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Background: Currently, main treatment strategies for early-stage non-small cell lung cancer (ES-NSCLC) disease are surgery or stereotactic body radiation therapy (SBRT), with successful local control rates for both approaches. However, regional and distant failure remain critical in SBRT, and it is paramount to identify predictive factors of response to identify high-risk patients who may benefit from more aggressive approaches. The main endpoint of the MONDRIAN trial is to identify multi-omic biomarkers of SBRT response integrating information from the individual fields of radiomics, genomics and proteomics. Methods: MONDRIAN is a prospective observational explorative cohort clinical study, with a data-driven, bottom-up approach. It is expected to enroll 100 ES-NSCLC SBRT candidates treated at an Italian tertiary cancer center with well-recognized expertise in SBRT and thoracic surgery. To identify predictors specific to SBRT, MONDRIAN will include data from 200 patients treated with surgery, in a 1:2 ratio, with comparable clinical characteristics. The project will have an overall expected duration of 60 months, and will be structured into five main tasks: (i) Clinical Study; (ii) Imaging/ Radiomic Study, (iii) Gene Expression Study, (iv) Proteomic Study, (v) Integrative Model Building. Discussion: Thanks to its multi-disciplinary nature, MONDRIAN is expected to provide the opportunity to characterize ES-NSCLC from a multi-omic perspective, with a Radiation Oncology-oriented focus. Other than contributing to a mechanistic understanding of the disease, the study will assist the identification of high-risk patients in a largely unexplored clinical setting. Ultimately, this would orient further clinical research efforts on the combination of SBRT and systemic treatments, such as immunotherapy, with the perspective of improving oncological outcomes in this subset of patients. Trial registration: The study was prospectively registered at clinicaltrials.gov (NCT05974475). [ABSTRACT FROM AUTHOR]

Simple Summary: The therapeutic potential of proton therapy (PT) was first recognized in 1946 by Robert Wilson, and nowadays, over 100 proton centers are in operation worldwide, and more than 60 are under construction or planned. Bibliometrics allows us to assess the growth and development of the field and provides an overview of the most relevant sources, authors, and institutions in the chosen field. Additionally, keyword analysis can be performed to provide details that could aid in identifying research trends and hot topics. Indeed, bibliometrics allows for identifying the basic and dominant themes and trends, as well as those emerging or declining. The aim of this study is to provide a comprehensive bibliometric analysis of the current status and trends in scientific literature in the PT field. The analysis will give an overview of past scientific production and, most importantly, provide a useful point of view on the future directions of the research activities. Background: The therapeutic potential of proton therapy (PT) was first recognized in 1946 by Robert Wilson, and nowadays, over 100 proton centers are in operation worldwide, and more than 60 are under construction or planned. Bibliometric data can be used to perform a structured analysis of large amounts of scientific data to provide new insights, e.g., to assess the growth and development of the field and to identify research trends and hot topics. The aim of this study is to provide a comprehensive bibliometric analysis of the current status and trends in scientific literature in the PT field. Methods: The literature on PT until the 31st December 2022 in the Scopus database was searched, including the following keywords: proton AND radiotherapy AND cancer/tumor in title, abstract, and/or keywords. The open-source R Studio's Bibliometrix package and Biblioshiny software (version 2.0) were used to perform the analysis. Results: A total of 7335 documents, mainly articles (n = 4794, 65%) and reviews (n = 1527, 21%), were collected from 1946 to 2022 from 1054 sources and 21,696 authors. Of these, roughly 84% (n = 6167) were produced in the last 15 years (2008–2022), in which the mean annual growth rate was 13%. Considering the corresponding author's country, 79 countries contributed to the literature; the USA was the top contributor, with 2765 (38%) documents, of whom 84% were single-country publications (SCP), followed by Germany and Japan, with 535 and 531 documents of whom 66% and 93% were SCP. Considering the themes subanalysis (2002–2022), a total of 7192 documents were analyzed; among all keywords used by authors, the top three were radiotherapy (n = 1394, 21% of documents), intensity-modulated radiotherapy (n = 301, 5%), and prostate cancer (n = 301, 5%). Among disease types, prostate cancer is followed by chordoma, head and neck, and breast cancer. The change in trend themes demonstrated the fast evolution of hotspots in PT; among the most recent trends, the appearance of flash, radiomics, relative biological effectiveness (RBE), and linear energy transfer (LET) deserve to be highlighted. Conclusions: The results of the present bibliometric analysis showed that PT is an active and rapidly increasing field of research. Themes of the published works encompass the main aspects of its application in clinical practice, such as the comparison with the actual photon-based standard of care technique and the continuing technological advances. This analysis gives an overview of past scientific production and, most importantly, provides a useful point of view on the future directions of the research activities. [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]

