Abstract
A diagnosis of bone metastasis indicates an advanced cancer stage with a median survival of a few months and presenting with limited therapeutic options. Hence, it is crucial to make an early assessment of bone metastasis to determine the appropriate therapeutic measure to diminish the risks of skeletal adverse events impacting on the survival rates and quality of life of patients. The objectives of this study are to develop a base convolutional neural network classifier and to determine the batch size that generate the best prediction performance for bone metastasis using bone scans. Several experiments to determine optimum batch size were made for both the base and pre-trained models. Results showed that all models favored the use of smaller batch size as larger batch sizes did not yield better performance. The findings showed the best base and pre-trained models obtained very good accuracy, precision, and superior specificity. ResNet50 bested all other models as to performance using Matthew’s correlation coefficient. However, all models had similar metric values suggesting that any of them can be used as a decision support tool for doctors in their clinical practice. Coupled with the known excellent sensitivity of bone scan as an imaging modality for bone metastasis, use of these models with superior specificity, very good precision and accuracy indicates their clinical utility leading to an enhanced diagnostic accuracy of bone scans.
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
Huang, K.: Global trends of researches on bone metastasis: a bibliometric and visualization study. Medicine 101(6), e28761 (2022). https://doi.org/10.1097/MD.0000000000028761
Shao, H., Varamini, P.: Breast cancer bone metastasis: a narrative review of emerging targeted drug delivery systems. Cells 11(3), 388 (2022). https://doi.org/10.3390/cells11030388
Valenti, M.T., Mottes, M., Dalle Carbonare, L., Feron, O.: Editorial: Bone metastases. Front. Oncol. 11, 3030 (2021). https://doi.org/10.3389/fonc.2021.741515
Coleman, R.E., Croucher, P.I., Padhani, A.R., Clézardin, P., Chow, E., Fallon, M., Guise, T., Colangeli, S., Capanna, R., Costa, L.: Bone metastases. Nature Rev. Dis. Primers 6(1), 83 (2020). https://doi.org/10.1038/s41572-020-00216
Orcajo-Rincon, J., Muñoz-Langa, J., Sepúlveda-Sánchez, J.M., et al.: Review of imaging techniques for evaluating morphological and functional responses to the treatment of bone metastases in prostate and breast cancer. Clin. Transl. Oncol. 24, 1290–1310 (2022). https://doi.org/10.1007/s12094-022-02784-0
Papandrianos, N., Papageorgiou, E., Anagnostis, A., Feleki, A.: A deep-learning approach for diagnosis of metastatic breast cancer in bones from whole-body scans. Appl. Sci. 10(3), 997 (2020). https://doi.org/10.3390/app10030997
Papandrianos, N., Papageorgiou, E., Anagnostis, A., Papageorgiou, K.: Bone metastasis classification using whole body images from prostate cancer patients based on convolutional neural networks application. PLoS ONE 15(8), e0237213 (2020). https://doi.org/10.1371/journal.pone.0237213
Liu, Y., Yang, P., Pi, Y., Jiang, L., Zhong, X., Cheng, J., Xiang, Y., Wei, J., Li, L., Yi, Z., Cai, H., Zhao, Z.: Automatic identification of suspicious bone metastatic lesions in bone scintigraphy using convolutional neural network. BMC Med. Imaging 21(1), 131 (2021). https://doi.org/10.1186/s12880-021-00662-9
Wuestemann, J., Hupfeld, S., Kupitz, D., Genseke, P., Schenke, S., Pech, M., Kreissl, M.C., Grosser, O.S.: Analysis of bone scans in various tumor entities using a deep-learning-based artificial neural network algorithm-evaluation of diagnostic performance. Cancers 12(9), 2654 (2020). https://doi.org/10.3390/cancers12092654
Zhao, Z., Pi, Y., Jiang, L., Xiang, Y., Wei, J., Yang, P., Zhang, W., Zhong, X., Zhou, K., Li, Y., Li, L., Yi, Z., Cai, H.: Deep neural network based artificial intelligence assisted diagnosis of bone scintigraphy for cancer bone metastasis. Sci. Rep. 10(1), 17046 (2020). https://doi.org/10.1038/s41598-020-74135-4
Magboo, V.P., Abu, P.A.: Deep neural network for diagnosis of bone metastasis. In: 2022 the 5th International Conference on Software Engineering and Information Management (ICSIM) (ICSIM 2022), pp. 144–151. Association for Computing Machinery, New York, NY, USA (2022). https://doi.org/10.1145/3520084.3520107
Ntakolia, C., Diamantis, D.E., Papandrianos, N., Moustakidis, S., Papageorgiou, E.I.: A lightweight convolutional neural network architecture applied for bone metastasis classification in nuclear medicine: a case study on prostate cancer patients. Healthcare (Basel, Switzerland) 8(4), 493 (2020). https://doi.org/10.3390/healthcare8040493
Abdulnabi, N.Z.T., Altun, O.: Batch size for training convolutional neural networks for sentence classification. J. Adv. Technol. Eng. Res. A/Prof. Akbar A. Khatibi 2(5), 156–163 (2016)
Kandell, I., Castelli, M.: The effect of batch size on the generalizability of the convolutional neural networks on a histopathology dataset. ICT Express 6(4), 312–315 (2020). https://doi.org/10.1016/j.icte.2020.04.010
Radiuk, P.M.: Impact of training set batch size on the performance of convolutional neural networks for diverse datasets. Inf. Technol. Manag. Sci. 20(1), 20–24 (2017). https://doi.org/10.1515/itms-2017-0003. https://itms-journals.rtu.lv/article/view/itms-2017-0003
Narin, A., Pamuk, Z.: Effect of different batch size parameters on predicting of covid19 cases. Akıllı Sistemler ve Uygulamaları Dergisi 74 (J. Intell. Syst. Appl.) 3(2), 69–72 (2020). https://doi.org/10.54856/jiswa.202012119
Masters, D., Luschi, C.: Revisiting Small Batch Training for Deep Neural Networks. arXiv preprint (2018). https://doi.org/10.48550/arxiv.1804.07612
Van den Wyngaert, T., Strobel, K., Uwe Kampen, W., Kuwert, T., van der Bruggen, W., Mohan, H., Gnanasegaran, G., Delgado-Bolton, R., Weber, W., Beheshti, M., Langsteger, W., Giammarile, F., Mottaghy, F., Paycha, F., EANM Bone & Joint Committee and the Oncology Committee: The EANM practice guidelines for bone scintigraphy. Eur. J. Nucl. Med. Mol. Imaging 43(9), 1723–1738 (2016). https://doi.org/10.1007/s00259-016-3415-4
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Magboo, V.P.C., Abu, P.A.R. (2023). Analysis of Batch Size in the Assessment of Bone Metastasis from Bone Scans in Various Convolutional Neural Networks. In: Jezic, G., Chen-Burger, J., Kusek, M., Sperka, R., Howlett, R.J., Jain, L.C. (eds) Agents and Multi-agent Systems: Technologies and Applications 2023. KES-AMSTA 2023. Smart Innovation, Systems and Technologies, vol 354. Springer, Singapore. https://doi.org/10.1007/978-981-99-3068-5_20
Download citation
DOI: https://doi.org/10.1007/978-981-99-3068-5_20
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-3067-8
Online ISBN: 978-981-99-3068-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)