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Abstract:

The generation of Synthetic Computed Tomography (sCT) images has become a pivotal methodology in modern clinical practice, particularly in the context of Radiotherapy (RT) treatment planning. The use of sCT enables the calculation of doses, pushing towards Magnetic Resonance Imaging (MRI) guided radiotherapy treatments. Moreover, with the introduction of MRI-Positron Emission Tomography (PET) hybrid scanners, the derivation of sCT from MRI can improve the attenuation correction of PET images.Deep learning methods for MRI-to-sCT have shown promising results, but their reliance on single-centre training dataset limits generalisation capabilities to diverse clinical settings. Moreover, creating centralised multi-centre datasets may pose privacy concerns. To address the aforementioned issues, we introduced FedSynthCT-Brain, an approach based on the Federated Learning (FL) paradigm for MRI-to-sCT in brain imaging. This is among the first applications of FL for MRI-to-sCT, employing a cross-silo horizontal FL approach that allows multiple centres to collaboratively train a U-Net-based deep learning model. We validated our method using real multicentre data from four European and American centres, simulating heterogeneous scanner types and acquisition modalities, and tested its performance on an independent dataset from a centre outside the federation.In the case of the unseen centre, the federated model achieved a median Mean Absolute Error (MAE) of 102.0 HU across 23 patients, with an interquartile range of 96.7–110.5 HU. The median (interquartile range) for the Structural Similarity Index (SSIM) and the Peak Signal to Noise Ratio (PNSR) were 0.89 (0.86–0.89) and 26.58 (25.52–27.42), respectively.The analysis of the results showed acceptable performances of the federated approach, thus highlighting the potential of FL to enhance MRI-to-sCT to improve generalisability and advancing safe and equitable clinical applications while fostering collaboration and preserving data privacy.

Abstract:

PurposeIdentifying and quantifying coronary artery calcification (CAC) is crucial for preoperative planning, as it helps to estimate both the complexity of the 2D coronary angiography (2DCA) procedure and the risk of developing intraoperative complications. Despite the relevance, the actual practice relies upon visual inspection of the 2DCA image frames by clinicians. This procedure is prone to inaccuracies due to the poor contrast and small size of the CAC; moreover, it is dependent on the physician’s experience. To address this issue, we developed a workflow to assist clinicians in identifying CAC within 2DCA using data from 44 image acquisitions across 14 patients.MethodsOur workflow consists of three stages. In the first stage, a classification backbone based on ResNet-18 is applied to guide the CAC identification by extracting relevant features from 2DCA frames. In the second stage, a U-Net decoder architecture, mirroring the encoding structure of the ResNet-18, is employed to identify the regions of interest (ROI) of the CAC. Eventually, a post-processing step refines the results to obtain the final ROI. The workflow was evaluated using a leave-out cross-validation.ResultsThe proposed method outperformed the comparative methods by achieving an F1-score for the classification step of 0.87 (0.77-0.94) (median ± quartiles), while for the CAC identification step the intersection over minimum (IoM) was 0.64 (0.46-0.86) (median ± quartiles).ConclusionThis is the first attempt to propose a clinical decision support system to assist the identification of CAC within 2DCA. The proposed workflow holds the potential to improve both the accuracy and efficiency of CAC quantification, with promising clinical applications. As future work, the concurrent use of multiple auxiliary tasks could be explored to further improve the segmentation performance.

Abstract:

BACKGROUND AND OBJECTIVE: Radiomics extracts quantitative features from magnetic resonance images (MRI) and is especially useful in the presence of subtle pathological changes within human soft tissues. This scenario, however, may not cover the effects of intrinsic, e.g., aging-related, (physiological) neurodegeneration of normal brain tissue. The aim of the work was to study the repeatability of radiomic features extracted from an apparently normal area in longitudinally acquired T1-weighted MR brain images using three different intensity normalization approaches typically used in radiomics: Z-score, WhiteStripe and Nyul. METHODS: Fifty-nine images of hearing impaired, yet cognitively intact, patients were repeatedly acquired in two different time points within six months. Ninety-one radiomic features were obtained from an area within the pons region, considered to be a healthy brain tissue according to previous analyses and reports. The Intraclass Correlation Coefficient (ICC) and the Concordance Correlation Coefficient (CCC) in the repeatability study were used as metrics. RESULTS: Features extracted from the MRI normalized with Z-score showed results comparable in both ICC (0.90 (0.82-0.98)) and CCC (0.82 (0.69-0.95)) distribution values, in terms of median and quartiles, with those extracted from the images normalized with WhiteStripe (0.89 (0.84-0.92)) and (0.80 (0.73-0.84)), respectively. CONCLUSION: Our findings underline the importance of, providing useful guidelines for, the intensity normalization of brain MRI prior to a longitudinal radiomic analysis.

