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Current approach to identify BRCA 1/2 carriers in the general population is ineffective as most of the carriers remain undiagnosed. Radiomics is an emerging tool for large scale quantitative analysis of features from standard diagnostic imaging and has been applied also to identify gene mutational status. The objective of this study was to evaluate the clinical and economic impact of integrating a radiogenomics model with clinical and family history data in identifying BRCA mutation carriers in the general population. This cost‐effective analysis compares three different approaches to women selection for BRCA testing: established clinical criteria/family history (model 1); established clinical criteria/family history and the currently available radiogenomic model (49% sensitivity and 87% specificity) based on ultrasound images (model 2); same approach used in model 2 but simulating an improvement of the performances of the radiogenomic model (80% sensitivity and 95% specificity) (model 3). All models were trained with literature data. Direct costs were calculated according to the rates currently used in Italy. The analysis was performed simulating different scenarios on the generation of 18‐year‐old girls in Italy (274,000 people). The main outcome was to identify the most effective model comparing the number of years of BRCA‐cancer healthy life expectancy (HLYs). An incremental cost‐effectiveness ratio (ICER) was also derived to determine the cost in order to increase BRCA carriers‐healthy life span by 1 year. Compared to model 1, model 2 increases the detection rate of BRCA carriers by 41.8%, reduces the rate of BRCA‐related cancers by 23.7%, generating over a 62‐year observation period a cost increase by 2.51 €/Year/Person. Moreover, model 3 further increases BRCA carriers detection (+ 68.3%) and decrease in BRCA‐related cancers (− 38.4%) is observed compared to model 1. Model 3 increases costs by 0.7 €/Year/Person. After one generation, the estimated ICER in the

Introduction: New digital technologies can become a tool for welcoming the patient through the artistic dimension. Cancer patients, in particular, need support that accompanies and supports them throughout their treatment. Materials and methods: The Art4ART project consist in the structural proposal to cancer patients of a web-based digital platform containing several forms of art as video-entertainments; a multimedia immersive room; an art-based welcoming of the patients with several original paintings; an environment with a peacefulness vertical garden; a reconceptualization of the chemotherapy-infusion seats. Data regarding patients’ preference and choices will be stored and analysed also using artificial intelligence (AI) algorithm to measure and predict impact indicators regarding clinical outcomes (survival and toxicity), psychological indicators. Moreover, the same digital platform will contribute to a better organization of the activities. Discussion: Through the systematic acquisition of patient preferences and through integration with other clinical parameters, it will be possible to measure the clinical, psychological, organisational, and social impact of the newly implemented Art4ART project. The use of digital technology leads us to apply the reversal of viewpoint from therapeutic acts to patient-centred care.

Personalized medicine (PM) bridges several disciplines for understanding and addressing prevalent, complex, or rare situations in human health (e.g., complex phenotyping, risk stratification, etc.); therefore, digital and technological solutions have been integrated in the field to boost innovation and new knowledge generation. The open innovation (OI) paradigm proposes a method by which to respectfully manage disruptive change in biomedical organizations, as experienced by many organizations during digital transformation and the COVID-19 pandemic. In this article, we focus on how this paradigm has catalyzed the transition from PM to personalized digital medicine in a large-volume research hospital. Methods, challenges, and results are discussed. This case study is an endeavor to confirm that OI strategies could help manage urgent needs from the healthcare environment, while achieving sustainability-oriented, accountable innovation.

Purpose: The aim of this study is to determine if radiomics features extracted from staging magnetic resonance (MR) images could predict 2-year long-term clinical outcome in patients with locally advanced cervical cancer (LACC) after neoadjuvant chemoradiotherapy (NACRT). Materials and methods: We retrospectively enrolled patients with LACC diagnosis who underwent NACRT followed by radical surgery in two different institutions. Radiomics features were extracted from pre-treatment 1.5 T T2w MR images. The predictive performance of each feature was quantified in terms of Wilcoxon–Mann–Whitney test. Among the significant features, Pearson correlation coefficient (PCC) was calculated to quantify the correlation among the different predictors. A logistic regression model was calculated considering the two most significant features at the univariate analysis showing the lowest PCC value. The predictive performance of the model created was quantified out using the area under the receiver operating characteristic curve (AUC). Results: A total of 175 patients were retrospectively enrolled (142 for the training cohort and 33 for the validation one). 1896 radiomic feature were extracted, 91 of which showed significance (p < 0.05) at the univariate analysis. The radiomic model showing the highest predictive value combined the features calculated starting from the gray level co-occurrence-based features. This model achieved an AUC of 0.73 in the training set and 0.91 in the validation set. Conclusions: The proposed radiomic model showed promising performances in predicting 2-year overall survival before NACRT. Nevertheless, the observed results should be tested in larger studies with consistent external validation cohorts, to confirm their potential clinical use.

Introduction: Stereotactic ablative radiotherapy (SABR) has been shown to increase survival in oligometastatic disease, but local control of colorectal metastases remains poor. We aimed to identify potential predictive factors of SBRT response through a multicenter large retrospective database and to investigate the progression to the polymetastatic disease (PMD). Material and methods: The study involved 23 centers, and was approved by the Ethical Committee (Prot. Negrar 2019-ZT). 1033 lung metastases were reported. Clinical and biological parameters were evaluated as predictive for freedom from local progression-free survival (FLP). Secondary end-point was the time to the polymetastatic conversion (tPMC). Results: Two-year FLP was 75.4%. Two-year FLP for lesions treated with a BED < 00 Gy, 100–124 Gy, and ≥125 Gy was 76.1%, 70.6%, and 94% (p = 0.000). Two-year FLP for lesion measuring ≤10 mm, 10–20 mm, and >20 mm was 79.7%, 77.1%, and 66.6% (p = 0.027). At the multivariate analysis a BED ≥125 Gy significantly reduced the risk of local progression (HR 0.24, 95%CI 0.11–0.51; p = 0.000). Median tPMC was 26.8 months. Lesions treated with BED ≥125 Gy reported a significantly longer tPMC as compared to lower BED. The median tPMC for patients treated to 1, 2–3 or 4–5 simultaneous oligometastases was 28.5, 25.4, and 9.8 months (p = 0.035). Conclusion: The present is the largest series of lung colorectal metastases treated with SABR. The results support the use of SBRT in lung oligometastatic colorectal cancer patients as it might delay the transition to PMD or offer relatively long disease-free period in selected cases. Predictive factors were identified for treatment personalization.