Radiomics and Clinical Data for the Diagnosis of Incidental Pulmonary Nodules and Lung Cancer Screening: Radiolung Integrative Predictive Model.

INTRODUCTION: Early diagnosis of lung cancer (LC) is crucial to improve survival rates. Radiomics models hold promise for enhancing LC diagnosis. This study assesses the impact of integrating a clinical and a radiomic model based on deep learning to predict the malignancy of pulmonary nodules (PN). METHODOLOGY: Prospective cross-sectional study of 97 PNs from 93 patients. Clinical data included epidemiological risk factors and pulmonary function tests. The region of interest of each chest CT containing the PN was analysed. The radiomic model employed a pre-trained convolutional network to extract visual features. From these features, 500 with a positive standard deviation were chosen as inputs for an optimised neural network. The clinical model was estimated by a logistic regression model using clinical data. The malignancy probability from the clinical model was used as the best estimate of the pre-test probability of disease to update the malignancy probability of the radiomic model using a nomogram for Bayes' theorem. RESULTS: The radiomic model had a positive predictive value (PPV) of 86%, an accuracy of 79% and an AUC of 0.67. The clinical model identified DLCO, obstruction index and smoking status as the most consistent clinical predictors associated with outcome. Integrating the clinical features into the deep-learning radiomic model achieves a PPV of 94%, an accuracy of 76% and an AUC of 0.80. CONCLUSIONS: Incorporating clinical data into a deep-learning radiomic model improved PN malignancy assessment, boosting predictive performance. This study supports the potential of combined image-based and clinical features to improve LC diagnosis.

BronchoPose: an analysis of data and model configuration for vision-based bronchoscopy pose estimation.

BACKGROUND AND OBJECTIVE: Recent advances in neural networks and temporal image processing have provided new results and opportunities for vision-based bronchoscopy tracking. However, such progress has been hindered by the lack of comparative experimental data conditions. We address the issue by sharing a novel synthetic dataset, which allows for a fair comparison of methods. Moreover, as incorporating deep learning advances in temporal structures is not yet explored in bronchoscopy navigation, we investigate several neural network architectures for learning temporal information at different levels of subject personalization, providing new insights and results. METHODS: Using our own shared synthetic dataset for bronchoscopy navigation and tracking, we explore deep learning temporal information architectures (Recurrent Neural Networks and 3D convolutions), which have not been fully explored on bronchoscopy tracking, putting a special focus on network efficiency by using a modern backbone (EfficientNet-B0) and ShuffleNet blocks. Finally, we provide a study of different losses for rotation tracking and population modeling schemes (personalized vs. population) for bronchoscopy tracking. RESULTS: Temporal information architectures provide performance improvements, both in position and angle estimation. Additionally, population scheme analysis illustrates the benefits of offering a personalized model, while loss analysis indicates the benefits of using an adequate metric, improving results. We finally compare with a state-of-the-art model obtaining better results both in performance, with  12.2% and  18.7% improvement for position and rotation respectively, and around  67.6% reduction in memory consumption. CONCLUSIONS: Proposed advances in temporal information architectures, loss configuration, and population scheme definition allow for improving the current state of the art in bronchoscopy analysis. Moreover, the publication of the first synthetic dataset allows for further improving bronchoscopy research by enabling proper comparison grounds among methods.

A novel intelligent radiomic analysis of perfusion SPECT/CT images to optimize pulmonary embolism diagnosis in COVID-19 patients.

