Personalized prognosis stratification of newly diagnosed glioblastoma applying a statistical decision tree model
Purpose Glioblastoma (GBM) is the most frequent glioma in adults with a high treatment resistance resulting into limited survival. The individual prognosis varies depending on individual prognostic factors, that must be considered while counseling patients with newly diagnosed GBM. The aim of this s...
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| Veröffentlicht in: | Journal of neuro-oncology 2024-07, Vol.168 (3), p.425-433 |
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| container_title | Journal of neuro-oncology |
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| creator | Conrad, Katharina Löber-Handwerker, Ronja Hazaymeh, Mohammad Rohde, Veit Malinova, Vesna |
| description | Purpose
Glioblastoma (GBM) is the most frequent glioma in adults with a high treatment resistance resulting into limited survival. The individual prognosis varies depending on individual prognostic factors, that must be considered while counseling patients with newly diagnosed GBM. The aim of this study was to elaborate a risk stratification algorithm based on reliable prognostic factors to facilitate a personalized prognosis estimation early on after diagnosis.
Methods
A consecutive patient cohort with confirmed GBM treated between 2010 and 2021 was retrospectively analyzed. Clinical, radiological, and molecular parameters were assessed and included in the analysis. Overall survival (OS) was the primary outcome parameter. After identifying the strongest prognostic factors, a risk stratification algorithm was elaborated with estimated odds of survival.
Results
A total of 462 GBM patients were analyzed. The strongest prognostic factors were Charlson Comorbidity Index (CCI), extent of tumor resection, and adjuvant treatment. Patients with CCI ≤ 1 receiving tumor resection had the highest survival odds (88% for 10 months). On the contrary, patients with CCI > 3 receiving no adjuvant treatment had the lowest survival odds (0% for 10 months). The 10-months survival rate in patients with CCI > 3 receiving adjuvant treatment was 56% for patients younger than 70 years and 22% for patients older than 70 years.
Conclusion
A risk stratification algorithm based on significant prognostic factors allowed a personalized early prognosis estimation at the time of GBM diagnosis, that can contribute to a more personalized patient counseling. |
| doi_str_mv | 10.1007/s11060-024-04683-6 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_pubme</sourceid><recordid>TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_11186892</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>3043072994</sourcerecordid><originalsourceid>FETCH-LOGICAL-c426t-38388d8169f6adad50138a849d37b16f6d529d5315b210862f3c791fcf7209d53</originalsourceid><addsrcrecordid>eNp9kU1vFSEYhUmjaa_VP9CFIXHjZpSv4WNlTONX0kQXNumOcAcYaRiYwlzN9dfLeNtaXbiBxXnO4X05AJxh9AojJF5XjBFHHSKsQ4xL2vEjsMG9oJ2ggj4CG4S56HrFrk7Ak1qvEUJMUHwMTqjkVMmebcD8xZWak4nhp7NwLnlMuYYK61LMEnwY2pkTzB4m9yPuoQ1mJRo7xpC30dQlTwaaeY77kEZomrNZ6tKcEVo3hLr6l-IcnLJ18Sl47E2s7tntfQou37_7ev6xu_j84dP524tuYIQvHZVUSisxV54ba2yPMJVGMmWp2GLuue2Jsj3F_ZZgJDnxdBAK-8ELglbhFLw55M677eTs4FLbKOq5hMmUvc4m6L-VFL7pMX_XGGPJpSIt4eVtQsk3O1cXPYU6uBhNcnlXNUWMIkGUYg198Q96nXel_epKccX7NiBtFDlQQ8m1Fufvp8FIr43qQ6O6Nap_N6p5Mz1_uMe95a7CBtADUJuURlf-vP2f2F-UaK6X</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>3069658623</pqid></control><display><type>article</type><title>Personalized prognosis stratification of newly diagnosed glioblastoma applying a statistical decision tree model</title><source>MEDLINE</source><source>SpringerNature Journals</source><creator>Conrad, Katharina ; Löber-Handwerker, Ronja ; Hazaymeh, Mohammad ; Rohde, Veit ; Malinova, Vesna</creator><creatorcontrib>Conrad, Katharina ; Löber-Handwerker, Ronja ; Hazaymeh, Mohammad ; Rohde, Veit ; Malinova, Vesna</creatorcontrib><description>Purpose
Glioblastoma (GBM) is the most frequent glioma in adults with a high treatment resistance resulting into limited survival. The individual prognosis varies depending on individual prognostic factors, that must be considered while counseling patients with newly diagnosed GBM. The aim of this study was to elaborate a risk stratification algorithm based on reliable prognostic factors to facilitate a personalized prognosis estimation early on after diagnosis.
