Viral presence and immunopathology in a kidney transplant recipient with fatal COVID-19: a clinical autopsy report

COVID-19 is of special concern to immunocompromised individuals, including organ transplant recipients. However, the exact implications of COVID-19 for the immunocompromised host remain unclear. Existing theories regarding this matter are controversial and mainly based on clinical observations. Here, the postmortem histopathology, immunopathology, and viral presence in various tissues of a kidney transplant recipient with COVID-19 were compared to those of 2 nontransplanted patients with COVID-19 matched for age, sex, length of intensive care unit stay, and admission period in the pandemic. None of the tissues of the kidney transplant recipient demonstrated the presence of SARS-CoV-2. In lung tissues of both controls, some samples showed viral positivity with high Ct values with quantitative reverse transcription polymerase chain reaction. The lungs of the kidney transplant recipient and controls demonstrated similar pathology, consisting of acute fibrinous and organizing pneumonia with thrombosis and an inflammatory response with T cells, B cells, and macrophages. The kidney allograft and control kidneys showed a similar pattern of interstitial lymphoplasmacytic infiltration. No myocarditis could be observed in the hearts of the kidney transplant recipient and controls, although all cases contained scattered lymphoplasmacytic infiltrates in the myocardium, pericardium, and atria. The brainstems of the kidney transplant recipient and controls showed a similar pattern of lymphocytic inflammation with microgliosis. This research report highlights the possibility that, based on the results obtained from this single case, at time of death, the immune response in kidney transplant recipients with long-term antirejection immunosuppression use prior to severe illness is similar to nontransplanted deceased COVID-19 patients.

Multicentric development and evaluation of 18F-FDG PET/CT and MRI radiomics models to predict para-aortic lymph node involvement in locally advanced cervical cancer

PURPOSE

To develop machine learning models to predict para-aortic lymph node (PALN) involvement in patients with locally advanced cervical cancer (LACC) before chemoradiotherapy (CRT) using 18F-FDG PET/CT and MRI radiomics combined with clinical parameters.

METHODS

We retrospectively collected 178 patients (60% for training and 40% for testing) in 2 centers and 61 patients corresponding to 2 further external testing cohorts with LACC between 2010 to 2022 and who had undergone pretreatment analog or digital 18F-FDG PET/CT, pelvic MRI and surgical PALN staging. Only primary tumor volumes were delineated. Radiomics features were extracted using the Radiomics toolbox®. The ComBat harmonization method was applied to reduce the batch effect between centers. Different prediction models were trained using a neural network approach with either clinical, radiomics or combined models. They were then evaluated on the testing and external validation sets and compared.

RESULTS

In the training set (n = 102), the clinical model achieved a good prediction of the risk of PALN involvement with a C-statistic of 0.80 (95% CI 0.71, 0.87). However, it performed in the testing (n = 76) and external testing sets (n = 30 and n = 31) with C-statistics of only 0.57 to 0.67 (95% CI 0.36, 0.83). The ComBat-radiomic (GLDZM_HISDE_PET_FBN64 and Shape_maxDiameter2D3_PET_FBW0.25) and ComBat-combined (FIGO 2018 and same radiomics features) models achieved very high predictive ability in the training set and both models kept the same performance in the testing sets, with C-statistics from 0.88 to 0.96 (95% CI 0.76, 1.00) and 0.85 to 0.92 (95% CI 0.75, 0.99), respectively.

CONCLUSIONS

Radiomic features extracted from pre-CRT analog and digital 18F-FDG PET/CT outperform clinical parameters in the decision to perform a para-aortic node staging or an extended field irradiation to PALN. Prospective validation of our models should now be carried out.

External validation of a radiomic signature to predict p16 (HPV) status from standard CT images of anal cancer patients

The paper deals with the evaluation of the performance of an existing and previously validated CT based radiomic signature, developed in oropharyngeal cancer to predict human papillomavirus (HPV) status, in the context of anal cancer. For the validation in anal cancer, a dataset of 59 patients coming from two different centers was collected. The primary endpoint was HPV status according to p16 immunohistochemistry. Predefined statistical tests were performed to evaluate the performance of the model. The AUC obtained here in anal cancer is 0.68 [95% CI (0.32-1.00)] with F1 score of 0.78. This signature is TRIPOD level 4 (57%) with an RQS of 61%. This study provides proof of concept that this radiomic signature has the potential to identify a clinically relevant molecular phenotype (i.e., the HPV-ness) across multiple cancers and demonstrates potential for this radiomic signature as a CT imaging biomarker of p16 status.

Radiomics and Delta-Radiomics Signatures to Predict Response and Survival in Patients with Non-Small-Cell Lung Cancer Treated with Immune Checkpoint Inhibitors

The aim of our study was to determine the potential role of CT-based radiomics in predicting treatment response and survival in patients with advanced NSCLC treated with immune checkpoint inhibitors. We retrospectively included 188 patients with NSCLC treated with PD-1/PD-L1 inhibitors from two independent centers. Radiomics analysis was performed on pre-treatment contrast-enhanced CT. A delta-radiomics analysis was also conducted on a subset of 160 patients who underwent a follow-up contrast-enhanced CT after 2 to 4 treatment cycles. Linear and random forest (RF) models were tested to predict response at 6 months and overall survival. Models based on clinical parameters only and combined clinical and radiomics models were also tested and compared to the radiomics and delta-radiomics models. The RF delta-radiomics model showed the best performance for response prediction with an AUC of 0.8 (95% CI: 0.65−0.95) on the external test dataset. The Cox regression delta-radiomics model was the most accurate at predicting survival with a concordance index of 0.68 (95% CI: 0.56−0.80) (p = 0.02). The baseline CT radiomics signatures did not show any significant results for treatment response prediction or survival. In conclusion, our results demonstrated the ability of a CT-based delta-radiomics signature to identify early on patients with NSCLC who were more likely to benefit from immunotherapy.

Deep learning based identification of bone scintigraphies containing metastatic bone disease foci

PURPOSE

Metastatic bone disease (MBD) is the most common form of metastases, most frequently deriving from prostate cancer. MBD is screened with bone scintigraphy (BS), which have high sensitivity but low specificity for the diagnosis of MBD, often requiring further investigations. Deep learning (DL) - a machine learning technique designed to mimic human neuronal interactions- has shown promise in the field of medical imaging analysis for different purposes, including segmentation and classification of lesions. In this study, we aim to develop a DL algorithm that can classify areas of increased uptake on bone scintigraphy scans.

METHODS

We collected 2365 BS from three European medical centres. The model was trained and validated on 1203 and 164 BS scans respectively. Furthermore we evaluated its performance on an external testing set composed of 998 BS scans. We further aimed to enhance the explainability of our developed algorithm, using activation maps. We compared the performance of our algorithm to that of 6 nuclear medicine physicians.

RESULTS

The developed DL based algorithm is able to detect MBD on BSs, with high specificity and sensitivity (0.80 and 0.82 respectively on the external test set), in a shorter time compared to the nuclear medicine physicians (2.5 min for AI and 30 min for nuclear medicine physicians to classify 134 BSs). Further prospective validation is required before the algorithm can be used in the clinic.

Distinction of Lymphoma from Sarcoidosis on 18F-FDG PET/CT: Evaluation of Radiomics-Feature-Guided Machine Learning Versus Human Reader Performance

