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BACKGROUND: Standard high-flow nasal cannula (HFNC) is a respiratory support device widely used to manage post-extubation hypoxemic acute respiratory failure (hARF) due to greater comfort, oxygenation, alveolar recruitment, humidification, and reduction of dead space, as compared to conventional oxygen therapy. On the contrary, the effects of the new asymmetrical HFNC interface (Optiflow® Duet system (Fisher & Paykel, Healthcare, Auckland, New Zealand) is still under discussion. Our aim is investigating whether the use of asymmetrical HFNC interface presents any relevant difference, compared with the standard configuration, on lung aeration (as assessed by end-expiratory lung impedance (EELI) measured by electrical impedance tomography (EIT)), diaphragm ultrasound thickening fraction (TFdi) and excursion (DE), ventilatory efficiency (estimated by corrected minute ventilation (MV)), gas exchange, dyspnea, and comfort. METHODS: Pilot physiological crossover randomized controlled study enrolling 20 adults admitted to the Intensive Care unit, invasively ventilated for at least 24 h, and developing post-extubation hARF, i.e., PaO2/set FiO2

Abstract Background The application of semi-conductor detectors such as cadmium–zinc–telluride (CZT) in nuclear medicine improves extrinsic energy resolution and count sensitivity due to the direct conversion of gamma photons into electric signals. A 3D-ring pixelated CZT system named StarGuide was recently developed and implemented by GE HealthCare for SPECT acquisition. The system consists of 12 detector columns with seven modules of 16 × 16 CZT pixelated crystals, each with an integrated parallel-hole tungsten collimator. The axial coverage is 27.5 cm. The detector thickness is 7.25 mm, which allows acquisitions in the energy range [40–279] keV. Since there is currently no performance characterization specific to 3D-ring CZT SPECT systems, the National Electrical Manufacturers Association (NEMA) NU 1-2018 clinical standard can be tailored to these cameras. The aim of this study was to evaluate the performance of the SPECT/CT StarGuide system according to the NEMA NU 1-2018 clinical standard specifically adapted to characterize the new 3D-ring CZT. Results Due to the integrated collimator, the system geometry and the pixelated nature of the detector, some NEMA tests have been adapted to the features of the system. The extrinsic measured energy resolution was about 5–6% for the tested isotopes (99mTc, 123I and 57Co); the maximum count rate was 760 kcps and the observed count rate at 20% loss was 917 kcps. The system spatial resolution in air extrapolated at 10 cm with 99mTc was 7.2 mm, while the SPECT spatial resolutions with scatter were 4.2, 3.7 and 3.6 mm in a central, radial and tangential direction respectively. Single head sensitivity value for 99mTc was 97 cps/MBq; with 12 detector columns, the system volumetric sensitivity reached 520 kcps MBq−1 cc−1. Conclusions The performance tests of the StarGuide can be performed according to the NEMA NU 1-2018 standard with some adaptations. The system has shown promising results, particularly in terms of energy resolution, spatial resolution and volumetric sensitivity, potentially leading to higher quality clinical images.

Abstract Background Standard high-flow nasal cannula (HFNC) is a respiratory support device widely used to manage post-extubation hypoxemic acute respiratory failure (hARF) due to greater comfort, oxygenation, alveolar recruitment, humidification, and reduction of dead space, as compared to conventional oxygen therapy. On the contrary, the effects of the new asymmetrical HFNC interface (Optiflow® Duet system (Fisher & Paykel, Healthcare, Auckland, New Zealand) is still under discussion. Our aim is investigating whether the use of asymmetrical HFNC interface presents any relevant difference, compared with the standard configuration, on lung aeration (as assessed by end-expiratory lung impedance (EELI) measured by electrical impedance tomography (EIT)), diaphragm ultrasound thickening fraction (TFdi) and excursion (DE), ventilatory efficiency (estimated by corrected minute ventilation (MV)), gas exchange, dyspnea, and comfort. Methods Pilot physiological crossover randomized controlled study enrolling 20 adults admitted to the Intensive Care unit, invasively ventilated for at least 24 h, and developing post-extubation hARF, i.e., PaO2/set FiO2

Measurement(s) Radiomic Features Technology Type(s) Digital Phantoms and Radiomic Software Tools

Abstract In this study, we tested and compared radiomics and deep learning-based approaches on the public LUNG1 dataset, for the prediction of 2-year overall survival (OS) in non-small cell lung cancer patients. Radiomic features were extracted from the gross tumor volume using Pyradiomics, while deep features were extracted from bi-dimensional tumor slices by convolutional autoencoder. Both radiomic and deep features were fed to 24 different pipelines formed by the combination of four feature selection/reduction methods and six classifiers. Direct classification through convolutional neural networks (CNNs) was also performed. Each approach was investigated with and without the inclusion of clinical parameters. The maximum area under the receiver operating characteristic on the test set improved from 0.59, obtained for the baseline clinical model, to 0.67 ± 0.03, 0.63 ± 0.03 and 0.67 ± 0.02 for models based on radiomic features, deep features, and their combination, and to 0.64 ± 0.04 for direct CNN classification. Despite the high number of pipelines and approaches tested, results were comparable and in line with previous works, hence confirming that it is challenging to extract further imaging-based information from the LUNG1 dataset for the prediction of 2-year OS.

