Contribution of F-18 fluorodeoxyglucose PET/CT and contrast-enhanced thoracic CT texture analyses to the differentiation of benign and malignant mediastinal lymph nodes

Background Texture analysis and machine learning methods are useful in distinguishing between benign and malignant tissues. Purpose To discriminate benign from malignant or metastatic mediastinal lymph nodes using F-18 fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT...

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Published in:Acta radiologica (1987) 2023-04, Vol.64 (4), p.1443-1454
Main Authors: Bülbül, Ogün, Bülbül, Hande Melike, Tertemiz, Kemal Can, Çapa Kaya, Gamze, Gürel, Duygu, Ulukuş, Emine Çağnur, Gezer, Naciye Sinem
Format: Article
Language:English
Subjects:
Adenocarcinoma - pathology
Bayes Theorem
Carcinoma, Squamous Cell - pathology
Cell Differentiation
Fluorodeoxyglucose F18
Humans
Lung Neoplasms - diagnostic imaging
Lung Neoplasms - pathology
Lymph Nodes - diagnostic imaging
Lymph Nodes - pathology
Positron Emission Tomography Computed Tomography
Positron-Emission Tomography - methods
Radiopharmaceuticals
Small Cell Lung Carcinoma - diagnostic imaging
Small Cell Lung Carcinoma - pathology
Tomography, X-Ray Computed - methods
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Summary:Background Texture analysis and machine learning methods are useful in distinguishing between benign and malignant tissues. Purpose To discriminate benign from malignant or metastatic mediastinal lymph nodes using F-18 fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) and contrast-enhanced computed tomography (CT) texture analyses with machine learning and determine lung cancer subtypes based on the analysis of lymph nodes. Material and Methods Suitable texture features were entered into the algorithms. Features that statistically significantly differed between the lymph nodes with small cell lung cancer (SCLC), adenocarcinoma (ADC), and squamous cell carcinoma (SCC) were determined. Results The most successful algorithms were decision tree with the sensitivity, specificity, and area under the curve (AUC) values of 89%, 50%, and 0.692, respectively, and naive Bayes (NB) with the sensitivity, specificity, and AUC values of 50%, 81%, and 0.756, respectively, for PET/CT, and NB with the sensitivity, specificity, and AUC values of 10%, 96%, and 0.515, respectively, and logistic regression with the sensitivity, specificity, and AUC values of 21%, 83%, and 0.631, respectively, for CT. In total, 13 features were able to differentiate SCLC and ADC, two features SCLC and SCC, and 33 features ADC and SCC lymph node metastases in PET/CT. One feature differed between SCLC and ADC metastases in CT. Conclusion Texture analysis is beneficial to discriminate between benign and malignant lymph nodes and differentiate lung cancer subtypes based on the analysis of lymph nodes.
ISSN:0284-1851
1600-0455
DOI:10.1177/02841851221130620