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Giulia Marvaso,1,2,* Stefania Volpe,1,2,* Matteo Pepa,1,* Mattia Zaffaroni,1,* Giulia Corrao,1,2,* Matteo Augugliaro,1,* Franco Nolè,3,* Ottavio De Cobelli,2,4,* Barbara Alicja Jereczek-Fossa1,2,* 1Division of Radiation Oncology, IEO European Institute of Oncology IRCCS, Milan, Italy; 2Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy; 3Medical Oncology Division of Urogenital & Head & Neck Tumors, IEO European Institute of Oncology IRCCS, Milan, Italy; 4Department of Urology, IEO, European Institute of Oncology IRCCS, Milan, Italy*These authors contributed equally to this workCorrespondence: Matteo PepaDivision of Radiation Oncology, IEO, European Institute of Oncology IRCCS, Milan, ItalyEmail matteo.pepa@ieo.itAbstract: After primary treatment for prostate cancer with either radical prostatectomy or radiotherapy, a significant proportion of patients are at risk of developing metastases. In recent years, a deeper understanding of the underlying biology together with improved imaging techniques and the advent of new therapeutic options including metastases-directed therapies and new drugs have revolutionized the management of low-burden metastatic disease, also known as oligometastatic state. The purpose of this narrative review is to report the recent developments in the management of hormone-sensitive oligometastatic prostate cancer patients.Keywords: oligometastatic prostate cancer, metastasis-directed therapies, narrative review, imaging, biology

Background: Patients with HER2-positive breast cancer have a significant risk of developing brain metastases (BrM), which have detrimental effects on survival outcomes and quality of life. Although there are several systemic treatment options available that may delay the appearance of BrM and secondary progression of previously treated BrM, there are still substantial unmet needs for this patient population and primary prevention remains elusive., Methods: A group of experts created consensus statements, through a modified Delphi process, to bridge the gap between current unmet needs, available evidence, and international guidelines., Results: The steering committee reviewed all relevant literature and formed research questions to be answered by the subsequent consensus statements. In total, 61 contributors provided feedback on the consensus statements, with 34 statements reaching agreement out of the 55 statements that were voted on altogether. Statements with consensus aimed to define BrM primary and secondary prevention, screening procedures, assessment of symptoms, treatment efficacy, and preventing the occurrence and progression of BrM, while acknowledging the possibilities and limitations in daily clinical practice. Some statements did not reach agreement for a variety of reasons, mostly due to lack of evidence., Conclusions: The consensus statements outlined in this publication provide a point of reference for daily clinical practice and can act as recommendations for clinical trial procedures and future guidelines., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Objectives: To test the performance of ex vivo fluorescence confocal microscopy (FCM; Vivascope 2500M-G4), as compared to intra-operative frozen section (IFS) analysis, to evaluate surgical margins during robot-assisted radical prostatectomy (RARP), with final pathology as the reference standard., Methods: Overall, 54 margins in 45 patients treated with RARP were analysed with: (1) ex vivo FCM; (2) IFS analysis; and (3) final pathology. FCM margins were evaluated by two different pathologists (experienced [M.I.: 10 years] vs highly experienced [G.R.: >30 years]) as strongly negative, probably negative, doubtful, probably positive, or strongly positive. First, inter-observer agreement (Cohen's κ) between pathologists was tested. Second, we reported the sensitivity, specificity, positive predictive (PPV) and negative predictive value (NPV) of ex vivo FCM. Finally, agreement between ex vivo FCM and IFS analysis (Cohen's κ) was reported. For all analyses, four combinations of FCM results were evaluated., Results: At ex vivo FCM, the inter-observer agreement between pathologists ranged from moderate (κ = 0.74) to almost perfect (κ = 0.90), according to the four categories of results. Indeed, at ex vivo FCM, the highly experienced pathologist reached the best balance between sensitivity (70.5%) specificity (91.8%), PPV (80.0%) and NPV (87.1%). Conversely, on IFS analysis, the sensitivity, specificity, PPV and NPV were, respectively, 88.2% vs 100% vs 100% vs 94.8%. The agreement between the ex vivo FCM and IFS analyses ranged from moderate (κ = 0.62) to strong (κ = 0.86), according to the four categories of results., Conclusion: Evaluation of prostate margins at ex vivo FCM appears to be feasible and reliable. The agreement between readers encourages its widespread use in daily practice. Nevertheless, as of today, the performance of FCM seems to be sub-par when compared to the established standard of care (IFS analysis)., (© 2024 BJU International.)

