Prognostic model based on magnetic resonance imaging, whole-tumour apparent diffusion coefficient values and HPV genotyping for stage IB-IV cervical cancer patients following chemoradiotherapy
Objectives To develop and validate a prognostic model of integrating whole-tumour apparent diffusion coefficient (ADC) from pretreatment diffusion-weighted (DW) magnetic resonance (MR) imaging with human papillomavirus (HPV) genotyping in predicting the overall survival (OS) and disease-free surviva...
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| Veröffentlicht in: | European radiology 2019-02, Vol.29 (2), p.556-565 |
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| creator | Lin, Gigin Yang, Lan-Yan Lin, Yu-Chun Huang, Yu-Ting Liu, Feng-Yuan Wang, Chun-Chieh Lu, Hsin-Ying Chiang, Hsin-Ju Chen, Yu-Ruei Wu, Ren-Chin Ng, Koon-Kwan Hong, Ji-Hong Yen, Tzu-Chen Lai, Chyong-Huey |
| description | Objectives
To develop and validate a prognostic model of integrating whole-tumour apparent diffusion coefficient (ADC) from pretreatment diffusion-weighted (DW) magnetic resonance (MR) imaging with human papillomavirus (HPV) genotyping in predicting the overall survival (OS) and disease-free survival (DFS) for women with stage IB–IV cervical cancer following concurrent chemoradiotherapy (CCRT).
Methods
We retrospectively analysed three prospectively collected cohorts comprising 300 patients with stage IB–IV cervical cancer treated with CCRT in 2007–2014 and filtered 134 female patients who underwent MR imaging at 3.0 T for final analysis (age, 24–92 years; median, 54 years). Univariate and multivariate Cox regression analyses were used to evaluate the whole-tumour ADC histogram parameters, HPV genotyping and relevant clinical variables in predicting OS and DFS. The dataset was randomly split into training (
n
= 88) and testing (
n
= 46) datasets for construction and independent bootstrap validation of the models.
Results
The median follow-up time for surviving patients was 69 months (range, 9–126 months). Non-squamous cell type, ADC
10 |
| doi_str_mv | 10.1007/s00330-018-5651-4 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_2078593400</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2078593400</sourcerecordid><originalsourceid>FETCH-LOGICAL-c372t-66fa888fc11d1805818a2f0ccc4d45844afb3df8aa47d5bdb7ba52e95eca2ca03</originalsourceid><addsrcrecordid>eNp1kUFv1DAQhSMEokvhB3BBlrhwIDBOnMQ5QgV0pUr0AL1GE3ucdZXYwU5a7b_jp-FoC0hInCw9f--NPS_LXnJ4xwGa9xGgLCEHLvOqrnguHmU7Lsoi5yDF42wHbSnzpm3FWfYsxlsAaLlonmZnJUDFuSh22c_r4Afn42IVm7ymkfUYSTPv2ISDo00PFL1Dp4jZpFk3vGX3Bz9SvqyTXwPDecZAbmHaGrNGm7zKkzFW2U29w3GlyNBpdnl9wwZyfjnOKYYZH1hccCC2_5jvb5iicGcVjkxt0wKbcdkSYgLH0d9vFnWgyQfU1i8HCjgfn2dPDI6RXjyc59n3z5--XVzmV1-_7C8-XOWqbIolr2uDUkqjONdcQiW5xMKAUkpoUUkh0PSlNhJRNLrqdd_0WBXUVqSwUAjlefbmlDsH_yP9Z-kmGxWNIzrya-wKaGTVlgI29PU_6G1ak0uv6wpetTWIti4SxU-UCj7GQKabQ9pvOHYcuq3e7lRvl-rttno7kTyvHpLXfiL9x_G7zwQUJyCmKzdQ-Dv6_6m_AOjitRU</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2159604962</pqid></control><display><type>article</type><title>Prognostic model based on magnetic resonance imaging, whole-tumour apparent diffusion coefficient values and HPV genotyping for stage IB-IV cervical cancer patients following chemoradiotherapy</title><source>MEDLINE</source><source>SpringerNature Journals</source><creator>Lin, Gigin ; Yang, Lan-Yan ; Lin, Yu-Chun ; Huang, Yu-Ting ; Liu, Feng-Yuan ; Wang, Chun-Chieh ; Lu, Hsin-Ying ; Chiang, Hsin-Ju ; Chen, Yu-Ruei ; Wu, Ren-Chin ; Ng, Koon-Kwan ; Hong, Ji-Hong ; Yen, Tzu-Chen ; Lai, Chyong-Huey</creator><creatorcontrib>Lin, Gigin ; Yang, Lan-Yan ; Lin, Yu-Chun ; Huang, Yu-Ting ; Liu, Feng-Yuan ; Wang, Chun-Chieh ; Lu, Hsin-Ying ; Chiang, Hsin-Ju ; Chen, Yu-Ruei ; Wu, Ren-Chin ; Ng, Koon-Kwan ; Hong, Ji-Hong ; Yen, Tzu-Chen ; Lai, Chyong-Huey</creatorcontrib><description>Objectives
To develop and validate a prognostic model of integrating whole-tumour apparent diffusion coefficient (ADC) from pretreatment diffusion-weighted (DW) magnetic resonance (MR) imaging with human papillomavirus (HPV) genotyping in predicting the overall survival (OS) and disease-free survival (DFS) for women with stage IB–IV cervical cancer following concurrent chemoradiotherapy (CCRT).
