MRI-based radiomic signatures for pretreatment prognostication in cervical cancer

Accurate pretherapeutic prognostication is important for tailoring treatment in cervical cancer (CC).

To investigate whether pretreatment MRI-based radiomic signatures predict disease-specific survival (DSS) in CC.

Retrospective.

CC patients (n = 133) allocated into training_(T) (n_T  = 89)/validation_(V) (n_V  = 44) cohorts.

T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) at 1.5T or 3.0T.

Radiomic features from segmented tumors were extracted from T2WI and DWI (high b-value DWI and apparent diffusion coefficient (ADC) maps).

Radiomic signatures for prediction of DSS from T2WI (T2_rad ) and T2WI with DWI (T2 + DWI_rad ) 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 (MAX_size ), 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.

The radiomic signatures T2_rad and T2 + DWI_rad yielded AUC_T /AUC_V 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 MAX_size (AUC_T /AUC_V : 0.69/0.65) and FIGO (AUC_T /AUC_V : 0.77/0.64) and were significant predictors of DSS after adjusting for FIGO (HR_T /HR_V for T2_rad : 4.0/2.5 and T2 + DWI_rad : 4.8/2.1). Adding T2_rad and T2 + DWI_rad to FIGO significantly improved DSS prediction compared to FIGO alone in cohort_(T) (AUC_T 0.86 and 0.88 vs. 0.77), and FIGO with T2 + DWI_rad tended to the same in cohort_(V) (AUC_V 0.75 vs. 0.64, p = 0.07). High radiomic score for T2 + DWI_rad was significantly associated with reduced DSS in both cohorts.

Radiomic signatures from T2WI and T2WI with DWI may provide added value for pretreatment risk assessment and for guiding tailored treatment strategies in CC.