The method and efficacy of support vector machine classifiers based on texture features and multi-resolution histogram from 18F-FDG PET-CT images for the evaluation of mediastinal lymph nodes in patients with lung cancer

European Journal of Radiology

Volumn 84, Issue 2, 2015, Pages 312-317

  Gao, Xuan ^a   Chu, Chunyu ^b   Li, Yingci ^a   Lu, Peiou ^a   Wang, Wenzhi ^a   Liu, Wanyu ^b   Yu, Lijuan ^a  

^a HARBIN MEDICAL UNIVERSITY   (China)

^b HARBIN INSTITUTE OF TECHNOLOGY   (China)

Author keywords

18F FDG; Lung carcinoma; PET CT; Support vector machine

Indexed keywords

FLUORODEOXYGLUCOSE F 18; RADIOPHARMACEUTICAL AGENT;

ADULT; ARTICLE; CANCER DIAGNOSIS; CANCER PATIENT; CANCER STAGING; COMPUTER ASSISTED EMISSION TOMOGRAPHY; COMPUTER ASSISTED TOMOGRAPHY; CONTROLLED STUDY; DATA BASE; DIAGNOSTIC ACCURACY; DIAGNOSTIC TEST ACCURACY STUDY; EMISSION TOMOGRAPHY; FEMALE; HISTOGRAM; HUMAN; IMAGE ANALYSIS; LUNG CANCER; LYMPH NODE METASTASIS; MAJOR CLINICAL STUDY; MALE; MEDIASTINUM LYMPH NODE; PET-CT SCANNER; PRIORITY JOURNAL; RECEIVER OPERATING CHARACTERISTIC; SENSITIVITY AND SPECIFICITY; SUPPORT VECTOR MACHINE; TUMOR VOLUME; AGED; ALGORITHM; LUNG TUMOR; LYMPH NODE; MIDDLE AGED; PATHOLOGY; POSITRON EMISSION TOMOGRAPHY; PROCEDURES; RETROSPECTIVE STUDY; SCINTISCANNING; VERY ELDERLY;

AGED; AGED, 80 AND OVER; ALGORITHMS; FEMALE; FLUORODEOXYGLUCOSE F18; HUMANS; LUNG NEOPLASMS; LYMPH NODES; LYMPHATIC METASTASIS; MALE; MIDDLE AGED; POSITRON-EMISSION TOMOGRAPHY; RADIOPHARMACEUTICALS; RETROSPECTIVE STUDIES; ROC CURVE; SUPPORT VECTOR MACHINE; TOMOGRAPHY, X-RAY COMPUTED;

EID: 84920654725     PISSN: 0720048X     EISSN: 18727727     Source Type: Journal    
DOI: 10.1016/j.ejrad.2014.11.006     Document Type: Article

References (25)

