Abstract
The accurate detection of lung lesions as well as the precise measurement of their sizes on Computed Tomography (CT) images is known to be crucial for the response to therapy assessment of cancer patients. The goal of this study is to investigate the feasibility of using mobile tele-radiology for this task in order to improve efficiency in radiology. Lung CT Images were obtained from The Cancer Imaging Archive (TCIA). The Bland-Altman analysis method was used to compare and assess conventional radiology and mobile radiology based lesion size measurements. Percentage of correctly detected lesions at the right image locations was also recorded. Sizes of 183 lung lesions between 5 and 52 mm in CT images were measured by two experienced radiologists. Bland-Altman plots were drawn, and limits of agreements (LOA) were determined as 0.025 and 0.975 percentiles (−1.00, 0.00), (−1.39, 0.00). For lesions of 10 mm and higher, these intervals were found to be much smaller than the decision interval (−30% and +20%) recommended by the RECIST 1.1 criteria. In average, observers accurately detected 98.2% of the total 271 lesions on the medical monitor, while they detected 92.8% of the nodules on the iPhone.
In conclusion, mobile tele-radiology can be a feasible alternative for the accurate measurement of lung lesions on CT images. A higher resolution display technology such as iPad may be preferred in order to detect new small <5 mm lesions more accurately. Further studies are needed to confirm these results with more mobile technologies and types of lesions.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bankier, A.A., MacMahon, H., Goo, J.M., Rubin, G.D., Schaefer-Prokop, C.M., Naidich, D.P.: Recommendations for measuring pulmonary nodules at CT: a statement from the Fleischner Society. Radiology 285(2), 584–600 (2017)
American College of Radiology. Lung CT Screening Reporting and Data System (Lung-RADS) (2014). http://www.acr.org/Quality-Safety/Resources/LungRADS. Accessed 20 May 2020
Lathrop, K., Kaklamani, V.: The response evaluation criteria in solid tumors (RECIST). In: Badve, S., Kumar, G.L. (eds.) Predictive Biomarkers in Oncology, pp. 501–511. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-95228-4_46
Kuhl, C.K.: RECIST needs revision: a wake-up call for radiologists. Radiology 292(1) (2019)
Yoon, S.H., Kim, K.W., Goo, J.M., Kim, D.W., Hahn, S.: Observer variability in RECIST-based tumor burden measurements: a meta-analysis. Eur. J. Cancer 53, 5–15 (2016)
Dinkel, J., Khalilzadeh, O., Hintze, C., Fabel, M., Puderbach, M., Eichinger, M., et al.: Inter-observer reproducibility of semi-automatic tumor diameter measurement and volumetric analysis in patients with lung cancer. Lung Cancer 82(1), 76–82 (2013)
Mendel, J., Lee, J.T., Dhiman, N., Swanson, J.A.: Humanitarian teleradiology. Curr. Radiol. Rep. 7(6), 17 (2019)
Nicholas, J.L.: Technology-mediated education in global radiology: opportunities and challenges. Curr. Radiol. Rep. 7(5), 1–6 (2019). https://doi.org/10.1007/s40134-019-0323-y
John, S., Poh, A.C., Lim, T.C., Chan, E.H.: The iPad tablet computer for mobile on-call radiology diagnosis? Auditing discrepancy in CT and MRI reporting. J. Digit. Imaging 25(5), 628–634 (2012)
Krupinski, E.: Tele-radiology: current perspectives. Rep. Med. Imaging 7(1), 5–14 (2014)
Armato, S.G., III: Data from LIDC-IDRI. The Cancer Imaging Archive (2015). https://doi.org/10.7937/K9/TCIA.2015.LO9QL9SX
Armato, S.G., 3rd., et al.: The lung image database consortium (LIDC) and image database resource initiative (IDRI): a completed reference database of lung nodules on CT scans. Med. Phys. 38, 915–931 (2011). https://doi.org/10.1118/1.3528204
Clark, K., et al.: The cancer imaging archive (TCIA): maintaining and operating a public information repository. J. Digit. Imaging 26(6), 1045–1057 (2013). https://doi.org/10.1007/s10278-013-9622-7
Bland, J.M., Altman, D.G.: Statistical methods for assessing agreement between two methods of clinical measurement. Int. J. Nurs. Stud. 47(8), 931–936 (2010)
Sui, X., Meinel, F.G., Song, W., Xu, X., Wang, Z., Wang, Y., et al.: Detection and size measurements of pulmonary nodules in ultra-low-dose CT with iterative reconstruction compared to low dose CT. Eur. J. Radiol. 85(3), 564–570 (2016)
Nishino, M.: New response evaluation criteria in solid tumors (RECIST) guidelines for advanced non–small cell lung cancer: comparison with original RECIST and impact on assessment of tumor response to targeted therapy. Am. J. Roentgenol. 195(3), W221-8 (2010)
Faggioni, L., Neri, E., Sbragia, P., Angeli, S., Angeli, S., Bartolozzi, C.: Chest CT and the iPad2®: preliminary 2D assessment of pulmonary nodules. Presented at RSNA, 29 November 2011
Acknowledgements
The authors acknowledge the National Cancer Institute and the Foundation for the National Institutes of Health, and their critical role in the creation of the free publicly available LIDC/IDRI Database used in this study.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Kaya, O., Kara, E., Inan, I., Kara, E., Matur, M., Guvenis, A. (2022). Evaluating Mobile Tele-radiology Performance for the Task of Analyzing Lung Lesions on CT Images. In: Su, R., Zhang, YD., Liu, H. (eds) Proceedings of 2021 International Conference on Medical Imaging and Computer-Aided Diagnosis (MICAD 2021). MICAD 2021. Lecture Notes in Electrical Engineering, vol 784. Springer, Singapore. https://doi.org/10.1007/978-981-16-3880-0_13
Download citation
DOI: https://doi.org/10.1007/978-981-16-3880-0_13
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-3879-4
Online ISBN: 978-981-16-3880-0
eBook Packages: EngineeringEngineering (R0)