Methods
A consecutive patient cohort with confirmed GBM treated between 2010 and 2021 was retrospectively analyzed. Clinical, radiological, and molecular parameters were assessed and included in the analysis. Overall survival (OS) was the primary outcome parameter. After identifying the strongest prognostic factors, a risk stratification algorithm was elaborated with estimated odds of survival.
Results
A total of 462 GBM patients were analyzed. The strongest prognostic factors were Charlson Comorbidity Index (CCI), extent of tumor resection, and adjuvant treatment. Patients with CCI ≤ 1 receiving tumor resection had the highest survival odds (88% for 10 months). On the contrary, patients with CCI > 3 receiving no adjuvant treatment had the lowest survival odds (0% for 10 months). The 10-months survival rate in patients with CCI > 3 receiving adjuvant treatment was 56% for patients younger than 70 years and 22% for patients older than 70 years.
Conclusion
A risk stratification algorithm based on significant prognostic factors allowed a personalized early prognosis estimation at the time of GBM diagnosis, that can contribute to a more personalized patient counseling.</description><identifier>ISSN: 0167-594X</identifier><identifier>ISSN: 1573-7373</identifier><identifier>EISSN: 1573-7373</identifier><identifier>DOI: 10.1007/s11060-024-04683-6</identifier><identifier>PMID: 38639854</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>Adjuvant therapy ; Adult ; Aged ; Aged, 80 and over ; Algorithms ; Brain Neoplasms - diagnosis ; Brain Neoplasms - mortality ; Brain Neoplasms - therapy ; Comorbidity ; Decision Trees ; Diagnosis ; Female ; Follow-Up Studies ; Glioblastoma ; Glioblastoma - diagnosis ; Glioblastoma - mortality ; Glioblastoma - therapy ; Glioma ; Humans ; Male ; Medical prognosis ; Medicine ; Medicine & Public Health ; Middle Aged ; Neurology ; Oncology ; Patients ; Precision Medicine ; Prognosis ; Retrospective Studies ; Risk Assessment ; Statistical models ; Survival ; Survival Rate ; Treatment resistance ; Tumors</subject><ispartof>Journal of neuro-oncology, 2024-07, Vol.168 (3), p.425-433</ispartof><rights>The Author(s) 2024</rights><rights>2024. The Author(s).</rights><rights>The Author(s) 2024. 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><cites>FETCH-LOGICAL-c426t-38388d8169f6adad50138a849d37b16f6d529d5315b210862f3c791fcf7209d53</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/s11060-024-04683-6$$EPDF$$P50$$Gspringer$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s11060-024-04683-6$$EHTML$$P50$$Gspringer$$Hfree_for_read</linktohtml><link.rule.ids>230,314,780,784,885,27924,27925,41488,42557,51319</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/38639854$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Conrad, Katharina</creatorcontrib><creatorcontrib>Löber-Handwerker, Ronja</creatorcontrib><creatorcontrib>Hazaymeh, Mohammad</creatorcontrib><creatorcontrib>Rohde, Veit</creatorcontrib><creatorcontrib>Malinova, Vesna</creatorcontrib><title>Personalized prognosis stratification of newly diagnosed glioblastoma applying a statistical decision tree model</title><title>Journal of neuro-oncology</title><addtitle>J Neurooncol</addtitle><addtitle>J Neurooncol</addtitle><description>Purpose
Glioblastoma (GBM) is the most frequent glioma in adults with a high treatment resistance resulting into limited survival. The individual prognosis varies depending on individual prognostic factors, that must be considered while counseling patients with newly diagnosed GBM. The aim of this study was to elaborate a risk stratification algorithm based on reliable prognostic factors to facilitate a personalized prognosis estimation early on after diagnosis.
Methods
A consecutive patient cohort with confirmed GBM treated between 2010 and 2021 was retrospectively analyzed. Clinical, radiological, and molecular parameters were assessed and included in the analysis. Overall survival (OS) was the primary outcome parameter. After identifying the strongest prognostic factors, a risk stratification algorithm was elaborated with estimated odds of survival.
