1,721,135 research outputs found
Sarcoïdose: Een adembenemende aandoening
No full textSarcoidosis occurs most frequently in young or middle-aged patients. The disease affects different organ systems, but the lungs are the preferred localisation. Other organs which may be involved are eyes, skin, heart, liver, spleen, salivary glands, muscles, bone, kidneys and central nervous system. Despite intensive research the etiology of this intriguing disorder remains still unknown. This disease is microscopically characterised by the occurrence of non-caseating granulomata. The diagnosis is based on a combination of clinical, radiological and histopathological findings. Prognosis correlates with host characteristics and the way symptoms do occur as well with the severity and the extent of the disease. Corticosteroids are still the mainstay of treatment, but guidelines for the clinician in setting dose and duration of treatment do not exist. In addition the evolution of the disease is very variable: it can reactivate following an initial improvement, or a spontaneous remission may occur. Thus, treating sarcoidosis remains one of the most challenging topics in interstitial lung diseases
Clinical use of biomarkers of survival in pulmonary fibrosis
No full textBackground: Biologic predictors or biomarkers of survival in pulmonary fibrosis with a worse prognosis, more specifically in idiopathic pulmonary fibrosis would help the clinician in deciding whether or not to treat since treatment carries a potential risk for adverse events. These decisions are made easier if accurate and objective measurements of the patients' clinical status can predict the risk of progression to death
Detection of Lung Cancer via Blood Plasma and <sup>1</sup>H-NMR Metabolomics: Validation by a Semi-Targeted and Quantitative Approach Using a Protein-Binding Competitor
No full textMetabolite profiling of blood plasma, by proton nuclear magnetic resonance (1H-NMR) spectroscopy, offers great potential for early cancer diagnosis and unraveling disruptions in cancer metabolism. Despite the essential attempts to standardize pre-analytical and external conditions, such as pH or temperature, the donor-intrinsic plasma protein concentration is highly overlooked. However, this is of utmost importance, since several metabolites bind to these proteins, resulting in an underestimation of signal intensities. This paper describes a novel 1H-NMR approach to avoid metabolite binding by adding 4 mM trimethylsilyl-2,2,3,3-tetradeuteropropionic acid (TSP) as a strong binding competitor. In addition, it is demonstrated, for the first time, that maleic acid is a reliable internal standard to quantify the human plasma metabolites without the need for protein precipitation. Metabolite spiking is further used to identify the peaks of 62 plasma metabolites and to divide the 1H-NMR spectrum into 237 well-defined integration regions, representing these 62 metabolites. A supervised multivariate classification model, trained using the intensities of these integration regions (areas under the peaks), was able to differentiate between lung cancer patients and healthy controls in a large patient cohort (n = 160), with a specificity, sensitivity, and area under the curve of 93%, 85%, and 0.95, respectively. The robustness of the classification model is shown by validation in an independent patient cohort (n = 72)
Impact of Surgery on Functional and Patient-reported Outcomes in Patients With Early-stage Non-small Cell Lung Cancer
No full textRationale In patients with early-stage non-small cell lung cancer (NSCLC), the treatment of choice is surgical resection, with or without (neo)adjuvant chemotherapy. As a result of the disease and its treatment, patients have an increased risk for poor functional performances, decreased quality of life and high symptom burden. Current knowledge is mainly based on cross-sectional evaluations after treatment; longitudinal changes have been poorly characterized. Therefore, we aimed to investigate functional and patient-reported outcomes in patients with early-stage NSCLC before treatment and 12 weeks after treatment. Methods Patients with early-stage NSCLC (stage I-IIIB) were assessed before surgery and 12 weeks after treatment initiation. Functional outcome measures were a six-minute walk distance (6MWD), 1-minute sit-to-stand test (1-MSTST), quadriceps muscle strength (QMS; microFET), and handgrip strength (HGS; Jamar). Patient-reported outcome measures were the European Organization for the Research and Treatment of Cancer Questionnaire and lung cancer module (EORTC QLQ-C30-LC13), multidimensional fatigue inventory (MFI-20), and San Diego shortness of breath questionnaire (SOBQ). Analyses were performed using JMP PRO 14.2.0. Paired t-tests and Wilcoxon Signed rank tests were used to compare differences between both timepoints. Results Fifteen patients were included (10 males; age 65±9yrs; 5 with COPD). Patients had NSCLC stage IA (n=10), IB (n=1), IIB (n=2) or IIIA (n=2) and were treated via VATS only (n=11) or VATS and adjuvant chemotherapy (n=4). Results are presented in Figure 1. Twelve weeks after treatment, a significant worsening was found for 1-MSTST (27reps vs. 23reps, p=0.008), HGS (36kg vs. 31kg, p=0.036), and SOBQ score (11 vs. 21, p=0.010). No significant differences were found for the other outcomes. Conclusion In early-stage NSCLC, the treatment mainly affected the performance on the 1-MSTST, peripheral muscle strength, and shortness of breath. In contrast to previous findings, we did not observe a significant decrease in 6MWD, quality of life, and fatigue levels
