Artificial intelligence of imaging and clinical neurological data for predictive, preventive and personalized (P3) medicine for Parkinson Disease: The NeuroArtP3 protocol for a multi-center research study.

BACKGROUND: The burden of Parkinson Disease (PD) represents a key public health issue and it is essential to develop innovative and cost-effective approaches to promote sustainable diagnostic and therapeutic interventions. In this perspective the adoption of a P3 (predictive, preventive and personalized) medicine approach seems to be pivotal. The NeuroArtP3 (NET-2018-12366666) is a four-year multi-site project co-funded by the Italian Ministry of Health, bringing together clinical and computational centers operating in the field of neurology, including PD. OBJECTIVE: The core objectives of the project are: i) to harmonize the collection of data across the participating centers, ii) to structure standardized disease-specific datasets and iii) to advance knowledge on disease's trajectories through machine learning analysis. METHODS: The 4-years study combines two consecutive research components: i) a multi-center retrospective observational phase; ii) a multi-center prospective observational phase. The retrospective phase aims at collecting data of the patients admitted at the participating clinical centers. Whereas the prospective phase aims at collecting the same variables of the retrospective study in newly diagnosed patients who will be enrolled at the same centers. RESULTS: The participating clinical centers are the Provincial Health Services (APSS) of Trento (Italy) as the center responsible for the PD study and the IRCCS San Martino Hospital of Genoa (Italy) as the promoter center of the NeuroartP3 project. The computational centers responsible for data analysis are the Bruno Kessler Foundation of Trento (Italy) with TrentinoSalute4.0 -Competence Center for Digital Health of the Province of Trento (Italy) and the LISCOMPlab University of Genoa (Italy). CONCLUSIONS: The work behind this observational study protocol shows how it is possible and viable to systematize data collection procedures in order to feed research and to advance the implementation of a P3 approach into the clinical practice through the use of AI models.

Pre-pectoral Breast Reconstruction: Surgical and Patient-Reported Outcomes of Two-Stages vs Single-Stage Implant-Based Breast Reconstruction.

BACKGROUND: Two-stages pre-pectoral breast reconstruction may confer advantages over direct to implant (DTI) and subpectoral reconstruction in selected patients who have no indication for autologous reconstruction. The primary endpoint of the study was to evaluate and compare the incidence of capsular contracture in the pre-pectoral two-stages technique versus the direct to implant technique. Complications related to the two surgical techniques and patient satisfaction were also evaluated. METHODS: A retrospective review of 45 two stages and 45 Direct-to-implant, DTI patients was completed. Acellular dermal matrix was used in all patients. An evaluation of anthropometric and clinical parameters, surgical procedures and complications was conducted. Minimum follow-up was 12 months after placement of the definitive implant. RESULTS: There was no statistically significant difference in the rate of capsular contracture in the two groups. Rippling occurred more in DTI reconstruction. In the two-stages reconstruction, lipofilling was applied more often and there was a higher incidence of seroma. Patient satisfaction extrapolated from the Breast Q questionnaire was better for patients submitted to two-stage implant-based breast reconstruction. CONCLUSION: Dual-stage pre-pectoral reconstruction with acellular dermal matrix appears to be a good reconstructive solution in patients with relative contraindications for one-stage heterologous reconstruction with definitive prosthesis and no desire for autologous reconstruction.

Whole-Exome Sequencing and cfDNA Analysis Uncover Genetic Determinants of Melanoma Therapy Response in a Real-World Setting.

Although several studies have explored the molecular landscape of metastatic melanoma, the genetic determinants of therapy resistance are still largely unknown. Here, we aimed to determine the contribution of whole-exome sequencing and circulating free DNA (cfDNA) analysis in predicting response to therapy in a consecutive real-world cohort of 36 patients, undergoing fresh tissue biopsy and followed during treatment. Although the underpowered sample size limited statistical analysis, samples from non-responders had higher copy number variations and mutations in melanoma driver genes compared to responders in the BRAF V600+ subset. In the BRAF V600- subset, Tumor Mutational Burden (TMB) was twice that in responders vs. non-responders. Genomic layout revealed commonly known and novel potential intrinsic/acquired resistance driver gene variants. Among these, RAC1, FBXW7, GNAQ mutations, and BRAF/PTEN amplification/deletion were present in 42% and 67% of patients, respectively. Both Loss of Heterozygosity (LOH) load and tumor ploidy were inversely associated with TMB. In immunotherapy-treated patients, samples from responders showed higher TMB and lower LOH and were more frequently diploid compared to non-responders. Secondary germline testing and cfDNA analysis proved their efficacy in finding germline predisposing variants carriers (8.3%) and following dynamic changes during treatment as a surrogate of tissue biopsy, respectively.

