Fusion of Quantitative Image and Genomic Biomarkers to Improve Prognosis Assessment of Early Stage Lung Cancer Patients

IEEE Transactions on Biomedical Engineering

Volumn 63, Issue 5, 2016, Pages 1034-1043

  Emaminejad, Nastaran ^a   Qian, Wei ^b,c   Guan, Yubao ^d   Tan, Maxine ^a   Qiu, Yuchen ^a   Liu, Hong ^a   Zheng, Bin ^a  

^a University of Oklahoma   (United States)

^b UNIVERSITY OF TEXAS   (United States)

^c NORTHEASTERN UNIVERSITY   (United States)

^d GUANGZHOU MEDICAL UNIVERSITY   (China)

Author keywords

Computer aided diagnosis; Fusion of image features and genomic biomarkers; Prediction of lung cancer recurrence risk; Quantitative image feature analysis; Radiomics

Indexed keywords

BAYESIAN NETWORKS; BIOLOGICAL ORGANS; BIOMARKERS; CLASSIFICATION (OF INFORMATION); COMPUTER AIDED ANALYSIS; COMPUTER AIDED DIAGNOSIS; COMPUTERIZED TOMOGRAPHY; DIAGNOSIS; DISEASES; FEATURE EXTRACTION; FORECASTING; GENE EXPRESSION; GENES; IMAGE ANALYSIS; IMAGE FUSION; IMAGE SEGMENTATION; RISK ASSESSMENT; SURGERY; TUMORS;

COMPUTED TOMOGRAPHY IMAGES; IMAGE FEATURES; LUNG CANCER; MULTI-LAYER PERCEPTRON CLASSIFIERS; NON SMALL CELL LUNG CANCER; QUANTITATIVE IMAGES; RADIOMICS; SYNTHETIC MINORITY OVER-SAMPLING TECHNIQUES;

IMAGE CLASSIFICATION;

BIOLOGICAL MARKER; EXCISION REPAIR CROSS COMPLEMENTING PROTEIN 1; RIBONUCLEOTIDE REDUCTASE; RIBONUCLEOTIDE REDUCTASE 1; UNCLASSIFIED DRUG; TUMOR MARKER;

ADULT; AGED; ARTICLE; CANCER PATIENT; CANCER PROGNOSIS; CANCER RECURRENCE; CANCER STAGING; COMPUTER ASSISTED TOMOGRAPHY; FEMALE; HUMAN; IMAGING; LUNG CANCER; LUNG TUMOR; MAJOR CLINICAL STUDY; MALE; MIDDLE AGED; PERCEPTRON; PREDICTION; PROTEIN EXPRESSION; QUANTITATIVE IMAGE; RETROSPECTIVE STUDY; COMPUTER ASSISTED DIAGNOSIS; DIAGNOSTIC IMAGING; GENETICS; GENOMICS; LUNG NEOPLASMS; PROCEDURES; PROGNOSIS; TUMOR RECURRENCE; VERY ELDERLY; X-RAY COMPUTED TOMOGRAPHY;

ADULT; AGED; AGED, 80 AND OVER; BIOMARKERS, TUMOR; DIAGNOSIS, COMPUTER-ASSISTED; FEMALE; GENOMICS; HUMANS; LUNG NEOPLASMS; MALE; MIDDLE AGED; NEOPLASM RECURRENCE, LOCAL; PROGNOSIS; RADIOGRAPHIC IMAGE INTERPRETATION, COMPUTER-ASSISTED; TOMOGRAPHY, X-RAY COMPUTED;

EID: 84966930831     PISSN: 00189294     EISSN: 15582531     Source Type: Journal    
DOI: 10.1109/TBME.2015.2477688     Document Type: Article

References (39)