BACKGROUND: COVID-19 infection, especially in cases with pneumonia, is associated with a high rate of pulmonary embolism (PE). In patients with contraindications for CT pulmonary angiography (CTPA) or non-diagnostic CTPA, perfusion single-photon emission computed tomography/computed tomography (Q-SPECT/CT) is a diagnostic alternative. The goal of this study is to develop a radiomic diagnostic system to detect PE based only on the analysis of Q-SPECT/CT scans. METHODS: This radiomic diagnostic system is based on a local analysis of Q-SPECT/CT volumes that includes both CT and Q-SPECT values for each volume point. We present a combined approach that uses radiomic features extracted from each scan as input into a fully connected classification neural network that optimizes a weighted cross-entropy loss trained to discriminate between three different types of image patterns (pixel sample level): healthy lungs (control group), PE and pneumonia. Four types of models using different configuration of parameters were tested. RESULTS: The proposed radiomic diagnostic system was trained on 20 patients (4,927 sets of samples of three types of image patterns) and validated in a group of 39 patients (4,410 sets of samples of three types of image patterns). In the training group, COVID-19 infection corresponded to 45% of the cases and 51.28% in the test group. In the test group, the best model for determining different types of image patterns with PE presented a sensitivity, specificity, positive predictive value and negative predictive value of 75.1%, 98.2%, 88.9% and 95.4%, respectively. The best model for detecting pneumonia presented a sensitivity, specificity, positive predictive value and negative predictive value of 94.1%, 93.6%, 85.2% and 97.6%, respectively. The area under the curve (AUC) was 0.92 for PE and 0.91 for pneumonia. When the results obtained at the pixel sample level are aggregated into regions of interest, the sensitivity of the PE increases to 85%, and all metrics improve for pneumonia. CONCLUSION: This radiomic diagnostic system was able to identify the different lung imaging patterns and is a first step toward a comprehensive intelligent radiomic system to optimize the diagnosis of PE by Q-SPECT/CT. HIGHLIGHTS: Artificial intelligence applied to Q-SPECT/CT is a diagnostic option in patients with contraindications to CTPA or a non-diagnostic test in times of COVID-19.

Photonic Label-Free Biosensors for Fast and Multiplex Detection of Swine Viral Diseases.

In this paper we present the development of photonic integrated circuit (PIC) biosensors for the label-free detection of six emerging and endemic swine viruses, namely: African Swine Fever Virus (ASFV), Classical Swine Fever Virus (CSFV), Porcine Reproductive and Respiratory Syndrome Virus (PPRSV), Porcine Parvovirus (PPV), Porcine Circovirus 2 (PCV2), and Swine Influenza Virus A (SIV). The optical biosensors are based on evanescent wave technology and, in particular, on Resonant Rings (RRs) fabricated in silicon nitride. The novel biosensors were packaged in an integrated sensing cartridge that included a microfluidic channel for buffer/sample delivery and an optical fiber array for the optical operation of the PICs. Antibodies were used as molecular recognition elements (MREs) and were selected based on western blotting and ELISA experiments to ensure the high sensitivity and specificity of the novel sensors. MREs were immobilized on RR surfaces to capture viral antigens. Antibody-antigen interactions were transduced via the RRs to a measurable resonant shift. Cell culture supernatants for all of the targeted viruses were used to validate the biosensors. Resonant shift responses were dose-dependent. The results were obtained within the framework of the SWINOSTICS project, contributing to cover the need of the novel diagnostic tools to tackle swine viral diseases.

Segmentation of distal airways using structural analysis.

Segmentation of airways in Computed Tomography (CT) scans is a must for accurate support of diagnosis and intervention of many pulmonary disorders. In particular, lung cancer diagnosis would benefit from segmentations reaching most distal airways. We present a method that combines descriptors of bronchi local appearance and graph global structural analysis to fine-tune thresholds on the descriptors adapted for each bronchial level. We have compared our method to the top performers of the EXACT09 challenge and to a commercial software for biopsy planning evaluated in an own-collected data-base of high resolution CT scans acquired under different breathing conditions. Results on EXACT09 data show that our method provides a high leakage reduction with minimum loss in airway detection. Results on our data-base show the reliability across varying breathing conditions and a competitive performance for biopsy planning compared to a commercial solution.

Design and Development of Photonic Biosensors for Swine Viral Diseases Detection.

In this paper we introduce a field diagnostic device based on the combination of advanced bio-sensing and photonics technologies, to tackle emerging and endemic viruses causing swine epidemics, and consequently significant economic damage in farms. The device is based on the use of microring resonators fabricated in silicon nitride with CMOS compatible techniques. In the paper, the designed and fabricated photonic integrated circuit (PIC) sensors are presented and characterized, showing an optimized performance in terms of optical losses (30 dB per ring) and extinction ration for ring resonances (15 dB). Furthermore, the results of an experiment for porcine circovirus 2 (PCV2) detection by using the developed biosensors are presented. Positive detection for different virus concentrations has been obtained. The device is currently under development in the framework of the EU Commission co-funded project SWINOSTICS.

Selection of Radiomics Features based on their Reproducibility.

