Objective: The objectives of our study were to assess the association of radiomic and genomic data with histology and patient outcome in non-small cell lung cancer (NSCLC). Methods: In this retrospective single-centre observational study, we selected 151 surgically treated patients with adenocarcinoma or squamous cell carcinoma who performed baseline [18F] FDG PET/CT. A subgroup of patients with cancer tissue samples at the Institutional Biobank (n = 74/151) was included in the genomic analysis. Features were extracted from both PET and CT images using an in-house tool. The genomic analysis included detection of genetic variants, fusion transcripts, and gene expression. Generalised linear model (GLM) and machine learning (ML) algorithms were used to predict histology and tumour recurrence. Results: Standardised uptake value (SUV) and kurtosis (among the PET and CT radiomic features, respectively), and the expression of TP63, EPHA10, FBN2, and IL1RAP were associated with the histotype. No correlation was found between radiomic features/genomic data and relapse using GLM. The ML approach identified several radiomic/genomic rules to predict the histotype successfully. The ML approach showed a modest ability of PET radiomic features to predict relapse, while it identified a robust gene expression signature able to predict patient relapse correctly. The best-performing ML radiogenomic rule predicting the outcome resulted in an area under the curve (AUC) of 0.87. Conclusions: Radiogenomic data may provide clinically relevant information in NSCLC patients regarding the histotype, aggressiveness, and progression. Gene expression analysis showed potential new biomarkers and targets valuable for patient management and treatment. The application of ML allows to increase the efficacy of radiogenomic analysis and provides novel insights into cancer biology.

Objective. Transcutaneous electrical nerve stimulation (TENS) has been recently introduced in neurorehabilitation and neuroprosthetics as a promising, non-invasive sensory feedback restoration alternative to implantable neurostimulation. Yet, the adopted stimulation paradigms are typically based on single-parameter modulations (e.g. pulse amplitude (PA), pulse-width (PW) or pulse frequency (PF)). They elicit artificial sensations characterized by a low intensity resolution (e.g. few perceived levels), low naturalness and intuitiveness, hindering the acceptance of this technology. To address these issues, we designed novel multiparametric stimulation paradigms, featuring the simultaneous modulation of multiple parameters, and implemented them in real-time tests of performance when exploited as artificial sensory inputs. Approach. We initially investigated the contribution of PW and PF variations to the perceived sensation magnitude through discrimination tests. Then, we designed three multiparametric stimulation paradigms comparing them with a standard PW linear modulation in terms of evoked sensation naturalness and intensity. The most performant paradigms were then implemented in real-time in a Virtual Reality—TENS platform to assess their ability to provide intuitive somatosensory feedback in a functional task. Main results. Our study highlighted a strong negative correlation between perceived naturalness and intensity: less intense sensations are usually deemed as more similar to natural touch. In addition, we observed that PF and PW changes have a different weight on the perceived sensation intensity. As a result, we adapted the activation charge rate (ACR) equation, proposed for implantable neurostimulation to predict the perceived intensity while co-modulating the PF and charge per pulse, to TENS (ACR_T). ACR_T allowed to design different multiparametric TENS paradigms with the same absolute perceived intensity. Although not reported as more natural, the multiparametric paradigm, based on sinusoidal PF modulation, resulted being more intuitive and subconsciously integrated than the standard linear one. This allowed subjects to achieve a faster and more accurate functional performance. Significance. Our findings suggest that TENS-based, multiparametric neurostimulation, despite not consciously perceived naturally, can provide integrated and more intuitive somatosensory information, as functionally proved. This could be exploited to design novel encoding strategies able to improve the performance of non-invasive sensory feedback technologies.

Bioelectronic therapies modulating the vagus nerve are promising for cardiovascular, inflammatory, and mental disorders. Clinical applications are however limited by side-effects such as breathing obstruction and headache caused by non-specific stimulation. To design selective and functional stimulation, we engineered VaStim, a realistic and efficient in-silico model. We developed a protocol to personalize VaStim in-vivo using simple muscle responses, successfully reproducing experimental observations, by combining models with trials conducted on five pigs. Through optimized algorithms, VaStim simulated the complete fiber population in minutes, including often omitted unmyelinated fibers which constitute 80% of the nerve. The model suggested that all Aα-fibers across the nerve affect laryngeal muscle, while heart rate changes were caused by B-efferents in specific fascicles. It predicted that tripolar paradigms could reduce laryngeal activity by 70% compared to typically used protocols. VaStim may serve as a model for developing neuromodulation therapies by maximizing efficacy and specificity, reducing animal experimentation.