Methods
We retrospectively analysed three prospectively collected cohorts comprising 300 patients with stage IB–IV cervical cancer treated with CCRT in 2007–2014 and filtered 134 female patients who underwent MR imaging at 3.0 T for final analysis (age, 24–92 years; median, 54 years). Univariate and multivariate Cox regression analyses were used to evaluate the whole-tumour ADC histogram parameters, HPV genotyping and relevant clinical variables in predicting OS and DFS. The dataset was randomly split into training (
n
= 88) and testing (
n
= 46) datasets for construction and independent bootstrap validation of the models.
Results
The median follow-up time for surviving patients was 69 months (range, 9–126 months). Non-squamous cell type, ADC
10
<0.77 × 10
-3
mm
2
/s, T3-4, M1 stage and high-risk HPV status were selected to generate a model, in which the OS and DFS for the low, intermediate and high-risk groups were significantly stratified (
p
< 0.0001). The prognostic model improved the prediction significantly compared with the International Federation of Gynaecology and Obstetrics (FIGO) stage for both the training and independent testing datasets (
p
< 0.0001).
Conclusions
The prognostic model based on integrated clinical and imaging data could be a useful clinical biomarker to predict OS and DFS in patients with stage IB–IV cervical cancer treated with CCRT.
Key points
• ADC
10
is the best prognostic factor among ADC parameters in cervical cancer treated with CCRT
• A novel prognostic model was built based on histology, ADC
10
, T and M stage and HPV status
• The prognostic model outperforms FIGO stage in the survival prediction</description><identifier>ISSN: 0938-7994</identifier><identifier>EISSN: 1432-1084</identifier><identifier>DOI: 10.1007/s00330-018-5651-4</identifier><identifier>PMID: 30051142</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Adult ; Aged ; Aged, 80 and over ; Biomarkers ; Cancer ; Cervical cancer ; Cervix ; Chemoradiotherapy ; Chemoradiotherapy - methods ; Chemotherapy ; Datasets ; Diagnostic Radiology ; Diffusion ; Diffusion coefficient ; Diffusion Magnetic Resonance Imaging - methods ; Female ; Genotype ; Genotyping ; Genotyping Techniques - methods ; Health risk assessment ; Histograms ; Histology ; Human papillomavirus ; Humans ; Image Interpretation, Computer-Assisted - methods ; Imaging ; Internal Medicine ; Interventional Radiology ; Kaplan-Meier Estimate ; Magnetic resonance imaging ; Mathematical models ; Medical imaging ; Medical prognosis ; Medicine ; Medicine & Public Health ; Middle Aged ; Neoplasm Staging ; Neuroradiology ; Obstetrics ; Oncology ; Papillomaviridae - classification ; Papillomaviridae - genetics ; Parameters ; Patients ; Predictions ; Prognosis ; Radiation therapy ; Radiology ; Regression analysis ; Resonance ; Retrospective Studies ; Risk groups ; Survival ; Training ; Tumors ; Ultrasound ; Uterine Cervical Neoplasms - diagnostic imaging ; Uterine Cervical Neoplasms - pathology ; Uterine Cervical Neoplasms - therapy ; Uterine Cervical Neoplasms - virology ; Young Adult</subject><ispartof>European radiology, 2019-02, Vol.29 (2), p.556-565</ispartof><rights>European Society of Radiology 2018</rights><rights>European Radiology is a copyright of Springer, (2018). All Rights Reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c372t-66fa888fc11d1805818a2f0ccc4d45844afb3df8aa47d5bdb7ba52e95eca2ca03</citedby><cites>FETCH-LOGICAL-c372t-66fa888fc11d1805818a2f0ccc4d45844afb3df8aa47d5bdb7ba52e95eca2ca03</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/s00330-018-5651-4$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00330-018-5651-4$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,41488,42557,51319</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/30051142$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Lin, Gigin</creatorcontrib><creatorcontrib>Yang, Lan-Yan</creatorcontrib><creatorcontrib>Lin, Yu-Chun</creatorcontrib><creatorcontrib>Huang, Yu-Ting</creatorcontrib><creatorcontrib>Liu, Feng-Yuan</creatorcontrib><creatorcontrib>Wang, Chun-Chieh</creatorcontrib><creatorcontrib>Lu, Hsin-Ying</creatorcontrib><creatorcontrib>Chiang, Hsin-Ju</creatorcontrib><creatorcontrib>Chen, Yu-Ruei</creatorcontrib><creatorcontrib>Wu, Ren-Chin</creatorcontrib><creatorcontrib>Ng, Koon-Kwan</creatorcontrib><creatorcontrib>Hong, Ji-Hong</creatorcontrib><creatorcontrib>Yen, Tzu-Chen</creatorcontrib><creatorcontrib>Lai, Chyong-Huey</creatorcontrib><title>Prognostic model based on magnetic resonance imaging, whole-tumour apparent diffusion coefficient values and HPV genotyping for stage IB-IV cervical cancer patients following chemoradiotherapy</title><title>European radiology</title><addtitle>Eur Radiol</addtitle><addtitle>Eur Radiol</addtitle><description>Objectives
To develop and validate a prognostic model of integrating whole-tumour apparent diffusion coefficient (ADC) from pretreatment diffusion-weighted (DW) magnetic resonance (MR) imaging with human papillomavirus (HPV) genotyping in predicting the overall survival (OS) and disease-free survival (DFS) for women with stage IB–IV cervical cancer following concurrent chemoradiotherapy (CCRT).
Methods
We retrospectively analysed three prospectively collected cohorts comprising 300 patients with stage IB–IV cervical cancer treated with CCRT in 2007–2014 and filtered 134 female patients who underwent MR imaging at 3.0 T for final analysis (age, 24–92 years; median, 54 years). Univariate and multivariate Cox regression analyses were used to evaluate the whole-tumour ADC histogram parameters, HPV genotyping and relevant clinical variables in predicting OS and DFS. The dataset was randomly split into training (
n
= 88) and testing (
n
= 46) datasets for construction and independent bootstrap validation of the models.
Results
The median follow-up time for surviving patients was 69 months (range, 9–126 months). Non-squamous cell type, ADC
10
<0.77 × 10
-3
mm
2
/s, T3-4, M1 stage and high-risk HPV status were selected to generate a model, in which the OS and DFS for the low, intermediate and high-risk groups were significantly stratified (
p
< 0.0001). The prognostic model improved the prediction significantly compared with the International Federation of Gynaecology and Obstetrics (FIGO) stage for both the training and independent testing datasets (
p
< 0.0001).
Conclusions
The prognostic model based on integrated clinical and imaging data could be a useful clinical biomarker to predict OS and DFS in patients with stage IB–IV cervical cancer treated with CCRT.
