Literatura académica sobre el tema "LUNG CANCER DETECTION"
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Artículos de revistas sobre el tema "LUNG CANCER DETECTION"
M, Jerin Jose, Janani B.R, Janani Priya K, Jeevitha J y Swathi S. "Lung Cancer Detection using Artificial Neural Netw". SIJ Transactions on Computer Science Engineering & its Applications (CSEA) 05, n.º 06 (27 de diciembre de 2017): 01–05. http://dx.doi.org/10.9756/sijcsea/v5i6/05010050101.
Texto completoReddy, Y. Venkat Sai, G. Chandana, G. Chetan Redddy, Ayush Kumar, Suvarna Kumar y Dr Syed Siraj Ahmed. "Lung Cancer Detection using YOLO CNN Algorithm". International Journal of Research Publication and Reviews 4, n.º 5 (junio de 2023): 5297–300. http://dx.doi.org/10.55248/gengpi.4.523.43476.
Texto completoM, Albertini. "Canine Scent Detection of Lung Cancer: Preliminary Results". Open Access Journal of Veterinary Science & Research 1, n.º 4 (2016): 1–5. http://dx.doi.org/10.23880/oajvsr-16000118.
Texto completoPatel, Bindiya, Dr Pankaj Kumar Mishra y Prof Amit Kolhe. "Lung Cancer Detection on CT Images by using Image Processing". International Journal of Trend in Scientific Research and Development Volume-2, Issue-3 (30 de abril de 2018): 2525–31. http://dx.doi.org/10.31142/ijtsrd11674.
Texto completoChoe, Wonho, Jeong Don Chae, Byoung-Hoon Lee, Sang-Hoon Kim, So Young Park, Satish Balasaheb Nimse, Junghoon Kim et al. "9G TestTM Cancer/Lung: A Desirable Companion to LDCT for Lung Cancer Screening". Cancers 12, n.º 11 (30 de octubre de 2020): 3192. http://dx.doi.org/10.3390/cancers12113192.
Texto completoYektaei, Homayoon y Mohammad Manthouri. "DIAGNOSIS OF LUNG CANCER USING MULTISCALE CONVOLUTIONAL NEURAL NETWORK". Biomedical Engineering: Applications, Basis and Communications 32, n.º 05 (12 de agosto de 2020): 2050030. http://dx.doi.org/10.4015/s1016237220500301.
Texto completoKaur, Jatinder, Ravi Prakash Chaturvedi y Sameer Asthana. "Lung cancer detection a machine learning approach". YMER Digital 21, n.º 03 (23 de marzo de 2022): 337–48. http://dx.doi.org/10.37896/ymer21.03/37.
Texto completoInage, Terunaga, Takahiro Nakajima, Ichiro Yoshino y Kazuhiro Yasufuku. "Early Lung Cancer Detection". Clinics in Chest Medicine 39, n.º 1 (marzo de 2018): 45–55. http://dx.doi.org/10.1016/j.ccm.2017.10.003.
Texto completoSmith, Robert A. y Thomas J. Glynn. "Early lung cancer detection". Cancer 89, S11 (1 de diciembre de 2000): 2327–28. http://dx.doi.org/10.1002/1097-0142(20001201)89:11+<2327::aid-cncr1>3.0.co;2-r.
Texto completoThanzeem Mohamed Sheriff, S., J. Venkat Kumar, S. Vigneshwaran, Aida Jones y Jose Anand. "Lung Cancer Detection using VGG NET 16 Architecture". Journal of Physics: Conference Series 2040, n.º 1 (1 de octubre de 2021): 012001. http://dx.doi.org/10.1088/1742-6596/2040/1/012001.
Texto completoTesis sobre el tema "LUNG CANCER DETECTION"
田珮芝 y Pui-chi Tin. "Detection of EGFR mutation in lung adenocarcinoma and paired plasma". Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2008. http://hub.hku.hk/bib/B40737044.
Texto completoKalubowilage, Madumali. "Liquid biopsies of solid tumors: non-small-cell lung and pancreatic cancer". Diss., Kansas State University, 2017. http://hdl.handle.net/2097/35385.
