PURPOSE: ARTO (ClinicalTrials.gov identifier: NCT03449719 ) is a multicenter, phase II randomized clinical trial testing the benefit of adding stereotactic body radiation therapy (SBRT) to abiraterone acetate and prednisone (AAP) in patients with oligometastatic castrate-resistant prostate cancer (CRPC). MATERIALS AND METHODS: All patients were affected by oligometastatic CRPC as defined as three or less nonvisceral metastatic lesions. Patients were randomly assigned 1:1 to receive either AAP alone (control arm) or AAP with concomitant SBRT to all the sites of disease (experimental arm). Primary end point was the rate of biochemical response (BR), defined as a prostate-specific antigen (PSA) decrease ≥50% from baseline measured at 6 months from treatment start. Complete BR (CBR), defined as PSA < 0.2 ng/mL at 6 months from treatment, and progression-free survival (PFS) were secondary end points. RESULTS: One hundred and fifty-seven patients were enrolled between January 2019 and September 2022. BR was detected in 79.6% of patients (92% v 68.3% in the experimental v control arm, respectively), with an odds ratio (OR) of 5.34 (95% CI, 2.05 to 13.88; P = .001) in favor of the experimental arm. CBR was detected in 38.8% of patients (56% v 23.2% in the experimental v control arm, respectively), with an OR of 4.22 (95% CI, 2.12 to 8.38; P < .001). SBRT yielded a significant PFS improvement, with a hazard ratio for progression of 0.35 (95% CI, 0.21 to 0.57; P < .001) in the experimental versus control arm. CONCLUSION: The trial reached its primary end point of biochemical control and PFS, suggesting a clinical advantage for SBRT in addition to first-line AAP treatment in patients with metastatic castration-resistant prostate cancer.

Decision Support Systems (DSSs) are increasingly exploited in the area of prognostic evaluations. For predicting the effect of therapies on patients, the trend is now to use image features, i.e. information that can be automatically computed by considering images resulting by analysis. The DSSs application as predictive tools is particularly suitable for cancer treatment, given the peculiarities of the disease -which is highly localised and lead to significant social costs- and the large number of images that are available for each patient. At the state of the art, there exists tools that allow to handle image features for prognostic evaluations, but they are not designed for medical experts. They require either a strong engineering or computer science background since they do not integrate all the required functions, such as image retrieval and storage. In this paper we fill this gap by proposing Moddicom, a user-friendly complete library specifically designed to be exploited by physicians. A preliminary experimental analysis, performed by a medical expert that used the tool, demonstrates the efficiency and the effectiveness of Moddicom.

The advances in diagnostic and treatment technology are responsible for a remarkable transformation in the internal medicine concept with the establishment of a new idea of personalized medicine. Inter- and intra-patient tumor heterogeneity and the clinical outcome and/or treatment's toxicity's complexity, justify the effort to develop predictive models from decision support systems. However, the number of evaluated variables coming from multiple disciplines: oncology, computer science, bioinformatics, statistics, genomics, imaging, among others could be very large thus making traditional statistical analysis difficult to exploit. Automated data-mining processes and machine learning approaches can be a solution to organize the massive amount of data, trying to unravel important interaction. The purpose of this paper is to describe the strategy to collect and analyze data properly for decision support and introduce the concept of an ‘umbrella protocol’ within the framework of ‘rapid learning healthcare’.

The interaction between implementation of new technologies and different outcomes can allow a broad range of researches to be expanded. The purpose of this paper is to introduce the VAlidation of high TEchnology based on large database analysis by learning machine (VATE) project that aims to combine new technologies with outcomes related to rectal cancer in terms of tumor control and normal tissue sparing. Using automated computer bots and the knowledge for screening data it is possible to identify the factors that can mostly influence those outcomes. Population-based observational studies resulting from the linkage of different datasets will be conducted in order to develop predictive models that allow physicians to share decision with patients into a wider concept of tailored treatment