MRI‐based radiomic signatures for pretreatment prognostication in cervical cancer

Author:

Wagner‐Larsen Kari S.12ORCID,Hodneland Erlend13,Fasmer Kristine E.12,Lura Njål12,Woie Kathrine4,Bertelsen Bjørn I.5,Salvesen Øyvind6,Halle Mari K.47,Smit Noeska18,Krakstad Camilla47,Haldorsen Ingfrid S.12

Affiliation:

1. Mohn Medical Imaging and Visualization Centre (MMIV), Department of Radiology Haukeland University Hospital Bergen Norway

2. Section for Radiology, Department of Clinical Medicine University of Bergen Bergen Norway

3. Department of Mathematics University of Bergen Bergen Norway

4. Department of Obstetrics and Gynecology Haukeland University Hospital Bergen Norway

5. Department of Pathology Haukeland University Hospital Bergen Norway

6. Clinical Research Unit, Department of Clinical and Molecular Medicine Norwegian University of Science and Technology Trondheim Norway

7. Centre for Cancer Biomarkers (CCBIO), Department of Clinical Science University of Bergen Bergen Norway

8. Department of Informatics University of Bergen Bergen Norway

Abstract

AbstractBackgroundAccurate pretherapeutic prognostication is important for tailoring treatment in cervical cancer (CC).PurposeTo investigate whether pretreatment MRI‐based radiomic signatures predict disease‐specific survival (DSS) in CC.Study TypeRetrospective.PopulationCC patients (n = 133) allocated into training(T) (nT = 89)/validation(V) (nV = 44) cohorts.Field Strength/SequenceT2‐weighted imaging (T2WI) and diffusion‐weighted imaging (DWI) at 1.5T or 3.0T.AssessmentRadiomic features from segmented tumors were extracted from T2WI and DWI (high b‐value DWI and apparent diffusion coefficient (ADC) maps).Statistical TestsRadiomic signatures for prediction of DSS from T2WI (T2rad) and T2WI with DWI (T2 + DWIrad) 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 (MAXsize), 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.ResultsThe radiomic signatures T2rad and T2 + DWIrad yielded AUCT/AUCV 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 MAXsize (AUCT/AUCV: 0.69/0.65) and FIGO (AUCT/AUCV: 0.77/0.64) and were significant predictors of DSS after adjusting for FIGO (HRT/HRV for T2rad: 4.0/2.5 and T2 + DWIrad: 4.8/2.1). Adding T2rad and T2 + DWIrad to FIGO significantly improved DSS prediction compared to FIGO alone in cohort(T) (AUCT 0.86 and 0.88 vs. 0.77), and FIGO with T2 + DWIrad tended to the same in cohort(V) (AUCV 0.75 vs. 0.64, p = 0.07). High radiomic score for T2 + DWIrad was significantly associated with reduced DSS in both cohorts.Data ConclusionRadiomic signatures from T2WI and T2WI with DWI may provide added value for pretreatment risk assessment and for guiding tailored treatment strategies in CC.

Funder

Helse Vest

Norges Forskningsråd

Trond Mohn stiftelse

Publisher

Wiley

Subject

Cancer Research,Radiology, Nuclear Medicine and imaging,Oncology

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