Quantitative CT texture and shape analysis: Can it differentiate benign and malignant mediastinal lymph nodes in patients with primary lung cancer?

European Radiology

Volumn 25, Issue 2, 2015, Pages 480-487

  Bayanati, Hamid ^a   E Thornhill, Rebecca ^a   Souza, Carolina A ^a   Sethi Virmani, Vineeta ^a   Gupta, Ashish ^a   Maziak, Donna ^a   Amjadi, Kayvan ^a   Dennie, Carole ^a  

^a OTTAWA HOSPITAL RESEARCH INSTITUTE   (Canada)

Author keywords

Computed tomography; Lung cancer; Lymph node; Staging; Texture analysis

Indexed keywords

AGED; ANALYSIS; AREA UNDER THE CURVE; ARTICLE; CANCER PATIENT; CANCER STAGING; CLINICAL ARTICLE; COMPUTER ASSISTED TOMOGRAPHY; DIAGNOSTIC TEST ACCURACY STUDY; FEMALE; HUMAN; HUMAN TISSUE; LUNG CANCER; MALE; MEDIASTINUM LYMPH NODE; OUTCOME ASSESSMENT; PRIMARY TUMOR; PRIORITY JOURNAL; QUANTITATIVE STUDY; RECEIVER OPERATING CHARACTERISTIC; RETROSPECTIVE STUDY; SENSITIVITY AND SPECIFICITY; SHAPE ANALYSIS; TEXTURE ANALYSIS; ADULT; BIOPSY; DIFFERENTIAL DIAGNOSIS; FOLLOW UP; LUNG TUMOR; LYMPH NODE; LYMPH NODE METASTASIS; LYMPHATIC SYSTEM DISEASE; MEDIASTINUM; MIDDLE AGED; PATHOLOGY; PROCEDURES; RADIOGRAPHY; SECONDARY; VERY ELDERLY;

ADULT; AGED; AGED, 80 AND OVER; BIOPSY; DIAGNOSIS, DIFFERENTIAL; FEMALE; FOLLOW-UP STUDIES; HUMANS; LUNG NEOPLASMS; LYMPH NODES; LYMPHATIC DISEASES; LYMPHATIC METASTASIS; MALE; MEDIASTINUM; MIDDLE AGED; NEOPLASM STAGING; RETROSPECTIVE STUDIES; ROC CURVE; TOMOGRAPHY, X-RAY COMPUTED;

EID: 84920858276     PISSN: 09387994     EISSN: 14321084     Source Type: Journal    
DOI: 10.1007/s00330-014-3420-6     Document Type: Article

References (29)