Background: Breast-conserving surgery (BCS) and whole breast radiation therapy (WBRT) are the standard of care for early-stage breast cancer (BC). Based on the observation that most local recurrences occurred near the tumor bed, accelerated partial breast irradiation (APBI), consisting of a higher dose per fraction to the tumor bed over a reduced treatment time, has been gaining ground as an attractive alternative in selected patients with low-risk BC. Although more widely delivered in postoperative setting, preoperative APBI has also been investigated in a limited, though increasing, and number of studies. The aim of this study is to test the feasibility, safety and efficacy of preoperative radiotherapy (RT) in a single fraction for selected BC patients.Methods: This is a phase I/II, single-arm and open-label single-center clinical trial using CyberKnife. The clinical investigation is supported by a preplanning section which addresses technical and dosimetric issues. The primary endpoint for the phase I study, covering the 1st and 2nd year of the research project, is the identification of the maximum tolerated dose (MTD) which meets a specific target toxicity level (no grade 3-4 toxicity). The primary endpoint for the phase II study (3rd to 5th year) is the evaluation of treatment efficacy measured in terms of pathological complete response rate.Discussion: The study will investigate the response of BC to the preoperative APBI from different perspectives. While preoperative APBI represents a form of anticipated boost, followed by WBRT, different are the implications for the scientific community. The study may help to identify good responders for whom surgery could be omitted. It is especially appealing for patients unfit for surgery due to advanced age or severe co-morbidities, in addition to or instead of systemic therapies, to ensure long-term local control. Moreover, patients with oligometastatic disease synchronous with primary BC may benefit from APBI on the intact tumor in terms of tumor progression free survival. The study of response to RT can provide useful information about BC radiobiology, immunologic reactions, genomic expression, and radiomics features, to be tested on a larger scale.Trial Registration: The study was prospectively registered at clinicaltrials.gov ( NCT04679454 ). [ABSTRACT FROM AUTHOR]

The purpose of this European Society for Radiotherapy and Oncology (ESTRO) project, endorsed by the European Association of Urology, is to explore expert opinion on the management of patients with oligometastatic and oligoprogressive renal cell carcinoma by means of stereotactic ablative radiotherapy (SABR) on extracranial metastases, with the aim of developing consensus recommendations for patient selection, treatment doses, and concurrent systemic therapy. A questionnaire on SABR in oligometastatic renal cell carcinoma was prepared by a core group and reviewed by a panel of ten prominent experts in the field. The Delphi consensus methodology was applied, sending three rounds of questionnaires to clinicians identified as key opinion leaders in the field. At the end of the third round, participants were able to find consensus on eight of the 37 questions. Specifically, panellists agreed to apply no restrictions regarding age (25 [100%) of 25) and primary renal cell carcinoma histology (23 [92%] of 25) for SABR candidates, on the upper threshold of three lesions to offer ablative treatment in patients with oligoprogression, and on the concomitant administration of immune checkpoint inhibitor. SABR was indicated as the treatment modality of choice for renal cell carcinoma bone oligometatasis (20 [80%] of 25) and for adrenal oligometastases 22 (88%). No consensus or major agreement was reached regarding the appropriate schedule, but the majority of the poll (54%–58%) retained the every-other-day schedule as the optimal choice for all the investigated sites. The current ESTRO Delphi consensus might provide useful direction for the application of SABR in oligometastatic renal cell carcinoma and highlight the key areas of ongoing debate, perhaps directing future research efforts to close knowledge gaps. [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]

Simple Summary: This study aims to compare effectiveness and cost profile in non-small-cell lung cancer (NSCLC) patients harboring synchronous brain metastases (BMs) who received non-chemo first-line systemic therapy with or without advanced radiotherapy (aRT). A total of 177 lung cancer patients, of whom 58 were treated with systemic treatment (either TKIs or pembrolizumab) plus aRT (STRT) and 119 with systemic treatment alone, were selected. The addition of aRT to systemic treatment was associated with a significantly better OS (p = 0.020) and PFS (p = 0.041) than systemic therapy alone. The incremental cost-effectiveness ratio (ICER) value indicated an average cost of €3792 for each month of survival after STRT and confirmed clinical effectiveness but higher healthcare costs. This real-world study suggests that upfront aRT in this setting represents a valid treatment strategy, boosting the efficacy of emerging drug classes with sustainable costs for the health service. Aim: Healthcare administrative databases represent a valuable source for real-life data analysis. The primary aim of this study is to compare effectiveness and cost profile in non-small-cell lung cancer (NSCLC) patients harboring synchronous brain metastases (BMs) who received non-chemo first-line systemic therapy with or without advanced radiotherapy (aRT). Methods: Diagnostic ICD-9-CM codes were used for identifying all patients with a new diagnosis of lung cancer between 2012 and 2019. Among these, patients who had started a first-line systemic treatment with either TKIs or pembrolizumab, alone or in combination with intensity-modulated or stereotactic RT, were selected. Clinical outcomes investigated included overall survival (OS), progression-free survival (PFS), and time-to-treatment failure (TTF). The cost outcome was defined as the average per capita cumulative healthcare direct costs of the treatment, including all inpatient and outpatient costs. Results: The final cohort included 177 patients, of whom 58 were treated with systemic treatment plus aRT (STRT) and 119 with systemic treatment alone. The addition of aRT to systemic treatment was associated with a significantly better OS (p = 0.020) and PFS (p = 0.041) than systemic therapy alone. The ICER (incremental cost-effectiveness ratio) value indicated an average cost of €3792 for each month of survival after STRT treatment and confirmed clinical effectiveness but higher healthcare costs. Conclusions: This real-world study suggests that upfront aRT for NCLSC patients with synchronous BMs represents a valid treatment strategy, boosting the efficacy of novel and emerging drug classes with sustainable costs for the health service. Translational relevance: The present real-world study reports that the use of upfront advanced radiotherapyaRT and new-generation systemic agents, such as TKIs and pembrolizumab, may have higher oncological control and an improved cost-effectiveness profile than the use of new-generation systemic agents alone in NCLSC patients with synchronous brain metastases. Acquired evidence can also be used to inform policymakers that adding advanced radiotherapy results is a sustainable cost for the health service. Since approximately 50% of patients do not meet RCT inclusion criteria, a significant proportion of them is receiving treatment that is not evidence-informed; therefore, these results warrant further studies to identify the best radiotherapy timing and possible dose escalation approaches to improving treatment efficacy in patient subgroups not typically represented in randomized controlled trials. [ABSTRACT FROM AUTHOR]

