Background Artificial Intelligence (AI) is increasingly integrated into oncology, offering opportunities to improve diagnostics, treatment planning, and operational efficiency. However, patient perspectives on AI, especially regarding data protection and ethical implications, remain underexplored. Objective The objective of this study is to investigate cancer patients' attitudes toward the use of Artificial Intelligence (AI) in healthcare, focusing on their awareness of data protection, perceived risks and benefits, and the conditions under which AI is considered acceptable. Additionally, the study aims to examine how demographic and educational factors influence patients' views within the context of an Italian comprehensive cancer center. Methods A cross-sectional survey was conducted with 117 cancer patients who completed a 28-item online questionnaire. The survey evaluated levels of AI knowledge, perceptions of data privacy, concerns about AI in medical contexts, and willingness to share health data for research. Results Most participants demonstrated moderate awareness of AI (70.1%) and its medical applications (85.5%), with higher familiarity observed among younger and more educated individuals. While data protection understanding varied, 76.9% were willing to share personal health data for research aimed at improving cancer care. Concerns included reduced physician autonomy (52.1%) and diminished physician-patient interaction (63.3%). However, 82.9% of respondents found AI acceptable when clinical decisions remained under physician control. AI was most favorably viewed for administrative support and care process optimization. Conclusion Cancer patients generally view AI in healthcare positively, especially when it maintains physician oversight and safeguards data privacy. To ensure equitable and informed adoption, targeted educational initiatives and transparent communication strategies should address generational, educational, and digital literacy differences.

The exponential growth of healthcare data, driven by advancements in medical research and digital health technologies, has underscored the critical need for interoperability and standardization. However, the heterogeneous nature of real-world clinical data poses significant challenges to ensuring seamless data exchange and secondary use for research purposes. These challenges include syntactic inconsistencies (e.g., variable use of terminologies like ICD-10 vs SNOMED CT), semantic mismatches (e.g., differing conceptualizations of disease staging across institutions), and structural fragmentation (e.g., laboratory results encoded in free text rather than structured fields). Fast Healthcare Interoperability Resources (FHIR) has emerged as a leading standard for structuring and harmonizing healthcare data, enabling integration across diverse systems. This work presents a FHIR-based transformation pipeline that leverages Resource Description Framework (RDF) to convert raw, conceptually heterogeneous oncology data into research-ready, semantically enriched datasets. By representing FHIR resources as RDF graphs, our approach enables semantic interoperability, enhances data linkage across heterogeneous sources, and supports automated reasoning through ontology-based queries and inference mechanisms. The pipeline employs a templated conversion strategy, allowing for the declarative definition of mappings that enable domain experts to focus on the data model. In Cancer Virtual Lab, we applied this methodology to a real-world oncology dataset comprising 36,335 anonymized patient records, successfully converting 1,093,705 clinical records into 1,151,559 distinct RDF-based FHIR resource types. The process incorporated syntactic and semantic validation, along with expert review, to ensure technical correctness and clinical relevance. Our results demonstrate the feasibility of semantically integrating oncology data using FHIR and RDF, fostering machine-readable, interoperable knowledge representation. This enriched representation supports data quality monitoring and improvement, data harmonization, longitudinal analysis, advanced analytics, and AI-driven decision support, promoting large-scale secondary use.

Objectives: The aim of this study was to investigate the treatment patterns and outcomes in two propensity score-matched cohorts of patients with neuroendocrine tumours (NETs) treated with first-line somatostatin analogue (SSA). Methods: Metastatic NET patients treated with first-line SSA (2009–2022) were retrospectively examined. First-line lanreotide vs. octreotide cohorts were matched 1:1 by propensity scores for demographics, tumour characteristics, and diagnosis year. Progression-free survival (PFS) and overall survival (OS) were analysed using Kaplan–Meier analysis and the Cox proportional hazards model. Results: Among 441 patients, 310 were matched (155 in both the octreotide and lanreotide groups). First-line SSA was monotherapy (63.5%) or combination with other medications (36.5%). A total of 77% of second-line patients (188/244) maintained their initial SSA medication in combination with other therapies. Radioligand therapy with lanreotide (N = 72; 29.5%) or octreotide (N = 70; 28.7%) was the most common second-line treatment. First-line lanreotide and octreotide cohorts had similar median PFS (15.5; 95% CI: 13.6–19.1 vs. 14.0; 95% CI: 12.0–15.8 months), despite octreotide having a 36% higher likelihood of moving to the second line than lanreotide (95% CI: 1.05–1.76, p = 0.018). Multiple metastases (HR = 1.45; p = 0.004, 95% CI: 1.13–1.87) and Ki-67 > 20% (HR = 2.34; p < 0.001, 95% CI: 1.43–3.83) were significantly associated with the worst PFS. First-line lanreotide patients had a median OS of 10.4 years (95% CI: 7.5-NA) and octreotide 9.2 years (95% CI: 7.3-NA) (p = 0.537). Bone metastases increased death risk by 91% (p = 0.014; 95% CI: 1.14–3.20). Conclusions: SSA monotherapy is the main first-line treatment and most subsequent treatments include SSA with additional medications. Cohorts had similar PFS/OS, but octreotide demonstrated a 36% significantly higher likelihood of moving to the second-line treatment.

Background/Objectives: Management of cancer treatment-induced bone loss (CTIBL) is essential for preserving quality of life among breast cancer (BC) patients receiving endocrine therapy. However, bone-modifying agents (BMAs) remain underused and delayed. In 2014, IRST launched the first bone health outpatient service in Romagna (the eastern area of the Emilia-Romagna region). A multi-centre, retrospective observational study with propensity score matching (PSM) was conducted to evaluate the impact of the IRST organisational model on bone health. Methods: The PSM matched the Emilia-Romagna patients who underwent BC surgery between 2014 and 2022 and were in follow-up in the Romagna area. Patients were grouped as follows: (1) IRST and (2) other Romagna hospitals (without bone health service, i.e., the control group). The matching was based on age, in situ/invasive cancer, and type of early-stage treatment (hormone treatment vs. chemotherapy). Logistic regression and Cox proportional-hazard models assessed factors associated with bone care treatment initiation and timings, respectively. Results: After PSM, we matched 3112 of the 8021 eligible patients into the two cohorts. IRST patients were 39% more likely to receive BMAs (OR: 1.393; 95% CI: 1.236–1.571) and initiated treatment approximately 12 months earlier. We observed that patients with invasive tumours were 77% more likely to initiate bone therapy than those with in situ tumours (OR: 1.766; 95% CI: 1.237–2.585). The early initiation of bone health therapy was influenced by age (p < 0.001) and neoadjuvant chemotherapy treatment (p < 0.001). Conclusions: The IRST model demonstrates responsiveness to bone health needs in BC patients and may be implemented elsewhere to support integrated CTIBL care.