1. Wu Y, Li P, Zhang H, Shi Y, Wu H, Zhang J, et al. Diagnostic value of fluorine 18 fluorodeoxyglucose positron emission tomography/computed tomography for the detection of metastases in non-small-cell lung cancer patients. Int J Cancer 2013;132(January (2)):E37-47 [Epub 01.09.12].
2. 18F-fluorodeoxyglucose uptake on positron emission tomography predicts lymph node metastasis and invasiveness in clinical stage IA non-small cell lung cancer. Interact Cardiovasc Thorac Surg 2009;9(July (1)):79-82 [Epub 14.04.09].
3. Takenaka T, Yano T, Morodomi Y, Ito K, Miura N, Kawano D, et al. Prediction of true-negative lymph node metastasis in clinical IA non-small cell lung cancer by measuring standardized uptake values on positron emission tomography. Surg Today 2012;42(October (10)):934-9 [Epub 03.08.12].
4. Sachs S, Bilfinger TV, Komaroff E, Franceschi D. Increased standardized uptake value in the primary lesion predicts nodal or distant metastases at presentation in lung cancer. Clin Lung Cancer 2005;6(March (5)):310-3.
5. Kawata Y, Niki N, Ohmatsu H. Computer aided differential diagnosis of pulmonary nodules using curvature based analysis. In: ICIAP'99. 1999. p. 470-5.
6. Shiraishi J, Abe H, Engelmann R, Aoyama M, MacMahon H, Doi K. Computer-aided diagnosis to distinguish benign from malignant solitary pulmonary nodules on radiographs. Radiology 2003;227:474-98.
7. Boroczky L, Zhao L. Feature subset selection for improving the performance of false positive reduction in lung nodule cad. IEEE Trans Inf Technol Biomed 2006;10(3):504-11.
8. Yankelevitz D, Reeves A, Kostis W. Small pulmonary nodules: volumetrically determined growth rates based on CT evaluation. Radiology 2000;217:251-6.
9. Revel M, Lefort C, Bissery A. Pulmonary nodules: preliminary experience with three-dimensional evaluation. Radiology 2004;231:459-66.
10. Kostis W, Yankelevitz D, Reeves A. Small pulmonary nodules: reproducibility of 3D volumetric measurement and estimation of time to follow-up CT. Radiology 2004;231:446-52.
11. Li F, Aoyama M, Shiraishi J, Abe H, Li Q, Suzuki K, et al. Radiologists' performance for differentiating benign from malignant lung nodules on high-resolution CT using computer-estimated likelihood of malignancy. Am J Roentgenol 2004;183(5):1209-15.
12. Mehta S, Grabowski TJ, Trivedi Y, Damasio H. Evaluation of voxel-based morphometry for focal lesion detection in individuals. Neuroimage 2003;20(3):1438-54.
13. Giger ML, Bae KT, Macmahon H. Computerized detection of pulmonary nodules in computed tomography images. Invest Radiol 1994;29(4):459-65.
14. Toshioka S, Kanazawa K, Niki N. Computer aided diagnosis system for lung cancer based on helical CT images. SPIE 1997;3034:975-84.
15. Hadjidemetriou E, Grossberg MD, Nayar SK. Multiresolution histograms and their use for recognition. IEEE Trans Pattern Anal Mach Intell 2004;26(7):831-47.
16. Hadjidemetriou E, Grossberg M, Nayar S. Spatial information in multiresolution histograms. In: Spatial Information in Multi-resolution Histograms Computer Vision and Pattern Recognition Conference. 2001. p. 702-9.
17. Hadjidemetriou E, Grossberg MD, Nayar SK. Histogram preserving image transformations. In: IEEE Conference on Proceedings of Computer Vision and Pattern Recognition. 2000. p. 410-41.
18. Belikova TP, Yashunskaya I, Kogan AE. Computer-aided differential diagnosis of small solitary pulmonary nodules. Comput Biomed Res 1996;29:48-62.
19. Brown MS, Goldin JG, Rogers S, Kim HJ, Suh RD, McNitt-Gray MF, et al. Computer-aided lung nodule detection in CT: results of large-scale observer test. Acad Radiol 2005;12(6):681-6.
20. Rusch VW, Asamura H, Watanabe H, Giroux DJ, Rami-Porta R, Goldstraw P, et al. The IASLC lung cancer staging project: a proposal for a new international lymph node map in the forthcoming seventh edition of the TNM classification for lung cancer. J Thorac Oncol 2009;4(May (5)):568-77, http://dx.doi.org/10.1097/JTO.0b013e3181a0d82e.
21. El Naqa I, Grigsby P, Apte A, Kidd E, Donnelly E, Khullar D, et al. Exploring feature-based approaches in PET images for predicting cancer treatment outcomes. Pattern Recognit 2009;42(June (6)):1162-71.
22. Vaidya M, Creach KM, Frye J, Dehdashti F, Bradley JD, El Naqa. I. Combined PET/CT image characteristics for radiotherapy tumor response in lung cancer. Radiother Oncol 2012;102(February (2)):239-45, http://dx.doi.org/10.1016/j.radonc.2011.10.014 [Epub 16.11.11].
23. Chicklore S, Goh V, Siddique M, Roy A, Marsden PK, Cook GJ. Quantifying tumour heterogeneity in 18F-FDG PET/CT imaging by texture analysis. Eur J Nucl Med Mol Imaging 2013;40(January (1)):133-40, http://dx.doi.org/10.1007/s00259-012-2247-0 [Epub 13.10.12].
24. Matsubara R, Kawano S, Chikui T, Kiyosue T, Goto Y, Hirano M, et al. Clinical significance of combined assessment of the maximum standardized uptake value of F-18 FDG PET with nodal size in the diagnosis of cervical lymph node metastasis of oral squamous cell carcinoma. Acad Radiol 2012;19(June (6)):708-17, http://dx.doi.org/10.1016/j.acra.2012.02.009 [Epub 07.04.12].
25. Nambu A, Kato S, Motosugi U, Araki T, Okuwaki H, Nishikawa K, et al. Thin-section CT of the mediastinum in preoperative N-staging of non-small cell lung cancer: comparison with FDG PET. Eur J Radiol 2010;73(March (3)):510-7, http://dx.doi.org/10.1016/j.ejrad.2009.01.021 [Epub 25.02.09].