Abstract:

Radiotherapy (RT) is a cancer treatment technique that involves exposing cells to ionizing radiation, including X-rays, electrons, or protons. RT offers promise to treat cancer, however, some inherent limitations can hamper its performance. Radio-resistance, whether innate or acquired, refers to the ability of tumor cells to withstand treatment, making it a key factor in RT failure. This perspective hypothesizes that nanoscale surface topography can impact on the topology of cancer cells network under radiation, and that this understanding can possibly advance the assessment of cell radio-resistance in RT applications. An experimental plan is proposed to test this hypothesis, using cancer cells exposed to various RT forms. By examining the influence of 2D surface and 3D scaffold nanoscale architecture on cancer cells, this approach diverges from traditional methodologies, such as clonogenic assays, offering a novel viewpoint that integrates fields such as tissue engineering, artificial intelligence, and nanotechnology. The hypotheses at the base of this perspective not only may advance cancer treatment but also offers insights into the broader field of structural biology. Nanotechnology and label-free Raman phenotyping of biological samples are lenses through which scientists can possibly better elucidate the structure-function relationship in biological systems.

Abstract:

BACKGROUND: Longitudinal Displacement (LD) is the relative motion of the intima-media upon adventitia of the arterial wall during the cardiac cycle, probably linked to atherosclerosis. It has a direction, physiologically first backward in its main components with respect to the arterial flow. Here, LD was investigated in various disease and in presence of a unilateral carotid stent. METHODS: Carotid acquisitions were performed by ultrasound imaging on both body sides of 75 participants (150 Arteries). LD was measured in its percent quantity and direction. RESULTS: Obesity (p = 0.001) and carotid plaques (p = 0.01) were independently associated to quantity decrease of LD in the whole population. In a subgroup analysis, it was respectively 143% in healthy (n = 48 carotids), 129% (n = 34) in presence of cardiovascular risk factors, 121% (n = 20) in MACE patients, 119% (n = 24) in the carotid contralateral to a stent, 110% (n = 24) in carotids with stents. Regarding the direction of LD, in a subgroup analysis an inverted movement was identified in aged (p = 0.001) and diseased (p = 0.001) participants who also showed less quantity of LD (p = 0.001), but independently with age only (p = 0.002) in the whole population. CONCLUSIONS: This observational study suggests that LD within carotid wall layers is lower additively with ageing, cardiovascular risk factors, cardiovascular diseases, and stent. Even if stent is surely beneficial, these data might shed some light on stent restenosis, emphasising the need for interventional studies.

Abstract:

In recent years, synthetic Computed Tomography (CT) images generated from Magnetic Resonance (MR) or Cone Beam Computed Tomography (CBCT) acquisitions have been shown to be comparable to real CT images in terms of dose computation for radiotherapy simulation. However, until now, there has been no independent strategy to assess the quality of each synthetic image in the absence of ground truth. In this work, we propose a Deep Learning (DL)-based framework to predict the accuracy of synthetic CT in terms of Mean Absolute Error (MAE) without the need for a ground truth (GT). The proposed algorithm generates a volumetric map as an output, informing clinicians of the predicted MAE slice-by-slice. A cascading multi-model architecture was used to deal with the complexity of the MAE prediction task. The workflow was trained and tested on two cohorts of head and neck cancer patients with different imaging modalities: 27 MR scans and 33 CBCT. The algorithm evaluation revealed an accurate HU prediction (a median absolute prediction deviation equal to 4 HU for CBCT-based synthetic CTs and 6 HU for MR-based synthetic CTs), with discrepancies that do not affect the clinical decisions made on the basis of the proposed estimation. The workflow exhibited no systematic error in MAE prediction. This work represents a proof of concept about the feasibility of synthetic CT evaluation in daily clinical practice, and it paves the way for future patient-specific quality assessment strategies.

