Multi-Habitat Based Radiomics for the Prediction of Treatment Response to Concurrent Chemotherapy and Radiation Therapy in Locally Advanced Cervical Cancer - Fang, Mengjie; Kan, Yangyang; Dong, Di; Yu, Tao; Zhao, Nannan; Jiang, Wenyan; Zhong, Lianzhen; Hu, Chaoen; Luo, Yahong; Tian, Jie (Frontiers in Oncology, 2020)
Objectives: To develop a radiomic model based on multiparametric magnetic resonance imaging (MRI) for predicting treatment response prior to commencing concurrent chemotherapy and radiation therapy (CCRT) for locally advanced cervical cancer. Materials and Methods: The retrospective study enrolled 120 patients (allocated to a training or a test set) with locally advanced cervical cancer who underwent CCRT between December 2014 and June 2017. All patients enrolled underwent MRI with 9 sequences before treatment and again at the end of the fourth week of treatment. Responses were evaluated by MRI according to RECIST standards, and patients were divided into responder group or non-responder group. For every MRI sequence, a total of 114 radiomic features were extracted from the outlined tumour habitat. On the training set, the least absolute shrinkage and selection operator method was used to select key features and to construct 9 habitat signatures. Then, three kinds of machine learning models were compared and applied to integrate these predictive signatures and the clinical characteristics into a radiomic model. The discrimination ability, reliability and calibration of our radiomic model were evaluated. Results: The radiomic model, which consisted of 3 habitat signatures from sagittal T2 image, axial T1 enhanced-MRI image and ADC image respectively, has shown good predictive performance, with area under the curve of 0.820 (95% CI: 0.713-0.927) in the training set and 0.798 (95% CI: 0.678-0.917) in the test set. Meanwhile, the model was proved to perform better than each single signature or clinical characteristic. Conclusions: A radiomic model employing features from multiple tumour habitats held the ability for predicting treatment response in patients with locally advanced cervical cancer, before commencing CCRT. These results illustrated a potential new tool for improving medical decision-making and therapeutic strategies.