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• Robust proton vs x-ray planning on free-breathing (FB) STAGE III NSCLC was tested. • A reduction of NTCP risk of pulmonary and cardiac toxicity was scored with protons. • A reduction in the dose to the immune system was observed with protons. • Robust optimization is an interesting and novel solution also for x-ray therapy. • Robust FB proton therapy could be an effective option for selected STAGE III NSCLC. To assess the robustness and to define the dosimetric and NTCP advantages of pencil-beam-scanning proton therapy (PBSPT) compared with VMAT for unresectable Stage III non-small lung cancer (NSCLC) in the immunotherapy era. 10 patients were re-planned with VMAT and PBSPT using: 1) ITV-based robust optimization with 0.5 cm setup uncertainties and (for PBSPT) 3.5 % range uncertainties on free-breathing CT 2) CTV-based RO including all 4DCTs anatomies. Target coverage (TC), organs at risk dose and TC robustness (TCR), set at V95%, were compared. The NTCP risk for radiation pneumonitis (RP), 24-month mortality (24MM), G2 + acute esophageal toxicity (ET), the dose to the immune system (EDIC) and the left anterior descending (LAD) coronary artery V15 < 10 % were registered. Wilcoxon test was used. Both PBSPT methods improved TC and TCR (p < 0.01). The mean lung dose and lung V20 were lower with PBSPT (p < 0.01). Median mean heart dose reduction with PBSPT was 8 Gy (p < 0.001). PT lowered median LAD V15 (p = 0.004). ΔNTCP > 5 % with PBSPT was observed for two patients for RP and for five patients for 24 MM. ΔNTCP for ≥ G2 ET was not in favor of PBSPT for all patients. PBSPT halved median EDIC (4.9/5.1 Gy for ITV/CTV-based VMAT vs 2.3 Gy for both ITV/CTV-based PBSPT, p < 0.01). PBSPT is a robust approach with significant dosimetric and NTCP advantages over VMAT; the EDIC reduction could allow for a better integration with immunotherapy. A clinical benefit for a subset of NSCLC patients is expected. [ABSTRACT FROM AUTHOR]

[Display omitted] • Accuracy is needed to deliver a high dose to a small volume identified on MRI. • There are no accepted metrics to quantify the accuracy of deformable registration. • Rigid coregistration seems accurate in radiotherapy of prostate cancer recurrence. To evaluate the accuracy of rigid coregistration between multiparametric magnetic resonance (mpMR) and computed tomography (CT) images for radiotherapy of prostate bed cancer recurrence. Fifty-three patients (59 nodules) accrued in a prospective study on salvage radiotherapy for prostatic bed recurrence were suitable for the analysis. Patients underwent a pre radiotherapy mpMR exam and a planning CT in the same treatment position and with control of organ filling. The site of recurrence was delineated on mpMR images and contours transferred on planning CT images using both rigid and deformable registrations. Coregistrations were evaluated by mathematical operators that quantify deformation (Jacobian determinant and vector curl) and similarity indices (Dice and Jaccard coefficients). Dose coverage was evaluated. Deformable registration did not change volumes, (p = 0.92 MW test). The Jacobian coefficient and the vector curl revealed no important image deformations. Dice and Jaccard coefficients indicated dislocation of the nodule volumes. Dislocation magnitude was d = (5.6 ± 3.1) mm. Organ filling was not correlated with deformation or dislocation. Volumes were covered by the 95% isodose in 96% of cases when rigid registration was performed versus 75% of cases when deformed. Rigid image coregistration is sufficiently accurate in this setting. The results indicate that the deformable registration tends to shrink the voxels and to dislocate the ROI, the adopted expansion for the recurrence volume adequately accounts for the observed deformation and dislocation, provided that organ filling is controlled. [ABSTRACT FROM AUTHOR]