Sarcoidosis and lymphoma often share common features on ¹⁸F-FDG PET/CT, such as intense hypermetabolic lesions in lymph nodes and multiple organs. We aimed at developing and validating radiomics signatures to differentiate sarcoidosis from Hodgkin lymphoma (HL) and diffuse large B-cell lymphoma (DLBCL). Methods: We retrospectively collected 420 patients (169 sarcoidosis, 140 HL, and 111 DLBCL) who underwent pretreatment ¹⁸F-FDG PET/CT at the University Hospital of Liege. The studies were randomly distributed to 4 physicians, who gave their diagnostic suggestion among the 3 diseases. The individual and pooled performance of the physicians was then calculated. Interobserver variability was evaluated using a sample of 34 studies interpreted by all physicians. Volumes of interest were delineated over the lesions and the liver using MIM software, and 215 radiomics features were extracted using the RadiomiX Toolbox. Models were developed combining clinical data (age, sex, and weight) and radiomics (original and tumor-to-liver TLR radiomics), with 7 different feature selection approaches and 4 different machine-learning (ML) classifiers, to differentiate sarcoidosis and lymphomas on both lesion-based and patient-based approaches. Results: For identifying lymphoma versus sarcoidosis, physicians' pooled sensitivity, specificity, area under the receiver-operating-characteristic curve (AUC), and accuracy were 0.99 (95% CI, 0.97-1.00), 0.75 (95% CI, 0.68-0.81), 0.87 (95% CI, 0.84-0.90), and 89.3%, respectively, whereas for identifying HL in the tumor population, it was 0.58 (95% CI, 0.49-0.66), 0.82 (95% CI, 0.74-0.89), 0.70 (95% CI, 0.64-0.75) and 68.5%, respectively. Moderate agreement was found among observers for the diagnosis of lymphoma versus sarcoidosis and HL versus DLBCL, with Fleiss κ-values of 0.66 (95% CI, 0.45-0.87) and 0.69 (95% CI, 0.45-0.93), respectively. The best ML models for identifying lymphoma versus sarcoidosis showed an AUC of 0.94 (95% CI, 0.93-0.95) and 0.85 (95% CI, 0.82-0.88) in lesion- and patient-based approaches, respectively, using TLR radiomics (plus age for the second). To differentiate HL from DLBCL, we obtained an AUC of 0.95 (95% CI, 0.93-0.96) in the lesion-based approach using TLR radiomics and 0.86 (95% CI, 0.80-0.91) in the patient-based approach using original radiomics and age. Conclusion: Characterization of sarcoidosis and lymphoma lesions is feasible using ML and radiomics, with very good to excellent performance, equivalent to or better than that of physicians, who showed significant interobserver variability in their assessment.

The Role of Imaging in the Detection of Non-COVID-19 Pathologies during the Massive Screening of the First Pandemic Wave

During the COVID-19 pandemic induced by the SARS-CoV-2, numerous chest scans were carried out in order to establish the diagnosis, quantify the extension of lesions but also identify the occurrence of potential pulmonary embolisms. In this perspective, the performed chest scans provided a varied database for a retrospective analysis of non-COVID-19 chest pathologies discovered de novo. The fortuitous discovery of de novo non-COVID-19 lesions was generally not detected by the automated systems for COVID-19 pneumonia developed in parallel during the pandemic and was thus identified on chest CT by the radiologist. The objective is to use the study of the occurrence of non-COVID-19-related chest abnormalities (known and unknown) in a large cohort of patients having suffered from confirmed COVID-19 infection and statistically correlate the clinical data and the occurrence of these abnormalities in order to assess the potential of increased early detection of lesions/alterations. This study was performed on a group of 362 COVID-19-positive patients who were prescribed a CT scan in order to diagnose and predict COVID-19-associated lung disease. Statistical analysis using mean, standard deviation (SD) or median and interquartile range (IQR), logistic regression models and linear regression models were used for data analysis. Results were considered significant at the 5% critical level (p < 0.05). These de novo non-COVID-19 thoracic lesions detected on chest CT showed a significant prevalence in cardiovascular pathologies, with calcifying atheromatous anomalies approaching nearly 35.4% in patients over 65 years of age. The detection of non-COVID-19 pathologies was mostly already known, except for suspicious nodule, thyroid goiter and the ascending thoracic aortic aneurysm. The presence of vertebral compression or signs of pulmonary fibrosis has shown a significant impact on inpatient length of stay. The characteristics of the patients in this sample, both from a demographic and a tomodensitometric point of view on non-COVID-19 pathologies, influenced the length of hospital stay as well as the risk of intra-hospital death. This retrospective study showed that the potential importance of the detection of these non-COVID-19 lesions by the radiologist was essential in the management and the intra-hospital course of the patients.

Lesion-specific radiomics analysis shows promising results for early-stage efficacy assessment of IOA-244 in uveal melanoma

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Background: Radiomics is an image based approach that allows for characterization and quantification of tumor lesions in cancer patients. Radiomics has been proven capable of potentially adding value in the diagnostic and prognostic patient managment. In this study we evaluated the potential of Radiomics to bring additional insight also in early drug development. Methods: All the visible malignant lung and liver metastasis lesions of 7 uveal melanoma patients (86% of women, 60±11y) treated with IOA-244 (EudraCT 2019-000686-20) were manually segmented and analyzed in their size and shape via a radiomics approach. The CT scans at baseline and first follow-up (8 weeks) were included in the study and compared. Descriptive statistics and linear mixed effect (LME) models were used to quantify volumetric lesion-specific response to treatment. Response has been defined both as continuous variable and in three discrete categories (lesion shrinkage, stable and progressive disease for a volume change of [-100%;-0%];[0%-+25%] and > 25%, respectively). The influence of lesion shape at baseline (e.g. compactness, elongation or surface roughness among others) on the treatment response has been explore through LME models as well. Results: We identified and segmented 126 metastatic lesions (70 lung and 56 liver) from baseline scans and 122 lesions (71 lung and 51 liver) from post treatment scans. Of those, 64% could be consistently mapped between visits, resulting in a total of 147 matching lesions on which the radiomics analysis was performed. We found 19% of complete response and 16% of new lesions appearing. 8 weeks after treatment start, we observed non progressive disease in 61% of all lesions, of which 42% was shrinking. LME did not show a significant change in lesion volume between visits, but the mean difference between visits was negative. LME did show that lesion shape is significantly different between progressors and non-progressors at baseline for lung lesions (compact and irregular lesions are more likely to respond), and that there are moderate correlations (0.4-0.7) between tumor shape and volume change for liver lesions (compact lesions have a larger volume drop). Conclusions: This work demonstrates both the clinical potential of IOA-244 for treatment of Uveal Melanoma patients with lesions in the lung and in the liver and the potential of radiomics individual lesion analysis for clinical research in the very early stages of drug development. Lesion evolution volumetric assessment has allowed a more accurate and sensitive understanding of IOA-244 efficacy and impact across different lesions, in both lung and liver. Radiomics showed a promising response of selected population to IOA-244 over the first time point (W0-W8). A further radiomics analysis on next follow-up scans would allow a radiological proof of treatment-induced changes and long-term patient outcome prediction.

Nodule vascularity as novel radiomics imaging endpoint for lung cancer diagnosis and prognosis

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Background: The vascularization of lung nodules has been proven as severe risk factor for malignancy, and in lung cancer, indication of worse prognosis (1,2). For this reason, we developed a novel imagining endpoint based on the vasculature surrounding a lung mass and we tested this endpoint for the prediction of malignancy for lung nodules. Methods: The vasculature of the nodules (both arteries and veins) has been computed using the surface intersection between the nodule and the vascular structure 3D meshes. Both 3D structures were obtained by converting the segmentations of the nodule and of the vessels to meshes with a marching cubes algorithm. Nodule and vessels segmentation has been obtained with an in-house deep learning segmentation model. The features considered are the numbers of intersections, the total area of intersection and the mean area of intersection. These features have been used to predict nodule malignancy on thoracic CT scans from the Lung Image Database Consortium image collection (3). Quality controls on clinical data completeness and imaging parameters resulted in a cohort of 894 scans (715 for training and 179 for testing), from the original 1018 cases. The malignancy status is defined as high risk and low risk, based on the consensus classification of a panel of four radiologists. Firstly, an univariate analysis is performed to assess the variability of the features grouped by the malignancy score by using Mann-Whitney and ANOVA tests. After, seven combinations of features have been used to train generalized linear models (GLM) to predict nodule malignancy. To compare the models, the Area Under the Curve (AUC) is used as the main performance metric. Results: Univariate analysis of each feature grouped by the malignancy outcome showed that all the three features have good univariate discriminative power between high risk and low risk categories ( p value ≤ 0.05), with nb_connections as the most predictive singular feature ( p value of 1.343277 × 10-36). All the GLM models developed showed a good performance (AUC equal or higher than 0.7), with the best model in testing based on the combination of mean_area and sum_area (AUC of 0.84). Conclusions: The radiomics vascularity endpoint has been proven capable of predicting nodule malignancy with very good performance. The singular feature that is most related to malignancy is the number of vessels intersecting the nodule while the total area of intersection followed by the number of intersections are the most useful to model risk of malignancy. Wang et al., Lung Cancer 114: 38–43, 2017. Hamanaka et al., Diagn Pathol 10,17, 2015. G. Armato et al., Med. Phys., 38: 915-931, 2011.