Results
A total of 462 GBM patients were analyzed. The strongest prognostic factors were Charlson Comorbidity Index (CCI), extent of tumor resection, and adjuvant treatment. Patients with CCI ≤ 1 receiving tumor resection had the highest survival odds (88% for 10 months). On the contrary, patients with CCI > 3 receiving no adjuvant treatment had the lowest survival odds (0% for 10 months). The 10-months survival rate in patients with CCI > 3 receiving adjuvant treatment was 56% for patients younger than 70 years and 22% for patients older than 70 years.
Conclusion
A risk stratification algorithm based on significant prognostic factors allowed a personalized early prognosis estimation at the time of GBM diagnosis, that can contribute to a more personalized patient counseling.</description><subject>Adjuvant therapy</subject><subject>Adult</subject><subject>Aged</subject><subject>Aged, 80 and over</subject><subject>Algorithms</subject><subject>Brain Neoplasms - diagnosis</subject><subject>Brain Neoplasms - mortality</subject><subject>Brain Neoplasms - therapy</subject><subject>Comorbidity</subject><subject>Decision Trees</subject><subject>Diagnosis</subject><subject>Female</subject><subject>Follow-Up Studies</subject><subject>Glioblastoma</subject><subject>Glioblastoma - diagnosis</subject><subject>Glioblastoma - mortality</subject><subject>Glioblastoma - therapy</subject><subject>Glioma</subject><subject>Humans</subject><subject>Male</subject><subject>Medical prognosis</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Middle Aged</subject><subject>Neurology</subject><subject>Oncology</subject><subject>Patients</subject><subject>Precision Medicine</subject><subject>Prognosis</subject><subject>Retrospective Studies</subject><subject>Risk Assessment</subject><subject>Statistical models</subject><subject>Survival</subject><subject>Survival Rate</subject><subject>Treatment resistance</subject><subject>Tumors</subject><issn>0167-594X</issn><issn>1573-7373</issn><issn>1573-7373</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>C6C</sourceid><sourceid>EIF</sourceid><recordid>eNp9kU1vFSEYhUmjaa_VP9CFIXHjZpSv4WNlTONX0kQXNumOcAcYaRiYwlzN9dfLeNtaXbiBxXnO4X05AJxh9AojJF5XjBFHHSKsQ4xL2vEjsMG9oJ2ggj4CG4S56HrFrk7Ak1qvEUJMUHwMTqjkVMmebcD8xZWak4nhp7NwLnlMuYYK61LMEnwY2pkTzB4m9yPuoQ1mJRo7xpC30dQlTwaaeY77kEZomrNZ6tKcEVo3hLr6l-IcnLJ18Sl47E2s7tntfQou37_7ev6xu_j84dP524tuYIQvHZVUSisxV54ba2yPMJVGMmWp2GLuue2Jsj3F_ZZgJDnxdBAK-8ELglbhFLw55M677eTs4FLbKOq5hMmUvc4m6L-VFL7pMX_XGGPJpSIt4eVtQsk3O1cXPYU6uBhNcnlXNUWMIkGUYg198Q96nXel_epKccX7NiBtFDlQQ8m1Fufvp8FIr43qQ6O6Nap_N6p5Mz1_uMe95a7CBtADUJuURlf-vP2f2F-UaK6X</recordid><startdate>20240701</startdate><enddate>20240701</enddate><creator>Conrad, Katharina</creator><creator>Löber-Handwerker, Ronja</creator><creator>Hazaymeh, Mohammad</creator><creator>Rohde, Veit</creator><creator>Malinova, Vesna</creator><general>Springer US</general><general>Springer Nature B.V</general><scope>C6C</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>7TK</scope><scope>K9.</scope><scope>7X8</scope><scope>5PM</scope></search><sort><creationdate>20240701</creationdate><title>Personalized prognosis stratification of newly diagnosed glioblastoma applying a statistical decision tree model</title><author>Conrad, Katharina ; Löber-Handwerker, Ronja ; Hazaymeh, Mohammad ; Rohde, Veit ; Malinova, Vesna</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c426t-38388d8169f6adad50138a849d37b16f6d529d5315b210862f3c791fcf7209d53</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Adjuvant therapy</topic><topic>Adult</topic><topic>Aged</topic><topic>Aged, 80 and over</topic><topic>Algorithms</topic><topic>Brain Neoplasms - diagnosis</topic><topic>Brain Neoplasms - mortality</topic><topic>Brain Neoplasms - therapy</topic><topic>Comorbidity</topic><topic>Decision Trees</topic><topic>Diagnosis</topic><topic>Female</topic><topic>Follow-Up Studies</topic><topic>Glioblastoma</topic><topic>Glioblastoma - diagnosis</topic><topic>Glioblastoma - mortality</topic><topic>Glioblastoma - therapy</topic><topic>Glioma</topic><topic>Humans</topic><topic>Male</topic><topic>Medical prognosis</topic><topic>Medicine</topic><topic>Medicine & Public Health</topic><topic>Middle Aged</topic><topic>Neurology</topic><topic>Oncology</topic><topic>Patients</topic><topic>Precision