Pediatric sarcomas, rare malignancies of mesenchymal origin, pose diagnostic and therapeutic challenges. In this review, we explore the role of radiomics in reshaping our understanding of pediatric sarcomas, emphasizing methodological considerations and applications such as diagnostics and predictive modeling. A systematic review conducted up to November 2023 identified 72 papers on radiomics analysis in pediatric sarcoma from PubMed/MEDLINE, Web of Knowledge, and Scopus. Following inclusion and exclusion criteria, 10 reports were included in this review. The studies, predominantly retrospective, focus on Ewing sarcoma and osteosarcoma, utilizing diverse imaging modalities, including CT, MRI, PET/CT, and PET/MRI. Manual segmentation is common, with a median of 35 features extracted. Radiomics Quality Score (RQS) and Methodological Radiomics Score (METRICS) assessments reveal a consistent emphasis on non-radiomic features, validation criteria, and improved methodological rigor in recent publications. Diagnostic applications dominate, with innovative studies exploring prognostic and treatment response aspects. Challenges include feature heterogeneity and sample size variations. The evolving landscape underscores the need for standardized methodologies. Despite challenges, the diagnostic and predictive potential of radiomics in pediatric oncology is evident, paving the way for precision medicine advancements.

An unprecedented amount of personal health data, with the potential to revolutionise precision medicine, is generated at healthcare institutions worldwide. The exploitation of such data using artificial intelligence relies on the ability to combine heterogeneous, multicentric, multimodal and multiparametric data, as well as thoughtful representation of knowledge and data availability. Despite these possibilities, significant methodological challenges and ethico-legal constraints still impede the real-world implementation of data models. The EuCanImage is an international consortium aimed at developing AI algorithms for precision medicine in oncology and enabling secondary use of the data based on necessary ethical approvals. The use of well-defined clinical data standards to allow interoperability was a central element within the initiative. The consortium is focused on three different cancer types and addresses seven unmet clinical needs. This article synthesises our experience and procedures for healthcare data interoperability and standardisation. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 952103. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes This study describes a new process to harmonize and standardize clinical data. The data will be available upon request to the authors.

Purpose: To investigate the potential role of radiomics-based machine learning in differentiating small renal oncocytoma (RO) from clear cells carcinoma (ccRCC) on contrast-enhanced CT (CECT). Material and methods: Fifty-two patients with small renal masses who underwent CECT before surgery between January 2016 and December 2020 were retrospectively included in the study. At pathology examination 39 ccRCC and 13 RO were identified. All lesions were manually delineated unenhanced (B), arterial (A) and venous (V) phases. Radiomics features were extracted using three different fixed bin widths (bw) of 25 HU, 10 HU, and 5 HU from each phase (B, A, V), and with different combinations (B+A, B+V, B+A+V, A+V), leading to 21 different datasets. Montecarlo Cross Validation technique was used to quantify the estimator performance. The final model built using the hyperparameter selected with Optuna was trained again on the training set and the final performance evaluation was made on the test set. Results: The A+V bw 10 achieved the greater median (IQR) balanced accuracy considering all the models of 0.70 (0.64-0.75), while A bw 10 considering only the monophasic ones. The A bw 10 model achieved a median (IQR) sensitivity of 0.60 (0.40-0.60), specificity of 0.80 (0.73-0.87), AUC-ROC of 0.77 (0.66-0.84), accuracy of 0.75 (0.70-0.80), and a Phi Coefficient of 0.38 (0.20-0.47). None of the nine models with the lowest mean balanced accuracy values implemented features from A. Conclusion: The A bw 10 model was identified as the most efficient mono-phasic model in differentiating small RO from ccRCC.

Artificial intelligence (AI) has undergone cycles of enthusiasm and stagnation, often referred to as “AI winters.” The introduction of large language models (LLMs), such as OpenAI’s ChatGPT in late 2022, has revitalized interest in AI, particularly within health-care applications, including radiology. The roots of AI in language processing can be traced back to Alan Turing’s 1950 work, which established foundational principles for natural language processing (NLP). Early iterations of NLP primarily concentrated on natural language understanding (NLU) and natural language generation (NLG), but they faced significant challenges related to contextual comprehension and the handling of lengthy text sequences. Recent advancements in NLP have demonstrated considerable promise in automating the analysis of unstructured data, including electronic health records and radiology reports. LLMs, which are based on the transformer architecture introduced in 2017, excel at capturing complex language dependencies and facilitating tasks, such as report generation and clinical decision support. This review critically examines the evolution from traditional NLP to LLMs, highlighting their transformative potential within the field of radiology. Despite the advantages presented by LLMs, challenges persist, including concerns regarding data privacy, the potential for generating misinformation, and the imperative for rigorous validation protocols. Addressing these challenges is crucial for harnessing the full potential of LLMs to enhance diagnostic precision and workflow efficiency in radiology, ultimately improving patient care and outcomes.