Texto completoDepartment of Chemistry
Stefan H. Bossmann
Cancer is a group of diseases that are characterized by uncontrolled growth and spread of cells. In order to treat cancer successfully, it is important to diagnose cancers in their early stages, because survival often depends on the stage of cancer detection. For that purpose, highly sensitive and selective methods must be developed, taking advantage of suitable biomarkers. The expression levels of proteases differ from one cancer type to the other, because different cancers arise from different cell types. According to the literature, there are significant differences between the protease expression levels of cancer patients and healthy people, because solid tumors rely on proteases for survival, angiogenesis and metastasis. Development of fluorescence-based nanobiosensors for the early detection of pancreatic cancer and non-small-cell lung cancer is discussed in this thesis. The nanobiosensors are capable of detecting protease/arginase activities in serum samples over a broad range. The functionality of the nanobiosensor is based on Förster resonance energy transfer and surface energy transfer mechanisms. The nanobiosensors for protease detection feature dopamine-coated Fe/Fe₃O₄ nanoparticles, consensus (cleavage) peptide sequences, meso-tetra(4-carboxyphenyl)porphine (TCPP), and cyanine 5.5. The consensus peptide sequences were synthesized by solid-supported peptide synthesis. In this thesis, improved consensus sequences were used, which permit faster synthesis and higher signal intensities. TCPP, which is the fluorophore of the nanoplatform, was connected to the N-terminal end of the oligopeptides while it was still on the resin. After the addition of TCPP, the TCPP-oligopeptide was cleaved off the resin and linked to the primary amine groups of Fe/Fe₃O₄-bound via a stable amide bond. In the presence of a particular protease, the consensus sequences attached to the nanoparticle can be cleaved and release TCPP to the aqueous medium. Upon releasing the dye, the emission intensity increases significantly and can be detected by fluorescence spectroscopy or, similarly, by using a fluorescence plate reader. In sensing of arginase, posttranslational modification of the peptide sequence will occur, transforming arginine to ornithine. This changes the conformational dynamics of the oligopeptide tether, leading to the increase of the TCPP signal. This is a highly selective technology, which has a very low limit of detection (LOD) of 1 x 10⁻¹⁶ molL⁻¹ for proteases and arginase. The potential of this nanobiosensor technology to detect early pancreatic and lung cancer was demonstrated by using serum samples, which were collected from patients who have been diagnosed with pancreatic cancer and non-small cell lung cancer at the South Eastern Nebraska Cancer Center (lung cancer) and the University of Kansas Cancer Center (pancreatic cancer). As controls, serum samples collected from healthy volunteers were analyzed. In pancreatic cancer detection, the protease/arginase signature for the detection of pancreatic adenocarcinomas in serum was identified. It comprises arginase, MMPs -1, - 3, and -9, cathepsins -B and -E, urokinase plasminogen activator, and neutrophil elastase. For lung cancer detection, the specificity and sensitivity of the nanobiosensors permit the accurate measurements of the activities of nine signature proteases in serum samples. Cathepsin -L and MMPs-1, -3, and -7 permit detecting non-small-cell lung-cancer at stage 1.
Al, Mohammad Badera. "Lung Cancer Detection on Chest Computed Tomography Scan: Observer Performance and the Effect of Cancer Nodules’ Characteristics". Thesis, The University of Sydney, 2018. http://hdl.handle.net/2123/20054.
Texto completoRakhit, Callum Paul. "Circulating DNA for the in vivo detection and monitoring of lung cancer". Thesis, University of Leicester, 2018. http://hdl.handle.net/2381/42486.
Texto completoSomers, Veerle Anne-Marie Christine. "The role of K-ras point mutation detection in lung cancer towards a strategy for early detection /". [Maastricht : Maastricht : Universiteit Maastricht] ; University Library, Maastricht University [Host], 1999. http://arno.unimaas.nl/show.cgi?fid=8568.
Texto completoCraig, Daniel John. "Low Frequency Airway Epithelial Cell Mutation Pattern Associated with Lung Cancer Risk". University of Toledo Health Science Campus / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=mco1556918218571742.
Texto completoAlves, Jeovane Honório. "A lung cancer detection approach based on shape index and curvedness superpixel candidate selection". reponame:Repositório Institucional da UFPR, 2016. http://hdl.handle.net/1884/45760.
