Radiomics of locally advanced rectal cancer: machine learning-based prediction of response to neoadjuvant chemoradiotherapy using pre-treatment sagittal T2-weighted MRI

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dc.authoridkocak, burak/0000-0002-7307-396X
dc.authoridYardimci, Veysi Hakan/0000-0003-1395-3882
dc.authoridCin, Merve/0000-0002-0656-497X
dc.contributor.authorYardimci, Aytul Hande
dc.contributor.authorKocak, Burak
dc.contributor.authorSel, Ipek
dc.contributor.authorBulut, Hasan
dc.contributor.authorBektas, Ceyda Turan
dc.contributor.authorCin, Merve
dc.contributor.authorDursun, Nevra
dc.date.accessioned2024-09-11T19:50:28Z
dc.date.available2024-09-11T19:50:28Z
dc.date.issued2023
dc.departmentİstanbul Gelişim Üniversitesien_US
dc.description.abstractPurpose Variable response to neoadjuvant chemoradiotherapy (nCRT) is observed among individuals with locally advanced rectal cancer (LARC), having a significant impact on patient management. In this work, we aimed to investigate the potential value of machine learning (ML)-based magnetic resonance imaging (MRI) radiomics in predicting therapeutic response to nCRT in patients with LARC. Materials and methods Seventy-six patients with LARC were included in this retrospective study. Radiomic features were extracted from pre-treatment sagittal T2-weighted MRI images, with 3D segmentation. Dimension reduction was performed with a reliability analysis, pair-wise correlation analysis, analysis of variance, recursive feature elimination, Kruskal-Wallis, and Relief methods. Models were created using four different algorithms. In addition to radiomic models, clinical only and different combined models were developed and compared. The reference standard was tumor regression grade (TRG) based on the Modified Ryan Scheme (TRG 0 vs TRG 1-3). Models were compared based on net reclassification index (NRI). Clinical utility was assessed with decision curve analysis (DCA). Results Number of features with excellent reliability is 106. The best result was achieved with radiomic only model using eight features. The area under the curve (AUC), accuracy, sensitivity, and specificity for validation were 0.753 (standard deviation [SD], 0.082), 81.1%, 83.8%, and 75.0%; for testing, 0.705 (SD, 0.145), 73.9%, 81.2%, and 57.1%, respectively. Based on the clinical only model as reference, NRI for radiomic only model was the best. DCA also showed better clinical utility for radiomic only model. Conclusions ML-based T2-weighted MRI radiomics might have a potential in predicting response to nCRT in patients with LARC.en_US
dc.identifier.doi10.1007/s11604-022-01325-7
dc.identifier.endpage82en_US
dc.identifier.issn1867-1071
dc.identifier.issn1867-108X
dc.identifier.issue1en_US
dc.identifier.pmid35962933en_US
dc.identifier.scopus2-s2.0-85136156971en_US
dc.identifier.startpage71en_US
dc.identifier.urihttps://doi.org/10.1007/s11604-022-01325-7
dc.identifier.urihttps://hdl.handle.net/11363/7634
dc.identifier.volume41en_US
dc.identifier.wosWOS:000840299500001en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofJapanese Journal of Radiologyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz20240903_Gen_US
dc.subjectRectal canceren_US
dc.subjectNeoadjuvant chemoradiotherapyen_US
dc.subjectMachine learningen_US
dc.subjectRadiomicsen_US
dc.subjectTexture analysisen_US
dc.titleRadiomics of locally advanced rectal cancer: machine learning-based prediction of response to neoadjuvant chemoradiotherapy using pre-treatment sagittal T2-weighted MRIen_US
dc.typeArticleen_US

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