapy</topic><topic>Regression analysis</topic><topic>Survival</topic><topic>Tumors</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Kim, Nalee</creatorcontrib><creatorcontrib>Chang, Jee Suk</creatorcontrib><creatorcontrib>Wee, Chan Woo</creatorcontrib><creatorcontrib>Kim, In Ah</creatorcontrib><creatorcontrib>Chang, Jong Hee</creatorcontrib><creatorcontrib>Lee, Hye Sun</creatorcontrib><creatorcontrib>Kim, Se Hoon</creatorcontrib><creatorcontrib>Kang, Seok-Gu</creatorcontrib><creatorcontrib>Kim, Eui Hyun</creatorcontrib><creatorcontrib>Yoon, Hong In</creatorcontrib><creatorcontrib>Kim, Jun Won</creatorcontrib><creatorcontrib>Hong, Chang-Ki</creatorcontrib><creatorcontrib>Cho, Jaeho</creatorcontrib><creatorcontrib>Kim, Eunji</creatorcontrib><creatorcontrib>Kim, Tae Min</creatorcontrib><creatorcontrib>Kim, Yu Jung</creatorcontrib><creatorcontrib>Park, Chul-Kee</creatorcontrib><creatorcontrib>Kim, Jin Wook</creatorcontrib><creatorcontrib>Kim, Chae-Yong</creatorcontrib><creatorcontrib>Choi, Seung Hong</creatorcontrib><creatorcontrib>Kim, Jae Hyoung</creatorcontrib><creatorcontrib>Park, Sung-Hye</creatorcontrib><creatorcontrib>Choe, Gheeyoung</creatorcontrib><creatorcontrib>Lee, Soon-Tae</creatorcontrib><creatorcontrib>Kim, Il Han</creatorcontrib><creatorcontrib>Suh, Chang-Ok</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Nursing & Allied Health Database</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>ProQuest SciTech Collection</collection><collection>ProQuest Technology 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 Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central</collection><collection>Technology 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>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Nursing & Allied Health Premium</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</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>MEDLINE - Academic</collection><jtitle>Strahlentherapie und Onkologie</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Kim, Nalee</au><au>Chang, Jee Suk</au><au>Wee, Chan Woo</au><au>Kim, In Ah</au><au>Chang, Jong Hee</au><au>Lee, Hye Sun</au><au>Kim, Se Hoon</au><au>Kang, Seok-Gu</au><au>Kim, Eui Hyun</au><au>Yoon, Hong In</au><au>Kim, Jun Won</au><au>Hong, Chang-Ki</au><au>Cho, Jaeho</au><au>Kim, Eunji</au><au>Kim, Tae Min</au><au>Kim, Yu Jung</au><au>Park, Chul-Kee</au><au>Kim, Jin Wook</au><au>Kim, Chae-Yong</au><au>Choi, Seung Hong</au><au>Kim, Jae Hyoung</au><au>Park, Sung-Hye</au><au>Choe, Gheeyoung</au><au>Lee, Soon-Tae</au><au>Kim, Il Han</au><au>Suh, Chang-Ok</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Validation and optimization of a web-based nomogram for predicting survival of patients with newly diagnosed glioblastoma</atitle><jtitle>Strahlentherapie und Onkologie</jtitle><stitle>Strahlenther Onkol</stitle><addtitle>Strahlenther Onkol</addtitle><date>2020</date><risdate>2020</risdate><volume>196</volume><issue>1</issue><spage>58</spage><epage>69</epage><pages>58-69</pages><issn>0179-7158</issn><eissn>1439-099X</eissn><abstract>Purpose
To optimize and validate a current (NRG [a newly constituted National Clinical Trials Network group through National Surgical Adjuvant Breast and Bowel Project [NSABP], the Radiation Therapy Oncology Group [RTOG] and the Gynecologic Oncology Group (GOG)]) nomogram for glioblastoma patients as part of continuous validation.
Methods
We identified patients newly diagnosed with glioblastoma who were treated with temozolomide-based chemoradiotherapy between 2006 and 2016 at three large-volume hospitals. The extent of resection was determined via postoperative MRI. The discrimination and calibration abilities of the prediction algorithm were assessed; if additional factors were identified as independent prognostic factors, updated models were developed using the data from two hospitals and were externally validated using the third hospital. Models were internally validated using cross-validation and bootstrapping.
Results
A total of 837 patients met the eligibility criteria. The median overall survival (OS) was 20.0 (95% CI 18.5–21.5) months. The original nomogram was able to estimate the 6‑, 12-, and 24-month OS probabilities, but it slightly underestimated the OS values. In multivariable Cox regression analysis, MRI-defined total resection had a greater impact on OS than that shown by the original nomogram, and two additional factors—
IDH1
mutation and tumor contacting subventricular zone—were newly identified as independent prognostic values. An updated nomogram incorporating these new variables outperformed the original nomogram (C-index at 6, 12, 24, and 36 months: 0.728, 0.688, 0.688, and 0.685, respectively) and was well calibrated. External validation using an independent cohort showed C‑indices of 0.787, 0.751, 0.719, and 0.702 at 6, 12, 24, and 36 months, respectively, and was well calibrated.
Conclusion
An updated and validated nomogram incorporating the contemporary parameters can estimate individual survival outcomes in patients with glioblastoma with better accuracy.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><pmid>31489457</pmid><doi>10.1007/s00066-019-01512-y</doi><tpages>12</tpages></addata></record> |
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| source | SpringerLink Journals |
| subjects | Algorithms Brain cancer Calibration Hospitals Identification methods Mathematical models Medical prognosis Medicine Medicine & Public Health Mutation Nomograms Oncology Optimization Original Article Parameter estimation Predictions Radiation therapy Radiotherapy Regression analysis Survival Tumors |
| title | Validation and optimization of a web-based nomogram for predicting survival of patients with newly diagnosed glioblastoma |
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