Lung Cancer Detection Educational Overview William J. Furiosi

Lung Cancer Detection Educational Overview William J. Furiosi

Lung Cancer Detection Educational Overview William J. Furiosi II Oviedo High School Epidemiology Cancer is the second leading cause of death in the U.S. behind heart disease.[1] Lung cancer is the leading cause of all cancer related deaths in the U.S. and the world.[2] Lung cancer makes up over a quarter of all cancerrelated deaths in the U.S.[2] Incidence of death by cause

Diseases of the heart Cancer Chronic lower respiratory disease Accidents Stroke lzheimer's disease Disabetes mellitus Influenza and pneumonia Kidney related disease Suicide Other Deaths by cancer type 27.19% Brain/Nervous System

Female Breast Colon & Rectum Leukemia Liver Lung & Bronchus Non-Hodgkin Lymphoma Ovary Pancreas 29.44% Prostate Other High-Risk Populations The U.S. Preventive Service Task Force identified the following risk factors:[3] Older than age 50

Current smokers Former smokers who quit within past 15 years Occupational exposures, including radon and asbestos History of other lung ailments Family history of cancer Approximately 87% of all cases of lung cancer in the United States are related to cigarette smoking.[5] Relatedness to smoking makes it the most preventable form of cancer. Healthy vs. Diseased Lungs: The image to the left, courtesy

of American Lung Association, shows the difference between a smoker and non-smokers lungs.[23] Etiology: Genetic Predisposition Individuals with inherited mutations in DNA repair and cell cycle genes are at a greater risk for developing lung cancer. Some individuals have genetic abnormalities that predispose them to cancer, including: Family history of defects in chromosome 6.[19] Changes in p53, p73, and p16

tumor suppressor genes.[19, 20] p53 Pathway: p53 pathway shows just how integral the p53 tumor suppressor is in gene regulation, cell growth, metabolic processes, and among other functions.[21] Etiology: Acquired Genetic Mutations Over 50 chemicals in tobacco are known carcinogens, including: [20] Polycyclic aromatic hydrocarbons (PAHs) Radioactive elements including radon and polonium Carcinogenic chemicals covalently bind to DNA to form DNA adducts that require repair [20] or release energy causing mutation. Failure to repair DNA adducts leads to

carcinogenesis, or unregulated cell growth, or mutagenesis, mutations in the DNA. DNA Adduct: The chemical model above shows the DNA structure modified into a DNA adduct by a metabolite of benzo[a]pyrene, a known tobacco smoke carcinogen.[20, 24] Etiology: Acquired Genetic Mutations Examples[20] Stimulation of K-RAS and ALK oncogenes. Modification of EGFR gene pathways. Changes in human repair gene, hMSH2. Mutations in cytochrome P450 and GST enzymes. Genetic variations in chromosome 13. Interactions with genes coding for NAT2 and mEH, genes responsible for handling cigarette smoke chemicals and metabolites. Dysregulation of apoptosis genes like FAS and FASL.

Epigenetic changes including DNA and histone modifications. Variation in telomerase reverse transcriptase (TERT) gene. Detection Methods Chest x-ray Computed tomography (CT) scan Utilizes a multidimensional x-ray machine to create three-dimensional models from two-dimensional image layers. Biopsy Most reliable, but most invasive method. Sputum analysis Most effective when collected for three days. Most useful when used in conjunction with imaging tools. [6] Molecular changes, especially involving p53, in bronchial epithelium can be used to detect lung cancer. [6]

Computed Tomography (CT) Multiple types of CT are available: Standard CT High-resolution CT Low-dose CT Most preferred for lung cancer detection is low-dose CT scan. Multiple 2-D images are taken and stacked to make a 3-D image, similar to a loaf of bread. [7] Basis of Computed Tomography: The diagram shows the general design of a CT machine. Notice how the x-ray source moves in a helical fashion around the patient while x-ray

detectors rotate with it.[7] National Lung Screening Trial (NLST) In response to health ailments related to smoking, the NLST was funded by the National Institute of Health and completed by many institutions and organizations. KEY FINDING: There is a 20.0% decrease in mortality from lung cancer when comparing those receiving lowdose CT versus standard radiography.[4] CT Risks Low-dose CT involves higher radiation doses than a typical chest x-ray. [1] Over-prescription of CT scans for low-risk patients or those with no appreciable change in nodule appearance.[2] Some prescribe CT scans every three months. Recommended timeframe is 12 18 months.