Background and purpose: Prediction models may be reliable decision-support tools to reduce the workload associated with the measurement-based patient-specific quality assurance (PSQA) of radiotherapy plans. This study compared the effectiveness of three different models based on delivery parameters, complexity metrics and sinogram radiomics features as tools for virtual-PSQA (vPSQA) of helical tomotherapy (HT) plans. Materials and methods: A dataset including 881 RT plans created with two different treatment planning systems (TPSs) was collected. Sixty-five indicators including 12 delivery parameters (DP) and 53 complexity metrics (CM) were extracted using a dedicated software library. Additionally, 174 radiomics features (RF) were extracted from the plans’ sinograms. Three groups of variables were formed: A (DP), B (DP + CM) and C (DP + CM + RF). Regression models were trained to predict the gamma index passing rate PRγ(3%G, 2mm) and the impact of each group of variables was investigated. ROC-AUC analysis measured the ability of the models to accurately discriminate between 'deliverable' and 'non-deliverable' plans. Results: The best performance was achieved by model C which allowed detecting around 16% and 63% of the 'deliverable' plans with 100% sensitivity for the two TPSs, respectively. In a real clinical scenario, this would have decreased the whole PSQA workload by approximately 35%. Conclusions: The combination of delivery parameters, complexity metrics and sinogram radiomics features allows for robust and reliable PSQA gamma passing rate predictions and high-sensitivity detection of a fraction of deliverable plans for one of the two TPSs. Promising yet improvable results were obtained for the other one. The results foster a future adoption of vPSQA programs for HT.

Background: Complexity metrics are mathematical quantities designed to quantify aspects of radiotherapy treatment plans that may affect both their deliverability and dosimetric accuracy. Despite numerous studies investigating their utility, there remains a notable absence of shared tools for their extraction. Purpose: This study introduces UCoMX (Universal Complexity Metrics Extractor), a software package designed for the extraction of complexity metrics from the DICOM‐RT plan files of radiotherapy treatments. Methods: UCoMX is developed around two extraction engines: VCoMX (VMAT Complexity Metrics Extractor) for VMAT/IMRT plans, and TCoMX (Tomotherapy Complexity Metrics Extractor) tailored for Helical Tomotherapy plans. The software, built using Matlab, is freely available in both Matlab‐based and stand‐alone versions. More than 90 complexity metrics, drawn from relevant literature, are implemented in the package: 43 for VMAT/IMRT and 51 for Helical Tomotherapy. Results: The package is designed to read DICOM‐RT plan files generated by most commercially available Treatment Planning Systems (TPSs), across various treatment units. A reference dataset containing VMAT, IMRT, and Helical Tomotherapy plans is provided to serve as a reference for comparing UCoMX with other in‐house systems available at other centers. Conclusion: UCoMX offers a straightforward solution for extracting complexity metrics from radiotherapy plans. Its versatility is enhanced through different versions, including Matlab‐based and stand‐alone, and its compatibility with a wide range of commercially available TPSs and treatment units. UCoMX presents a free, user‐friendly tool empowering researchers to compute the complexity of treatment plans efficiently. [ABSTRACT FROM AUTHOR]

Abstract Purpose This study presents patient-specific quality assurance (QA) results from the first 395 clinical cases for the new helical TomoTherapy® platform (Radixact) coupled with dedicated Precision TPS. Methods The passing rate of the Gamma Index (GP%) of 395 helical QA of patient-specific tomotherapy, acquired with ArcCHECK, is presented, analysed and correlated to various parameters of the plan. Following TG-218 recommendations, the clinic specific action limit (ALcs) and tolerance limit (TLcs) were calculated for our clinic and monitored during the analysed period. Results The mean values ​​(± 1 standard deviation) of GP% (3%/2 mm) (both global and local normalization) are: 97.6% and 90.9%, respectively. The proposed ALcs and TLcs, after a period of two years’ process monitoring are 89.4% and 91.1% respectively. Conclusions The phantom measurements closely match the planned dose distributions, demonstrating that the calculation accuracy of the new Precision TPS and the delivery accuracy of the Radixact unit are adequate, with respect to international guidelines and reports. Furthermore, a first correlation with the planning parameters was made. Action and tolerance limits have been set for the new Radixact Linac.

Purpose: This study aims to establish whether metabolic parameters obtainable from FCH PET/CT can predict long-term response to radical radiotherapy (rRT) in patients with localized prostate cancer (PCa). Methods: Drawing on a single-center database, we retrospectively reviewed the pre-treatment FCH PET/CT scans of 50 patients who underwent rRT between 2012 and 2017. Patients were enrolled if they had a follow-up of at least 3 years after rRT. Various metabolic parameters were considered for each PET/CT, including FCH multifocality. rRT was administered to all patients for a total equivalent dose of 76–80 Gy, using a standard or hypofractionated schedule. Patients were classified as disease-free (DF) if their PSA levels after rRT rose by

Lung cancer represents the second most common malignancy worldwide and lymph node (LN) involvement serves as a crucial prognostic factor for tailoring treatment approaches. Invasive methods, such as mediastinoscopy and endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA), are employed for preoperative LN staging. Among the preoperative non-invasive diagnostic methods, computed tomography (CT) and, recently, positron emission tomography (PET)/CT with fluorine-18-fludeoxyglucose ([18F]FDG) are routinely recommended by several guidelines; however, they can both miss pathologically proven LN metastases, with an incidence up to 26% for patients staged with [18F]FDG PET/CT. These undetected metastases, known as occult LN metastases (OLMs), are usually cases of micro-metastasis or small LN metastasis (shortest radius below 10 mm). Hence, it is crucial to find novel approaches to increase their discovery rate. Radiomics is an emerging field that seeks to uncover and quantify the concealed information present in biomedical images by utilising machine or deep learning approaches. The extracted features can be integrated into predictive models, as numerous reports have emphasised their usefulness in the staging of lung cancer. However, there is a paucity of studies examining the detection of OLMs using quantitative features derived from images. Hence, the objective of this review was to investigate the potential application of PET- and/or CT-derived quantitative radiomic features for the identification of OLMs.