Rosario De Feo

INTRODUCTIONCancer pain is a complex, multidimensional syndrome affecting approximately 55% of patients with cancer, depending on tumor stage and treatment, and is associated with poorer clinical outcomes and reduced quality of life. Pain assessment and management become particularly challenging when patients experience transient exacerbations of pain despite otherwise controlled baseline pain with opioid therapy, a condition known as breakthrough cancer pain (BTcP), which affects up to 70% of patients with chronic cancer pain. Artificial intelligence (AI) and advanced statistical approaches offer new opportunities to investigate the complex relationships among clinical variables contributing to BTcP. The Ruggi Study (ClinicalTrials.gov: NCT07038434) is a single-center, non-profit, observational-interventional study designed to characterize chronic pain through multimodal AI-based assessments in adult oncologic and non-oncologic patients.OBJECTIVESThis preliminary analysis aimed to investigate the causal relationship between opioid therapy and BTcP, evaluating the mediating role of chronic pain phenotype in a subcohort of 99 cancer patients assessed at their first follow-up visit.METHODSA Bayesian network was constructed including age, smoking status, body surface area (BSA), comorbidities, cardiovascular therapy, cancer severity (presence of metastases or bone metastases), ongoing anticancer treatment at enrollment, time from diagnosis, treatment duration, opioid therapy, chronic pain phenotype, BTcP, and quality of life assessed by the Brief Pain Inventory (BPI, item 9g). The network structure was learned using the Bayesian Information Criterion, allowing the identification of statistical dependencies among variables. Missing data were preliminarily assessed using Little’s test and subsequently imputed through a model-based approach.RESULTSOverall, 22% of the dataset contained missing values. Little’s test excluded a missing-not-at-random mechanism (p = 0.14), supporting the imputation strategy, which mainly affected variables related to cancer therapies. The median patient age was 65 years, with a median BSA of 1.4 m²; approximately 98% had a sex-adjusted normal BSA. Most patients had comorbidities, 66% had a time from diagnosis exceeding 12 months, 17% presented with bone metastases, and 39% were receiving opioid therapy. Neuropathic pain was the most frequent chronic pain phenotype (>60%), while eight patients experienced BTcP. Twenty-six patients reported impaired quality of life (BPI item 9g >4). The Bayesian network did not identify a direct causal relationship between opioid therapy and BTcP. Instead, chronic pain phenotype emerged as a mediator: patients with nociceptive pain receiving opioids showed a higher probability of developing BTcP than those with neuropathic or mixed pain (25% vs 19%).CONCLUSIONSThese preliminary findings suggest that the association between opioid therapy and BTcP is influenced by the underlying chronic pain phenotype rather than representing a direct causal relationship. Bayesian network analysis provides a valuable framework for disentangling complex clinical interactions and identifying patient subgroups at increased risk of BTcP, potentially supporting more personalized pain management strategies. Larger prospective studies are warranted to validate these findings and assess their clinical applicability.

Chronic subdural hematoma (CSDH) is traditionally described as a post-traumatic blood collection within the subdural space; however, both its anatomical localization and pathophysiology have been increasingly questioned. Ultrastructural and histopathological evidence demonstrates that no true subdural space exists under physiological conditions and that CSDH originates instead within the dural border cell (DBC) layer, a mechanically fragile and biologically active meningeal interface. Accordingly, chronic “subdural” hematoma may be more accurately interpreted as an intradural border cell lesion. Beyond anatomy, CSDH is a dynamic, self-sustaining disease driven by chronic inflammation, pathological angiogenesis, vascular immaturity, and localized hemostatic dysregulation. Hypoxia-induced HIF-1α/VEGF activation promotes fragile, hyperpermeable neovessels, while local hyperfibrinolysis and kallikrein–kinin activation prevent stable clot formation, driving recurrent microbleeding and plasma exudation. Consequently, hematoma persistence and recurrence represent a biological failure rather than a purely technical surgical shortcoming. This conceptual shift provides a coherent rationale for dural-targeted therapies, including middle meningeal artery embolization and pharmacological modulation of angiogenesis and fibrinolysis. Reframing CSDH as a chronic intradural and biologically active disorder has important implications for terminology, classification, and the development of mechanism-oriented, multidisciplinary management strategies.

Although emotions play a fundamental role in modulating pain perception, their objective assessment in clinical contexts remains challenging. Recent advances in artificial intelligence (AI) have opened new opportunities to measure emotional states through facial expression analysis, physiological signal modeling, natural language processing (NLP), and multimodal data integration. In affective computing, the field that focuses on technologies designed to recognize, interpret, process, and simulate human emotions, facial expression-based emotion recognition has progressed from traditional machine learning methods to advanced deep learning approaches, including convolutional neural networks (CNNs), attention-based hybrid models, and transformer architectures. Similarly, recurrent neural networks and self-supervised learning methods have been implemented for developing models from physiological signals such as electrocardiography, photoplethysmography, galvanic skin response, and related biosignals. Additionally, NLP systems can extract affective information from naturalistic text, using both lexicon-based and transformer-based models. Finally, multimodal fusion and alignment techniques allow the integration of heterogeneous data streams, providing richer and more ecologically valid emotion representations. Collectively, these strategies offer powerful tools for advancing automatic pain assessment (APA) in cancer care, with the potential to support personalized, emotion-aware therapeutic approaches. However, from an AI perspective, several open challenges remain, including multimodal representation learning under weak supervision, robustness to missing or degraded modalities, limited explainability of affective inference models, lack of standardized benchmarking protocols, and the presence of bias and domain shift in emotion datasets. Given the inherently subjective, context-dependent, and culturally mediated features of the emotional experience, further research is needed to address these technical limitations, integrating technological advances with the intrinsic complexity of emotion interpretation.