A 2007 report estimates that 1.5% to 2.0% of all cancers are due to CT radiation,[7] and may be even greater with increased CT usage. Is it Cancer? The following characteristics increase the chance of the nodule being malignant: Spiculation Size >7 mm Presence in right upper lobe Scalloped margins Volumetric growth, specifically doubling times, are used to determine risk of malignancy. Tumors are usually observed by the time it reaches 9 mm in diameter, which accounts for roughly 30 doublings. [22, 23] Death usually ensues after 40 doublings. [22]

The absence of growth over a two year span is the greatest sign of benignity. [22, 23] Structures found in the lung larger than 3 cm are no longer called nodules, but instead called masses.[13] Ruling-out Cancer The following characteristics favor benign or slow growing carcinomas: [9, 22, 23] calcification smooth margins presence of fat clustering smaller than 7 mm presence in lower versus upper lung High hounsfield unit (HU) suggests the

deposit is calcified and benign.[10] The clustering here suggests sarcoidosis as opposed to being malignant nodules.[4] Single nodule with calcification, also with smooth edge and less than 3 cm suggests that this is a hamartoma.[11] Likely causes are: scarring inadvertent tissue build-up infection inflammation

Error in Detection One study states an overall miss rate of lung cancer between 10% and 22%[14] Several studies have shown that only 50% of 1-cm lesions are detected.[13] Nodules greater than 7 mm are the greatest risk for malignancy. Errors arise primarily from Poor technique. Failures of perception. Lack of knowledge[5] Misjudgments. Error Due to Imaging Tool Many nodules are missed by radiologists in simple chest xrays (CXR).[13] Superimposed structures account for a miss rate of 71%. [14] Obstruction by the clavicle, with one study stating it obscured 22% of the missed cancers.[14]

Most missed are in right upper lobe or on the periphery. [13] Other Errors in Detection Satisfaction of search (SOS) error Notable, but unrelated, images diverts the doctors attention. [15] Intentional underreading by neglecting shadows in order to limit false-positive results.[15] Confirmation of positive diagnosis was primarily by further noninvasive screenings and rarely by invasive methods.[4] In the NLST, 24.2% of low-dose CT were positive results. 96.4% of the low-dose CT group were false-positive results. In the same study above, 6.9% of CXRs were positive for lung cancer. [4] 94.5% of the radiography group were false-positive results. Doctors fail to compare current images to previous CXRs. [13] Nearly a third of lung cancers were identified after their radiographs were

re-reviewed.[16] 3D Slicer: Improving Detection Utilized and developed by programmers and researchers as an open-source unrestricted medical imaging analysis software. Allows for programmers, engineers, and clinicians alike to work on a rapidly developed, universal platform. Figure utilized from a journal describing the potential of 3D printing from 3D slicer in reference to diseases of the lungs[17] Imaging & Analysis Utilizes file types specialized for medical imaging field, like MRML and DICOM, or general image formats, like RAW, JPG, and PNG. DICOM (Diagnostic Imaging and Communications in Medicine) allows for metadata

regarding patient records and is a widely-accepted standard in radiology. MRML (Medical Reality Markup Language) provides coordinates needed to map images in 2D and 3D space. Anatomical Views Views are always in reference to the patients perspective. Because CT scans give cross-sectional views of the body, its important to understand which perspective the cuts were made. Anatomical plane diagrams necessary to understand CT images and orientations in 3D Slicer. (U. Bagci, personal communication, July 2016). Power of 3D Slicer Axial 3D

Sagittal Coronal UCFs Center for Computer Vision is working on an algorithm to improve detection of malignant lung nodules, similar to shown below. The figure above shows the difference between manual radiologists segmentation of a nodule and segmentation by 3D-Slicer-based algorithms. [25] Mechanism of Detecting Nodules The simplest algorithm to detect a region of interest (ROI) is called region growing.