Data harmonisation for information fusion in digital healthcare: A state-of-the-art systematic review, meta-analysis and future research directions

Removing the bias and variance of multicentre data has always been a challenge in large scale digital healthcare studies, which requires the ability to integrate clinical features extracted from data acquired by different scanners and protocols to improve stability and robustness. Previous studies have described various computational approaches to fuse single modality multicentre datasets. However, these surveys rarely focused on evaluation metrics and lacked a checklist for computational data harmonisation studies. In this systematic review, we summarise the computational data harmonisation approaches for multi-modality data in the digital healthcare field, including harmonisation strategies and evaluation metrics based on different theories. In addition, a comprehensive checklist that summarises common practices for data harmonisation studies is proposed to guide researchers to report their research findings more effectively. Last but not least, flowcharts presenting possible ways for methodology and metric selection are proposed and the limitations of different methods have been surveyed for future research.

An externally validated fully automated deep learning algorithm to classify COVID-19 and other pneumonias on chest CT

Purpose

In this study, we propose an artificial intelligence (AI) framework based on three-dimensional convolutional neural networks to classify computed tomography (CT) scans of patients with coronavirus disease 2019 (COVID-19), influenza/community-acquired pneumonia (CAP), and no infection, after automatic segmentation of the lungs and lung abnormalities.

Methods

The AI classification model is based on inflated three-dimensional Inception architecture and was trained and validated on retrospective data of CT images of 667 adult patients (no infection n=188, COVID-19 n=230, influenza/CAP n=249) and 210 adult patients (no infection n=70, COVID-19 n=70, influenza/CAP n=70), respectively. The model's performance was independently evaluated on an internal test set of 273 adult patients (no infection n=55, COVID-19 n= 94, influenza/CAP n=124) and an external validation set from a different centre (305 adult patients: COVID-19 n=169, no infection n=76, influenza/CAP n=60).

Results

The model showed excellent performance in the external validation set with area under the curve of 0.90, 0.92 and 0.92 for COVID-19, influenza/CAP and no infection, respectively. The selection of the input slices based on automatic segmentation of the abnormalities in the lung reduces analysis time (56 s per scan) and computational burden of the model. The Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) score of the proposed model is 47% (15 out of 32 TRIPOD items).

Conclusion

This AI solution provides rapid and accurate diagnosis in patients suspected of COVID-19 infection and influenza.

A fully automated artificial intelligence-based network is proposed to classify CT volumes of patients affected with COVID-19 or influenza/CAP, and in the uninfectedhttps://bit.ly/3MJrVRi

ACE2 Protein Expression During Childhood, Adolescence, and Early Adulthood

Purpose and context. Angiotensin-converting enzyme 2 is the entry receptor for SARS-CoV and SARS-CoV-2. Variations in ACE2 expression might explain age-related symptomatology of COVID-19, that is, more gastro-intestinal symptoms and less pulmonary complaints. This study qualitatively investigated ACE2 protein expression in various organs from the fetal to the young adolescent stage. Method. Autopsy samples from lung, heart, liver, stomach, small intestine, pancreas, kidney, adrenals, and brain (when available) were obtained from twenty subjects aged 24 weeks gestational age through 28 years. Formalin-fixed paraffin-embedded 4-um-thick tissue sections were stained against ACE2. Key results. We showed that the extent of ACE2 expression is age-related. With age, expression increases in lungs and decreases in intestines. In the other examined organs, ACE2 protein expression did not change with age. In brain tissue, ACE2 was expressed in astrocytes and endothelial cells. Conclusions. Age-related ACE2 expression differences could be one substrate of the selective clinical vulnerability of the respiratory and gastro-intestinal system to SARS-CoV-2 infection during infancy.

Tumor-immune landscape patterns before and after chemoradiation in resectable esophageal adenocarcinomas

Immunotherapy is a new anti‐cancer treatment option, showing promising results in clinical trials. To investigate potential immune biomarkers in esophageal adenocarcinoma (EAC), we explored immune landscape patterns in the tumor microenvironment before and after neoadjuvant chemoradiation (nCRT). Sections from matched pretreatment biopsies and post‐nCRT resection specimens (n = 188) were stained for (1) programmed death‐ligand 1 (PD‐L1, CD274); (2) programmed cell death protein 1 (PD‐1, CD279), forkhead box P3 (FOXP3), CD8, pan‐cytokeratin multiplex; and (3) an MHC class I, II duplex. The densities of tumor‐associated immune cells (TAICs) were calculated using digital image analyses and correlated to histopathological nCRT response [tumor regression grade (TRG)], survival, and post‐nCRT immune patterns. PD‐L1 positivity defined by a combined positive score of >1 was associated with a better response post‐nCRT (TRG 1–3 versus 4, 5, p = 0.010). In addition, high combined mean densities of CD8⁺, FOXP3⁺, and PD‐1⁺ TAICs in the tumor epithelium and stroma of biopsies were associated with a better response (TRG 1–3 versus 4, 5, p = 0.025 and p = 0.044, respectively). Heterogeneous TAIC density patterns were observed post‐nCRT, with significantly higher CD8⁺ and PD‐1⁺ TAIC mean densities compared with biopsies (both p = 0.000). Three immune landscape patterns were defined post‐nCRT: ‘inflamed’, ‘invasive margin’, and ‘desert’, of which ‘inflamed’ was the most frequent (57%). Compared with matched biopsies, resection specimens with ‘inflamed’ tumors showed a significantly higher increase in CD8⁺ density compared with non‐inflamed tumors post‐nCRT (p = 0.000). In this cohort of EAC patients, higher TAIC densities in pretreatment biopsies were associated with response to nCRT. This warrants future research into the potential of the tumor‐immune landscape for patient stratification and novel (immune) therapeutic strategies. © 2021 The Authors. The Journal of Pathology published by John Wiley & Sons, Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Privacy preserving distributed learning classifiers - Sequential learning with small sets of data

BACKGROUND

Artificial intelligence (AI) typically requires a significant amount of high-quality data to build reliable models, where gathering enough data within a single institution can be particularly challenging. In this study we investigated the impact of using sequential learning to exploit very small, siloed sets of clinical and imaging data to train AI models. Furthermore, we evaluated the capacity of such models to achieve equivalent performance when compared to models trained with the same data over a single centralized database.

METHODS

We propose a privacy preserving distributed learning framework, learning sequentially from each dataset. The framework is applied to three machine learning algorithms: Logistic Regression, Support Vector Machines (SVM), and Perceptron. The models were evaluated using four open-source datasets (Breast cancer, Indian liver, NSCLC-Radiomics dataset, and Stage III NSCLC).

FINDINGS

The proposed framework ensured a comparable predictive performance against a centralized learning approach. Pairwise DeLong tests showed no significant difference between the compared pairs for each dataset.

INTERPRETATION

Distributed learning contributes to preserve medical data privacy. We foresee this technology will increase the number of collaborative opportunities to develop robust AI, becoming the default solution in scenarios where collecting enough data from a single reliable source is logistically impossible. Distributed sequential learning provides privacy persevering means for institutions with small but clinically valuable datasets to collaboratively train predictive AI while preserving the privacy of their patients. Such models perform similarly to models that are built on a larger central dataset.

A review in radiomics: Making personalized medicine a reality via routine imaging

Radiomics is the quantitative analysis of standard-of-care medical imaging; the information obtained can be applied within clinical decision support systems to create diagnostic, prognostic, and/or predictive models. Radiomics analysis can be performed by extracting hand-crafted radiomics features or via deep learning algorithms. Radiomics has evolved tremendously in the last decade, becoming a bridge between imaging and precision medicine. Radiomics exploits sophisticated image analysis tools coupled with statistical elaboration to extract the wealth of information hidden inside medical images, such as computed tomography (CT), magnetic resonance (MR), and/or Positron emission tomography (PET) scans, routinely performed in the everyday clinical practice. Many efforts have been devoted in recent years to the standardization and validation of radiomics approaches, to demonstrate their usefulness and robustness beyond any reasonable doubts. However, the booming of publications and commercial applications of radiomics approaches warrant caution and proper understanding of all the factors involved to avoid "scientific pollution" and overly enthusiastic claims by researchers and clinicians alike. For these reasons the present review aims to be a guidebook of sorts, describing the process of radiomics, its pitfalls, challenges, and opportunities, along with its ability to improve clinical decision-making, from oncology and respiratory medicine to pharmacological and genotyping studies.

