Objective To evaluate whether the initial chest X-ray (CXR) severity assessed by an AI system may have prognostic utility in patients with COVID-19. Methods This retrospective single-center study included adult patients presenting to the emergency department (ED) between February 25 and April 9, 2020, with SARS-CoV-2 infection confirmed on real-time reverse transcriptase polymerase chain reaction (RT-PCR). Initial CXRs obtained on ED presentation were evaluated by a deep learning artificial intelligence (AI) system and compared with the Radiographic Assessment of Lung Edema (RALE) score, calculated by two experienced radiologists. Death and critical COVID-19 (admission to intensive care unit (ICU) or deaths occurring before ICU admission) were identified as clinical outcomes. Independent predictors of adverse outcomes were evaluated by multivariate analyses. Results Six hundred ninety-seven 697 patients were included in the study: 465 males (66.7%), median age of 62 years (IQR 52–75). Multivariate analyses adjusting for demographics and comorbidities showed that an AI system-based score ≥ 30 on the initial CXR was an independent predictor both for mortality (HR 2.60 (95% CI 1.69 − 3.99; p < 0.001)) and critical COVID-19 (HR 3.40 (95% CI 2.35–4.94; p < 0.001)). Other independent predictors were RALE score, older age, male sex, coronary artery disease, COPD, and neurodegenerative disease. Conclusion AI- and radiologist-assessed disease severity scores on CXRs obtained on ED presentation were independent and comparable predictors of adverse outcomes in patients with COVID-19. Trial registration ClinicalTrials.gov NCT04318366 (https://clinicaltrials.gov/ct2/show/NCT04318366). Key Points • AI system–based score ≥ 30 and a RALE score ≥ 12 at CXRs performed at ED presentation are independent and comparable predictors of death and/or ICU admission in COVID-19 patients. • Other independent predictors are older age, male sex, coronary artery disease, COPD, and neurodegenerative disease. • The comparable performance of the AI system in relation to a radiologist-assessed score in predicting adverse outcomes may represent a game-changer in resource-constrained settings.

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• No residual tumour (RT) at surgery is the most important prognostic factor in OC. • Radiomic features related to mass size, randomness and homogeneity were associated with RT. • Progression of disease within 12 months (PD12) indicates worse prognosis in OC. • A model including clinical and radiomic features performed better than only-clinical model to predict PD12.

Limited attention has been given to the psychological impact of primary treatments in patients with prostate cancer. Aim of our analysis was to critically analyse the current evidence on the psychological impact of different primary treatments (surgery, radiotherapy and active surveillance), in patients with prostate cancer, using validated questionnaires. We searched in the MEDLINE and Cochrane library database from the literature of the past 15 years (primary fields: prostate neoplasm, AND radical prostatectomy or radiotherapy or active surveillance AND psychological distress or anxiety or depression; secondary fields: urinary, sexual, bowel modifications, non‐randomised and randomised trials). Overall eighteen original and review articles were included and critically evaluated. Either radical prostatectomy or active surveillance and radiotherapy are well‐tolerated in terms of definite anxiety and depression during the post‐treatment follow‐up. A mutual influence between functional and psychological modifications induced by treatments has been demonstrated. Urinary symptoms related to incontinence more than sexual and bowel dysfunction are able to induce psychological distress worsening. In conclusion, patients and their clinicians might wish to know how functional and psychological aspects may differently be influenced by treatment choice.