Texto completoDissertação (mestrado) - Universidade Federal do Paraná, Setor de Tecnologia, Programa de Pós-Graduação em Engenharia Elétrica. Defesa: Curitiba, 29/08/2016
Inclui referências : f. 72-76
Área de concentração: Sistemas eletrônicos
Resumo: Câncer é uma das causas com mais mortalidade mundialmente. Câncer de pulmão é o tipo de câncer mais comum (excluíndo câncer de pele não-melanoma). Seus sintomas aparecem em estágios mais avançados, o que dificulta o seu tratamento. Para diagnosticar o paciente, a tomografia computadorizada é utilizada. Ela é composta de diversos cortes, que mapeiam uma região 3D de interesse. Apesar de fornecer muitos detalhes, por serem gerados vários cortes, a análise de exames de tomografia computadorizada se torna exaustiva, o que pode influenciar negativamente no diagnóstico feito pelo especialista. O objetivo deste trabalho é o desenvolvimento de métodos para a segmentação do pulmão e a detecção de nódulos em imagens de tomografia computadorizada do tórax. As imagens são segmentadas para separar o pulmão das outras estruturas e após, detecção de nódulos utilizando a técnicas de superpixeis são aplicadas. A técnica de Rótulamento dos Eixos teve uma média de preservação de nódulos de 93,53% e a técnica Monotone Chain Convex Hull apresentou melhores resultados com uma taxa de 97,78%. Para a detecção dos nódulos, as técnicas Felzenszwalb e SLIC são empregadas para o agrupamento de regiões de nódulos em superpixeis. Uma seleção de candidatos à nódulos baseada em shape index e curvedness é aplicada para redução do número de superpixeis. Para a classificação desses candidatos, foi utilizada a técnica de Florestas Aleatórias. A base de imagens utilizada foi a LIDC, que foi dividida em duas sub-bases: uma de desenvolvimento, composta pelos pacientes 0001 a 0600, e uma de validação, composta pelos pacientes 0601 a 1012. Na base de validação, a técnica Felzenszwalb obteve uma sensibilidade de 60,61% e 7,2 FP/exame. Palavras-chaves: Câncer de pulmão. Detecção de nódulos. Superpixel. Shape index.
Abstract: Cancer is one of the causes with more mortality worldwide. Lung cancer is the most common type (excluding non-melanoma skin cancer). Its symptoms appear mostly in advanced stages, which difficult its treatment. For patient diagnostic, computer tomography (CT) is used. CT is composed of many slices, which maps a 3D region of interest. Although it provides many details, its analysis is very exhaustive, which may has negatively influence in the specialist's diagnostic. The objective of this work is the development of lung segmentation and nodule detection methods in chest CT images. These images are segmented to separate the lung region from other parts and, after that, nodule detection using superpixel methods is applied. The Axes' Labeling had a mean of nodule preservation of 93.53% and the Monotone Chain Convex Hull method presented better results, with a mean of 97.78%. For nodule detection, the Felzenszwalb and SLIC methods are employed to group nodule regions. A nodule candidate selection based on shape index and curvedness is applied for superpixel reduction. Then, classification of these candidates is realized by the Random Forest. The LIDC database was divided into two data sets: a development data set composed of the CT scans of patients 0001 to 0600, and a untouched, validation data set, composed of patients 0601 to 1012. For the validation data set, the Felzenszwalb method had a sensitivity of 60.61% and 7.2 FP/scan. Key-words: Lung cancer. Nodule detection. Superpixel. Shape index.
Senden, Nicole Hubertina Maria. "NSP-reticulons characterization and use for the detection of neuroendocrine differentiation in lung cancer /". Maastricht : Maastricht : Universitaire Pers Maastricht ; University Library, Maastricht University [Host], 1995. http://arno.unimaas.nl/show.cgi?fid=8353.
Texto completoTRAVERSO, ALBERTO. "Development and application in clinical practice of Computer-aided Diagnosis systems for the early detection of lung cancer". Doctoral thesis, Politecnico di Torino, 2017. http://hdl.handle.net/11583/2686725.
Texto completoBroyelle, Antoine. "Automated Pulmonary Nodule Detection on Computed Tomography Images with 3D Deep Convolutional Neural Network". Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-231930.
