The aim of this study was to create a radiomics model for Locally Advanced Cervical Cancer (LACC) patients to predict pathological complete response (pCR) after neoadjuvant chemoradiotherapy (NACRT) analysing T2-weighted 1.5 T magnetic resonance imaging (MRI) acquired before treatment start. Patients with LACC and an International Federation of Gynecology and Obstetrics stage from IB2 to IVA at diagnosis were retrospectively enrolled for this study. All patients underwent NACRT, followed by radical surgery; pCR―assessed on surgical specimen―was defined as absence of any residual tumour. Finally, 1889 features were extracted from MR images; features showing statistical significance in predicting pCR at the univariate analysis were selected following an iterative method, which was ad-hoc developed for this study. Based on this method, 15 different classifiers were trained considering the most significant features selected. Model selection was carried out using the area under the receiver operating characteristic curve (AUC) as target metrics. One hundred eighty-three patients from two institutions were analysed. The model, showing the highest performance with an AUC of 0.80, was the random forest method initialised with default parameters. Radiomics appeared to be a reliable tool in pCR prediction for LACC patients undergoing NACRT, supporting the identification of patient risk groups, which paves treatment pathways tailored according to the predicted outcome.

Purpose To compare the survival and toxicity outcomes in patients with endometrial cancer treated with either high-dose-rate (HDR) or low-dose-rate (LDR) vaginal brachytherapy (VBT) following external beam radiotherapy (EBRT). Material and methods From January 2000 to December 2014, patients...

Background: Recent studies have highlighted the potentialities of a radiobiological parameter, the early regression index (ERI_TCP), in the treatment response prediction for rectal cancer patients treated with chemoradiotherapy followed by surgery. The aim of this study is to evaluate the performance of this parameter in predicting pathological complete response (pCR) in the context of low field MR guided radiotherapy (MRgRT) for cervical cancer (CC). Methods: A total of 16 patients affected by CC were enrolled. All patients underwent a MRgRT treatment, with prescription of 50.6 Gy in 22 fractions. A daily MR acquisition was performed at simulation and on each treatment fraction. Gross tumor volume (GTV) was delineated on the MR images acquired at the following biological effective dose (BED) levels: 14, 28, 42, 54 and 62 Gy. The ERI_TCP was calculated at the different BED levels and its predictive performance was quantified in terms of receiver operating characteristic (ROC) curve. Results: pCR was observed in 11/16 cases. The highest discriminative power of ERI_TCP was reported when a BED value of 28 Gy is reached, obtaining an area under curve (AUC) of 0.84. Conclusion: This study confirmed ERI_TCP as a promising response biomarker also for CC, although further studies with larger cohort of patients are recommended.