Loading…

P13.02 Conventional MRI radiomics in the diagnosis of early- and pseudo-progression

Abstract BACKGROUND After radiochemotherapy, 20% to 30% of patients with early worsening MRI experience pseudoprogression (Psp) which is not distinguishable from early progression (EP). We aimed to assess the diagnostic performance and survival predictive ability of radiomics in patients with suspec...

Full description

Saved in:
Bibliographic Details
Published in:Neuro-oncology (Charlottesville, Va.) Va.), 2019-09, Vol.21 (Supplement_3), p.iii62-iii62
Main Authors: Bani Sadr, A, Eker, O F, Berner, L, Ameli, R, Hermier, M, Barritault, M, Meyronet, D, Guyotat, J, Jouanneau, E, Honnorat, J, Ducray, F, Berthezène, Y
Format: Article
Language:English
Subjects:
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by
cites
container_end_page iii62
container_issue Supplement_3
container_start_page iii62
container_title Neuro-oncology (Charlottesville, Va.)
container_volume 21
creator Bani Sadr, A
Eker, O F
Berner, L
Ameli, R
Hermier, M
Barritault, M
Meyronet, D
Guyotat, J
Jouanneau, E
Honnorat, J
Ducray, F
Berthezène, Y
description Abstract BACKGROUND After radiochemotherapy, 20% to 30% of patients with early worsening MRI experience pseudoprogression (Psp) which is not distinguishable from early progression (EP). We aimed to assess the diagnostic performance and survival predictive ability of radiomics in patients with suspected EP or Psp. MATERIAL AND METHODS Radiomics features (RF) of 76 patients (53 EP and 23 Psp) retrospectively identified were extracted from conventional MRI based on four volumes-of-interest. Subjects were randomly assigned into training and validation groups. Classification model (EP versus Psp) consisted of a random forest algorithm after univariate filtering. Overall (OS) and progression-free survivals (PFS) were predicted using a semi-supervised principal component analysis and, forecasts were evaluated using C index and integrated Brier scores (IBS). RESULTS Using 11 RFs, radiomics classified patients with 75.0% and 76.0% accuracy, 81.6% and 94.1% sensitivity, 50.0% and 37.5% specificity, respectively in training and validation phases. Addition of MGMT promoter status improved accuracy to 83% and 79.2% and, specificity to 63.6% and 75%. OS model included 14 RFs and stratified low- and high-risk patients both in the training (hazard ratio (HR), 3.63, p=0.002) and the validation (HR, 3.76, p=0.001) phases. Similarly, PFS model stratified patients during training (HR, 2.58, p=0.005) and validation (HR, 3.58, p=0.004) phases using 5 RF. OS and PFS forecasts had C index of 0.65 and 0.69 and IBS of 0.122 and 0.147, respectively. CONCLUSION Conventional MRI radiomics has promising diagnostic value, especially when combined with MGMT promoter status, but specificity remains moderate. In addition, our results suggest a potential for predicting OS and PFS.
doi_str_mv 10.1093/neuonc/noz126.223
format article
fullrecord <record><control><sourceid>oup_pubme</sourceid><recordid>TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_6796042</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><oup_id>10.1093/neuonc/noz126.223</oup_id><sourcerecordid>10.1093/neuonc/noz126.223</sourcerecordid><originalsourceid>FETCH-LOGICAL-c1773-b1397e48b7547046a9715ae237a75c85a2215312f7e4df59f5f9167c5190529c3</originalsourceid><addsrcrecordid>eNqNkNtKAzEQhoMoWKsP4F0ewG0zySbZ3AhSPBQqiofrkGazbaRNlqQr1Kd3dUXwzqsZmP__GD6EzoFMgCg2Da6LwU5D_AAqJpSyAzQCTlnBKyEOv3daVBzkMTrJ-Y0QClzACD0_ApsQimcxvLuw8zGYDb5_muNkah-33mbsA96tHa69WYWYfcaxwc6kzb7AJtS4za6rY9GmuEou555wio4as8nu7GeO0evN9cvsrlg83M5nV4vCgpSsWAJT0pXVUvJSklIYJYEbR5k0ktuKG9r_yIA2fahuuGp4o0BIy0ERTpVlY3Q5cNtuuXW17f9PZqPb5Lcm7XU0Xv-9BL_Wq_iuhVSClLQHwACwKeacXPPbBaK_tOpBqx606l5r37kYOrFr_xH_BCVIfOE</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype></control><display><type>article</type><title>P13.02 Conventional MRI