Semiautomatic methods for segmentation of the proliferative tumour volume on sequential FLT PET/CT images in head and neck carcinomas and their relation to clinical outcome
Purpose Radiotherapy of head and neck cancer induces changes in tumour cell proliferation during treatment, which can be depicted by the PET tracer 18 F-fluorothymidine (FLT). In this study, three advanced semiautomatic PET segmentation methods for delineation of the proliferative tumour volume (PV)...
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creator | Arens, Anne I. J. Troost, Esther G. C. Hoeben, Bianca A. W. Grootjans, Willem Lee, John A. Grégoire, Vincent Hatt, Mathieu Visvikis, Dimitris Bussink, Johan Oyen, Wim J. G. Kaanders, Johannes H. A. M. Visser, Eric P. |
description | Purpose
Radiotherapy of head and neck cancer induces changes in tumour cell proliferation during treatment, which can be depicted by the PET tracer
18
F-fluorothymidine (FLT). In this study, three advanced semiautomatic PET segmentation methods for delineation of the proliferative tumour volume (PV) before and during (chemo)radiotherapy were compared and related to clinical outcome.
Methods
The study group comprised 46 patients with 48 squamous cell carcinomas of the head and neck, treated with accelerated (chemo)radiotherapy, who underwent FLT PET/CT prior to treatment and in the 2nd and 4th week of therapy. Primary gross tumour volumes were visually delineated on CT images (GTV
CT
). PVs were visually determined on all PET scans (PV
VIS
). The following semiautomatic segmentation methods were applied to sequential PET scans: background-subtracted relative-threshold level (PV
RTL
), a gradient-based method using the watershed transform algorithm and hierarchical clustering analysis (PV
W&C
), and a fuzzy locally adaptive Bayesian algorithm (PV
FLAB
).
Results
Pretreatment PV
VIS
correlated best with PV
FLAB
and GTV
CT
. Correlations with PV
RTL
and PV
W&C
were weaker although statistically significant. During treatment, the PV
VIS
, PV
W&C
and PV
FLAB
significant decreased over time with the steepest decline over time for PV
FLAB
. Among these advanced segmentation methods, PV
FLAB
was the most robust in segmenting volumes in the third scan (67 % of tumours as compared to 40 % for PV
W&C
and 27 % for PV
RTL
). A decrease in PV
FLAB
above the median between the pretreatment scan and the scan obtained in the 4th week was associated with better disease-free survival (4 years 90 % versus 53 %).
Conclusion
In patients with head and neck cancer, FLAB proved to be the best performing method for segmentation of the PV on repeat FLT PET/CT scans during (chemo)radiotherapy. This may potentially facilitate radiation dose adaptation to changing PV. |
doi_str_mv | 10.1007/s00259-013-2651-0 |
format | Article |
fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_1520386386</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>3268407921</sourcerecordid><originalsourceid>FETCH-LOGICAL-c405t-fdf0f31b4eb6787d99c67bc621b09c742d975ed22d6fd06e68c1ef4b60f125e03</originalsourceid><addsrcrecordid>eNp1kd9qFDEUxoMotl19AG_kgDfejE0yM5nJpSytFhYUXK-HmeRkN3WS1CRT8J18SLNOLSIIgYST3_nOn4-QV4y-Y5R2l4lS3sqKsrriomUVfULOmWCy6mgvnz6-O3pGLlK6pZT1vJfPyRlv6kY0rDknP7-gs-OSgxuzVeAwH4NOYEKEhAeHPpd48BAM5CPCXQyzNRhL8B4hLy4sEe7DvDiEQiX8vpQUO85wvdvD56v95XYP1o0HTGA9HHHUMHoNHtU3UGNU1pfK6Xes6NsIEee1Yg6gZuutKmJhySo4fEGemXFO-PLh3pCv11f77cdq9-nDzfb9rlINbXNltKGmZlODk-j6TkupRDcpwdlEpeoarmXXouZcC6OpQNErhqaZBDWMt0jrDXm76pZxy0ApD84mhfM8egxLGljLad2L09mQN_-gt2UlvnRXKFZzKVrZFIqtlIohpYhmuItlK_HHwOhwsnJYrRyKlcPJyuHUxOsH5WVyqB8z_nhXAL4CqXz5A8a_Sv9X9RdPl6wK</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1513296594</pqid></control><display><type>article</type><title>Semiautomatic methods for segmentation of the proliferative tumour volume on sequential FLT PET/CT images in head and neck carcinomas and their relation to clinical outcome</title><source>MEDLINE</source><source>SpringerLink Journals - AutoHoldings</source><creator>Arens, Anne I. J. ; Troost, Esther G. C. ; Hoeben, Bianca A. W. ; Grootjans, Willem ; Lee, John A. ; Grégoire, Vincent ; Hatt, Mathieu ; Visvikis, Dimitris ; Bussink, Johan ; Oyen, Wim J. G. ; Kaanders, Johannes H. A. M. ; Visser, Eric P.</creator><creatorcontrib>Arens, Anne I. J. ; Troost, Esther G. C. ; Hoeben, Bianca A. W. ; Grootjans, Willem ; Lee, John A. ; Grégoire, Vincent ; Hatt, Mathieu ; Visvikis, Dimitris ; Bussink, Johan ; Oyen, Wim J. G. ; Kaanders, Johannes H. A. M. ; Visser, Eric P.</creatorcontrib><description>Purpose
Radiotherapy of head and neck cancer induces changes in tumour cell proliferation during treatment, which can be depicted by the PET tracer
18
F-fluorothymidine (FLT). In this study, three advanced semiautomatic PET segmentation methods for delineation of the proliferative tumour volume (PV) before and during (chemo)radiotherapy were compared and related to clinical outcome.