Freehand 1.5T MR-Guided Vacuum-Assisted Breast Biopsy (MR-VABB): Contribution of Radiomics to the Differentiation of Benign and Malignant Lesions.

Radiomics and artificial intelligence have been increasingly applied in breast MRI. However, the advantages of using radiomics to evaluate lesions amenable to MR-guided vacuum-assisted breast biopsy (MR-VABB) are unclear. This study includes patients scheduled for MR-VABB, corresponding to subjects with MRI-only visible lesions, i.e., with a negative second-look ultrasound. The first acquisition of the multiphase dynamic contrast-enhanced MRI (DCE-MRI) sequence was selected for image segmentation and radiomics analysis. A total of 80 patients with a mean age of 55.8 years ± 11.8 (SD) were included. The dataset was then split into a training set (50 patients) and a validation set (30 patients). Twenty out of the 30 patients with a positive histology for cancer were in the training set, while the remaining 10 patients with a positive histology were included in the test set. Logistic regression on the training set provided seven features with significant p values (<0.05): (1) 'AverageIntensity', (2) 'Autocorrelation', (3) 'Contrast', (4) 'Compactness', (5) 'StandardDeviation', (6) 'MeanAbsoluteDeviation' and (7) 'InterquartileRange'. AUC values of 0.86 (95% C.I. 0.73-0.94) for the training set and 0.73 (95% C.I. 0.54-0.87) for the test set were obtained for the radiomics model. Radiological evaluation of the same lesions scheduled for MR-VABB had AUC values of 0.42 (95% C.I. 0.28-0.57) for the training set and 0.4 (0.23-0.59) for the test set. In this study, a radiomics logistic regression model applied to DCE-MRI images increased the diagnostic accuracy of standard radiological evaluation of MRI suspicious findings in women scheduled for MR-VABB. Confirming this performance in large multicentric trials would imply that using radiomics in the assessment of patients scheduled for MR-VABB has the potential to reduce the number of biopsies, in suspicious breast lesions where MR-VABB is required, with clear advantages for patients and healthcare resources.

EEG in extreme conditions: An advanced analysis pipeline for the human electroencephalographic signals recorded in space during the ALTEA experiment.

The Anomalous Long Term Effects in Astronauts (ALTEA) project originally aimed at disentangling the mechanisms behind astronauts' perception of light flashes. To this end, an experimental apparatus was set up in order to concurrently measure the tracks of cosmic radiation particles in the astronauts' head and the electroencephalographic (EEG) signals generated by their brain. So far, the ALTEA data set has never been analyzed with the broader intent to study possible interference between cosmic radiation and the brain, regardless of light flashes. The aim of this work is to define a pipeline to systematically pre-process the ALTEA EEG data. Compared to the analysis of standard EEG recording, this task is made more difficult by the presence of unconventional artifacts due to the extreme recording conditions that, in particular, require the EEG cap to be positioned next to another noisy electronic device, namely the particle detectors. Here we show how standard tools for the analysis of EEG data can be tuned to deal with these unconventional artifacts. After pre-processing the available data we were able to elucidate a shift of the center frequency of the α rhythm induced by visual stimulation, thus proving the effectiveness of the implemented pipeline. This work represents the first study presenting results of signal processing of ALTEA EEG time series. Further, it is the starting point of a future work aimed at analyzing the interaction between EEG and cosmic radiation.

Gene's expression underpinning the divergent predictive value of [18F]F-fluorodeoxyglucose and prostate-specific membrane antigen positron emission tomography in primary prostate cancer: a bioinformatic and experimental study.