radiomics in the diagnosis of early- and pseudo-progression</title><source>Open Access: PubMed Central</source><source>Oxford Journals Online</source><creator>Bani Sadr, A ; Eker, O F ; Berner, L ; Ameli, R ; Hermier, M ; Barritault, M ; Meyronet, D ; Guyotat, J ; Jouanneau, E ; Honnorat, J ; Ducray, F ; Berthezène, Y</creator><creatorcontrib>Bani Sadr, A ; Eker, O F ; Berner, L ; Ameli, R ; Hermier, M ; Barritault, M ; Meyronet, D ; Guyotat, J ; Jouanneau, E ; Honnorat, J ; Ducray, F ; Berthezène, Y</creatorcontrib><description>Abstract BACKGROUND After radiochemotherapy, 20% to 30% of patients with early worsening MRI experience pseudoprogression (Psp) which is not distinguishable from early progression (EP). We aimed to assess the diagnostic performance and survival predictive ability of radiomics in patients with suspected EP or Psp. MATERIAL AND METHODS Radiomics features (RF) of 76 patients (53 EP and 23 Psp) retrospectively identified were extracted from conventional MRI based on four volumes-of-interest. Subjects were randomly assigned into training and validation groups. Classification model (EP versus Psp) consisted of a random forest algorithm after univariate filtering. Overall (OS) and progression-free survivals (PFS) were predicted using a semi-supervised principal component analysis and, forecasts were evaluated using C index and integrated Brier scores (IBS). RESULTS Using 11 RFs, radiomics classified patients with 75.0% and 76.0% accuracy, 81.6% and 94.1% sensitivity, 50.0% and 37.5% specificity, respectively in training and validation phases. Addition of MGMT promoter status improved accuracy to 83% and 79.2% and, specificity to 63.6% and 75%. OS model included 14 RFs and stratified low- and high-risk patients both in the training (hazard ratio (HR), 3.63, p=0.002) and the validation (HR, 3.76, p=0.001) phases. Similarly, PFS model stratified patients during training (HR, 2.58, p=0.005) and validation (HR, 3.58, p=0.004) phases using 5 RF. OS and PFS forecasts had C index of 0.65 and 0.69 and IBS of 0.122 and 0.147, respectively. CONCLUSION Conventional MRI radiomics has promising diagnostic value, especially when combined with MGMT promoter status, but specificity remains moderate. In addition, our results suggest a potential for predicting OS and PFS.</description><identifier>ISSN: 1522-8517</identifier><identifier>EISSN: 1523-5866</identifier><identifier>DOI: 10.1093/neuonc/noz126.223</identifier><language>eng</language><publisher>US: Oxford University Press</publisher><subject>Poster Presentations</subject><ispartof>Neuro-oncology (Charlottesville, Va.), 2019-09, Vol.21 (Supplement_3), p.iii62-iii62</ispartof><rights>The Author(s) 2019. Published by Oxford University Press on behalf of the Society for Neuro-Oncology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com 2019</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6796042/pdf/$$EPDF$$P50$$Gpubmedcentral$$H</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6796042/$$EHTML$$P50$$Gpubmedcentral$$H</linktohtml><link.rule.ids>230,314,727,780,784,885,27924,27925,53791,53793</link.rule.ids></links><search><creatorcontrib>Bani Sadr, A</creatorcontrib><creatorcontrib>Eker, O F</creatorcontrib><creatorcontrib>Berner, L</creatorcontrib><creatorcontrib>Ameli, R</creatorcontrib><creatorcontrib>Hermier, M</creatorcontrib><creatorcontrib>Barritault, M</creatorcontrib><creatorcontrib>Meyronet, D</creatorcontrib><creatorcontrib>Guyotat, J</creatorcontrib><creatorcontrib>Jouanneau, E</creatorcontrib><creatorcontrib>Honnorat, J</creatorcontrib><creatorcontrib>Ducray, F</creatorcontrib><creatorcontrib>Berthezène, Y</creatorcontrib><title>P13.02 Conventional MRI radiomics in the diagnosis of early- and pseudo-progression</title><title>Neuro-oncology (Charlottesville, Va.)