Methods
The study group comprised 46 patients with 48 squamous cell carcinomas of the head and neck, treated with accelerated (chemo)radiotherapy, who underwent FLT PET/CT prior to treatment and in the 2nd and 4th week of therapy. Primary gross tumour volumes were visually delineated on CT images (GTV
CT
). PVs were visually determined on all PET scans (PV
VIS
). The following semiautomatic segmentation methods were applied to sequential PET scans: background-subtracted relative-threshold level (PV
RTL
), a gradient-based method using the watershed transform algorithm and hierarchical clustering analysis (PV
W&C
), and a fuzzy locally adaptive Bayesian algorithm (PV
FLAB
).
Results
Pretreatment PV
VIS
correlated best with PV
FLAB
and GTV
CT
. Correlations with PV
RTL
and PV
W&C
were weaker although statistically significant. During treatment, the PV
VIS
, PV
W&C
and PV
FLAB
significant decreased over time with the steepest decline over time for PV
FLAB
. Among these advanced segmentation methods, PV
FLAB
was the most robust in segmenting volumes in the third scan (67 % of tumours as compared to 40 % for PV
W&C
and 27 % for PV
RTL
). A decrease in PV
FLAB
above the median between the pretreatment scan and the scan obtained in the 4th week was associated with better disease-free survival (4 years 90 % versus 53 %).
Conclusion
In patients with head and neck cancer, FLAB proved to be the best performing method for segmentation of the PV on repeat FLT PET/CT scans during (chemo)radiotherapy. This may potentially facilitate radiation dose adaptation to changing PV.</description><identifier>ISSN: 1619-7070</identifier><identifier>EISSN: 1619-7089</identifier><identifier>DOI: 10.1007/s00259-013-2651-0</identifier><identifier>PMID: 24346414</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Adult ; Aged ; Carcinoma, Squamous Cell - diagnostic imaging ; Carcinoma, Squamous Cell - pathology ; Cardiology ; Clinical outcomes ; Dideoxynucleosides ; Drug therapy ; Female ; Head & neck cancer ; Head and Neck Neoplasms - diagnostic imaging ; Head and Neck Neoplasms - pathology ; Humans ; Image Processing, Computer-Assisted - methods ; Imaging ; Male ; Medicine ; Medicine & Public Health ; Middle Aged ; Multimodal Imaging ; Nuclear Medicine ; Oncology ; Original Article ; Orthopedics ; Positron-Emission Tomography ; Prognosis ; Radiology ; Radiopharmaceuticals ; Tomography, X-Ray Computed ; Tumor Burden ; Tumors</subject><ispartof>European journal of nuclear medicine and molecular imaging, 2014-05, Vol.41 (5), p.915-924</ispartof><rights>Springer-Verlag Berlin Heidelberg 2013</rights><rights>Springer-Verlag Berlin Heidelberg 2014</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c405t-fdf0f31b4eb6787d99c67bc621b09c742d975ed22d6fd06e68c1ef4b60f125e03</citedby><cites>FETCH-LOGICAL-c405t-fdf0f31b4eb6787d99c67bc621b09c742d975ed22d6fd06e68c1ef4b60f125e03</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/s00259-013-2651-0$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00259-013-2651-0$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27923,27924,41487,42556,51318</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/24346414$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Arens, Anne I. J.</creatorcontrib><creatorcontrib>Troost, Esther G. C.</creatorcontrib><creatorcontrib>Hoeben, Bianca A. W.</creatorcontrib><creatorcontrib>Grootjans, Willem</creatorcontrib><creatorcontrib>Lee, John A.</creatorcontrib><creatorcontrib>Grégoire, Vincent</creatorcontrib><creatorcontrib>Hatt, Mathieu</creatorcontrib><creatorcontrib>Visvikis, Dimitris</creatorcontrib><creatorcontrib>Bussink, Johan</creatorcontrib><creatorcontrib>Oyen, Wim J. G.</creatorcontrib><creatorcontrib>Kaanders, Johannes H. A. M.</creatorcontrib><creatorcontrib>Visser, Eric P.</creatorcontrib><title>Semiautomatic methods for segmentation of the proliferative tumour volume on sequential FLT PET/CT images in head and neck carcinomas and their relation to clinical outcome</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