• An "Ad hoc" phantom is designed and used to collect a large database of CT images. • Convolutional Neural Netwoks (CNNs) for denoise and segmentation tasks are developed. • Quality evaluation of CNNs processing is achieved by conventional and radiomic approaches. • Performances of different multi-tasks CNNs are investigated. • Radiomic features are proposed as indicators of texture alterations induced by CNNs. We investigate, by an extensive quality evaluation approach, performances and potential side effects introduced in Computed Tomography (CT) images by Deep Learning (DL) processing. We selected two relevant processing steps, denoise and segmentation, implemented by two Convolutional Neural Networks (CNNs) models based on autoencoder architecture (encoder-decoder and UNet) and trained for the two tasks. In order to limit the number of uncontrolled variables, we designed a phantom containing cylindrical inserts of different sizes, filled with iodinated contrast media. A large CT image dataset was collected at different acquisition settings and two reconstruction algorithms. We characterized the CNNs behavior using metrics from the signal detection theory, radiological and conventional image quality parameters, and finally unconventional radiomic features analysis. The UNet, due to the deeper architecture complexity, outperformed the shallower encoder-decoder in terms of conventional quality parameters and preserved spatial resolution. We also studied how the CNNs modify the noise texture by using radiomic analysis, identifying sensitive and insensitive features to the denoise processing. The proposed evaluation approach proved effective to accurately analyze and quantify the differences in CNNs behavior, in particular with regard to the alterations introduced in the processed images. Our results suggest that even a deeper and more complex network, which achieves good performances, is not necessarily a better network because it can modify texture features in an unwanted way. [ABSTRACT FROM AUTHOR]

Purpose In the treatment of Head-and-Neck Squamous Cell Carcinoma (HNSCC), the early prediction of residual malignant lymph nodes (LNs) is currently required. Here, we investigated the potential of a multi-modal characterization (combination of CT, T2w-MRI and DW-MRI) at baseline and at mid-treatment, based on texture analysis (TA), for the early prediction of LNs response to chemo-radiotherapy (CRT). Methods 30 patients with pathologically confirmed HNSCC treated with CRT were considered. All patients underwent a planning CT and two serial MR examinations (including T2w and DW images), one before and one at mid-CRT. For each patient the largest malignant LN was selected and within each LN, morphological and textural features were estimated from T2w-MRI and CT, besides a quantification of the apparent diffusion coefficient (ADC) from DW-MRI. After a median follow-up time of 26.6 months, 19 LNs showed regional control, while 11 LNs showed regional failure at a median time of 4.6 months. Linear discriminant analysis was used to test the accuracy of the image-based features in predicting the final response. Results Pre-treatment features showed higher predictive power than mid-CRT features, the ADC having the highest accuracy (80%); CT-based indices were found not predictive. When ADC was combined with TA, the classification performance increased (accuracy = 82.8%). If only T2w-MRI features were considered, the best combination of pre-CRT indices and their variation reached an equivalent accuracy (81.8%). Conclusion Our results may suggest that TA on T2w-MRI and ADC can be combined together to obtain a more accurate prediction of response to CRT. [ABSTRACT FROM AUTHOR]

This comprehensive review addresses most organs at risk involved in planning optimization for prostate cancer. It can be considered an update of a previous educational review that was published in 2009 (Fiorino et al., 2009). The literature was reviewed based on PubMed and MEDLINE database searches (from January 2009 up to September 2015), including papers in press; for each section/subsection, key title words were used and possibly combined with other more general key-words (such as radiotherapy, dose-volume effects, NTCP, DVH, and predictive model). Publications generally dealing with toxicity without any association with dose–volume effects or correlations with clinical risk factors were disregarded, being outside the aim of the review. A focus was on external beam radiotherapy, including post-prostatectomy, with conventional fractionation or moderate hypofractionation (<4 Gy/fraction); extreme hypofractionation is the topic of another paper in this special issue. Gastrointestinal and urinary toxicity are the most investigated endpoints, with quantitative data published in the last 5 years suggesting both a dose–response relationship and the existence of a number of clinical/patient related risk factors acting as dose–response modifiers. Some results on erectile dysfunction, bowel toxicity and hematological toxicity are also presented. [ABSTRACT FROM AUTHOR]