Background:The new immunotherapies have not only changed the oncological therapeutic approach but have also made it necessary to develop new imaging methods for assessing the response to treatment. Delta radiomics consists of the analysis of radiomic features variation between different medical images, usually before and after therapy. Purpose:This review aims to evaluate the role of delta radiomics in the immunotherapy response assessment. Methods:A systematic search was performed in PubMed, Scopus, and Web Of Science using "delta radiomics AND immunotherapy" as search terms. The included articles' methodological quality was measured using the Radiomics Quality Score (RQS) tool. Results:Thirteen articles were finally included in the systematic review. Overall, the RQS of the included studies ranged from 4 to 17, with a mean RQS total of 11,15 ± 4,18 with a corresponding percentage of 30.98 ± 11.61 %. Eleven articles out of 13 performed imaging at multiple time points. All the included articles performed feature reduction. No study carried out prospective validation, decision curve analysis, or cost-effectiveness analysis. Conclusions:Delta radiomics has been demonstrated useful in evaluating the response in oncologic patients undergoing immunotherapy. The overall quality was found law, due to the lack of prospective design and external validation. Thus, further efforts are needed to bring delta radiomics a step closer to clinical implementation.

The use of low-rank approximation filters in the field of NMR is increasing due to their flexibility and effectiveness. Despite their ability to reduce the Mean Square Error between the processed signal and the true signal is well known, the statistical distribution of the residual noise is still undescribed. In this article, we show that low-rank approximation filters are equivalent to linear filters, and we calculate the mean and the covariance matrix of the processed data. We also show how to use this knowledge to build a maximum likelihood estimator, and we test the estimator's performance with a Montecarlo simulation of a 13C pyruvate metabolic tracer. While the article focuses on NMR spectroscopy experiment with hyperpolarized tracer, we also show that the results can be applied to tensorial data (e.g. using HOSVD) or 1D data (e.g. Cadzow filter).

The spleen, often referred to as the “forgotten organ”, plays numerous important roles in various diseases. Recently, there has been an increased interest in the application of radiomics in different areas of medical imaging. This systematic review aims to assess the current state of the art and evaluate the methodological quality of radiomics applications in spleen imaging. A systematic search was conducted on PubMed, Scopus, and Web of Science. All the studies were analyzed, and several characteristics, such as year of publication, research objectives, and number of patients, were collected. The methodological quality was evaluated using the radiomics quality score (RQS). Fourteen articles were ultimately included in this review. The majority of these articles were published in non-radiological journals (78%), utilized computed tomography (CT) for extracting radiomic features (71%), and involved not only the spleen but also other organs for feature extraction (71%). Overall, the included papers achieved an average RQS total score of 9.71 ± 6.37, corresponding to an RQS percentage of 27.77 ± 16.04. In conclusion, radiomics applications in spleen imaging demonstrate promising results in various clinical scenarios. However, despite all the included papers reporting positive outcomes, there is a lack of consistency in the methodological approaches employed.

The application of radiomics for non-oncologic diseases is currently emerging. Despite its relative infancy state, the evidence highlights the potential of radiomics approaches to serve as neuroimaging biomarkers in the field of the neurodegenerative brain. This systematic review presents the last progress and potential application of radiomics in the field of neurodegenerative nuclear imaging applied to positron-emission tomography (PET) and single-photon emission computed tomography (SPECT) by focusing mainly on the two most common neurodegenerative disorders, Alzheimer's (AD) and Parkinson's disease (PD). A comprehensive review of the current literature was performed using the PubMed and Web of Science databases up to November 2022. The final collection of eighteen relevant publications was grouped as AD-related and PD-related. The main efforts in the field of AD dealt with radiomics-based early diagnosis of preclinical AD and the prediction of MCI to AD conversion, meanwhile, in the setting of PD, the radiomics techniques have been used in the attempt to improve the assessment of PD diagnosis, the differential diagnosis between PD and other parkinsonism, severity assessment, and outcome prediction. Although limited evidence with relatively small cohort studies, it seems that radiomics-based analysis using nuclear medicine tools, mainly [18F]Fluorodeoxyglucose (FDG) and β-amyloid (Aβ) PET, and dopamine transporter (DAT) SPECT, can be used for computer-aided diagnoses in AD-continuum and parkinsonian disorders. Combining nuclear radiomics analysis with clinical factors and introducing a multimodality approach can significantly improve classification and prediction efficiency in neurodegenerative disorders.