Radiotherapy of head and neck cancer induces changes in tumour cell proliferation during treatment, which can be depicted by the PET tracer
18
F-fluorothymidine (FLT). In this study, three advanced semiautomatic PET segmentation methods for delineation of the proliferative tumour volume (PV) before and during (chemo)radiotherapy were compared and related to clinical outcome.
Methods
The study group comprised 46 patients with 48 squamous cell carcinomas of the head and neck, treated with accelerated (chemo)radiotherapy, who underwent FLT PET/CT prior to treatment and in the 2nd and 4th week of therapy. Primary gross tumour volumes were visually delineated on CT images (GTV
CT
). PVs were visually determined on all PET scans (PV
VIS
). The following semiautomatic segmentation methods were applied to sequential PET scans: background-subtracted relative-threshold level (PV
RTL
), a gradient-based method using the watershed transform algorithm and hierarchical clustering analysis (PV
W&C
), and a fuzzy locally adaptive Bayesian algorithm (PV
FLAB
).
Results
Pretreatment PV
VIS
correlated best with PV
FLAB
and GTV
CT
. Correlations with PV
RTL
and PV
W&C
were weaker although statistically significant. During treatment, the PV
VIS
, PV
W&C
and PV
FLAB
significant decreased over time with the steepest decline over time for PV
FLAB
. Among these advanced segmentation methods, PV
FLAB
was the most robust in segmenting volumes in the third scan (67 % of tumours as compared to 40 % for PV
W&C
and 27 % for PV
RTL
). A decrease in PV
FLAB
above the median between the pretreatment scan and the scan obtained in the 4th week was associated with better disease-free survival (4 years 90 % versus 53 %).
Conclusion
In patients with head and neck cancer, FLAB proved to be the best performing method for segmentation of the PV on repeat FLT PET/CT scans during (chemo)radiotherapy. This may potentially facilitate radiation dose adaptation to changing PV.</description><subject>Adult</subject><subject>Aged</subject><subject>Carcinoma, Squamous Cell - diagnostic imaging</subject><subject>Carcinoma, Squamous Cell - pathology</subject><subject>Cardiology</subject><subject>Clinical outcomes</subject><subject>Dideoxynucleosides</subject><subject>Drug therapy</subject><subject>Female</subject><subject>Head & neck cancer</subject><subject>Head and Neck Neoplasms - diagnostic imaging</subject><subject>Head and Neck Neoplasms - pathology</subject><subject>Humans</subject><subject>Image Processing, Computer-Assisted - methods</subject><subject>Imaging</subject><subject>Male</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Middle Aged</subject><subject>Multimodal Imaging</subject><subject>Nuclear Medicine</subject><subject>Oncology</subject><subject>Original Article</subject><subject>Orthopedics</subject><subject>Positron-Emission Tomography</subject><subject>Prognosis</subject><subject>Radiology</subject><subject>Radiopharmaceuticals</subject><subject>Tomography, X-Ray Computed</subject><subject>Tumor Burden</subject><subject>Tumors</subject><issn>1619-7070</issn><issn>1619-7089</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2014</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp1kd9qFDEUxoMotl19AG_kgDfejE0yM5nJpSytFhYUXK-HmeRkN3WS1CRT8J18SLNOLSIIgYST3_nOn4-QV4y-Y5R2l4lS3sqKsrriomUVfULOmWCy6mgvnz6-O3pGLlK6pZT1vJfPyRlv6kY0rDknP7-gs-OSgxuzVeAwH4NOYEKEhAeHPpd48BAM5CPCXQyzNRhL8B4hLy4sEe7DvDiEQiX8vpQUO85wvdvD56v95XYP1o0HTGA9HHHUMHoNHtU3UGNU1pfK6Xes6NsIEee1Yg6gZuutKmJhySo4fEGemXFO-PLh3pCv11f77cdq9-nDzfb9rlINbXNltKGmZlODk-j6TkupRDcpwdlEpeoarmXXouZcC6OpQNErhqaZBDWMt0jrDXm76pZxy0ApD84mhfM8egxLGljLad2L09mQN_-gt2UlvnRXKFZzKVrZFIqtlIohpYhmuItlK_HHwOhwsnJYrRyKlcPJyuHUxOsH5WVyqB8z_nhXAL4CqXz5A8a_Sv9X9RdPl6wK</recordid><startdate>20140501</startdate><enddate>20140501</enddate><creator>Arens, Anne I. J.