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Background: Prostate cancer (PCa) is a highly heterogeneous disease, making tailored treatment approaches challenging. Magnetic resonance imaging (MRI), notably diffusion‐weighted imaging (DWI) and the derived Apparent Diffusion Coefficient (ADC) maps, plays a crucial role in PCa characterization. In this context, radiomics is a very promising approach able to disclose insights from MRI data. However, the sensitivity of radiomic features to MRI settings, encompassing DWI protocols and multicenter variations, requires the development of robust and generalizable models. Purpose: To develop a comprehensive radiomics framework for noninvasive PCa characterization using ADC maps, focusing on identifying reliable imaging biomarkers against intra‐ and inter‐institution variations. Materials and methods: Two patient cohorts, including an internal cohort (118 PCa patients) used for both training (75%) and hold‐out testing (25%), and an external cohort (50 PCa patients) for independent testing, were employed in the study. DWI images were acquired with three different DWI protocols on two different MRI scanners: two DWI protocols acquired on a 1.5‐T scanner for the internal cohort, and one DWI protocol acquired on a 3‐T scanner for the external cohort. One hundred and seven radiomics features (i.e., shape, first order, texture) were extracted from ADC maps of the whole prostate gland. To address variations in DWI protocols and multicenter variability, a dedicated pipeline, including two‐way ANOVA, sequential‐feature‐selection (SFS), and ComBat features harmonization was implemented. Mann–Whitney U‐tests (α = 0.05) were performed to find statistically significant features dividing patients with different tumor characteristics in terms of Gleason score (GS) and T‐stage. Support‐Vector‐Machine models were then developed to predict GS and T‐stage, and the performance was assessed through the area under the curve (AUC) of receiver‐operating‐characteristic curves. Results: Downstream of ANOVA, two subsets of 38 and 41 features stable against DWI protocol were identified for GS and T‐stage, respectively. Among these, SFS revealed the most predictive features, yielding an AUC of 0.75 (GS) and 0.70 (T‐stage) in the hold‐out test. Employing ComBat harmonization improved the external‐test performance of the GS model, raising AUC from 0.72 to 0.78. Conclusion: By incorporating stable features with a harmonization procedure and validating the model on an external dataset, model robustness, and generalizability were assessed, highlighting the potential of ADC and radiomics for PCa characterization. [ABSTRACT FROM AUTHOR]

Objective: To test the ability of high-performance machine learning (ML) models employing clinical, radiological, and radiomic variables to improve non-invasive prediction of the pathological status of prostate cancer (PCa) in a large, single-institution cohort. Methods: Patients who underwent multiparametric MRI and prostatectomy in our institution in 2015–2018 were considered; a total of 949 patients were included. Gradient-boosted decision tree models were separately trained using clinical features alone and in combination with radiological reporting and/or prostate radiomic features to predict pathological T, pathological N, ISUP score, and their change from preclinical assessment. Model behavior was analyzed in terms of performance, feature importance, Shapley additive explanation (SHAP) values, and mean absolute error (MAE). The best model was compared against a naïve model mimicking clinical workflow. Results: The model including all variables was the best performing (AUC values ranging from 0.73 to 0.96 for the six endpoints). Radiomic features brought a small yet measurable boost in performance, with the SHAP values indicating that their contribution can be critical to successful prediction of endpoints for individual patients. MAEs were lower for low-risk patients, suggesting that the models find them easier to classify. The best model outperformed (p ≤ 0.0001) clinical baseline, resulting in significantly fewer false negative predictions and overall was less prone to under-staging. Conclusions: Our results highlight the potential benefit of integrative ML models for pathological status prediction in PCa. Additional studies regarding clinical integration of such models can provide valuable information for personalizing therapy offering a tool to improve non-invasive prediction of pathological status. Clinical relevance statement: The best machine learning model was less prone to under-staging of the disease. The improved accuracy of our pathological prediction models could constitute an asset to the clinical workflow by providing clinicians with accurate pathological predictions prior to treatment. Key Points: • Currently, the most common strategies for pre-surgical stratification of prostate cancer (PCa) patients have shown to have suboptimal performances. • The addition of radiological features to the clinical features gave a considerable boost in model performance. Our best model outperforms the naïve model, avoiding under-staging and resulting in a critical advantage in the clinic. •Machine learning models incorporating clinical, radiological, and radiomics features significantly improved accuracy of pathological prediction in prostate cancer, possibly constituting an asset to the clinical workflow. [ABSTRACT FROM AUTHOR]

Featured Application: Setup optimization in pediatric particle therapy. The purpose of this retrospective study was to simulate a daily pre-alignment strategy to mitigate systematic positioning errors in image-guided pediatric hadron therapy. All pediatric patients (32 patients, 853 fractions) treated from December 2021 and September 2022 at our Institution were retrospectively considered. For all fractions, daily correction vectors (CVs) resulting from image registration for patient positioning were retrieved in the form of txt files from the hospital database. For each fraction, an adjusted correction vector (V′) was then computed as the difference between the actual one (V) and the algebraic average of the previous ones, as to simulate patient pre-alignment before imaging. The Euclidean norm of each V′ was computed and normalized with respect to that of the corresponding V to derive N. Pre-correcting all the coordinate values led to a 46% average reduction (min 20%, max 60%) in CVs, considering the first 27 fractions (average value in this cohort of patients). Such a potential improvement (N < 1) was observed for the most patients' fractions (781/853, 91.6%). For the remaining 72/853 cases (8.4%), a remarkable worsening (N > 2) involved only 7/853 (0.82%) fractions. The presented strategy shows promising outcomes in order to ameliorate pediatric patient setup before imaging. However, further investigations to identify patients most likely to benefit from this approach are warranted. [ABSTRACT FROM AUTHOR]

