The Image Biomarker Standardization Initiative validated consensus-based reference values for 169 radiomics features, thus enabling calibration and verification of radiomics software. Key results: • research teams found agreement for calculation of 169 radiomics features derived from a digital phantom and a human lung cancer on CT scan. • Of these 169 candidate radiomics features, good to excellent reproducibility was achieved for 167 radiomics features using MRI, 18F-FDG PET and CT images obtained in 51 patients with soft-tissue sarcoma.

Magnetic Resonance-guided radiotherapy (MRgRT) marks the beginning of a new era. MR is a versatile and suitable imaging modality for radiotherapy, as it enables direct visualization of the tumor and the surrounding organs at risk. Moreover, MRgRT provides real-time imaging to characterize and eventually track anatomical motion. Nevertheless, the successful translation of new technologies into clinical practice remains challenging. To date, the initial availability of next-generation hybrid MR-linac (MRL) systems is still limited and therefore, the focus of the present preview was on the initial applicability in current clinical practice and on future perspectives of this new technology for different treatment sites. MRgRT can be considered a groundbreaking new technology that is capable of creating new perspectives towards an individualized, patient-oriented planning and treatment approach, especially due to the ability to use daily online adaptation strategies. Furthermore, MRL systems overcome the limitations of conventional image-guided radiotherapy, especially in soft tissue, where target and organs at risk need accurate definition. Nevertheless, some concerns remain regarding the additional time needed to re-optimize dose distributions online, the reliability of the gating and tracking procedures and the interpretation of functional MR imaging markers and their potential changes during the course of treatment. Due to its continuous technological improvement and rapid clinical large-scale application in several anatomical settings, further studies may confirm the potential disruptive role of MRgRT in the evolving oncological environment.

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.

This MR-based, vendor-independent model can be helpful for predicting pCR probability in locally advanced rectal cancer (LARC) patients only using pre-treatment imaging.

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.

Background Different studies have proved in recent years that hypofractionated radiotherapy (RT) improves overall survival of patients affected by locally advanced, unresectable, pancreatic cancer. The clinical management of these patients generally leads to poor results and is considered very challenging, due to different factors, heavily influencing treatment delivery and its outcomes. Firstly, the dose prescribed to the target is limited by the toxicity that the highly radio-sensitive organs at risk (OARs) surrounding the disease can develop. Treatment delivery is also complicated by the significant inter-fractional and intra-fractional variability of therapy volumes, mainly related to the presence of hollow organs and to the breathing cycle. Main body of the abstract The recent introduction of magnetic resonance guided radiotherapy (MRgRT) systems leads to the opportunity to control most of the aforementioned sources of uncertainty influencing RT treatment workflow in pancreatic cancer. MRgRT offers the possibility to accurately identify radiotherapy volumes, thanks to the high soft-tissue contrast provided by the Magnetic Resonance imaging (MRI), and to monitor the tumour and OARs positions during the treatment fraction using a high-temporal cine MRI. However, the main advantage offered by the MRgRT is the possibility to online adapt the RT treatment plan, changing the dose distribution while the patient is still on couch and successfully addressing most of the sources of variability. Short conclusion Aim of this study is to present and discuss the state of the art, the main pitfalls and the innovative opportunities offered by online adaptive MRgRT in pancreatic cancer treatment.

Introduction: Deep Learning (DL) is a machine learning technique that uses deep neural networks to create a model. The application areas of deep learning in radiation oncology include image segmentation and detection, image phenotyping, and radiomic signature discovery, clinical outcome prediction, image dose quantification, dose-response modeling, radiation adaptation, and image generation. In this review, we explain the methods used in DL and perform a literature review using the Medline database to identify studies using deep learning in radiation oncology. The search was conducted in April 2018, and identified studies published between 1997 and 2018, strongly skewed toward 2015 and later.Methods: A literature review was performed using PubMed/Medline in order to identify important recent publications to be synthesized into a review of the current status of Deep Learning in radiation oncology, directed at a clinically-oriented reader. The search strategy included the search terms “radiotherapy” and “deep learning.” In addition, reference lists of selected articles were hand searched for further potential hits of relevance to this review. The search was conducted in April 2018, and identified studies published between 1997 and 2018, strongly skewed toward 2015 and later.Results: Studies using DL for image segmentation were identified in Brain (n = 2), Head and Neck (n = 3), Lung (n = 6), Abdominal (n = 2), and Pelvic (n = 6) cancers. Use of Deep Learning has also been reported for outcome prediction, such as toxicity modeling (n = 3), treatment response and survival (n = 2), or treatment planning (n = 5).Conclusion: Over the past few years, there has been a significant number of studies assessing the performance of DL techniques in radiation oncology. They demonstrate how DL-based systems can aid clinicians in their daily work, be it by reducing the time required for or the variability in segmentation, or by helping to predict treatment outcomes and toxicities. It still remains to be seen when these techniques will be employed in routine clinical practice.

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