Key points
• ADC
10
is the best prognostic factor among ADC parameters in cervical cancer treated with CCRT
• A novel prognostic model was built based on histology, ADC
10
, T and M stage and HPV status
• The prognostic model outperforms FIGO stage in the survival prediction</description><subject>Adult</subject><subject>Aged</subject><subject>Aged, 80 and over</subject><subject>Biomarkers</subject><subject>Cancer</subject><subject>Cervical cancer</subject><subject>Cervix</subject><subject>Chemoradiotherapy</subject><subject>Chemoradiotherapy - methods</subject><subject>Chemotherapy</subject><subject>Datasets</subject><subject>Diagnostic Radiology</subject><subject>Diffusion</subject><subject>Diffusion coefficient</subject><subject>Diffusion Magnetic Resonance Imaging - methods</subject><subject>Female</subject><subject>Genotype</subject><subject>Genotyping</subject><subject>Genotyping Techniques - methods</subject><subject>Health risk assessment</subject><subject>Histograms</subject><subject>Histology</subject><subject>Human papillomavirus</subject><subject>Humans</subject><subject>Image Interpretation, Computer-Assisted - methods</subject><subject>Imaging</subject><subject>Internal Medicine</subject><subject>Interventional Radiology</subject><subject>Kaplan-Meier Estimate</subject><subject>Magnetic resonance imaging</subject><subject>Mathematical models</subject><subject>Medical imaging</subject><subject>Medical prognosis</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Middle Aged</subject><subject>Neoplasm Staging</subject><subject>Neuroradiology</subject><subject>Obstetrics</subject><subject>Oncology</subject><subject>Papillomaviridae - classification</subject><subject>Papillomaviridae - genetics</subject><subject>Parameters</subject><subject>Patients</subject><subject>Predictions</subject><subject>Prognosis</subject><subject>Radiation therapy</subject><subject>Radiology</subject><subject>Regression analysis</subject><subject>Resonance</subject><subject>Retrospective Studies</subject><subject>Risk groups</subject><subject>Survival</subject><subject>Training</subject><subject>Tumors</subject><subject>Ultrasound</subject><subject>Uterine Cervical Neoplasms - diagnostic imaging</subject><subject>Uterine Cervical Neoplasms - pathology</subject><subject>Uterine Cervical Neoplasms - therapy</subject><subject>Uterine Cervical Neoplasms - virology</subject><subject>Young Adult</subject><issn>0938-7994</issn><issn>1432-1084</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</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>eNp1kUFv1DAQhSMEokvhB3BBlrhwIDBOnMQ5QgV0pUr0AL1GE3ucdZXYwU5a7b_jp-FoC0hInCw9f--NPS_LXnJ4xwGa9xGgLCEHLvOqrnguHmU7Lsoi5yDF42wHbSnzpm3FWfYsxlsAaLlonmZnJUDFuSh22c_r4Afn42IVm7ymkfUYSTPv2ISDo00PFL1Dp4jZpFk3vGX3Bz9SvqyTXwPDecZAbmHaGrNGm7zKkzFW2U29w3GlyNBpdnl9wwZyfjnOKYYZH1hccCC2_5jvb5iicGcVjkxt0wKbcdkSYgLH0d9vFnWgyQfU1i8HCjgfn2dPDI6RXjyc59n3z5--XVzmV1-_7C8-XOWqbIolr2uDUkqjONdcQiW5xMKAUkpoUUkh0PSlNhJRNLrqdd_0WBXUVqSwUAjlefbmlDsH_yP9Z-kmGxWNIzrya-wKaGTVlgI29PU_6G1ak0uv6wpetTWIti4SxU-UCj7GQKabQ9pvOHYcuq3e7lRvl-rttno7kTyvHpLXfiL9x_G7zwQUJyCmKzdQ-Dv6_6m_AOjitRU</recordid><startdate>20190201</startdate><enddate>20190201</enddate><creator>Lin, Gigin</creator><creator>Yang, Lan-Yan</creator><creator>Lin, Yu-Chun</creator><creator>Huang, Yu-Ting</creator><creator>Liu, Feng-Yuan</creator><creator>Wang, Chun-Chieh</creator><creator>Lu, Hsin-Ying</creator><creator>Chiang, Hsin-Ju</creator><creator>Chen, Yu-Ruei</creator><creator>Wu, Ren-Chin</creator><creator>Ng, Koon-Kwan</creator><creator>Hong, Ji-Hong</creator><creator>Yen, Tzu-Chen</creator><creator>Lai, Chyong-Huey</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>7QO</scope><scope>7RV</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8FD</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>FR3</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>P64</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>7X8</scope></search><sort><creationdate>20190201</creationdate><title>Prognostic model based on magnetic resonance imaging, whole-tumour apparent diffusion coefficient values and HPV genotyping for stage IB-IV cervical cancer patients following chemoradiotherapy</title><author>Lin, Gigin ; Yang, Lan-Yan ; Lin, Yu-Chun ; Huang, Yu-Ting ; Liu, Feng-Yuan ; Wang, Chun-Chieh ; Lu, Hsin-Ying ; Chiang, Hsin-Ju ; Chen, Yu-Ruei ; Wu, Ren-Chin ; Ng, Koon-Kwan ; Hong, Ji-Hong ; Yen, Tzu-Chen ; Lai, Chyong-Huey</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c372t-66fa888fc11d1805818a2f0ccc4d45844afb3df8aa47d5bdb7ba52e95eca2ca03</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Adult</topic><topic>Aged</topic><topic>Aged, 80 and over</topic><topic>Biomarkers</topic><topic>Cancer</topic><topic>Cervical cancer</topic><topic>Cervix</topic><topic>Chemoradiotherapy</topic><topic>Chemoradiotherapy - methods</topic><topic>Chemotherapy</topic><topic>Datasets</topic><topic>Diagnostic Radiology</topic><topic>Diffusion</topic><topic>Diffusion coefficient</topic><topic>Diffusion Magnetic Resonance Imaging - methods</topic><topic>Female</topic><topic>Genotype</topic><topic>Genotyping</topic><topic>Genotyping Techniques - methods</topic><topic>Health risk assessment</topic><topic>Histograms</topic><topic>Histology</topic><topic>Human papillomavirus</topic><topic>Humans</topic><topic>Image Interpretation, Computer-Assisted - methods</topic><topic>Imaging</topic><topic>Internal Medicine</topic><topic>Interventional Radiology</topic><topic>Kaplan-Meier Estimate</topic><topic>Magnetic resonance imaging</topic><topic>Mathematical models</topic><topic>Medical imaging</topic><topic>Medical prognosis</topic><topic>Medicine</topic><topic>Medicine & Public Health</topic><topic>Middle Aged</topic><topic>Neoplasm Staging</topic><topic>Neuroradiology</topic><topic>Obstetrics</topic><topic>Oncology</topic><topic>Papillomaviridae - classification</topic><topic>Papillomaviridae - genetics</topic><topic>Parameters</topic><topic>Patients</topic><topic>Predictions</topic><topic>Prognosis</topic><topic>Radiation therapy</topic><topic>Radiology</topic><topic>Regression analysis</topic><topic>Resonance</topic><topic>Retrospective Studies</topic><topic>Risk groups</topic><topic>Survival</topic><topic>Training</topic><topic>Tumors</topic><topic>Ultrasound</topic><topic>Uterine Cervical Neoplasms - diagnostic imaging</topic><topic>Uterine Cervical Neoplasms - pathology</topic><topic>Uterine Cervical Neoplasms - therapy</topic><topic>Uterine Cervical Neoplasms - virology</topic><topic>Young Adult</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Lin, Gigin</creatorcontrib><creatorcontrib>Yang, Lan-Yan</creatorcontrib><creatorcontrib>Lin, Yu-Chun</creatorcontrib><creatorcontrib>Huang, Yu-Ting</creatorcontrib><creatorcontrib>Liu, Feng-Yuan</creatorcontrib><creatorcontrib>Wang, Chun-Chieh</creatorcontrib><creatorcontrib>Lu, Hsin-Ying</creatorcontrib><creatorcontrib>Chiang, Hsin-Ju</creatorcontrib><creatorcontrib>Chen, Yu-Ruei</creatorcontrib><creatorcontrib>Wu, Ren-Chin</creatorcontrib><creatorcontrib>Ng, Koon-Kwan</creatorcontrib><creatorcontrib>Hong, Ji-Hong</creatorcontrib><creatorcontrib>Yen, Tzu-Chen</creatorcontrib><creatorcontrib>Lai, Chyong-Huey</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>Biotechnology Research Abstracts</collection><collection>Proquest Nursing & Allied Health Source</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>Technology Research Database</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>Engineering Research Database</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>Biotechnology and BioEngineering Abstracts</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>MEDLINE - Academic</collection><jtitle>European radiology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Lin, Gigin</au><au>Yang, Lan-Yan</au><au>Lin, Yu-Chun</au><au>Huang, Yu-Ting</au><au>Liu, Feng-Yuan</au><au>Wang, Chun-Chieh</au><au>Lu, Hsin-Ying</au><au>Chiang, Hsin-Ju</au><au>Chen, Yu-Ruei</au><au>Wu, Ren-Chin</au><au>Ng, Koon-Kwan</au><au>Hong, Ji-Hong</au><au>Yen, Tzu-Chen</au><au>Lai, Chyong-Huey</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Prognostic model based on magnetic resonance imaging, whole-tumour apparent diffusion coefficient values and HPV genotyping for stage IB-IV cervical cancer patients following chemoradiotherapy</atitle><jtitle>European radiology</jtitle><stitle>Eur Radiol</stitle><addtitle>Eur Radiol</addtitle><date>2019-02-01</date><risdate>2019</risdate><volume>29</volume><issue>2</issue><spage>556</spage><epage>565</epage><pages>556-565</pages><issn>0938-7994</issn><eissn>1432-1084</eissn><abstract>Objectives
To develop and validate a prognostic model of integrating whole-tumour apparent diffusion coefficient (ADC) from pretreatment diffusion-weighted (DW) magnetic resonance (MR) imaging with human papillomavirus (HPV) genotyping in predicting the overall survival (OS) and disease-free survival (DFS) for women with stage IB–IV cervical cancer following concurrent chemoradiotherapy (CCRT).