The work investigates the implementation of personalized radiotherapy boluses by means of additive manufacturing technologies. Boluses materials that are currently used need an excessive amount of human intervention which leads to reduced repeatability in terms of dosimetry. Additive manufacturing can solve this problem by eliminating the human factor in the process of fabrication. Planar boluses with fixed geometry and personalized boluses printed starting from a computed tomography scan of a radiotherapy phantom were produced. First, a dosimetric characterization study on planar bolus designs to quantify the effects of print parameters such as infill density and geometry on the radiation beam was made. Secondly, a volumetric quantification of air gap between the bolus and the skin of the patient as well as dosimetric analyses were performed. The optimization process according to the obtained dosimetric and airgap results allowed us to find a combination of parameters to have the 3D-printed bolus performing similarly to that in conventional use. These preliminary results confirm those in the relevant literature, with 3D-printed boluses showing a dosimetric performance similar to conventional boluses with the additional advantage of being perfectly conformed to the patient geometry. [ABSTRACT FROM AUTHOR]

Featured Application: Setup optimization in ocular proton therapy. This study describes a method for setup optimization in patient simulation for ocular proton therapy (OPT) at the National Center for Oncological Hadrontherapy (CNAO) in Pavia, Italy, with the aim of minimizing the occupancy time of clinical areas and streamlining the actual procedure. Setup repeatability is ensured by patient-specific immobilization tools and relies on the patient's ability to maintain a stable gaze direction according to the treatment plan. This is facilitated by aligning a light source (LED) on a patient-specific base along the prescribed gaze direction. At CNAO, a dedicated Eye-Tracking System (ETS) was designed to provide the patient with a visible source of light aligned to the desired gaze direction. The ETS position is defined prior to treatment planning, relying on optical-tracking guidance and comparing the position of passive markers fixed on the ETS chassis with patient-specific models prepared offline in accordance with the desired geometry. OPT at CNAO started in 2016 and may be considered as a consolidated clinical routine. However, all the preparation phases, including patient-specific ETS models and setup, still require long sessions in clinical areas such as the computed tomography (CT) and the treatment rooms, with a non-negligible impact on other activities. This study describes a novel approach for patient-specific definition of the ETS position and orientation, aiming at minimizing the time required for preparatory activities inside clinical areas. To minimize the occurrence of biases and to reproduce as much as possible a real end-to-end approach, we included in the analysis data of patients that received OPT in our facility. The study was performed in parallel, carrying out the alignment with the standard method currently used in the clinical workflow of CNAO and with the proposed method. Results are presented as 3D residuals and gaze deviations, comparing ETS alignment based on the new approach with respect to the clinical standard method. The preliminary results of this study are evidence of the capability of the procedure to align the ETS position, allowing performing of the procedure in a non-clinical dedicated room. [ABSTRACT FROM AUTHOR]

Aim: Radiation-induced oral mucositis (RIOM) is the most frequent side effect in head and neck cancer (HNC) patients treated with curative radiotherapy (RT). A standardized strategy for preventing and treating RIOM has not been defined. Aim of this study was to perform a real-life survey on RIOM management among Italian RT centers. Methods: A 40-question survey was administered to 25 radiation oncologists working in 25 different RT centers across Italy. Results: A total of 1554 HNC patients have been treated in the participating centers in 2021, the majority (median across the centers 91%) with curative intent. Median treatment time was 41 days, with a mean percentage of interruption due to toxicity of 14.5%. Eighty percent of responders provide written oral cavity hygiene recommendations. Regarding RIOM prevention, sodium bicarbonate mouthwashes, oral mucosa barrier agents, and hyaluronic acid-based mouthwashes were the most frequent topic agents used. Regarding RIOM treatment, 14 (56%) centers relied on literature evidence, while internal guidelines were available in 13 centers (44%). Grade (G)1 mucositis is mostly treated with sodium bicarbonate mouthwashes, oral mucosa barrier agents, and steroids, while hyaluronic acid-based agents, local anesthetics, and benzydamine were the most used in mucositis G2/G3. Steroids, painkillers, and anti-inflammatory drugs were the most frequent systemic agents used independently from the RIOM severity. Conclusion: Great variety of strategies exist among Italian centers in RIOM management for HNC patients. Whether different strategies could impact patients’ compliance and overall treatment time of the radiation course is still unclear and needs further investigation. [ABSTRACT FROM AUTHOR]

Background: Radiomics represents an emerging field of precision‐medicine. Its application in head and neck is still at the beginning. Methods: Retrospective study about magnetic resonance imaging (MRI) based radiomics in oral tongue squamous cell carcinoma (OTSCC) surgically treated (2010–2019; 79 patients). All preoperative MRIs include different sequences (T1, T2, DWI, ADC). Tumor volume was manually segmented and exported to radiomic‐software, to perform feature extraction. Statistically significant variables were included in multivariable analysis and related to survival endpoints. Predictive models were elaborated (clinical, radiomic, clinical‐radiomic models) and compared using C‐index. Results: In almost all clinical‐radiomic models radiomic‐score maintained statistical significance. In all cases C‐index was higher in clinical‐radiomic models than in clinical ones. ADC provided the best fit to the models (C‐index 0.98, 0.86, 0.84 in loco‐regional recurrence, cause‐specific mortality, overall survival, respectively). Conclusion: MRI‐based radiomics in OTSCC represents a promising noninvasive method of precision medicine, improving prognosis prediction before surgery. [ABSTRACT FROM AUTHOR]

