Treatment plans for prostate cancer patients undergoing stereotactic body radiation therapy (SBRT) are often challenging due to the proximity of organs at risk. Today, there are no objective criteria to determine whether an optimal treatment plan has been achieved, and physicians rely on their personal experience to evaluate the plan's quality. In this study, we propose a method for determining rectal and bladder dose constraints achievable for a given patient's anatomy. We expect that this method will improve the overall plan quality and consistency, and facilitate comparison of clinical outcomes across different institutions. The 3D proximity of the organs at risk to the target is quantified by means of the expansion‐intersection volume (EIV), which is defined as the intersection volume between the target and the organ at risk expanded by 5 mm. We determine a relationship between EIV and relevant dosimetric parameters, such as the volume of bladder and rectum receiving 75% of the prescription dose (V75%). This relationship can be used to establish institution‐specific criteria to guide the treatment planning and evaluation process.A database of 25 prostate patients treated with CyberKnife SBRT is used to validate this approach. There is a linear correlation between EIV and V75% of bladder and rectum, confirming that the dose delivered to rectum and bladder increases with increasing extension and proximity of these organs to the target. This information can be used during the planning stage to facilitate the plan optimization process, and to standardize plan quality and consistency. We have developed a method for determining customized dose constraints for prostate patients treated with robotic SBRT. Although the results are technology‐specific and based on the experience of a single institution, we expect that the application of this method by other institutions will result in improved standardization of clinical practice.PACS numbers: 87.55.‐x, 87.55.D‐, 87.55.de, 87.55.dk

Purpose: to predict vestibular schwannoma (VS) response to radiosurgery by applying machine learning (ML) algorithms on radiomic features extracted from pre-treatment magnetic resonance (MR) images. Methods: patients with VS treated with radiosurgery in two Centers from 2004 to 2016 were retrospectively evaluated. Brain T1-weighted contrast-enhanced MR images were acquired before and at 24 and 36 months after treatment. Clinical and treatment data were collected contextually. Treatment responses were assessed considering the VS volume variation based on pre- and post-radiosurgery MR images at both time points. Tumors were semi-automatically segmented and radiomic features were extracted. Four ML algorithms (Random Forest, Support Vector Machine, Neural Network, and extreme Gradient Boosting) were trained and tested for treatment response (i.e., increased or non-increased tumor volume) using nested cross-validation. For training, feature selection was performed using the Least Absolute Shrinkage and Selection Operator, and the selected features were used as input to separately build the four ML classification algorithms. To overcome class imbalance during training, Synthetic Minority Oversampling Technique was used. Finally, trained models were tested on the corresponding held out set of patients to evaluate balanced accuracy, sensitivity, and specificity. Results: 108 patients treated with Cyberknife^® were retrieved; an increased tumor volume was observed at 24 months in 12 patients, and at 36 months in another group of 12 patients. The Neural Network was the best predictive algorithm for response at 24 (balanced accuracy 73% ± 18%, specificity 85% ± 12%, sensitivity 60% ± 42%) and 36 months (balanced accuracy 65% ± 12%, specificity 83% ± 9%, sensitivity 47% ± 27%). Conclusions: radiomics may predict VS response to radiosurgery avoiding long-term follow-up as well as unnecessary treatment.

Purpose: Definition of the optimal treatment schedule for high-risk prostate cancer is under debate. A combination of photon intensity modulated radiotherapy (IMRT) on pelvis with a carbon ion boost might be the optimal treatment scheme to escalate the dose on prostate and deliver curative dose with respect to normal tissue and quality of dose distributions. In fact, carbon ion beams offer the advantage to deliver hypofractionated radiotherapy (RT) using a significantly smaller number of fractions compared to conventional RT without increasing risks of late effects. Methods: This study is a prospective phase II clinical trial exploring safety and feasibility of a mixed beam scheme of carbon ion prostate boost followed by photon IMRT on pelvis. The study is designed to enroll 65 patients with localized high-risk prostate cancer at 3 different oncologic hospitals: Istituto Europeo di Oncologia, Fondazione IRCCS Istituto Nazionale dei Tumori, and Centro Nazionale di Adroterapia Oncologica. The primary endpoint is the evaluation of safety and feasibility with acute toxicity scored up to 1 month after the end of RT. Secondary endpoints are treatment early (3 months after the end of RT) and long-term tolerability, quality of life, and efficacy. Results: The study is not yet recruiting; in silico studies are ongoing and we expect to start recruitment by 2017. Conclusions: The present clinical trial aims at improving the current treatment for high-risk prostate cancer, evaluating safety and feasibility of a new RT mixed-beam scheme including photons and carbon ions. Encouraging results are coming from carbon ion facilities worldwide on the treatment of different tumors including prostate cancers. Carbon ions combine physical properties allowing for high dose conformity and advantageous radiobiological characteristics. The proposed mixed beam treatment has the advantage to combine a photon high conformity standard of care IMRT phase with a hypofractionated carbon ion RT boost delivered in a short overall treatment time.