</creator><creator>Troost, Esther G. C.</creator><creator>Hoeben, Bianca A. W.</creator><creator>Grootjans, Willem</creator><creator>Lee, John A.</creator><creator>Grégoire, Vincent</creator><creator>Hatt, Mathieu</creator><creator>Visvikis, Dimitris</creator><creator>Bussink, Johan</creator><creator>Oyen, Wim J. G.</creator><creator>Kaanders, Johannes H. A. M.</creator><creator>Visser, Eric P.</creator><general>Springer Berlin Heidelberg</general><general>Springer Nature B.V</general><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>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>7QO</scope><scope>8FD</scope><scope>FR3</scope><scope>P64</scope></search><sort><creationdate>20140501</creationdate><title>Semiautomatic methods for segmentation of the proliferative tumour volume on sequential FLT PET/CT images in head and neck carcinomas and their relation to clinical outcome</title><author>Arens, Anne I. J. ; Troost, Esther G. C. ; Hoeben, Bianca A. W. ; Grootjans, Willem ; Lee, John A. ; Grégoire, Vincent ; Hatt, Mathieu ; Visvikis, Dimitris ; Bussink, Johan ; Oyen, Wim J. G. ; Kaanders, Johannes H. A. M. ; Visser, Eric P.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c405t-fdf0f31b4eb6787d99c67bc621b09c742d975ed22d6fd06e68c1ef4b60f125e03</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2014</creationdate><topic>Adult</topic><topic>Aged</topic><topic>Carcinoma, Squamous Cell - diagnostic imaging</topic><topic>Carcinoma, Squamous Cell - pathology</topic><topic>Cardiology</topic><topic>Clinical outcomes</topic><topic>Dideoxynucleosides</topic><topic>Drug therapy</topic><topic>Female</topic><topic>Head & neck cancer</topic><topic>Head and Neck Neoplasms - diagnostic imaging</topic><topic>Head and Neck Neoplasms - pathology</topic><topic>Humans</topic><topic>Image Processing, Computer-Assisted - methods</topic><topic>Imaging</topic><topic>Male</topic><topic>Medicine</topic><topic>Medicine & Public Health</topic><topic>Middle Aged</topic><topic>Multimodal Imaging</topic><topic>Nuclear Medicine</topic><topic>Oncology</topic><topic>Original Article</topic><topic>Orthopedics</topic><topic>Positron-Emission Tomography</topic><topic>Prognosis</topic><topic>Radiology</topic><topic>Radiopharmaceuticals</topic><topic>Tomography, X-Ray Computed</topic><topic>Tumor Burden</topic><topic>Tumors</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Arens, Anne I. J.</creatorcontrib><creatorcontrib>Troost, Esther G. C.</creatorcontrib><creatorcontrib>Hoeben, Bianca A. W.</creatorcontrib><creatorcontrib>Grootjans, Willem</creatorcontrib><creatorcontrib>Lee, John A.</creatorcontrib><creatorcontrib>Grégoire, Vincent</creatorcontrib><creatorcontrib>Hatt, Mathieu</creatorcontrib><creatorcontrib>Visvikis, Dimitris</creatorcontrib><creatorcontrib>Bussink, Johan</creatorcontrib><creatorcontrib>Oyen, Wim J. G.</creatorcontrib><creatorcontrib>Kaanders, Johannes H. A. M.</creatorcontrib><creatorcontrib>Visser, Eric P.</creatorcontrib><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>Nursing & Allied Health Database</collection><collection>Neurosciences Abstracts</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>ProQuest SciTech Collection</collection><collection>ProQuest Technology 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 Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>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>Nursing & Allied Health Database (Alumni Edition)</collection><collection>ProQuest Biological Science Collection</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Biological Science 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>Biotechnology Research Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Biotechnology and BioEngineering Abstracts</collection><jtitle>European journal of nuclear medicine and molecular imaging</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Arens, Anne I. J.</au><au>Troost, Esther G. C.</au><au>Hoeben, Bianca A. W.