Ultization of Radiological Techniques in Early Diagnosis of Lunc Cancer

Authors

  • Sisi Yulianti Universitas Andalas
  • M A rif Budiman Puskesmas Siak
  • Miftachul Amri Universitas Negeri Surabaya

DOI:

https://doi.org/10.61991/ijeet.v2i2.35

Keywords:

Lung cancer, Early diagnosis, Radiological techniques, Computed Tomography

Abstract

Lung cancer remains a leading cause of mortality worldwide, underscoring the critical need for early detection to improve patient outcomes. Radiological techniques play a pivotal role in the timely diagnosis of lung cancer, offering non-invasive approaches that facilitate early intervention and treatment planning. This paper comprehensively reviews the utilization of radiological modalities, including computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET), and chest X-ray, in the early detection of lung cancer. Emphasizing the significance of each modality in identifying suspicious pulmonary nodules, characterizing lesions, and staging disease progression, the review highlights advancements in imaging technologies that enhance sensitivity and specificity. Furthermore, it addresses challenges such as false positives, radiation exposure, and cost-effectiveness, proposing strategies to mitigate these limitations. The review also explores emerging trends such as artificial intelligence (AI) algorithms and molecular imaging techniques, which hold promise for further improving diagnostic accuracy and personalized treatment approaches. By synthesizing current evidence and future directions in the utilization of radiological techniques for early lung cancer diagnosis, this review contributes to advancing clinical practice and ultimately reducing the burden of lung cancer morbidity and mortality.

 

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References

AL-Salman, H. N. K., Hsu, C. Y., Nizar Jawad, Z., Mahmoud, Z. H., Mohammed, F., Saud, A., Al-Mashhadani, Z. I., Sami Abu Hadal, L., &; Kianfar, E. (2024). Graphene oxide-based biosensors for detection of lung cancer: A review. Results in Chemistry, 7(December 2023), 101300. https://doi.org/10.1016/j.rechem.2023.101300

Ali, A., Goffin, J. R., Arnold, A., & Ellis, P. M. (2013). Survival of patients with non-small-cell lung cancer after a diagnosis of brain metastases. Current Oncology, 20(4), 300–306. https://doi.org/10.3747/co.20.1481

Desai, S., &; Guddati, A. K. (2023). Carcinoembryonic Antigen, Carbohydrate Antigen 19-9, Cancer Antigen 125, Prostate-Specific Antigen and Other Cancer Markers: A Primer on Commonly Used Cancer Markers. World Journal of Oncology, 14(1), 4–14. https://doi.org/10.14740/wjon1425

In Capua, D., Bracken-Clarke, D., Ronan, K., Baird, A. M., &; Finn, S. (2021). The liquid biopsy for lung cancer: State of the art, limitations and future developments. Cancers, 13(16), 1–22. https://doi.org/10.3390/cancers13163923

Gillies, R. J., &; Schabath, M. B. (2020). Radiomics improves cancer screening and early detection. Cancer Epidemiology Biomarkers and Prevention, 29(12), 2556–2567. https://doi.org/10.1158/1055-9965.EPI-20-0075

Jung, T., &; Vij, N. (2021). Early diagnosis and real-time monitoring of regional lung function changes to prevent chronic obstructive pulmonary disease progression to severe emphysema. Journal of Clinical Medicine, 10(24). https://doi.org/10.3390/jcm10245811

Kitko, C. L., Pidala, J., Schoemans, H. M., Lawitschka, A., Flowers, M. E., Cowen, E. W., Tkaczyk, E., Farhadfar, N., Jain, S., Steven, P., Luo, Z. K., Ogawa, Y., Stern, M., Yanik, G. A., Cuvelier, G. D. E., Cheng, G. S., Holtan, S. G., Schultz, K. R., Martin, P. J., ... Cutler, C. (2021). National Institutes of Health Consensus Development Project on Criteria for Clinical Trials in Chronic Graft-versus-Host Disease: IIa. The 2020 Clinical Implementation and Early Diagnosis Working Group Report. Transplantation and Cellular Therapy, 27(7), 545–557. https://doi.org/10.1016/j.jtct.2021.03.033

Kumar, S., Kumar, H., Kumar, G., Singh, S. P., Bijalwan, A., &; Diwakar, M. (2024). A methodical exploration of imaging modalities from dataset to detection through machine learning paradigms in prominent lung disease diagnosis: a review. BMC Medical Imaging, 24(1), 1–42. https://doi.org/10.1186/s12880-024-01192-w

Linet, M. S., Slovis, T. L., Miller, D. L., Kleinerman, R., Lee, C., Rajaraman, P., &; Berrington de Gonzalez, A. (2012). Cancer risks associated with external radiation from diagnostic imaging procedures. CA: A Cancer Journal for Clinicians, 62(2), 75–100. https://doi.org/10.3322/caac.21132

Liu, X., Shao, C., &; Fu, J. (2021). Promising biomarkers of radiation-induced lung injury: A review. Biomedicines, 9(9), 1–20. https://doi.org/10.3390/biomedicines9091181