Simple Summary: Oropharyngeal squamous cell carcinoma (OPSCC) has one of the most rapidly increasing incidences of any cancer in high-income countries. The aim of this study is to test whether radiomic and blood-derived biomarkers are good candidates for refining the prognostic stratification in OPSCC. The results show that the integration of clinical, immunological, and computed tomography-derived features generally yields an improvement, regardless of the HPV status, in the prognostic stratification of OPSCC patients who are candidates for curative-intent radiotherapy. Specifically, we documented a significant role of the Lymphocyte-to-Monocyte Ratio (LMR) in this population, which has been scarcely investigated in OPSCC, as well as the detrimental effects of lymphopenia and anemia. Results are promising, and model performances compare favorably with available radiomic scores in the same setting. Further investigations on our findings are warranted to validate the results and include a more in-depth study of the prognostic role of the LMR in OPSCC. Future analyses of this dataset are planned to provide a more complete overview of the tumor-immune system interplay. Aims: To assess whether CT-based radiomics and blood-derived biomarkers could improve the prediction of overall survival (OS) and locoregional progression-free survival (LRPFS) in patients with oropharyngeal cancer (OPC) treated with curative-intent RT. Methods: Consecutive OPC patients with primary tumors treated between 2005 and 2021 were included. Analyzed clinical variables included gender, age, smoking history, staging, subsite, HPV status, and blood parameters (baseline hemoglobin levels, neutrophils, monocytes, and platelets, and derived measurements). Radiomic features were extracted from the gross tumor volumes (GTVs) of the primary tumor using pyradiomics. Outcomes of interest were LRPFS and OS. Following feature selection, a radiomic score (RS) was calculated for each patient. Significant variables, along with age and gender, were included in multivariable analysis, and models were retained if statistically significant. The models' performance was compared by the C-index. Results: One hundred and five patients, predominately male (71%), were included in the analysis. The median age was 59 (IQR: 52–66) years, and stage IVA was the most represented (70%). HPV status was positive in 63 patients, negative in 7, and missing in 35 patients. The median OS follow-up was 6.3 (IQR: 5.5–7.9) years. A statistically significant association between low Hb levels and poorer LRPFS in the HPV-positive subgroup (p = 0.038) was identified. The calculation of the RS successfully stratified patients according to both OS (log-rank p < 0.0001) and LRPFS (log-rank p = 0.0002). The C-index of the clinical and radiomic model resulted in 0.82 [CI: 0.80–0.84] for OS and 0.77 [CI: 0.75–0.79] for LRPFS. Conclusions: Our results show that radiomics could provide clinically significant informative content in this scenario. The best performances were obtained by combining clinical and quantitative imaging variables, thus suggesting the potential of integrative modeling for outcome predictions in this setting of patients. [ABSTRACT FROM AUTHOR]

Background: Contouring of anatomical regions is a crucial step in the medical workflow and is both time-consuming and prone to intra- and inter-observer variability. This study compares different strategies for automatic segmentation of the prostate in T2-weighted MRIs. Methods: This study included 100 patients diagnosed with prostate adenocarcinoma who had undergone multi-parametric MRI and prostatectomy. From the T2-weighted MR images, ground truth segmentation masks were established by consensus from two expert radiologists. The prostate was then automatically contoured with six different methods: (1) a multi-atlas algorithm, (2) a proprietary algorithm in the Syngo.Via medical imaging software, and four deep learning models: (3) a V-net trained from scratch, (4) a pre-trained 2D U-net, (5) a GAN extension of the 2D U-net, and (6) a segmentation-adapted EfficientDet architecture. The resulting segmentations were compared and scored against the ground truth masks with one 70/30 and one 50/50 train/test data split. We also analyzed the association between segmentation performance and clinical variables. Results: The best performing method was the adapted EfficientDet (model 6), achieving a mean Dice coefficient of 0.914, a mean absolute volume difference of 5.9%, a mean surface distance (MSD) of 1.93 pixels, and a mean 95th percentile Hausdorff distance of 3.77 pixels. The deep learning models were less prone to serious errors (0.854 minimum Dice and 4.02 maximum MSD), and no significant relationship was found between segmentation performance and clinical variables. Conclusions: Deep learning-based segmentation techniques can consistently achieve Dice coefficients of 0.9 or above with as few as 50 training patients, regardless of architectural archetype. The atlas-based and Syngo.via methods found in commercial clinical software performed significantly worse (0.855 - 0.887 Dice). [ABSTRACT FROM AUTHOR]

Although radiation therapy (RT) provides several therapeutic advantages in terms of cancer control and quality of life, it continues to be a poorly understood field by most students and health workers. Theoretical lessons are not sufficient, while practical exercitations are time-consuming, both in terms of man- and machine-hours. Furthermore, RT candidates often have several prejudices that may affect their treatment choices, favoring the more well-known surgical or chemotherapy approaches or resulting in a high level of anxiety during treatment. Moreover, the misperception of low treatment control and its related side effects could worsen the patients' psychological distress, already brought by a cancer diagnosis. Augmented reality (AR) and virtual reality (VR) could be a valid instrument for promoting the awareness of radiation oncology as a discipline with its own identity and respect in the scientific community. The aim of the present work is to provide a glance at the recent developments in AR/VR to support students' education, personnel training and patients' empowerment in this clinical setting. The main findings of our work show that such technologies have already become a reality in many institutions worldwide and it has been shown to be an effective strategy for raising educational standards, improving health workers' skills and promoting patients' well-being and compliance. These results seem to promote the further implementation of AR/VR technologies and their development as a driving force of a much-hoped-for revolution in the way patients are treated and radiation oncology is taught. [ABSTRACT FROM AUTHOR]