Medicine</topic><topic>Prognosis</topic><topic>Retrospective Studies</topic><topic>Risk Assessment</topic><topic>Statistical models</topic><topic>Survival</topic><topic>Survival Rate</topic><topic>Treatment resistance</topic><topic>Tumors</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Conrad, Katharina</creatorcontrib><creatorcontrib>Löber-Handwerker, Ronja</creatorcontrib><creatorcontrib>Hazaymeh, Mohammad</creatorcontrib><creatorcontrib>Rohde, Veit</creatorcontrib><creatorcontrib>Malinova, Vesna</creatorcontrib><collection>Springer Nature OA Free Journals</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Neurosciences Abstracts</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Journal of neuro-oncology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Conrad, Katharina</au><au>Löber-Handwerker, Ronja</au><au>Hazaymeh, Mohammad</au><au>Rohde, Veit</au><au>Malinova, Vesna</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Personalized prognosis stratification of newly diagnosed glioblastoma applying a statistical decision tree model</atitle><jtitle>Journal of neuro-oncology</jtitle><stitle>J Neurooncol</stitle><addtitle>J Neurooncol</addtitle><date>2024-07-01</date><risdate>2024</risdate><volume>168</volume><issue>3</issue><spage>425</spage><epage>433</epage><pages>425-433</pages><issn>0167-594X</issn><issn>1573-7373</issn><eissn>1573-7373</eissn><abstract>Purpose
Glioblastoma (GBM) is the most frequent glioma in adults with a high treatment resistance resulting into limited survival. The individual prognosis varies depending on individual prognostic factors, that must be considered while counseling patients with newly diagnosed GBM. The aim of this study was to elaborate a risk stratification algorithm based on reliable prognostic factors to facilitate a personalized prognosis estimation early on after diagnosis.
Methods
A consecutive patient cohort with confirmed GBM treated between 2010 and 2021 was retrospectively analyzed. Clinical, radiological, and molecular parameters were assessed and included in the analysis. Overall survival (OS) was the primary outcome parameter. After identifying the strongest prognostic factors, a risk stratification algorithm was elaborated with estimated odds of survival.
Results
A total of 462 GBM patients were analyzed. The strongest prognostic factors were Charlson Comorbidity Index (CCI), extent of tumor resection, and adjuvant treatment. Patients with CCI ≤ 1 receiving tumor resection had the highest survival odds (88% for 10 months). On the contrary, patients with CCI > 3 receiving no adjuvant treatment had the lowest survival odds (0% for 10 months). The 10-months survival rate in patients with CCI > 3 receiving adjuvant treatment was 56% for patients younger than 70 years and 22% for patients older than 70 years.
Conclusion
A risk stratification algorithm based on significant prognostic factors allowed a personalized early prognosis estimation at the time of GBM diagnosis, that can contribute to a more personalized patient counseling.</abstract><cop>New York</cop><pub>Springer US</pub><pmid>38639854</pmid><doi>10.1007/s11060-024-04683-6</doi><tpages>9</tpages><oa>free_for_read</oa></addata></record> |
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| identifier | ISSN: 0167-594X |
| ispartof | Journal of neuro-oncology, 2024-07, Vol.168 (3), p.425-433 |
| issn | 0167-594X 1573-7373 1573-7373 |
| language | eng |
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| source | MEDLINE; SpringerNature Journals |
| subjects | Adjuvant therapy Adult Aged Aged, 80 and over Algorithms Brain Neoplasms - diagnosis Brain Neoplasms - mortality Brain Neoplasms - therapy Comorbidity Decision Trees Diagnosis Female Follow-Up Studies Glioblastoma Glioblastoma - diagnosis Glioblastoma - mortality Glioblastoma - therapy Glioma Humans Male Medical prognosis Medicine Medicine & Public Health Middle Aged Neurology Oncology Patients Precision Medicine Prognosis Retrospective Studies Risk Assessment Statistical models Survival Survival Rate Treatment resistance Tumors |
| title | Personalized prognosis stratification of newly diagnosed glioblastoma applying a statistical decision tree model |
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