Mediastinal Lymph Nodes Staging By 18F-FDG PET/CT For Early-stage Non-small Cell Lung Cancer: A Multicenter Study

Li, X.; Zhang, H.; J, Yu; Xing, Ligang; Ma, Huali; Chen, M.; Zhang, L.; Xu, Wengui; Yang, Guanghui

doi:10.1016/j.ijrobp.2011.06.1128

Cited by 12 publications

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“…reported a 9% rate of occult nodal metastasis in Chinese NSCLC N0 patients. 24 In the present study, the SUVmax of the primary tumor, adenocarcinoma, and tumor size were independent predictors of occult LN metastasis for patients with NSCLC by integrated FDG-PET/CT on multivariate analysis.…”

Section: Discussionsupporting

confidence: 48%

Identification of false‐negative and false‐positive diagnoses of lymph node metastases in non‐small cell lung cancer patients staged by integrated ^18F‐fluorodeoxyglucose‐positron emission tomography/computed tomography: A retrospective cohort study

et al. 2016

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BackgroundThe aim of this study was to evaluate the diagnostic accuracy of integrated 18 F‐fluorodeoxyglucose positron emission tomography/computed tomography (FDG‐PET/CT) in hilar and mediastinal lymph node (HMLN) staging of non‐small cell lung cancer (NSCLC), and to investigate potential risk factors for false‐negative and false‐positive HMLN metastases.MethodsWe examined the data of 388 surgically resected NSCLC patients preoperatively staged by integrated FDG‐PET/CT. Risk factors for false‐negative and false‐positive HMLN metastases were analyzed using univariate and multivariate analyses of clinicopathological factors.ResultsThe sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of integrated FDG‐PET/CT in detecting HMLN metastases were 47.4%, 91.0%, 56.3%, 87.7%, and 82.5%, respectively. On multivariate analysis, the maximum standardized uptake value (SUVmax) of the tumor (P = 0.042), adenocarcinoma (P = 0.003), and tumor size (P = 0.017) were risk factors for false‐negative HMLN metastases, and history of lung disease (P = 0.006) and tumor location (central; P = 0.025) were risk factors for false‐positive HMLN metastases.ConclusionsThe present study identified risk factors for false‐negative and false‐positive HMLN metastases in NSCLC patients staged by preoperative integrated FDG‐PET/CT. These findings would be helpful in selecting appropriate candidates for mediastinoscopy or endobronchial ultrasound‐guided transbronchial needle aspiration.

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Section: Discussionsupporting

confidence: 48%

Identification of false‐negative and false‐positive diagnoses of lymph node metastases in non‐small cell lung cancer patients staged by integrated ^18F‐fluorodeoxyglucose‐positron emission tomography/computed tomography: A retrospective cohort study

et al. 2016

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“…Evidence suggests that 18 F-FDG PET findings should not replace histological confirmation. A recent multicenter study indicated that 18 F-FDG PET/CT had high NPV (91 %) and specificity (83 %) but a low positive predictive value (PPV) (29 %) [20]. Similar findings were confirmed in a prospective trial where the mediastinal staging of 149 patients by 18 F-FDG PET/CT was confirmed histopathologically; overall sensitivity, specificity, PPV, and NPV were 70, 94, 64, and 95 %, respectively [21].…”

Section: Locoregional Lymph Node Staging (N Staging)mentioning

confidence: 99%

Present and future roles of FDG-PET/CT imaging in the management of lung cancer

et al. 2016

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Integrated positron emission tomography/computed tomography (PET/CT) using 2-[(18)F]fluoro-2-deoxy-D-glucose ((18)F-FDG) has emerged as a powerful tool for combined metabolic and anatomic evaluation in clinical oncologic imaging. This review discusses the utility of (18)F-FDG PET/CT as a tool for managing patients with lung cancer. We discuss different patient management stages, including diagnosis, initial staging, therapy planning, early treatment response assessment, re-staging, and prognosis.