BACKGROUND: Positron Emission Tomography (PET) imaging with Prostate-Specific Membrane Antigen (PSMA) and Fluorodeoxyglucose (FDG) represent promising biomarkers for risk-stratification of Prostate Cancer (PCa). We verified whether the expression of genes encoding for PSMA and enzymes regulating FDG cellular uptake are independent and additive prognosticators in PCa. METHODS: mRNA expression of genes involved in glucose metabolism and PSMA regulation obtained from primary PCa specimens were retrieved from open-source databases and analyzed using an integrative bioinformatics approach. Machine Learning (ML) techniques were used to create predictive Progression-Free Survival (PFS) models. Cellular models of primary PCa with different aggressiveness were used to compare [18F]F-PSMA-1007 and [18F]F-FDG uptake kinetics in vitro. Confocal microscopy, immunofluorescence staining, and quantification analyses were performed to assess the intracellular and cellular membrane PSMA expression. RESULTS: ML analyses identified a predictive functional network involving four glucose metabolism-related genes: ALDOB, CTH, PARP2, and SLC2A4. By contrast, FOLH1 expression (encoding for PSMA) did not provide any additive predictive value to the model. At a cellular level, the increase in proliferation rate and migratory potential by primary PCa cells was associated with enhanced FDG uptake and decreased PSMA retention (paralleled by the preferential intracellular localization). CONCLUSIONS: The overexpression of a functional network involving four glucose metabolism-related genes identifies a higher risk of disease progression since the earliest phases of PCa, in agreement with the acknowledged prognostic value of FDG PET imaging. By contrast, the prognostic value of PSMA PET imaging is independent of the expression of its encoding gene FOLH1. Instead, it is influenced by the protein docking to the cell membrane, regulating its accessibility to tracer binding.

Prediction of severe thunderstorm events with ensemble deep learning and radar data.

The problem of nowcasting extreme weather events can be addressed by applying either numerical methods for the solution of dynamic model equations or data-driven artificial intelligence algorithms. Within this latter framework, the most used techniques rely on video prediction deep learning methods which take in input time series of radar reflectivity images to predict the next future sequence of reflectivity images, from which the predicted rainfall quantities are extrapolated. Differently from the previous works, the present paper proposes a deep learning method, exploiting videos of radar reflectivity frames as input and lightning data to realize a warning machine able to sound timely alarms of possible severe thunderstorm events. The problem is recast in a classification one in which the extreme events to be predicted are characterized by a an high level of precipitation and lightning density. From a technical viewpoint, the computational core of this approach is an ensemble learning method based on the recently introduced value-weighted skill scores for both transforming the probabilistic outcomes of the neural network into binary predictions and assessing the forecasting performance. Such value-weighted skill scores are particularly suitable for binary predictions performed over time since they take into account the time evolution of events and predictions paying attention to the value of the prediction for the forecaster. The result of this study is a warning machine validated against weather radar data recorded in the Liguria region, in Italy.

Longitudinal Cluster Analysis of Hemodialysis Patients with COVID-19 in the Pre-Vaccination Era.

Coronavirus disease 2019 (COVID-19) in hemodialysis patients (HD) is characterized by heterogeneity of clinical presentation and outcomes. To stratify patients, we collected clinical and laboratory data in two cohorts of HD patients at COVID-19 diagnosis and during the following 4 weeks. Baseline and longitudinal values were used to build a linear mixed effect model (LME) and define different clusters. The development of the LME model in the derivation cohort of 17 HD patients (66.7 ± 12.3 years, eight males) allowed the characterization of two clusters (cl1 and cl2). Patients in cl1 presented a prevalence of females, higher lymphocyte count, and lower levels of lactate dehydrogenase, C-reactive protein, and CD8 + T memory stem cells as a possible result of a milder inflammation. Then, this model was tested in an independent validation cohort of 30 HD patients (73.3 ± 16.3 years, 16 males) assigned to cl1 or cl2 (16 and 14 patients, respectively). The cluster comparison confirmed that cl1 presented a milder form of COVID-19 associated with reduced disease activity, hospitalization, mortality rate, and oxygen requirement. Clustering analysis on longitudinal data allowed patient stratification and identification of the patients at high risk of complications. This strategy could be suitable in different clinical settings.

First Hard X-Ray Imaging Results by Solar Orbiter STIX.

The Spectrometer/Telescope for Imaging X-rays (STIX) is one of six remote sensing instruments on-board Solar Orbiter. The telescope applies an indirect imaging technique that uses the measurement of 30 visibilities, i.e., angular Fourier components of the solar flare X-ray source. Hence, the imaging problem for STIX consists of the Fourier inversion of the data measured by the instrument. In this work, we show that the visibility amplitude and phase calibration of 24 out of 30 STIX sub-collimators has reached a satisfactory level for scientific data exploitation and that a set of imaging methods is able to provide the first hard X-ray images of solar flares from Solar Orbiter. Four visibility-based image reconstruction methods and one count-based are applied to calibrated STIX observations of six events with GOES class between C4 and M4 that occurred in May 2021. The resulting reconstructions are compared to those provided by an optimization algorithm used for fitting the amplitudes of STIX visibilities. We show that the five imaging methods produce results morphologically consistent with the ones provided by the Atmospheric Imaging Assembly on board the Solar Dynamic Observatory (SDO/AIA) in UV wavelengths. The χ 2 values and the parameters of the reconstructed sources are comparable between methods, thus confirming their robustness.