In this paper, we aim to evaluate the entity of inter-fraction organ motion and deformation in stereotactic body radiotherapy (SBRT) treatments for nodal oligometastatic prostate cancer (PCa). Thirty-three patients with lymph nodes showing oligometastatic PCa treated with SBRT were included. Organs at risk (OARs) were delineated using both simulation computer tomography (s-CT) and daily cone beam CTs (CBCTs) using the Raystation planning system. For each OAR, the union volume (UV) between all the CBCTs and s-CT was computed. An expanded volume (EV) of the s-CT OARs was applied using six different margins (3, 5, 8, 10, 15, and 20 mm). A percentage volume (V%) was computed to assess the intersection between each EV and UV. The OAR deformation and motion were further evaluated using the dice similarity coefficient (DSC) and mean distance to agreement (Mean_DA). The percentage maximum dose variations for all the OARs were estimated. A recalculation with higher dose prescriptions was performed by prescribing 36 Gy/3 fx, as well as 45 Gy/3 fx. The cauda showed the highest matching (DSC = 0.72; Mean_DA = 0.14 cm), and the colon showed the lowest one (DSC = 0.37; Mean_DA = 0.44 cm). The minimum margin, which ensured a V% > 95%, was 3 mm (97.5%) for the cauda and 15 mm (96.6%) for the colon. All the OARs reached the compliance of the constraints in each session. Regarding 36 Gy-plans, the ileum punctual compliance Dmax failed in 58.8% of patients, and it failed in 70.6% of the patients for the ileum, 7.14% for the colon, and 12.5% for the bladder in the case of 45 Gy-plans. This study is an ancillary study of the RADIOSA clinical trial (AIRC IG-22159) and can be used as a benchmark for dose escalation. [ABSTRACT FROM AUTHOR]

Radiotherapy represents a highly targeted and efficient treatment choice in many cancer types, both with curative and palliative intents. Nevertheless, radioresistance, consisting in the adaptive response of the tumor to radiation-induced damage, represents a major clinical problem. A growing body of the literature suggests that mechanisms related to mitochondrial changes and metabolic remodeling might play a major role in radioresistance development. In this work, the main contributors to the acquired cellular radioresistance and their relation with mitochondrial changes in terms of reactive oxygen species, hypoxia, and epigenetic alterations have been discussed. We focused on recent findings pointing to a major role of mitochondria in response to radiotherapy, along with their implication in the mechanisms underlying radioresistance and radiosensitivity, and briefly summarized some of the recently proposed mitochondria-targeting strategies to overcome the radioresistant phenotype in cancer. [ABSTRACT FROM AUTHOR]

Objective: Deploying an automatic segmentation model in practice should require rigorous quality assurance (QA) and continuous monitoring of the model's use and performance, particularly in high-stakes scenarios such as healthcare. Currently, however, tools to assist with QA for such models are not available to AI researchers. In this work, we build a deep learning model that estimates the quality of automatically generated contours. Methods: The model was trained to predict the segmentation quality by outputting an estimate of the Dice similarity coefficient given an image contour pair as input. Our dataset contained 60 axial T2-weighted MRI images of prostates with ground truth segmentations along with 80 automatically generated segmentation masks. The model we used was a 3D version of the EfficientDet architecture with a custom regression head. For validation, we used a fivefold cross-validation. To counteract the limitation of the small dataset, we used an extensive data augmentation scheme capable of producing virtually infinite training samples from a single ground truth label mask. In addition, we compared the results against a baseline model that only uses clinical variables for its predictions. Results: Our model achieved a mean absolute error of 0.020 ± 0.026 (2.2% mean percentage error) in estimating the Dice score, with a rank correlation of 0.42. Furthermore, the model managed to correctly identify incorrect segmentations (defined in terms of acceptable/unacceptable) 99.6% of the time. Conclusion: We believe that the trained model can be used alongside automatic segmentation tools to ensure quality and thus allow intervention to prevent undesired segmentation behavior. [ABSTRACT FROM AUTHOR]

After primary treatment for prostate cancer with either radical prostatectomy or radiotherapy, a significant proportion of patients are at risk of developing metastases. In recent years, a deeper understanding of the underlying biology together with improved imaging techniques and the advent of new therapeutic options including metastases-directed therapies and new drugs have revolutionized the management of low-burden metastatic disease, also known as oligometastatic state. The purpose of this narrative review is to report the recent developments in the management of hormone-sensitive oligometastatic prostate cancer patients. [ABSTRACT FROM AUTHOR]

Background and Purpose: Machine learning (ML) is emerging as a feasible approach to optimize patients' care path in Radiation Oncology. Applications include autosegmentation, treatment planning optimization, and prediction of oncological and toxicity outcomes. The purpose of this clinically oriented systematic review is to illustrate the potential and limitations of the most commonly used ML models in solving everyday clinical issues in head and neck cancer (HNC) radiotherapy (RT). Materials and Methods: Electronic databases were screened up to May 2021. Studies dealing with ML and radiomics were considered eligible. The quality of the included studies was rated by an adapted version of the qualitative checklist originally developed by Luo et al. All statistical analyses were performed using R version 3.6.1. Results: Forty-eight studies (21 on autosegmentation, four on treatment planning, 12 on oncological outcome prediction, 10 on toxicity prediction, and one on determinants of postoperative RT) were included in the analysis. The most common imaging modality was computed tomography (CT) (40%) followed by magnetic resonance (MR) (10%). Quantitative image features were considered in nine studies (19%). No significant differences were identified in global and methodological scores when works were stratified per their task (i.e., autosegmentation). Discussion and Conclusion: The range of possible applications of ML in the field of HN Radiation Oncology is wide, albeit this area of research is relatively young. Overall, if not safe yet, ML is most probably a bet worth making. [ABSTRACT FROM AUTHOR]