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.)

Purpose: Intraoperative radiation therapy with electrons (IOERT) may represent a viable choice for partial breast reirradiation after repeat quadrantectomy for local recurrence (LR) for primary breast cancer (BC) in lieu of mastectomy., Methods and Materials: A database collecting data on partial breast reirradiation with IOERT from 8 Italian centers was set up in 2016 to 2018, providing data on cumulative incidence (CumI) of second LR and survival with a long follow-up., Results: From 2002 to 2015, 109 patients underwent the conservative retreatment. The median primary BC first LR interval was 11.1 years (range, 2.4-27.7). The median first LR size was 0.9 cm (range, 0.3-3.0), and 43.6% cases were luminal A. Median IOERT dose was 18 Gy (range, 12-21), and median collimator diameter was 4 cm (range, 3-6). Median follow-up duration was 11.7 years (IQR, 7.7-14.6). The second LR CumI was 12.2% (95% CI, 6.8%-19.2%) at 5 years and 32.3% at 10 years (95% CI, 22.8%-42.2%), occurring in the same site as the first LR in about half of the cases. Human epidermal growth factor receptor 2 status and collimator size were independent LR predictors. The 5- and 10-year overall survival rates were 95.2% and 88.3%, respectively, whereas 5- and 10-year BC-specific survival rates were 98% and 94.5%, respectively. The development of a second LR significantly reduced BC-specific survival (hazard ratio, 9.40; P < .001). Grade ≥3 fibrosis rate was 18.9%. Patient-reported cosmesis was good/excellent in 59.7% of the cases., Conclusions: Second LR CumI was within the range of the literature but higher than expected, opening questions on radiation field extension and fractionation schedule. Because a second LR worsened the outcome, salvage modality must be carefully planned., Competing Interests: Disclosures The Division of Radiation Oncology of the European Institute of Oncology (IEO) received research funding from the Italian Association for Cancer Research (AIRC), Fondazione Istituto Europeo di Oncologia-Centro Cardiologico Monzino (IEO-CCM), Accuray, and Ion Beam Applications (IBA), all outside the current project. Barbara Alicja Jereczek-Fossa received speaker fees from Bayer, Accuray, Astellas, IBA, Ipsen, Astra Zeneca, Tecnologie Avanzate, Recordati, and Novartis; participated in the Advisory Board of Accuray, Bayer, and Seagen, all outside the current project. Maria Cristina Leonardi received a speakers fee from Accuray, and Samantha Dicuonzo received a speakers fee from Accuray Asia (all outside the current project). The remaining authors declare no conflicts of interest that are relevant to the content of this article., (Copyright © 2024 American Society for Radiation Oncology. Published by Elsevier Inc. All rights reserved.)

Background and Objectives: Prostate cancer treatments paradigms are in continuous evolution, especially in metastatic setting. In this context, the Genito-Urinary (GU) Group of Italian Association of Radiotherapy and Clinical Oncology (AIRO) aimed to create a Consensus on radiotherapy indication in de novo metastatic hormone sensitive prostate cancer both on primary tumour and metastatic sites., Methods: A panel of experts, involved in clinical management of prostate cancer, through the estimate-talk-estimate (ETE) method, developed a list of items and correspondent statements on the identified topic., Key Findings: Seven conclusive items were identified with 12 statements about the chosen topic, radiotherapy in metastatic hormone sensitive prostate cancer on primary tumour and metastatic sites., Conclusions and Clinical Implications: This consensus might help clinicians in prostate cancer managing in daily clinical practice., Competing Interests: Conflict of Interest Statement BAJF reported research funding from ACCURAY, IBA, AIRC (Italian Association for Cancer Research) and Fondazione IEO-CCM (Istituto Europeo di Oncologia-Centro Cardiologico Monzino). NGL reported funds from AstraZeneca, Amgen, Janseen. AL, ARA, GP, FP, LT, EC, AM, SF, GF, AR, RDA, CS had no funds to report., (Copyright © 2024. Published by Elsevier Inc.)