Purpose This work deal with the waiting time that patient that receive radiotherapy treatments pass in the queue [1] , in order to organize treatment processes effectively and efficiently and to ensure both the patient health and to facilities work optimization [2,3]. Methods Data belong to patients undergoing radiotherapy related to an observation period of 2 years were extrapolated from the record and verified system and classified into 8 macro categories. A simulation on the waiting time estimation was performed for all patients in the dataset. Our system considers: a priority assignment system coded by our institute staff; a characterization of machines and of their operation and maintenance; the historical data about patients and treatments; some different methods for calculating waiting time. Have been tested: a deterministic model, a Hidden Markov Model (HMM) to estimate the component not explained by the above parameters and a model based on a Support Vector Machine (SVM). Results The first results demonstrate an average accuracy (forecast – real waiting time, measured on historical data) around 5–10 days. Conclusions The implications of waiting list for radiotherapy treatment include perceived lower cancer control rates and patient suffering. The model represents a tool for effectively managing the capacity in a radiotherapy department to optimize the waiting lists for treatment. [ABSTRACT FROM AUTHOR]

Purpose Image-guided radiotherapy (IGRT) is daily used to correct patient setup errors based on the bony anatomy and soft-tissue contrast. When setup errors are larger than expected ones, on-line imaging are repeated to verify the fulfillment of alignment criteria but number, frequency and causes of repetition cannot be investigated with the conventional information registered in the record and verify (R&V) systems. This study aims at quantitative evaluating daily on-line shift in prostate cancer patients using control charts and at investigating their potential causes in order to reduce the time needed for patients' setup thus improving the treatment. Methods A subset of 24 prostate cancer treatments has been enrolled in this work. A homemade software was developed for pre-treatment data collection to complete the information automatically registered in the R&V system. Control charts method has been used to study the CBCT-based setup errors and to investigate how a process changes over time. Multivariate analysis was performed for the variable repetition, for the 3D error and for single direction error. Results For all investigated patients, the reasons of repetition have been extrapolated from the software. Multivariate analysis revealed that significant prognostic variable of CBCT repetition were: the drinking time before treatment, the inclusion of pelvic nodes in the treatment, the rectum volume and length, the bladder volume. The process appeared to be not under control and requires corrective actions. Conclusion Our software allows collecting information useful to be discussed in a clinical audit with the aim of decreasing the in-room time of patients without losing the benefit of the image-based investigation. The systematic and random setup errors calculated for each patient through the use of control charts permit to take under control the entire process and identify corrective actions. [ABSTRACT FROM AUTHOR]

Purpose Proton therapy reduces lung dose and mean heart dose in breast cancer irradiation in adjuvant breast cancer, in particular with nodal involvement [1]. In this work we compare 10 selected intensity modulated photon therapy (IMXT) treatment cases selected from the sending institution with PBS proton therapy. Methods and materials The eligibly criteria of the sending institution were: poor coverage of the target volumes, unfavourable dose volume histograms (DVH) to the organs at risk (OAR) lung and heart or wide irradiated volume (IV) with static IMXT. The type of case were heterogeneous as shown in Table 1. All plans were performed on the CT and volumes send applying a class solution of 3 proton field with range shifter with treatment planning system (TPS) Raystation. All plans were performed in robust Single Field Optimization technique (rSFO) optimizing the target and the OAR heart and the ipsilateral lung. The optimization was done with clinical pencil beam algorithm and 5 mm geometrical robust optimization tool provided by the TPS. No robust optimization of the range was applied. Results Comparing the proton and photon plans there was a significant improvement in at least one of the following parameter: IV, DVH of lung, DVH of heart for each case (e.g. Fig. 1). Conclusion The results of this study are promising. The heuristic eligibility method provide positive answer in 100% of the cases. Further work are necessary to assess role of range errors of proton peak. [ABSTRACT FROM AUTHOR]