Dimensionality reduction is key to alleviate machine learning artifacts in clinical applications with Small Sample Size (SSS) unbalanced datasets. Existing methods rely on either the probabilistic distribution of training data or the discriminant power of the reduced space, disregarding the impact of repeatability and uncertainty in features.In the present study is proposed the use of reproducibility of radiomics features to select features with high inter-class correlation coefficient (ICC). The reproducibility includes the variability introduced in the image acquisition, like medical scans acquisition parameters and convolution kernels, that affects intensity-based features and tumor annotations made by physicians, that influences morphological descriptors of the lesion.For the reproducibility of radiomics features three studies were conducted on cases collected at Vall Hebron Oncology Institute (VHIO) on responders to oncology treatment. The studies focused on the variability due to the convolution kernel, image acquisition parameters, and the inter-observer lesion identification. The features selected were those features with a ICC higher than 0.7 in the three studies.The selected features based on reproducibility were evaluated for lesion malignancy classification using a different database. Results show better performance compared to several state-of-the-art methods including Principal Component Analysis (PCA), Kernel Discriminant Analysis via QR decomposition (KDAQR), LASSO, and an own built Convolutional Neural Network.

BronchoX: bronchoscopy exploration software for biopsy intervention planning.

Virtual bronchoscopy (VB) is a non-invasive exploration tool for intervention planning and navigation of possible pulmonary lesions (PLs). A VB software involves the location of a PL and the calculation of a route, starting from the trachea, to reach it. The selection of a VB software might be a complex process, and there is no consensus in the community of medical software developers in which is the best-suited system to use or framework to choose. The authors present Bronchoscopy Exploration (BronchoX), a VB software to plan biopsy interventions that generate physician-readable instructions to reach the PLs. The authors' solution is open source, multiplatform, and extensible for future functionalities, designed by their multidisciplinary research and development group. BronchoX is a compound of different algorithms for segmentation, visualisation, and navigation of the respiratory tract. Performed results are a focus on the test the effectiveness of their proposal as an exploration software, also to measure its accuracy as a guiding system to reach PLs. Then, 40 different virtual planning paths were created to guide physicians until distal bronchioles. These results provide a functional software for BronchoX and demonstrate how following simple instructions is possible to reach distal lesions from the trachea.

Positive Airway Pressure to Enhance Computed Tomography Imaging for Airway Segmentation for Virtual Bronchoscopic Navigation.

RATIONALE: Virtual bronchoscopic navigation (VBN) guidance to peripheral pulmonary lesions is often limited by insufficient segmentation of the peripheral airways. OBJECTIVES: To test the effect of applying positive airway pressure (PAP) during CT acquisition to improve segmentation, particularly at end-expiration. METHODS: CT acquisitions in inspiration and expiration with 4 PAP protocols were recorded prospectively and compared to baseline inspiratory acquisitions in 20 patients. The 4 protocols explored differences between devices (flow vs. turbine), exposures (within seconds vs. 15-min) and pressure levels (10 vs. 14 cmH2O). Segmentation quality was evaluated with the number of airways and number of endpoints reached. A generalized mixed-effects model explored the estimated effect of each protocol. MEASUREMENTS AND MAIN RESULTS: Patient characteristics and lung function did not significantly differ between protocols. Compared to baseline inspiratory acquisitions, expiratory acquisitions after 15 min of 14 cmH2O PAP segmented 1.63-fold more airways (95% CI 1.07-2.48; p = 0.018) and reached 1.34-fold more endpoints (95% CI 1.08-1.66; p = 0.004). Inspiratory acquisitions performed immediately under 10 cmH2O PAP reached 1.20-fold (95% CI 1.09-1.33; p < 0.001) more endpoints; after 15 min the increase was 1.14-fold (95% CI 1.05-1.24; p < 0.001). CONCLUSIONS: CT acquisitions with PAP segment more airways and reach more endpoints than baseline inspiratory acquisitions. The improvement is particularly evident at end-expiration after 15 min of 14 cmH2O PAP. Further studies must confirm that the improvement increases diagnostic yield when using VBN to evaluate peripheral pulmonary lesions.

Advances on photodynamic therapy of melanoma through novel ring-fused 5,15-diphenylchlorins.