Abstract:

Limited medical image data hinders the training of deep learning (DL) models in the biomedical field. Image augmentation can reduce the data-scarcity problem by generating variations of existing images. However, currently implemented methods require coding, excluding non-programmer users from this opportunity.We therefore present ImageAugmenter, an easy-to-use and open-source module for 3D Slicer imaging computing platform. It offers a simple and intuitive interface for applying over 20 simultaneous MONAI Transforms (spatial, intensity, etc.) to medical image datasets, all without programming.ImageAugmenter makes accessible medical image augmentation, enabling a wider range of users to improve the performance of DL models in medical image analysis by increasing the number of samples available for training.

Abstract:

Background: Extraction of mandibular third molars (M3Ms) is a routine procedure in oral and maxillofacial surgery, often associated with postoperative symptoms like pain, facial swelling, and trismus. This study aimed to introduce a standardized and automated protocol for swelling analysis following M3M surgery, presenting results regarding clinical conditions immediately and one-week after surgery.Methods: In a prospective study, 35 patients were enrolled (mean age: 24.4 ± 5.8 years) for removal of 54 M3Ms. Facial swelling was evaluated through 3D facial scans before surgery (T0), at three days (T1), and seven days (T2) post-surgery. The open-source software 3DSlicer facilitated automated analysis, including data anonymization, orientation, surface registration, qualitative comparisons, linear measurements, and volumetric quantification. Pairwise superimposition of facial models enabled qualitative, vectorial, and quantitative assessments, comparing initial conditions with swelling development at T1 and T2. Additionally, changes between T1 and T2 were also evaluated. Secondary outcomes encompassed clinical evaluations of pain, trismus (maximum mouth opening), and surgery time. Statistical analysis involved the paired Student t-test to assess longitudinal changes and analysis of variance to evaluate outcome variables concerning difficulty scores. Linear regression models correlated primary outcome variables with secondary study variables (α < 0.05).Results: Longitudinal analysis demonstrated significant but variable facial swelling, pain, and trismus at T1, followed by improvement at T2 (p < 0.001). Linear and volumetric differences correlated positively with surgery time (p < 0.05). A direct proportionality between linear and volume differences was observed, higher values at T1 correlated with higher values at T2 (p < 0.05).Conclusions: An innovative digital workflow for precise quantification of postoperative facial changes was implemented, incorporating volumetric measurements that surpass linear assessments. Clinical conditions demonstrated a direct correlation with surgery time, deteriorating immediately and improving one-week after surgery.

Abstract:

Simulation models and artificial intelligence (AI) are largely used to address healthcare and biomedical engineering problems. Both approaches showed promising results in the analysis and optimization of healthcare processes. Therefore, the combination of simulation models and AI could provide a strategy to further boost the quality of health services. In this work, a systematic review of studies applying a hybrid simulation models and AI approach to address healthcare management challenges was carried out. Scopus, Web of Science, and PubMed databases were screened by independent reviewers. The main strategies to combine simulation and AI as well as the major healthcare application scenarios were identified and discussed. Moreover, tools and algorithms to implement the proposed approaches were described. Results showed that machine learning appears to be the most employed AI strategy in combination with simulation models, which mainly rely on agent-based and discrete-event systems. The scarcity and heterogeneity of the included studies suggested that a standardized framework to implement hybrid machine learning-simulation approaches in healthcare management is yet to be defined. Future efforts should aim to use these approaches to design novel intelligent in-silico models of healthcare processes and to provide effective translation to the clinics.

Abstract:

