Image Embeddings Extracted from CNNs Outperform Other Transfer Learning Approaches in Classification of Chest Radiographs

To identify the best transfer learning approach for the identification of the most frequent abnormalities on chest radiographs (CXRs), we used embeddings extracted from pretrained convolutional neural networks (CNNs). An explainable AI (XAI) model was applied to interpret black-box model predictions and assess its performance. Seven CNNs were trained on CheXpert. Three transfer learning approaches were thereafter applied to a local dataset. The classification results were ensembled using simple and entropy-weighted averaging. We applied Grad-CAM (an XAI model) to produce a saliency map. Grad-CAM maps were compared to manually extracted regions of interest, and the training time was recorded. The best transfer learning model was that which used image embeddings and random forest with simple averaging, with an average AUC of 0.856. Grad-CAM maps showed that the models focused on specific features of each CXR. CNNs pretrained on a large public dataset of medical images can be exploited as feature extractors for tasks of interest. The extracted image embeddings contain relevant information that can be used to train an additional classifier with satisfactory performance on an independent dataset, demonstrating it to be the optimal transfer learning strategy and overcoming the need for large private datasets, extensive computational resources, and long training times

Radiomic Gradient in Peritumoural Tissue of Liver Metastases: A Biomarker for Clinical Practice? Analysing Density, Entropy, and Uniformity Variations with Distance from the Tumour

The radiomic analysis of the tissue surrounding colorectal liver metastases (CRLM) enhances the prediction accuracy of pathology data and survival. We explored the variation of the textural features in the peritumoural tissue as the distance from CRLM increases. We considered patients with hypodense CRLMs >10 mm and high-quality computed tomography (CT). In the portal phase, we segmented (1) the tumour, (2) a series of concentric rims at a progressively increasing distance from CRLM (from one to ten millimetres), and (3) a cylinder of normal parenchyma (Liver-VOI). Sixty-three CRLMs in 51 patients were analysed. Median peritumoural HU values were similar to Liver-VOI, except for the first millimetre around the CRLM. Entropy progressively decreased (from 3.11 of CRLM to 2.54 of Liver-VOI), while uniformity increased (from 0.135 to 0.199, p < 0.001). At 10 mm from CRLM, entropy was similar to the Liver-VOI in 62% of cases and uniformity in 46%. In small CRLMs (≤30 mm) and responders to chemotherapy, normalisation of entropy and uniformity values occurred in a higher proportion of cases and at a shorter distance. The radiomic analysis of the parenchyma surrounding CRLMs unveiled a wide halo of progressively decreasing entropy and increasing uniformity despite a normal radiological aspect. Underlying pathology data should be investigated

Lung Ultrasound in Pediatrics and Neonatology: An Update

The potential role of ultrasound for the diagnosis of pulmonary diseases is a recent field of research, because, traditionally, lungs have been considered unsuitable for ultrasonography for the high presence of air and thoracic cage that prevent a clear evaluation of the organ. The peculiar anatomy of the pediatric chest favors the use of lung ultrasound (LUS) for the diagnosis of respiratory conditions through the interpretation of artefacts generated at the pleural surface, correlating them to disease-specific patterns. Recent studies demonstrate that LUS can be a valid alternative to chest X-rays for the diagnosis of pulmonary diseases, especially in children to avoid excessive exposure to ionizing radiations. This review focuses on the description of normal and abnormal findings during LUS of the most common pediatric pathologies. Current literature demonstrates usefulness of LUS that may become a fundamental tool for the whole spectrum of lung pathologies to guide both diagnostic and therapeutic decisions

Acute Bronchiolitis: Is There a Role for Lung Ultrasound?

Viral bronchiolitis is a common cause of lower respiratory tract infection in the first year of life, considered a health burden because of its morbidity and costs. Its diagnosis is based on history and physical examination and the role of radiographic examination is limited to atypical cases. Thus far, Lung Ultrasound (LUS) is not considered in the diagnostic algorithm for bronchiolitis

Lung Ultrasound and Clinical Progression of Acute Bronchiolitis: A Prospective Observational Single-Center Study