Texto completoObjektdetektering i naturliga bilder har reducerates till en enstegs process tack vare genombrott i djupa neurala nätverk. Automatisk detektering av pulmonella nodulärer är vanligtvis ett trestegsproblem: segmentering av lunga, generering av nodulärkandidater och reducering av falska positiva utfall. Det här projektet tar sig an nodulärdetektering med en enstegsmodell med hjälp av ett djupt neuralt nätverk. Pulmonella nodulärer har unika karaktärsdrag som inte finns utanför lungorna. Modellen förväntas fånga dessa drag och enbart fokusera på element inuti lungorna när den arbetar med datortomografibilder. Nodulärer är små och glest föredelade. Vi visar att ett vältränat nätverk kan finna relevanta särdrag samt föreslå ett lågt antal intresseregioner utan extra för- eller efter- behandling. På grund av den visuella karaktären av det här problemet så designade vi ett tredimensionellt s.k. convolutional neural network med residualkopplingar. Projektet inspirerades av Faster R-CNN, ett nätverk som utmärker sig i sin förmåga att detektera intresseregioner. Nätverket utvärderades på ett dataset vid namn LUNA16. Det slutgiltiga nätverket testade 0.826, vilket är genomsnittlig sensitivitet vid 0.125, 0.25, 0.5, 1, 2, 4, och 8 falska positiva per utvärdering. Detta kan anses vara genomsnittligt jämfört med andra deltagande i tävlingen, men lösningen som föreslås här är en enstegslösning som utför detektering från början till slut och har färre träningsbara parametrar.
La détection d’objets sur les images naturelles est devenue au fil du temps un processus réalisé de bout en bout en une seule étape grâce aux évolutions récentes des architectures de neurones artificiels profonds. En revanche, la détection automatique de nodules pulmonaires est généralement un processus en trois étapes : la segmentation des poumons (pré-traitement), la génération de zones d’intérêt (modèle) et la réduction des faux positifs (post-traitement). Ce projet s’attaque à la détection des nodules pulmonaires en une seule étape avec un réseau profond de neurones artificiels. Les nodules pulmonaires ont des formes et des structures uniques qui ne sont pas présentes en dehors de cet organe. Nous nous attendons à ce qu’un modèle soit capable de capturer ces caractéristiques et de se focaliser uniquement sur les éléments à l’intérieur des poumons alors même qu’il reçoit des images brutes (sans segmentation des poumons). Les nodules sont petits, peu fréquents et répartis aléatoirement. Nous montrons qu’un modèle correctement entraîné peut repérer les éléments caractéristiques des nodules et générer peu de localisations sans pré-traitement ni post-traitement. Du fait de la nature visuelle de la tâche, nous avons développé un réseau neuronal convolutif tridimensionnel. L’architecture utilisée est inspirée du méta-algorithme de détection Faster R-CNN. L’évaluation est réalisée avec le jeu de données du challenge LUNA16. Le score final est de 0.826 qui représente la sensibilité moyenne pour les valeurs de 0.125, 0.25, 0.5, 1, 2, 4 et 8 faux positifs par scanner. Il peut être considéré comme un score moyen comparé aux autres contributions du challenge. Cependant, la solution décrite montre la faisabilité d’un modèle en une seule étape, entraîné de bout en bout. Le réseau comporte moins de paramètres que la majorité des solutions.
Libros sobre el tema "LUNG CANCER DETECTION"
Canadian Institute for Radiation Safety. CAIRS early lung cancer detection program: Report. Toronto, Ont: CARIS, Canadian Institute for Radiation Safety, 1989.
Buscar texto completoSzamosi, Alfred. Lung cancer: The art of detection by conventional radiography. Stockholm, Sweden: The Foundation, 1995.
Buscar texto completoBunn, Paul A., Harubumi Kato y James L. Textbook of Prevention and Detection of Early Lung Cancer. Editado por Fred R. Hirsch. Abingdon, UK: Taylor & Francis, 1988. http://dx.doi.org/10.4324/9780203324523.
Texto completoR, Hirsch Fred y International Association for the Study of Lung Cancer., eds. International Association for the Study of Lung Cancer textbook of prevention and detection of early lung cancer. London: Taylor & Francis, 2006.
Buscar texto completoOksa, Panu. Asbestosis, its detection, and predictors of progression and cancer. Helsinki: Finnish Institute of Occupational Health, 1998.
Buscar texto completoKim, Ho-jung. array-CGH rŭl iyong han piso sepʻo pʻyeam ŭi chogi chaebal pʻyojija mit chindan mohyŏng kaebal =: Development of early-recurrence detection marker and diagnostic model using array-CGH in NSCLC. [Seoul]: Pogŏn Pokchibu, 2007.
Buscar texto completoKing, Stephen. Night Shift. New York, USA: Anchor Books, 2011.