Significant discordance rates in HER2 status between primary breast cancer (BC) and brain metastases (BCBM) have been reported, potentially affecting clinical decision-making. Biological recharacterization of BCBMs is difficult due to inherent neurosurgery risks. We here assess the potential of radiomics to non-invasively assess HER2 status of BCBMs. We retrospectively identified BC patients from 2 centers, for which HER2 status evaluated on BCBMs (HER2-positive versus HER2-negative by ASCO-CAP) and preoperative brain MRI were available. A total of 218 radiomics features, complying with Imaging Biomarker Standardization Initiative feature definition, were extracted from manually segmented BCBMs using Python. Robust features (selected using LASSO) associated with HER2 status in univariate analysis (p<0.05) were tested in multivariate analysis. Linear regression model (LRM) and non-linear logistic regression model (NLRM) were constructed. Machine learning (ML) algorithms (decision tree, k-nearest neighbor, and support vector machine [SVM]), were tested, by spliting the dataset into 70% for training, 20% for testing, and 10% for validation. Classifiers were trained and tested using the cross-validation approach 10 times; median values for AUC, accuracy, sensitivity and specificity were obtained. The performance of the best classifiers was evaluated on the validation dataset. Analyses were performed using MATLAB R2021b Statistics and Machine Toolbox (MathWorks). We identified 54 BC patients, for which HER2 status evaluated on 56 BCBMs (HER2+ N=27, HER2- N=29) and preoperative brain MRI was available. At univariate analysis, 9 features were significantly associated with HER2 status, but diagnostic accuracy was low (maximum 67%). Using LRM and NLRM, including the 9 significant features at univariate analysis, accuracy increased to 79%. Using the ML approach, the best classifier among the pattern recognition approaches tested was a SVM including the 9 significant features, which reached an AUC of 0.97 and an accuracy of 91% and 86% on test and validation set, respectively. Radiomics using machine learning approach shows potential to non-invasively predict HER2 status of BCBMs.

Magnesium (Mg) implants have shown to cause image artefacts or distortions in magnetic resonance imaging (MRI). Yet, there is a lack of information on how the degradation of Mg-based implants influences the image quality of MRI examinations. In this study, Mg-based implants are analysed in vitro, ex vivo, and in the clinical setting for various magnetic field strengths with the aim to quantify metallic artefact behaviour. In vitro corroded Mg-based screws and a titanium (Ti) equivalent were imaged according to the ASTM F2119. Mg-based and Ti pins were also implanted into rat femurs for different time points and scanned to provide insights on the influence of soft and hard tissue on metallic artefact. Additionally, MRI data of patients with scaphoid fractures treated with CE-approved Mg-based compression screws (MAGNEZIX®) were analysed at various time points post-surgery. The artefact production of the Mg-based material decreased as implant material degraded in all settings. The worst-case imaging scenario was determined to be when the imaging plane was selected to be perpendicular to the implant axis. Moreover, the Mg-based implant outperformed the Ti equivalent in all experiments by producing lower metallic artefact (p < 0.05). This investigation demonstrates that Mg-based implants generate significantly lower metallic distortion in MRI when compared to Ti. Our positive findings suggest and support further research into the application of Mg-based implants including post-operative care facilitated by MRI monitoring of degradation kinetics and bone/tissue healing processes.

Hyperpolarized 13C magnetic resonance (MR) is a promising technique for the noninvasive assessment of the regional cardiac metabolism since it permits heart physiology studies in pig and mouse models. The main objective of the present study is to resume the work carried out at our electromagnetic laboratory in the field of radio frequency (RF) coil design, building, and testing. In this paper, first, we review the principles of RF coils, coil performance parameters, and estimation methods by using simulations, workbench, and MR imaging experiments. Then, we describe the simulation, design, and testing of different 13C coil configurations and acquisition settings for hyperpolarized studies on pig and mouse heart with a clinical 3T MRI scanner. The coil simulation is performed by developing a signal-to-noise ratio (SNR) model in terms of coil resistance, sample-induced resistance, and magnetic field pattern. Coil resistance was calculated from Ohm’s law and sample-induced resistances were estimated with a finite-difference time-domain (FDTD) algorithm. In contrast, the magnetic field per unit current was calculated by magnetostatic theory and a FDTD algorithm. The information could be of interest to graduate students and researchers working on the design and development of an MR coil to be used in 13C studies.