Manning, D. J., Ethell, S. C., & Donovan, T. (2014). Detection or decision errors? Missed lung cancer from the posteroanterior chest radiograph. British Journal of Radiology, 77(915), 231–235. https://doi.org/10.1259/bjr/28883951

Mantovani, C., Gastino, A., Cerrato, M., Badellino, S., Ricardi, U., &; Levis, M. (2021). Modern Radiation Therapy for the Management of Brain Metastases From Non-Small Cell Lung Cancer: Current Approaches and Future Directions. Frontiers in Oncology, 11(November), 1–24. https://doi.org/10.3389/fonc.2021.772789

McManigle, W., Petkovich, B., Smith, H., Chopra, A., &; Huggins, J. T. (2023). Ultrasound-guided pleural biopsy following a non-diagnostic thoracentesis for non-small cell lung cancer. Respiratory Medicine Case Reports, 45(December 2022), 101875. https://doi.org/10.1016/j.rmcr.2023.101875

Nageswaran et al. (2024). Retracted: Lung Cancer Classification and Prediction Using Machine Learning and Image Processing. BioMed Research International, 1–9. https://doi.org/10.1155/2024/9851527

Neal Joshua, E. S., Bhattacharyya, D., Chakkravarthy, M., & Byun, Y. C. (2021). 3D CNN with Visual Insights for Early Detection of Lung Cancer Using Gradient-Weighted Class Activation. Journal of Healthcare Engineering, 2021. https://doi.org/10.1155/2021/6695518

Pal, A., Ali, A., Young, T. R., Oostenbrink, J., Prabhakar, A., Prabhakar, A., Deacon, N., Arnold, A., Eltayeb, A., Yap, C., Young, D. M., Tang, A., Lakshmanan, S., Lim, Y. Y., Pokarowski, M., &; Kakodkar, P. (2021). Comprehensive literature review on the radiographic findings, imaging modalities, and the role of radiology in the COVID-19 pandemic. World Journal of Radiology, 13(9), 258–282. https://doi.org/10.4329/wjr.v13.i9.258

Pulumati, A., Pulumati, A., Dwarakanath, B. S., Verma, A., &; Papineni, R. V. L. (2023). Technological advancements in cancer diagnostics: Improvements and limitations. Cancer Reports, 6(2), 1–17. https://doi.org/10.1002/cnr2.1764

Rastogi, A., Yadav, K., Mishra, A., Singh, M. S., Chaudhary, S., Manohar, R., &; Parmar, A. S. (2022). Early diagnosis of lung cancer using magnetic nanoparticles-integrated systems. Nanotechnology Reviews, 11(1), 544–574. https://doi.org/10.1515/ntrev-2022-0032

Shin, D., Fishman, M. D. C., Ngo, M., Wang, J., &; LeBedis, C. A. (2022). The Impact of Social Determinants of Health on Lung Cancer Screening Utilization. Journal of the American College of Radiology, 19(1), 122–130. https://doi.org/10.1016/j.jacr.2021.08.026

Srinivasulu, A., Ramanjaneyulu, K., Neelaveni, R., Karanam, S. R., Majji, S., Jothilingam, M., &; Patnala, T. R. (2023). RETRACTED ARTICLE: Advanced lung cancer prediction based on blockchain material using extended CNN. Applied Nanoscience (Switzerland), 13(2), 985. https://doi.org/10.1007/s13204-021-01897-2

Tandberg, D. J., Tong, B. C., Ackerson, B. G., & Kelsey, C. R. (2018). Surgery versus stereotactic body radiation therapy for stage I non–small cell lung cancer: A comprehensive review. Cancer, 124(4), 667–678. https://doi.org/10.1002/cncr.31196

Wilson, D. O., Weissfeld, J. L., Balkans, A., Schragin, J. G., Fuhrman, C. R., Fisher, S. N., Wilson, J., Leader, J. K., Siegfried, J. M., Shapiro, S. D., &; Sciurba, F. C. (2008). Association of radiographic emphysema and airflow obstruction with lung cancer. American Journal of Respiratory and Critical Care Medicine, 178(7), 738–744. https://doi.org/10.1164/rccm.200803-435OC

Wolf, A. J., Miller, P. M., Burk, J. R., Vigness, R. M., &; Hollingsworth, J. W. (2023). Ability of single anesthesia for combined robotic-assisted bronchoscopy and surgical lobectomy to reduce time between detection and treatment in stage I non–small cell lung cancer. Baylor University Medical Center Proceedings, 36(4), 434–438. https://doi.org/10.1080/08998280.2023.2193134

Zablotska, L. B., &; Neugut, A. I. (2003). Lung carcinoma after radiation therapy in women treated with lumpectomy or mastectomy for primary breast carcinoma. Cancer, 97(6), 1404–1411. https://doi.org/10.1002/cncr.11214

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Published

2024-03-15

How to Cite

Sisi Yulianti, Budiman, M. A. rif, & Miftachul Amri. (2024). Ultization of Radiological Techniques in Early Diagnosis of Lunc Cancer. Indonesia Journal of Engineering and Education Technology (IJEET), 2(2), 191–197. https://doi.org/10.61991/ijeet.v2i2.35