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“…In recent years, diagnosis of non-small cell lung cancer (NSCLC) from PET/CT images became a popular research topic [1][2][3][4]. Many studies focused on assessing the efficacy of 18 F-FDG PET/CT for diagnosing mediastinal lymph node metastasis [5][6][7][8][9][10][11][12], whereas the judgment of metastasis was mostly based on thresholding the image features such as maximum short diameter, maximum standardized uptake value (SUV max ), or mean standardized uptake value (SUV mean ). Due to the limited number of image features and the simplicity of feature thresholding strategy, the diagnostic power of PET/CT might not have been fully explored.…”

Section: Introductionmentioning

confidence: 99%

Comparison of machine learning methods for classifying mediastinal lymph node metastasis of non-small cell lung cancer from 18F-FDG PET/CT images

et al. 2017

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BackgroundThis study aimed to compare one state-of-the-art deep learning method and four classical machine learning methods for classifying mediastinal lymph node metastasis of non-small cell lung cancer (NSCLC) from 18F-FDG PET/CT images. Another objective was to compare the discriminative power of the recently popular PET/CT texture features with the widely used diagnostic features such as tumor size, CT value, SUV, image contrast, and intensity standard deviation. The four classical machine learning methods included random forests, support vector machines, adaptive boosting, and artificial neural network. The deep learning method was the convolutional neural networks (CNN). The five methods were evaluated using 1397 lymph nodes collected from PET/CT images of 168 patients, with corresponding pathology analysis results as gold standard. The comparison was conducted using 10 times 10-fold cross-validation based on the criterion of sensitivity, specificity, accuracy (ACC), and area under the ROC curve (AUC). For each classical method, different input features were compared to select the optimal feature set. Based on the optimal feature set, the classical methods were compared with CNN, as well as with human doctors from our institute.ResultsFor the classical methods, the diagnostic features resulted in 81~85% ACC and 0.87~0.92 AUC, which were significantly higher than the results of texture features. CNN’s sensitivity, specificity, ACC, and AUC were 84, 88, 86, and 0.91, respectively. There was no significant difference between the results of CNN and the best classical method. The sensitivity, specificity, and ACC of human doctors were 73, 90, and 82, respectively. All the five machine learning methods had higher sensitivities but lower specificities than human doctors.ConclusionsThe present study shows that the performance of CNN is not significantly different from the best classical methods and human doctors for classifying mediastinal lymph node metastasis of NSCLC from PET/CT images. Because CNN does not need tumor segmentation or feature calculation, it is more convenient and more objective than the classical methods. However, CNN does not make use of the import diagnostic features, which have been proved more discriminative than the texture features for classifying small-sized lymph nodes. Therefore, incorporating the diagnostic features into CNN is a promising direction for future research.

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Mediastinal Lymph Nodes Staging By 18F-FDG PET/CT For Early-stage Non-small Cell Lung Cancer: A Multicenter Study

Cited by 12 publications

References 0 publications

Identification of false‐negative and false‐positive diagnoses of lymph node metastases in non‐small cell lung cancer patients staged by integrated ^18F‐fluorodeoxyglucose‐positron emission tomography/computed tomography: A retrospective cohort study

Identification of false‐negative and false‐positive diagnoses of lymph node metastases in non‐small cell lung cancer patients staged by integrated ^18F‐fluorodeoxyglucose‐positron emission tomography/computed tomography: A retrospective cohort study

Present and future roles of FDG-PET/CT imaging in the management of lung cancer

Comparison of machine learning methods for classifying mediastinal lymph node metastasis of non-small cell lung cancer from 18F-FDG PET/CT images

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Mediastinal Lymph Nodes Staging By 18F-FDG PET/CT For Early-stage Non-small Cell Lung Cancer: A Multicenter Study

Cited by 12 publications

References 0 publications

Identification of false‐negative and false‐positive diagnoses of lymph node metastases in non‐small cell lung cancer patients staged by integrated 18F‐fluorodeoxyglucose‐positron emission tomography/computed tomography: A retrospective cohort study

Identification of false‐negative and false‐positive diagnoses of lymph node metastases in non‐small cell lung cancer patients staged by integrated 18F‐fluorodeoxyglucose‐positron emission tomography/computed tomography: A retrospective cohort study

Present and future roles of FDG-PET/CT imaging in the management of lung cancer

Comparison of machine learning methods for classifying mediastinal lymph node metastasis of non-small cell lung cancer from 18F-FDG PET/CT images

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Identification of false‐negative and false‐positive diagnoses of lymph node metastases in non‐small cell lung cancer patients staged by integrated ^18F‐fluorodeoxyglucose‐positron emission tomography/computed tomography: A retrospective cohort study

Identification of false‐negative and false‐positive diagnoses of lymph node metastases in non‐small cell lung cancer patients staged by integrated ^18F‐fluorodeoxyglucose‐positron emission tomography/computed tomography: A retrospective cohort study