Multi-omics staging of locally advanced rectal cancer predicts treatment response: a pilot study.

Treatment response assessment of rectal cancer patients is a critical component of personalized cancer care and it allows to identify suitable candidates for organ-preserving strategies. This pilot study employed a novel multi-omics approach combining MRI-based radiomic features and untargeted metabolomics to infer treatment response at staging. The metabolic signature highlighted how tumor cell viability is predictively down-regulated, while the response to oxidative stress was up-regulated in responder patients, showing significantly reduced oxoproline values at baseline compared to non-responder patients (p-value < 10-4). Tumors with a high degree of texture homogeneity, as assessed by radiomics, were more likely to achieve a major pathological response (p-value < 10-3). A machine learning classifier was implemented to summarize the multi-omics information and discriminate responders and non-responders. Combining all available radiomic and metabolomic features, the classifier delivered an AUC of 0.864 (± 0.083, p-value < 10-3) with a best-point sensitivity of 90.9% and a specificity of 81.8%. Our results suggest that a multi-omics approach, integrating radiomics and metabolomic data, can enhance the predictive value of standard MRI and could help to avoid unnecessary surgical treatments and their associated long-term complications.

Air embolism as a rare complication of lung biopsy: A case report.

Lung biopsy is an important interventional radiology procedure allowing the characterization of lesions with suspected malignancy. The most frequent complications are pneumothorax and hemorrhage. Air embolism is a rare but potentially fatal occurrence. In this case report, we present an air embolism after core needle CT-guided biopsy showing CT and MRI features that radiologists should expect in the everyday clinical practice.

Musculoskeletal disorders in padel: from biomechanics to sonography.

Padel is a racket sport, combining high-frequency and low-intensity athletic gestures, that has been gaining growing scientific interest in recent years. Musculoskeletal injuries are very common among padel players with an incidence rate of 3 per 1000 h of training and 8 per 1000 matches. To the best of our knowledge, a comprehensive collection describing the most common sonographic findings in padel players with musculoskeletal injuries is lacking in the pertinent literature. In this sense, starting from the biomechanical features of padel-specific gestures we have reported the ultrasonographic patterns of most frequent injuries involving the upper limb, the trunk, and the lower limb. Indeed, comprehensive knowledge of the biomechanical and clinical features of musculoskeletal injuries in padel is paramount to accurately perform a detailed ultrasound examination of the affected anatomical site. So, the present investigation aims to provide a practical guide, simple and ready-to-use in daily practice, to optimize the sonographic assessment of padel players by combining it with the clinical findings and the biomechanical features of athletic gestures.

Machine Learning Analysis in Diffusion Kurtosis Imaging for Discriminating Pediatric Posterior Fossa Tumors: A Repeatability and Accuracy Pilot Study.

Background and purpose: Differentiating pediatric posterior fossa (PF) tumors such as medulloblastoma (MB), ependymoma (EP), and pilocytic astrocytoma (PA) remains relevant, because of important treatment and prognostic implications. Diffusion kurtosis imaging (DKI) has not yet been investigated for discrimination of pediatric PF tumors. Estimating diffusion values from whole-tumor-based (VOI) segmentations may improve diffusion measurement repeatability compared to conventional region-of-interest (ROI) approaches. Our purpose was to compare repeatability between ROI and VOI DKI-derived diffusion measurements and assess DKI accuracy in discriminating among pediatric PF tumors. Materials and methods: We retrospectively analyzed 34 children (M, F, mean age 7.48 years) with PF tumors who underwent preoperative examination on a 3 Tesla magnet, including DKI. For each patient, two neuroradiologists independently segmented the whole solid tumor, the ROI of the area of maximum tumor diameter, and a small 5 mm ROI. The automated analysis pipeline included inter-observer variability, statistical, and machine learning (ML) analyses. We evaluated inter-observer variability with coefficient of variation (COV) and Bland-Altman plots. We estimated DKI metrics accuracy in discriminating among tumor histology with MANOVA analysis. In order to account for class imbalances, we applied SMOTE to balance the dataset. Finally, we performed a Random Forest (RF) machine learning classification analysis based on all DKI metrics from the SMOTE dataset by partitioning 70/30 the training and testing cohort. Results: Tumor histology included medulloblastoma (15), pilocytic astrocytoma (14), and ependymoma (5). VOI-based measurements presented lower variability than ROI-based measurements across all DKI metrics and were used for the analysis. DKI-derived metrics could accurately discriminate between tumor subtypes (Pillai's trace: p < 0.001). SMOTE generated 11 synthetic observations (10 EP and 1 PA), resulting in a balanced dataset with 45 instances (34 original and 11 synthetic). ML analysis yielded an accuracy of 0.928, which correctly predicted all but one lesion in the testing set. Conclusions: VOI-based measurements presented improved repeatability compared to ROI-based measurements across all diffusion metrics. An ML classification algorithm resulted accurate in discriminating PF tumors on a SMOTE-generated dataset. ML techniques based on DKI-derived metrics are useful for the discrimination of pediatric PF tumors.

Treatment response assessment of acute pyelonephritis: A multi-reader DWI-based MRI approach.