Understanding omics data of lung cancer patients: Correlations between metabolomics and radiomics
No full textEur J Nucl Med Mol Imaging (2021) 48 (Suppl 1): S1-S648 predicting response and survival was obtained by combining clinical data with PET and CT texture parameters (AUC 0.87). Conclusion: PET/CT derived parameters demonstrated better performances-than the clinical parameters in predicting the response and overall survival confirming the interest in considering a radiomics based approach for the optimization of therapy management in patients with head and neck cancer. References: none Aim/Introduction: Qualitative and semi-quantitative parameters of PET and CT images are used to assist decision making for cancer treatment. In initial studies PET and CT radiomic features have shown promising results in disease prognostication and treatment outcome prediction in cancer. These features are specific to the outcome and different features show association with different outcomes. Hence finding the scalability of radiomic features from one modality to another can have promising impact. In our study we have tried to check the scalability of radiomic features across the modalities, PET and CT. We have performed a study to predict CT radiomic features using PET radiomic features and vice versa. Materials and Methods: This study was approved by the institutional ethics committee as retrospective study. 104 NSCLC patients who underwent pre-treatment PET-CT scan were included in this study. Primary lung tumor was delineated by SUV cutoff (42%) method on PET images and saved as RTStructure for PET and CT series. These Images and RTStructures were used for radiomic extraction using bin-width of 25 and 5 for CT and PET respectively using pyradiomic 2.1.0 software and in-house developed python script. Subsequently, concordance correlation coefficient (CCC) was calculated between PET and CT features and top 25 correlated features (excluding shape features) were selected to develop a prediction model. Entire set of data was split into training and validation sets (70:30). For each PET radiomic feature; a set of CT features were selected and vice versa using Recursive Feature Elimination(RFE). For individual feature prediction across modalities, a multivariate linear regression model was developed using selected features. Model performance was assessed based on accuracy of prediction (C-index) on validation set. Results: Around 54% and 46 % radiomic features show positive and negative correlation across PET and CT respectively. Only 91(8.33%), 69(6.3%) and 51(4.67%) features have 0.5<CCC<0.7, 0.7≤CCC<0.9 and CCC≥0.9 respectively. Top 25 selected radiomic features had CCC equal to or more than 0.99. The average C-Index and p-value in validation set for 25 PET radiomic features prediction was found to be 0.988(±0.019) and <0.0001 respectively. Similarly, average C-Index value and p-value in validation set for 25 CT radiomic features prediction was found to be 0.987(±0.016) and <0.0001 respectively. Conclusion: As per our findings, very few radiomic features have good correlation between PET and CT. These features show excellent capability to predict features across these modalities. References: none Aim/Introduction: Treatment of lung cancer remains challenging, partly due to the late-stage diagnosis of patients. With a strong focus on non-small cell lung cancer (NSCLC), this pilot study examines the diagnostic and prognostic potential of combining specific metabolic biomarkers from blood plasma (metabolomics) with features out of medical images (radiomics). This way, metabolomics and radiomics might be at the base of developing a more personalized treatment plan for lung cancer patients. This study aims to combine a metabolomics and radiomics dataset from lung cancer patients and to unravel the underlying correlations between the techniques. Materials and Methods: The initial patient cohort consisted of 32 patients, all diagnosed with early-stage NSCLC. All patients underwent a lobectomy as part of their standard-of-care treatment plan. The PET-CT images of all the patients were collected using 18 F-FDG (Biograph Horizon camera, Siemens). The PET-CT images were then segmented using a semi-automatic tool (ACCURATE), creating specific volumes of interest (VOIs) of the lung lesions for each patient. By loading the VOIs into the second tool (RADIOMICS), 486 radiomics parameters were extracted from each VOI (Both tools developed by R.B.) Simultaneously, 238 metabolic parameters representing 62 plasma metabolites were determined from the same patients using proton nuclear magnetic resonance (1 H-NMR) spectroscopy. A correlation coefficient test was used on the total omics-dataset to find a correlation between these two sets of parameters. Results: The correlation values found between the radiomics and metabolomics parameters showed R 2 values between 0.5 and 0.65 (positive correlation) or between-0.5 and-0.65 (negative correlation). The positive correlations found in the metabolomics dataset were mainly related to the concentration of plasma glucose. The radiomics S50