Bad and Good Errors: Value-Weighted Skill Scores in Deep Ensemble Learning.

Forecast verification is a crucial task for assessing the predictive power of prognostic model forecasts and it is usually implemented by checking quality-based skill scores. In this article, we propose a novel approach to realize forecast verification focusing not just on the forecast quality but rather on its value. Specifically, we introduce a strategy for assessing the severity of forecast errors based on the evidence that, on the one hand, a false alarm just anticipating an occurring event is better than one in the middle of consecutive nonoccurring events, and that, on the other hand, a miss of an isolated event has a worse impact than a miss of a single event, which is part of several consecutive occurrences. Relying on this idea, we introduce a notion of value-weighted skill scores giving greater importance to the value of the prediction rather than to its quality. Then, we introduce an ensemble strategy to maximize quality-based and value-weighted skill scores independently of one another. We test it on the predictions provided by deep learning methods for binary classification in the case of four applications concerned with pollution, space weather, stock price, and IoT data stream forecasting. Our experimental studies show that using the ensemble strategy for maximizing the value-weighted skill scores generally improves both the value and quality of the forecast.

Transfreq: A Python package for computing the theta-to-alpha transition frequency from resting state electroencephalographic data.

A classic approach to estimate individual theta-to-alpha transition frequency (TF) requires two electroencephalographic (EEG) recordings, one acquired in a resting state condition and one showing alpha desynchronisation due, for example, to task execution. This translates into long recording sessions that may be cumbersome in studies involving patients. Moreover, an incomplete desynchronisation of the alpha rhythm may compromise TF estimates. Here we present transfreq, a publicly available Python library that allows TF computation from resting state data by clustering the spectral profiles associated to the EEG channels based on their content in alpha and theta bands. A detailed overview of transfreq core algorithm and software architecture is provided. Its effectiveness and robustness across different experimental setups are demonstrated on a publicly available EEG data set and on in-house recordings, including scenarios where the classic approach fails to estimate TF. We conclude with a proof of concept of the predictive power of transfreq TF as a clinical marker. Specifically, we present a scenario where transfreq TF shows a stronger correlation with the mini mental state examination score than other widely used EEG features, including individual alpha peak and median/mean frequency. The documentation of transfreq and the codes for reproducing the analysis of the article with the open-source data set are available online at https://elisabettavallarino.github.io/transfreq/. Motivated by the results showed in this article, we believe our method will provide a robust tool for discovering markers of neurodegenerative diseases.

Neurological long-COVID in the outpatient clinic: Two subtypes, two courses.

INTRODUCTION: Symptoms referable to central and peripheral nervous system involvement are often evident both during the acute phase of COVID-19 infection and during long-COVID. In this study, we evaluated a population of patients with prior COVID-19 infection who showed signs and symptoms consistent with neurological long-COVID. METHODS: We prospectively collected demographic and acute phase course data from patients with prior COVID-19 infection who showed symptoms related to neurological involvement in the long-COVID phase. Firstly, we performed a multivariate logistic linear regression analysis to investigate the impact of demographic and clinical data, the severity of the acute COVID-19 infection and hospitalization course, on the post-COVID neurological symptoms at three months follow-up. Secondly, we performed an unsupervised clustering analysis to investigate whether there was evidence of different subtypes of neurological long COVID-19. RESULTS: One hundred and nine patients referred to the neurological post-COVID outpatient clinic. Clustering analysis on the most common neurological symptoms returned two well-separated and well-balanced clusters: long-COVID type 1 contains the subjects with memory disturbances, psychological impairment, headache, anosmia and ageusia, while long-COVID type 2 contains all the subjects with reported symptoms related to PNS involvement. The analysis of potential risk-factors among the demographic, clinical presentation, COVID 19 severity and hospitalization course variables showed that the number of comorbidities at onset, the BMI, the number of COVID-19 symptoms, the number of non-neurological complications and a more severe course of the acute infection were all, on average, higher for the cluster of subjects with reported symptoms related to PNS involvement. CONCLUSION: We analyzed the characteristics of neurological long-COVID and presented a method to identify well-defined patient groups with distinct symptoms and risk factors. The proposed method could potentially enable treatment deployment by identifying the optimal interventions and services for well-defined patient groups, so alleviating long-COVID and easing recovery.