Abstract 354: Mucinous phenotype is associated with response to neoadjuvant chemotherapy in microsatellite instable resectable gastric cancer

Background: Epstein-Barr virus positivity (EBV+) and microsatellite instability (MSI-high) have been shown to be positive prognostic factors for long term survival in resectable gastric cancer (GC) in several studies. However, the benefit of perioperative treatment in patients with MSI-high tumors remains topic of discussion. Here, we present the clinicopathological outcome of patients with EBV+ and MSI-high GCs treated with surgery only in the Dutch D1/D2 trial, and treated with chemotherapy or chemoradiotherapy after preoperative chemotherapy and surgery in the CRITICS trial.

Patients and methods: EBV was determined in tumor tissue using EBV-encoded RNA in situ hybridization (EBER-ISH). PCR and/or immunohistochemistry were performed to determine MSI status. Results were correlated to histopathological response, morphological tumor characteristics and survival.

Results: In the Dutch D1/D2 trial 10.5% (47/447) of tumors were EBV+ and 10.5% (47/447) were MSI-high. In the CRITICS trial 5.5% (25/451) of tumors were EBV+ and 5.5% (25/451) were MSI-high tumors. In the Dutch D1/D2 trial, five-year overall survival probability was 51.1% for EBV+, 46.8% for MSI-high, and 42.5% for EBV-/MSS (P=0.19). In the CRITICS trial, five-year overall survival was 56.0% for EBV+, 47.3% for MSI-high, and 36.5% for EBV-/MSS (P=0.22). In the CRITICS trial, 3 (12.5%) MSI-high tumors showed moderate to complete histopathological response. Interestingly, all three showed a mucinous phenotype. Eight (36.4%) EBV+ and 114 (29.9%) EBV-/MSS tumors showed moderate to complete histopathological response. None of the EBV+ GCs showed mucinous differentiation.

Conclusions: The favorable outcome of GC patients with resectable EBV+ or MSI-high tumors compared to EBV-/MSS tumors remains after perioperative chemotherapy. In MSI-high tumors significant histopathological response to neoadjuvant chemotherapy was found only in those with a mucinous phenotype.

Citation Format: H.D. Biesma, A.T.T.D. Soeratram, K. Sikorska, I.A. Caspers, H.F. van Essen, J.M.P. Egthuijsen, A. Mookhoek, D.M. Hoek, W. Vos, H.W.M. van Laarhoven, M. Nordsmark, D.L. van der Peet, F.A.R.M. Warmerdam, M.M. Geenen, O.J.L. Loosveld, J.E.A. Portielje, M. Los, E. Meershoek - Klein Kranenbarg, H.H. Hartgrink, J. van Sandick, C.J.H. van de Velde, M. Verheij, A. Cats, B. Ylstra, N.C.T. van Grieken, On behalf of the CRITICS investigators. Mucinous phenotype is associated with response to neoadjuvant chemotherapy in microsatellite instable resectable gastric cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 354.

An Inhaled PI3Kδ Inhibitor Improves Recovery in Acutely Exacerbating COPD Patients: A Randomized Trial

Purpose

This study evaluated the safety and efficacy of inhaled nemiralisib, a phosphoinositide 3-kinase δ (PI3Kδ) inhibitor, in patients with an acute exacerbation of chronic obstructive pulmonary disease (COPD).

Methods

In this double-blind, placebo-controlled study, 126 patients (40–80 years with a post-bronchodilator forced expiratory volume in 1 sec (FEV₁) ≤80% of predicted (previously documented)) were randomized 1:1 to once daily inhaled nemiralisib (1 mg) or placebo for 84 days, added to standard of care. The primary endpoint was specific imaging airway volume (siVaw) after 28 treatment days and was analyzed using a Bayesian repeated measures model (clintrials.gov: NCT02294734).

Results

A total of 126 patients were randomized to treatment; 55 on active treatment and 49 on placebo completed the study. When comparing nemiralisib and placebo-treated patients, an 18% placebo-corrected increase from baseline in distal siVaw (95% credible intervals (Cr I) (−1%, 42%)) was observed on Day 28. The probability that the true treatment ratio was >0% (Pr(θ>0)) was 96%, suggestive of a real treatment effect. Improvements were observed across all lung lobes. Patients treated with nemiralisib experienced a 107.3 mL improvement in posterior median FEV₁ (change from baseline, 95% Cr I (−2.1, 215.5)) at day 84, compared with placebo. Adverse events were reported by 41 patients on placebo and 49 on nemiralisib, the most common being post-inhalation cough on nemiralisib (35%) vs placebo (3%).

Conclusion

These data show that addition of nemiralisib to usual care delivers more effective recovery from an acute exacerbation and improves lung function parameters including siVaw and FEV₁. Although post-inhalation cough was identified, nemiralisib was otherwise well tolerated, providing a promising novel therapy for this acutely ill patient group.

Exploring PI3Kδ Molecular Pathways in Stable COPD and Following an Acute Exacerbation, Two Randomized Controlled Trials

Background

Inhibition of phosphoinositide 3-kinase δ (PI3Kδ) exerts corrective effects on the dysregulated migration characteristics of neutrophils isolated from patients with chronic obstructive pulmonary disease (COPD).

Objective

To develop novel, induced sputum endpoints to demonstrate changes in neutrophil phenotype in the lung by administering nemiralisib, a potent and selective inhaled PI3Kδ inhibitor, to patients with stable COPD or patients with acute exacerbation (AE) of COPD.

Methods

In two randomized, double-blind, placebo-controlled clinical trials patients with A) stable COPD (N=28, randomized 3:1) or B) AECOPD (N=44, randomized 1:1) received treatment with inhaled nemiralisib (1mg). Endpoints included induced sputum at various time points before and during treatment for the measurement of transcriptomics (primary endpoint), inflammatory mediators, functional respiratory imaging (FRI), and spirometry.

Results

In stable COPD patients, the use of nemiralisib was associated with alterations in sputum neutrophil transcriptomics suggestive of an improvement in migration phenotype; however, the same nemiralisib-evoked effects were not observed in AECOPD. Inhibition of sputum inflammatory mediators was also observed in stable but not AECOPD patients. In contrast, a placebo-corrected improvement in forced expiratory volume in 1 sec of 136 mL (95% Credible Intervals -46, 315mL) with a probability that the true treatment ratio was >0% (Pr(θ>0)) of 93% was observed in AECOPD. However, FRI endpoints remained unchanged.

Conclusion

We provide evidence for nemiralisib-evoked changes in neutrophil migration phenotype in stable COPD but not AECOPD, despite improving lung function in the latter group. We conclude that induced sputum can be used for measuring evidence of alteration of neutrophil phenotype in stable patients, and our study provides a data set of the sputum transcriptomic changes during recovery from AECOPD.

External validation of a radiomic signature to predict HPV (p16) status from standard CT images of anal and vulvar cancer patients

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Background: HPV status of anal and vulvar cancers cannot be predicted by visual inspection as well as for oropharyngeal cancers. Radiomics applied on computed tomography images can extract features that may better characterize the structure and the underlying biology of the tumor. Methods: In this multi-center study, we validated a CT based radiomic signature to predict HPV (p16) status, developed in head & neck cancer, in anal and vulvar cancer patients. The patients cohort was composed of 68 anal cancer patients and 7 vulvar cancer patients, with p16 status determined by immunohistochemistry, while a control cohort was composed of 422 lung cancer patients. The patient cohorts come from 4 different centers (Maastro Clinic - the Netherlands, CHU Liege – Belgium, St Luke’s Hospital – Ireland, Cork University Hospital - Ireland). The primary tumor volume was manually delineated for each patient on axial CT images. Prior to analysis, all images were resampled to isotropic voxels of 2 mm, using linear interpolation. A total of 37 radiomics features were calculated from five groups: tumor intensity, shape, texture, Wavelet and Laplacian of Gaussian. The signature was built using regularized logistic regression [1]. The signature was evaluated according to the Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD) and the Radiomics Quality Score (RQS). Results: The signature classified anal and vulvar cancers based on their HPV status (positive or negative), with an AUROC of 0.760 comparable to the performance of the original signature developed in oropharyngeal squamous cell carcinomas (AUROC of 0.764) [1]. The model, tested in the control cohort of lung cancer patients, predicted the HPV positive status of 1% of the patients which is in line with expected European prevalence (0 – 10%). This signature is TRIPOD level 4 (57%) with an RQS of 61%. Conclusions: This study supplies an additional insight into HPV imaging phenotype, providing a proof of concept that molecular information can be inferred from standard medical images by means of radiomics. These preliminary but encouraging results may pave the road for further generalization of CT image features of HPV-related tumors and aid in the optimization of future therapy developments [2]. Reference [1] Ralph TH Leijenaar et al., The British Journal of Radiology 2018 91:1086 [2] Immunotherapy Drug with Two Targets Shows Promise against HPV-Related Cancers - accessed on 12/02/2021 - https://www.cancer.gov/