Simple Summary: Several retrospective studies have shown worse oncological outcome and toxicity in sarcopenic patients with head and neck cancer undergoing radiotherapy. The current study focuses on patients with oropharyngeal carcinoma, especially HPV-associated, to investigate whether sarcopenia, not only present at baseline before starting treatment, but also arising during radiotherapy, could impact oncological outcomes and toxicity in these good-prognosis patients. Indeed, the intensification of a tailored approach with prompt nutritional intervention, trying to intercept patients before the onset of sarcopenia, could improve oncological outcomes. The current study aims to profile sarcopenic condition (both at baseline and developed during treatment) in oropharyngeal carcinoma (OPC) patients treated with curative radiotherapy (RT) +/− chemotherapy and to evaluate its impact on oncological outcomes and toxicity. A total of 116 patients were included in this retrospective single-center study. Sarcopenia assessment at baseline and at 50 Gy re-evaluation CT was obtained from two different methodologies: (i) the L3-skeletal muscle index (SMI) derived from the contouring of the cross-sectional area (CSA) of the masticatory muscles (CSA-MM); and (ii) the paravertebral and sternocleidomastoid muscles at the level of the third cervical vertebra (CSA-C3). Based on L3-SMI from CSA-MM, developing sarcopenic condition during RT (on-RT sarcopenia) was associated with worse progression-free survival (PFS) (p = 0.03) on multivariable analysis and a trend of correlation with overall survival (OS) was also evident (p = 0.05). According to L3-SMI derived from CSA-C3, on-RT sarcopenia was associated with worse PFS (p = 0.0096) and OS (p = 0.013) on univariate analysis; these associations were not confirmed on multivariable analysis. A significant association was reported between becoming on-RT sarcopenia and low baseline haemoglobin (p = 0.03) and the activation of nutritional counselling (p = 0.02). No significant associations were found between sarcopenia and worse RT toxicity. Our data suggest that the implementation of prompt nutritional support to prevent the onset of sarcopenia during RT could improve oncological outcomes in OPC setting. [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]

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.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.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.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.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.• 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.Methods: 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.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.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.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.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.• 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.Results: 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.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.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.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.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.• 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.Conclusions: 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.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.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.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.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.• 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.Clinical relevance statement: 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.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.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.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.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.• 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.Key Points: 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.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.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.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.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.• 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]

Background: Paucity and low evidence-level data on proton therapy (PT) represent one of the main issues for the establishment of solid indications in the PT setting. Aim of the present registry, the POWER registry, is to provide a tool for systematic, prospective, harmonized, and multidimensional high-quality data collection to promote knowledge in the field of PT with a particular focus on the use of hypofractionation. Methods: All patients with any type of oncologic disease (benign and malignant disease) eligible for PT at the European Institute of Oncology (IEO), Milan, Italy, will be included in the present registry. Three levels of data collection will be implemented: Level (1) clinical research (patients outcome and toxicity, quality of life, and cost/effectiveness analysis); Level (2) radiological and radiobiological research (radiomic and dosiomic analysis, as well as biological modeling); Level (3) biological and translational research (biological biomarkers and genomic data analysis). Endpoints and outcome measures of hypofractionation schedules will be evaluated in terms of either Treatment Efficacy (tumor response rate, time to progression/percentages of survivors/median survival, clinical, biological, and radiological biomarkers changes, identified as surrogate endpoints of cancer survival/response to treatment) and Toxicity. The study protocol has been approved by the IEO Ethical Committee (IEO 1885). Other than patients treated at IEO, additional PT facilities (equipped with Proteus®ONE or Proteus®PLUS technologies by IBA, Ion Beam Applications, Louvain-la-Neuve, Belgium) are planned to join the registry data collection. Moreover, the registry will be also fully integrated into international PT data collection networks. [ABSTRACT FROM AUTHOR]

