The aim of this study was to evaluate the variation of radiomics features, defined as “delta radiomics”, in patients undergoing neoadjuvant radiochemotherapy (RCT) for rectal cancer treated with hybrid magnetic resonance (MR)-guided radiotherapy (MRgRT). The delta radiomics features were then correlated with clinical complete response (cCR) outcome, to investigate their predictive power. A total of 16 patients were enrolled, and 5 patients (31%) showed cCR at restaging examinations. T 2*/ T 1 MR images acquired with a hybrid 0.35 T MRgRT unit were considered for this analysis. An imaging acquisition protocol of 6 MR scans per patient was performed: the first MR was acquired at first simulation ( t 0) and the remaining ones at fractions 5, 10, 15, 20 and 25. Radiomics features were extracted from the gross tumour volume (GTV), and each feature was correlated with the corresponding delivered dose. The variations of each feature during treatment were quantified, and the ratio between the values calculated at different dose levels and the one extracted at t 0 was calculated too. The Wilcoxon–Mann–Whitney test was performed to identify the features whose variation can be predictive of cCR, assessed with a MR acquired 6 weeks after RCT and digital examination. The most predictive feature ratios in cCR prediction were the L_least and glnu ones, calculated at the second week of treatment (22 Gy) with a p value = 0.001. Delta radiomics approach showed promising results and the quantitative analysis of images throughout MRgRT treatment can successfully predict cCR offering an innovative personalized medicine approach to rectal cancer treatment.

A method for the in vivo determination of the isocenter dose, Diso, and mid-plane dose, Dm, using the transmitted signal St measured by 25 central pixels of an aSi-based EPID is here reported. The method has been applied to check the conformal radiotherapy of pelvic tumors and supplies accurate in vivo dosimetry avoiding many of the disadvantages associated with the use of two diode detectors (at the entrance and exit of the patient) as their periodic recalibration and their positioning. Irradiating water-equivalent phantoms of different thicknesses, a set of correlation functions F(w, l) were obtained by the ratio between St and Dm as a function of the phantom thickness, w, for a different field width, l. For the in vivo determination of Diso and Dm values, the water-equivalent thickness of the patients (along the beam central axis) was evaluated by means of the treatment planning system that uses CT scans calibrated in terms of the electron densities. The Diso and Dm values experimentally determined were compared with the stated doses D(iso,TPS) and D(m,TPS), determined by the treatment planning system for ten pelvic treatments. In particular, for each treatment four fields were checked in six fractions. In these conditions the agreement between the in vivo dosimetry and stated doses at the isocenter point were within 3%. Comparing the 480 dose values obtained in this work with those obtained for 30 patients tested with a similar method, which made use of a small ion-chamber positioned on the EPIDs to obtain the transmitted signal, a similar agreement was observed. The method here proposed is very practical and can be applied in every treatment fraction, supplying useful information about eventual patient dose variations due to the incorrect application of the quality assurance program based on the check of patient setup, machine setting, and calculations.

The aim of this study was to propose a methodology to investigate the tumour heterogeneity and evaluate its ability to predict pathologically complete response (pCR) after chemo-radiotherapy (CRT) in locally advanced rectal cancer (LARC). This approach consisted in normalising the pixel intensities of the tumour and identifying the different sub-regions using an intensity-based thresholding. The spatial organisation of these subpopulations was quantified using the fractal dimension (FD). This approach was implemented in a radiomic workflow and applied to 198 T2-weighted pre-treatment magnetic resonance (MR) images of LARC patients. Three types of features were extracted from the gross tumour volume (GTV): morphological, statistical and fractal features. Feature selection was performed using the Wilcoxon test and a logistic regression model was calculated to predict the pCR probability after CRT. The model was elaborated considering the patients treated in two institutions: Fondazione Policlinico Universitario "Agostino Gemelli" of Rome (173 cases, training set) and University Medical Centre of Maastricht (25 cases, validation set). The results obtained showed that the fractal parameters of the subpopulations have the highest performance in predicting pCR. The predictive model elaborated had an area under the curve (AUC) equal to 0.77 ± 0.07. The model reliability was confirmed by the validation set (AUC = 0.79 ± 0.09). This study suggests that the fractal analysis can play an important role in radiomics, providing valuable information not only about the GTV structure, but also about its inner subpopulations.