</title><description>Abstract BACKGROUND After radiochemotherapy, 20% to 30% of patients with early worsening MRI experience pseudoprogression (Psp) which is not distinguishable from early progression (EP). We aimed to assess the diagnostic performance and survival predictive ability of radiomics in patients with suspected EP or Psp. MATERIAL AND METHODS Radiomics features (RF) of 76 patients (53 EP and 23 Psp) retrospectively identified were extracted from conventional MRI based on four volumes-of-interest. Subjects were randomly assigned into training and validation groups. Classification model (EP versus Psp) consisted of a random forest algorithm after univariate filtering. Overall (OS) and progression-free survivals (PFS) were predicted using a semi-supervised principal component analysis and, forecasts were evaluated using C index and integrated Brier scores (IBS). RESULTS Using 11 RFs, radiomics classified patients with 75.0% and 76.0% accuracy, 81.6% and 94.1% sensitivity, 50.0% and 37.5% specificity, respectively in training and validation phases. Addition of MGMT promoter status improved accuracy to 83% and 79.2% and, specificity to 63.6% and 75%. OS model included 14 RFs and stratified low- and high-risk patients both in the training (hazard ratio (HR), 3.63, p=0.002) and the validation (HR, 3.76, p=0.001) phases. Similarly, PFS model stratified patients during training (HR, 2.58, p=0.005) and validation (HR, 3.58, p=0.004) phases using 5 RF. OS and PFS forecasts had C index of 0.65 and 0.69 and IBS of 0.122 and 0.147, respectively. CONCLUSION Conventional MRI radiomics has promising diagnostic value, especially when combined with MGMT promoter status, but specificity remains moderate. In addition, our results suggest a potential for predicting OS and PFS.</description><subject>Poster Presentations</subject><issn>1522-8517</issn><issn>1523-5866</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><recordid>eNqNkNtKAzEQhoMoWKsP4F0ewG0zySbZ3AhSPBQqiofrkGazbaRNlqQr1Kd3dUXwzqsZmP__GD6EzoFMgCg2Da6LwU5D_AAqJpSyAzQCTlnBKyEOv3daVBzkMTrJ-Y0QClzACD0_ApsQimcxvLuw8zGYDb5_muNkah-33mbsA96tHa69WYWYfcaxwc6kzb7AJtS4za6rY9GmuEou555wio4as8nu7GeO0evN9cvsrlg83M5nV4vCgpSsWAJT0pXVUvJSklIYJYEbR5k0ktuKG9r_yIA2fahuuGp4o0BIy0ERTpVlY3Q5cNtuuXW17f9PZqPb5Lcm7XU0Xv-9BL_Wq_iuhVSClLQHwACwKeacXPPbBaK_tOpBqx606l5r37kYOrFr_xH_BCVIfOE</recordid><startdate>20190906</startdate><enddate>20190906</enddate><creator>Bani Sadr, A</creator><creator>Eker, O F</creator><creator>Berner, L</creator><creator>Ameli, R</creator><creator>Hermier, M</creator><creator>Barritault, M</creator><creator>Meyronet, D</creator><creator>Guyotat, J</creator><creator>Jouanneau, E</creator><creator>Honnorat, J</creator><creator>Ducray, F</creator><creator>Berthezène, Y</creator><general>Oxford University Press</general><scope>AAYXX</scope><scope>CITATION</scope><scope>5PM</scope></search><sort><creationdate>20190906</creationdate><title>P13.02 Conventional MRI radiomics in the diagnosis of early- and pseudo-progression</title><author>Bani Sadr, A ; Eker, O F ; Berner, L ; Ameli, R ; Hermier, M ; Barritault, M ; Meyronet, D ; Guyotat, J ; Jouanneau, E ; Honnorat, J ; Ducray, F ; Berthezène, Y</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c1773-b1397e48b7547046a9715ae237a75c85a2215312f7e4df59f5f9167c5190529c3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Poster Presentations</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Bani Sadr, A</creatorcontrib><creatorcontrib>Eker, O F</creatorcontrib><creatorcontrib>Berner, L</creatorcontrib><creatorcontrib>Ameli, R</creatorcontrib><creatorcontrib>Hermier, M</creatorcontrib><creatorcontrib>Barritault, M</creatorcontrib><creatorcontrib>Meyronet, D</creatorcontrib><creatorcontrib>Guyotat, J</creatorcontrib><creatorcontrib>Jouanneau, E</creatorcontrib><creatorcontrib>Honnorat, J</creatorcontrib><creatorcontrib>Ducray, F</creatorcontrib><creatorcontrib>Berthezène, Y</creatorcontrib><collection>CrossRef</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Neuro-oncology (Charlottesville, Va.)