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MRI‐based radiomic signatures for pretreatment prognostication in cervical cancer

BACKGROUND: Accurate pretherapeutic prognostication is important for tailoring treatment in cervical cancer (CC). PURPOSE: To investigate whether pretreatment MRI‐based radiomic signatures predict disease‐specific survival (DSS) in CC. STUDY TYPE: Retrospective. POPULATION: CC patients (n = 133) all...

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Autores principales: Wagner‐Larsen, Kari S., Hodneland, Erlend, Fasmer, Kristine E., Lura, Njål, Woie, Kathrine, Bertelsen, Bjørn I., Salvesen, Øyvind, Halle, Mari K., Smit, Noeska, Krakstad, Camilla, Haldorsen, Ingfrid S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10652318/
https://www.ncbi.nlm.nih.gov/pubmed/37840437
http://dx.doi.org/10.1002/cam4.6526
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author Wagner‐Larsen, Kari S.
Hodneland, Erlend
Fasmer, Kristine E.
Lura, Njål
Woie, Kathrine
Bertelsen, Bjørn I.
Salvesen, Øyvind
Halle, Mari K.
Smit, Noeska
Krakstad, Camilla
Haldorsen, Ingfrid S.
author_facet Wagner‐Larsen, Kari S.
Hodneland, Erlend
Fasmer, Kristine E.
Lura, Njål
Woie, Kathrine
Bertelsen, Bjørn I.
Salvesen, Øyvind
Halle, Mari K.
Smit, Noeska
Krakstad, Camilla
Haldorsen, Ingfrid S.
author_sort Wagner‐Larsen, Kari S.
collection PubMed
description BACKGROUND: Accurate pretherapeutic prognostication is important for tailoring treatment in cervical cancer (CC). PURPOSE: To investigate whether pretreatment MRI‐based radiomic signatures predict disease‐specific survival (DSS) in CC. STUDY TYPE: Retrospective. POPULATION: CC patients (n = 133) allocated into training((T)) (n (T) = 89)/validation((V)) (n (V) = 44) cohorts. FIELD STRENGTH/SEQUENCE: T2‐weighted imaging (T2WI) and diffusion‐weighted imaging (DWI) at 1.5T or 3.0T. ASSESSMENT: Radiomic features from segmented tumors were extracted from T2WI and DWI (high b‐value DWI and apparent diffusion coefficient (ADC) maps). STATISTICAL TESTS: Radiomic signatures for prediction of DSS from T2WI (T2(rad)) and T2WI with DWI (T2 + DWI(rad)) were constructed by least absolute shrinkage and selection operator (LASSO) Cox regression. Area under time‐dependent receiver operating characteristics curves (AUC) were used to evaluate and compare the prognostic performance of the radiomic signatures, MRI‐derived maximum tumor size ≤/> 4 cm (MAX(size)), and 2018 International Federation of Gynecology and Obstetrics (FIGO) stage (I–II/III–IV). Survival was analyzed using Cox model estimating hazard ratios (HR) and Kaplan–Meier method with log‐rank tests. RESULTS: The radiomic signatures T2(rad) and T2 + DWI(rad) yielded AUC(T)/AUC(V) of 0.80/0.62 and 0.81/0.75, respectively, for predicting 5‐year DSS. Both signatures yielded better or equal prognostic performance to that of MAX(size) (AUC(T)/AUC(V): 0.69/0.65) and FIGO (AUC(T)/AUC(V): 0.77/0.64) and were significant predictors of DSS after adjusting for FIGO (HR(T)/HR(V) for T2(rad): 4.0/2.5 and T2 + DWI(rad): 4.8/2.1). Adding T2(rad) and T2 + DWI(rad) to FIGO significantly improved DSS prediction compared to FIGO alone in cohort((T)) (AUC(T) 0.86 and 0.88 vs. 0.77), and FIGO with T2 + DWI(rad) tended to the same in cohort((V)) (AUC(V) 0.75 vs. 0.64, p = 0.07). High radiomic score for T2 + DWI(rad) was significantly associated with reduced DSS in both cohorts. DATA CONCLUSION: Radiomic signatures from T2WI and T2WI with DWI may provide added value for pretreatment risk assessment and for guiding tailored treatment strategies in CC.
