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Soft tissue sarcomas (STS) are uncommon, heterogeneous malignant tumors of mesodermal origin. Other than conservative surgery (CS), neoadjuvant or adjuvant radiotherapy (RT) is recommended when the risk of local recurrence is high. The aim of this study is to present our Institutional experience in adjuvant RT for treatment of STS of extremities and trunk (with either brachytherapy (BRT), external beam RT (EBRT), or both) and to provide an insight of toxicity and oncological outcomes for each RT modality. According to the RT treatment approach, patients were divided into three categories: 1) BRT alone; 2) EBRT alone; 3) combined BRT+EBRT. Differences among the three groups were assessed by the Chi-squared test. Patients' follow-up was performed every 6 months for the first two years after the end of RT and then once a year. Data from 90 patients were analyzed. The overall 3-year distant relapse-free survival (DRFS), progression-free survival (PFS), and overall survival (OS) were 84%, 80%, and 97%, respectively. Acute erythema was the most frequent side effect, although severe grade 3 toxicity was present in 5 patients. Chronic toxicity of any grade was reported in 14 patients. The incidence of chronic toxicity did not show any association with treatment modality. Multivariate analysis suggested a significant correlation between acute toxicity and tumor size, RT modality, and RT dose. In conclusion, good local control and toxicity profile were observed, despite negative patients' selection at baseline. Further investigation on wider series is warranted in order to define the optimal combination with systemic therapy. [ABSTRACT FROM AUTHOR]

CyberKnife® Lung Optimized Treatment (LOT) allows the treatment of lung cancer without invasive fiducial implantation. The aim of this retrospective analysis was to evaluate the feasibility, toxicity and clinical outcome. One hundred fifteen patients (124 lesions) were treated with CyberKnife® using LOT. The median age was 72.6 years (range 31.8-90.3). From 124 treated lesions, 52 were with histopathological confirmation (41 primitive pulmonary cancers, 8 pulmonary metastases) and 72 as untyped tumors. For 5 patients (6 lesions) treatment was an in-field re-irradiation. Concomitant therapy was administered in 7 patients. Zero-View tracking was applied in 69 patients, 1-View in 33 patients, 2-View in 22 patients. The median total dose was 45 Gy (range 18-54), median dose/fraction was 15 Gy (range 4-18) with a median prescription isodose of 80% (range 68-85). The median planning target volume (PTV) was 25 cm3 (range 3-195). The median follow-up was 20 months (range 7-47). Thirty-seven patients (32%) were alive with no evidence of disease, 39 patients (34%) were alive with clinically evident disease, and 38 patients (33%) died of the disease. The 1- and 2-year overall survival (OS) rate was 83% and 61%. The median time to progression was 19 months (95% confidence interval: 11-19 months), 1- and 2-year progression-free survival (PFS) rates were 62% and 41%, respectively. Smaller PTV was significantly associated with better OS, PFS and in-field PFS in univariate and multivariate analyses. Acute toxicity was observed in 36 patients (41%). Late toxicity was registered in 25 patients (29%). G3 late toxicity was observed in one patient (1.1%). Our data suggest that fiducial less-stereotactic body radiation therapy (SBRT) is a feasible, well-tolerated and potentially effective treatment with high compliance in the setting of inoperable patients due to concomitant disease or previous treatments. [ABSTRACT FROM AUTHOR]

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., (© 2024 The Author(s). Medical Physics published by Wiley Periodicals LLC on behalf of American Association of Physicists in Medicine.)

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., (© 2024. The Author(s), under exclusive licence to European Society of Radiology.)

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., (© 2024. The Author(s).)