Aim: The present work reports updated oncological results and patients-reported outcomes at 5 years of phase II trial "Short-term high precision RT for early prostate cancer with SIB to the dominant intraprostatic lesion (DIL) for patients with early-stage PCa”. Methods: Data from patients enrolled within AIRC IG-13218 (NCT01913717) trial were analyzed. Clinical and GU/GI toxicity assessment and PSA measurements were performed every 3 months for at least 2 years after RT end. QoL of enrolled patients was assessed by IPSS, EORTC QLQ-C30, EORTC QLQ-PR25, and IIEF-5. Patients’ score changes were calculated at the end of RT and at 1, 12, and 60 months after RT. Results: A total of 65 patients were included. At a median follow-up of 5 years, OS resulted 86%. Biochemical and clinical progression-free survival at 5 years were 95%. The median PSA at baseline was 6.07 ng/ml, while at last follow-up resulted 0.25 ng/ml. IPSS showed a statistically significant variation in urinary function from baseline (p = 0.002), with the most relevant deterioration 1 month after RT, with a recovery toward baseline at 12 months (p ≤ 0.0001). A numerical improvement in QoL according to the EORTC QLQ-C30 has been reported although not statistically significant. No change in sexual activity was recorded after RT. Conclusions: The study confirms that extreme hypofractionation with a DIL boost is safe and effective, with no severe effects on the QoL. The increasing dose to the DIL does not worsen the RT toxicity, thus opening the possibility of an even more escalated treatment. [ABSTRACT FROM AUTHOR]

Simple Summary: Automatic segmentation of organs and other regions of interest is a promising approach for reducing the workload of doctors in radiotherapeutic planning, but it can be hard for doctors and researchers to keep up with current developments. This review evaluates 807 papers and reveals trends, commonalities, and gaps in the existing corpus. A set of recommendations for conducting effective segmentation studies is also provided. This review provides a formal overview of current automatic segmentation studies that use deep learning in radiotherapy. It covers 807 published papers and includes multiple cancer sites, image types (CT/MRI/PET), and segmentation methods. We collect key statistics about the papers to uncover commonalities, trends, and methods, and identify areas where more research might be needed. Moreover, we analyzed the corpus by posing explicit questions aimed at providing high-quality and actionable insights, including: "What should researchers think about when starting a segmentation study?", "How can research practices in medical image segmentation be improved?", "What is missing from the current corpus?", and more. This allowed us to provide practical guidelines on how to conduct a good segmentation study in today's competitive environment that will be useful for future research within the field, regardless of the specific radiotherapeutic subfield. To aid in our analysis, we used the large language model ChatGPT to condense information. [ABSTRACT FROM AUTHOR]

Simple Summary: The concept of "oligometastatic state" has brought about a shift in our understanding of cancer's metastasis and progression, and its management remains an active field of research, with ongoing efforts to develop new treatment options. The aim of the present study is to report a bibliometric analysis of the oligometastatic disease/state to provide an overview of the topic. A total of 3304 documents were collected from 1995 to 2022. Among all keywords used by authors, the top three were oligometastases (19%), SBRT (18%), and radiation therapy (8%). In particular, a link can be observed between radiotherapy and topics of side-fields like immunotherapy and targeted therapies, representing an opportunity for further combinatorial studies in this setting. This bibliometric analysis represents a detailed summary of the most influential studies dealing with the oligometastatic state and sheds light on the area for the attention of researchers worldwide. The term "oligometastasis" represents a relatively novel idea, which denotes a condition characterized by cancer dissemination with a limited number of lesions (usually fewer than five). The aim of the present study is to report a bibliometric analysis of the oligometastatic disease/state, incorporating all relevant studies on the topic for more than 20 years. The research strategy included at least one the terms "Oligmetastases", "Oligometastasis", "Oligometastatic", "Oligoprogression, "Oligoprogressive", "Oligorecurrent", or "Oligorecurrency" in the title, abstract, and/or keywords. All English-language documents from 1 January 1995 (the year of the earliest available document in Scopus) to 31 December 2022 were considered for the analysis. R code (R version 4.2.0) with R Studio (version 2022.12.0-353) and the Bibliometrix package (version 4.0.1) were used for the analysis. A total of 3304 documents, mainly articles (n = 2083, 63.0%) and reviews (n = 813, 24.6%), were collected from 1995 to 2022. The average annual growth rate of literature on the topic was 26.7%. Overall 15,176 authors published on the topic, with an average of eight authors/publication. From 1995, 69 countries contributed to the literature, with the USA and Italy being the top contributors. Among all keywords used by authors, the top three were oligometastases (19%), SBRT (18%), and radiation therapy (8%). Themes regarding "locoregional treatment", "organ motion", and "immunotherapy" were the most recent trend topics, mainly developed from 2019 to 2022, while "high-dose chemotherapy", "whole-brain radiotherapy", and "metastatic breast cancer" saw their main development during 2009–2018. Our study shows the exceptionally flourishing scientific production on the oligometastatic state, summarizing the most influential studies and highlighting the future developments and interests. This analysis will serve as a benchmark to identify this area for the attention of researchers worldwide and contribute to the increasing scientific work. [ABSTRACT FROM AUTHOR]

