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Background and objectivesInvestigations of the prognosis are vital for better patient management and decision-making in patients with advanced metastatic renal cell carcinoma (mRCC). The purpose of this study is to evaluate the capacity of emerging Artificial Intelligence (AI) technologies to predict three- and five-year overall survival (OS) for mRCC patients starting their first-line of systemic treatment.Patients and methodsThe retrospective study included 322 Italian patients with mRCC who underwent systemic treatment between 2004 and 2019. Statistical analysis included the univariate and multivariate Cox proportional-hazard model and the Kaplan-Meier analysis for the prognostic factors’ investigation. The patients were split into a training cohort to establish the predictive models and a hold-out cohort to validate the results. The models were evaluated by the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. We assessed the clinical benefit of the models using decision curve analysis (DCA). Then, the proposed AI models were compared with well-known pre-existing prognostic systemsResultsThe median age of patients in the study was 56.7 years at RCC diagnosis and 78% of participants were male. The median survival time from the start of systemic treatment was 29.2 months; 95% of the patients died during the follow-up that finished by the end of 2019. The proposed predictive model, which was constructed as an ensemble of three individual predictive models, outperformed all well-known prognostic models to which it was compared. It also demonstrated better usability in supporting clinical decisions for 3- and 5-year OS. The model achieved (0.786 and 0.771) AUC and (0.675 and 0.558) specificity at sensitivity 0.90 for 3 and 5 years, respectively. We also applied explainability methods to identify the important clinical features that were found to be partially matched with the prognostic factors identified in the Kaplan-Meier and Cox analyses.ConclusionsOur AI models provide best predictive accuracy and clinical net benefits over well-known prognostic models. As a result, they can potentially be used in clinical practice for providing better management for mRCC patients starting their first-line of systemic treatment. Larger studies would be needed to validate the developed model

The host’s immune system plays a crucial role in determining the clinical outcome of many cancers, including breast cancer. Peripheral blood neutrophils and lymphocytes counts may be surrogate markers of systemic inflammation and potentially reflect survival outcomes. The aim of the present study is to assess the role of preoperative systemic inflammatory biomarkers to predict local or distant relapse in breast cancer. In particular we investigated ER+ HER2- early breast cancer, considering its challenging risk stratification. A total of 1,763 breast cancer patients treated at tertiary referral Breast Unit were reviewed. Neutrophil-to-lymphocyte (NLR), platelet-to-lymphocyte (PLR) and lymphocyte-to-monocyte (LMR) ratios were assessed from the preoperative blood counts. Multivariate analyses for 5-years locoregional recurrence-free (LRRFS), distant metastases-free (DMFS) and disease-free survivals (DFS) were performed, taking into account both blood inflammatory biomarkers and clinical-pathological variables. Low NLR and high LMR were independent predictors of longer LRRFS, DMFS and DFS, and low PLR was predictive of better LRRFS and DMFS in the study population. In 999 ER+ HER2- early breast cancers, high PLR was predictive of worse LRRFS (HR 0.42, p=0.009), while high LMR was predictive of improved LRRFS (HR 2.20, p=0.02) and DFS (HR 2.10, p=0.01). NLR was not an independent factor of 5-years survival in this patients’ subset. Inflammatory blood biomarkers and current clinical assessment of the disease were not in agreement in terms of estimate of relapse risk (K-Cohen from -0.03 to 0.02). In conclusion, preoperative lymphocyte ratios, in particular PLR and LMR, showed prognostic relevance in ER+ HER2- early breast cancer. Therefore, they may be used in risk stratification and therapy escalation/de-escalation in patients with this type of tumor.

Big data technologies are nowadays providing health care with powerful instruments to gather and analyze large volumes of heterogeneous data collected for different purposes, including clinical care, administration, and research. This makes possible to design IT infrastructures that favor the implementation of the so-called “Learning Healthcare System Cycle,” where healthcare practice and research are part of a unique and synergic process. In this paper we highlight how “Big Data enabled” integrated data collections may support clinical decision-making together with biomedical research. Two effective implementations are reported, concerning decision support in Diabetes and in Inherited Arrhythmogenic Diseases.