Buscar texto completoKing, Stephen. Night Shift. London: New English Library, 1986.
Buscar texto completoKing, Stephen. Night shift. Oxford: ISIS Large Print, 1994.
Buscar texto completoKing, Stephen. Night shift. Thorndike, Me: G.K. Hall, 1994.
Buscar texto completoCapítulos de libros sobre el tema "LUNG CANCER DETECTION"
Jen, Rachel y Stephen Lam. "Detection and Treatment of Preneoplastic Lesions". En Lung Cancer, 129–43. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781118468791.ch7.
Texto completoKim, Edward S. y Fadlo R. Khuri. "Prevention and Early Detection of Lung Cancer". En Lung Cancer, 256–79. New York, NY: Springer New York, 2003. http://dx.doi.org/10.1007/0-387-22652-4_14.
Texto completoBrambilla, Elisabeth. "Early Lung Cancer Detection". En Clinical and Biological Basis of Lung Cancer Prevention, 39–56. Basel: Birkhäuser Basel, 1998. http://dx.doi.org/10.1007/978-3-0348-8924-7_4.
Texto completoHassanein, Mohamed, Melinda C. Aldrich, Stephen A. Deppen, Karl E. Krueger, Eric L. Grogan y Pierre P. Massion. "Early Detection of Lung Cancer". En Biomarkers in Cancer Screening and Early Detection, 163–84. Chichester, UK: John Wiley & Sons, Ltd, 2017. http://dx.doi.org/10.1002/9781118468869.ch14.
Texto completoWojtowicz, Malgorzata y Eva Szabo. "Commonalities in Lung Cancer and COPD". En Biomarkers in Cancer Screening and Early Detection, 185–96. Chichester, UK: John Wiley & Sons, Ltd, 2017. http://dx.doi.org/10.1002/9781118468869.ch15.
Texto completoNakajima, Takahiro y Kazuhiro Yasufuku. "Early Lung Cancer: Methods for Detection". En Interventions in Pulmonary Medicine, 245–56. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-58036-4_14.
Texto completoSuzuki, Kenji. "Computer-Aided Detection of Lung Cancer". En Image-Based Computer-Assisted Radiation Therapy, 9–40. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-2945-5_2.
Texto completoMahima, S., S. Kezia y E. Grace Mary Kanaga. "Deep Learning-Based Lung Cancer Detection". En Lecture Notes in Electrical Engineering, 633–41. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2177-3_59.
Texto completoSaranya, N., N. Kanthimathi, P. Saravanakumar, M. Kiruthika, G. Kavitha y R. Narthika. "Lung Cancer Detection Using SVM Classification". En Advances in Intelligent Systems and Computing, 715–28. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-7330-6_53.
Texto completoYasufuku, Kazuhiro. "Early Lung Cancer: Methods for Detection". En Interventions in Pulmonary Medicine, 211–19. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-6009-1_13.
Texto completoActas de conferencias sobre el tema "LUNG CANCER DETECTION"
Monif, Mamdouh, Kinan Mansour, Waad Ammar y Maan Ammar. "Automatic Detection and Extraction of Lungs Cancer Nodules Using Connected Components Labeling and Distance Measure Based Classification". En 11th International Conference on Computer Science and Information Technology (CCSIT 2021). AIRCC Publishing Corporation, 2021. http://dx.doi.org/10.5121/csit.2021.110705.
Texto completoWang, Lulu y Hu Peng. "A Feasibility Study of Lung Cancer Detection Using Holographic Microwave Imaging". En ASME 2017 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/imece2017-70062.
Texto completo"Lung Cancer Prediction Using Machine Learning: A Systematic Review". En International Conference on Women Researchers in Electronics and Computing. AIJR Publisher, 2021. http://dx.doi.org/10.21467/proceedings.114.3.
Texto completoVas, Moffy y Amita Dessai. "Lung cancer detection system using lung CT image processing". En 2017 International Conference on Computing, Communication, Control and Automation (ICCUBEA). IEEE, 2017. http://dx.doi.org/10.1109/iccubea.2017.8463851.
Texto completoGunaydin, Ozge, Melike Gunay y Oznur Sengel. "Comparison of Lung Cancer Detection Algorithms". En 2019 Scientific Meeting on Electrical-Electronics & Biomedical Engineering and Computer Science (EBBT). IEEE, 2019. http://dx.doi.org/10.1109/ebbt.2019.8741826.