Purpose: This study represents a descriptive analysis of preliminary results of a Phase II trial on a novel mixed beam radiotherapy (RT) approach, consisting of carbon ions RT (CIRT) followed by intensity-modulated photon RT, in combination with hormonal therapy, for high-risk prostate cancer (HR PCa) with a special focus on acute toxicity. Methods: Primary endpoint was the evaluation of safety in terms of acute toxicity. Secondary endpoints were early and long-term tolerability of treatment, quality of life (QoL), and efficacy. Data on acute and late toxicities were collected according to RTOG/EORTC. QoL of enrolled patients was assessed by IPSS, EORTC QLQ-C30, EORTC QLQ-PR25, and sexual activity by IIEF-5. Results: Twenty-six patients were enrolled in the study, but only 15 completed so far the RT course and were included. Immediately after CIRT, no patients experienced GI/GU toxicity. At 1 and 3 months from the whole course RT completion, no GI/GU toxicities greater than grade 2 were observed. QoL scores were overall satisfactory. Conclusions: The feasibility of the proposed mixed treatment schedule was assessed, and an excellent acute toxicity profile was recorded. Such findings instil confidence in the continuation of this mixed approach, with evaluation of long-term tolerability and efficacy. [ABSTRACT FROM AUTHOR]

Purpose: We present the preliminary results of the STRA-MI-VT Study (NCT04066517), a spontaneous, phase Ib/II study, designed to prospectively test the safety and efficacy of stereotactic body radiotherapy (SBRT) in patientswith advanced cardiac disease and intractable ventricular tachycardia (VT). Methods: Cardiac computed tomography (CT) integrated by electroanatomical mapping was used for substrate identification and merged with dedicated CT scans for treatment plan preparation. A single 25-Gy radioablation dose was delivered by a LINAC-based volumetric modulated arc therapy technique in a non-invasive matter. The primary safety endpoint was treatment-related adverse effects during acute and long-term follow-up (FU), obtained by regular in-hospital controls and implantable cardioverter defibrillator (ICD) remote monitoring. The primary efficacy endpoint was the reduction at 3 and 6 months of VT episodes and ICD shocks. Results: Seven out of eight patients (men; age, 70 ± 7 years; ejection fraction, 27 ± 11%; 3 ischemic, 4 non-ischemic cardiomyopathies) underwent SBRT. At a median 8-month FU, no treatment-related serious adverse event occurred. Three patients died from non-SBRT-related causes. Four patients completed the 6-month FU: the number of VT decreased from 29 ± 33 to 11 ± 9 (p =.05) and 2 ± 2 (p =.08), at 3 and 6 months, respectively; shocks decreased from 11 to 0 and 2, respectively. At 6 months, all patients. showed a significant reduction of VT episodes and no electrical storm recurrence, with the complete regression of iterative VTs in 2/2 patients. Conclusion: The STRA-MI-VT Study suggests that SBRT can be considered an alternative option for the treatment of VT in patients with structural heart disease and highlights the need for further clinical investigation addressing safety and efficacy. [ABSTRACT FROM AUTHOR]

Rectum and bladder volumes play an important role in the dose distribution reproducibility in prostate cancer adenocarcinoma (PCa) radiotherapy, especially for particle therapy, where density variation can strongly affect the dose distribution. We investigated the reliability and reproducibility of our image-guided radiotherapy (IGRT) and treatment planning protocol for carbon ion radiotherapy (CIRT) within the phase II mixed beam study (AIRC IG 14300) for the treatment of high-risk PCa. In order to calculate the daily dose distribution, a set of synthetic computed tomography (sCT) images was generated from the cone beam computed tomography (CBCT) images acquired in each treatment session. Planning target volume (PTV) together with rectum and bladder volume variation was evaluated with sCT dose-volume histogram (DVH) metric deviations from the planning values. The correlations between the bladder and rectum volumes, and the corresponding DVH metrics, were also assessed. No significant difference in the bladder, rectum, and PTV median volumes between the planning computed tomography (pCT) and the sCT was found. In addition, no significant difference was assessed when comparing the average DVHs and median DVH metrics between pCT and sCT. Dose deviations determined by bladder and rectum filling variations demonstrated that dose distributions were reproducible in terms of both target coverage and organs at risk (OARs) sparing. [ABSTRACT FROM AUTHOR]

Various definitions are currently in use to describe high-risk prostate cancer. This variety in definitions is important for patient counseling, since predicted outcomes depend on which classification is applied to identify patient's prostate cancer risk category. Historically, strategies for the treatment of localized high-risk prostate cancer comprise local approaches such as surgery and radiotherapy, as well as systemic approaches such as hormonal therapy. Nevertheless, since high-risk prostate cancer patients remain the group with higher-risk of treatment failure and mortality rates, nowadays, novel treatment strategies, comprising hypofractionated-radiotherapy, second-generation antiandrogens, and hadrontherapy, are being explored in order to improve their long-term oncological outcomes. This narrative review aims to report the current management of high-risk prostate cancer and to explore the future perspectives in this clinical setting. [ABSTRACT FROM AUTHOR]