Purpose: Hippocampal sparing whole-brain radiotherapy (HS-WBRT) showed significantly lower long-term side effects compared to standard WBRT. Aim of this study is to describe a HS-WBRT real-world monoinstitutional experience within a retrospective cohort., Methods: Patients who completed HS-WBRT course, with Karnofsky Performance Status ⩾ 60 and radiological diagnosis of brain metastases (BMs) were enrolled. Treatment was performed using helical Tomotherapy scheduled in 30 Gy in 10 or 12 fractions or 25 Gy in 10 fractions. Oncological outcomes were clinically and radiologically assessed every three months. Toxicity was graded according to the National Cancer Institute Common Terminology Criteria for Adverse Events 4.3., Results: One hundred and nineteen patients from 2016 to 2020 met inclusion criteria; after a median follow-up of 18 months, 29 patients were alive; 6- and 12-months overall survival rates were 66% and 41%, respectively. HS-WBRT response was assessed for 72 patients. Median time to any progression and intracranial failure (IF) was 4.5 and 13.7 months, respectively. The 6- and 12-month IF rates were 85% and 57%. Among 40 patients (34%) who experienced IF, 17 (42%) were oligometastatic, 23 (58%) polymetastatic and 15/40 developed IF within the hippocampi avoidance zone. No grade (G) ⩾ 2 acute toxicities were reported and one G2 (dizziness) late toxicity was described., Conclusions: HS-WBRT is well tolerated, and despite the hippocampal sparing region, the oncological control is satisfying. Further investigation is warranted to find patients who could most benefit from a HS-WBRT approach., Competing Interests: Declaration of conflicting interestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

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., (© 2023. The Author(s).)

This study quantified the incidental dose to the first axillary level (L1) in locoregional treatment plan for breast cancer. Eighteen radiotherapy centres contoured L1-L4 on three different patients (P1,2,3), created the L2-L4 planning target volume (single centre planning target volume, SC-PTV) and elaborated a locoregional treatment plan. The L2-L4 gold standard clinical target volume (CTV) along with the gold standard L1 contour (GS-L1) were created by an expert consensus. The SC-PTV was then replaced by the GS-PTV and the incidental dose to GS-L1 was measured. Dosimetric data were analysed with Kruskal-Wallis test. Plans were intensity modulated radiotherapy (IMRT)-based. P3 with 90° arm setup had statistically significant higher L1 dose across the board than P1 and P2, with the mean dose (Dmean) reaching clinical significance. Dmean of P1 and P2 was consistent with the literature (77.4% and 74.7%, respectively). The incidental dose depended mostly on L1 proportion included in the breast fields, underlining the importance of the setup, even in case of IMRT., Competing Interests: Declaration of Conflicting InterestThe authors declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: Division of Radiotherapy IEO received research funding from AIRC (Italian Association for Cancer Research) and Fondazione IEO-CCM (Istituto Europeo di Oncologia-Centro Cardiologico Monzino) (all outside the current project). BAJF received speakers fee from Roche, Bayer, Janssen, Carl Zeiss, Ipsen, Accuray, Astellas, Elekta, IBA Astra Zeneca (all outside the current project). MCL received a speaker fee from Accuray Inc (outside the current project). SD received speakers fee from Accuray Asia (outside the current project). MGV was supported by a research fellowship from AIRC entitled “Radioablation ± hormonotherapy for prostate cancer oligorecurrences (RADIOSA trial): potential of imaging and biology”, registered at ClinicalTrials.gov NCT03940235, approved by the Ethics Committee of IRCCS Istituto Europeo di Oncologia and Centro Cardiologico Monzino (IEO-997). IEO, the European Institute of Oncology IRCCS, was partially supported by the Italian Ministry of Health with Ricerca Corrente and 5x1000 funds and by institutional grants from Accuray Inc. The sponsors did not play any role in the study design, collection, analysis and interpretation of data, nor in the writing of the manuscript, nor in the decision to submit the manuscript for publication. The remaining authors declare no conflict of interest that are relevant to the content of this article.

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., (© 2023 The Authors. Head & Neck published by Wiley Periodicals LLC.)

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.

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[Formula: see text]0.887 Dice)., (© 2023. The Author(s).)