Background and Objective: Innovations have improved outcomes in advanced prostate cancer (PC). Nonetheless, we continue to lack high-level evidence on a variety of topics that greatly impact daily practice. The 2024 Advanced Prostate Cancer Consensus Conference (APCCC) surveyed experts on key questions in clinical management in order to supplement evidence-based guidelines. Here we present voting results for questions from APCCC 2024., Methods: Before the conference, a panel of 120 international PC experts used a modified Delphi process to develop 183 multiple-choice consensus questions on eight different topics. Before the conference, these questions were administered via a web-based survey to the voting panel members ("panellists")., Key Findings and Limitations: Consensus was a priori defined as ≥75% agreement, with strong consensus defined as ≥90% agreement. The voting results show varying degrees of consensus, as discussed in this article and detailed in the Supplementary material. These findings do not include a formal literature review or meta-analysis., Conclusions and Clinical Implications: The voting results can help physicians and patients navigate controversial areas of clinical management for which high-level evidence is scant or conflicting. The findings can also help funders and policymakers in prioritising areas for future research. Diagnostic and treatment decisions should always be individualised on the basis of patient and cancer characteristics, and should incorporate current and emerging clinical evidence, guidelines, and logistic and economic factors. Enrolment in clinical trials is always strongly encouraged. Importantly, APCCC 2024 once again identified important gaps (areas of nonconsensus) that merit evaluation in specifically designed trials., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

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.)