Seed: Initial start point determined by the user that all neighboring pixels are compared against. Threshold: Maximum tolerance or difference accepted between the seed and neighboring pixels. An algorithm compares the seed, threshold, and neighboring pixel to determine whether it should be included in the selection or not. If it is within the threshold, its included. If its outside the threshold, its excluded. The checking continues until all neighboring pixels fail to be included. Diagrams showing the mechanism of region growing in terms of individual

pixels.[18] References [1] National Center for Health Statistics. (2016). Health, United States, 2015: With special feature on racial and ethnic health disparities. Washington, DC: U.S. Government Printing Office. [2] American Cancer Society. (2014). Cancer Facts & Figures 2014. Atlanta, GA: American Cancer Society, Inc. [3] Movsas, B., Brahmer, J., & Paller, C. Kernstine, K.H. (2001). Non-small cell lung cancer. Cancer Network: Cancer Management, 1-43. [4] The National Lung Screening Trial Research Team. (2001). Reduced lung-cancer mortality with low-dose computed tomographic screening. New England Journal of Medicine, 365(5), 395-409. doi: 10.1056/NEJMoa1102873 [5] Moyer, V.A. (2014). Screening for lung cancer: Recommendations from the U.S. Preventive Services Task Force. Annals of Internal Medicine, 160(5), 1-40. doi:10.7326/P14-9009 [6] Thunninssen, F. B. J. M. (2003). Sputum examination for early detection of lung cancer. Journal of Clinical Pathology, 56(11), 805-810. [7] Brenner, D. J., & Hall, E. J. (2007). Computed tomography an increase source of radiation exposure. New England Journal of Medicine, 357(22), 2277-2284. doi: 10.1056/NEJMra072149 [8] American College of Chest Physicians & American Thoracic Society. (2013, October 27). Five things physicians and patients should question [PDF document]. Retrieved from http://www.choosingwisely.org/wp-content/uploads/2015/02/ACCPATS-Choosing-Wisely-List.pdf

[9] MacMahon, H., Austin, J. H. M., Gamsu, G., Herold, C. J., Jett, J. R., D. P. Naidich, Swensen, S. J. (2005). Guidelines for management of small pulmonary nodules detected on CT scans: A statement from the Fleischner Society. Radiology, 237(2), 395-400. doi: 10.1148/radiol.2372041887 [10]: Alavi, A., & Kamangar, N. (2014). Solitary pulmonary nodule. Retrieved from http://emedicine.medscape.com/article/2139920-overview [11]: Alkhaderi, S. (2015). Imaging of focal lung lesions [PowerPoint slides]. Retrieved from http://www.slideshare.net/sakherkh/focal-lung-lesion [12]: Nunes, H., Uzunhan, Y., Gille, T., Lamberto, C., Valeyre, Brillet, P. (2012). Imaging of sarcoidosis of the airways and lung parenchyma correlation with lung function. European Respiratory Journal, 40(3), 750-765. doi: 10.1183/09031936.00025212 References [13] Gould, M. K., Fletcher, J., Iannettoni, M. D., Lynch, W. R., Midthun, D. E., Naidich, D. P., Ost, D. E. (2007). Evaluation of patients with pulmonary nodules: When is it lung cancer?: ACCP evidence-based clinical practice guidelines (2nd edition). Chest, 132(3), 108S-130S. doi: 10.1378/chest.07-1353 [14] Singh, H., Sethi, S., Raber, M., & Petersen, L.A. (2007). Errors in cancer diagnosis: Current understanding and future directions. Journal of Clinical Oncology, 25(31), 5009-5018. doi: 10.1200/JCO.2007.13.2142 [15] Pinto, A. & Brunese, L. (2010). Spectrum of diagnostic errors in radiology. World Journal of Radiology, 2(10), 377-383. doi: 10.4329/wjr.v2.i10.377