The synthesis, photophysical behaviour and photosensitization ability of novel 4,5,6,7-tetrahydropyrazolo[1,5-a]pyridine-fused 5,15-diphenylchlorins against melanoma cells are described. All studied chlorins were found to be extremely active against melanoma cell lines A375 showing IC50 values below 20 nM. Furthermore, a dihydroxymethyl diphenylchlorin was identified as an excellent candidate to allow modulating of different types of cell death, apoptosis vs. necrosis, by varying its concentration. This can be explored as a tool to improve the effectiveness of PDT since inflammatory response resulting from necrotic cell death after PDT can activate the antitumor immune response with implications also regarding the vascular damage. This feature combined with very low cytotoxicity against human melanoma cells in the absence of light activation and against human fibroblast HFF-1 cells makes this chlorin a candidate of choice as a photosensitizer for PDT. A comprehensive photophysical investigation including the determination of quantum yields for fluorescence, singlet oxygen sensitization and internal conversion, lifetimes and rate constants of all the excited state deactivation processes has been undertaken.

Objective Endoscopic Measurements of Central Airway Stenosis: A Pilot Study.

BACKGROUND: Endoscopic estimation of the degree of stenosis in central airway obstruction is subjective and highly variable. OBJECTIVE: To determine the benefits of using SENSA (System for Endoscopic Stenosis Assessment), an image-based computational software, for obtaining objective stenosis index (SI) measurements among a group of expert bronchoscopists and general pulmonologists. METHODS: A total of 7 expert bronchoscopists and 7 general pulmonologists were enrolled to validate SENSA usage. The SI obtained by the physicians and by SENSA were compared with a reference SI to set their precision in SI computation. We used SENSA to efficiently obtain this reference SI in 11 selected cases of benign stenosis. A Web platform with three user-friendly microtasks was designed to gather the data. The users had to visually estimate the SI from videos with and without contours of the normal and the obstructed area provided by SENSA. The users were able to modify the SENSA contours to define the reference SI using morphometric bronchoscopy. RESULTS: Visual SI estimation accuracy was associated with neither bronchoscopic experience (p = 0.71) nor the contours of the normal and the obstructed area provided by the system (p = 0.13). The precision of the SI by SENSA was 97.7% (95% CI: 92.4-103.7), which is significantly better than the precision of the SI by visual estimation (p < 0.001), with an improvement by at least 15%. CONCLUSION: SENSA provides objective SI measurements with a precision of up to 99.5%, which can be calculated from any bronchoscope using an affordable scalable interface. Providing normal and obstructed contours on bronchoscopic videos does not improve physicians' visual estimation of the SI.

Toward online quantification of tracheal stenosis from videobronchoscopy.

PURPOSE: Lack of objective measurement of tracheal obstruction degree has a negative impact on the chosen treatment prone to lead to unnecessary repeated explorations and other scanners. Accurate computation of tracheal stenosis in videobronchoscopy would constitute a breakthrough for this noninvasive technique and a reduction in operation cost for the public health service. METHODS: Stenosis calculation is based on the comparison of the region delimited by the lumen in an obstructed frame and the region delimited by the first visible ring in a healthy frame. We propose a parametric strategy for the extraction of lumen and tracheal ring regions based on models of their geometry and appearance that guide a deformable model. To ensure a systematic applicability, we present a statistical framework to choose optimal parametric values and a strategy to choose the frames that minimize the impact of scope optical distortion. RESULTS: Our method has been tested in 40 cases covering different stenosed tracheas. Experiments report a non- clinically relevant [Formula: see text] of discrepancy in the calculated stenotic area and a computational time allowing online implementation in the operating room. CONCLUSIONS: Our methodology allows reliable measurements of airway narrowing in the operating room. To fully assess its clinical impact, a prospective clinical trial should be done.

Labelling improves false belief understanding. A training study.

A total of 104 children aged between 41 and 47 months were selected to study the relationship between language and false belief understanding. Participants were assigned to four different training conditions: discourse, labelling, control (all with deceptive objects), and sentential complements (involving non-deceptive objects). Post-test results showed an improvement in children's false belief understanding in the discourse and the labelling conditions, but not in the sentential complements with non-deceptive objects or the control group. Furthermore, the most remarkable improvement in false belief understanding occurred in the labelling group. These results suggest that some types of linguistic experience promote the development of false belief understanding, provided that differing perspectives are confronted.