Delta-Like Ligand-Notch1 Signaling Is Selectively Modulated by HPV16 E6 to Promote Squamous Cell Proliferation and Correlates with Cervical Cancer Prognosis

Human papillomavirus (HPV) drives high-grade intraepithelial neoplasia and cancer; for unknown reasons, this occurs most often in the cervical transformation zone. Either mutation or HPV E6-driven inhibition of Notch1 can drive neoplastic development in stratified squamous epithelia. However, the contribution of Notch1 and its Delta-like ligands (DLL) to site susceptibility remains poorly understood. Here, we map DLL1/DLL4 expression in cell populations present in normal cervical biopsies by immunofluorescence. In vitro keratinocyte 2D monolayer models, growth assays, and organotypic raft cultures were used to assess the functional role of DLL-Notch signaling in uninfected cells and its modulation by HPV16 in neoplasia. An RNA sequencing-based gene signature was used to suggest the cell of origin of 279 HPV-positive cervical carcinomas from The Cancer Genome Atlas and to relate this to disease prognosis. Finally, the prognostic impact of DLL4 expression was investigated in three independent cervical cancer patient cohorts. Three molecular cervical carcinoma subtypes were identified, with reserve cell tumors the most common and linked to relatively good prognosis. Reserve cells were characterized as DLL1-/DLL4+, a proliferative phenotype that is temporarily observed during squamous metaplasia and wound healing but appears to be sustained by HPV16 E6 in raft models of low-grade and, more prominently, high-grade neoplasia. High expression of DLL4 was associated with an increased likelihood of cervical cancer-associated death and recurrence. Taken together, DLL4-Notch1 signaling reflects a proliferative cellular state transiently present during physiologic processes but inherent to cervical reserve cells, making them strongly resemble neoplastic tissue even before HPV infection has occurred. SIGNIFICANCE: This study investigates cervical cancer cell-of-origin populations and describes a DLL-Notch1 phenotype that is associated with disease prognosis and that might help identify cells that are susceptible to HPV-induced carcinogenesis.

Development and Validation of an Automated Radiomic CT Signature for Detecting COVID-19

The coronavirus disease 2019 (COVID-19) outbreak has reached pandemic status. Drastic measures of social distancing are enforced in society and healthcare systems are being pushed to and beyond their limits. To help in the fight against this threat on human health, a fully automated AI framework was developed to extract radiomics features from volumetric chest computed tomography (CT) exams. The detection model was developed on a dataset of 1381 patients (181 COVID-19 patients plus 1200 non COVID control patients). A second, independent dataset of 197 RT-PCR confirmed COVID-19 patients and 500 control patients was used to assess the performance of the model. Diagnostic performance was assessed by the area under the receiver operating characteristic curve (AUC). The model had an AUC of 0.882 (95% CI: 0.851-0.913) in the independent test dataset (641 patients). The optimal decision threshold, considering the cost of false negatives twice as high as the cost of false positives, resulted in an accuracy of 85.18%, a sensitivity of 69.52%, a specificity of 91.63%, a negative predictive value (NPV) of 94.46% and a positive predictive value (PPV) of 59.44%. Benchmarked against RT-PCR confirmed cases of COVID-19, our AI framework can accurately differentiate COVID-19 from routine clinical conditions in a fully automated fashion. Thus, providing rapid accurate diagnosis in patients suspected of COVID-19 infection, facilitating the timely implementation of isolation procedures and early intervention.

Viral presence and immunopathology in patients with lethal COVID-19: a prospective autopsy cohort study

BACKGROUND

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) targets multiple organs and causes severe coagulopathy. Histopathological organ changes might not only be attributable to a direct virus-induced effect, but also the immune response. The aims of this study were to assess the duration of viral presence, identify the extent of inflammatory response, and investigate the underlying cause of coagulopathy.

METHODS

This prospective autopsy cohort study was done at Amsterdam University Medical Centers (UMC), the Netherlands. With informed consent from relatives, full body autopsy was done on 21 patients with COVID-19 for whom autopsy was requested between March 9 and May 18, 2020. In addition to histopathological evaluation of organ damage, the presence of SARS-CoV-2 nucleocapsid protein and the composition of the immune infiltrate and thrombi were assessed, and all were linked to disease course.

FINDINGS

Our cohort (n=21) included 16 (76%) men, and median age was 68 years (range 41-78). Median disease course (time from onset of symptoms to death) was 22 days (range 5-44 days). In 11 patients tested for SARS-CoV-2 tropism, SARS-CoV-2 infected cells were present in multiple organs, most abundantly in the lungs, but presence in the lungs became sporadic with increased disease course. Other SARS-CoV-2-positive organs included the upper respiratory tract, heart, kidneys, and gastrointestinal tract. In histological analyses of organs (sampled from nine to 21 patients per organ), an extensive inflammatory response was present in the lungs, heart, liver, kidneys, and brain. In the brain, extensive inflammation was seen in the olfactory bulbs and medulla oblongata. Thrombi and neutrophilic plugs were present in the lungs, heart, kidneys, liver, spleen, and brain and were most frequently observed late in the disease course (15 patients with thrombi, median disease course 22 days [5-44]; ten patients with neutrophilic plugs, 21 days [5-44]). Neutrophilic plugs were observed in two forms: solely composed of neutrophils with neutrophil extracellular traps (NETs), or as aggregates of NETs and platelets..

INTERPRETATION

In patients with lethal COVID-19, an extensive systemic inflammatory response was present, with a continued presence of neutrophils and NETs. However, SARS-CoV-2-infected cells were only sporadically present at late stages of COVID-19. This suggests a maladaptive immune response and substantiates the evidence for immunomodulation as a target in the treatment of severe COVID-19.

FUNDING

Amsterdam UMC Corona Research Fund.

Influence of preanalytical variables on performance of delta-like protein 3 (DLL3) predictive immunohistochemistry

DLL3 might become a predictive immunohistochemical marker in small cell carcinoma of the lung (SCLC). We investigated the influence of pre-analytical handling of samples on the performance of DLL3 immunohistochemistry (IHC) using DLL3 SP347 ready to use assay (Ventana). DLL3 positive cell lines were subjected to different experimental conditions mimicking the pre-analytical variation in daily clinical practice. Formalin fixation of 24 h led to the most optimal results of DLL3 IHC. Longstanding fixation in Cytolyt, methanol-based fixative for cytology samples, but also decalcification using a mix of formic- and hydrochloracid resulted in decreased DLL3 staining. Postponed staining of blanc slides for 3 months also decreased DLL3 IHC. Postponed fixation of the SCLC cell lines did not influence the performance of DLL3 IHC, although this might be different in the tissues than in the cell lines. In conclusion, different pre-analytical variables decrease the performance of DLL3 IHC. These findings are essential for implementing novel predictive immunohistochemical biomarkers in daily pathology practice.

Machine Learning Algorithms Utilizing Functional Respiratory Imaging May Predict COPD Exacerbations

RATIONALE AND OBJECTIVES

Acute chronic obstructive pulmonary disease exacerbations (AECOPD) have a significant negative impact on the quality of life and accelerate progression of the disease. Functional respiratory imaging (FRI) has the potential to better characterize this disease. The purpose of this study was to identify FRI parameters specific to AECOPD and assess their ability to predict future AECOPD, by use of machine learning algorithms, enabling a better understanding and quantification of disease manifestation and progression.

MATERIALS AND METHODS

A multicenter cohort of 62 patients with COPD was analyzed. FRI obtained from baseline high resolution CT data (unenhanced and volume gated), clinical, and pulmonary function test were analyzed and incorporated into machine learning algorithms.

RESULTS

A total of 11 baseline FRI parameters could significantly distinguish ( p < 0.05) the development of AECOPD from a stable period. In contrast, no baseline clinical or pulmonary function test parameters allowed significant classification. Furthermore, using Support Vector Machines, an accuracy of 80.65% and positive predictive value of 82.35% could be obtained by combining baseline FRI features such as total specific image-based airway volume and total specific image-based airway resistance, measured at functional residual capacity. Patients who developed an AECOPD, showed significantly smaller airway volumes and (hence) significantly higher airway resistances at baseline.