1. R. Siegel et al., "Cancer statistics, 2015," CA, Cancer J. Clin., vol. 65, pp. 5-29, Jan./Feb. 2015.
2. S. Swensen et al., "CT screening for lung cancer: Five-year prospective experience," Radiology, vol. 235, no. 235, pp. 259-265, Apr. 2005.
3. P. Bach et al., "Computed tomography screening and lung cancer outcomes," J. Amer. Med. Assoc., vol. 297, no. 9, pp. 953-961, Mar. 2007.
4. L. Humphrey et al., "Screening for lung cancer with low-dose computed tomography: A systematic review to update the U.S. preventive services task force recommendation," Ann. Internal Med., vol. 159, no. 6, pp. 411-420, Sep. 2013.
5. S. Singhal et al., "Prognostic implications of cell cycle, apoptosis, and angiogenesis biomarkers in non-small cell lung cancer: A review," Clin. Cancer Res., vol. 11, pp. 3974-3986, 2005.
6. American Cancer Society. (2014, Nov.). Non-Small Cell Lung Cancer Survival Rates by Stage. [Online]. Available: http://www.cancer.org/cancer/lungcancer-nonsmallcell/detailedguide/non-small-celllung-cancer-survival-rates
7. R. Rosell et al., "Screening for epidermal growth factor receptormutations in lung cancer," N. Engl. J. Med., vol. 361, pp. 958-967, Sep. 2009.
8. G. Bepler et al., "ERCC1 and RRM1 in the international adjuvant lung trial by automated quantitiative in situ analysis," Amer. J. Pathol., vol. 178, no. 1, pp. 69-78, Jan. 2011.
9. J.C. Soria, "ERCC1-tailored chemotherapy in lung cancer: The first prospective randomized trial," J. Clin. Oncol., vol. 25, pp. 2648-2649, 2007.
10. G. Bepler et al., "RRM1 and PTEN as prognostic parameters for overall and disease-free survival in patients with non-small-cell lung cancer," J. Clin. Oncol., vol. 22, no. 10, pp. 1878-1885, May 2004.
11. C. Poleri et al., "Risk of recurrence in patients with surgically resected stage I non-small cell lung carcinoma: Histopathologic and immunohistochemical analysis," Chest, vol. 123, no. 6, pp. 1858-1867, Jun. 2003.
12. G. loannidis et al., "How close are we to customizing chemotheraphy in early non-small-cell lung cancer," Ther. Adv. Med. Oncol., vol. 3, no. 4, pp. 185-205, Jul. 2011.
13. E. P. Diamandis, "Mass spectrometry as a diagnostic and a cancer biomarker discovery tool: Opportunities and potential limitations," Mol. Cell Proteomics, vol. 3, no. 4, pp. 367-378, Apr. 2004.
14. H. Li et al., "Cost-effectiveness of a novel molecular test for cytologically indeterminate thyroid nodules," J. Clin. Endocrinol. Metab., vol. 96, no. 11, pp. E1719-E1726, Nov. 2011.
15. R. Wahl et al., "Staging of mediastinal non-small cell lung cancer with FDG PET, CT, and fusion images: preliminary prospective evaluation," Radiology, vol. 191, no. 2, pp. 371-377, May 1994.
16. D. Lardinois et al., "Staging of non-small cell lung cancer with integrated positron-emission tomography and computed tomography," N. Engl. J. Med., vol. 348, no. 25, pp. 2500-2507, Jun. 2003.
17. K. Hisanobu et al., "Magnetic resonance imaging for lung cancer," J. Thoracic Imag., vol. 28, no. 3, pp. 138-150, May 2013.
18. E. Eisenhauer et al., "New response evaluation criteria in solid tumors: Revised RECIST guideline (version 1.1)," Eur. J. Cancer, vol. 45, no. 2, pp. 228-247, Jan. 2009.
19. J. O'conner et al., "Quantitative imaging biomarkers in the clinical development of targeted therapeutics: Current and future perspectives," Lancet Oncol., vol. 9, no. 8, pp. 766-776, Aug. 2008.