Background and Objectives:Recent literature suggests that lung ultrasound might have a role in the diagnosis and management of bronchiolitis. The aim of the study is to evaluate the relationship between an ultrasound score and the clinical progression of bronchiolitis: need for supplemental oxygen, duration of oxygen therapy and hospital stay.Materials and Methods:This was a prospective observational single-center study, conducted in a pediatric unit during the 2017-2018 epidemic periods. All consecutive patients admitted with clinical signs of acute bronchiolitis, but without the need for supplemental oxygen, underwent a lung ultrasound in the first 24 h of hospital care. The lung involvement was graded based on the ultrasound score. During clinical progression, need for supplemental oxygen, duration of oxygen therapy and duration of hospital stay were recorded.Results:The final analysis included 83 patients, with a mean age of 4.5 +/- 4.1 months. The lung ultrasound score in patients that required supplemental oxygen during hospitalization was 4.5 +/- 1.7 (range: 2.0-8.0), different from the one of the not supplemented infants (2.5 +/- 1.8; range: 0.0-6.0;p< 0.001). Ultrasound score was associated with the need for supplemental oxygen (OR = 2.2; 95% CI = 1.5-3.3;p< 0.0001). Duration of oxygen therapy was not associated with LUS score (p> 0.05). Length of hospital stay (coef. = 0.5; 95% CI = 0.2-0.7;p< 0.0001) correlates with LUS score.Conclusion:Lung ultrasound score correlates with the need of supplemental oxygen and length of hospital stay in infants with acute bronchiolitis

Mapping the research landscape of HPV-positive oropharyngeal cancer: a bibliometric analysis

Objective: The aim of the study is to evaluate the scientific interest, the collaboration patterns and the emerging trends regarding HPV+ OPSCC diagnosis and treatment. Materials and methods: A cross-sectional bibliometric analysis of articles reporting on HPV+ OPSCC within Scopus database was performed and all documents published up to December 31th, 2022 were eligible for analysis. Outcomes included the exploration of key characteristics (number of manuscripts published per year, growth rate, top productive countries, most highly cited papers, and the most well-represented journals), collaboration parameters (international collaboration ratio and networks, co-occurrence networks), keywords analysis (trend topics, factorial analysis). Results: A total of 5200 documents were found, published from March, 1987 to December, 2022. The number of publications increased annually with an average growth rate of 19.94%, reaching a peak of 680 documents published in 2021. The 10 most cited documents (range 1105-4645) were published from 2000 to 2012. The keywords factorial analysis revealed two main clusters: one on epidemiology, diagnosis, prevention and association with other HPV tumors; the other one about the therapeutic options. According to the frequency of keywords, new items are emerging in the last three years regarding the application of Artifical Intelligence (machine learning and radiomics) and the diagnostic biomarkers (circulating tumor DNA). Conclusions: This bibliometric analysis highlights the importance of research efforts in prevention, diagnostics, and treatment strategies for this disease. Given the urgency of optimizing treatment and improving clinical outcomes, further clinical trials are needed to bridge unaddressed gaps in the management of HPV+ OPSCC patients

CT Cadaveric dataset for Radiomics features stability assessment in lumbar vertebrae

Abstract Radiomics features (RFs) studies have showed limitations in the reproducibility of RFs in different acquisition settings. To date, reproducibility studies using CT images mainly rely on phantoms, due to the harness of patient exposure to X-rays. The provided CadAIver dataset has the aims of evaluating how CT scanner parameters effect radiomics features on cadaveric donor. The dataset comprises 112 unique CT acquisitions of a cadaveric truck acquired on 3 different CT scanners varying KV, mA, field-of-view, and reconstruction kernel settings. Technical validation of the CadAIver dataset comprises a comprehensive univariate and multivariate GLM approach to assess stability of each RFs extracted from lumbar vertebrae. The complete dataset is publicly available to be applied for future research in the RFs field, and could foster the creation of a collaborative open CT image database to increase the sample size, the range of available scanners, and the available body districts

Volume-of-Interest Aware Deep Neural Networks for Rapid Chest CT-Based COVID-19 Patient Risk Assessment

Since December 2019, the world has been devastated by the Coronavirus Disease 2019 (COVID-19) pandemic. Emergency Departments have been experiencing situations of urgency where clinical experts, without long experience and mature means in the fight against COVID-19, have to rapidly decide the most proper patient treatment. In this context, we introduce an artificially intelligent tool for effective and efficient Computed Tomography (CT)-based risk assessment to improve treatment and patient care. In this paper, we introduce a data-driven approach built on top of volume-of-interest aware deep neural networks for automatic COVID-19 patient risk assessment (discharged, hospitalized, intensive care unit) based on lung infection quantization through segmentation and, subsequently, CT classification. We tackle the high and varying dimensionality of the CT input by detecting and analyzing only a sub-volume of the CT, the Volume-of-Interest (VoI). Differently from recent strategies that consider infected CT slices without requiring any spatial coherency between them, or use the whole lung volume by applying abrupt and lossy volume down-sampling, we assess only the “most infected volume” composed of slices at its original spatial resolution. To achieve the above, we create, present and publish a new labeled and annotated CT dataset with 626 CT samples from COVID-19 patients. The comparison against such strategies proves the effectiveness of our VoI-based approach. We achieve remarkable performance on patient risk assessment evaluated on balanced data by reaching 88.88%, 89.77%, 94.73% and 88.88% accuracy, sensitivity, specificity and F1-score, respectively