1. McLoud TC, Bourgouin PM, Greenberg RW et al (1992) Bronchogenic carcinoma: analysis of staging in the mediastinum with CT by correlative lymph node mapping and sampling. Radiology 182:319–323
2. Webb WR, Gatsonis C, Zerhouni EA et al (1991) CT and MR imaging in staging non-small cell bronchogenic carcinoma: report of the Radiologic Diagnostic Oncology Group. Radiology 178:705–713
3. Gupta NC, Graeber GM, Bishop HA (2000) Comparative efficacy of positron emission tomography with fluorodeoxyglucose in evaluation of small (<1 cm), intermediate (1 to 3 cm), and large (>3 cm) lymph node lesions. Chest 117:773–778
4. Patz EF Jr, Lowe VJ, Goodman PC, Herndon J (1995) Thoracic nodal staging with PET imaging with 18FDG in patients with bronchogenic carcinoma. Chest 108:1617–1621
5. Kassner A, Thornhill RE (2010) Texture analysis: a review of neurologic MR imaging applications. AJNR Am J Neuroradiol 31:809–816
6. Tourassi GD (1999) Journey toward computer-aided diagnosis: role of image texture analysis. Radiology 213:317–320
7. Ganeshan B, Goh V, Mandeville HC, Ng QS, Hoskin PJ, Miles KA (2013) Non-small cell lung cancer: histopathologic correlates for texture parameters at CT. Radiology 266:326–336
8. Ganeshan B, Panayiotou E, Burnand K, Dizdarevic S, Miles K (2012) Tumour heterogeneity in non-small cell lung carcinoma assessed by CT texture analysis: a potential marker of survival. Eur Radiol 22:796–802
9. Miles KA, Ganeshan B, Griffiths MR, Young RC, Chatwin CR (2009) Colorectal cancer: texture analysis of portal phase hepatic CT images as a potential marker of survival. Radiology 250:444–452
10. Ng F, Ganeshan B, Kozarski R, Miles KA, Goh V (2013) Assessment of primary colorectal cancer heterogeneity by using whole-tumor texture analysis: contrast-enhanced CT texture as a biomarker of 5-year survival. Radiology 266:177–184
11. Ravanelli M, Farina D, Morassi M et al (2013) Texture analysis of advanced non-small cell lung cancer (NSCLC) on contrast-enhanced computed tomography: prediction of the response to the first-line chemotherapy. Eur Radiol 23:3450–3455
12. Collewet G, Strzelecki M, Mariette F (2004) Influence of MRI acquisition protocols and image intensity normalization methods on texture classification. Magn Reson Imaging 22:81–91
13. Mayerhoefer ME, Breitenseher MJ, Kramer J, Aigner N, Hofmann S, Materka A (2005) Texture analysis for tissue discrimination on T1-weighted MR images of the knee joint in a multicenter study: transferability of texture features and comparison of feature selection methods and classifiers. J Magn Reson Imaging 22:674–680
14. Galloway M (1975) Texture analysis using gray level run lenghts. Comp Graph Image Process 4:172–179
15. Haralick RM, Shanmugan K, Dinstein I (1973) Textural features for image classification. IEEE Trans Syst Man Cybern 3:610–621
16. Hanley JA, McNeil BJ (1982) The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143:29–36
17. Chang CC, Lin CJ (2011) LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol 2:1–27
18. Witten IH, Frank E (2005) Data mining - practical machine learning tools and technique, 2nd edn. Elsevier, Burlington
19. Lunt SJ, Chaudary N, Hill RP (2009) The tumor microenvironment and metastatic disease. Clin Exp Metastasis 26:19–34
20. Davnall F, Yip CS, Ljungqvist G et al (2012) Assessment of tumor heterogeneity: an emerging imaging tool for clinical practice? Insights Imaging 3:573–589
21. Spira D, Wecker M, Spira SM et al (2013) Does volume perfusion computed tomography enable differentiation of metastatic and non-metastatic mediastinal lymph nodes in lung cancer patients? A feasibility study. Cancer Imaging 13:323–331
22. Thornhill RE, Shabana W, Kajal D (2012) Diameter-independent Computer-Aided Classification of Mesorectal Lymph Nodes in Rectal cancer: Preliminary Results - 20th Meeting International Society of Magnetic Resonance in Medicine, Melbourne, Australia, pp 3038
23. Brown G, Richards CJ, Bourne MW et al (2003) Morphologic predictors of lymph node status in rectal cancer with use of high-spatial-resolution MR imaging with histopathologic comparison. Radiology 227:371–377
24. Nguyen P, Bashirzadeh F, Hundloe J et al (2012) Optical differentiation between malignant and benign lymphadenopathy by grey scale texture analysis of endobronchial ultrasound convex probe images. Chest 141:709–715
25. Fujiwara T, Yasufuku K, Nakajima T et al (2010) The utility of sonographic features during endobronchial ultrasound-guided transbronchial needle aspiration for lymph node staging in patients with lung cancer: a standard endobronchial ultrasound image classification system. Chest 138:641–647
26. Chen DR, Chang RF, Huang YL (1999) Computer-aided diagnosis applied to US of solid breast nodules by using neural networks. Radiology 213:407–412
27. Chen SJ, Lin CH, Chang CY et al (2012) Characterizing the major sonographic textural difference between metastatic and common benign lymph nodes using support vector machine with histopathologic correlation. Clin Imaging 36:e352
28. Herlidou-Meme S, Constans JM, Carsin B et al (2003) MRI texture analysis on texture test objects, normal brain and intracranial tumors. Magn Reson Imaging 21:989–993
29. Sarunas JR, Anil JK (1991) Small sample size effects in statistical pattern recognition: recommendations for practioners. IEEE Trans Pattern Anal Mach Intell 13:252–264