Electrical impedance tomography (EIT) is a medical imaging technique based on the injection of a current or voltage pattern through electrodes on the skin of the patient, and on the reconstruction of the internal conductivity distribution from the voltages collected by the electrodes. Compared to other imaging techniques, EIT shows significant advantages: it does not use ionizing radiation, is non-invasive and is characterized by high temporal resolution. Moreover, its low cost and high portability make it suitable for real-time, bedside monitoring. However, EIT is also characterized by some technical limitations that cause poor spatial resolution. The possibility to design wearable devices based on EIT has recently given a boost to this technology. In this paper we reviewed EIT physical principles, hardware design and major clinical applications, from the classical to a wearable setup. A wireless and wearable EIT system seems a promising frontier of this technology, as it can both facilitate making clinical measurements and open novel scenarios to EIT systems, such as home monitoring.

Carbon Ion Radiotherapy (CIRT) is one of the most promising therapeutic options to reduce Local Recurrence (LR) in Sacral Chordomas (SC). The aim of this work is to compare the performances of survival models fed with dosiomics features and conventional DVH metrics extracted from relative biological effectiveness (RBE)-weighted dose (D_RBE) and dose-averaged Linear Energy Transfer (LET_d) maps, towards the identification of possible prognostic factors for LR in SC patients treated with CIRT. This retrospective study included 50 patients affected by SC with a focus on patients that presented a relapse in a high-dose region. Survival models were built to predict both LR and High-Dose Local Recurrencies (HD-LR). The models were evaluated through Harrell Concordance Index (C-index) and patients were stratified into high/low-risk groups. Local Recurrence-free Kaplan–Meier curves were estimated and evaluated through log-rank tests. The model with highest performance (median(interquartile-range) C-index of 0.86 (0.22)) was built on features extracted from LET_d maps, with D_RBE models showing promising but weaker results (C-index of 0.83 (0.21), 0.80 (0.21)). Although the study should be extended to a wider patient population, LET_d maps show potential as a prognostic factor for SC HD-LR in CIRT, and dosiomics appears to be the most promising approach against more conventional methods (e.g., DVH-based).

To investigate the role of dosiomics features extracted from physical dose (D_PHYS), RBE-weighted dose (D_RBE) and dose-averaged Linear Energy Transfer (LET_d), to predict the risk of local recurrence (LR) in skull base chordoma (SBC) treated with Carbon Ion Radiotherapy (CIRT). Thus, define and evaluate dosiomics-driven tumor control probability (TCP) models. 54 SBC patients were retrospectively selected for this study. A regularized Cox proportional hazard model (r-Cox) and Survival Support Vector Machine (s-SVM) were tuned within a repeated Cross Validation (CV) and patients were stratified in low/high risk of LR. Models' performance was evaluated through Harrell's concordance statistic (C-index), and survival was represented through Kaplan-Meier (KM) curves. A multivariable logistic regression was fit to the selected feature sets to generate a dosiomics-driven TCP model for each map. These were compared to a reference model built with clinical parameters in terms of f-score and accuracy. The LETd maps reached a test C-index of 0.750 and 0.786 with r-Cox and s-SVM, and significantly separated KM curves. D_PHYS maps and clinical parameters showed promising CV outcomes with C-index above 0.8, despite a poorer performance on the test set and patients stratification. The LETd-based TCP showed a significatively higher f-score (0.67[0.52-0.70], median[IQR]) compared to the clinical model (0.4[0.32-0.63], p < 0.025), while D_PHYS achieved a significatively higher accuracy (D_PHYS: 0.73[0.65-0.79], Clinical: 0.6 [0.52-0.72]). This analysis supports the role of LETd as relevant source of prognostic factors for LR in SBC treated with CIRT. This is reflected in the TCP modeling, where LETd and D_PHYS showed an improved performance with respect to clinical models.

To integrate patient-specific cell count data from diffusion-weighted MRI (DWI) into the linear-quadratic (LQ) Poisson tumor control probability (TCP) model for sacral chordomas (SC) treated with carbon ion radiotherapy (CIRT), aiming to improve local control (LC) and local relapse (LR) prediction. We considered data from 37 of the first 50 SC patients consecutively treated at the National Centre for Oncological Hadrontherapy (CNAO, Pavia, Italy). LQ Poisson formalism was revised to integrate either a linear (TCP_LIN) or logarithmic (TCP_LOG) dependence on clonogenic cell count, derived from baseline DWI through an optimal match with in-silico simulations. The models were compared with the case of a uniform cell density of 10⁷ cells/cm³, as widely adopted in the literature. All models were fitted on 27 patients and tested on 10 held-out cases to assess the performance, both in terms of area under the receiver-operator curve (AUC) and considering the statistical differences in TCP between LR and LC. In contrast to the constant cell density model, DWI-based models significantly separated the TCP of LC and LR patients, with TCP_LOG describing an average TCP of 71.3 % ± 9.56 % for LC patients, compared to 48.9 % ± 9.49 % for LR test cases. AUC values of 0.92 and 0.96 were respectively achieved by TCP_LIN and TCP_LOG, compared to 0.88 for constant cell density, on the test set. DWI-based cell count data can significantly improve the performance of TCP models in predicting the probability of LC of SC treated with CIRT.

