Fabrication of a Beeswax-Based Chest Phantom Incorporating Anadara antiquata Shell-Derived CaCO3 for Adult Lung Cancer Simulation

Authors

  • Ardilah Ardilah Department of Physics, Faculty of Sciences and Mathematics, Diponegoro University, Semarang 50275, Indonesia
  • Choirul Anam Department of Physics, Faculty of Sciences and Mathematics, Diponegoro University, Semarang 50275, Indonesia
  • Ilham Alkian Research Center for Nanotechnology System, National Research and Innovation Agency (BRIN), South Tangerang 15314, Indonesia
  • Heri Sutanto Department of Physics, Faculty of Sciences and Mathematics, Diponegoro University, Semarang 50275, Indonesia
  • Nurul Sabihah Zakaria Physics Section, School of Distance Education, Universiti Sains Malaysia (USM), Pulau Pinang 11800, Malaysia

DOI:

https://doi.org/10.25077/jif.19.1.40-52.2027

Keywords:

lung cancer, chest phantom, radiodiagnostic, thoracic CT, image quality

Abstract

Lung cancer remains a major cause of cancer mortality, while chest CT requires careful radiation dose optimization. Commercial anthropomorphic phantoms are costly, and shell-derived bone substitutes for chest phantoms with simulated pulmonary nodules remain insufficiently evaluated. This study developed and assessed a low-cost adult chest phantom using locally available materials: polymethyl methacrylate (PMMA) for soft tissue, polyurethane (PU) foam for lung tissue, calcium carbonate derived from Anadara antiquata shell waste for bone, and beeswax for a simulated pulmonary nodule. The phantom was scanned using a 128-slice Multi-Slice Computed Tomography (MSCT) scanner with Automatic Exposure Control (AEC) at 80, 100, and 120 kV. Radiometric performance was evaluated using CT numbers, Signal-to-Noise Ratio (SNR), and Contrast-to-Noise Ratio (CNR). Measured CT numbers were −62.59 ± 17.59 HU for beeswax, −915.34 ± 15.65 HU for lung tissue, 113.48 ± 14.64 HU for soft tissue, and 1155.35 ± 170.70 HU for bone. These values were compared with published tissue attenuation ranges. The phantom shows potential for CT image quality assessment and optimization, although further validation is needed to improve lesion representation and establish broader clinical applicability. This approach also explores the reuse of shell waste.

Downloads

Download data is not yet available.

References

Alikhani, B., Jamali, L., Raatschen, H. J., Wacker, F., & Werncke, T. (2017). Impact of CT parameters on the physical quantities related to image quality for two MDCT scanners using the ACR accreditation phantom: A phantom study. Radiography, 23(3), 202–210. https://doi.org/10.1016/j.radi.2017.03.013

Amini, I., Akhlaghi, P., & Sarbakhsh, P. (2018). Construction and verification of a physical chest phantom from suitable tissue equivalent materials for computed tomography examinations. Radiation Physics and Chemistry, 150, 51–57. https://doi.org/10.1016/j.radphyschem.2018.04.020

Anderson, I. J., & Davis, A. M. (2018). Incidental Pulmonary Nodules Detected on CT Images. JAMA - Journal of the American Medical Association, 320(21), 2260–2261. https://doi.org/10.1001/jama.2018.16336

Bushberg, J. T., Seibert, J. A., Leidholdt, E. M., Jr., & Boone, J. M. (2012). The essential physics of medical imaging (3rd ed.). Lippincott Williams & Wilkins.

Choi, Y., Gil, B. M., Chung, M. H., Yoo, W. J., Jung, N. Y., Kim, Y. H., Kwon, S. S., & Kim, J. (2019). Comparing attenuations of malignant and benign solitary pulmonary nodule using semi-automated region of interest selection on contrast-enhanced CT. Journal of Thoracic Disease, 11(6), 2392–2401. https://doi.org/10.21037/jtd.2019.05.56

Dance, D. R., Christofides, S., Maidment, A. D. A., McLean, I. D., & Ng, K. H. (2014). Diagnostic Radiology Physics. In International Atomic Energy Agency (IAEA). https://doi.org/10.1201/9781482266641

Dewi, P. S., Ratini, N. N., & Trisnawati, N. L. P. (2022). Effect of x-ray tube voltage variation to value of contrast to noise ratio (CNR) on computed tomography (CT) Scan at RSUD Bali Mandara. International Journal of Physical Sciences and Engineering, 6(2), 82–90. https://doi.org/10.53730/ijpse.v6n2.9656

Frellesen, C., Kaup, M., Wichmann, J. L., Hüsers, K., Scholtz, J. E., Albrecht, M. H., Metzger, S. C., Bauer, R. W., Kerl, J. M., Lehnert, T., Vogl, T. J., & Bodelle, B. (2016). Noise-optimized advanced image-based virtual monoenergetic imaging for improved visualization of lung cancer: Comparison with traditional virtual monoenergetic imaging. European Journal of Radiology, 85(3), 665–672. https://doi.org/10.1016/j.ejrad.2015.12.022

