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Diagnostic performance of deep learning models for detecting bone metastasis on whole-body bone scan in prostate cancer

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European Journal of Nuclear Medicine and Molecular Imaging Aims and scope Submit manuscript

Abstract

Purpose

We evaluated the performance of deep learning classifiers for bone scans of prostate cancer patients.

Methods

A total of 9113 consecutive bone scans (5342 prostate cancer patients) were initially evaluated. Bone scans were labeled as positive/negative for bone metastasis using clinical reports and image review for ground truth diagnosis. Two different 2D convolutional neural network (CNN) architectures were proposed: (1) whole body–based (WB) and (2) tandem architectures integrating whole body and local patches, here named as “global–local unified emphasis” (GLUE). Both models were trained using abundant (72%:8%:20% for training:validation:test sets) and limited training data (10%:40%:50%). The allocation of test sets was rotated across all images: therefore, fivefold and twofold cross-validation test results were available for abundant and limited settings, respectively.

Results

A total of 2991 positive and 6142 negative bone scans were used as input. For the abundant training setting, the receiver operating characteristics curves of both the GLUE and WB models indicated excellent diagnostic ability in terms of the area under the curve (GLUE: 0.936–0.955, WB: 0.933–0.957, P > 0.05 in four of the fivefold tests). The overall accuracies of the GLUE and WB models were 0.900 and 0.889, respectively. With the limited training setting, the GLUE models showed significantly higher AUCs than the WB models (0.894–0.908 vs. 0.870–0.877, P < 0.0001).

Conclusion

Our 2D-CNN models accurately classified bone scans of prostate cancer patients. While both showed excellent performance with the abundant dataset, the GLUE model showed higher performance than the WB model in the limited data setting.

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Acknowledgements

We appreciate the substantial contributions and support from members in the Division of Research Information, Department of Data Convergence, Asan Medical Center, Seoul, Korea, for retrieving clinical information on the study population.

Funding

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (Ministry of Science and ICT; No. NRF-2020M2D9A1094074; 2021R1A2C3009056) and by a grant from the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (HI18C2383).

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Authors and Affiliations

  1. Department of Nuclear Medicine, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, Korea

    Sangwon Han, Jungsu S. Oh & Jong Jin Lee

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  1. Sangwon Han
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Contributions

Sangwon Han, Jungsu S. Oh, and Jong Jin Lee contributed to the study conceptualization, data acquisition, data analysis, data interpretation, writing, and editing of the manuscript.

Corresponding authors

Correspondence to Jungsu S. Oh or Jong Jin Lee.

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Ethics approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and the principles of the 1964 Declaration of Helsinki and its subsequent amendments or comparable ethical standards.

Informed consent

This retrospective study was approved by the local institutional review board (IRB No. 2020–1098). The need for informed consent was waived by the committee.

Conflict of interest

The authors declare no competing interests.

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Jong Jin Lee and Jungsu S. Oh contributed equally to this work as the corresponding authors.

This article is part of the Topical Collection on Advanced Image Analyses (Radiomics and Artificial Intelligence).

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Han, S., Oh, J.S. & Lee, J.J. Diagnostic performance of deep learning models for detecting bone metastasis on whole-body bone scan in prostate cancer. Eur J Nucl Med Mol Imaging 49, 585–595 (2022). https://doi.org/10.1007/s00259-021-05481-2

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