Readme zur Dokumentation von Forschungsdatensätzen

Ersteller*in Readme:				Wehlte, Lukas

Erstellungsdatum Readme:				2024-04-01
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Titel des Forschungsdatensatzes:			The Association between the Body Mass Index, Chronic Obstructive Pulmonary Disease and SUV of the Non-Tumorous Lung in the Pretreatment [18F]FDG-PET/CT of Patients with Lung Cancer
						
Ort der Datenerhebung:				München, Bayern, Deutschland

Version:						1.0
Versionenänderungen:				

Herausgeber*in:					Universitätsbibliothek der Ludwig-Maximilians-Universität München
Herausgeber*in Identifier:			[Angabe Institution per GND oder ROR]
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Ersteller*in 1 des Datensatzes:			Wehlte, Lukas 
Ersteller*in 1 Identifier:			https://orcid.org/0009-0001-2436-8630
Ersteller*in 1 Affiliation:			Department of Medicine V, LMU University Hospital, München (Germany)		

Beteiligte[*] 1:					Daisenberger, Lea
Beteiligte 1 Identifier:			
Beteiligte 1 Affiliation :			Department of Dermatology and Allergy, LMU University Hospital, München (Germany)

[*] An der Sammlung von Forschungsdaten beteiligte Personen.
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Projektname:					Pneumonitis:
						All lung cancer patients who were treated with an immune checkpoint inhibitor therapy at the Department of Medicine V at LMU Klinikum between 2014 and 2022.
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Abstract:					This research project aimed to elucidate the association between clinical variables and lung standardized uptake value (SUV) in pretherapeutic [18F]FDG-PET/CT scans of lung cancer patients. The study examined SUV measurements in non-tumorous regions of the lungs from 240 lung cancer patients, comparing SUVMEAN, SUVMAX, SUV95, and ratios of lung tissue to liver tissue and blood pool SUV with various patient characteristics and comorbidities. The analyzed clinical variables included sex, age, BMI, smoking status, pack years, COPD, tumor stage, previous lung operation, previous lung radiation, pleural effusion (PE), pericardial effusion (PCE), diabetes mellitus type II, asthma, coronary heart disease (CHD) and blood hemoglobin level (Hb). This comprehensive analysis aimed to shed light on the interplay between these clinical factors and lung SUV in lung cancer patients, potentially informing more tailored and effective management strategies for the disease.

This dataset comprises essential components for replicating the research findings presented in my dissertation. Included are a README file offering an overview of the dataset's contents and usage instructions, a cohort file containing the dataset used in the study, and R scripts facilitating the reproduction of the analyses outlined in the dissertation. These resources aim to enhance transparency and reproducibility, enabling fellow researchers to validate and build upon the findings of the original research article.
						
Schlagwörter:					Medicine, Lung cancer, PET/CT, SUV, COPD, BMI

Methode der Datenerhebung: 			For clinical variables a semi-automatic tool was built to analyze text of doctor’s documentations using keywords for those characteristics. FDG-PET/CT images were evaluated using Visage®7, Visage Imaging, Inc., San Diego, USA

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Datei:						COHORT (Data frame)
Dateiformate: 					XLSX

Datei:						Results.R (Runs all R files and includes explanations for each file
						1_Patient_characteristics
						2_Image_analysis
						3.1_SUV
						3.2_scanner.R
						3.3a_characteristics.R
						3.3b_characteristics_subgroup.R
						3.4_correlation_bmi_copd.R
						3.5a_multilinear_regression.R
						3.5b_multilinear_regression_subgroup.R
Dateiformate: 					XLSX; R

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Anmerkungen: 					This project was funded by the Deutsche Forschungsgemeinschaft (DFG) under Germany’s Excellence Strategy within the framework of the Munich Cluster for Systems Neurology (EXC 2145 SyNergy—ID 390857198)
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