Fu, W., Sharma, S., Solomon, J., Ria, F., Setiawan, H., Ding, A., Segars, W. P., & Samei, E. (2025). Patient-specific organ dose and in-vivo image quality assessment in clinical CT. Physica Medica, 136(June). https://doi.org/10.1016/j.ejmp.2025.105017

Goo, H. W. (2012). CT radiation dose optimization and estimation: An update for radiologists. Korean Journal of Radiology, 13(1), 1–11. https://doi.org/10.3348/kjr.2012.13.1.1

Greffier, J., Pereira, F., Macri, F., Beregi, J. P., & Larbi, A. (2016). CT dose reduction using Automatic Exposure Control and iterative reconstruction: A chest paediatric phantoms study. Physica Medica, 32(4), 582–589. https://doi.org/10.1016/j.ejmp.2016.03.007

Hassan, S. U, Abdulkadir, S. J., Fayyaz, A. M., Al-Selwi, S. M., & Ismail, A. O. A. (2026). Interpretable MedDeepNet: Deep feature learning with atom search optimization for explainable lung cancer detection in CT images. Machine Learning with Applications, 25(August). https://doi.org/10.1016/j.mlwa.2026.100973

Heryani, H., Sutanto, H., Anam, C., & Reskianto, A. D. (2020). Development of Synthetic Bones for ANSI Chest Phantom Dosimetry. European Journal of Advances in Engineering and Technology, 2020(9), 2–7.

Hong, D., Moon, S., Seo, J. B., & Kim, N. (2023). Development of a patient-specific chest computed tomography imaging phantom with realistic lung lesions using silicone casting and three-dimensional printing. Scientific Reports, 13(1), 1–9. https://doi.org/10.1038/s41598-023-31142-5

Jamal, N. H. M., Sayed, I. S., & Syed, W. S. (2020). Estimation of organ absorbed dose in pediatric chest X-ray examination: A phantom study. Radiation Physics and Chemistry, 166(September), 108472. https://doi.org/10.1016/j.radphyschem.2019.108472

Kamal, I., Razak, H. R. A., Karim, M. K. A., Mashohor, S., Liew, J. Y. C., Low, Y. J., Zaaba, N. A., Norkhairunnisa, M., & Rafi, N. A. S. M. (2022). Mechanical and Imaging Properties of a Clinical-Grade Kidney Phantom Based on Polydimethylsiloxane and Elastomer. Polymers, 14(3). https://doi.org/10.3390/polym14030535

Khallouqi, A., Halimi, A., & El rhazouani, O. (2024a). Comparative dosimetry of an epoxy resin pediatric head phantom and PMMA phantom for CT imaging. Radiation Physics and Chemistry, 216(May 2023), 111350. https://doi.org/10.1016/j.radphyschem.2023.111350

Khallouqi, A., Halimi, A., & El rhazouani, O. (2024b). Evaluating polyester resin as a viable substitute for PMMA in computed tomography dosimetry phantoms. Nuclear Engineering and Technology, 56(9), 3758–3763. https://doi.org/10.1016/j.net.2024.04.024

Larke, F. J., Kruger, R. L., Cagnon, C. H., Flynn, M. J., McNitt-Gray, M. M., Wu, X., Judy, P. F., & Cody, D. D. (2011). Estimated radiation dose associated with low-dose chest CT of average-size participants in the national lung screening trial. American Journal of Roentgenology, 197(5), 1165–1169. https://doi.org/10.2214/AJR.11.6533

McCollough, C. H., Primak, A. N., Braun, N., Kofler, J., Yu, L., & Christner, J. (2009). Strategies for Reducing Radiation Dose in CT. Radiologic Clinics of North America, 47(1), 27–40. https://doi.org/10.1016/j.rcl.2008.10.006

Miftahuddin, D., Prayitno, A. G., Hariyanto, A. P., Gani, M. R. A., & Endarko, E. (2024). Evaluation of low-dose pediatric chest CT examination using in-house developed various age-size pediatric chest phantoms. European Journal of Radiology, 177(February), 1–9. https://doi.org/10.1016/j.ejrad.2024.111599

Mohammed Ali, A., & Al-Murshedi, S. (2023). Low-cost chest paediatric phantom for dose optimisation: construction and validation. Radiologia, 65(4), 327–337. https://doi.org/10.1016/j.rx.2022.11.011

Nathan, V., Prithishkumar, I., & Livingstone, R. (2021). Estimation of anatomical dimensions of the thorax from computed tomography images of the adult and pediatric Indian population for developing optimal radiological protocols. Journal of Medical Physics, 46(4), 295–299. https://doi.org/10.4103/jmp.JMP_19_21