Background: The global coronavirus disease 2019 (COVID-19) pandemic has posed substantial challenges for healthcare systems, notably the increased demand for chest computed tomography (CT) scans, which lack automated analysis. Our study addresses this by utilizing artificial intelligence-supported automated computer analysis to investigate lung involvement distribution and extent in COVID-19 patients. Additionally, we explore the association between lung involvement and intensive care unit (ICU) admission, while also comparing computer analysis performance with expert radiologists’ assessments.Methods: A total of 81 patients from an open-source COVID database with confirmed COVID-19 infection were included in the study. Three patients were excluded. Lung involvement was assessed in 78 patients using CT scans, and the extent of infiltration and collapse was quantified across various lung lobes and regions. The associations between lung involvement and ICU admission were analysed. Additionally, the computer analysis of COVID-19 involvement was compared against a human rating provided by radiological experts.Results: The results showed a higher degree of infiltration and collapse in the lower lobes compared to the upper lobes (P<0.05). No significant difference was detected in the COVID-19-related involvement of the left and right lower lobes. The right middle lobe demonstrated lower involvement compared to the right lower lobes (P<0.05). When examining the regions, significantly more COVID-19 involvement was found when comparing the posterior vs. the anterior halves and the lower vs. the upper half of the lungs. Patients, who required ICU admission during their treatment exhibited significantly higher COVID-19 involvement in their lung parenchyma according to computer analysis, compared to patients who remained in general wards. Patients with more than 40% COVID-19 involvement were almost exclusively treated in intensive care. A high correlation was observed between computer detection of COVID-19 affections and the rating by radiological experts.Conclusions: The findings suggest that the extent of lung involvement, particularly in the lower lobes, dorsal lungs, and lower half of the lungs, may be associated with the need for ICU admission in patients with COVID-19. Computer analysis showed a high correlation with expert rating, highlighting its potential utility in clinical settings for assessing lung involvement. This information may help guide clinical decision-making and resource allocation during ongoing or future pandemics. Further studies with larger sample sizes are warranted to validate these findings.

Abstract:

A minimally-invasive manipulator characterized by hyper-redundant kinematics and embedded sensing modules is presented in this work. The bending angles (tilt and pan) of the robot tip are controlled through tendon-driven actuation; the transmission of the actuation forces to the tip is based on a Bowden-cable solution integrating some channels for optical fibers. The viability of the real-time measurement of the feedback control variables, through optoelectronic acquisition, is evaluated for automated bending of the flexible endoscope and trajectory tracking of the tip angles. Indeed, unlike conventional catheters and cannulae adopted in neurosurgery, the proposed robot can extend the actuation and control of snake-like kinematic chains with embedded sensing solutions, enabling real-time measurement, robust and accurate control of curvature, and tip bending of continuum robots for the manipulation of cannulae and microsurgical instruments in neurosurgical procedures. A prototype of the manipulator with a length of 43 mm and a diameter of 5.5 mm has been realized via 3D printing. Moreover, a multiple regression model has been estimated through a novel experimental setup to predict the tip angles from measured outputs of the optoelectronic modules. The sensing and control performance has also been evaluated during tasks involving tip rotations.

Abstract:

Clonogenic assays are routinely used to evaluate the response of cancer cells to external radiation fields, assess their radioresistance and radiosensitivity, estimate the performance of radiotherapy. However, classic clonogenic tests focus on the number of colonies forming on a substrate upon exposure to ionizing radiation, and disregard other important characteristics of cells such their ability to generate structures with a certain shape. The radioresistance and radiosensitivity of cancer cells may depend less on the number of cells in a colony and more on the way cells interact to form complex networks. In this study, we have examined whether the topology of 2D cancer-cell graphs is influenced by ionizing radiation. We subjected different cancer cell lines, i.e. H4 epithelial neuroglioma cells, H460 lung cancer cells, PC3 bone metastasis of grade IV of prostate cancer and T24 urinary bladder cancer cells, cultured on planar surfaces, to increasing photon radiation levels up to 6 Gy. Fluorescence images of samples were then processed to determine the topological parameters of the cell-graphs developing over time. We found that the larger the dose, the less uniform the distribution of cells on the substrate—evidenced by high values of small-world coefficient (cc), high values of clustering coefficient (cc), and small values of characteristic path length (cpl). For all considered cell lines, 𝑠𝑤>1 for doses higher or equal to 4 Gy, while the sensitivity to the dose varied for different cell lines: T24 cells seem more distinctly affected by the radiation, followed by the H4, H460 and PC3 cells. Results of the work reinforce the view that the characteristics of cancer cells and their response to radiotherapy can be determined by examining their collective behavior—encoded in a few topological parameters—as an alternative to classical clonogenic assays.

