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PubblicazioneThe Challenge of Liver Metastases: Evolving Knowledge and Treatment( 2023)The liver is a common site of metastasis for many different tumours due to its anatomical, immunological and metabolic characteristics. For years, metastatic disease, even if oligometastatic, for many tumours was a definite limitation to the surgical option and curative intent. In recent years, the range of therapeutic options for both surgical and ablative and oncological treatment has expanded enormously. New surgical strategies to make what is initially not surgically treatable have emerged in the treatment of metastases, and new innovative medical therapies that rely not only on the local response but also on the systemic response of the patient are outlining exciting future scenarios leading to a complete evolution and revolution of the concept of metastases.
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PubblicazioneAnatomical Computerized Exploration to Excise Malignancies in Deep Facial Compartments: An Advanced Virtual Reality Protocol for a Tailored Surgical Approach( 2022)Objective/Hypothesis: This study describes the design and application of a novel advanced protocol for virtual three-dimensional anatomical reconstruction of the deep facial compartments, aiming to improve the preoperative understanding and the intraoperative assistance in complex resective surgeries performed for malignant diseases which extend in complex spaces, including the pterygomaxillopalatine fossa, the masticator space, and the infratemporal fossa. Methods: This study is a non-profit, retrospective, and single-institution case series. The authors clearly describe in detail imaging acquisition protocols which are suitable to segment each target, and a multilayer reconstruction technique is presented to simulate anatomical structures, with particular focus on vascular networks. Virtual surgical planning techniques are individually designed for each case to provide the most effective access to the deep facial compartments. Intraoperative guidance systems, including navigation and virtual endoscopy, are presented, and their role is analyzed. Results: The study included seven patients with malignant disease located in the deep facial compartments requiring radical resection, and all patients underwent successful application of the protocol. All lesions, except one, were subject to macroscopically radical resection. Vascular structures were identified with overall reconstruction rates superior to 90% for major caliber vessels. Prominent landmarks for virtual endoscopy were identified for each case. Conclusions: Virtual surgical planning and multilayer anatomical reconstruction are valuable methods to implement for surgeries in deep facial compartments, providing the surgeon with improved understanding of the preoperative condition and intraoperative guidance in critical phases for both open and endoscopic phases. Such techniques allow to tailor each surgical access, limiting morbidity to strictly necessary approaches to reach the disease target.
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PubblicazioneContextual Priors Shape Action Understanding before and beyond the Unfolding of Movement Kinematics( 2024)Previous studies have shown that contextual information may aid in guessing the intention underlying others’ actions in conditions of perceptual ambiguity. Here, we aimed to evaluate the temporal deployment of contextual influence on action prediction with increasing availability of kinematic information during the observation of ongoing actions. We used action videos depicting an actor grasping an object placed on a container to perform individual or interpersonal actions featuring different kinematic profiles. Crucially, the container could be of different colors. First, in a familiarization phase, the probability of co-occurrence between each action kinematics and color cues was implicitly manipulated to 80% and 20%, thus generating contextual priors. Then, in a testing phase, participants were asked to predict action outcome when the same action videos were occluded at five different timeframes of the entire movement, ranging from when the actor was still to when the grasp of the object was fully accomplished. In this phase, all possible action–contextual cues’ associations were equally presented. The results showed that for all occlusion intervals, action prediction was more facilitated when action kinematics deployed in high- than low-probability contextual scenarios. Importantly, contextual priors shaped action prediction even in the latest occlusion intervals, where the kinematic cues clearly unveiled an action outcome that was previously associated with low-probability scenarios. These residual contextual effects were stronger in individuals with higher subclinical autistic traits. Our findings highlight the relative contribution of kinematic and contextual information to action understanding and provide evidence in favor of their continuous integration during action observation.
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PubblicazioneA Bayesian defect-based physics-guided neural network model for probabilistic fatigue endurance limit evaluation( 2024)Accurate fatigue assessment of material plagued by defects is of utmost importance to guarantee safety and service continuity in engineering components. This study shows how state-of-the-art semi-empirical models can be endowed with additional defect descriptors to probabilistically predict the occurrence of fatigue failures by exploiting advanced Bayesian Physics-guided Neural Network (B-PGNN) approaches. A B-PGNN is thereby developed to predict the fatigue failure probability of a sample containing defects, referred to a given fatigue endurance limit. In this framework, a robustly calibrated El Haddad's curve is exploited as the prior physics reinforcement of the probabilistic model, i.e., prior knowledge. Following, a likelihood function is built and the B-PGNN is trained via Bayesian Inference, thus calculating the posterior of the parameters. The arbitrariness of the choice of the related architecture is circumvented through a Bayesian model selection strategy. A case-study is analysed to prove the robustness of the proposed approach. This methodology proposes an advanced practical approach to help support the probabilistic design against fatigue failure.