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Bani Sadr, A</au><au>Eker, O F</au><au>Berner, L</au><au>Ameli, R</au><au>Hermier, M</au><au>Barritault, M</au><au>Meyronet, D</au><au>Guyotat, J</au><au>Jouanneau, E</au><au>Honnorat, J</au><au>Ducray, F</au><au>Berthezène, Y</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>P13.02 Conventional MRI radiomics in the diagnosis of early- and pseudo-progression</atitle><jtitle>Neuro-oncology (Charlottesville, Va.)</jtitle><date>2019-09-06</date><risdate>2019</risdate><volume>21</volume><issue>Supplement_3</issue><spage>iii62</spage><epage>iii62</epage><pages>iii62-iii62</pages><issn>1522-8517</issn><eissn>1523-5866</eissn><abstract>Abstract BACKGROUND After radiochemotherapy, 20% to 30% of patients with early worsening MRI experience pseudoprogression (Psp) which is not distinguishable from early progression (EP). We aimed to assess the diagnostic performance and survival predictive ability of radiomics in patients with suspected EP or Psp. MATERIAL AND METHODS Radiomics features (RF) of 76 patients (53 EP and 23 Psp) retrospectively identified were extracted from conventional MRI based on four volumes-of-interest. Subjects were randomly assigned into training and validation groups. Classification model (EP versus Psp) consisted of a random forest algorithm after univariate filtering. Overall (OS) and progression-free survivals (PFS) were predicted using a semi-supervised principal component analysis and, forecasts were evaluated using C index and integrated Brier scores (IBS). RESULTS Using 11 RFs, radiomics classified patients with 75.0% and 76.0% accuracy, 81.6% and 94.1% sensitivity, 50.0% and 37.5% specificity, respectively in training and validation phases. Addition of MGMT promoter status improved accuracy to 83% and 79.2% and, specificity to 63.6% and 75%. OS model included 14 RFs and stratified low- and high-risk patients both in the training (hazard ratio (HR), 3.63, p=0.002) and the validation (HR, 3.76, p=0.001) phases. Similarly, PFS model stratified patients during training (HR, 2.58, p=0.005) and validation (HR, 3.58, p=0.004) phases using 5 RF. OS and PFS forecasts had C index of 0.65 and 0.69 and IBS of 0.122 and 0.147, respectively. CONCLUSION Conventional MRI radiomics has promising diagnostic value, especially when combined with MGMT promoter status, but specificity remains moderate. In addition, our results suggest a potential for predicting OS and PFS.</abstract><cop>US</cop><pub>Oxford University Press</pub><doi>10.1093/neuonc/noz126.223</doi><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 1522-8517
ispartof Neuro-oncology (Charlottesville, Va.), 2019-09, Vol.21 (Supplement_3), p.iii62-iii62
issn 1522-8517
1523-5866
language eng
recordid cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_6796042
source Open Access: PubMed Central; Oxford Journals Online
subjects Poster Presentations
title P13.02 Conventional MRI radiomics in the diagnosis of early- and pseudo-progression
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-06T00%3A12%3A54IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-oup_pubme&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=P13.02%20Conventional%20MRI%20radiomics%20in%20the%20diagnosis%20of%20early-%20and%20pseudo-progression&rft.jtitle=Neuro-oncology%20(Charlottesville,%20Va.)&rft.au=Bani%20Sadr,%20A&rft.date=2019-09-06&rft.volume=21&rft.issue=Supplement_3&rft.spage=iii62&rft.epage=iii62&rft.pages=iii62-iii62&rft.issn=1522-8517&rft.eissn=1523-5866&rft_id=info:doi/10.1093/neuonc/noz126.223&rft_dat=%3Coup_pubme%3E10.1093/neuonc/noz126.223%3C/oup_pubme%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c1773-b1397e48b7547046a9715ae237a75c85a2215312f7e4df59f5f9167c5190529c3%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_id=info:pmid/&rft_oup_id=10.1093/neuonc/noz126.223&rfr_iscdi=true