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spelling pubmed-106523182023-10-16 MRI‐based radiomic signatures for pretreatment prognostication in cervical cancer Wagner‐Larsen, Kari S. Hodneland, Erlend Fasmer, Kristine E. Lura, Njål Woie, Kathrine Bertelsen, Bjørn I. Salvesen, Øyvind Halle, Mari K. Smit, Noeska Krakstad, Camilla Haldorsen, Ingfrid S. Cancer Med RESEARCH ARTICLES BACKGROUND: Accurate pretherapeutic prognostication is important for tailoring treatment in cervical cancer (CC). PURPOSE: To investigate whether pretreatment MRI‐based radiomic signatures predict disease‐specific survival (DSS) in CC. STUDY TYPE: Retrospective. POPULATION: CC patients (n = 133) allocated into training((T)) (n (T) = 89)/validation((V)) (n (V) = 44) cohorts. FIELD STRENGTH/SEQUENCE: T2‐weighted imaging (T2WI) and diffusion‐weighted imaging (DWI) at 1.5T or 3.0T. ASSESSMENT: Radiomic features from segmented tumors were extracted from T2WI and DWI (high b‐value DWI and apparent diffusion coefficient (ADC) maps). STATISTICAL TESTS: Radiomic signatures for prediction of DSS from T2WI (T2(rad)) and T2WI with DWI (T2 + DWI(rad)) were constructed by least absolute shrinkage and selection operator (LASSO) Cox regression. Area under time‐dependent receiver operating characteristics curves (AUC) were used to evaluate and compare the prognostic performance of the radiomic signatures, MRI‐derived maximum tumor size ≤/> 4 cm (MAX(size)), and 2018 International Federation of Gynecology and Obstetrics (FIGO) stage (I–II/III–IV). Survival was analyzed using Cox model estimating hazard ratios (HR) and Kaplan–Meier method with log‐rank tests. RESULTS: The radiomic signatures T2(rad) and T2 + DWI(rad) yielded AUC(T)/AUC(V) of 0.80/0.62 and 0.81/0.75, respectively, for predicting 5‐year DSS. Both signatures yielded better or equal prognostic performance to that of MAX(size) (AUC(T)/AUC(V): 0.69/0.65) and FIGO (AUC(T)/AUC(V): 0.77/0.64) and were significant predictors of DSS after adjusting for FIGO (HR(T)/HR(V) for T2(rad): 4.0/2.5 and T2 + DWI(rad): 4.8/2.1). Adding T2(rad) and T2 + DWI(rad) to FIGO significantly improved DSS prediction compared to FIGO alone in cohort((T)) (AUC(T) 0.86 and 0.88 vs. 0.77), and FIGO with T2 + DWI(rad) tended to the same in cohort((V)) (AUC(V) 0.75 vs. 0.64, p = 0.07). High radiomic score for T2 + DWI(rad) was significantly associated with reduced DSS in both cohorts. DATA CONCLUSION: Radiomic signatures from T2WI and T2WI with DWI may provide added value for pretreatment risk assessment and for guiding tailored treatment strategies in CC. John Wiley and Sons Inc. 2023-10-16 /pmc/articles/PMC10652318/ /pubmed/37840437 http://dx.doi.org/10.1002/cam4.6526 Text en © 2023 The Authors. Cancer Medicine published by John Wiley & Sons Ltd. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle RESEARCH ARTICLES
Wagner‐Larsen, Kari S.
Hodneland, Erlend
Fasmer, Kristine E.
Lura, Njål
Woie, Kathrine
Bertelsen, Bjørn I.
Salvesen, Øyvind
Halle, Mari K.
Smit, Noeska
Krakstad, Camilla
Haldorsen, Ingfrid S.
MRI‐based radiomic signatures for pretreatment prognostication in cervical cancer
title MRI‐based radiomic signatures for pretreatment prognostication in cervical cancer
title_full MRI‐based radiomic signatures for pretreatment prognostication in cervical cancer
title_fullStr MRI‐based radiomic signatures for pretreatment prognostication in cervical cancer
title_full_unstemmed MRI‐based radiomic signatures for pretreatment prognostication in cervical cancer
title_short MRI‐based radiomic signatures for pretreatment prognostication in cervical cancer
title_sort mri‐based radiomic signatures for pretreatment prognostication in cervical cancer
topic RESEARCH ARTICLES
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10652318/
https://www.ncbi.nlm.nih.gov/pubmed/37840437
http://dx.doi.org/10.1002/cam4.6526
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