A multi-scanner study of MRI radiomics in uterine cervical cancer: prediction of in-field tumor control after definitive radiotherapy based on a machine learning method including peritumoral regions.

PURPOSE: This study aimed to identify the most appropriate volume of interest (VOI) setting in prognostic prediction using pretreatment magnetic resonance imaging (MRI) radiomic analysis for cervical cancer (CC) treated with definitive radiotherapy.
MATERIALS AND METHODS: The study participants were 87 patients who had undergone pretreatment MRI and definitive radiotherapy for CC. VOItumor was created with tumor alone and VOI+4 mm-VOI+20 mm mechanically expanded by 4-20 mm around each VOItumor in axial T2-weighted images (T2WI) and an apparent diffusion coefficient (ADC) map. A model was constructed to predict recurrence within the irradiation field within 2 years after treatment using imaging features from the VOI of each sequence. Sorting ability was evaluated by area under the receiver operator characteristic curve (AUC-ROC) analysis.
RESULTS: VOI expansion improved AUC-ROCs compared with the predictive models of VOItumor (0.59 and 0.67 in T2WI and ADC, respectively). The AUC-ROCs of the models with imaging features from expanded VOI+4 mm in T2WI and VOI+4 mm and VOI+8 mm in ADC were 0.82, 0.82, and 0.86, respectively.
CONCLUSION: Recurrence could be predicted with high accuracy using expanded VOI for CC treated with definitive radiotherapy, suggesting that including the pathological characteristics of invasive margins in radiomics may improve predictive ability.