Objective: To evaluate the impact of adjuvant radiotherapy (aRT) in patients with prostate cancer (PCa) found to have pathological positive lymph nodes (pN1s) after radical prostatectomy (RP) and extended pelvic lymph node dissection (ePLND) with regard to distant recurrence‐free survival (RFS), according to both main tumour pathological characteristics and number of positive lymph nodes. Biochemical RFS, local RFS, overall survival (OS) and acute and late toxicity were assessed as secondary endpoints. Patients and Methods: A retrospective cohort of 187 consecutive patients with pN1 PCa were treated with aRT at the IEO, European Institute of Oncology IRCCS, Milan, Italy. aRT on the tumour bed and pelvis was administered within 6 months of RP. Androgen deprivation therapy was administered according to the guidelines. Univariate and multivariate Cox regression analyses predicting biochemical RFS, local RFS, distant RFS and OS rates were performed to assess whether the number of pN1s represented an independent prognostic factor. The Youden index was computed to find the optimal threshold for the number of pN1s able to discriminate between patients with or without biochemical and clinical relapse. Results: At 5 years, local RFS, distant RFS, biochemical RFS and OS were 68%, 71%, 56% and 94%, respectively. The median follow‐up was 49 months. The number of pN1s was significantly associated with biochemical RFS, local RFS and distant RFS. The best threshold for discriminating between patients with or without biochemical and clinical relapse was five pN1s. In multivariate analyses, the number of pN1s was confirmed to be an independent predictor of biochemical RFS, local RFS and distant RFS, but not of OS. Multivariate analyses also showed an increased risk of biochemical relapse for increasing values of initial prostate‐specific antigen and for patients with tumour vascular invasion. Local and distant RFS were also inversely correlated with significantly reduced risk for International Society of Urological Pathology grade group <3 (group 1 or 2 compared to group 3). Conclusions: Our data confirmed the encouraging outcomes of patients with pN1 PCa treated with adjuvant treatments and the key role represented by the number of pN1s in predicting biochemical RFS, clinical RFS and distant RFS. Large prospective cohort studies and randomized clinical trials are needed to confirm these results and to identify the subgroup of patients with pN1 PCa who would most benefit from aRT. [ABSTRACT FROM AUTHOR]

Objectives: Radiomic involves testing the associations of a large number of quantitative imaging features with clinical characteristics. Our aim was to extract a radiomic signature from axial T2-weighted (T2-W) magnetic resonance imaging (MRI) of the whole prostate able to predict oncological and radiological scores in prostate cancer (PCa). Methods: This study included 65 patients with localized PCa treated with radiotherapy (RT) between 2014 and 2018. For each patient, the T2-W MRI images were normalized with the histogram intensity scale standardization method. Features were extracted with the IBEX software. The association of each radiomic feature with risk class, T-stage, Gleason score (GS), extracapsular extension (ECE) score, and Prostate Imaging Reporting and Data System (PI-RADS v2) score was assessed by univariate and multivariate analysis. Results: Forty-nine out of 65 patients were eligible. Among the 1702 features extracted, 3 to 6 features with the highest predictive power were selected for each outcome. This analysis showed that texture features were the most predictive for GS, PI-RADS v2 score, and risk class; intensity features were highly associated with T-stage, ECE score, and risk class, with areas under the receiver operating characteristic curve (ROC AUC) ranging from 0.74 to 0.94. Conclusions: MRI-based radiomics is a promising tool for prediction of PCa characteristics. Although a significant association was found between the selected features and all the mentioned clinical/radiological scores, further validations on larger cohorts are needed before these findings can be applied in the clinical practice. Key Points: • A radiomic model was used to classify PCa aggressiveness. • Radiomic analysis was performed on T2-W magnetic resonance images of the whole prostate gland. • The most predictive features belong to the texture (57%) and intensity (43%) domains. [ABSTRACT FROM AUTHOR]

Background: Italy experienced one of the world's severest COVID-19 outbreak, with Lombardy being the most afflicted region. However, the imposed safety measures allowed to flatten the epidemic curve and hence to ease the restrictions and inaugurate, on the 4th of May 2020, the Italian phase (P) 2 of the pandemic. The present survey study, endorsed by CODRAL and AIRO-L, aimed to assess how radiotherapy (RT) departments in Lombardy have dealt with the recovery.Materials and Methods: A questionnaire dealing with the management of pandemic was developed online and sent to all CODRAL Directors on the 10th of June 2020. Answers were collected in full anonymity one week after.Results: All the 33 contacted RT facilities (100%) responded to the survey. Despite the scale of the pandemic, during P1 14 (42.4%) centres managed to safely continue the activity (≤ 10% reduction). During P2, 10 (30.3%) centres fully recovered and 14 (42.4%) reported an increase. Nonetheless, 6 (18.2%) declared no changes and, interestingly, 3 (9.1%) reduced activities. Overall, 21 centres (63.6%) reported suspected or positive cases within healthcare workforce since the beginning of the pandemic. Staff units were quarantined in 19 (57.6%) and 6 (18.2%) centres throughout P1 and P2, respectively. In the two phases, about two thirds centres registered positive or suspected cases amongst patients.Conclusion: The study revealed a particular attention to anti-contagion measures and a return to normal or even higher clinical workload in most RT centres in Lombardy, necessary to carry out current and previously deferred treatments. [ABSTRACT FROM AUTHOR]