Abstract:

Purpose Primary central nervous system lymphoma (PCNSL) is a rare, aggressive form of extranodal non-Hodgkin lym- phoma. To predict the overall survival (OS) in advance is of utmost importance as it has the potential to aid clinical decision-making. Though radiomics-based machine learning (ML) has demonstrated the promising performance in PCNSL, it demands large amounts of manual feature extraction efforts from magnetic resonance images beforehand. deep learning (DL) overcomes this limitation.Methods In this paper, we tailored the 3D ResNet to predict the OS of patients with PCNSL. To overcome the limitation of data sparsity, we introduced data augmentation and transfer learning, and we evaluated the results using r stratified k-fold cross-validation. To explain the results of our model, gradient-weighted class activation mapping was applied.Results We obtained the best performance (the standard error) on post-contrast T1-weighted (T1Gd)—area under curve = 0.81(0.03), accuracy = 0.87(0.07), precision = 0.88(0.07), recall = 0.88(0.07) and F1-score = 0.87(0.07), while compared with ML-based models on clinical data and radiomics data, respectively, further confirming the stability of our model. Also, we observed that PCNSL is a whole-brain disease and in the cases where the OS is less than 1 year, it is more difficult to distinguish the tumor boundary from the normal part of the brain, which is consistent with the clinical outcome. Conclusions All these findings indicate that T1Gd can improve prognosis predictions of patients with PCNSL. To the best of our knowledge, this is the first time to use DL to explain model patterns in OS classification of patients with PCNSL. Future work would involve collecting more data of patients with PCNSL, or additional retrospective studies on different patient populations with rare diseases, to further promote the clinical role of our model.

Abstract:

Primary Central Nervous System Lymphoma (PCNSL) is an aggressive neoplasm with a poor prognosis. Although therapeutic progresses have significantly improved Overall Survival (OS), a number of patients do not respond to HD–MTX-based chemotherapy (15–25%) or experience relapse (25–50%) after an initial response. The reasons underlying this poor response to therapy are unknown. Thus, there is an urgent need to develop improved predictive models for PCNSL. In this study, we investigated whether radiomics features can improve outcome prediction in patients with PCNSL. A total of 80 patients diagnosed with PCNSL were enrolled. A patient sub-group, with complete Magnetic Resonance Imaging (MRI) series, were selected for the stratification analysis. Following radiomics feature extraction and selection, different Machine Learning (ML) models were tested for OS and Progression-free Survival (PFS) prediction. To assess the stability of the selected features, images from 23 patients scanned at three different time points were used to compute the Interclass Correlation Coefficient (ICC) and to evaluate the reproducibility of each feature for both original and normalized images. Features extracted from Z-score normalized images were significantly more stable than those extracted from non-normalized images with an improvement of about 38% on average (p-value < 10−12). The area under the ROC curve (AUC) showed that radiomics-based prediction overcame prediction based on current clinical prognostic factors with an improvement of 23% for OS and 50% for PFS, respectively. These results indicate that radiomics features extracted from normalized MR images can improve prognosis stratification of PCNSL patients and pave the way for further study on its potential role to drive treatment choice.

Abstract:

This reprint showcases a selection of bleeding-edge articles about medical image processing and segmentation workflows based on artificial intelligence algorithms. The proposed papers are applied to multiple and different anatomical districts and clinical scenarios.

Abstract:

Mitral regurgitation is a structural heart condition affecting the mitral valve that can be treated with the MitraClip system ™, a device that allows a percutaneous intervention for the deployment of a catheter-embedded clip on the valve leaflets to prevent blood-backflow from the left ventricle to the left atrium. Despite its efficacy, the procedure presents technical challenges, relying on fluoroscopy guidance and surgeon expertise. In the context of human-machine interface for autonomous robotic catheter cardiac intervention, this study aims to evaluate the effectiveness of Augmented Reality (AR) for training surgeons in the MitraClip procedure. Users using an AR Interface demonstrated better performance compared to those using an interface emulating traditional visualization methodologies (fluoroscopy and transesophageal echocardiogra-phy). AR-based training offers a more engaging and effective learning experience, leading to improved surgical dexterity and safety in the procedure.

Abstract:

Timely and accurate diagnosis of severe Aortic Stenosis (AS) is crucial to prevent severe clinical implications. The most commonly used parameter for diagnostic purposes is the mean transvalvular pressure gradient, measured by echocardio-graphy (>= 40 mmHg). However, its use for detecting severe AS has several limitations, including technical, pathophysiological, and clinical reasons. This study aimed to develop a Deep Learning (DL) model for identifying severe AS using ColorDoppler Echocardiography video data. The new DL model used is called ViViT (Video Vision Transformers). To limit the overfitting problem, the data augmentation technique was applied during the training phase. The model achieved an accuracy of 87% in classifying patients with severe AS compared to healthy subjects in the testing group. Future efforts will focus on enhancing model accuracy, increasing the initial dataset, and refining the classification process by implementing multi-classification of AS with varying degrees of severity.