Aim: To evaluate acute toxicity at 6 months after stereotactic body radiotherapy (SBRT) in patients with oligometastatic cancer within the OligoCare cohort., Material and Methods: OligoCare is a prospective, registry-based, single-arm, observational study that aims to report prospective real-world data of patients with oligometastases from solid cancer treated with SBRT (NCT03818503). Primary tumor included non-small cell lung cancer (NSCLC), breast cancer (BC), colorectal cancer (CRC), and prostate cancer (PC). This analysis addresses a secondary endpoint of the trial, acute toxicity within 6 months after SBRT., Results: Out of 1,597registered patients, 1'468 patients were evaluated for acute toxicity. Globally, 290 (20 %) had NSCLC primary disease, 227 (16 %) had BC, 293 (20 %) had CRC, and 658 (45 %) had PC. Concomitant systemic treatment was administered in 527 (35.9 %) patients. According to the EORTC/ESTRO oligometastatic disease (OMD) classification, 828 (56 %) patients had de novo OMD, 464 (32 %) repeat OMD, and 176 (12 %) induced OMD. Acute grade ≥ 3 SBRT related adverse events were reported in 8 (0.5 %) patients, including 2 (0.1 %) fatal AEs. In particular, 6 (0.4 %) grade 3 events were: 1 empyema, 1 pneumonia, 1 radiation pneumonitis, 1 radiation skin injury, 1 decreased appetite, and 1 bone pain. Among those 2 occurred in NSCLC patients, 2 in BC patients, and 1 in CRC and PC patients each. The two (0.1 %) grade 5 toxicity were represented by: pneumonitis and cerebral hemorrhage., Conclusion: OligoCare is the largest prospective registry cohort on oligometastatic disease. Acute toxicity within 6 months was low, confirming the safety of SBRT in the treatment of oligometastases., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Purpose: To focus on the ecological footprint of radiotherapy (RT), on opportunities for sustainable practices, on future research directions., Methods: Different databases were interrogated using the following terms: Carbon Footprint, Sustainab*, Carbon Dioxide, Radiotherapy, and relative synonyms., Results: 931 records were retrieved; 15 reports were included in the review. Eight main thematic areas have been identified. Nine research works analyzed the environmental impact of photon-based external beam RT. Particle therapy was the subject of one work. Other thematic areas were brachytherapy, intra-operative RT, telemedicine, travel-related issues, and the impact of COVID-19., Conclusion: This review demonstrates the strong interest in identifying novel strategies for a more environmentally friendly RT and serves as a clarion call to unveil the environmental impact of carbon footprints entwined with radiation therapy. Future research should address current gaps to guide the transition towards greener practices, reducing the environmental footprint and maintaining high-quality care., Competing Interests: Declaration of Competing Interest 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 speaker fees from Roche, Bayer, Janssen, Carl Zeiss, Ipsen, Accuray, Astellas, Elekta, IBA, Astra Zeneca (all outside the current project). IEO, the European Institute of Oncology IRCCS, was partially supported by institutional grants from Accuray Inc. and IBA (Ion Beam Applications, Louvain-la-Neuve, ‎Belgium). 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 conflicts of interest that are relevant to the content of this article., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Artificial intelligence (AI) has made a tremendous impact in the space of healthcare, and proton therapy is not an exception. Proton therapy has witnessed growing popularity in oncology over recent decades, and researchers are increasingly looking to develop AI and machine learning tools to aid in various steps of the treatment planning and delivery processes. This review delves into the emergent role of AI in proton therapy, evaluating its development, advantages, intended clinical contexts, and areas of application. Through the analysis of 76 studies, we aim to underscore the importance of AI applications in advancing proton therapy and to highlight their prospective influence on clinical practices., Competing Interests: Declaration of Competing Interest BAJF received speaker fees from Roche, Bayer, Janssen, Ipsen, Accuray, Astellas, Elekta, IBA and Astra Zeneca (all outside the current project). EGCT is member of the scientific advisory board of IBA and received speaker fees from Elekta (all outside the current manuscript). M.G.V received a research fellowship from the Associazione Italiana per la Ricerca sul Cancro (AIRC) entitled “Radioablation ± hormonotherapy for prostate cancer oligorecurrences (RADIOSA trial): potential of imaging and biology” registered at Clinical Trials.gov NCT03940235, approved by the Ethics Committee of IEO and Centro Cardiologico Monzino (IEO-997). GC reports speakers fee and advisory board from Roche, Novartis, Lilly, Pfizer, Astra Zeneca, Daichii Sankyo, Ellipsis, Veracyte, Exact Science, Celcuity, Merck, BMS, Gilead, Sanofi, Menarini. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results. The remaining authors declare no conflicts of interest., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Purpose: To assess the ability of tumor apparent diffusion coefficient (ADC) values obtained from multiparametric magnetic resonance imaging (mpMRI) to predict the risk of 5-year biochemical recurrence (BCR) after radical prostatectomy (RP)., Materials and Methods: This retrospective analysis included 1207 peripheral and 232 non-peripheral zone prostate cancer (PCa) patients who underwent mpMRI before RP (2012-2015), with the outcome of interest being 5-year BCR. ADC was evaluated as a continuous variable and as categories: low (< 850 µm 2 /s), intermediate (850-1100 µm 2 /s), and high (> 1100 µm 2 /s). Kaplan-Meier curves with log-rank testing of BCR-free survival, multivariable Cox proportional hazard regression models were formed to estimate the risk of BCR., Results: Among the 1439 males with median age 63 (± 7) years, the median follow-up was 59 months, and 306 (25%) patients experienced BCR. Peripheral zone PCa patients with BCR had lower tumor ADC values than those without BCR (874 versus 1025 µm 2 /s, p < 0.001). Five-year BCR-free survival rates were 52.3%, 74.4%, and 87% for patients in the low, intermediate, and high ADC value categories, respectively (p < 0.0001). Lower ADC was associated with BCR, both as continuously coded variable (HR: 5.35; p < 0.001) and as ADC categories (intermediate versus high ADC-HR: 1.56, p = 0.017; low vs. high ADC-HR; 2.36, p < 0.001). In the non-peripheral zone PCa patients, no association between ADC and BCR was observed., Conclusion: Tumor ADC values and categories were found to be predictive of the 5-year BCR risk after RP in patients with peripheral zone PCa and may serve as a prognostic biomarker., (© 2024. Italian Society of Medical Radiology.)