Aim: To evaluate the role of baseline neutrophil‐to‐lymphocyte ratio (NLR) as prognostic marker in squamous cell carcinoma of the oropharynx (OPC) treated with definitive chemoradiotherapy (CRT) in the era of HPV status. Patients and methods: A retrospective analysis of 125 patients (pts) affected with locally advanced OPC was performed. Inclusion criteria were age >18 years, stage III or IV (TNM 7th ed.) and definitive CRT. Haematological marker for their independent role as prognostic biomarkers for progression‐free survival (PFS) and overall survival (OS). Logistic models were used to assess the association with downstage in TNM 8th ed. Results: Seventy‐seven (61.6%) pts had HPV/p16 + related OPC. Therapeutic choice consisted in sequential and concurrent CRT. Median follow‐up was 50 months. A value of NLR ≥3 was associated with poorer OS. Two‐year OS was 91% and 81% in pts with NLR <3 and ≥3, respectively. Conclusion: A baseline NLR ≥ 3 at treatment initiation represented a negative prognostic marker for OPC treated with definitive CRT. These results are in line with literature data, and prognostic value of NLR has been confirmed restaging our cohort with new TNM staging (8th ed.). Therefore, NLR could be considered a valuable biomarker for risk stratification in pts with OPC. [ABSTRACT FROM AUTHOR]

Purpose: We herein present the results of the first Italian Association of Radiotherapy and Clinical Oncology (AIRO) survey regarding salvage external beam re-irradiation of local prostate cancer relapse named PROLAPSE. Methods: A questionnaire with 12 items was administered to the 775 Italian radiation oncologist members of the AIRO. Results: One hundred of the members completed the survey. The survey highlighted that 59% of the participants are currently performing prostate re-EBRT, while nearly two-thirds (65%) affirmed that they are taking into consideration the procedure in case of intraprostatic relapse. Regarding the clinical target volume (CTV), only a minority (16%) declared to always prefer the partial prostate re-irradiation, while a consistent portion (nearly two-thirds) relied on clinical considerations of the choice towards partial or whole gland irradiation. The main techniques used for re-irradiation resulted to be intensity-modulated RT (IMRT)/volumetric modulated arc therapy (VMAT) and SBRT, having received approximately 40% of responses each. Regarding the criteria for patients' selection, more than 75% of responders agreed on the use of positron emission tomography (PET)/computed tomography (CT)-choline to exclude distant metastases and of multiparametric magnetic resonance imaging (mp-MRI) to detect intraprostatic recurrence. A sufficient timeframe (> 3 years) between primary RT and reirradiation was indicated by more than half of participants as an important driver in decision-making, while histological confirmation of the relapse was considered not essential by more than two-thirds. For the use of concomitant androgen deprivation therapy (ADT), most AIRO members (79%) agreed that the prescription should be based on a case-by-case analysis. Extreme hypofractionation (> 5 Gy/fraction) was preferred by the majority (52%) of the AIRO members. In most centers (more than 74%), the planning dose–volume constraints were generally extrapolated from the published data. In half of the cases, the interviewed responders affirmed that no major gastrointestinal (GI) and genitourinary (GU) toxicities were registered in the follow-up of their re-EBRT patients. Bladder complications represented the most commonly observed form of toxicity, with an incidence of 67%. Conclusion: This first AIRO survey about salvage prostate re-EBRT provides an interesting snapshot and suggests increasing interest in re-EBRT patients in Italy. Consensus about some aspects of patients' selection, the necessity of biopsy, fractionation, and highly selective techniques seems feasible, but other key points such as irradiated volume, dosimetry parameters, and hormonal treatment association need to be clarified. [ABSTRACT FROM AUTHOR]

In order to limit radiotherapy (RT)-related side effects, effective toxicity prediction and assessment schemes are essential. In recent years, the growing interest toward artificial intelligence and machine learning (ML) within the science community has led to the implementation of innovative tools in RT. Several researchers have demonstrated the high performance of ML-based models in predicting toxicity, but the application of these approaches in clinics is still lagging, partly due to their low interpretability. Therefore, an overview of contemporary research is needed in order to familiarize practitioners with common methods and strategies. Here, we present a review of ML-based models for predicting and classifying RT-induced complications from both a methodological and a clinical standpoint, focusing on the type of features considered, the ML methods used, and the main results achieved. Our work overviews published research in multiple cancer sites, including brain, breast, esophagus, gynecological, head and neck, liver, lung, and prostate cancers. The aim is to define the current state of the art and main achievements within the field for both researchers and clinicians. [ABSTRACT FROM AUTHOR]

To determine interobserver variability in axillary nodal contouring in breast cancer (BC) radiotherapy (RT) by comparing the clinical target volume of participating single centres (SC-CTV) with a gold-standard CTV (GS-CTV). The GS-CTV of three patients (P1, P2, P3) with increasing complexity was created in DICOM format from the median contour of axillary CTVs drawn by BC experts, validated using the simultaneous truth and performance-level estimation and peer-reviewed. GS-CTVs were compared with the correspondent SC-CTVs drawn by radiation oncologists, using validated metrics and a total score (TS) integrating all of them. Eighteen RT centres participated in the study. Comparative analyses revealed that, on average, the SC-CTVs were smaller than GS-CTV for P1 and P2 (by −29.25% and −27.83%, respectively) and larger for P3 (by +12.53%). The mean Jaccard index was greater for P1 and P2 compared to P3, but the overlap extent value was around 0.50 or less. Regarding nodal levels, L4 showed the highest concordance with the GS. In the intra-patient comparison, L2 and L3 achieved lower TS than L4. Nodal levels showed discrepancy with GS, which was not statistically significant for P1, and negligible for P2, while P3 had the worst agreement. DICE similarity coefficient did not exceed the minimum threshold for agreement of 0.70 in all the measurements. Substantial differences were observed between SC- and GS-CTV, especially for P3 with altered arm setup. L2 and L3 were the most critical levels. The study highlighted these key points to address. The present study compares, by means of validated geometric indexes, manual segmentations of axillary lymph nodes in breast cancer from different observers and different institutions made on radiotherapy planning CT images. Assessing such variability is of paramount importance, as geometric uncertainties might lead to incorrect dosimetry and compromise oncological outcome. [ABSTRACT FROM AUTHOR]