CONCLUSION

This study indicates that FRI is a sensitive tool (PPV 82.35%) for predicting future AECOPD on a patient specific level in contrast to classical clinical parameters.

Use of functional respiratory imaging to characterize the effect of inhalation profile and particle size on lung deposition of inhaled corticosteroid/long-acting β2-agonists delivered via a pressurized metered-dose inhaler

Background:

Functional respiratory imaging (FRI) uses three-dimensional models of human lungs and computational fluid dynamics to simulate functional changes within airways and predict the deposition of inhaled drugs. This study used FRI to model the effects of different patient inhalation and drug formulation factors on lung deposition of an inhaled corticosteroid/long-acting β₂-agonist (ICS/LABA) combination, administered by a pressurized metered-dose inhaler.

Methods:

Three-dimensional models of the lungs of six patients with asthma (mean forced expiratory volume in 1 s, 83%), treated with an ICS/LABA, were included. FRI modelling was used to simulate (1) the effects on lung deposition of inhalation duration and particle size [fine particle fraction (FPF), proportion of particles <5 µm; and mass median aerodynamic diameter (MMAD), average size of inhalable particles]; (2) deposition of fluticasone propionate/formoterol (FP/FORM) 125/5 µg; and (3) how inhalation profiles and flow rates affected FP/FORM deposition.

Results:

Total lung depositions (TLDs) following 1-, 3- and 5-s inhalations were 22.8%, 36.1% and 41.6% (metered dose), respectively, and central-to-peripheral deposition (C:P) ratios were 1.81, 0.86 and 0.61, respectively. TLD increased with increasing FPF, from ~8% at 10% FPF to ~36% at 40% FPF (metered dose); by contrast, MMAD had little effect on TLD, which was similar across MMADs (1.5–4.5 µm) at each FPF. FP/FORM deposited throughout central and peripheral airways with gradual (sinusoidal) and sharp (rapid) inhalations. TLD ranged from 35.8 to 44.0% (metered dose) for gradual and sharp inhalations at 30 and 60 L/min mean flow rates.

Conclusions:

These data provide important insights into the potential effects of inhalation characteristics (inhalation profile and duration) and aerosol formulation (FPF) on lung deposition of inhaled therapies. FRI thus represents a useful alternative to scintigraphy techniques. Future FRI studies will further our understanding of the deposition of inhaled drugs and help improve the management of asthma.

Machine Learning Algorithms Utilizing Quantitative CT Features May Predict Eventual Onset of Bronchiolitis Obliterans Syndrome After Lung Transplantation

RATIONALE AND OBJECTIVES

Long-term survival after lung transplantation (LTx) is limited by bronchiolitis obliterans syndrome (BOS), defined as a sustained decline in forced expiratory volume in the first second (FEV₁) not explained by other causes. We assessed whether machine learning (ML) utilizing quantitative computed tomography (qCT) metrics can predict eventual development of BOS.

MATERIALS AND METHODS

Paired inspiratory-expiratory CT scans of 71 patients who underwent LTx were analyzed retrospectively (BOS [n = 41] versus non-BOS [n = 30]), using at least two different time points. The BOS cohort experienced a reduction in FEV₁ of >10% compared to baseline FEV₁ post LTx. Multifactor analysis correlated declining FEV₁ with qCT features linked to acute inflammation or BOS onset. Student t test and ML were applied on baseline qCT features to identify lung transplant patients at baseline that eventually developed BOS.

RESULTS

The FEV₁ decline in the BOS cohort correlated with an increase in the lung volume (P = .027) and in the central airway volume at functional residual capacity (P = .018), not observed in non-BOS patients, whereas the non-BOS cohort experienced a decrease in the central airway volume at total lung capacity with declining FEV₁ (P = .039). Twenty-three baseline qCT parameters could significantly distinguish between non-BOS patients and eventual BOS developers (P < .05), whereas no pulmonary function testing parameters could. Using ML methods (support vector machine), we could identify BOS developers at baseline with an accuracy of 85%, using only three qCT parameters.

CONCLUSIONS

ML utilizing qCT could discern distinct mechanisms driving FEV₁ decline in BOS and non-BOS LTx patients and predict eventual onset of BOS. This approach may become useful to optimize management of LTx patients.

A randomized study using functional respiratory imaging to characterize bronchodilator effects of glycopyrrolate/formoterol fumarate delivered by a metered dose inhaler using co-suspension delivery technology in patients with COPD

Background

Functional respiratory imaging (FRI) uses high-resolution computed tomography (HRCT) scans to assess changes in airway volume and resistance.

Patients and methods

In this randomized, double-blind, 2-week, crossover, Phase IIIB study, patients with moderate-to-severe COPD received twice-daily glycopyrrolate/formoterol fumarate delivered by a metered dose inhaler (GFF MDI, 18/9.6 μg) and placebo MDI, formulated using innovative co-suspension delivery technology. Co-primary endpoints included the following: specific image-based airway volume (siVaw) and specific image-based airway resistance (siRaw) at Day 15, measured using FRI. Secondary and other endpoints included the following: change from baseline in post-dose forced expiratory volume in 1 second (FEV₁) and inspiratory capacity (IC; spirometry) and ratio to baseline in post-dose functional residual capacity (FRC) and residual volume (RV; body plethysmography).

Results

Twenty patients (46-78 years of age) were randomized and treated; of whom 19 completed the study. GFF MDI treatment increased siVaw by 75% and reduced siRaw by 71% vs placebo MDI (both P<0.0001). Image-based airway volume (iVaw) and image-based airway resistance (iRaw), without adjusting for lobe volume, demonstrated corresponding findings to the co-primary endpoint, as lobe volumes did not change with either treatment. Approximately 48% of the delivered dose of glycopyrronium and formoterol fumarate was estimated to be deposited in the lungs. Compared with placebo, GFF MDI treatment improved post-dose FEV₁ and IC (443 mL and 454 mL, respectively; both P<0.001) and reduced FRC and RV (13% and 22%, respectively; both P<0.0001). There were no significant safety findings.

Conclusion

GFF MDI demonstrated significant, clinically meaningful benefits on FRI-based airway volume and resistance in patients with moderate-to-severe COPD. Benefits were associated with improvements in FEV₁, IC, and hyperinflation.

Clinical trial registration

ClinicalTrials.gov: NCT02643082.

Functional respiratory imaging (FRI) for optimizing therapy development and patient care

Functional imaging techniques offer the possibility of improved visualization of anatomical structures such as; airways, lobe volumes and blood vessels. Computer-based flow simulations with a three-dimensional element add functionality to the images. By providing valuable detailed information about airway geometry, internal airflow distribution and inhalation profile, functional respiratory imaging can be of use routinely in the clinic. Three dimensional visualization allows for highly detailed follow-up in terms of disease progression or in assessing effects of interventions. Here, we explore the usefulness of functional respiratory imaging in different respiratory diseases. In patients with asthma and COPD, functional respiratory imaging has been used for phenotyping these patients, to predict the responder and non-responder phenotype and to evaluate different innovative therapeutic interventions.

Functional respiratory imaging: heterogeneity of acute exacerbations of COPD

Background

Exacerbations of COPD are a major burden to patients, and yet little is understood about heterogeneity. It contributes to the current persistent one-size-fits-all treatment. To replace this treatment by more personalized, precision medicine, new insights are required. We assessed the heterogeneity of exacerbations by functional respiratory imaging (FRI) in 3-dimensional models of airways and lungs.

Methods

The trial was designed as a multicenter trial of patients with an acute exacerbation of COPD who were assessed by FRI, pulmonary function tests, and patient-reported outcomes, both in the acute stage and during resolution.

Results

Forty seven patients were assessed. FRI analyses showed significant improvements in hyperinflation (a decrease in total volume at functional residual capacity of −0.25±0.61 L, p≤0.01), airway volume at total lung capacity (+1.70±4.65 L, p=0.02), and airway resistance. As expected, these improvements correlated partially with changes in the quality of life and in conventional lung function test parameters. Patients with the same changes in pulmonary function differ in regional disease activity measured by FRI.

Conclusion

FRI is a useful tool to get a better insight into exacerbations of COPD, and significant improvements in its indices can be demonstrated from the acute phase to resolution even in relatively small groups. It clearly visualizes the marked variability within and between individuals in ventilation and resistance during exacerbations and is a tool for the assessment of the heterogeneity of COPD exacerbations.