PURPOSE: To evaluate the diagnostic accuracy of a structured reporting score (SRS) in treatment response assessment for acute pyelonephritis (APN) using a diffusion-weighted imaging (DWI) -based MRI approach. Additionally, we explored the influence of reader experience on the interpretation of SRS and DWI, including lesion conspicuity and measurements of Apparent Diffusion Coefficient (ADC) maps. METHODS: Follow-up DWI-based MRIs of 36 patients treated for APN between September 2021 and June 2023 were retrospectively reviewed by three readers. Follow-up blood inflammatory markers were used as reference standard. Treatment response was assessed using a structured reporting score (SRS). Each reader assigned a score from 1 to 3 to the "conspicuity" of the residual disease on DWI. Quantitative ADC measurements were compared with the Mann-Whitney U test. Descriptive statistics and Intraclass Correlation Coefficient (ICC) were calculated. RESULTS: The diagnostic accuracy of SRS was 80.6 %, 76.9 %, and 72.2 % for the Reader 1, 2, and 3 respectively. ICC decreased from 0.82 (Reader 1 and 2), to 0.68 when considering all readers. The average conspicuity varied between 2.3 and 2.7. ADC values were significantly higher in complete responders for Reader 1 and 2 (153.5-154.5 vs 107.7-116.2, p < 0.001). The ICC was good (0.89) for Reader 1 and 2 and moderate (0.60) when considering all readers. CONCLUSIONS: Treatment response of pyelonephritis can be accurately assessed by a DWI-based MRI, potentially avoiding unnecessary contrast agent administration and radiation exposure. SRS and DWI analysis showed a good inter-observer agreement but a certain learning curve may be necessary for less expert readers.

Point-Of-Care low-field MRI in acute Stroke (POCS): protocol for a multicentric prospective open-label study evaluating diagnostic accuracy.

INTRODUCTION: Fast and accurate diagnosis of acute stroke is crucial to timely initiate reperfusion therapies. Conventional high-field (HF) MRI yields the highest accuracy in discriminating early ischaemia from haemorrhages and mimics. Rapid access to HF-MRI is often limited by contraindications or unavailability. Low-field (LF) MRI (<0.5T) can detect several types of brain injury, including ischaemic and haemorrhagic stroke. Implementing LF-MRI in acute stroke care may offer several advantages, including extended applicability, increased safety, faster administration, reduced staffing and costs. This multicentric prospective open-label trial aims to evaluate the diagnostic accuracy of LF-MRI, as a tool to guide treatment decision in acute stroke. METHODS AND ANALYSIS: Consecutive patients accessing the emergency department with suspected stroke dispatch will be recruited at three Italian study units: Azienda Sanitaria Locale (ASL) Abruzzo 1 and 2, Istituto di Ricerca e Cura a Carattere Scientifico (IRCCS) Humanitas Research Hospital. The estimated sample size is 300 patients. Anonymised clinical and LF-MRI data, along with conventional neuroimaging data, will be independently assessed by two external units: Marche Polytechnic University and 'G. Martino' Polyclinic University Hospital. Both units will independently adjudicate the best treatment option, while the latter will provide historical HF-MRI data to develop artificial intelligence algorithms for LF-MRI images interpretation (Free University of Bozen-Bolzano). Agreement with conventional neuroimaging will be evaluated at different time points: hyperacute, acute (24 hours), subacute (72 hours), at discharge and chronic (4 weeks). Further investigations will include feasibility study to develop a mobile stroke unit equipped with LF-MRI and cost-effectiveness analysis. This trial will provide necessary data to validate the use of LF-MRI in acute stroke care. ETHICS AND DISSEMINATION: The study was approved by the Research Ethics Committee of the Abruzzo Region (CEtRA) on 11 May 2023 (approval code: richyvgrg). Results will be disseminated in peer-reviewed journals and presented in academic conferences. TRIAL REGISTRATION NUMBER: NCT05816213; Pre-Results.

BOLD cardiorespiratory pulsatility in the brain: from noise to signal of interest.

Functional magnetic resonance imaging (fMRI) based on the Blood Oxygen Level Dependent (BOLD) contrast has been extensively used to map brain activity and connectivity in health and disease. Standard fMRI preprocessing includes different steps to remove confounds unrelated to neuronal activity. First, this narrative review explores how signal fluctuations due to cardiac and respiratory activity, usually considered as "physiological noise" and regressed out from fMRI time series. However, these signal components bear useful information about some mechanisms of brain functioning (e.g., glymphatic clearance) or cerebrovascular compliance in response to arterial pressure waves. Aging and chronic diseases can cause stiffening of the aorta and other main arteries, with a reduced dampening effect resulting in greater transmission of pressure impulses to the brain. Importantly, the continuous hammering of cardiac pulsations can produce local alterations of the mechanical properties of the small cerebral vessels, with a progressive deterioration that ultimately affects neuronal functionality. Second, the review emphasizes how fMRI can study the brain patterns most affected by cardiac pulsations in health and disease with high spatiotemporal resolution, offering the opportunity to identify much more specific risk markers than systemic factors based on measurements of the vascular compliance of large arteries or other global risk factors. In this regard, modern fast fMRI acquisition techniques allow a better characterization of these pulsatile signal components due to reduced aliasing effects, turning what has been traditionally considered as noise in a signal of interest that can be used to develop novel non-invasive biomarkers in different clinical contexts.