Nisa, K. A., Anam, C., Alkian, I., Naufal, A., & Sutanto, H. (2025). Design and fabrication of paediatric chest phantom using polymethyl methacrylate-polyurethane foam for pneumonia imaging evaluation. Radiation Physics and Chemistry, 232(November 2024). https://doi.org/10.1016/j.radphyschem.2025.112610

Novitasari, D., Anam, C., Setiawati, E., Amilia, R., Naufal, A., & Reskianto, A. D. (2024). Evaluation of IndoQCT for automatic measurement of contrast-to-noise ratio (CNR) on American college of radiology (ACR) CT phantom images. AIP Conference Proceedings, 3210(1). https://doi.org/10.1063/5.0228091

Pengpan, T., Rattanarungruangchai, N., Dechjaithat, J., Panthim, P., Siricharuwong, P., & Prapan, A. (2022). Optimization of Image Quality and Organ Absorbed Dose for Pediatric Chest X-Ray Examination: In-House Developed Chest Phantom Study. Radiology Research and Practice, 2022, 1–10. https://doi.org/10.1155/2022/3482458

Pratama, N. G., & Widayanti, A. (2025). Karakteristik Limbah Cangkang Kerang Bulu dan Material Alam sebagai Campuran Perkerasan pada Lapis Asphalt Concrete-Wearing Course (AC-WC). Jurnal Media Publikasi Terapan Transportasi, 3(3), 240–246. https://doi.org/10.26740/mitrans.v3n3.p240-246

QRM GmbH. (2020). Thorax/abdomen phantom specifications [Product specification sheet]. QRM GmbH - A PTW Company.

Rahmawati, T. N., Anam, C., Setiawati, E., Amilia, R., Naufal, A., & Widhianto, R. W. (2024, August). Evaluation of contrast-to-noise ratio measurements using IndoQCT on images of the American association of physicists in medicine (AAPM) CT performance phantom. In AIP Conference Proceedings (Vol. 3210, No. 1, p. 030007). AIP Publishing LLC. doi: https://doi.org/10.1063/5.0228089

Rodgers, S., Atkinson, J., Cryer, D., Storm, C., Nezich, R., Ebert, M. A., & Rowshanfarzad, P. (2024). Construction and validation of an infant chest phantom for paediatric computed tomography. Physical and Engineering Sciences in Medicine, 47(2), 491–501. https://doi.org/10.1007/s13246-023-01379-5

Sawall, S., Maier, J., Ziener, C. H., Schlemmer, H. P., Do, T. D., Kauczor, H. U., Schoenberg, S. O., Froelich, M. F., & Kachelrieß, M. (2025). Lung cancer screening CT acquisition protocols for three generations of CT systems conforming to German legislation. Physica Medica, 139(September). https://doi.org/10.1016/j.ejmp.2025.105208

Sergi, C., Sarasini, F., & Tirillò, J. (2022). The compressive behavior and crashworthiness of cork: A review. Polymers, 14(1). https://doi.org/10.3390/polym14010134

Sookpeng, S., Cheebsumon, P., Pengpan, T., & Martin, C. (2016). Comparison of computed tomography dose index in polymethyl methacrylate and nylon dosimetry phantoms. Journal of Medical Physics, 41(1), 45–51. https://doi.org/10.4103/0971-6203.177287

Sung, H., Ferlay, J., Siegel, R. L., Laversanne, M., Soerjomataram, I., Jemal, A., & Bray, F. (2021). Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA: A Cancer Journal for Clinicians, 71(3), 209–249. https://doi.org/10.3322/caac.21660

Swensen, S. J., Viggiano, R. W., Midthun, D. E., Müller, N. L., Sherrick, A., Yamashita, K., Naidich, D. P., Patz, E. F., Hartman, T. E., Muhm, J. R., & Weaver, A. L. (2000). Lung nodule enhancement at CT: Multicenter study. Radiology, 214(1), 73–80. https://doi.org/10.1148/radiology.214.1.r00ja1473

Tang, K., Wang, L., Li, R., Lin, J., Zheng, X., & Cao, G. (2012). Effect of low tube voltage on image quality, radiation dose, and low-contrast detectability at abdominal multidetector CT: Phantom study. Journal of Biomedicine and Biotechnology, 2012(Lcd). https://doi.org/10.1155/2012/130169

World Health Organization (2023). WHO - Lung cancer. https://www.who.int/newsroom/fact-sheets/detail/lung-cancer. (Accessed 06 October 2026).

Downloads

Published

2026-11-08

How to Cite

Ardilah, A., Anam, C., Alkian, I., Sutanto, H., & Zakaria, N. S. (2026). Fabrication of a Beeswax-Based Chest Phantom Incorporating Anadara antiquata Shell-Derived CaCO3 for Adult Lung Cancer Simulation. JURNAL ILMU FISIKA | UNIVERSITAS ANDALAS, 19(1), 40–52. https://doi.org/10.25077/jif.19.1.40-52.2027

Citation Check