Purpose: Radiomics is an emerging field that utilizes quantitative features extracted from medical images to predict clinically meaningful outcomes. Validating findings is crucial to assess radiomics applicability. We aimed to validate previously published magnetic resonance imaging (MRI) radiomics models to predict oncological outcomes in oral tongue squamous cell carcinoma (OTSCC)., Materials and Methods: Retrospective multicentric study on OTSCC surgically treated from 2010 to 2019. All patients performed preoperative MRI, including contrast-enhanced T1-weighted (CE-T1), diffusion-weighted sequences and apparent diffusion coefficient map. We evaluated overall survival (OS), locoregional recurrence-free survival (LRRFS), cause-specific mortality (CSM). We elaborated different models based on clinical and radiomic data. C-indexes assessed the prediction accuracy of the models., Results: We collected 112 consecutive independent patients from three Italian Institutions to validate the previously published MRI radiomic models based on 79 different patients. The C-indexes for the hybrid clinical-radiomic models in the validation cohort were lower than those in the training cohort but remained > 0.5 in most cases. CE-T1 sequence provided the best fit to the models: the C-indexes obtained were 0.61, 0.59, 0.64 (pretreatment model) and 0.65, 0.69, 0.70 (posttreatment model) for OS, LRRFS and CSM, respectively., Conclusion: Our clinical-radiomic models retain a potential to predict OS, LRRFS and CSM in heterogeneous cohorts across different centers. These findings encourage further research, aimed at overcoming current limitations, due to the variability of imaging acquisition, processing and tumor volume delineation., (© 2024. The Author(s).)

Objective: Poly (ADP-ribose) polymerase inhibitors (PARPi) have become a new standard of care for the maintenance treatment of advanced epithelial ovarian cancer. This study aims to evaluate the efficacy and safety of combining stereotactic body radiotherapy with PARPi continuation as a strategy to treat ovarian cancer oligoprogression on PARPi., Methods: This is a multicenter retrospective study including ovarian cancer patients treated with stereotactic body radiotherapy and PARPi continuation for oligoprogression under PARPi maintenance therapy between June 2012 and May 2023 in three Italian centers. PARPi treatment was continued until further disease progression or unacceptable toxicity. The primary endpoint was the next-line systemic therapy-free interval. The Kaplan-Meier method was used to assess local control, progression-free survival, and overall survival. Univariate and multivariate Cox regression analyses were performed to evaluate potential clinical outcomes predictors., Results: 46 patients were included, with a total of 89 lesions treated over 63 radiotherapy treatments. Lymph nodes were the most frequently treated lesions (80, 89.9%), followed by visceral lesions (8, 9%) and one case with a bone lesion (1.1%). Median follow-up was 25.9 months (range 2.8-122). The median next-line systemic therapy-free interval was 12.4 months (95% CI 8.3 to 19.5). A number of prior chemotherapy lines greater than five was significantly associated with a reduced next-line systemic therapy-free interval (HR 3.21, 95% CI 1.11 to 9.32, p=0.032). At the time of analysis, 32 (69.6%) patients started a new systemic therapy regimen, while 14 (30.4%) remained on the PARPi regimen. The 2-year progression-free survival, local failure-free survival, and overall survival rates were 10.7%, 78.1%, and 76.5%, respectively. Four patients (8.7%) experienced acute toxicity with G1 gastrointestinal events., Conclusion: Stereotactic body radiotherapy combined with PARPi continuation may be an effective and safe strategy for managing ovarian cancer patients with oligoprogression on PARPi maintenance therapy. Prospective research is warranted to shed more light on this approach., Competing Interests: Competing interests: BAJ-F has received speakers fees from Roche, Bayer, Janssen, Carl Zeiss, Ipsen, Accuray, Astellas, Elekta, and IBA Astra Zeneca, all outside the current project., (© IGCS and ESGO 2024. No commercial re-use. See rights and permissions. Published by BMJ.)