20. H. Aerts et al., "Decoding tumor phenotype by noninvasive imaging using a quantitative radiomics approach," Nature Commun., vol. 5, pp. 4006-1-4006-8, Aug. 2014.
21. J. Kuhnigk et al., "Morphological segmentation and partial volume analysis for volumetry of solid pulmonary lesions in thoracic CT scans," IEEE Trans. Med. Imag., vol. 25, no. 4, pp. 417-434, Apr. 2006.
22. S. Iwano et al., "Semi-automatic volumetric measurement of lung cancer using multi-detector CT," Acad. Radiol., vol. 16, no. 10, pp. 1179-1186, Oct. 2009.
23. B. Zheng et al., "A method to improve visual similarity of breast masses for an interactive computer-aided diagnosis environment," Med. Phys., vol. 33, no. 1, pp. 111-117, Jan. 2006.
24. I. Witten. (2011). Data Mining: Practical Machine Learning Tools Techniques. [Online]. Available: http://www.cs.waikato.ac.nz/ml/weka/
25. E. Burns et al., "Best-first heuristic search for multicore machines," J. Artif. Intell. Res., vol. 39, pp. 689-743, Dec. 2010.
26. J. Schrader et al., "Matrix stiffness modulates proliferation, chemotherapeutic response, and dormancy in hepatocellular carcinoma cells," Hepatology, vol. 53, no. 4, pp. 1192-1205, Apr. 2011.
27. M. Haidekker, "Image Analysis: A Perspective," in Advanced Biomedical Image Analysis. Hoboken, NJ, USA: Wiley, 2010.
28. X. Wei. (2007, Nov.). Gray Level Run Length Matrix Toolbox v1.0. [Online]. Available: http://www.mathworks.com/matlabcentral/fileexchange/17482-graylevel-run-length-matrix-toolbox
29. T. M. Mitchell, Machine Learning. Boston, MA, USA: McGraw-Hill, 1997.
30. C. E. Kahn et al., "Construction of a Bayesian network for mammographic diagnosis of breast cancer," Comput. Biol. Med., vol. 27, no. 1, pp. 1929-1940, Jan. 1997.
31. X. Wang et al., "Computer-assisted diagnosis of breast cancer using a data-driven Bayesian belief network," Int. J. Med. Informat., vol. 54, no. 2, pp. 115-126, May 1999.
32. Q. Li and K. Doi, "Reduction of bias and variance for evaluation of computer-aided diagnosis schemes," Med. Phys., vol. 33, no. 4, pp. 868-875, Apr. 2006.
33. N. V. Chawla et al., "SMOTE: Synthetic minority over-sampling technique," J. Artif. Intell. Res., vol. 16, pp. 321-357, Jun. 2002.
34. D. Lederman et al., "Improving breast cancer risk stratification using resonance-frequency electrical impedance spectroscopy through fusion of multiple classifiers," Ann. Biomed. Eng., vol. 39, no. 3, pp. 931-945, Mar. 2011.
35. S. Park et al., "Computer-aided detection of early interstitial lung diseases using low-dose CT images," Phys. Med. Biol., vol. 56, no. 2, pp. 1139-1153, Jan. 2011.
36. S. Park et al., "A multistage approach to improve performance of computer-aided detection of pulmonary embolisms depicted on CT images: Preliminary investigation," IEEE Trans. Biomed. Eng., vol. 58, no. 6, pp. 1519-1527, Jun. 2011.
37. M. Tan et al., "A novel computer-aided lung nodule detection system for CT images," Med. Phys., vol. 38, no. 10, pp. 5630-5645, Sep. 2011.
38. D. Butcher et al., "A tense situation: forcing tumor progression," Nature Rev. Cancer, vol. 9, no. 2, pp. 108-122, Feb. 2009.
39. F. Collins and H. Varmus, "A new iniative on precision medicine," N. Engl. J. Med., vol. 372, no. 9, pp. 793-795, Feb. 2015.