Density Features of Screened Lung Tumors in Low-Dose Computed Tomography

Academic Radiology

Volumn 21, Issue 1, 2014, Pages 41-51

  Shen, Wei Chih ^a   Liu, Juhn Cherng ^b,c   Shieh, Shwn Huey ^c   Yang, Su Tso ^b,d   Tseng, Guan Chin ^b   Hsu, Wu Huei ^b,e   Chen, Chih Yi ^b   Yu, Yang Hao ^b,e  

^a ASIA UNIVERSITY   (Taiwan)

^b CHINA MEDICAL UNIVERSITY HOSPITAL   (Taiwan)

^c CHINA MEDICAL UNIVERSITY   (Taiwan)

^d CHINA MEDICAL UNIVERSITY   (Taiwan)

^e CHINA MEDICAL UNIVERSITY   (Taiwan)

Author keywords

Computer aided diagnosis; Density feature; Low dose computed tomography; Lung tumor

Indexed keywords

ADULT; ARTICLE; BENIGN TUMOR; CANCER SIZE; CLINICAL ARTICLE; COMPUTED TOMOGRAPHY SCANNER; COMPUTER ASSISTED TOMOGRAPHY; DENSITY; DIFFERENTIAL DIAGNOSIS; ENTROPY; FEMALE; HISTOPATHOLOGY; HUMAN; HUMAN TISSUE; LOW DOSE COMPUTED TOMOGRAPHY; LUNG ADENOCARCINOMA; LUNG ADENOMA; LUNG TUMOR; MALE; MALIGNANT NEOPLASTIC DISEASE; PHYSICIAN; PREDICTIVE VALUE; PRIORITY JOURNAL; RETROSPECTIVE STUDY;

COMPUTER-AIDED DIAGNOSIS; DENSITY FEATURE; LOW-DOSE COMPUTED TOMOGRAPHY; LUNG TUMOR;

ABSORPTIOMETRY, PHOTON; ADENOCARCINOMA; ALGORITHMS; CARCINOMA IN SITU; EARLY DETECTION OF CANCER; FEMALE; HUMANS; LUNG NEOPLASMS; MALE; MIDDLE AGED; RADIATION DOSAGE; RADIATION PROTECTION; RADIOGRAPHIC IMAGE INTERPRETATION, COMPUTER-ASSISTED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; TOMOGRAPHY, X-RAY COMPUTED;

EID: 84890233228     PISSN: 10766332     EISSN: 18784046     Source Type: Journal    
DOI: 10.1016/j.acra.2013.09.021     Document Type: Article

References (34)