Breast conserving treatment typically involves surgical excision of tumor and adjuvant radiotherapy targeting the breast area or tumor bed. Accurately defining the tumor bed is challenging and lead to irradiation of greater volume of healthy tissues. Preoperative stereotactic body radiotherapy (SBRT) which target tumor may solves that issues. We conducted a systematic literature review to evaluates the early toxicity and cosmetic outcomes of this promising treatment approach. Secondary we reviewed pathological complete response (pCR) rates, late toxicity, patient selection criteria and radiotherapy protocols. We retrieved literature from PubMed, Scopus, Web of Science, Cochrane, ScienceDirect, and ClinicalTrials.gov. The study adhered to the PRISMA 2020 guidelines. Ten prospective clinical trials (7 phase II, 3 phase I), encompassing 188 patients (aged 18-75 years, cT1-T3 cN0-N3 cM0, primarily with ER/PgR-positive, HER2-negative status,), were analyzed. Median follow-up was 15 months (range 3-30). Treatment involved single-fraction SBRT (15-21Gy) in five studies and fractionated (19.5-31.5Gy in 3 fractions) in the rest. Time interval from SBRT to surgery was 9.5 weeks (range 1-28). Acute and late G2 toxicity occurred in 0-17% and 0-19% of patients, respectively, G3 toxicity was rarely observed. The cosmetic outcome was excellent in 85-100%, fair in 0-10% and poor in only 1 patient. pCR varied, showing higher rates (up to 42%) with longer intervals between SBRT and surgery and when combined with neoadjuvant systemic therapy (up to 90%). Preoperative SBRT significantly reduce overall treatment time, enabling to minimalize volumes. Early results indicate excellent cosmetic effects and low toxicity., (© 2024. The Author(s).)

Introduction: Radiation induced lymphopenia (RIL) deteriorate survival and diminishes the benefit of immune checkpoint inhibitors in combined treatment of lung cancer. Given the inconsistent data across various studies on the predictors of RIL, we aim to methodically elucidate these predictors and formulate a practical guide for clinicians., Methods: We conducted observational cohort study in four tertiary cancer centers. Patients with non-small cell lung cancer and small cell lung cancer, without lymphopenia grade >1, who underwent standalone radiotherapy (RT) in minimum 15 fractions were eligible. Dose-volume parameters of structures and clinical factors were comprehensively analyzed using various predictors selection methods and statistical models (Linear Regressors, Elastic Net, Bayesian Regressors, Huber Regression, regression based on k-nearest neighbors, Gaussian Process Regressor, Decision Tree Regressor, Random Forest Regressor, eXtreme Gradient Boosting, Automated Machine Learning) and were ranked to predict lymphocytes count nadir (alc&#95;nadir)., Results: Two hundred thirty eight patients (stage I-3.4%, II-17.6%, III-75.2%, IV-3.8%) who underwent RT to median dose of 60 Gy were analyzed. Median alc&#95;nadir was 0.68K/mm 3 . The 60 feature sets were evaluated in 600 models (RMSE 0.27-0.41K/mm ³ ). The most important features were baseline lymphocyte count (alc&#95;1), mean lung&#95;dose, lung v05, lung v10, heart v05 and effective dose to immune cells (edic). In patients with alc&#95;1 ≤ 2.005K/mm 3 , median alc&#95;nadir predictions were 0.54K/mm3 for lung&#95;v05p > 51.8% and 0.76K/mm 3 for lung&#95;v05p ≤ 51.8%. Lymphopenia was rare in patients with alc&#95;1 > 2.005K/mm 3 ., Discussion: RIL was most severe in patients with low early lymphocyte counts, primarily triggered by low RT doses in the heart and lungs., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Kuncman, Pajdziński, Smółka, Bilski, Socha, Stando, Peszyńska-Piorun, Korab, Jereczek-Fossa and Fijuth.)