Simple Summary: The oligometastatic prostate cancer state is defined as the presence of a number of lesions ≤ 5 and has been significantly correlated with better survival if compared to a number of metastases > 5. In particular, patients in an oligometastatic setting could benefit from a metastates directed therapy, which could control the disease delaying the start of systemic therapies. For this reason, the selection of true-oligometastatic patients who could benefit from such approach is particularly important in this setting. The aim of the present narrative review is to report the current state of the art on the liquid biopsy-derived analytes and their reliability as biomarkers in the clinics for the identification of true-oligometastatic patients. This kind of molecular profiling could refine current developments in the era of precision oncology allowing patients' stratification and leading to more refined therapeutic strategies. In recent years, a growing interest has been directed towards oligometastatic prostate cancer (OMPC), as patients with three to five metastatic lesions have shown a significantly better survival as compared with those harboring a higher number of lesions. The efficacy of local ablative treatments directed on metastatic lesions (metastases-directed treatments) was extensively investigated, with the aim of preventing further disease progression and delaying the start of systemic androgen deprivation therapies. Definitive diagnosis of prostate cancer is traditionally based on histopathological analysis. Nevertheless, a bioptic sample—static in nature—inevitably fails to reflect the dynamics of the tumor and its biological response due to the dynamic selective pressure of cancer therapies, which can profoundly influence spatio-temporal heterogeneity. Furthermore, even with new imaging technologies allowing an increasingly early detection, the diagnosis of oligometastasis is currently based exclusively on radiological investigations. Given these premises, the development of minimally-invasive liquid biopsies was recently promoted and implemented as predictive biomarkers both for clinical decision-making at pre-treatment (baseline assessment) and for monitoring treatment response during the clinical course of the disease. Through liquid biopsy, different biomarkers, commonly extracted from blood, urine or saliva, can be characterized and implemented in clinical routine to select targeted therapies and assess treatment response. Moreover, this approach has the potential to act as a tissue substitute and to accelerate the identification of novel and consistent predictive analytes cost-efficiently. However, the utility of tumor profiling is currently limited in OMPC due to the lack of clinically validated predictive biomarkers. In this scenario, different ongoing trials, such as the RADIOSA trial, might provide additional insights into the biology of the oligometastatic state and on the identification of novel biomarkers for the outlining of true oligometastatic patients, paving the way towards a wider ideal approach of personalized medicine. The aim of the present narrative review is to report the current state of the art on the solidity of liquid biopsy-related analytes such as CTCs, cfDNA, miRNA and epi-miRNA, and to provide a benchmark for their further clinical implementation. Arguably, this kind of molecular profiling could refine current developments in the era of precision oncology and lead to more refined therapeutic strategies in this subset of oligometastatic patients. [ABSTRACT FROM AUTHOR]

Management of patients with head and neck cancers (HNCs) is challenging for the Radiation Oncologist, especially in the COVID-19 era. The Italian Society of Radiotherapy and Clinical Oncology (AIRO) identified the need of practice recommendations on logistic issues, treatment delivery and healthcare personnel's protection in a time of limited resources. A panel of 15 national experts on HNCs completed a modified Delphi process. A five-point Likert scale was used; the chosen cut-offs for strong agreement and agreement were 75% and 66%, respectively. Items were organized into two sections: (1) general recommendations (10 items) and (2) special recommendations (45 items), detailing a set of procedures to be applied to all specific phases of the Radiation Oncology workflow. The distribution of facilities across the country was as follows: 47% Northern, 33% Central and 20% Southern regions. There was agreement or strong agreement across the majority (93%) of proposed items including treatment strategies, use of personal protection devices, set-up modifications and follow-up re-scheduling. Guaranteeing treatment delivery for HNC patients is well-recognized in Radiation Oncology. Our recommendations provide a flexible tool for management both in the pandemic and post-pandemic phase of the COVID-19 outbreak. [ABSTRACT FROM AUTHOR]

As part of the AIRC IG-13218 (NCT01913717), we analyzed data from patients with low- and intermediate-risk prostate cancer treated with extreme hypofractionated radiotherapy (RT) and simultaneous boost to the intraprostatic lesion. The aim of the study is to identify clinically meaningful information through the analysis of validated questionnaires testing gastrointestinal (GI) and genitourinary (GU) RT-related toxicity and their impact on quality of life (QoL). At the end of RT treatment, clinical assessment and prostate-specific antigen (PSA) measurements were performed every 3 months for at least 2 years and GI and GU toxicities were evaluated contextually. QoL of enrolled patients was assessed by International Prostate Symptoms score (IPSS), European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire-Core 30 (EORTC QLQ-C30), EORTC QLQ prostate specific (QLQ-PR25), and sexual activity by International Index of Erectile Function (IIEF-5). Patients' score changes were calculated at the end of RT, at one month after RT and at 12 and 24 months. Sixty-five prospectively enrolled patients were analyzed. Extensive analysis of different QoL assessments showed that patients' tolerance was satisfactory across all the considered time points, with no statistically significant change of QoL from baseline compared to that before RT. Overall survival and biochemical progression-free survival at 2-years were of 98% and 97%, respectively. Despite the toxicity of extreme hypofractionation was low and tumor control was encouraging, a longer follow-up is necessary to confirm our findings. The increasing dose to the dominant intraprostatic lesion does not worsen the RT toxicity and consequently does not affect patients' QoL, thus questioning the possibility of an even more escalated treatment. [ABSTRACT FROM AUTHOR]