Background: Sacral chordoma (SC) is a rare and locally aggressive tumor, characterized by a high risk of local recurrence (LR). Advanced imaging biomarkers capturing tumor heterogeneity and treatment response may help refine patient stratification and improve outcomes. Purpose: This study aims to explore the potential of microstructural parameters and habitat analysis derived from diffusion‐weighted MRI (DW‐MRI) to characterize intratumoral heterogeneity, assess treatment‐induced changes, following carbon ion radiotherapy (CIRT), and investigate their association with recurrence patterns in SC patients. Methods: A retrospective analysis was performed on 40 SC patients treated with CIRT. Microstructural parameters—cell radius ( R ), eccentricity (ecc), diffusion coefficient ( D ), cell volume fraction (vf), and apparent cellularity (ρ‐app)—were estimated from DW‐MRI using a computational model based on Monte Carlo simulations. Longitudinal changes were assessed from DW‐MRI scans acquired at baseline, first follow‐up (3 months after CIRT), and, for LR patients, at the time of recurrence. Habitat imaging analysis was performed by applying K ‐means clustering ( k = 2) independently to each map of the six microstructural parameters. A joint clustering approach was then used to combine these binary maps, resulting in 64 unique microstructural configurations (2 ⁶ habitats). The spatial distribution of these habitats was quantified within the gross tumor volume (GTV) and recurrence regions. Associations with clinical outcomes were explored by analyzing habitat fractions in patients with early (early‐LR) vs. late recurrence (late‐LR), and by comparing recurrences predominantly located within the high‐dose clinical target volume irradiated during CIRT (in‐field‐LR) vs. those partially outside the target (marginal‐LR). Statistical tests, multiregional spatial interaction (MSI) matrix analysis, Cox regression, Spearman correlation and Kaplan–Meier survival analyses were performed. Results: Treatment‐induced changes in microstructural parameters were statistically significant in both local control (LC) and LR patients, characterized by an increase in cell radius, eccentricity, ADC, and diffusion, and a concomitant reduction in cell volume fraction and apparent cellularity. This pattern suggested a reduction in cell density and increased tissue disorganization, likely reflecting CIRT‐induced cell damage and extracellular matrix remodeling. Habitat analysis revealed distinct microstructural configurations associated with clinical outcome. A favorable microstructural pattern—high diffusivity and low cellularity—was predominantly found in LC patients (HABITAT47_212221), whereas more aggressive configurations (low diffusivity and high cellularity) were enriched in LR patients (HABITAT26_122112 and HABITAT28_122122). Univariate Cox regression identified these high‐risk habitats as significantly associated with recurrence (hazard ratio > 1, p ‐value < 0.05). Moreover, habitat fractions showed a progressive trend across recurrence risk subgroups (LC, late‐LR, early‐LR; LC, in‐field‐LR, marginal‐LR), while spatial analyses revealed increased co‐localization of high‐risk habitats in LR patients based on MSI matrix analysis. Finally, Kaplan–Meier analysis based on hazard ratios predicted at first follow‐up using Cox models trained on baseline habitat fractions revealed significant differences in progression‐free survival. Conclusion: This study demonstrates the potential of DW‐MRI‐derived microstructural parameters and habitat imaging to capture intratumoral heterogeneity, assess treatment‐induced microstructural changes, and identify high‐risk patterns associated with LR in SC patients treated with CIRT. These findings support the integration of microstructural and habitat‐based imaging as a non‐invasive tool for risk stratification and personalized treatment planning in CIRT for SC.

Diffusion-Weighted Magnetic Resonance Imaging (DW-MRI) is a key tool for probing tissue microstructure by measuring water motion. Given the invasiveness and limited sampling of biopsies, Apparent Diffusion Coefficient (ADC), derived from DW-MRI acquisitions, is clinically used as a non-invasive biomarker for tumor heterogeneity, although it provides a simplified representation of tissue microstructure. Recent work maps in vivo DW-MRI signals to simulated dictionaries generated from in silico cellular models, allowing estimation of sub-voxel microstructural properties using clinically feasible acquisitions. However, these methods remain constrained by oversimplified tissue models and require re-optimization when acquisition parameters change, limiting their generalizability. This work seeks to address these limitations by implementing a generalized Machine Learning framework adaptable to different acquisition protocols and trained on realistic in silico cellular models directly derived from real microscopy images. We created a library of realistic and dynamic 3D in silico tumor models, derived from microscopy images of real cell lines, along with their corresponding in silico DW-MRI signals via GPU-accelerated Monte Carlo simulations. Using this library, we trained a Neural Network to estimate microstructural parameters from in vivo DW-MRI acquisitions of prostate and breast cancer patients, independently of the acquisition protocol. Results showed that the distributions of extracted microstructural parameters across lesions effectively stratified patients according to histological grade. Preliminary validation further indicated that breast-derived in silico substrates yielded the closest agreement with the parameters extracted from the breast cancer cohorts. This approach enables fast, non-invasive, and spatially resolved characterization of tumor heterogeneity, improving on ADC-based and simple-geometry approaches, ultimately providing a clinically applicable and robust framework for tumor microstructure characterization across different imaging protocols.