Methods
We retrospectively analysed three prospectively collected cohorts comprising 300 patients with stage IB–IV cervical cancer treated with CCRT in 2007–2014 and filtered 134 female patients who underwent MR imaging at 3.0 T for final analysis (age, 24–92 years; median, 54 years). Univariate and multivariate Cox regression analyses were used to evaluate the whole-tumour ADC histogram parameters, HPV genotyping and relevant clinical variables in predicting OS and DFS. The dataset was randomly split into training (
n
= 88) and testing (
n
= 46) datasets for construction and independent bootstrap validation of the models.
Results
The median follow-up time for surviving patients was 69 months (range, 9–126 months). Non-squamous cell type, ADC
10
<0.77 × 10
-3
mm
2
/s, T3-4, M1 stage and high-risk HPV status were selected to generate a model, in which the OS and DFS for the low, intermediate and high-risk groups were significantly stratified (
p
< 0.0001). The prognostic model improved the prediction significantly compared with the International Federation of Gynaecology and Obstetrics (FIGO) stage for both the training and independent testing datasets (
p
< 0.0001).
Conclusions
The prognostic model based on integrated clinical and imaging data could be a useful clinical biomarker to predict OS and DFS in patients with stage IB–IV cervical cancer treated with CCRT.
Key points
• ADC
10
is the best prognostic factor among ADC parameters in cervical cancer treated with CCRT
• A novel prognostic model was built based on histology, ADC
10
, T and M stage and HPV status
• The prognostic model outperforms FIGO stage in the survival prediction</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><pmid>30051142</pmid><doi>10.1007/s00330-018-5651-4</doi><tpages>10</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0938-7994 |
| ispartof | European radiology, 2019-02, Vol.29 (2), p.556-565 |
| issn | 0938-7994 1432-1084 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_2078593400 |
| source | MEDLINE; SpringerNature Journals |
| subjects | Adult Aged Aged, 80 and over Biomarkers Cancer Cervical cancer Cervix Chemoradiotherapy Chemoradiotherapy - methods Chemotherapy Datasets Diagnostic Radiology Diffusion Diffusion coefficient Diffusion Magnetic Resonance Imaging - methods Female Genotype Genotyping Genotyping Techniques - methods Health risk assessment Histograms Histology Human papillomavirus Humans Image Interpretation, Computer-Assisted - methods Imaging Internal Medicine Interventional Radiology Kaplan-Meier Estimate Magnetic resonance imaging Mathematical models Medical imaging Medical prognosis Medicine Medicine & Public Health Middle Aged Neoplasm Staging Neuroradiology Obstetrics Oncology Papillomaviridae - classification Papillomaviridae - genetics Parameters Patients Predictions Prognosis Radiation therapy Radiology Regression analysis Resonance Retrospective Studies Risk groups Survival Training Tumors Ultrasound Uterine Cervical Neoplasms - diagnostic imaging Uterine Cervical Neoplasms - pathology Uterine Cervical Neoplasms - therapy Uterine Cervical Neoplasms - virology Young Adult |
| title | Prognostic model based on magnetic resonance imaging, whole-tumour apparent diffusion coefficient values and HPV genotyping for stage IB-IV cervical cancer patients following chemoradiotherapy |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-20T11%3A05%3A19IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Prognostic%20model%20based%20on%20magnetic%20resonance%20imaging,%20whole-tumour%20apparent%20diffusion%20coefficient%20values%20and%20HPV%20genotyping%20for%20stage%20IB-IV%20cervical%20cancer%20patients%20following%20chemoradiotherapy&rft.jtitle=European%20radiology&rft.au=Lin,%20Gigin&rft.date=2019-02-01&rft.volume=29&rft.issue=2&rft.spage=556&rft.epage=565&rft.pages=556-565&rft.issn=0938-7994&rft.eissn=1432-1084&rft_id=info:doi/10.1007/s00330-018-5651-4&rft_dat=%3Cproquest_cross%3E2078593400%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2159604962&rft_id=info:pmid/30051142&rfr_iscdi=true |