• Italy has been slowly reopening after a 10 week lockdown following COVID-19 outbreak. • A survey was administered to Italian RT departments to depict the current scenario. • In P1, RT departments managed to contain contagion with modest workload reduction. • All safety measures, maintained in P2, allowed RT departments to restore activities. • Reorganization of activities will be crucial to prevent the second wave of pandemic. [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]

• Boost dose is beneficial for young patients affected by breast cancer. • Intraoperative electrons (IOERT) boost and hypofractionated whole breast irradiation (HWBI) represent a suitable modality. • Local control with IOERT/HWBI was excellent, even in very young patients. • High tumor bed fibrosis was observed due to high cumulative EQD2 boost dose. To evaluate outcome of intraoperative electron boost (IOERT) and hypofractionated whole breast irradiation (HWBI) for breast cancer (BC) in young women. Women aged ≤ 48 with pT1-2 N0-1 BC received 12 Gy IOERT boost during conservative surgery followed by 3-dimensional conformal HWBI in 13 fractions (2.85 Gy/die). Local relapses (LR) and survival (disease-free, DFS; specific, BCSS; overall, OS) were analyzed. 481 consecutive BC patients, mostly node negative, with median age of 42 were treated between 2004 and 2014. Median tumor size was 1.48 cm and median IOERT collimator was 4 cm. After 25-day mean interval, HWBI was delivered. At a median follow-up of 9.6 years, there were 23 LRs (4.8 %, 9 of which were in the boost region). Ten-year LR cumulative incidence was 4.1 % (95 %CI, 2.5–6.3). Over time, local control rate decreased for Luminal A and HER2 positive with negative hormonal receptors, while remained steady for triple negative. At multivariate analysis, LR predictors included age < 40, extensive intraductal component and the use of 4-cm IOERT collimator size. Ten-year survival outcomes were as follows: DFS 80.0 % (95 % CI, 75.8–83.5), BCSS 97.5 % (95 % CI, 95.5–98.6 %), OS 96.5 % (95 % CI, 94.3–97.9). Luminal B HER2 negative had the worse survival outcomes. Perioperative complications were uncommon (16.4 %), acute toxicity was mild (<2% Grade 3), but moderate/severe fibrosis was described in 40.8 % of the cases. Cosmesis was scored as excellent/good in 86 % of the cases. ELIOT boost and HWBI achieved an excellent local control at the cost of tumor bed fibrosis. IOERT boost dose lower than 12 Gy is advisable. [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]

• Studies regarding hypofractionated photon RT or Proton Therapy were considered. • A total of 160 studies for localized PCa were included in the final analysis. • PT can prevent acute GI toxicities and improve 5-year BRFS in selected PCa patients. • Further efforts should be made to identify patient that could benefit most from PT. High-level evidence on hypofractionated proton therapy (PT) for localized and locally advanced prostate cancer (PCa) patients is currently missing. The aim of this study is to provide a systematic literature review to compare the toxicity and effectiveness of curative radiotherapy with photon therapy (XRT) or PT in PCa. PubMed, Embase, and the Cochrane Library databases were systematically searched up to April 2022. Men with a diagnosis of PCa who underwent curative hypofractionated RT treatment (PT or XRT) were included. Risk of grade (G) ≥ 2 acute and late genitourinary (GU) OR gastrointestinal (GI) toxicity were the primary outcomes of interest. Secondary outcomes were five-year biochemical relapse-free survival (b-RFS), clinical relapse-free, distant metastasis-free, and prostate cancer-specific survival. Heterogeneity between study-specific estimates was assessed using Chi-square statistics and measured with the I2 index (heterogeneity measure across studies). A total of 230 studies matched inclusion criteria and, due to overlapped populations, 160 were included in the present analysis. Significant lower rates of G ≥ 2 acute GI incidence (2 % vs 7 %) and improved 5-year biochemical relapse-free survival (95 % vs 91 %) were observed in the PT arm compared to XRT. PT benefits in 5-year biochemical relapse-free survival were maintained for the moderate hypofractionated arm (p-value 0.0122) and among patients in intermediate and low-risk classes (p-values < 0.0001 and 0.0368, respectively). No statistically relevant differences were found for the other considered outcomes. The present study supports that PT is safe and effective for localized PCa treatment, however, more data from RCTs are needed to draw solid evidence in this setting and further effort must be made to identify the patient subgroups that could benefit the most from PT. [ABSTRACT FROM AUTHOR]

