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dc.contributor.authorRundo, Leonardo
dc.contributor.authorBeer, Lucian
dc.contributor.authorEscudero Sanchez, Lorena
dc.contributor.authorCrispin-Ortuzar, Mireia
dc.contributor.authorReinius, Marika
dc.contributor.authorMcCague, Cathal
dc.contributor.authorSahin, Hilal
dc.contributor.authorBura, Vlad
dc.contributor.authorPintican, Roxana
dc.contributor.authorZerunian, Marta
dc.contributor.authorUrsprung, Stephan
dc.contributor.authorAllajbeu, Iris
dc.contributor.authorAddley, Helen
dc.contributor.authorMartin-Gonzalez, Paula
dc.contributor.authorBuddenkotte, Thomas
dc.contributor.authorSingh, Naveena
dc.contributor.authorSahdev, Anju
dc.contributor.authorFuningana, Ionut-Gabriel
dc.contributor.authorJimenez-Linan, Mercedes
dc.contributor.authorMarkowetz, Florian
dc.contributor.authorBrenton, James D
dc.contributor.authorSala, Evis
dc.contributor.authorWoitek, Ramona
dc.date.accessioned2022-06-30T08:00:32Z
dc.date.available2022-06-30T08:00:32Z
dc.date.issued2022
dc.date.submitted2022-02-02
dc.identifier.citationFrontiers in Oncology, volume 12, article-number 868265
dc.identifier.issn2234-943X
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/338626
dc.description.abstractBACKGROUND: Pathological response to neoadjuvant treatment for patients with high-grade serous ovarian carcinoma (HGSOC) is assessed using the chemotherapy response score (CRS) for omental tumor deposits. The main limitation of CRS is that it requires surgical sampling after initial neoadjuvant chemotherapy (NACT) treatment. Earlier and non-invasive response predictors could improve patient stratification. We developed computed tomography (CT) radiomic measures to predict neoadjuvant response before NACT using CRS as a gold standard. METHODS: Omental CT-based radiomics models, yielding a simplified fully interpretable radiomic signature, were developed using Elastic Net logistic regression and compared to predictions based on omental tumor volume alone. Models were developed on a single institution cohort of neoadjuvant-treated HGSOC (n = 61; 41% complete response to NCT) and tested on an external test cohort (n = 48; 21% complete response). RESULTS: The performance of the comprehensive radiomics models and the fully interpretable radiomics model was significantly higher than volume-based predictions of response in both the discovery and external test sets when assessed using G-mean (geometric mean of sensitivity and specificity) and NPV, indicating high generalizability and reliability in identifying non-responders when using radiomics. The performance of a fully interpretable model was similar to that of comprehensive radiomics models. CONCLUSIONS: CT-based radiomics allows for predicting response to NACT in a timely manner and without the need for abdominal surgery. Adding pre-NACT radiomics to volumetry improved model performance for predictions of response to NACT in HGSOC and was robust to external testing. A radiomic signature based on five robust predictive features provides improved clinical interpretability and may thus facilitate clinical acceptance and application.
dc.languageen
dc.publisherFrontiers Media SA
dc.subjectOncology
dc.subjectovarian cancer
dc.subjectradiomics
dc.subjectcomputed tomography
dc.subjectchemotherapy response score
dc.subjectneoadjuvant chemotherapy
dc.titleClinically Interpretable Radiomics-Based Prediction of Histopathologic Response to Neoadjuvant Chemotherapy in High-Grade Serous Ovarian Carcinoma.
dc.typeArticle
dc.date.updated2022-06-30T08:00:31Z
prism.publicationNameFront Oncol
dc.identifier.doi10.17863/CAM.86039
dcterms.dateAccepted2022-05-02
rioxxterms.versionofrecord10.3389/fonc.2022.868265
rioxxterms.versionVoR
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by/4.0/
dc.contributor.orcidMarkowetz, Florian [0000-0002-2784-5308]
dc.contributor.orcidBrenton, James [0000-0002-5738-6683]
dc.contributor.orcidSala, Evis [0000-0002-5518-9360]
dc.identifier.eissn2234-943X
pubs.funder-project-idEngineering and Physical Sciences Research Council (EP/P020259/1)
pubs.funder-project-idCancer Research UK (C96/A25177)
pubs.funder-project-idNational Institute for Health Research (IS-BRC-1215-20014)
pubs.funder-project-idWellcome Trust (215733/Z/19/Z)
cam.issuedOnline2022-06-16


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