The Role of Functional Respiratory Imaging in Treatment Selection of Children With Obstructive Sleep Apnea and Down Syndrome

STUDY OBJECTIVES

The complexity of the pathogenesis of obstructive sleep apnea (OSA) in children with Down syndrome (DS) is illustrated by a prevalence of residual OSA after adenotonsillectomy. The aim of this study was to investigate whether upper airway imaging combined with computation fluid dynamics could characterize treatment outcome after adenotonsillectomy in these children.

METHODS

Children with DS and OSA were prospectively included. All children underwent an evaluation of the upper airway and an ultra-low dose computed tomography scan of the upper airway before adenotonsillectomy. The upper airway tract was extracted from the scan and combined with computational fluid dynamics. Results were evaluated using control polysomnography after adenotonsillectomy.

RESULTS

Thirty-three children were included: 18 boys, age 4.3 ± 2.3 years, median body mass index z-score 0.6 (-2.9 to 3.0), and median obstructive apnea-hypopnea index was 15.7 (3-70) events/h. The minimal upper airway cross-sectional area was significantly smaller in children with more severe OSA (P = .03). Nineteen children underwent a second polysomnography after adenotonsillectomy. Seventy-nine percent had persistent OSA (obstructive apneahypopnea index > 2 events/h). A greater than 50% decrease in obstructive apnea-hypopnea index was observed in 79% and these children had a significantly higher volume of the regions below the tonsils.

CONCLUSIONS

This is the first study to characterize treatment outcome in children with DS and OSA using computed tomography upper airway imaging. At baseline, children with more severe OSA had a smaller upper airway. Children with a less favorable response to adenotonsillectomy had a smaller volume of regions below the tonsils, which could be due to enlargement of the lingual tonsils, glossoptosis, or macroglossia.

COMMENTARY

A commentary on this article appears in this issue on page 501.

A Multimodal Imaging Approach Based on Micro-CT and Fluorescence Molecular Tomography for Longitudinal Assessment of Bleomycin-Induced Lung Fibrosis in Mice

Idiopathic pulmonary fibrosis (IPF) is a fatal lung disease characterized by the progressive and irreversible destruction of lung architecture, which causes significant deterioration in lung function and subsequent death from respiratory failure. The pathogenesis of IPF in experimental animal models has been induced by bleomycin administration. In this study, we investigate an IPF-like mouse model induced by a double intratracheal bleomycin instillation. Standard histological assessments used for studying lung fibrosis are invasive terminal procedures. The goal of this work is to monitor lung fibrosis through noninvasive imaging techniques such as Fluorescent Molecular Tomography (FMT) and Micro-CT. These two technologies validated with histology findings could represent a revolutionary functional approach for real time non-invasive monitoring of IPF disease severity and progression. The fusion of different approaches represents a step further for understanding the IPF disease, where the molecular events occurring in a pathological condition can be observed with FMT and the subsequent anatomical changes can be monitored by Micro-CT.

Changes in ventilation-perfusion during and after an COPD exacerbation: an assessment using fluid dynamic modeling

Introduction

Severe exacerbations associated with chronic obstructive pulmonary disease (COPD) that require hospitalization significantly contribute to morbidity and mortality. Definitions for exacerbations are very broad, and it is unclear whether there is one predominant underlying mechanism that leads to them. Functional respiratory imaging (FRI) with modeling provides detailed information about airway resistance, hyperinflation, and ventilation–perfusion (V/Q) mismatch during and following an acute exacerbation.

Materials and methods

Forty-two patients with COPD participating in a multicenter study were assessed by FRI, pulmonary function tests, and self-reported outcome measures during an acute exacerbation and following resolution. Arterial blood gasses and lung function parameters were measured.

Results

A significant correlation was found between alveolar–arterial gradient and image-based V/Q (iV/Q), suggesting that iV/Q represents V/Q mismatch during an exacerbation (p<0.05).

Conclusion

Recovery of an exacerbation is due to decreased (mainly distal) airway resistance (p<0.05). Improvement in patient-reported outcomes were also associated with decreased distal airway resistance (p<0.05), but not with forced expiratory volume. FRI is, therefore, a sensitive tool to describe changes in airway caliber, ventilation, and perfusion during and after exacerbation. On the basis of the fact that FRI increased distal airway resistance seems to be the main cause of an exacerbation, therapy should mainly focus on decreasing it during and after the acute event.

Pathophysiological mechanism of long-term noninvasive ventilation in stable hypercapnic patients with COPD using functional respiratory imaging

Introduction

Patients with severe COPD often develop chronic hypercapnic respiratory failure. Their prognosis worsens and they are more likely to develop exacerbations. This has major influence on the health-related quality of life. Currently, there is no information about the success of long-term noninvasive ventilation (NIV) among patients who receive NIV in acute settings. Also, little is known about the pathophysiological mechanism of NIV.

Methods

Ten Global Initiative for Obstructive Lung Disease stage III and IV COPD patients with respiratory failure who were hospitalized following acute exacerbation were treated with NIV using a Synchrony BiPAP device for 6 months. Arterial blood gases and lung function parameters were measured. Low-dose computed tomography of the thorax was performed and used for segmentation. Further analyses provided lobe volume, airway volume, and airway resistance, giving an overall functional description of the separate airways and lobes. Ventilation perfusion (VQ) was calculated. Patient-reported outcomes were evaluated.

Results

PaCO₂ significantly improved from 50.03 mmHg at baseline to 44.75 mmHg after 1 month and 43.37 mmHg after 6 months (P=0.006). Subjects showed improvement in the 6-minute walk tests (6MWTs) by an average of 51 m (from 332 m at baseline to 359 m at 1 month and 383 m at 6 months). Patients demonstrated improvement in self-reported anxiety (P=0.018). The improvement in image-based VQ was positively associated with the 6MWT and the anxiety domain of the Severe Respiratory Insufficiency Questionnaire.

Conclusion

Though previous studies of long-term NIV have shown conflicting results, this study demonstrates that patients can benefit from long-term NIV treatment, resulting in improved VQ, gas exchange, and exercise tolerance.

Predicting the effect of treatment in paediatric OSA by clinical examination and functional respiratory imaging

OBJECTIVE

The aim of this study was to investigate whether functional respiratory imaging (FRI) or clinical examination could predict treatment outcome for obstructive sleep apnea (OSA) in normal-weight, non-syndromic children.

METHODS

Normal weight children diagnosed with OSA by polysomnography were prospectively included. All children got a thorough evaluation and an ultra-low dose computed tomography scan of the upper airway (UA). A 3-D reconstruction was built combined with computational fluid dynamics for FRI. Decisions on the need and type of surgery were based upon findings during drug-induced sleep endoscopy. A second polysomnography was performed 3-12 months after surgery.

RESULTS

Ninety-one children were included: 62 boys, 5.0 ± 2.7 years, and BMI z-score of -0.1 ± 1.2. Children with more severe OSA had a smaller volume of the overlap region between the adenoids and tonsils. Nineteen out of 60 patients had persistent OSA (oAHI >2/h). A lower conductance in the UA and a higher tonsil score predicted successful treatment.

CONCLUSIONS

A less constricted airway, as characterized by both FRI and a lower tonsil score, was associated with a less favorable response to (adeno) tonsillectomy. Further studies after treatment using FRI and DISE are warranted to further characterize the UA of these subjects.

[Functional respiratory imaging after neostigmine- or sugammadex-enhanced recovery from neuromuscular blockade in the anesthetised rat: a randomised controlled pilot study]

OBJECTIVES

Reductions in diaphragm activity are associated with the postoperative development of atelectasis. Neostigmine reversal is also associated with increased atelectasis. We assessed the effects of neostigmine, sugammadex, and spontaneous reversal on regional lung ventilation and airway flow.

METHODS

Six Sprague-Dawley rats were paralysed with rocuronium and mechanically ventilated until recovery of the train-of-four ratio to 0.5. We administered neostigmine (0.06mg.kg^-1), sugammadex (15mg.kg^-1), or saline (n=2 per group). Computed tomography scans were obtained during the breathing cycle. Three-dimensional models of lung lobes were generated using functional respiratory imaging technology, and lobar volumes were calculated during the breathing cycle. The diaphragmatic surface was segmented for the end-expiratory and end-inspiratory scans. The total change in volume was reported by the lung volume change from the end-expiratory scan to the end-inspiratory scan. Chest wall movement was defined as the lung volume change minus the volume change that resulted from diaphragm excursion.