</au><au>Grootjans, Willem</au><au>Lee, John A.</au><au>Grégoire, Vincent</au><au>Hatt, Mathieu</au><au>Visvikis, Dimitris</au><au>Bussink, Johan</au><au>Oyen, Wim J. G.</au><au>Kaanders, Johannes H. A. M.</au><au>Visser, Eric P.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Semiautomatic methods for segmentation of the proliferative tumour volume on sequential FLT PET/CT images in head and neck carcinomas and their relation to clinical outcome</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>2014-05-01</date><risdate>2014</risdate><volume>41</volume><issue>5</issue><spage>915</spage><epage>924</epage><pages>915-924</pages><issn>1619-7070</issn><eissn>1619-7089</eissn><abstract>Purpose
Radiotherapy of head and neck cancer induces changes in tumour cell proliferation during treatment, which can be depicted by the PET tracer
18
F-fluorothymidine (FLT). In this study, three advanced semiautomatic PET segmentation methods for delineation of the proliferative tumour volume (PV) before and during (chemo)radiotherapy were compared and related to clinical outcome.
Methods
The study group comprised 46 patients with 48 squamous cell carcinomas of the head and neck, treated with accelerated (chemo)radiotherapy, who underwent FLT PET/CT prior to treatment and in the 2nd and 4th week of therapy. Primary gross tumour volumes were visually delineated on CT images (GTV
CT
). PVs were visually determined on all PET scans (PV
VIS
). The following semiautomatic segmentation methods were applied to sequential PET scans: background-subtracted relative-threshold level (PV
RTL
), a gradient-based method using the watershed transform algorithm and hierarchical clustering analysis (PV
W&C
), and a fuzzy locally adaptive Bayesian algorithm (PV
FLAB
).
Results
Pretreatment PV
VIS
correlated best with PV
FLAB
and GTV
CT
. Correlations with PV
RTL
and PV
W&C
were weaker although statistically significant. During treatment, the PV
VIS
, PV
W&C
and PV
FLAB
significant decreased over time with the steepest decline over time for PV
FLAB
. Among these advanced segmentation methods, PV
FLAB
was the most robust in segmenting volumes in the third scan (67 % of tumours as compared to 40 % for PV
W&C
and 27 % for PV
RTL
). A decrease in PV
FLAB
above the median between the pretreatment scan and the scan obtained in the 4th week was associated with better disease-free survival (4 years 90 % versus 53 %).
Conclusion
In patients with head and neck cancer, FLAB proved to be the best performing method for segmentation of the PV on repeat FLT PET/CT scans during (chemo)radiotherapy. This may potentially facilitate radiation dose adaptation to changing PV.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><pmid>24346414</pmid><doi>10.1007/s00259-013-2651-0</doi><tpages>10</tpages></addata></record> |
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ispartof | European journal of nuclear medicine and molecular imaging, 2014-05, Vol.41 (5), p.915-924 |
issn | 1619-7070 1619-7089 |
language | eng |
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source | MEDLINE; SpringerLink Journals - AutoHoldings |
subjects | Adult Aged Carcinoma, Squamous Cell - diagnostic imaging Carcinoma, Squamous Cell - pathology Cardiology Clinical outcomes Dideoxynucleosides Drug therapy Female Head & neck cancer Head and Neck Neoplasms - diagnostic imaging Head and Neck Neoplasms - pathology Humans Image Processing, Computer-Assisted - methods Imaging Male Medicine Medicine & Public Health Middle Aged Multimodal Imaging Nuclear Medicine Oncology Original Article Orthopedics Positron-Emission Tomography Prognosis Radiology Radiopharmaceuticals Tomography, X-Ray Computed Tumor Burden Tumors |
title | Semiautomatic methods for segmentation of the proliferative tumour volume on sequential FLT PET/CT images in head and neck carcinomas and their relation to clinical outcome |
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