[16] Kvale, P. A., Johnson, C. C., Tammemgi, M., Marcus, P. M., Zylak, C. J., Spizarny, D. L., Prorok, P. (2014). Interval lung cancers not detected on screening chest x-rays: How are they different?. Lung Cancer, 86(1), 4146. doi: 10.1016/j.lungcan.2014.07.013 [17] Cheng, G. Z., Estepar, R. S. J., Folch, E., Onieva, J., Gangadharan, S., & Majid, A. (2016). Threedimensional printing and 3D slicer: Powerful tools in understanding and treating structural lung disease. Chest, 149(5), 1136-1142. doi: 10.1016/j.chest.2016.03.001 [18] Marshall, D. Region growing [Web page]. Retrieved from Vision Systems Web site: http://https://www.cs.cf.ac.uk/Dave/Vision_lecture/node35.html [19] Cancer.org. (2016). What causes non-small cell lung cancer?. Retrieved from http://www.cancer.org/cancer/lungcancer-non-smallcell/detailedguide/non-small-cell-lung-cancer-what-causes [20] Dela Cruz, C. S., Tanoue, L. T., & Matthay, R. A. (2011). Lung cancer: Epidemiology, etiology, and prevention. Clinics in Chest Medicine, 32(4), 605-644. doi: 10.1016/j.ccm.2011.09.001. [21] Zheltukhin, A. O & Chumakov, P. M. (2010). Constitutive and induced functions of the p53 gene. Biochemistry (Moscow), 75(13), 1692-1721. [22] ODonovan, P. B. (1997). The radiologic appearance of lung cancer. Oncology, 11(9), 1387-1402. [23] Ben-Joseph, E. P. (Ed.). (2016). Smoking [Image]. Retrieved from http://kidshealth.org/en/teens/smoking.html# References [24] Wheeler, R. (2007). Solution structure of a trans-opened (10S)-dA adduct of +)-(7S,8R,9S,10R)7,8-dihydroxy-9,10-epoxy-7,8,9,10-tetrahydrobenzo[a]pyrene in a DNA duplex [Image]. Retrieved

from https://en.wikipedia.org/wiki/DNA_adduct [25] Velazquez, E. R., Parmar, C., Jermoumi, M., Mak, R. H., van Baardwijk, A., Fennessy, F. M., Aerts, H. J. W. L. (2013). Volumetric CT-based segmentation of NSCLC using 3D-Slicer. Scientific Reports, 3, 1-7. doi: 10.1038/srep03529 [26] Fedorov, A., Beichel, R., Kalpathy-Cramer, J., Finet, J., Fillion-Robin, J-C., Pujol S., Kikinis R. (2012). 3D Slicer as an image computing platform for the quantitative imaging network. Magnetic Resonance Imaging, 30(9), 1323 1341. doi:10.1016/j.mri.2012.05.001 [27] Gering, D. T., Nabavi, A., Kikinis, R., Grimson, W. E. L., Hata, N., Everett, P., Wells, W. M. (1999). An integrated visualization system for surgical planning and guidance using image fusion and interventional imaging. In C. Taylor & A. Colchester (Eds.), Medical Image Computing and ComputerAssisted Intervention MICCAI 99 (pp. 809 819). Heidelberg, Germany: Springer-Verlag Berlin Heidelberg. doi: 10.1007/10704282. [28] Gering, D. T., Nabavi, A., Kikinis, R., Hata, N., ODonnell, L. J., Grimson, W. E. L., Wells, W. M. (2001). An integrated visualization system for surgical planning and guidance using image fusion and an open MR. Journal of Magnetic Resonance Imaging, 13(6), 967 975. doi: 10.1002/jmri.1139 [29] Pieper, S., Halle, M., & Kikinis, R. (2004). 3D Slicer. Proceedings of the 1st IEEE International Symposium on Biomedical Imaging: From Nano to Micro, 2004 (pp. 632 635). doi: 10.1109/ISBI.2004.1398617 [30] Pieper, S., Lorensen, B., Schroeder, W., & Kikinis, R. (2006). The NA-MIC kit: ITK, VTK, pipeines,

grids, and 3D Slicer as an open platform for the medical imaging community. 3rd IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2006 (pp. 698 701). doi: 10.1109/ISBI.2006.1625012 Lung Cancer DICOM Image References Smith K., Clark K., Bennett W., Nolan T., Kirby J., Wolfsberger M., Freymann J. Data From NSCLCRadiomics-Genomics. http://dx.doi.org/10.7937/K9/TCIA.2015.L4FRET6Z Clark K., Vendt B., Smith K., Freymann J., Kirby J., Koppel P., Prior F. (2013). The Cancer Imaging Archive (TCIA): Maintaining and operating a public information repository. Journal of Digital Imaging, 26(6), 1045-1057. Aerts, H. J. W. L., Velazques, E. R., Leijenaar, R. T. H., Parmar, C., Grossman, P., Carvalho, S., Lambin, P. (2014).Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nature Communications, 5, 1-8. doi: 10.1038/ncomms5006.

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