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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.)

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 or Objective-The aim of the study is to evaluate the efficacy and safety of SBRT on detectable prostate bed recurrence in RT-naïve prostate cancer patients., Materials and Methods: Eighty-six patients who underwent SBRT for macroscopic bed recurrence after prostatectomy were retrospectively included. Patients were treated based on mpMRI or choline/PSMA PET., Results: The median time to biochemical relapse (BCR) after RP was 46 months, with a median PSA at restaging of 1.04 ng/mL. Forty-six patients were staged with mpMRI and choline/PSMA PET, while ten and thirty were treated based on PET and MRI only, respectively. Only one late G ≥ 2 GI toxicity was observed. With a median BCR follow-up of 14 months, twenty-nine patients experienced a BCR with a median PSA at recurrence of 1.66 ng/mL and a median survival free from the event of 40.1 months. The median time to BCR was 17.9 months. Twenty-seven patients had clinical relapse (CR), with a median CR follow-up of 16.27 months and a median time to CR of 23.0 months. Biochemical recurrence-free survival at one and two years was 88% and 66%, respectively, while clinical recurrence-free survival at one and two years was 92% and 82%, respectively. Regarding local relapses, seven were in the field of treatment, while eight of them were outside the field of treatment., Conclusions: Data showed that SBRT targeting only the macroscopic bed recurrence instead of the whole prostate bed is safe and effective. Additional data and longer follow-ups will provide a clearer indication of the appropriate treatment and staging methodology for these patients.

Background: 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., Methods: 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)., Results: 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., Conclusion: 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., 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.)