1. National Lung Screening Trial Research Team, Aberle D.R., Adams A.M., Berg C.D., et al. Reduced lung-cancer mortality with low-dose computed tomographic screening. N Engl J Med 2011, 365:395-409.
2. Gohagan J., Marcus P., Fagerstrom R., et al. Baseline findings of a randomized feasibility trial of lung cancer screening with spiral CT scan vs chest radiograph: the Lung Screening Study of the National Cancer Institute. Chest 2004, 126:114-121.
3. van den Bergh K.A., Essink-Bot M.L., Bunge E.M., et al. Impact of computed tomography screening for lung cancer on participants in a randomized controlled trial (NELSON trial). Cancer 2008, 113:396-404.
4. Kalra M.K., Maher M.M., Toth T.L., et al. Strategies for CT radiation dose optimization. Radiology 2004, 230:619-628. Review.
5. Chasen M.H., McCarthy M.J., Gilliland J.D., et al. Concepts in computed tomography of the thorax. Radiographics 1986, 6:793-832.
6. Fischbach F., Knollmann F., Griesshaber V., et al. Detection of pulmonary nodules by multislice computed tomography: improved detection rate with reduced slice thickness. Eur Radiol 2003, 13:2378-2383.
7. Lee H.Y., Goo J.M., Lee H.J., et al. Usefulness of concurrent reading using thin-section and thick-section CT images in subcentimetre solitary pulmonary nodules. Clin Radiol 2009, 64:127-132.
8. Bushberg J.T., Seibert J.A., Leidholdt E.M., et al. The essential physics of medical imaging 2011, Lippincott Williams & Wilkins, Philadelphia, PA. 3rd ed.
9. Diederich S., Lenzen H., Windmann R., et al. Pulmonary nodules: experimental and clinical studies at low-dose CT. Radiology 1999 Oct, 213(1):289-298.
10. Godoy M.C., Naidich D.P. Subsolid pulmonary nodules and the spectrum of peripheral adenocarcinomas of the lung: recommended interim guidelines for assessment and management. Radiology 2009, 253:606-622. Review.
11. Leader J.K., Warfel T.E., Fuhrman C.R., et al. Pulmonary nodule detection with low-dose CT of the lung: agreement among radiologists. AJR Am J Roentgenol 2005, 185:973-978.
12. Gierada D.S., Pilgram T.K., Ford M., et al. Lung cancer: interobserver agreement on interpretation of pulmonary findings at low-dose CT screening. Radiology 2008, 246:265-272.
13. Takamochi K., Nagai K., Yoshida J., et al. Pathologic N0 status in pulmonary adenocarcinoma is predictable by combining serum carcinoembryonic antigen level and computed tomographic findings. J Thorac Cardiovasc Surg 2001, 122:325-330.
14. Okada M., Nishio W., Sakamoto T., et al. Correlation between computed tomographic findings, bronchioloalveolar carcinoma component, and biologic behavior of small-sized lung adenocarcinomas. J Thorac Cardiovasc Surg 2004, 127:857-861.
15. Okada M., Tauchi S., Iwanaga K., et al. Associations among bronchioloalveolar carcinoma components, positron emission tomographic and computed tomographic findings, and malignant behavior in small lung adenocarcinomas. J Thorac Cardiovasc Surg 2007, 133:1448-1454.
16. Okada M., Nishio W., Sakamoto T., et al. Discrepancy of computed tomographic image between lung and mediastinal windows as a prognostic implication in small lung adenocarcinoma. Ann Thorac Surg 2003, 76:1828-1832. discussion 1832.
17. Haraguchi N., Satoh H., Kikuchi N., et al. Prognostic value of tumor disappearance rate on computed tomography in advanced-stage lung adenocarcinoma. Clin Lung Cancer 2007, 8:327-330.
18. Henschke C.I., Yankelevitz D.F., Mirtcheva R., et al. CTscreening for lung cancer: frequency and significance of part-solid and nonsolid nodules. AJR Am J Roentgenol 2002, 178:1053-1057. ELCAP Group.
19. Kitami A., Kamio Y., Hayashi S., et al. One-dimensional mean computed tomography value evaluation of ground-glass opacity on high-resolution images. Gen Thorac Cardiovasc Surg 2012, 60:425-430.
20. Nomori H., Ohtsuka T., Naruke T., et al. Differentiating between atypical adenomatous hyperplasia and bronchioloalveolar carcinoma using the computed tomography number histogram. Ann Thorac Surg 2003, 76:867-871.
21. Ikeda K., Awai K., Mori T., et al. Differential diagnosis of ground-glass opacity nodules: CT number analysis by three-dimensional computerized quantification. Chest 2007, 132:984-990.
22. Ganeshan B., Abaleke S., Young R.C., et al. Texture analysis of non-small cell lung cancer on unenhanced computed tomography: initial evidence for a relationship with tumour glucose metabolism and stage. Cancer Imaging 2010, 10:137-143.
23. Wei-Chih Shen, Yang-Hao Yu, Cheng-Hung Chuang. Computer Aided Diagnosis for Pulmonary Nodule on Low-Dose Computed Tomography (LDCT) Using Density Features. In: Eighth International Conference on Computer Graphics, Imaging and Visualization (CGIV); 17-19 August, 2011; Singapore.
24. Wei-Chih Shen, Yang-Hao Yu, Shwn-Huey Shieh, etal. Ability of Density Feature in Low-Dose Computed Tomography for Evaluating Screened Lung Tumor. In: Sixth International Conference on Genetic and Evolutionary Computing (ICGEC); 25-28 August, 2012; Kitakyushu, Japan.
25. Travis W.D., Brambilla E., Muller-Hermlink H.K., et al. Pathology and genetics of tumours of the lung, pleura, thymus and heart. World Health Organization classification of tumours 2004, IARC Press, Lyon, France.
26. Travis W.D., Brambilla E., Noguchi M., et al. International association for the study of lung cancer/american thoracic society/european respiratory society international multidisciplinary classification of lung adenocarcinoma. J Thorac Oncol 2011, 6:244-285. Review.
27. Kanal K.M., Stewart B.K., Kolokythas O., et al. Impact of operator-selected image noise index and reconstruction slice thickness on patient radiation dose in 64-MDCT. AJR Am J Roentgenol 2007, 189:219-225.
28. Otsu N. A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 1979, 9:62-66.
29. Vincent Luc, Soille Pierre Watersheds in digital spaces: an efficient algorithm based on immersion simulations. IEEE Trans Pattern Anal Mach Intell 1991, 13:583-598.
30. PASW Statistics, version 18 2010, SPSS Inc, Chicago, IL.
31. Cohen J. Statistical power analysis for the behavioral sciences 1988, Lawrence Erlbaum, Hillsdale, NJ. 2nd ed.
32. Xu D.M., van Klaveren R.J., de Bock G.H., et al. Limited value of shape, margin and CT density in the discrimination between benign and malignant screen detected solid pulmonary nodules of the NELSON trial. Eur J Radiol 2008, 68:347-352.
33. Hartman T.E., Swensen S.J. CT screening for lung cancer. Semin Roentgenol 2005, 40:193-196. Review.
34. CT Screening for lung cancer: diagnoses resulting from the New York Early Lung Cancer Action Project. Radiology 2007, 243:239-249. New York Early Lung Cancer Action Project Investigators.