The size of mediastinal lymph nodes and its relation with metastatic involvement: a meta-analysis.
ABSTRACT Positron emission tomography with 18-fluorodeoxyglucose (FDG-PET) seems to be superior to computed tomography (CT) in staging the mediastinum in patients with non-small-cell lung cancer (NSCLC). However, recent results suggest that FDG-PET performance characteristics are conditional for nodal size as shown by CT: FDG-PET is more sensitive but less specific with lymph node enlargement on CT. The association between size and the probability of malignancy needs to be known to predict the post-test probabilities after PET, and finally, stratify patients for mediastinoscopy or thoracotomy depending on the PET and CT results. Therefore, we performed a meta-analysis of available studies reporting on the prevalence of metastatic involvement for different size categories of enlarged lymph nodes in patients with NSCLC and were able to include 14 studies. The prevalence of metastatic involvement and conditional test performance of CT and FDG-PET were calculated for lymph nodes measuring 10-15 mm, 16-20 mm and >20 mm. We found a post-test probability for N2 disease of 5% for lymph nodes measuring 10-15 mm on CT in patients with a negative FDG-PET result, suggesting that these patients should be planned for thoracotomy because the yield of mediastinoscopy will be extremely low. For patients with lymph nodes measuring > or =16 mm on CT and a negative FDG-PET result a post-test probability for N2 disease of 21% was found, suggesting that these patients should be planned for mediastinoscopy prior to possible thoracotomy to prevent too many unnecessary thoracotomies in this subset.
Article: Lymph node detection and segmentation in chest CT data using discriminative learning and a spatial prior.[show abstract] [hide abstract]
ABSTRACT: Lymph nodes have high clinical relevance and routinely need to be considered in clinical practice. Automatic detection is, however, challenging due to clutter and low contrast. In this paper, a method is presented that fully automatically detects and segments lymph nodes in 3-D computed tomography images of the chest. Lymph nodes can easily be confused with other structures, it is therefore vital to incorporate as much anatomical prior knowledge as possible in order to achieve a good detection performance. Here, a learned prior of the spatial distribution is used to model this knowledge. Different prior types with increasing complexity are proposed and compared to each other. This is combined with a powerful discriminative model that detects lymph nodes from their appearance. It first generates a number of candidates of possible lymph node center positions. Then, a segmentation method is initialized with a detected candidate. The graph cuts method is adapted to the problem of lymph nodes segmentation. We propose a setting that requires only a single positive seed and at the same time solves the small cut problem of graph cuts. Furthermore, we propose a feature set that is extracted from the segmentation. A classifier is trained on this feature set and used to reject false alarms. Cross-validation on 54 CT datasets showed that for a fixed number of four false alarms per volume image, the detection rate is well more than doubled when using the spatial prior. In total, our proposed method detects mediastinal lymph nodes with a true positive rate of 52.0% at the cost of only 3.1 false alarms per volume image and a true positive rate of 60.9% with 6.1 false alarms per volume image, which compares favorably to prior work on mediastinal lymph node detection.Medical image analysis 11/2012; · 3.09 Impact Factor
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ABSTRACT: In order to appropriately manage patients with lung cancer, it is necessary to properly stage the tumor. The ACR Appropriateness Criteria is designed to provide an overview of the value of different imaging techniques in the non-invasive staging of lung cancer and allow for the rational selection of imaging studies to arrive at the appropriate clinical stage.Journal of thoracic imaging 11/2010; 25(4):W107-11. · 1.42 Impact Factor
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ABSTRACT: Patients with clinical stage IIIAN2 non-small cell lung cancer (NSCLC) are a heterogeneous subgroup in term of prognosis and therapeutic management. The optimal management of this patient group is a major focus for thoracic oncology research and the concept of multimodality treatment has recently been introduced. This approach combines induction chemotherapy or radiochemotherapy followed by surgery in the case of mediastinal lymph node down-staging. positron emission tomography computed tomography with [18F]-fluorodesoxyglucose (FDG-PET) is a molecular and metabolic imaging modality which combines the metabolic data of PET with morphological data from CT. FDG-PET has become a standard in lung cancer management since the different indications listed in the standards, options and recommendations (SOR) of the FNCLCC. However, the potential specific importance of FDG-PET in IIIAN2 patients needs to be addressed further. In this setting, the authors' objective is to review the potential role of metabolic imaging in stage IIIAN2 NSCLC, taking into account new multimodality treatments. In stage IIIAN2, FDG-PET has performed better than morphoradiological imaging for baseline and postinduction lymph node staging, the identification of distant metastasis, and determining prognosis, as well as assessing the response to treatment.Revue des Maladies Respiratoires 02/2012; 29(2):149-60. · 0.59 Impact Factor