Simple Summary: The present study has been suggested by the previous experience of our group showing that patients treated with conventional radiotherapy (named 3D conformal radiotherapy—3D-CRT) performed after conservative surgery (CS) for tumors of the supraglottic regions experienced a high rate of severe long-term toxicity. Therefore, we reported the toxicity profile of a similar cohort of patients treated with a high-precision radiotherapy technique (named intensity-modulated radiotherapy—IMRT). Moreover, to investigate the advantage of IMRT, we performed a comparison with a historical cohort treated with 3D-CRT. Results showed that patients treated with IMRT achieved a very low toxicity profile and comparison with 3D-CRT was in favor of IMRTs. Therefore, we believe that the results of the present study provide preliminary findings on the potential of IMRT in improving the toxicity profile of patients treated with surgical organ preservation strategies for laryngeal tumors. The aim of the present study was to investigate the role of intensity-modulated radiotherapy (IMRT) on the toxicity profile of patients treated with conservative surgery (CS) of the supraglottic (SG) region. Data on patients treated with CS and postoperative radiotherapy (PORT)-IMRT were prospectively collected. Results. In total, 20 patients were analyzed. Of these, six patients (35%) required the positioning of a temporary tracheostomy. The functional larynx preservation rate was 95%. Females had a higher risk of both endoscopic intervention and chondronecrosis, while the median age was significantly higher in patients requiring enteral nutrition. The incidence of long-term severe toxicities was lower in patients treated with IMRT than in the historical 3D-CRT cohort. Patients who had received PORT-IMRT achieved a lower rate of permanent laryngeal and swallowing dysfunctions. Overall, results from the comparison with the historical 3D-CRT cohort favor the IMRTs. [ABSTRACT FROM AUTHOR]

The last edition of the American Joint Committee on Cancer (AJCC eighth) has introduced the depth of infiltration (DOI) as a new prognostic parameter in oral cavity squamous cell carcinomas (OCSCCs). The aim of this study is to analyze the impact of stage migration on the indication to post-operative radiotherapy (PORT). OCSCCs treated at two institutions between 2014 and 2019 were retrieved. As per the AJCC eighth, only pT3 primarily OCSCCs were considered; availability of the pathologic specimen was a further inclusion criterion. Risk factors considered for PORT were: pT3-pT4, nodal involvement, positive/close surgical margins, perineural and lymph vascular invasion. 149 patients staged as pT3 AJCC eighth were included. A four-fold increase in the number of patients staged as pT3 from the seventh to the eighth AJCC was found. Stage migration to pT3 was equally due to the downstaging from former pT4 (38%) and upstaging of former pT1-pT2 (35%). Considering the former pT1-pT2 53 patients, 13 (25%) had no risk factors for PORT other than DOI. Among 25 cases with former pT1-pT2 and negative lymph nodes, no additional risk factors were found in 11 (44%). 90% of patients had at least one risk factor besides DOI and would have received PORT also according to the AJCC seventh; notably, of former pT1-pT2N0, half of them have been upstaged to pT3 in the current TNM classification. The role of PORT in this cohort of patients has not been clarified yet. Other-than-DOI risk factors leading to PORT indication are highly prevalent in OCSSC patients classified as pT3 per the latest AJCC TNM staging system and should therefore be considered for a comprehensive oncological assessment. [ABSTRACT FROM AUTHOR]

The aim of this study is to evaluate feasibility of salvage 4-week hypofractionated whole breast radiotherapy (WBRT) in patients with in-breast recurrence after receiving intraoperative radiotherapy with electrons (IOERT) for primary breast cancer (BC). BC patients who had repeated quadrantectomy underwent modified WBRT with intensity-modulated radiotherapy using Helical Tomotherapy to underdose the IOERT region. This approach, called POLO (Partially Omitted Lobe), excluded the IOERT volume from receiving the full prescription dose. Nine patients were treated with this approach, receiving 45 Gy in 20 fractions. A simultaneous integrated boost of 2.5 Gy in 20 fractions was delivered in 6/9 patients. Dose constraints and planning objectives were reported. No severe toxicity was reported while local control and overall survival were 100%. The POLO approach is technically feasible and capable to achieve a significant reduction of radiation dose delivered to the previous treated IOERT area. The study demonstrates the technical and dosimetric feasibility of conservative salvage whole breast radiotherapy, while sparing the area already treated with IORT, in patients with in-breast recurrence. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of the study was to evaluate the toxicity, local control, overall and disease-free survival of elderly breast cancer (BC) patients treated with adjuvant once-weekly ultra-hypofractionated radiotherapy (RT) either with intensity-modulated RT (IMRT) or 3D conformal RT (3DCRT). Methods: From July 2011 to July 2018, BC patients receiving 5.7 Gy once a week for 5 weeks to the whole breast after breast-conserving surgery were considered for the study. Inclusion criteria were: T1–T3 invasive BC, no or limited axillary involvement, age ≥ 65 years or women with commuting difficulties or disabling diseases. Results: A total of 271 patients were included in the study. Median age was 76 (46–86) years. Most of BC were T1 (77%), while the remaining were T2 (22.2%) and T3 (0.4%). Axillary status was negative in 68.3% of the patients. The only severe acute toxicity (G3) at the end of RT was erythema (0.4%), registered in the 3DCRT group; no G3 edema or epitheliolysis was recorded. With 18 months of median follow-up, severe early–late toxicity (G3) was reported in terms of fibrosis and breast retraction, both with an incidence of 1.4%, mostly in the 3DCRT group. Oncological outcomes at a median follow-up of 2.9 years reported 249/271 (91.9%) patients alive and free from any event and 5 (1.8%) isolated locoregional recurrences. At 3 years, disease-free survival and overall survival were 94.9% and 97.8%, respectively. Breast volume > 500 cm3 was reported as predictive for moderate–severe (≥ G2) acute toxicity. Conclusions: Weekly ultra-hypofractionated whole breast RT seems feasible and effective. Toxicity was mild, local control was acceptable, and overall survival was 97.8% at 3 years. Rates of severe toxicity were reduced with the IMRT technique. [ABSTRACT FROM AUTHOR]