18F-FDG-PET correlates of aging and disease course in ALS as revealed by distinct PVC approaches.

PURPOSE: The partial volume effect (PVE) complicates PET studies of neurodegenerative diseases, since a decreased 18F-FDG retention might be influenced by atrophy-related changes of cortical regions. Multiple partial volume correction (PVC) methods have been therefore developed, but their application in amyotrophic lateral sclerosis (ALS) is still rare. Additionally, even if metabolic changes have been established in ALS, no study yet has investigated how these may be influenced by aging and disease course. The aim of the present study was therefore to apply and compare multiple PVC approaches to explore aging and disease course-related hypometabolism in ALS. METHODS: PET and MRI data from 15 ALS patients were analyzed using PETSurfer to implement 4 distinct PVC methods: noPVC, Meltzer (MZ), Müller-Gärtner (MG) and Symmetric Geometric Transfer Matrix (SGTM). For each method and Region of Interest (ROI), the 18F-FDG value was regressed against subject age and disease duration. RESULTS: MG/SGTM application almost halved the number of regions showing a significant age-related hypometabolism, while the same effect was not observed for disease course, where only the distribution of identified regions varied. Three distinct patterns emerged: regions showing a significant age/disease course-related effect across all the different methods, regions yielding significance only with MG/SGTM application, and regions maintaining significance only with noPVC/MZ application. CONCLUSIONS: Significant changes in the distribution of aging and disease course-related hypometabolism were observed when the effect of the underlying structural status was considered, supporting the need for investigate the impact of PVE on PET-assessed metabolic changes in clinical and research settings.

Opportunistic skeletal muscle metrics as prognostic tools in metastatic castration-resistant prostate cancer patients candidates to receive Radium-223.

OBJECTIVE: Androgen deprivation therapy alters body composition promoting a significant loss in skeletal muscle (SM) mass through inflammation and oxidative damage. We verified whether SM anthropometric composition and metabolism are associated with unfavourable overall survival (OS) in a retrospective cohort of metastatic castration-resistant prostate cancer (mCRPC) patients submitted to 18F-Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography (FDG PET/CT) imaging before receiving Radium-223. PATIENTS AND METHODS: Low-dose CT were opportunistically analysed using a cross-sectional approach to calculate SM and adipose tissue areas at the third lumbar vertebra level. Moreover, a 3D computational method was used to extract psoas muscles to evaluate their volume, Hounsfield Units (HU) and FDG retention estimated by the standardized uptake value (SUV). Baseline established clinical, lab and imaging prognosticators were also recorded. RESULTS: SM area predicted OS at univariate analysis. However, this capability was not additive to the power of mean HU and maximum SUV of psoas muscles volume. These factors were thus combined in the Attenuation Metabolic Index (AMI) whose power was tested in a novel uni- and multivariable model. While Prostate-Specific Antigen (PSA), Alkaline Phosphatase (ALP), Lactate Dehydrogenase and Hemoglobin, Metabolic Tumor Volume, Total Lesion Glycolysis and AMI were associated with long-term OS at the univariate analyses, only PSA, ALP and AMI resulted in independent prognosticator at the multivariate analysis. CONCLUSION: The present data suggest that assessing individual 'patients' SM metrics through an opportunistic operator-independent computational analysis of FDG PET/CT imaging provides prognostic insights in mCRPC patients candidates to receive Radium-223.

A Simple Way to Apply Moisture Chamber Eyeglasses During Oculoplastic Surgery.

Proper eyelid closure and a normal blink reflex are essential to maintain a stable tear film and a healthy corneal surface. We present a simple technique to apply moisture chamber eyeglasses after oculoplastic surgery. The Opti-Gard® protective eyewear is faster, easily applicable and well performing. It can be used with different face shape, and it does not require any additional trimming or modification. This technical note explains a very simple, economical and less time-consuming method to prevent a postoperative dry keratoconjunctivitis. It may be applied to all procedures within the field of ophthalmic surgery, including oculoplastic surgery.Level of Evidence V This journal requires that authors assign a level of evidence to each article. For a full description of these evidence-based medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Longitudinal analysis of atherosclerotic plaques evolution: an 18F-NaF PET/CT study.

