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Magnetic resonance imaging (MRI) has emerged as an important part of the diagnostic work-up in cervical cancer, with tumor volume and extent as important prognosticators. MRI radiomic tumor features may aid in prognostication and tailoring of treatment in cervical cancer. We extracted whole-volume radiomic texture features from 124 manually segmented tumors and performed unsupervised clustering yielding two distinct clusters. Overlapping clinicopathologic, genomic (whole exome sequencing, n=61), transcriptomic (L1000 arrays, n= 65) and molecular biomarker (n=82) data were applied to characterize the clusters. Independent of tumor size parameters, patients in cluster II had significantly reduced disease-specific survival as compared to those in cluster I (p>0.001), also within squamous cell carcinomas (n=96, p Citation Format: Mari Kyllesø Halle, Erlend Hodneland, Erling Hoivik, Kari Wagner-Larsen, Njål G. Lura, Julie Dybvik, David Forsse, Bjørn I. Bertelsen, Camilla Krakstad, Ingfrid S. Haldorsen, Olivera Bozickovic, Kathrine Woie. Radiomic profiles revealing targets for therapy in cervical cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 513. |