Based on literature, intensity‐modulated radiation therapy (IMRT) provides less related toxicity compared with conventional 2D/3D‐RT with no impact on oncological outcomes for oropharyngeal cancer. The aim of this systematic review and meta‐analysis is to assess whether IMRT might provide similar clinical outcomes with reduced related toxicity in comparison with conventional 2D/3D RT in patients treated for clinically advanced oropharyngeal cancer (OPC). Inclusion criteria for paper selection included: squamous OPC patients, treatment performed by concomitant CRT or RT alone, four treatment performed for curative intent, and presence of clinical outcome of interest, namely, overall survival (OS) and disease‐free survival (DFS) and full paper available in English. Acute and late toxicities were retrieved together with OS and DFS. Crude relative risk estimates of relapse and death comparing 2D/3D‐RT versus IMRT were calculated from tabular data, extracting events at 2–3 years of follow‐up. Eight studies were selected. Six of them were included in the meta‐analysis considering summary relative risk. Considering both acute and late toxicities, the considered studies evidenced advantages for IMRT populations, with the 2D/3D‐RT population showing higher frequencies than the IMRT one. No statistical difference between IMRT and 2D/3D‐RT in terms of death (SRR = 0.93, 95% CI: 0.83–1.04 with no heterogeneity I2 = 0%) and relapse (SRR = 0.92, 95% CI: 0.83–1.03, with no heterogeneity I2 = 0%) was found. Results of our study suggest the improvement in the therapeutic index with IMRT with evidenced reduced toxicity without any worsening in clinical outcome when compared to 2D/3DCRT. [ABSTRACT FROM AUTHOR]

• Positive sentinel node biopsy (SLNB) after neoadjuvant therapy (NAST) is an issue. • Positive SLNB is associated with high rate of residual positive lymph nodes. • Positive SLNB after NAST warrants treatment of axilla (dissection/radiotherapy-RT) • Predictors of positive axilla lymph nodes distribution might help to tailor RT fields. To assess the rate of positive non-sentinel lymph nodes (non-SLNs) after neoadjuvant systemic therapy (NAST) in breast cancer (BC) following positive sentinel lymph node biopsy (SLNB). From institutional database, 265 consecutive patients receiving NAST for cT1-3, any N, M0 BC between 2001 and 2018 were identified. Patients presented clinically negative axilla before surgery and were candidate for SLNB. Following metastatic SLNB, completion axillary lymph node dissection (AxLND) was performed. Non-SLNs rate was investigated using multivariate (MV) logistic regression models. The distribution of non-SLNs across the axilla was observed. Positive non-SLNs were found in 62.3% of cases and showed no correlation with SLN metastasis size. At MV, statistically significant variables associated with non-SLNs were older age (p = 0.025), clinically positive lymph nodes (p = 0.002), SLN extracapsular extension (ECE, p = 0.001), and higher ratio of positive SLNs/total SLNs (p = 0.016). ECE and higher nodal ratio were independent predictors of III axillary level positivity. By categorizing patients in intermediate- and high-risk groups using the study variables, positive non-SLNs were found in the range of 23–56% across the three axillary levels, rates which did not support radiotherapy volume de-escalation. The III axillary level lower involvement (6.3%) was better identified with the RAPCHEM trial criteria based on the ypN status after AxLND. Involved non-SLNs rate following positive SLNB after NAST is nearly double the rate observed after primary surgery, supporting some intervention on the axilla. If AxLND is limited to I and II level, the involvement of the III level up to 31% of the cases seems to require some additional treatment, while the omission in selected cases needs further investigation. [ABSTRACT FROM AUTHOR]