RESULTS

The two rats that received neostigmine exhibited a smaller relative contribution of diaphragm movement to the total change in lung volume compared with the two rats that received sugammadex or saline (chest wall contribution (%): 26.69 and 25.55 for neostigmine; -2.77 and 15.98 for sugammadex; 18.82 and 10.30 for saline).

CONCLUSION

This pilot study in rats demonstrated an increased relative contribution of chest wall expansion after neostigmine compared with sugammadex or saline. This smaller relative contribution of diaphragm movement may be explained by a neostigmine-induced decrease in phrenic nerve activity or by remaining occupied acetylcholine receptors after neostigmine.

Longitudinal assessment of bleomycin-induced lung fibrosis by Micro-CT correlates with histological evaluation in mice

Background: The intratracheal instillation of bleomycin in mice induces early damage to alveolar epithelial cells and development of inflammation followed by fibrotic tissue changes and represents the most widely used model of pulmonary fibrosis to investigate human IPF. Histopathology is the gold standard for assessing lung fibrosis in rodents, however it precludes repeated and longitudinal measurements of disease progression and does not provide information on spatial and temporal distribution of tissue damage. Here we investigated the use of the Micro-CT technique to allow the evaluation of disease onset and progression at different time-points in the mouse bleomycin model of lung fibrosis. Micro-CT was throughout coupled with histological analysis for the validation of the imaging results. Methods: In bleomycin-instilled and control mice, airways and lung morphology changes were assessed and reconstructed at baseline, 7, 14 and 21 days post-treatment based on Micro-CT images. Ashcroft score, percentage of collagen content and percentage of alveolar air area were detected on lung slides processed by histology and subsequently compared with Micro-CT parameters. Results: Extent (%) of fibrosis measured by Micro-CT correlated with Ashcroft score, the percentage of collagen content and the percentage of alveolar air area (r2 = 0.91; 0.77; 0.94, respectively). Distal airway radius also correlated with the Ashcroft score, the collagen content and alveolar air area percentage (r2 = 0.89; 0.78; 0.98, respectively). Conclusions: Micro-CT data were in good agreement with histological read-outs as micro-CT was able to quantify effectively and non-invasively disease progression longitudinally and to reduce the variability and number of animals used to assess the damage. This suggests that this technique is a powerful tool for understanding experimental pulmonary fibrosis and that its use could translate into a more efficient drug discovery process, also helping to fill the gap between preclinical setting and clinical practice.

Longitudinal assessment of bleomycin-induced lung fibrosis by Micro-CT correlates with histological evaluation in mice

Background

The intratracheal instillation of bleomycin in mice induces early damage to alveolar epithelial cells and development of inflammation followed by fibrotic tissue changes and represents the most widely used model of pulmonary fibrosis to investigate human IPF.

Histopathology is the gold standard for assessing lung fibrosis in rodents, however it precludes repeated and longitudinal measurements of disease progression and does not provide information on spatial and temporal distribution of tissue damage.

Here we investigated the use of the Micro-CT technique to allow the evaluation of disease onset and progression at different time-points in the mouse bleomycin model of lung fibrosis. Micro-CT was throughout coupled with histological analysis for the validation of the imaging results.

Methods

In bleomycin-instilled and control mice, airways and lung morphology changes were assessed and reconstructed at baseline, 7, 14 and 21 days post-treatment based on Micro-CT images. Ashcroft score, percentage of collagen content and percentage of alveolar air area were detected on lung slides processed by histology and subsequently compared with Micro-CT parameters.

Results

Extent (%) of fibrosis measured by Micro-CT correlated with Ashcroft score, the percentage of collagen content and the percentage of alveolar air area (r² = 0.91; 0.77; 0.94, respectively). Distal airway radius also correlated with the Ashcroft score, the collagen content and alveolar air area percentage (r² = 0.89; 0.78; 0.98, respectively).

Conclusions

Micro-CT data were in good agreement with histological read-outs as micro-CT was able to quantify effectively and non-invasively disease progression longitudinally and to reduce the variability and number of animals used to assess the damage. This suggests that this technique is a powerful tool for understanding experimental pulmonary fibrosis and that its use could translate into a more efficient drug discovery process, also helping to fill the gap between preclinical setting and clinical practice.

Functional respiratory imaging, regional strain, and expiratory time constants at three levels of positive end expiratory pressure in an ex vivo pig model

Heterogeneity in regional end expiratory lung volume (EELV) may lead to variations in regional strain (ε). High ε levels have been associated with ventilator-associated lung injury (VALI). While both whole lung and regional EELV may be affected by changes in positive end-expiratory pressure (PEEP), regional variations are not revealed by conventional respiratory system measurements. Differential rates of deflation of adjacent lung units due to regional variation in expiratory time constants (τE) may create localized regions of ε that are significantly greater than implied by whole lung measures. We used functional respiratory imaging (FRI) in an ex vivo porcine lung model to: (i) demonstrate that computed tomography (CT)-based imaging studies can be used to assess global and regional values of ε and τE and, (ii) demonstrate that the manipulation of PEEP will cause measurable changes in total and regional ε and τE values. Our study provides three insights into lung mechanics. First, image-based measurements reveal egional variation that cannot be detected by traditional methods such as spirometry. Second, the manipulation of PEEP causes global and regional changes in R, E, ε and τE values. Finally, regional ε and τE were correlated in several lobes, suggesting the possibility that regional τE could be used as a surrogate marker for regional ε.

Pulmonary vascular effects of pulsed inhaled nitric oxide in COPD patients with pulmonary hypertension

Introduction

Severe chronic obstructive pulmonary disease (COPD) is often associated with secondary pulmonary hypertension (PH), which worsens prognosis. PH can be lowered by oxygen, but also by inhaled nitric oxide (NO), which has the potential to improve the health status of these patients. NO is an important mediator in vascular reactions in the pulmonary circulation. Oral compounds can act through NO-mediated pathways, but delivering pulsed inhaled NO (iNO) directly to the airways and pulmonary vasculature could equally benefit patients. Therefore, a proof-of-concept study was performed to quantify pulmonary blood vessel caliber changes after iNO administration using computed tomography (CT)-based functional respiratory imaging (FRI).

Methods

Six patients with secondary PH due to COPD received “pulsed” iNO in combination with oxygen for 20 minutes via a nasal cannula. Patients underwent a high-resolution CT scan with contrast before and after iNO. Using FRI, changes in volumes of blood vessels and associated lobes were quantified. Oxygen saturation and blood pressure were monitored and patients were asked about their subjective feelings.

Results

Pulmonary blood vessel volume increased by 7.06%±5.37% after iNO. A strong correlation (Ω²₀=0.32, P=0.002) was obtained between ventilation and observed vasodilation, suggesting that using the pulsed system, iNO is directed toward the ventilated zones, which consequently experience more vasodilation. Patients did not develop oxygen desaturation, remained normotensive, and perceived an improvement in their dyspnea sensation.

Conclusion

Inhalation of pulsed NO with oxygen causes vasodilation in the pulmonary circulation of COPD patients, mainly in the well-ventilated areas. A high degree of heterogeneity was found in the level of vasodilation. Patients tend to feel better after the treatment. Chronic use trials are warranted.

Efficacy of inhaled medications in asthma and COPD related to disease severity

INTRODUCTION

The administration of medication by inhalation has become the most important route in treating airway diseases. The efficacy of this route depends on several factors like correct inhalation techniques, compliance and the size of the particles. The flow properties and internal flow distribution contribute to the deposition pattern. Areas covered: What has been less well studied is the effect of the internal flow distribution. We know from recent studies that using systemic anti-inflammatory compounds that open up the distal airways redistributes flow internally and enhances the deposition of inhaled particles to the active site of bronchoconstriction or airway inflammation. We discuss this in more detail in this paper, and also make reference to the use of functional respiratory imaging (FRI) that allows for the description of this flow pattern starting from chest CT followed by post processing with segmentation software and the application of fluid dynamics. Expert opinion: The method that was previously validated does show the importance of redistribution of flow in the final clinical results that could be obtained with inhaled medication, especially in more severe obstructive airway diseases. Based on these insights and novel diagnostic tools, patients in end stage respiratory failure would benefit from a personalized approach with inhaled medication.