PURPOSE: 18F-NaF-PET/CT can detect mineral metabolism within atherosclerotic plaques. To ascertain whether their 18F-NaF uptake purports progression, this index was compared with subsequent morphologic evolution. METHODS: 71 patients underwent two consecutive 18F-NaF-PET/CTs (PET1/PET2). In PET1, non-calcified 18F-NaF hot spots were identified in the abdominal aorta. Their mean/max HU was compared with those of a non-calcified control region (CR) and with corresponding areas in PET2. A target-to-background ratio (TBR), mean density (HU), and calcium score (CS) were calculated on calcified atherosclerotic plaques in PET1 and compared with those in PET2. A VOI including the entire abdominal aorta was drawn; mean TBR and total CS were calculated on PET1 and compared with those PET2. RESULTS: Hot spots in PET1 (N = 179) had a greater HU than CR (48 ± 8 vs 37 ± 9, P < .01). Mean hot spots HU increased to 59 ± 12 in PET2 (P < .001). New calcifications appeared at the hot spots site in 73 cases (41%). Baseline atherosclerotic plaque's (N = 375) TBR was proportional to percent HU and CS increase (P < .01 for both). Aortic CS increased (P < .001); the whole-aorta TBR in PET1 correlated with the CS increase between the baseline and the second PET/CT (R = .63, P < .01). CONCLUSIONS: 18F-NaF-PET/CT depicts the early stages of plaques development and tracks their evolution over time.

Computational quantification of global effects induced by mutations and drugs in signaling networks of colorectal cancer cells.

Colorectal cancer (CRC) is one of the most deadly and commonly diagnosed tumors worldwide. Several genes are involved in its development and progression. The most frequent mutations concern APC, KRAS, SMAD4, and TP53 genes, suggesting that CRC relies on the concomitant alteration of the related pathways. However, with classic molecular approaches, it is not easy to simultaneously analyze the interconnections between these pathways. To overcome this limitation, recently these pathways have been included in a huge chemical reaction network (CRN) describing how information sensed from the environment by growth factors is processed by healthy colorectal cells. Starting from this CRN, we propose a computational model which simulates the effects induced by single or multiple concurrent mutations on the global signaling network. The model has been tested in three scenarios. First, we have quantified the changes induced on the concentration of the proteins of the network by a mutation in APC, KRAS, SMAD4, or TP53. Second, we have computed the changes in the concentration of p53 induced by up to two concurrent mutations affecting proteins upstreams in the network. Third, we have considered a mutated cell affected by a gain of function of KRAS, and we have simulated the action of Dabrafenib, showing that the proposed model can be used to determine the most effective amount of drug to be delivered to the cell. In general, the proposed approach displays several advantages, in that it allows to quantify the alteration in the concentration of the proteins resulting from a single or multiple given mutations. Moreover, simulations of the global signaling network of CRC may be used to identify new therapeutic targets, or to disclose unexpected interactions between the involved pathways.

Radiomics and Artificial Intelligence for Outcome Prediction in Multiple Myeloma Patients Undergoing Autologous Transplantation: A Feasibility Study with CT Data.

Multiple myeloma is a plasma cell dyscrasia characterized by focal and non-focal bone lesions. Radiomic techniques extract morphological information from computerized tomography images and exploit them for stratification and risk prediction purposes. However, few papers so far have applied radiomics to multiple myeloma. A retrospective study approved by the institutional review board: n = 51 transplanted patients and n = 33 (64%) with focal lesion analyzed via an open-source toolbox that extracted 109 radiomics features. We also applied a dedicated tool for computing 24 features describing the whole skeleton asset. The redundancy reduction was realized via correlation and principal component analysis. Fuzzy clustering (FC) and Hough transform filtering (HTF) allowed for patient stratification, with effectiveness assessed by four skill scores. The highest sensitivity and critical success index (CSI) were obtained representing each patient, with 17 focal features selected via correlation with the 24 features describing the overall skeletal asset. These scores were higher than the ones associated with a standard cytogenetic classification. The Mann-Whitney U-test showed that three among the 17 imaging descriptors passed the null hypothesis. This AI-based interpretation of radiomics features stratified relapsed and non-relapsed MM patients, showing some potentiality for the determination of the prognostic image-based biomarkers in disease follow-up.