• A growing number of patients is being treated with postmastectomy radiotherapy. • The number of patients with implant-based breast reconstruction is increasing. • The optimal timing and sequence of reconstruction and radiotherapy is unanswered. • Hypofractionation has been gaining ground in implant-based breast reconstruction radiotherapy. To evaluate reconstruction failure (RF) rate in patients receiving implant-based immediate breast reconstruction (IBR) and hypofractionated (HF) postmastectomy radiation therapy (PMRT). Stage II–III breast cancer patients, treated with HF-PMRT using intensity modulated radiotherapy were stratified in two groups according to IBR: single-stage direct-to-implant (DTI-group) and two-stage expander and implant (TE/I-group). Irradiated patients were matched with non-irradiated ones who underwent the same IBR during the same period. Prescription dose was 40.05 Gy/15 fractions to chest wall and infra/supraclavicular nodal region. Primary endpoint was RF defined as the need of major revisional surgery (MaRS) for implant removal or conversion to autologous reconstruction. Secondary endpoint was the rate of minor revisional surgeries (MiRS) including implant repositioning or substitution with another implant. One hundred and seven irradiated patients (62 in TE/I-group, 45 in DTI-group) were matched with 107 non-irradiated subjects. Median follow-up was 4.2 years (0.1–6.1) In the TE/I setting, MaRS was performed in 8/62 irradiated patients (12.9%) of the irradiated TE/I group compared to 1/62 (1.6%) non-irradiated subjects (p = 0.015). In the DTI setting, MaRs occurred in 3/45 irradiated patients (6.7%) compared to 1/45 non-irradiated ones (2.2%) (p = 0.35). Overall MaRS rate was 10.3% in the irradiated group. MiRS was performed in 35.6% and 31.1% of the irradiated and non-irradiated DTI-groups (p = 0.65), respectively, and in 12.9% and 8.1% of the irradiated and non-irradiated TE/I groups (p = 0.38), respectively. Compared to the non-irradiated counterparts, the TE/I group showed higher rate of RF, while no statistically significant difference was observed for the DTI group. The use of hypofractionation and IMRT to implant-based IBR did not seem to increase the risk of RF which appeared to be in line with the literature [ABSTRACT FROM AUTHOR]

Aim of the present analysis was to report results of a systematic review of the literature in the setting of patients treated with hypoF PT for benign lesions of the central nervous system (CNS). The methodology complied with the PRISMA recommendations. PubMed, EMBASE and Scopus databases were interrogated in September 2022. Twelve papers have been selected including patients treated for base of the skull meningiomas (6 papers), vestibular schwannoma (3 papers) and pituitary adenomas (3 papers). Clinical outcomes were evaluated with both radiologic images and clinical parameters. Long-term toxicity was reported in all but one series with an incidence ranging from 2 % to 7 % in patients treated for base of skull meningioma and 1–9 % for schwannoma. HypoF PT is a safe and effective treatment in selected benign tumors of the CNS. Further dosimetric and clinical comparisons are required to better refine the patients' selection criteria. • Clinical data on hypofractionated Proton Therapy are rapidly increasing. • QoL is of particular importance for benign tumor of the CNS. • Hypofractionated PT confirmed to an effective alternative to the photon approach. [ABSTRACT FROM AUTHOR]

[Display omitted] • Using different platforms for radiomic extraction affects models' performance. • Variables' relevance is inconsistent among platforms. • MRI features are correlated to radiosurgery response in brain metastases from NSCLC. • Higher number of radiomic features does not necessarily imply better performance. Radiomics enables the mining of quantitative features from medical images. The influence of the radiomic feature extraction software on the final performance of models is still a poorly understood topic. This study aimed to investigate the ability of radiomic features extracted by two different radiomic platforms to predict clinical outcomes in patients treated with radiosurgery for brain metastases from non-small cell lung cancer. We developed models integrating pre-treatment magnetic resonance imaging (MRI)-derived radiomic features and clinical data. Pre-radiotherapy gadolinium enhanced axial T1-weighted MRI scans were used. MRI images were re-sampled, intensity-shifted, and histogram-matched before radiomic extraction by means of two different platforms (PyRadiomics and SOPHiA Radiomics). We adopted LASSO Cox regression models for multivariable analyses by creating radiomic, clinical, and combined models using three survival clinical endpoints (local control, distant progression, and overall survival). The statistical analysis was repeated 50 times with different random seeds and the median concordance index was used as performance metric of the models. We analysed 276 metastases from 148 patients. The use of the two platforms resulted in differences in both the quality and the number of extractable features. That led to mismatches in terms of end-to-end performance, statistical significance of radiomic scores, and clinical covariates found significant in combined models. This study shed new light on how extracting radiomic features from the same images using two different platforms could yield several discrepancies. That may lead to acute consequences on drawing conclusions, comparing results across the literature, and translating radiomics into clinical practice. [ABSTRACT FROM AUTHOR]