Background: Although telehealth strategies can be effectively adopted to manage cancer pain, identifying the optimal care pathway for tailoring interventions and allocating resources remains difficult. Artificial intelligence and machine learning (ML) may help clinicians develop more accurate strategies for predicting whether patients need remote consultations or in‐person evaluations. Methods: Data from two cohorts of cancer pain patients were analyzed. Variables included sociodemographic and clinical data, including age, sex, ECOG performance status, metastases, bone metastases, pain type, breakthrough cancer pain (BTCP), and rapid onset opioids (ROOs) therapy. The main outcome was the number of televisits (one versus multiple). For preprocessing, datasets from the two cohorts were harmonized by aligning variable definitions, coding schemes, and data formats. Six models were tested: logistic regression, random forest (RF), gradient boosting machine (GBM), support vector machine (SVM), k‐nearest neighbors (KNNs), and multilayer perceptron (MLP). Training and tuning used a 7‐repeated 5‐fold cross‐validation approach. Performance was evaluated on a hold‐out test set using F 1‐score, accuracy, and AUC‐ROC. A sensitivity analysis with two scenarios was performed to verify the effects of class weighting and excluding the cohort variable. Results: The final dataset included 270 patients. No statistically significant associations were identified between the available variables and the number of televisits. F 1‐scores across models ranged from 0.33 (RF) to 0.65 (MLP), accuracy from 0.45 (RF) to 0.55 (SVM), and AUC‐ROC from 0.43 (RF) to 0.65 (LR). DeLong tests showed no significant differences between algorithms ( p > 0.05). Although the MLP achieved the highest F 1‐score, it exhibited instability, with 91% of null F 1‐scores. Incorporating class weights slightly improved SVM ( F 1 = 0.58 and AUC = 0.62) and LR ( F 1 = 0.53 and AUC = 0.63) though not significantly. Conclusion: Although no model demonstrated strong predictive power, this ML‐based framework shows the potential of using structured telemedicine data to model clinical workload and optimize follow‐up strategies in cancer pain care. Trial Registration: ClinicalTrials.gov identifier: NCT04726228 and NCT07038434

Background: The integration of artificial intelligence (AI) in healthcare has the potential to revolutionize clinical practice, particularly in the management of complex conditions such as fibromyalgia (FM). Despite its promise, the adoption of this technology in practice faces several challenges, including limited knowledge and preparedness among healthcare professionals. Aim: To evaluate the level of knowledge before and after a workshop on AI in FM among clinicians of different disciplines. Methods: A survey was conducted at the end of the lab. An anonymous 21-item questionnaire was administered to participants. Results: This survey (n=26) revealed that while most had extensive clinical experience and some prior exposure to AI, the majority lacked sufficient knowledge and felt unprepared to integrate AI into FM management. Post-congress, perceptions of AI improved for many, but significant barriers remained, including lack of training, resistance to change, and cost concerns. Key benefits identified were symptom monitoring and decision support. Targeted training and technical support were highlighted as essential for effective AI adoption in clinical practice. Conclusion: Despite a generally positive shift in perception following the congress, many doctors still feel unprepared and lack the necessary knowledge to effectively utilize AI tools. These results underscore the importance of targeted training and support to implement research and facilitate the integration of AI tools in FM and other clinical settings.

Sleep is essential for overall health, yet various sleep disorders disrupt normal sleep patterns, affecting duration, quality, and timing. This pilot study investigate the impact of a food supplement (SPINOFF^®) on both sleep quality and mental well-being in 41 participants (mean age: 45.3 years). Initial assessments revealed sleep disturbances (Pittsburgh Sleep Quality Index—PSQ—mean score: 8.2) and insomnia symptoms (Insomnia Severity Index—ISI— mean score: 12.7). Mental health assessments showed psychological distress (Dass-21 Depression mean score: 4.2, Anxiety mean score: 6.9, Stress mean score: 11.6, Total mean score: 22.7). This study assessed sleep continuity using Awakenings per Night (ApN) via a smartwatch (HELO HEALTH^®) and conducted the study in two phases: baseline (T0) and after 30 days of treatment (T1) (Phase A). No placebo-control was used in this study. After 30 days (Phase B), 21 patients were selected for reassessment. Eleven continued treatment for another 30 days (T2), while ten discontinued. Following the intervention, we observed remarkable improvements in sleep quality and mental distress. The SPINOFF^® supplement significantly reduced the PSQI scores (22.4%), indicating enhanced sleep quality. Additionally, there was a 19.6% decrease in ISI scores, demonstrating a reduction in insomnia symptoms. Moreover, overall psychological distress decreased by 19.5% signifying improved psychological well-being. In the second phase, participants who continued treatment experienced more substantial improvements, with a mean decrease of 0.8 points in PSQI scores (±0.9) and a mean decrease of 0.9 points in ISI scores. Our findings suggest that the SPINOFF^® supplement has the potential to effectively address both sleep disturbances and psychological distress in our study population.