CT texture analysis can help differentiate between malignant and benign lymph nodes in the mediastinum in patients suspected for lung cancer

Andersen, Michael Brun; Harders, Stefan Walbom; Ganeshan, Balaji; Thygesen, Jesper; Madsen, Harold F.; Rasmussen, Finn

doi:10.1177/0284185115598808

Cited by 90 publications

(80 citation statements)

References 22 publications

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“…The SVMs performed equivalently to quantitative SUV max and mean nodal short-axis diameter in diagnosing malignant thoracic lymph nodes. In another study, the mean attenuation measurement was shown to be useful in predicting nodal metastasis in lung cancer patients (n = 46) [31]. The authors found a mean difference of 20 HU between benign and malignant lymph nodes.…”

Section: Imaging Heterogeneity In Lung Cancermentioning

confidence: 95%

See 1 more Smart Citation

Imaging Heterogeneity in Lung Cancer: Techniques, Applications, and Challenges

Bashir

Siddique

McLean

et al. 2016

American Journal of Roentgenology

131

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Texture analysis of lung cancer images has been applied successfully to FDG PET and CT scans. Different texture parameters have been shown to be predictive of the nature of disease and of patient outcome. In general, it appears that more heterogeneous tumors on imaging tend to be more aggressive and to be associated with poorer outcomes and that tumor heterogeneity on imaging decreases with treatment. Despite these promising results, there is a large variation in the reported data and strengths of association.

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Section: Imaging Heterogeneity In Lung Cancermentioning

confidence: 95%

“…Nevertheless, studies investigating texture analysis as a potential classification tool are showing promising results, and it is hoped that texture analysis may improve the accuracy of imaging [23,[30][31][32][33].…”

Section: Applications In Lesion Classificationmentioning

confidence: 99%

Imaging Heterogeneity in Lung Cancer: Techniques, Applications, and Challenges

Bashir

Siddique

McLean

et al. 2016

American Journal of Roentgenology

131

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“…Chest.-CTTA has also been applied to differentiating benign from malignant entities in the chest. It has been used to differentiate benign from malignant pulmonary nodules and mediastinal lymph nodes (3,(31)(32)(33)(34) and to differentiate radiation fibrosis from recurrent lung cancer (3,35,36).…”

Section: Lesion Characterizationmentioning

confidence: 99%

CT Texture Analysis: Definitions, Applications, Biologic Correlates, and Challenges

et al. 2017

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This review discusses potential oncologic and nononcologic applications of CT texture analysis ( CTTA CT texture analysis ), an emerging area of "radiomics" that extracts, analyzes, and interprets quantitative imaging features. CTTA CT texture analysis allows objective assessment of lesion and organ heterogeneity beyond what is possible with subjective visual interpretation and may reflect information about the tissue microenvironment. CTTA CT texture analysis has shown promise in lesion characterization, such as differentiating benign from malignant or more biologically aggressive lesions. Pretreatment CT texture features are associated with histopathologic correlates such as tumor grade, tumor cellular processes such as hypoxia or angiogenesis, and genetic features such as KRAS or epidermal growth factor receptor (EGFR) mutation status. In addition, and likely as a result, these CT texture features have been linked to prognosis and clinical outcomes in some tumor types. CTTA CT texture analysis has also been used to assess response to therapy, with decreases in tumor heterogeneity generally associated with pathologic response and improved outcomes. A variety of nononcologic applications of CTTA CT texture analysis are emerging, particularly quantifying fibrosis in the liver and lung. Although CTTA CT texture analysis seems to be a promising imaging biomarker, there is marked variability in methods, parameters reported, and strength of associations with biologic correlates. Before CTTA CT texture analysis can be considered for widespread clinical implementation, standardization of tumor segmentation and measurement techniques, image filtration and postprocessing techniques, and methods for mathematically handling multiple tumors and time points is needed, in addition to identification of key texture parameters among hundreds of potential candidates, continued investigation and external validation of histopathologic correlates, and structured reporting of findings. RSNA, 2017.

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“…Given that knowledge, there has also been investigation into the textural features of tumors outside of the primary site. CT textural features were recently shown to be correlated with malignant and benign lymph nodes 26,27 . However, to date there have been very few quantitative analyses of NSCLC lymph node features correlated with clinical outcomes.…”

Section: Introductionmentioning

confidence: 99%

Radiomic-Based Pathological Response Prediction from Primary Tumors and Lymph Nodes in NSCLC

Coroller

Agrawal

Huynh

et al. 2017

Journal of Thoracic Oncology

195

115

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Introduction Non-invasive biomarkers that capture the total tumor burden could provide important complementary information for precision medicine to aid clinical decision-making. We investigated the value of radiomic data, extracted from pre-treatment computed tomography (CT) images of the primary tumor and lymph nodes, in predicting pathological response following neoadjuvant chemoradiation prior to surgery. Methods Eighty-five patients with resectable locally-advanced (stage II-III) non-small cell lung cancer (NSCLC) (median age: 60.3 years; 65% female) treated from 2003-2013 were included in this IRB-approved study. Radiomics analysis was performed on 85 primary tumors and 178 lymph nodes to discriminate between pathological complete response (pCR) or gross residual disease (GRD). Twenty non-redundant and stable features (10 from each site) were evaluated using the area under the curve (AUC) (all p-values were corrected for multiple hypothesis testing). Classification performance of each feature set was evaluated using random forest and nested cross validation. Results Three radiomic features (describing primary tumor sphericity and lymph node homogeneity) were significantly predictive of pCR with similar performances (all AUC=0.67, p-value<0.05). Those two features (quantifying lymph node homogeneity) were predictive of GRD (AUC range: 0.72-0.75, p-value<0.05) and performed significantly better than the primary features (AUC=0.62). Multivariate analysis showed that for pCR, the radiomics feature set alone had the best performing classification (median AUC=0.68). Furthermore, for GRD classification, combining radiomic and clinical data significantly outperformed all other feature sets (median AUC=0.73). Conclusion Lymph node phenotypic information was significantly predictive for pathological response and showed higher classification performance than radiomic features obtained from the primary tumor.

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CT texture analysis can help differentiate between malignant and benign lymph nodes in the mediastinum in patients suspected for lung cancer

Cited by 90 publications

References 22 publications

Imaging Heterogeneity in Lung Cancer: Techniques, Applications, and Challenges

Imaging Heterogeneity in Lung Cancer: Techniques, Applications, and Challenges

CT Texture Analysis: Definitions, Applications, Biologic Correlates, and Challenges

Radiomic-Based Pathological Response Prediction from Primary Tumors and Lymph Nodes in NSCLC

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