Sodium (23Na) is the most abundant cation present in the human body and is involved in a large number of vital body functions. In the last few years, the interest in Sodium Magnetic Resonance Imaging (23Na MRI) has considerably increased for its relevance in physiological and physiopathological aspects. Indeed, sodium MRI offers the possibility to extend the anatomical imaging information by providing additional and complementary information on physiology and cellular metabolism with the heteronuclear Magnetic Resonance Spectroscopy (MRS). Constraints are the rapidly decaying of sodium signal, the sensitivity lack due to the low sodium concentration versus 1H-MRI induce scan times not clinically acceptable and it also constitutes a challenge for sodium MRI. With the available magnetic fields for clinical MRI scanners (1.5 T, 3 T, 7 T), and the hardware capabilities such as strong gradient strengths with high slew rates and new dedicated radiofrequency (RF) sodium coils, it is possible to reach reasonable measurement times (~10–15 min) with a resolution of a few millimeters, where it has already been applied in vivo in many human organs such as the brain, cartilage, kidneys, heart, as well as in muscle and the breast. In this work, we review the different geometries and setup of sodium coils described in the available literature for different in vivo applications in human organs with clinical MR scanners, by providing details of the design, modeling and construction of the coils.

Dissolution dynamic nuclear polarization allows in vivo studies of metabolic flux using 13 C-hyperpolarized tracers by enhancing signal intensity by up to four orders of magnitude. The T1 for in vivo applications is typically in the range of 10-50 s for the different 13 C-enriched metabolic substrates; the exponential loss of polarization due to various relaxation mechanisms leads to a strong reduction of the signal-to-noise ratio (SNR). A common solution to the problem of low SNR is the accumulation/averaging of consecutive spectra. However, some limitations related to long delays between consecutive scans occur: in particular, following biochemical kinetics and estimate apparent enzymatic constants becomes time critical when measurement scans are repeated with the typical delay of about 3 T1 . Here we propose a method to dramatically reduce the noise, and therefore also the acquisition times, by computing, via truncated singular value decomposition, a low-rank approximation to the individual complex time-domain signals. Moreover, this approach has the additional advantage that the phase correction can be applied to the spectra already denoised, thus greatly reducing phase correction errors. We have tested the method on (1) simulated data; (2) performing dissolution of hyperpolarized 1-13 C-pyruvate in standard conditions and (3) in vivo data sets, using a porcine model injected with hyperpolarized Na-1-13 C-acetate. It was shown that the presented method reduces the noise level in all the experimental data sets, allowing the retrieval of signals from highly noisy data without any prior phase correction pre-processing. The effects of the proposed approach on the quantification of metabolic kinetics parameters have to be shown by full quantification studies.

We present a processing method, based on the multivariate curve resolution approach (MCR), to denoise 2D solid-state NMR spectra, yielding a substantial S/N ratio increase while preserving the lineshapes and relative signal intensities. These spectral features are particularly important in the quantification of silicon species, where sensitivity is limited by the low natural abundance of the 29Si nuclei and by the dilution of the intrinsic protons of silica, but can be of interest also when dealing with other intermediate-to-low receptivity nuclei. This method also offers the possibility of coprocessing multiple 2D spectra that have the signals at the same frequencies but with different intensities (e.g.: as a result of a variation in the mixing time). The processing can be carried out on the time-domain data, thus preserving the possibility of applying further processing to the data. As a demonstration, we have applied Cadzow denoising on the MCR-processed FIDs, achieving a further increase in the S/N ratio and more effective denoising also on the transients at longer indirect evolution times. We have applied the combined denoising on a set of experimental data from a lysozyme–silica composite.

High dielectric contrast polymer dielectric mirrors are used to recycle non-absorbed photons in organic luminescent solar concentrators. A 10% increase in the concentrator optical efficiency is found and retained upon doubling its size paving the way to lightweight and cheap building integrated photovoltaic systems.

This study reports for the first time the use of a red-emitting AIEgen, i.e. TPE-AC, for the realization of efficient luminescent solar concentrators (LSCs) based on poly(methyl methacrylate) (PMMA) and polycarbonate (PC) thin films (25 ± 5 μm).