Impact of Surgery on Functional and Patient-reported Outcomes in Patients With Early-stage Non-small Cell Lung Cancer
No full textRationale In patients with early-stage non-small cell lung cancer (NSCLC), the treatment of choice is surgical resection, with or without (neo)adjuvant chemotherapy. As a result of the disease and its treatment, patients have an increased risk for poor functional performances, decreased quality of life and high symptom burden. Current knowledge is mainly based on cross-sectional evaluations after treatment; longitudinal changes have been poorly characterized. Therefore, we aimed to investigate functional and patient-reported outcomes in patients with early-stage NSCLC before treatment and 12 weeks after treatment. Methods Patients with early-stage NSCLC (stage I-IIIB) were assessed before surgery and 12 weeks after treatment initiation. Functional outcome measures were a six-minute walk distance (6MWD), 1-minute sit-to-stand test (1-MSTST), quadriceps muscle strength (QMS; microFET), and handgrip strength (HGS; Jamar). Patient-reported outcome measures were the European Organization for the Research and Treatment of Cancer Questionnaire and lung cancer module (EORTC QLQ-C30-LC13), multidimensional fatigue inventory (MFI-20), and San Diego shortness of breath questionnaire (SOBQ). Analyses were performed using JMP PRO 14.2.0. Paired t-tests and Wilcoxon Signed rank tests were used to compare differences between both timepoints. Results Fifteen patients were included (10 males; age 65±9yrs; 5 with COPD). Patients had NSCLC stage IA (n=10), IB (n=1), IIB (n=2) or IIIA (n=2) and were treated via VATS only (n=11) or VATS and adjuvant chemotherapy (n=4). Results are presented in Figure 1. Twelve weeks after treatment, a significant worsening was found for 1-MSTST (27reps vs. 23reps, p=0.008), HGS (36kg vs. 31kg, p=0.036), and SOBQ score (11 vs. 21, p=0.010). No significant differences were found for the other outcomes. Conclusion In early-stage NSCLC, the treatment mainly affected the performance on the 1-MSTST, peripheral muscle strength, and shortness of breath. In contrast to previous findings, we did not observe a significant decrease in 6MWD, quality of life, and fatigue levels
A New Missense Mutation in theCASRGene in Familial Interstitial Lung Disease with Hypocalciuric Hypercalcemia and Defective Granulocyte Function
No full textThe support of the Broere Charitable Foundation is
acknowledge
Validation of 1H-NMR-based metabolomics as a tool to detect lung cancer in human blood plasma
No full textAim: Until today no effective method permits the early detection of lung cancer. Evidence has shown that disturbances in biochemical pathways which occur during the development of cancer provoke, changes in the metabolic phenotype. Recently, our research group has constructed a statistical classifier by means of multivariate orthogonal partial least squares-discriminant analysis (OPLS-DA). This classifier (constructed with 110 variables) allows to discriminate between 190 lung cancer patients (71% male, 29% female, age: 68 ± 10, BMI: 25.8 ± 4.7) and 182 controls (53% male, 47% female, age: 69 + 11, BMI: 28.1 ± 4.8) with a sensitivity of 76% and a specificity of 89%, with an AUC of 0.86. When only the 19 most discriminating variables (VIP value > 0.8) were selected to construct a classifier (i.e. glucose, lactate, myo-inositol, threonine, alanine, isoleucine and lipids signals) a sensitivity of 69%, a specificity of 83% and an AUC of 0.81 is achieved. The present study aims to examine the predictive accuracy of these statistical classifiers in an independent cohort of 50 lung cancer patients (60% male, 40% female, age: 67 ± 9, BMI: 25.6 ± 4.3) and 58 controls (64% male, 36% female, age: 63 ± 13, BMI: 26.9 ± 5.7).
Methods: The metabolic phenotype of the plasma samples from this independent cohort is determined by 1H-NMR spectroscopy. Subsequently, the constructed classifiers are used to classify the independent samples. OPLS-DA is used as discriminant statistic.
Results: By using the classifier constructed with all 110 variables, 72% of the lung cancer patients and 72% of the controls are correctly classified, with an AUC of 0.79. Moreover, when the classifier constructed with only the 19 most discriminating variables is used to classify the independent samples, a sensitivity of 82%, a specificity of 64% and an AUC of 0.79 is achieved.
Conclusions: A statistical classifier constructed with only the most discriminating variables shows already a fair predictive accuracy, similar to this of the classifier build with all variables. Future experiments will investigate whether the constructed classifier can be used as a valid screening tool
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