Texto completoPrakash, Varsha y P. Smitha Vas. "Survey on Lung Cancer Detection Techniques". En 2020 International Conference on Computational Performance Evaluation (ComPE). IEEE, 2020. http://dx.doi.org/10.1109/compe49325.2020.9200019.
Texto completoKurkure, Manasee y Anuradha Thakare. "Lung cancer detection using Genetic approach". En 2016 International Conference on Computing Communication Control and automation (ICCUBEA). IEEE, 2016. http://dx.doi.org/10.1109/iccubea.2016.7860007.
Texto completoIgnatious, Sruthi y Robin Joseph. "Computer aided lung cancer detection system". En 2015 Global Conference on Communication Technologies (GCCT). IEEE, 2015. http://dx.doi.org/10.1109/gcct.2015.7342723.
Texto completoTejaswini, Chintakayala, P. Nagabushanam, Priyadharshini Rajasegaran, Palyam Rohith Johnson y S. Radha. "CNN Architecture for Lung Cancer Detection". En 2022 IEEE 11th International Conference on Communication Systems and Network Technologies (CSNT). IEEE, 2022. http://dx.doi.org/10.1109/csnt54456.2022.9787650.
Texto completoS, Bharathy, Pavithra R y Akshaya B. "Lung Cancer Detection using Machine Learning". En 2022 International Conference on Applied Artificial Intelligence and Computing (ICAAIC). IEEE, 2022. http://dx.doi.org/10.1109/icaaic53929.2022.9793061.
Texto completoInformes sobre el tema "LUNG CANCER DETECTION"
Spira, Avrum E. y Emily Maple. Detection of Early Lung Cancer Among Military Personnel (DECAMP). Fort Belvoir, VA: Defense Technical Information Center, octubre de 2012. http://dx.doi.org/10.21236/ada568356.
Texto completoLois, Augusto. Early Lung Cancer Detection via Global Protein Modification Profiles. Fort Belvoir, VA: Defense Technical Information Center, diciembre de 2013. http://dx.doi.org/10.21236/ada600735.
Texto completoWong, David. Salivary Proteomic and microRNA Biomarkers Development for Lung Cancer Detection. Fort Belvoir, VA: Defense Technical Information Center, agosto de 2014. http://dx.doi.org/10.21236/ada613286.
Texto completoWong, David. Salivary Proteomic and microRNA Biomarkers Development for Lung Cancer Detection. Fort Belvoir, VA: Defense Technical Information Center, agosto de 2013. http://dx.doi.org/10.21236/ada593384.
Texto completoDubinett, Steven M., Pierre Massion, Ignacio Wistuba y Avrum Spira. Molecular Profiles for Lung Cancer Pathogenesis and Detection in U.S. Veterans. Fort Belvoir, VA: Defense Technical Information Center, diciembre de 2014. http://dx.doi.org/10.21236/ada612659.
Texto completoWistuba, Ignacio I. y Humam Kadara. Molecular Profiles for Lung Cancer Pathogenesis and Detection in U.S. Veterans. Fort Belvoir, VA: Defense Technical Information Center, diciembre de 2014. http://dx.doi.org/10.21236/ada613782.
Texto completoMassion, Pierre, Steven M. Dubinett, Brigitte M. Gompers, Ignacio Wistuba y Avrum Spria. Molecular Profiles for Lung Cancer Pathogenesis and Detection in U.S. Veterans. Fort Belvoir, VA: Defense Technical Information Center, diciembre de 2014. http://dx.doi.org/10.21236/ada614423.
Texto completoSaccomanno, G. Early Lung Cancer Detection in Uranium Miners with Abnormal Sputum Cytology. Office of Scientific and Technical Information (OSTI), junio de 2000. http://dx.doi.org/10.2172/834057.
Texto completoSaccomanno, G. Early lung cancer detection in uranium miners with abnormal sputum cytology. Office of Scientific and Technical Information (OSTI), agosto de 1992. http://dx.doi.org/10.2172/7091811.
Texto completoWistuba, Ignacio I. y Humam Kadara. Molecular Profiles for Lung Cancer Pathogenesis and Detection in U.S. Veterans. Fort Belvoir, VA: Defense Technical Information Center, octubre de 2012. http://dx.doi.org/10.21236/ada574247.
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