Radiomic Profiles Improve Prognostication and Reveal Targets for Therapy in Cervical Cancer

Overview

Journal Sci Rep

Specialty Science

Date 2024 May 17

PMID 38760387

Authors

Mari Kylleso Halle

Erlend Hodneland

Kari S Wagner-Larsen

Njal G Lura

Kristine E Fasmer

Hege F Berg

Tomasz Stokowy

Aashish Srivastava

David Forsse

Erling A Hoivik

Kathrine Woie

Bjorn I Bertelsen

Camilla Krakstad

Ingfrid S Haldorsen

Affiliations

Soon will be listed here.

Abstract

Cervical cancer (CC) is a major global health problem with 570,000 new cases and 266,000 deaths annually. Prognosis is poor for advanced stage disease, and few effective treatments exist. Preoperative diagnostic imaging is common in high-income countries and MRI measured tumor size routinely guides treatment allocation of cervical cancer patients. Recently, the role of MRI radiomics has been recognized. However, its potential to independently predict survival and treatment response requires further clarification. This retrospective cohort study demonstrates how non-invasive, preoperative, MRI radiomic profiling may improve prognostication and tailoring of treatments and follow-ups for cervical cancer patients. By unsupervised clustering based on 293 radiomic features from 132 patients, we identify three distinct clusters comprising patients with significantly different risk profiles, also when adjusting for FIGO stage and age. By linking their radiomic profiles to genomic alterations, we identify putative treatment targets for the different patient clusters (e.g., immunotherapy, CDK4/6 and YAP-TEAD inhibitors and p53 pathway targeting treatments).

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